From 245ea3dd59c0cc67f90c31b9e66b04e5ccde0e17 Mon Sep 17 00:00:00 2001 From: Robert Jakob Date: Wed, 28 May 2025 13:52:15 +0200 Subject: [PATCH] docs: Update READMEs with cloud version info and remove private repo references --- Agent1_Peer_Review/README.md | 14 +- Agent1_Peer_Review/context/context.json | 4 +- Agent2_Outlet_Fit/README.md | 24 +- .../.gitignore | 42 - .../README.md | 78 - .../requirements.txt | 4 - .../requirements_1.txt | 15 - .../requirements_2.txt | 15 - .../src/main.py | 110 - .../src/openai_client.py | 134 - .../src/pdf_parser.py | 175 - .../src/requirements_checker.py | 80 - .../test_api.py | 38 - Backup/V3_Peer_Review/README.md | 81 - Backup/V3_Peer_Review/requirements.txt | 3 - Backup/V3_Peer_Review/review_criteria.json | 12 - Backup/V3_Peer_Review/src/main.py | 110 - Backup/V3_Peer_Review/src/openai_client.py | 124 - Backup/V3_Peer_Review/src/pdf_parser.py | 117 - .../V3_Peer_Review/src/peer_review_checker.py | 121 - Backup/V4_multi_agent/README.md | 126 - 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100644 Backup/V5_multi_agent2/tests/test_rigor_agents.py delete mode 100644 Backup/V5_multi_agent2/tests/test_writing_agents.py diff --git a/Agent1_Peer_Review/README.md b/Agent1_Peer_Review/README.md index e50bfaf..75932f3 100644 --- a/Agent1_Peer_Review/README.md +++ b/Agent1_Peer_Review/README.md @@ -1,4 +1,8 @@ -# AI Peer Reviewer +# Agent1_Peer_Review + +> **Note:** This is an open-source project under the MIT License. We welcome contributions from the community to help improve the AI Peer Reviewer system. Please feel free to submit issues, pull requests, or suggestions for improvements. + +> **Cloud Version Available:** A cloud version of the AI Peer Reviewer is now available at [https://www.rigorous.company/](https://www.rigorous.company/). Simply upload your manuscript, provide context on target journal and review focus, and receive a comprehensive PDF report via email within 1-2 working days. The cloud version is currently free for testing purposes. A multi-agent system for comprehensive manuscript analysis and review. @@ -198,13 +202,7 @@ MIT License ## Contributing -1. Fork the repository -2. Create a feature branch -3. Commit your changes -4. Push to the branch -5. Create a Pull Request - -For detailed guidelines on how to contribute, please see [CONTRIBUTING.md](CONTRIBUTING.md). +This project is open source under the MIT License. We welcome contributions from the community to help improve the AI Peer Reviewer system. Please feel free to submit issues, pull requests, or suggestions for improvements. ## Join the Project diff --git a/Agent1_Peer_Review/context/context.json b/Agent1_Peer_Review/context/context.json index 2a0837d..1d7a67f 100644 --- a/Agent1_Peer_Review/context/context.json +++ b/Agent1_Peer_Review/context/context.json @@ -3,12 +3,12 @@ "label": "Target Publication Outlets (optional but recommended)", "description": "This helps us tailor the review to your target venue's requirements.", "placeholder": "e.g., Nature Medicine, Science, or specific conferences like NeurIPS 2024", - "user_input": "appetite journal" + "user_input": "Journal of Medical Internet Research (JMIR)" }, "review_focus_areas": { "label": "Review Focus Areas (optional but recommended)", "description": "Specify any particular aspects you'd like the AI peer reviewers to focus on.", "placeholder": "e.g., statistical analysis, methodology, experimental design, motivation, or specific aspects you want reviewers to focus on", - "user_input": "background literature and contribution introduction" + "user_input": "Introduction and discussion" } } \ No newline at end of file diff --git a/Agent2_Outlet_Fit/README.md b/Agent2_Outlet_Fit/README.md index 6cca67d..fe08452 100644 --- a/Agent2_Outlet_Fit/README.md +++ b/Agent2_Outlet_Fit/README.md @@ -1,3 +1,25 @@ +# Agent2_Outlet_Fit + +> **Note:** This module is currently in active development. It will help reviewers evaluate manuscripts against specific journal/conference criteria and support desk rejection decisions. + +## Purpose + +Agent2_Outlet_Fit is designed to: +- Evaluate manuscript fit with target journals/conferences +- Support journals/conferences in desk rejection decisions +- Enable researchers to pre-check manuscripts before submission + +## Status + +🚧 **In Development** +- Core functionality being implemented +- Integration with Agent1_Peer_Review in progress +- Testing and validation ongoing + +## Contributing + +This project is open source under the MIT License. We welcome contributions from the community to help improve the AI Peer Reviewer system. Please feel free to submit issues, pull requests, or suggestions for improvements. + ## STATUS: 🚧 IN PLANNING PHASE This tool is currently in the planning and development phase. It aims to serve two key purposes: 1. Help reviewers evaluate manuscripts against specific journal/conference criteria @@ -78,7 +100,7 @@ A JSON report that includes: Build a multiagent pipeline that automatically reverse-engineers a target outlet's expectations and assesses a manuscript's fit. The tool serves three key purposes: 1. **For Reviewers**: Streamline the review process by automatically checking manuscripts against journal/conference criteria -2. **For Journals/Conferences**: Support desk rejection decisions by providing automated preliminary screening +2. **For Journals/Conferences**: Support faster desk rejection decisions by providing automated preliminary screening and fast feedback to authors 3. **For Researchers**: Enable pre-submission self-assessment to identify potential issues before formal submission This comprehensive approach aims to reduce desk rejection risk, improve submission strategy, and make the peer review process more efficient for all stakeholders. \ No newline at end of file diff --git a/Backup/V2_Editorial_First_Decision_Support/.gitignore b/Backup/V2_Editorial_First_Decision_Support/.gitignore deleted file mode 100644 index 8fdbb45..0000000 --- a/Backup/V2_Editorial_First_Decision_Support/.gitignore +++ /dev/null @@ -1,42 +0,0 @@ -# Environment variables -.env - -# Manuscripts -manuscripts/ -analysis_results/ - -# Python -__pycache__/ -*.py[cod] -*$py.class -*.so -.Python -env/ -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -*.egg-info/ -.installed.cfg -*.egg - -# Virtual Environment -venv/ -ENV/ - -# IDE -.idea/ -.vscode/ -*.swp -*.swo - -# OS -.DS_Store -Thumbs.db \ No newline at end of file diff --git a/Backup/V2_Editorial_First_Decision_Support/README.md b/Backup/V2_Editorial_First_Decision_Support/README.md deleted file mode 100644 index ed56ff6..0000000 --- a/Backup/V2_Editorial_First_Decision_Support/README.md +++ /dev/null @@ -1,78 +0,0 @@ -# V2 - Editorial First Decision Support - -A tool that analyzes academic manuscripts against editorial requirements using OpenAI's GPT models. - -## Features - -- Extracts text from PDF manuscripts -- Analyzes manuscript against a list of editorial requirements -- Identifies which requirements are met and which are not -- Provides specific evidence for unmet requirements -- Generates a desk rejection recommendation -- Processes multiple PDFs in batch - -## Setup - -1. Clone this repository -2. Install dependencies: - ```bash - pip install -r requirements.txt - ``` -3. Set up your OpenAI API key: - - Either set it as an environment variable: `export OPENAI_API_KEY=your-key-here` - - Or provide it via command line argument: `--api-key your-key-here` - -## Usage - -1. Place your PDF manuscripts in the `manuscripts/` directory -2. Create a text file with your editorial requirements (one per line) -3. Run the checker: - ```bash - python src/main.py --requirements path/to/requirements.txt - ``` - - Optional arguments: - - `--manuscripts-dir`: Directory containing PDFs (default: manuscripts) - - `--output-dir`: Directory for analysis results (default: analysis_results) - - `--api-key`: Your OpenAI API key - -## Project Structure - -``` -. -├── manuscripts/ # Directory for PDF manuscripts -├── analysis_results/ # Directory for analysis output files -├── src/ # Source code -│ ├── main.py -│ ├── pdf_parser.py -│ ├── openai_client.py -│ └── requirements_checker.py -├── requirements.txt # Python dependencies -└── example_requirements.txt # Example requirements file -``` - -## Example Requirements File - -``` -Manuscript must be under 5000 words -Abstract must be structured (Background, Methods, Results, Conclusion) -Figures must be in high resolution (300 DPI minimum) -``` - -## Output - -For each PDF in the manuscripts directory, the tool will: -1. Create a separate analysis file in the `analysis_results/` directory -2. Name the file `{manuscript_name}_analysis.txt` -3. Include: - - Analysis of each requirement (met/not met) - - Evidence for unmet requirements - - Final desk rejection recommendation with justification - -## Development - -The project structure is modular and easy to extend: -- `pdf_parser.py`: Handles PDF text extraction -- `openai_client.py`: Manages OpenAI API interactions -- `requirements_checker.py`: Orchestrates the analysis process -- `main.py`: Provides the CLI interface diff --git a/Backup/V2_Editorial_First_Decision_Support/requirements.txt b/Backup/V2_Editorial_First_Decision_Support/requirements.txt deleted file mode 100644 index 7aba6da..0000000 --- a/Backup/V2_Editorial_First_Decision_Support/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -openai>=1.0.0 -PyMuPDF>=1.23.0 -python-dotenv>=1.0.0 -pytest>=7.0.0 \ No newline at end of file diff --git a/Backup/V2_Editorial_First_Decision_Support/requirements_1.txt b/Backup/V2_Editorial_First_Decision_Support/requirements_1.txt deleted file mode 100644 index e179f58..0000000 --- a/Backup/V2_Editorial_First_Decision_Support/requirements_1.txt +++ /dev/null @@ -1,15 +0,0 @@ -Manuscript must be under 5000 words -Abstract must be structured with Background, Methods, Results, and Conclusion sections -All figures must be in high resolution (600 DPI minimum) with detailed captions -Methods section must include comprehensive statistical analysis procedures -Results must be presented with appropriate statistical tests and p-values -References must follow APA format -All abbreviations must be defined at first use with a list of abbreviations provided -Conflict of interest statement must be included -Ethics approval must be mentioned if human subjects were involved -Data availability statement must be included -Funding sources must be acknowledged -Author contributions must be specified -Limitations of the study must be discussed -Future research directions must be suggested -Key findings must be summarized in a conclusion section \ No newline at end of file diff --git a/Backup/V2_Editorial_First_Decision_Support/requirements_2.txt b/Backup/V2_Editorial_First_Decision_Support/requirements_2.txt deleted file mode 100644 index d31f7fe..0000000 --- a/Backup/V2_Editorial_First_Decision_Support/requirements_2.txt +++ /dev/null @@ -1,15 +0,0 @@ -Manuscript must be under 10000 words -Abstract should include key information about the study -Figures should be clear and readable -Methods section should describe the approach used -Results should be clearly presented -References should be consistent in format -Abbreviations should be explained where used -Conflict of interest statement is optional -Ethics approval should be mentioned if required by local regulations -Data availability statement is optional -Funding information can be included if relevant -Author contributions can be mentioned if desired -Study limitations can be discussed if relevant -Future work can be suggested if appropriate -A conclusion section is recommended \ No newline at end of file diff --git a/Backup/V2_Editorial_First_Decision_Support/src/main.py b/Backup/V2_Editorial_First_Decision_Support/src/main.py deleted file mode 100644 index 9658e99..0000000 --- a/Backup/V2_Editorial_First_Decision_Support/src/main.py +++ /dev/null @@ -1,110 +0,0 @@ -import argparse -import json -import os -from typing import List -from requirements_checker import RequirementsChecker - -def read_requirements(requirements_path: str) -> List[str]: - """ - Read requirements from a text file. - - Args: - requirements_path (str): Path to the requirements file - - Returns: - List[str]: List of requirements - """ - with open(requirements_path, 'r') as f: - return [line.strip() for line in f if line.strip()] - -def get_pdf_files(directory: str) -> List[str]: - """ - Get all PDF files from a directory. - - Args: - directory (str): Path to the directory - - Returns: - List[str]: List of PDF file paths - """ - pdf_files = [] - for file in os.listdir(directory): - if file.lower().endswith('.pdf'): - pdf_files.append(os.path.join(directory, file)) - return pdf_files - -def analyze_manuscript(checker: RequirementsChecker, pdf_path: str, requirements: List[str], output_dir: str) -> None: - """ - Analyze a single manuscript and save results to a file. - - Args: - checker (RequirementsChecker): The requirements checker instance - pdf_path (str): Path to the PDF file - requirements (List[str]): List of requirements to check - output_dir (str): Directory to save the results - """ - try: - # Get the base filename without extension - base_name = os.path.splitext(os.path.basename(pdf_path))[0] - - # Analyze manuscript - results = checker.check_manuscript(pdf_path, requirements) - - # Format results - formatted_results = checker.format_results(results) - - # Save results to file - output_file = os.path.join(output_dir, f"{base_name}_analysis.txt") - with open(output_file, 'w') as f: - f.write(formatted_results) - - print(f"Analysis completed for {base_name}") - print(f"Results saved to: {output_file}\n") - - except Exception as e: - print(f"Error processing {pdf_path}: {str(e)}\n") - -def main(): - parser = argparse.ArgumentParser(description='Manuscript Requirements Checker') - parser.add_argument('--manuscripts-dir', default='manuscripts', - help='Directory containing PDF manuscripts (default: manuscripts)') - parser.add_argument('--requirements', required=True, help='Path to the requirements text file') - parser.add_argument('--output-dir', default='analysis_results', - help='Directory to save analysis results (default: analysis_results)') - parser.add_argument('--api-key', help='OpenAI API key (optional if set in environment)') - - args = parser.parse_args() - - try: - # Create output directory if it doesn't exist - os.makedirs(args.output_dir, exist_ok=True) - - # Read requirements - requirements = read_requirements(args.requirements) - - # Get PDF files - pdf_files = get_pdf_files(args.manuscripts_dir) - - if not pdf_files: - print(f"No PDF files found in {args.manuscripts_dir}") - return 1 - - print(f"Found {len(pdf_files)} PDF files to analyze") - - # Initialize checker - checker = RequirementsChecker(api_key=args.api_key) - - # Process each PDF - for pdf_path in pdf_files: - analyze_manuscript(checker, pdf_path, requirements, args.output_dir) - - print("Analysis complete!") - - except Exception as e: - print(f"Error: {str(e)}") - return 1 - - return 0 - -if __name__ == '__main__': - exit(main()) \ No newline at end of file diff --git a/Backup/V2_Editorial_First_Decision_Support/src/openai_client.py b/Backup/V2_Editorial_First_Decision_Support/src/openai_client.py deleted file mode 100644 index 01659e0..0000000 --- a/Backup/V2_Editorial_First_Decision_Support/src/openai_client.py +++ /dev/null @@ -1,134 +0,0 @@ -import os -import json -from typing import List, Dict, Any -from openai import OpenAI -from dotenv import load_dotenv - -class OpenAIClient: - """A class to handle interactions with the OpenAI API.""" - - def __init__(self, api_key: str = None): - """ - Initialize the OpenAI client. - - Args: - api_key (str, optional): OpenAI API key. If not provided, will try to load from environment. - """ - # Try to load .env from the current directory - load_dotenv() - - # If API key is not found, try to load from parent directory - if not os.getenv("OPENAI_API_KEY"): - # Get the path to the parent directory (two levels up from this file) - parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")) - env_path = os.path.join(parent_dir, ".env") - if os.path.exists(env_path): - load_dotenv(env_path) - - self.api_key = api_key or os.getenv("OPENAI_API_KEY") - if not self.api_key: - raise ValueError("OpenAI API key is required") - self.client = OpenAI(api_key=self.api_key) - - def check_requirements(self, manuscript_text: str, requirements: List[str]) -> Dict[str, Any]: - """ - Check if the manuscript meets the given requirements using GPT-3.5-turbo. - - Args: - manuscript_text (str): The full text of the manuscript - requirements (List[str]): List of editorial requirements to check - - Returns: - Dict[str, Any]: Analysis results including requirement status and evidence - """ - # Truncate manuscript text to first 4000 words to reduce token usage - words = manuscript_text.split() - truncated_text = ' '.join(words[:4000]) if len(words) > 4000 else manuscript_text - - prompt = self._create_analysis_prompt(truncated_text, requirements) - - try: - response = self.client.chat.completions.create( - model="gpt-3.5-turbo", # Using standard model instead of 16k for cost efficiency - messages=[ - {"role": "system", "content": "You are an expert manuscript reviewer. Analyze manuscripts against requirements. Be strict and thorough. Only mark requirements as met with clear evidence. Provide specific quotes and exact numbers when applicable. Always respond with valid JSON."}, - {"role": "user", "content": prompt} - ], - temperature=0.3, - max_tokens=1000 # Limit response length - ) - - response_content = response.choices[0].message.content - print(f"OpenAI Response: {response_content}") # Debug print - - return self._parse_response(response_content) - - except Exception as e: - raise Exception(f"Failed to analyze manuscript: {str(e)}") - - def _create_analysis_prompt(self, manuscript_text: str, requirements: List[str]) -> str: - """ - Create a prompt for the requirements analysis. - - Args: - manuscript_text (str): The manuscript text - requirements (List[str]): List of requirements to check - - Returns: - str: Formatted prompt for the OpenAI API - """ - requirements_section = "\n".join([f"{i+1}. {req}" for i, req in enumerate(requirements)]) - - return f"""Please analyze the following manuscript against these requirements: - -{requirements_section} - -For each requirement: -1. Determine if it is met (YES/NO) -2. Provide evidence from the text -3. Give a brief explanation - -Manuscript text: -{manuscript_text} - -Please format your response as a JSON object with the following structure: -{{ - "requirements_analysis": [ - {{ - "requirement": "", - "is_met": , - "evidence": "", - "explanation": "" - }} - ], - "desk_rejection_recommendation": {{ - "should_reject": , - "justification": "" - }} -}}""" - - def _parse_response(self, response: str) -> Dict[str, Any]: - """ - Parse the OpenAI API response into a structured format. - - Args: - response (str): Raw response from the API - - Returns: - Dict[str, Any]: Parsed response - """ - try: - # Remove code block markers if present - cleaned_response = response.strip() - if cleaned_response.startswith("```"): - cleaned_response = cleaned_response.split("\n", 1)[1] # Remove first line - if cleaned_response.endswith("```"): - cleaned_response = cleaned_response.rsplit("\n", 1)[0] # Remove last line - if cleaned_response.startswith("json"): - cleaned_response = cleaned_response.split("\n", 1)[1] # Remove "json" line - - return json.loads(cleaned_response) - except json.JSONDecodeError as e: - print(f"Failed to parse JSON: {str(e)}") # Debug print - print(f"Response content: {response}") # Debug print - raise Exception("Failed to parse OpenAI response as JSON") \ No newline at end of file diff --git a/Backup/V2_Editorial_First_Decision_Support/src/pdf_parser.py b/Backup/V2_Editorial_First_Decision_Support/src/pdf_parser.py deleted file mode 100644 index 3aa8408..0000000 --- a/Backup/V2_Editorial_First_Decision_Support/src/pdf_parser.py +++ /dev/null @@ -1,175 +0,0 @@ -import fitz # PyMuPDF -import re -from typing import List, Dict, Optional, Tuple -from dataclasses import dataclass - -@dataclass -class TextBlock: - """Represents a block of text with its properties.""" - text: str - page: int - font_size: float - font_name: str - is_bold: bool - is_italic: bool - bbox: Tuple[float, float, float, float] - -class PDFParser: - """A class to parse PDF manuscripts with advanced text extraction capabilities.""" - - def __init__(self, pdf_path: str): - """ - Initialize the PDF parser. - - Args: - pdf_path (str): Path to the PDF file - """ - self.pdf_path = pdf_path - self.doc = None - self.text_blocks = [] - - def extract_text(self) -> str: - """ - Extract text from the PDF with structure preservation. - - Returns: - str: Extracted and structured text - """ - try: - self.doc = fitz.open(self.pdf_path) - self.text_blocks = [] - - # Process first 10 pages or less - max_pages = min(10, len(self.doc)) - - for page_num in range(max_pages): - page = self.doc[page_num] - blocks = self._extract_page_blocks(page, page_num) - self.text_blocks.extend(blocks) - - # Sort blocks by position and process - self.text_blocks.sort(key=lambda b: (b.page, b.bbox[1], b.bbox[0])) - - # Combine blocks into structured text - structured_text = self._combine_blocks() - - return structured_text - - except Exception as e: - raise Exception(f"Failed to extract text from PDF: {str(e)}") - finally: - if self.doc: - self.doc.close() - - def _extract_page_blocks(self, page: fitz.Page, page_num: int) -> List[TextBlock]: - """ - Extract text blocks from a page with formatting information. - - Args: - page (fitz.Page): PDF page - page_num (int): Page number - - Returns: - List[TextBlock]: List of text blocks with formatting - """ - blocks = [] - - # Get text with formatting information - text_dict = page.get_text("dict") - - for block in text_dict.get("blocks", []): - for line in block.get("lines", []): - for span in line.get("spans", []): - # Extract text and formatting - text = span.get("text", "").strip() - if not text: - continue - - font = span.get("font", "") - size = span.get("size", 0) - bbox = span.get("bbox", [0, 0, 0, 0]) - - # Check for bold/italic - flags = span.get("flags", 0) - is_bold = bool(flags & 2**1) # Check bold flag - is_italic = bool(flags & 2**0) # Check italic flag - - blocks.append(TextBlock( - text=text, - page=page_num + 1, - font_size=size, - font_name=font, - is_bold=is_bold, - is_italic=is_italic, - bbox=tuple(bbox) - )) - - return blocks - - def _combine_blocks(self) -> str: - """ - Combine text blocks into structured text. - - Returns: - str: Structured text - """ - structured_text = [] - current_section = None - - for block in self.text_blocks: - text = block.text - - # Detect headers based on font size and style - if block.font_size > 12 and block.is_bold: - if current_section: - structured_text.append("\n") - current_section = text - structured_text.append(f"\n{text}\n") - else: - # Regular text - structured_text.append(text) - - # Add space between paragraphs - if text.endswith(('.', '!', '?')): - structured_text.append("\n") - - return " ".join(structured_text) - - def get_word_count(self, text: str) -> int: - """ - Get the word count of the text. - - Args: - text (str): Text to count words in - - Returns: - int: Word count - """ - return len(text.split()) - - def detect_sections(self) -> Dict[str, List[str]]: - """ - Detect major sections in the document. - - Returns: - Dict[str, List[str]]: Dictionary of sections and their content - """ - sections = {} - current_section = "Introduction" - current_content = [] - - for block in self.text_blocks: - # Detect section headers - if block.font_size > 12 and block.is_bold: - if current_content: - sections[current_section] = current_content - current_section = block.text - current_content = [] - else: - current_content.append(block.text) - - # Add the last section - if current_content: - sections[current_section] = current_content - - return sections \ No newline at end of file diff --git a/Backup/V2_Editorial_First_Decision_Support/src/requirements_checker.py b/Backup/V2_Editorial_First_Decision_Support/src/requirements_checker.py deleted file mode 100644 index 29850e5..0000000 --- a/Backup/V2_Editorial_First_Decision_Support/src/requirements_checker.py +++ /dev/null @@ -1,80 +0,0 @@ -from typing import List, Dict, Any -from pdf_parser import PDFParser -from openai_client import OpenAIClient - -class RequirementsChecker: - """A class to check manuscript requirements using OpenAI's GPT model.""" - - def __init__(self, api_key: str = None): - """ - Initialize the requirements checker. - - Args: - api_key (str, optional): OpenAI API key - """ - self.openai_client = OpenAIClient(api_key) - - def check_manuscript(self, pdf_path: str, requirements: List[str]) -> Dict[str, Any]: - """ - Check if a manuscript meets the given requirements. - - Args: - pdf_path (str): Path to the PDF manuscript - requirements (List[str]): List of requirements to check - - Returns: - Dict[str, Any]: Analysis results - """ - # Parse PDF with structure preservation - pdf_parser = PDFParser(pdf_path) - manuscript_text = pdf_parser.extract_text() - - # Get sections for better context - sections = pdf_parser.detect_sections() - - # Calculate word count - word_count = len(manuscript_text.split()) - - # Add metadata and section information to the text - structured_text = f"""Document Metadata: -Word Count: {word_count} words - -Document Structure: -""" - for section, content in sections.items(): - section_text = ' '.join(content) - section_word_count = len(section_text.split()) - structured_text += f"\n{section} ({section_word_count} words):\n{section_text}\n" - - # Check requirements using OpenAI - analysis = self.openai_client.check_requirements(structured_text, requirements) - - return analysis - - def format_results(self, results: Dict[str, Any]) -> str: - """ - Format the analysis results into a readable string. - - Args: - results (Dict[str, Any]): Analysis results from OpenAI - - Returns: - str: Formatted results - """ - output = [] - output.append("=== Manuscript Requirements Analysis ===\n") - - # Format requirements analysis - for req_analysis in results["requirements_analysis"]: - output.append(f"Requirement: {req_analysis['requirement']}") - output.append(f"Status: {'✓ Met' if req_analysis['is_met'] else '✗ Not Met'}") - output.append(f"Evidence: {req_analysis['evidence']}") - output.append(f"Explanation: {req_analysis['explanation']}\n") - - # Format desk rejection recommendation - rejection = results["desk_rejection_recommendation"] - output.append("=== Final Recommendation ===") - output.append(f"Desk Rejection: {'Yes' if rejection['should_reject'] else 'No'}") - output.append(f"Justification: {rejection['justification']}") - - return "\n".join(output) \ No newline at end of file diff --git a/Backup/V2_Editorial_First_Decision_Support/test_api.py b/Backup/V2_Editorial_First_Decision_Support/test_api.py deleted file mode 100644 index 2dea7b6..0000000 --- a/Backup/V2_Editorial_First_Decision_Support/test_api.py +++ /dev/null @@ -1,38 +0,0 @@ -import os -from dotenv import load_dotenv -from openai import OpenAI - -def test_api_key(): - # Force reload of environment variables - load_dotenv(override=True) - - # Get API key - api_key = os.getenv("OPENAI_API_KEY") - print(f"API Key loaded: {'Yes' if api_key else 'No'}") - if api_key: - print(f"API Key starts with: {api_key[:7]}...") - print(f"API Key length: {len(api_key)}") - - # Print current working directory and .env file location - print(f"\nCurrent working directory: {os.getcwd()}") - env_path = os.path.join(os.getcwd(), '.env') - print(f"Looking for .env file at: {env_path}") - print(f".env file exists: {os.path.exists(env_path)}") - - try: - # Initialize OpenAI client - client = OpenAI(api_key=api_key) - - # Make a simple API call - response = client.chat.completions.create( - model="gpt-3.5-turbo", - messages=[{"role": "user", "content": "Hello!"}], - max_tokens=5 - ) - print("\nAPI call successful!") - print(f"Response: {response.choices[0].message.content}") - except Exception as e: - print(f"\nError: {str(e)}") - -if __name__ == "__main__": - test_api_key() \ No newline at end of file diff --git a/Backup/V3_Peer_Review/README.md b/Backup/V3_Peer_Review/README.md deleted file mode 100644 index 2230fd0..0000000 --- a/Backup/V3_Peer_Review/README.md +++ /dev/null @@ -1,81 +0,0 @@ -# Academic Manuscript Peer Review Tool - -This tool uses OpenAI's GPT-4 to perform automated peer reviews of academic manuscripts. It analyzes PDF manuscripts against a set of review criteria and provides detailed feedback, scores, and recommendations. - -## Features - -- Automated peer review of academic manuscripts -- Comprehensive analysis across multiple review criteria -- Detailed feedback with specific examples and suggestions -- Metadata extraction and document structure analysis -- Support for multiple PDF files -- Configurable review criteria - -## Installation - -1. Clone the repository -2. Install the required dependencies: - ```bash - pip install -r requirements.txt - ``` -3. Create a `.env` file in the root directory with your OpenAI API key: - ``` - OPENAI_API_KEY=your_api_key_here - ``` - Note: The tool will look for the .env file in the current directory first, then in the parent directory. - -## Usage - -1. Place your PDF manuscripts in the `manuscripts` directory -2. (Optional) Customize the review criteria in `review_criteria.json` -3. Run the review tool: - ```bash - python src/main.py --criteria review_criteria.json - ``` - -### Command Line Arguments - -- `--manuscripts-dir`: Directory containing PDF manuscripts (default: `manuscripts`) -- `--criteria`: Path to the review criteria JSON file (required) -- `--output-dir`: Directory to save review results (default: `analysis_results`) -- `--api-key`: OpenAI API key (optional if set in environment) - -## Review Criteria - -The tool evaluates manuscripts against the following criteria: - -1. Originality and Innovation -2. Methodology -3. Results and Analysis -4. Writing and Presentation -5. Technical Accuracy -6. Literature Review -7. Figures and Tables -8. References -9. Ethical Considerations -10. Impact and Significance - -Each criterion is scored on a scale of 1-5, with detailed feedback and specific examples provided. - -## Output Format - -The review results are saved in text files with the following sections: - -- Manuscript Metadata -- Document Statistics -- Overall Assessment -- Detailed Assessment (per criterion) - - Score - - Feedback - - Examples - - Suggestions for Improvement - -## Requirements - -- Python 3.7+ -- OpenAI API key -- PDF manuscripts to review - -## License - -This project is licensed under the MIT License - see the LICENSE file for details. \ No newline at end of file diff --git a/Backup/V3_Peer_Review/requirements.txt b/Backup/V3_Peer_Review/requirements.txt deleted file mode 100644 index d413891..0000000 --- a/Backup/V3_Peer_Review/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -openai>=1.0.0 -python-dotenv>=0.19.0 -PyPDF2>=3.0.0 \ No newline at end of file diff --git a/Backup/V3_Peer_Review/review_criteria.json b/Backup/V3_Peer_Review/review_criteria.json deleted file mode 100644 index 8ae077d..0000000 --- a/Backup/V3_Peer_Review/review_criteria.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "Originality and Innovation": "Assess the novelty and originality of the research. Consider if the work makes a significant contribution to the field and introduces new ideas or approaches.", - "Methodology": "Evaluate the research design, methods, and procedures. Consider if they are appropriate, well-described, and rigorously implemented.", - "Results and Analysis": "Assess the presentation and analysis of results. Consider if the data is properly analyzed, interpreted, and presented in a clear and logical manner.", - "Writing and Presentation": "Evaluate the clarity, organization, and quality of the writing. Consider if the manuscript is well-structured, easy to follow, and free of major language issues.", - "Technical Accuracy": "Assess the technical accuracy of the content, including mathematical derivations, statistical analyses, and experimental procedures.", - "Literature Review": "Evaluate the comprehensiveness and relevance of the literature review. Consider if it adequately covers the field and provides proper context for the research.", - "Figures and Tables": "Assess the quality and appropriateness of figures and tables. Consider if they are clear, well-labeled, and effectively support the text.", - "References": "Evaluate the completeness and accuracy of references. Consider if they are properly formatted and relevant to the research.", - "Ethical Considerations": "Assess if the research adheres to ethical standards and guidelines. Consider issues such as informed consent, data privacy, and conflict of interest.", - "Impact and Significance": "Evaluate the potential impact and significance of the research. Consider if it addresses an important question and has the potential to influence the field." -} \ No newline at end of file diff --git a/Backup/V3_Peer_Review/src/main.py b/Backup/V3_Peer_Review/src/main.py deleted file mode 100644 index 16d21f0..0000000 --- a/Backup/V3_Peer_Review/src/main.py +++ /dev/null @@ -1,110 +0,0 @@ -import argparse -import json -import os -from typing import Dict, List -from peer_review_checker import PeerReviewChecker - -def read_review_criteria(criteria_path: str) -> Dict[str, str]: - """ - Read review criteria from a JSON file. - - Args: - criteria_path (str): Path to the criteria file - - Returns: - Dict[str, str]: Dictionary of criteria and their descriptions - """ - with open(criteria_path, 'r') as f: - return json.load(f) - -def get_pdf_files(directory: str) -> List[str]: - """ - Get all PDF files from a directory. - - Args: - directory (str): Path to the directory - - Returns: - List[str]: List of PDF file paths - """ - pdf_files = [] - for file in os.listdir(directory): - if file.lower().endswith('.pdf'): - pdf_files.append(os.path.join(directory, file)) - return pdf_files - -def review_manuscript(checker: PeerReviewChecker, pdf_path: str, criteria: Dict[str, str], output_dir: str) -> None: - """ - Review a single manuscript and save results to a file. - - Args: - checker (PeerReviewChecker): The peer review checker instance - pdf_path (str): Path to the PDF file - criteria (Dict[str, str]): Review criteria - output_dir (str): Directory to save the results - """ - try: - # Get the base filename without extension - base_name = os.path.splitext(os.path.basename(pdf_path))[0] - - # Review manuscript - results = checker.review_manuscript(pdf_path, criteria) - - # Format results - formatted_results = checker.format_results(results) - - # Save results to file - output_file = os.path.join(output_dir, f"{base_name}_review.txt") - with open(output_file, 'w') as f: - f.write(formatted_results) - - print(f"Review completed for {base_name}") - print(f"Results saved to: {output_file}\n") - - except Exception as e: - print(f"Error processing {pdf_path}: {str(e)}\n") - -def main(): - parser = argparse.ArgumentParser(description='Academic Manuscript Peer Review Tool') - parser.add_argument('--manuscripts-dir', default='manuscripts', - help='Directory containing PDF manuscripts (default: manuscripts)') - parser.add_argument('--criteria', required=True, help='Path to the review criteria JSON file') - parser.add_argument('--output-dir', default='analysis_results', - help='Directory to save review results (default: analysis_results)') - parser.add_argument('--api-key', help='OpenAI API key (optional if set in environment)') - - args = parser.parse_args() - - try: - # Create output directory if it doesn't exist - os.makedirs(args.output_dir, exist_ok=True) - - # Read review criteria - criteria = read_review_criteria(args.criteria) - - # Get PDF files - pdf_files = get_pdf_files(args.manuscripts_dir) - - if not pdf_files: - print(f"No PDF files found in {args.manuscripts_dir}") - return 1 - - print(f"Found {len(pdf_files)} PDF files to review") - - # Initialize checker - checker = PeerReviewChecker(api_key=args.api_key) - - # Process each PDF - for pdf_path in pdf_files: - review_manuscript(checker, pdf_path, criteria, args.output_dir) - - print("Review process complete!") - - except Exception as e: - print(f"Error: {str(e)}") - return 1 - - return 0 - -if __name__ == '__main__': - exit(main()) \ No newline at end of file diff --git a/Backup/V3_Peer_Review/src/openai_client.py b/Backup/V3_Peer_Review/src/openai_client.py deleted file mode 100644 index 58df1e4..0000000 --- a/Backup/V3_Peer_Review/src/openai_client.py +++ /dev/null @@ -1,124 +0,0 @@ -import os -import json -from typing import List, Dict, Any -from openai import OpenAI -from dotenv import load_dotenv - -class OpenAIClient: - """A class to handle interactions with the OpenAI API for peer review.""" - - def __init__(self, api_key: str = None): - """ - Initialize the OpenAI client. - - Args: - api_key (str, optional): OpenAI API key. If not provided, will try to load from environment. - """ - # Try to load .env from the current directory - load_dotenv() - - # If API key is not found, try to load from parent directory - if not os.getenv("OPENAI_API_KEY"): - # Get the path to the parent directory (two levels up from this file) - parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")) - env_path = os.path.join(parent_dir, ".env") - if os.path.exists(env_path): - load_dotenv(env_path) - - self.api_key = api_key or os.getenv("OPENAI_API_KEY") - if not self.api_key: - raise ValueError("OpenAI API key is required") - self.client = OpenAI(api_key=self.api_key) - - def analyze_manuscript(self, manuscript_text: str, review_criteria: Dict[str, str]) -> Dict[str, Any]: - """ - Analyze a manuscript using GPT-4 for comprehensive peer review. - - Args: - manuscript_text (str): The full text of the manuscript - review_criteria (Dict[str, str]): Dictionary of review criteria and their descriptions - - Returns: - Dict[str, Any]: Analysis results including scores and detailed feedback - """ - # Truncate manuscript text to first 4000 words to reduce token usage - words = manuscript_text.split() - truncated_text = ' '.join(words[:4000]) if len(words) > 4000 else manuscript_text - - prompt = self._create_review_prompt(truncated_text, review_criteria) - - try: - response = self.client.chat.completions.create( - model="gpt-4", # Using GPT-4 for more sophisticated analysis - messages=[ - {"role": "system", "content": "You are an expert peer reviewer with extensive experience in academic publishing. Analyze manuscripts thoroughly and provide detailed, constructive feedback. Be objective and evidence-based in your assessment."}, - {"role": "user", "content": prompt} - ], - temperature=0.3, - max_tokens=2000 # Increased token limit for detailed feedback - ) - - return self._parse_response(response.choices[0].message.content) - - except Exception as e: - raise Exception(f"Failed to analyze manuscript: {str(e)}") - - def _create_review_prompt(self, manuscript_text: str, review_criteria: Dict[str, str]) -> str: - """ - Create a prompt for the peer review analysis. - - Args: - manuscript_text (str): The manuscript text - review_criteria (Dict[str, str]): Review criteria and descriptions - - Returns: - str: Formatted prompt for the OpenAI API - """ - criteria_section = "\n".join([f"- {criterion}: {description}" - for criterion, description in review_criteria.items()]) - - return f"""Please analyze the following manuscript according to these criteria: - -{criteria_section} - -For each criterion: -1. Provide a score from 1-5 (1 being lowest, 5 being highest) -2. Give detailed, constructive feedback -3. Support your assessment with specific examples from the text -4. Suggest specific improvements where applicable - -Manuscript text: -{manuscript_text} - -Please format your response as a JSON object with the following structure: -{{ - "overall_assessment": {{ - "score": <1-5>, - "summary": "" - }}, - "criteria_assessments": {{ - "": {{ - "score": <1-5>, - "feedback": "", - "examples": ["", ""], - "suggestions": ["", ""] - }} - }}, - "recommendation": "", - "confidence": <0-1> -}}""" - - def _parse_response(self, response: str) -> Dict[str, Any]: - """ - Parse the OpenAI API response into a structured format. - - Args: - response (str): Raw response from the API - - Returns: - Dict[str, Any]: Parsed response - """ - try: - return json.loads(response) - except json.JSONDecodeError: - raise Exception("Failed to parse OpenAI response as JSON") \ No newline at end of file diff --git a/Backup/V3_Peer_Review/src/pdf_parser.py b/Backup/V3_Peer_Review/src/pdf_parser.py deleted file mode 100644 index d12f3c4..0000000 --- a/Backup/V3_Peer_Review/src/pdf_parser.py +++ /dev/null @@ -1,117 +0,0 @@ -import os -import re -from typing import Dict, List, Tuple -import PyPDF2 - -class PDFParser: - """A class to parse PDF manuscripts and extract structured content.""" - - def __init__(self, pdf_path: str): - """ - Initialize the PDF parser. - - Args: - pdf_path (str): Path to the PDF file - """ - if not os.path.exists(pdf_path): - raise FileNotFoundError(f"PDF file not found: {pdf_path}") - self.pdf_path = pdf_path - - def extract_text(self) -> str: - """ - Extract text from the PDF file. - - Returns: - str: Extracted text - """ - try: - with open(self.pdf_path, 'rb') as file: - reader = PyPDF2.PdfReader(file) - text = "" - for page in reader.pages: - text += page.extract_text() + "\n" - return text.strip() - except Exception as e: - raise Exception(f"Failed to extract text from PDF: {str(e)}") - - def detect_sections(self) -> Dict[str, List[str]]: - """ - Detect and extract sections from the manuscript. - - Returns: - Dict[str, List[str]]: Dictionary of section names and their content - """ - text = self.extract_text() - - # Common section headers in academic papers - section_patterns = { - 'Abstract': r'Abstract[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)', - 'Introduction': r'Introduction[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)', - 'Methods': r'(Methods|Methodology|Materials and Methods)[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)', - 'Results': r'Results[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)', - 'Discussion': r'Discussion[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)', - 'Conclusion': r'(Conclusion|Conclusions)[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:)', - 'References': r'(References|Bibliography)[\s\S]*?(?=\n\n|\n[A-Z][a-z]+:|$)' - } - - sections = {} - for section_name, pattern in section_patterns.items(): - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - section_text = match.group(0).strip() - # Clean up the section text - section_text = re.sub(r'^\w+\s*', '', section_text) # Remove section header - sections[section_name] = section_text.split('\n') - - return sections - - def get_metadata(self) -> Dict[str, str]: - """ - Extract metadata from the PDF. - - Returns: - Dict[str, str]: Dictionary of metadata - """ - try: - with open(self.pdf_path, 'rb') as file: - reader = PyPDF2.PdfReader(file) - metadata = reader.metadata - - return { - 'title': metadata.get('/Title', 'Unknown'), - 'author': metadata.get('/Author', 'Unknown'), - 'creation_date': metadata.get('/CreationDate', 'Unknown'), - 'page_count': str(len(reader.pages)) - } - except Exception as e: - raise Exception(f"Failed to extract metadata from PDF: {str(e)}") - - def get_references(self) -> List[str]: - """ - Extract references from the manuscript. - - Returns: - List[str]: List of references - """ - sections = self.detect_sections() - if 'References' in sections: - return sections['References'] - return [] - - def get_figures_and_tables(self) -> Tuple[List[str], List[str]]: - """ - Extract figures and tables from the manuscript. - - Returns: - Tuple[List[str], List[str]]: Lists of figures and tables - """ - text = self.extract_text() - - # Simple pattern matching for figures and tables - figure_pattern = r'Figure \d+[.:].*?(?=\n\n|\n[A-Z][a-z]+:)' - table_pattern = r'Table \d+[.:].*?(?=\n\n|\n[A-Z][a-z]+:)' - - figures = re.findall(figure_pattern, text, re.IGNORECASE | re.DOTALL) - tables = re.findall(table_pattern, text, re.IGNORECASE | re.DOTALL) - - return figures, tables \ No newline at end of file diff --git a/Backup/V3_Peer_Review/src/peer_review_checker.py b/Backup/V3_Peer_Review/src/peer_review_checker.py deleted file mode 100644 index a30a45a..0000000 --- a/Backup/V3_Peer_Review/src/peer_review_checker.py +++ /dev/null @@ -1,121 +0,0 @@ -from typing import Dict, Any, List -from pdf_parser import PDFParser -from openai_client import OpenAIClient - -class PeerReviewChecker: - """A class to coordinate the peer review process.""" - - def __init__(self, api_key: str = None): - """ - Initialize the peer review checker. - - Args: - api_key (str, optional): OpenAI API key - """ - self.openai_client = OpenAIClient(api_key) - - def review_manuscript(self, pdf_path: str, review_criteria: Dict[str, str]) -> Dict[str, Any]: - """ - Review a manuscript using the specified criteria. - - Args: - pdf_path (str): Path to the PDF manuscript - review_criteria (Dict[str, str]): Dictionary of review criteria and their descriptions - - Returns: - Dict[str, Any]: Review results - """ - # Parse PDF - pdf_parser = PDFParser(pdf_path) - - # Get manuscript metadata - metadata = pdf_parser.get_metadata() - - # Extract text and structure - manuscript_text = pdf_parser.extract_text() - sections = pdf_parser.detect_sections() - - # Get references and figures/tables - references = pdf_parser.get_references() - figures, tables = pdf_parser.get_figures_and_tables() - - # Add metadata and structure information to the text - structured_text = f"""Document Metadata: -Title: {metadata['title']} -Author: {metadata['author']} -Pages: {metadata['page_count']} -Creation Date: {metadata['creation_date']} - -Document Structure: -""" - for section, content in sections.items(): - section_text = ' '.join(content) - section_word_count = len(section_text.split()) - structured_text += f"\n{section} ({section_word_count} words):\n{section_text}\n" - - # Add references and figures/tables information - structured_text += f"\nReferences ({len(references)}):\n" + "\n".join(references) - structured_text += f"\n\nFigures ({len(figures)}):\n" + "\n".join(figures) - structured_text += f"\n\nTables ({len(tables)}):\n" + "\n".join(tables) - - # Analyze manuscript using OpenAI - analysis = self.openai_client.analyze_manuscript(structured_text, review_criteria) - - # Add metadata to the analysis results - analysis['metadata'] = metadata - analysis['statistics'] = { - 'total_references': len(references), - 'total_figures': len(figures), - 'total_tables': len(tables), - 'total_sections': len(sections) - } - - return analysis - - def format_results(self, results: Dict[str, Any]) -> str: - """ - Format the review results into a readable string. - - Args: - results (Dict[str, Any]): Review results - - Returns: - str: Formatted results - """ - output = [] - - # Add metadata section - output.append("=== Manuscript Metadata ===") - for key, value in results['metadata'].items(): - output.append(f"{key}: {value}") - - # Add statistics section - output.append("\n=== Document Statistics ===") - for key, value in results['statistics'].items(): - output.append(f"{key}: {value}") - - # Add overall assessment - output.append("\n=== Overall Assessment ===") - output.append(f"Score: {results['overall_assessment']['score']}/5") - output.append(f"Summary: {results['overall_assessment']['summary']}") - output.append(f"Recommendation: {results['recommendation']}") - output.append(f"Confidence: {results['confidence']*100:.1f}%") - - # Add criteria assessments - output.append("\n=== Detailed Assessment ===") - for criterion, assessment in results['criteria_assessments'].items(): - output.append(f"\n{criterion}") - output.append(f"Score: {assessment['score']}/5") - output.append(f"Feedback: {assessment['feedback']}") - - if assessment['examples']: - output.append("\nExamples:") - for example in assessment['examples']: - output.append(f"- {example}") - - if assessment['suggestions']: - output.append("\nSuggestions for Improvement:") - for suggestion in assessment['suggestions']: - output.append(f"- {suggestion}") - - return "\n".join(output) \ No newline at end of file diff --git a/Backup/V4_multi_agent/README.md b/Backup/V4_multi_agent/README.md deleted file mode 100644 index d3df89f..0000000 --- a/Backup/V4_multi_agent/README.md +++ /dev/null @@ -1,126 +0,0 @@ -# Multi-Agent Scientific Paper Review System - -A sophisticated system that uses multiple AI agents to provide comprehensive peer review of scientific papers. The system simulates a team of expert reviewers, each with specific expertise and focus areas, to analyze papers from multiple perspectives. - -## Features - -### 1. Specialized Review Agents -- **Core Reviewers** (Always included): - - Language and Style Expert - - Methodology Expert - - Ethics and Compliance Expert - - Literature Review Expert - - Impact and Significance Expert - - Research Gap and Contribution Expert - -- **Domain-Specific Reviewers** (Added based on paper content): - - Technical Area Experts (e.g., Machine Learning, Healthcare Systems) - - Field-Specific Experts (e.g., Computer Science, Medical Research) - -- **Specialized Reviewers** (Added based on requirements): - - Data Analysis Expert - - Experimental Design Expert - - Literature Coverage Expert - - Research Gap and Contribution Expert - -### 2. Key Scientist Simulation -- Identifies influential scientists in the field -- Simulates their likely perspective and feedback -- Considers their research focus and typical review style -- Provides realistic reviewer comments - -### 3. Comprehensive Analysis -- Paper analysis and domain identification -- Research gap analysis -- Contribution positioning -- Literature coverage assessment -- Methodology evaluation -- Impact assessment - -### 4. Detailed Reporting -- Executive summary -- Individual reviewer feedback -- Thematic analysis across reviews -- Specific recommendations -- Final assessment and timeline - -## Technical Details - -### Model Configuration -- Supports multiple OpenAI models: - - Default: GPT-3.5-turbo (for testing) - - Production: GPT-4-turbo-preview (for high-quality reviews) -- Easy model switching for different use cases - -### Output Format -```json -{ - "review_plan": { - "paper_analysis": {...}, - "review_team": {...}, - "key_scientists": [...], - "simulated_feedback": {...} - }, - "final_report": { - "executive_summary": {...}, - "reviewer_feedback": {...}, - "thematic_analysis": {...}, - "specific_recommendations": {...}, - "final_assessment": {...} - } -} -``` - -## Setup - -1. Install dependencies: -```bash -pip install -r requirements.txt -``` - -2. Configure OpenAI API key: -- Create a `.env` file in the project root -- Add your OpenAI API key: -``` -OPENAI_API_KEY=your-api-key-here -``` - -3. Run the system: -```bash -python src/test_editor.py -``` - -## Usage - -1. Initialize the editor agent: -```python -from editor_agent import EditorAgent - -# Use default model (GPT-3.5-turbo) -editor = EditorAgent() - -# Or specify custom model configuration -editor = EditorAgent({ - "default": "gpt-3.5-turbo", - "production": "gpt-4-turbo-preview", - "current": "default" -}) -``` - -2. Generate a review: -```python -result = editor.generate_review_plan(paper_content) -``` - -3. Switch models if needed: -```python -editor.set_model("production") # Switch to GPT-4 -``` - -## Contributing - -Contributions are welcome! Please feel free to submit a Pull Request. - -## License - -This project is licensed under the MIT License - see the LICENSE file for details. \ No newline at end of file diff --git a/Backup/V4_multi_agent/requirements.txt b/Backup/V4_multi_agent/requirements.txt deleted file mode 100644 index 47a966d..0000000 --- a/Backup/V4_multi_agent/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ -openai>=1.0.0 -python-dotenv>=0.19.0 -PyPDF2>=3.0.0 -langchain>=0.1.0 -langchain-community>=0.0.10 -typing-extensions>=4.0.0 -requests>=2.31.0 -python-json-logger>=2.0.0 \ No newline at end of file diff --git a/Backup/V4_multi_agent/src/coordinator_agent.py b/Backup/V4_multi_agent/src/coordinator_agent.py deleted file mode 100644 index 6f91e66..0000000 --- a/Backup/V4_multi_agent/src/coordinator_agent.py +++ /dev/null @@ -1,201 +0,0 @@ -from typing import Dict, List, Any -import json -from openai_client import OpenAIClient - -class CoordinatorAgent: - """Coordinator agent for synthesizing specialized reviews.""" - - def __init__(self): - self.client = OpenAIClient() - self.paper_text = None # Store paper text for use across methods - - def synthesize_reviews(self, specialized_reviews: Dict[str, Dict[str, Any]], paper_text: str) -> Dict[str, Any]: - """Synthesize specialized reviews into a comprehensive report. - - Args: - specialized_reviews (Dict[str, Dict[str, Any]]): Reviews from specialized agents - paper_text (str): The text content of the paper - - Returns: - Dict[str, Any]: Comprehensive review report - """ - self.paper_text = paper_text # Store paper text for use in other methods - - prompt = f"""Synthesize these specialized reviews into a comprehensive report. - -Specialized Reviews: -{json.dumps(specialized_reviews, indent=2)} - -Paper text: -{paper_text[:8000]} # Limit text length to avoid token limits - -Provide a comprehensive synthesis in the following JSON format: -{{ - "paper_overview": {{ - "domain": "Main domain of the paper", - "key_contributions": ["List of key contributions"], - "technical_areas": ["List of technical areas covered"] - }}, - "specialized_reviews": {{ - "agent_id": {{ - "expertise": ["List of expertise areas"], - "findings": "Summary of findings", - "recommendations": ["List of recommendations"] - }} - }}, - "cross_domain_analysis": {{ - "interdependencies": ["List of interdependencies between domains"], - "conflicts": ["List of conflicts between domains"], - "synergies": ["List of synergies between domains"] - }}, - "comprehensive_assessment": {{ - "ratings": {{ - "technical_quality": {{"score": <1-10>, "justification": "text"}}, - "methodology": {{"score": <1-10>, "justification": "text"}}, - "innovation": {{"score": <1-10>, "justification": "text"}}, - "clarity": {{"score": <1-10>, "justification": "text"}}, - "ethics": {{"score": <1-10>, "justification": "text"}}, - "impact": {{"score": <1-10>, "justification": "text"}} - }}, - "key_strengths": ["List of key strengths"], - "key_weaknesses": ["List of key weaknesses"], - "critical_issues": ["List of critical issues"] - }}, - "action_plan": {{ - "priority_actions": ["List of priority actions"], - "timeline": "Estimated timeline for improvements", - "resource_requirements": ["List of resource requirements"] - }}, - "final_recommendation": {{ - "decision": "Accept/Minor Revision/Major Revision/Reject", - "justification": "Detailed justification", - "next_steps": ["List of next steps"] - }} -}} - -Ensure your response is valid JSON and includes all required fields.""" - - try: - response = self.client.analyze_manuscript(paper_text, { - "role": "expert scientific editor synthesizing specialized reviews", - "task": "synthesize reviews", - "prompt": prompt - }) - - if "error" in response: - raise Exception(f"Failed to synthesize reviews: {response['error']}") - - # Extract JSON from response if needed - if isinstance(response, str): - try: - start_idx = response.find('{') - end_idx = response.rfind('}') + 1 - if start_idx >= 0 and end_idx > start_idx: - response = json.loads(response[start_idx:end_idx]) - else: - raise ValueError("No JSON found in response") - except json.JSONDecodeError as e: - raise Exception(f"Failed to parse JSON response: {str(e)}") - - return response - - except Exception as e: - print(f"Error synthesizing reviews: {e}") - return { - "error": "Failed to synthesize reviews", - "details": str(e) - } - - def generate_final_report(self, specialized_reviews: Dict[str, Dict[str, Any]], synthesis: Dict[str, Any]) -> Dict[str, Any]: - """Generate the final comprehensive report. - - Args: - specialized_reviews (Dict[str, Dict[str, Any]]): Reviews from specialized agents - synthesis (Dict[str, Any]): Synthesis of reviews - - Returns: - Dict[str, Any]: Final comprehensive report - """ - if not self.paper_text: - raise ValueError("Paper text not available. Call synthesize_reviews first.") - - prompt = f"""Generate a final comprehensive report combining specialized reviews and synthesis. - -Specialized Reviews: -{json.dumps(specialized_reviews, indent=2)} - -Synthesis: -{json.dumps(synthesis, indent=2)} - -Paper text: -{self.paper_text[:8000]} # Limit text length to avoid token limits - -Provide the final report in the following JSON format: -{{ - "executive_summary": {{ - "paper_overview": "Brief overview of the paper", - "key_findings": ["List of key findings"], - "recommendation": "Final recommendation" - }}, - "detailed_reviews": {{ - "agent_id": {{ - "expertise": ["List of expertise areas"], - "detailed_analysis": "Detailed analysis", - "specific_recommendations": ["List of specific recommendations"] - }} - }}, - "cross_domain_analysis": {{ - "interdependencies": ["List of interdependencies"], - "conflicts": ["List of conflicts"], - "synergies": ["List of synergies"] - }}, - "comprehensive_assessment": {{ - "overall_quality": 0, - "key_strengths": ["List of key strengths"], - "key_weaknesses": ["List of key weaknesses"], - "critical_issues": ["List of critical issues"] - }}, - "action_plan": {{ - "priority_actions": ["List of priority actions"], - "timeline": "Estimated timeline", - "resource_requirements": ["List of resource requirements"] - }}, - "final_recommendation": {{ - "decision": "Accept/Minor Revision/Major Revision/Reject", - "justification": "Detailed justification", - "next_steps": ["List of next steps"] - }} -}} - -Ensure your response is valid JSON and includes all required fields.""" - - try: - response = self.client.analyze_manuscript(self.paper_text, { - "role": "expert scientific editor generating final report", - "task": "generate final report", - "prompt": prompt - }) - - if "error" in response: - raise Exception(f"Failed to generate final report: {response['error']}") - - # Extract JSON from response if needed - if isinstance(response, str): - try: - start_idx = response.find('{') - end_idx = response.rfind('}') + 1 - if start_idx >= 0 and end_idx > start_idx: - response = json.loads(response[start_idx:end_idx]) - else: - raise ValueError("No JSON found in response") - except json.JSONDecodeError as e: - raise Exception(f"Failed to parse JSON response: {str(e)}") - - return response - - except Exception as e: - print(f"Error generating final report: {e}") - return { - "error": "Failed to generate final report", - "details": str(e) - } \ No newline at end of file diff --git a/Backup/V4_multi_agent/src/editor_agent.py b/Backup/V4_multi_agent/src/editor_agent.py deleted file mode 100644 index ba11b8b..0000000 --- a/Backup/V4_multi_agent/src/editor_agent.py +++ /dev/null @@ -1,970 +0,0 @@ -from typing import Dict, List, Any -import json -import os -from openai_client import OpenAIClient - -class EditorAgent: - """Editor agent that analyzes papers and creates specialized review teams.""" - - def __init__(self, model_config: Dict[str, str] = None): - """Initialize the editor agent with configurable model settings. - - Args: - model_config (Dict[str, str], optional): Configuration for different models. Defaults to: - { - "default": "gpt-3.5-turbo", # Cheap model for testing - "production": "gpt-4-turbo-preview", # More expensive model for production - "current": "default" # Which model to use currently - } - """ - try: - self.client = OpenAIClient() - except Exception as e: - raise RuntimeError(f"Failed to initialize OpenAI client: {str(e)}") - - # Default model configuration - self.model_config = { - "default": "gpt-3.5-turbo", # Cheap model for testing - "production": "gpt-4-turbo-preview", # More expensive model for production - "current": "default" # Which model to use currently - } - - # Update with any provided configuration - if model_config: - self.model_config.update(model_config) - - # Core reviewers that are always included - self.core_reviewers = [ - { - "id": "audience_terminology_reviewer", - "role": "Audience and Terminology Expert", - "expertise": [ - "Audience Analysis", - "Field-Specific Terminology", - "Technical Language", - "Communication Strategy" - ], - "focus_areas": [ - "Audience Appropriateness", - "Terminology Usage", - "Technical Language Clarity", - "Communication Effectiveness" - ], - "review_criteria": [ - "Audience targeting", - "Terminology accuracy", - "Technical language appropriateness", - "Communication strategy effectiveness" - ], - "required_background": [ - "Audience analysis", - "Field-specific terminology", - "Technical communication", - "Communication strategies" - ] - }, - { - "id": "grammar_structure_reviewer", - "role": "Grammar and Structure Expert", - "expertise": [ - "Grammar", - "Sentence Structure", - "Paragraph Organization", - "Text Flow" - ], - "focus_areas": [ - "Grammatical Correctness", - "Sentence Construction", - "Paragraph Coherence", - "Text Flow and Transitions" - ], - "review_criteria": [ - "Grammar accuracy", - "Sentence structure", - "Paragraph organization", - "Text flow and coherence" - ], - "required_background": [ - "Grammar rules", - "Sentence construction", - "Paragraph organization", - "Text flow principles" - ] - }, - { - "id": "spelling_mechanics_reviewer", - "role": "Spelling and Mechanics Expert", - "expertise": [ - "Spelling", - "Punctuation", - "Capitalization", - "Formatting" - ], - "focus_areas": [ - "Spelling Accuracy", - "Punctuation Usage", - "Capitalization Rules", - "Formatting Consistency" - ], - "review_criteria": [ - "Spelling correctness", - "Punctuation accuracy", - "Capitalization appropriateness", - "Formatting consistency" - ], - "required_background": [ - "Spelling rules", - "Punctuation guidelines", - "Capitalization standards", - "Formatting conventions" - ] - }, - { - "id": "visual_presentation_reviewer", - "role": "Visual Presentation Expert", - "expertise": [ - "Figure Design", - "Table Layout", - "Visual Communication", - "Publication Format" - ], - "focus_areas": [ - "Figure Clarity and Design", - "Table Organization", - "Visual Communication Effectiveness", - "Publication Format Compliance" - ], - "review_criteria": [ - "Figure clarity and design", - "Table organization", - "Visual communication effectiveness", - "Publication format compliance" - ], - "required_background": [ - "Figure design principles", - "Table layout standards", - "Visual communication", - "Publication guidelines" - ] - }, - { - "id": "literature_review_expert", - "role": "Literature Review Expert", - "expertise": [ - "Literature Synthesis", - "Citation Analysis", - "Reference Management", - "Literature Coverage" - ], - "focus_areas": [ - "Citation Accuracy", - "Literature Coverage", - "Reference Consistency", - "Literature Gaps" - ], - "review_criteria": [ - "Citation completeness", - "Literature coverage", - "Reference formatting", - "Literature gap identification" - ], - "required_background": [ - "Citation standards", - "Literature review methodologies", - "Reference management", - "Literature analysis" - ] - }, - { - "id": "data_analysis_expert", - "role": "Data Analysis Expert", - "expertise": [ - "Statistical Methods", - "Data Interpretation", - "Analytical Rigor", - "Statistical Validity" - ], - "focus_areas": [ - "Statistical Validity", - "Data Interpretation Accuracy", - "Analytical Methods", - "Statistical Rigor" - ], - "review_criteria": [ - "Statistical appropriateness", - "Data interpretation quality", - "Analytical rigor", - "Statistical validity" - ], - "required_background": [ - "Statistical analysis", - "Data interpretation", - "Analytical methodologies", - "Statistical validation" - ] - }, - { - "id": "results_presentation_expert", - "role": "Results Presentation Expert", - "expertise": [ - "Results Organization", - "Data Visualization", - "Findings Presentation", - "Results Clarity" - ], - "focus_areas": [ - "Results Clarity", - "Data Presentation", - "Findings Organization", - "Results Impact" - ], - "review_criteria": [ - "Results presentation quality", - "Data visualization effectiveness", - "Findings clarity", - "Results impact" - ], - "required_background": [ - "Results presentation", - "Data visualization", - "Scientific communication", - "Impact assessment" - ] - }, - { - "id": "discussion_quality_expert", - "role": "Discussion Quality Expert", - "expertise": [ - "Discussion Depth", - "Interpretation Quality", - "Implications Analysis", - "Discussion Structure" - ], - "focus_areas": [ - "Discussion Thoroughness", - "Interpretation Accuracy", - "Implications Clarity", - "Discussion Flow" - ], - "review_criteria": [ - "Discussion quality", - "Interpretation depth", - "Implications presentation", - "Discussion structure" - ], - "required_background": [ - "Discussion methodologies", - "Interpretation frameworks", - "Implications analysis", - "Discussion structure" - ] - }, - { - "id": "conclusion_strength_expert", - "role": "Conclusion Strength Expert", - "expertise": [ - "Conclusion Formulation", - "Summary Quality", - "Future Directions", - "Conclusion Impact" - ], - "focus_areas": [ - "Conclusion Clarity", - "Summary Completeness", - "Future Directions Relevance", - "Conclusion Impact" - ], - "review_criteria": [ - "Conclusion strength", - "Summary quality", - "Future directions appropriateness", - "Conclusion impact" - ], - "required_background": [ - "Conclusion writing", - "Summary techniques", - "Future research planning", - "Impact assessment" - ] - }, - { - "id": "abstract_quality_expert", - "role": "Abstract Quality Expert", - "expertise": [ - "Abstract Writing", - "Summary Skills", - "Key Points Extraction", - "Abstract Structure" - ], - "focus_areas": [ - "Abstract Clarity", - "Summary Completeness", - "Key Points Presentation", - "Abstract Impact" - ], - "review_criteria": [ - "Abstract quality", - "Summary accuracy", - "Key points clarity", - "Abstract impact" - ], - "required_background": [ - "Abstract writing", - "Summary techniques", - "Key points extraction", - "Impact assessment" - ] - }, - { - "id": "introduction_quality_expert", - "role": "Introduction Quality Expert", - "expertise": [ - "Introduction Writing", - "Context Setting", - "Research Gap Identification", - "Introduction Structure" - ], - "focus_areas": [ - "Introduction Clarity", - "Context Completeness", - "Research Gap Presentation", - "Introduction Flow" - ], - "review_criteria": [ - "Introduction quality", - "Context setting", - "Research gap clarity", - "Introduction flow" - ], - "required_background": [ - "Introduction writing", - "Context setting", - "Research gap identification", - "Introduction structure" - ] - }, - { - "id": "limitations_analysis_expert", - "role": "Limitations Analysis Expert", - "expertise": [ - "Limitations Identification", - "Constraint Analysis", - "Boundary Assessment", - "Limitations Impact" - ], - "focus_areas": [ - "Limitations Completeness", - "Constraint Clarity", - "Boundary Definition", - "Limitations Impact" - ], - "review_criteria": [ - "Limitations coverage", - "Constraint presentation", - "Boundary clarity", - "Limitations impact" - ], - "required_background": [ - "Limitations analysis", - "Constraint assessment", - "Boundary definition", - "Impact assessment" - ] - }, - { - "id": "future_work_expert", - "role": "Future Work Expert", - "expertise": [ - "Future Research Planning", - "Extension Identification", - "Direction Setting", - "Future Impact" - ], - "focus_areas": [ - "Future Work Clarity", - "Extension Relevance", - "Direction Appropriateness", - "Future Impact" - ], - "review_criteria": [ - "Future work quality", - "Extension value", - "Direction clarity", - "Future impact" - ], - "required_background": [ - "Future research planning", - "Extension assessment", - "Direction setting", - "Impact assessment" - ] - }, - { - "id": "cross_reference_expert", - "role": "Cross-Reference Expert", - "expertise": [ - "Cross-Reference Accuracy", - "Internal Consistency", - "Reference Linking", - "Reference Management" - ], - "focus_areas": [ - "Cross-Reference Completeness", - "Internal Consistency", - "Reference Linking", - "Reference Accuracy" - ], - "review_criteria": [ - "Cross-reference accuracy", - "Internal consistency", - "Reference linking quality", - "Reference accuracy" - ], - "required_background": [ - "Cross-reference standards", - "Internal consistency", - "Reference linking", - "Reference management" - ] - }, - { - "id": "methodology_reviewer", - "role": "Research Methodology Expert", - "expertise": [ - "Research Design", - "Statistical Analysis", - "Experimental Methods", - "Data Collection", - "Reproducibility" - ], - "focus_areas": [ - "Research Design Quality", - "Statistical Rigor", - "Experimental Protocol", - "Data Collection Methods", - "Reproducibility Standards" - ], - "review_criteria": [ - "Methodological soundness", - "Statistical analysis appropriateness", - "Experimental design quality", - "Data collection rigor", - "Reproducibility potential" - ], - "required_background": [ - "Research methodology", - "Statistical analysis", - "Experimental design", - "Data collection standards", - "Reproducibility frameworks" - ] - }, - { - "id": "ethics_compliance_reviewer", - "role": "Ethics and Compliance Expert", - "expertise": [ - "Research Ethics", - "Data Privacy", - "Informed Consent", - "Conflict of Interest", - "Regulatory Compliance" - ], - "focus_areas": [ - "Ethical Considerations", - "Data Protection", - "Participant Rights", - "Conflict Management", - "Regulatory Requirements" - ], - "review_criteria": [ - "Ethical compliance", - "Data privacy measures", - "Informed consent process", - "Conflict of interest disclosure", - "Regulatory adherence" - ], - "required_background": [ - "Research ethics", - "Data protection regulations", - "Human subjects research", - "Conflict of interest guidelines", - "Research compliance" - ] - }, - { - "id": "technical_implementation_reviewer", - "role": "Technical Implementation Expert", - "expertise": [ - "Technical Architecture", - "Implementation Quality", - "Code/Algorithm Review", - "System Design", - "Performance Optimization" - ], - "focus_areas": [ - "Technical Architecture", - "Implementation Details", - "Code/Algorithm Quality", - "System Design", - "Performance Considerations" - ], - "review_criteria": [ - "Technical architecture soundness", - "Implementation quality", - "Code/algorithm efficiency", - "System design appropriateness", - "Performance optimization" - ], - "required_background": [ - "Software architecture", - "Implementation best practices", - "Code review standards", - "System design principles", - "Performance optimization" - ] - }, - { - "id": "impact_significance_reviewer", - "role": "Impact and Significance Expert", - "expertise": [ - "Field Impact Assessment", - "Scientific Contribution", - "Practical Applications", - "Future Research Directions", - "Knowledge Advancement" - ], - "focus_areas": [ - "Scientific Impact", - "Field Contribution", - "Practical Relevance", - "Future Implications", - "Knowledge Gap Addressing" - ], - "review_criteria": [ - "Scientific contribution significance", - "Field impact potential", - "Practical application value", - "Future research implications", - "Knowledge advancement" - ], - "required_background": [ - "Impact assessment", - "Scientific contribution evaluation", - "Practical application analysis", - "Future research trends", - "Knowledge gap identification" - ] - }, - { - "id": "literature_coverage_expert", - "role": "Literature Coverage Expert", - "expertise": [ - "Literature Comprehensiveness", - "Related Work Analysis", - "Citation Completeness", - "Field Coverage", - "Recent Developments" - ], - "focus_areas": [ - "Literature Coverage Completeness", - "Related Work Analysis", - "Citation Appropriateness", - "Field Coverage Breadth", - "Recent Literature Integration" - ], - "review_criteria": [ - "Literature coverage completeness", - "Related work analysis depth", - "Citation appropriateness", - "Field coverage breadth", - "Recent literature integration" - ], - "required_background": [ - "Literature review methodologies", - "Citation analysis", - "Field coverage assessment", - "Recent developments tracking", - "Related work analysis" - ] - }, - { - "id": "research_gap_contribution_expert", - "role": "Research Gap and Contribution Expert", - "expertise": [ - "Research Gap Analysis", - "Contribution Positioning", - "Literature Synthesis", - "Field Impact Assessment", - "Academic Diplomacy" - ], - "focus_areas": [ - "Research Gap Identification", - "Contribution Significance", - "Literature Positioning", - "Field Impact Balance", - "Academic Tone Management" - ], - "review_criteria": [ - "Research gap clarity and justification", - "Contribution significance and uniqueness", - "Literature positioning accuracy", - "Field impact balance", - "Academic tone appropriateness" - ], - "required_background": [ - "Research gap analysis methodologies", - "Contribution assessment frameworks", - "Literature synthesis techniques", - "Field impact evaluation", - "Academic communication strategies" - ] - } - ] - - def get_current_model(self) -> str: - """Get the currently configured model to use.""" - model_key = self.model_config["current"] - return self.model_config[model_key] - - def set_model(self, model_key: str) -> None: - """Set which model configuration to use. - - Args: - model_key (str): Key of the model to use ("default" or "production") - """ - if model_key not in self.model_config: - raise ValueError(f"Unknown model key: {model_key}. Must be one of: {list(self.model_config.keys())}") - self.model_config["current"] = model_key - - def analyze_paper(self, paper_text: str) -> Dict[str, Any]: - """Analyze the paper to determine required expertise and review criteria. - - Args: - paper_text (str): The text content of the paper - - Returns: - Dict[str, Any]: Analysis of paper requirements and review team structure - """ - try: - response = self.client.analyze_manuscript(paper_text, { - "role": "expert scientific editor", - "task": "analyze paper", - "model": self.get_current_model(), - "prompt": """Analyze this scientific paper and provide: - 1. Main domain and subdomains - 2. Technical areas requiring expertise - 3. Required review team composition - 4. Key evaluation criteria - - Return the analysis in JSON format with these exact keys: - { - "domains": { - "main_domain": "string", - "subdomains": ["string"] - }, - "technical_areas": ["string"], - "required_expertise": ["string"], - "evaluation_criteria": ["string"] - }""" - }) - - if "error" in response: - raise Exception(response["error"]) - - return response - - except Exception as e: - print(f"Error analyzing paper: {str(e)}") - return { - "error": "Failed to analyze paper", - "details": str(e) - } - - def create_review_team(self, analysis: Dict[str, Any]) -> Dict[str, Any]: - """Create a specialized review team based on paper analysis. - - Args: - analysis (Dict[str, Any]): Paper analysis from analyze_paper - - Returns: - Dict[str, Any]: Review team configuration - """ - prompt = f"""Based on this paper analysis, create a specialized review team with specific expertise and focus areas. - -Analysis: -{json.dumps(analysis, indent=2)} - -Provide the review team configuration in the following JSON format: -{{ - "review_team": {{ - "agents": [ - {{ - "id": "unique_agent_id", - "role": "Specific role (e.g., Computer Vision Expert)", - "expertise": ["List of expertise areas"], - "focus_areas": ["List of focus areas"], - "review_criteria": ["List of specific criteria"], - "required_background": ["List of required background knowledge"] - }} - ], - "review_process": {{ - "sequence": ["Order of review steps"], - "dependencies": ["Review dependencies"], - "coordination_points": ["Points where agents need to coordinate"] - }} - }} -}} - -Ensure your response is valid JSON and includes all required fields.""" - - try: - response = self.client.analyze_manuscript("", { - "role": "expert scientific editor", - "task": "create review team", - "analysis": analysis, - "prompt": prompt, - "model": self.get_current_model() # Use configured model - }) - - if "error" in response: - raise Exception(f"Failed to create review team: {response['error']}") - - # Extract JSON from response if needed - if isinstance(response, str): - try: - start_idx = response.find('{') - end_idx = response.rfind('}') + 1 - if start_idx >= 0 and end_idx > start_idx: - response = json.loads(response[start_idx:end_idx]) - else: - raise ValueError("No JSON found in response") - except json.JSONDecodeError as e: - raise Exception(f"Failed to parse JSON response: {str(e)}") - - return response - - except Exception as e: - print(f"Error creating review team: {e}") - return { - "error": "Failed to create review team", - "details": str(e) - } - - def identify_key_scientists(self, paper_content: str) -> List[Dict[str, Any]]: - """Identify key scientists in the field based on paper content and citations.""" - try: - response = self.client.analyze_manuscript(paper_content, { - "role": "expert scientific editor", - "task": "identify key scientists", - "model": self.get_current_model(), - "prompt": """Identify the key scientists mentioned or relevant to this paper. - For each scientist provide: - { - "name": "string", - "research_focus": "string", - "review_style": "string", - "potential_concerns": ["string"], - "appreciated_areas": ["string"] - } - - Return the list in JSON format.""" - }) - - if "error" in response: - raise Exception(response["error"]) - - return response.get("scientists", []) - - except Exception as e: - print(f"Error identifying scientists: {str(e)}") - return [] - - def simulate_scientist_feedback(self, paper_content: str, scientist: Dict[str, Any]) -> Dict[str, Any]: - """Simulate feedback from a specific scientist.""" - try: - response = self.client.analyze_manuscript(paper_content, { - "role": "expert scientific editor", - "task": "simulate scientist feedback", - "model": self.get_current_model(), - "prompt": f"""As {scientist['name']}, an expert in {scientist['research_focus']}, - review this paper and provide feedback in this JSON format: - {{ - "overall_assessment": "string", - "strengths": ["string"], - "concerns": ["string"], - "recommendations": ["string"], - "final_verdict": "string" - }}""" - }) - - if "error" in response: - raise Exception(response["error"]) - - return response - - except Exception as e: - print(f"Error simulating feedback: {str(e)}") - return { - "error": f"Failed to simulate feedback for {scientist['name']}", - "details": str(e) - } - - def generate_final_report(self, review_plan: Dict[str, Any]) -> Dict[str, Any]: - """Generate a comprehensive final report including all reviewer feedback. - - Args: - review_plan (Dict[str, Any]): The complete review plan including all feedback - - Returns: - Dict[str, Any]: Comprehensive final report - """ - try: - response = self.client.analyze_manuscript(str(review_plan), { - "role": "expert scientific editor", - "task": "generate final report", - "model": self.get_current_model(), - "prompt": """Generate a comprehensive final report in this JSON format: - { - "executive_summary": { - "overall_assessment": "string", - "key_strengths": ["string"], - "key_concerns": ["string"], - "main_recommendations": ["string"] - }, - "thematic_analysis": { - "common_themes": ["string"], - "conflicting_feedback": ["string"], - "consensus_points": ["string"] - }, - "final_assessment": { - "overall_rating": "string", - "publication_readiness": "string", - "required_changes": ["string"], - "estimated_timeline": "string" - } - }""" - }) - - if "error" in response: - raise Exception(response["error"]) - - return response - - except Exception as e: - print(f"Error generating final report: {str(e)}") - return { - "error": "Failed to generate final report", - "details": str(e) - } - - def generate_review_plan(self, paper_content: str) -> Dict[str, Any]: - """Generate a complete review plan including simulated expert feedback.""" - try: - # Get basic paper analysis - paper_analysis = self.analyze_paper(paper_content) - if "error" in paper_analysis: - raise Exception(paper_analysis["error"]) - - # Identify key scientists - key_scientists = self.identify_key_scientists(paper_content) - - # Simulate feedback from each scientist - simulated_feedback = {} - for scientist in key_scientists: - feedback = self.simulate_scientist_feedback(paper_content, scientist) - if "error" not in feedback: - simulated_feedback[scientist['name']] = feedback - - # Generate the complete review plan - review_plan = { - "paper_analysis": paper_analysis, - "key_scientists": key_scientists, - "simulated_feedback": simulated_feedback - } - - # Generate the final report - final_report = self.generate_final_report(review_plan) - - return { - "review_plan": review_plan, - "final_report": final_report - } - - except Exception as e: - print(f"Error generating review plan: {str(e)}") - return { - "error": "Failed to generate review plan", - "details": str(e), - "review_plan": { - "paper_analysis": paper_analysis if 'paper_analysis' in locals() else {"error": "Analysis failed"}, - "key_scientists": key_scientists if 'key_scientists' in locals() else [], - "simulated_feedback": simulated_feedback if 'simulated_feedback' in locals() else {} - }, - "final_report": { - "error": "Report generation failed", - "executive_summary": {"error": "Generation failed"}, - "thematic_analysis": {"error": "Generation failed"}, - "final_assessment": {"error": "Generation failed"} - } - } - - def get_agent_feedback(self, agent_name, paper): - """Get detailed feedback from a specific specialized reviewer agent.""" - # Find the agent in the core reviewers - agent = next((a for a in self.core_reviewers if a['id'] == agent_name), None) - if not agent: - return {"error": f"Agent {agent_name} not found in review team"} - - # Create the prompt for the specific agent - prompt = f"""As a {agent['role']} reviewer specializing in {', '.join(agent['expertise'])}, - please provide detailed feedback on the following paper: - - {paper} - - Focus specifically on: - {', '.join(agent['review_criteria'])} - - Provide your feedback in the following JSON format: - {{ - "overall_assessment": "Detailed assessment of the paper from your expertise perspective", - "strengths": [ - "List of specific strengths identified" - ], - "areas_for_improvement": [ - "List of areas needing improvement" - ], - "specific_recommendations": [ - "List of actionable recommendations" - ], - "final_verdict": "Your final verdict on the paper" - }} - - Base your review on your expertise in {', '.join(agent['required_background'])}. - Ensure your response is valid JSON. - """ - - # Get the feedback from the agent - response = self.client.analyze_manuscript(paper, { - "role": "expert scientific editor", - "task": "get agent feedback", - "model": self.get_current_model(), - "prompt": prompt - }) - - # Extract JSON from response if needed - if isinstance(response, str): - try: - start_idx = response.find('{') - end_idx = response.rfind('}') + 1 - if start_idx >= 0 and end_idx > start_idx: - response = json.loads(response[start_idx:end_idx]) - except json.JSONDecodeError: - response = {"error": "Failed to parse feedback"} - - return { - "agent_name": agent_name, - "role": agent['role'], - "expertise": agent['expertise'], - "focus_areas": agent['focus_areas'], - "feedback": response - } \ No newline at end of file diff --git a/Backup/V4_multi_agent/src/openai_client.py b/Backup/V4_multi_agent/src/openai_client.py deleted file mode 100644 index a0a9c97..0000000 --- a/Backup/V4_multi_agent/src/openai_client.py +++ /dev/null @@ -1,113 +0,0 @@ -import os -from typing import Dict, Any -from openai import OpenAI -from dotenv import load_dotenv, find_dotenv -import json -import re - -class OpenAIClient: - """Client for interacting with OpenAI's API.""" - - def __init__(self): - """Initialize the OpenAI client.""" - # Load environment variables from .env file - env_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env') - print(f"\nLooking for .env file at: {env_path}") - print(f"File exists: {os.path.exists(env_path)}") - - # Try to load the .env file - if load_dotenv(env_path): - print("Successfully loaded .env file") - else: - print("Failed to load .env file") - - # Get API key - api_key = os.getenv("OPENAI_API_KEY") - if not api_key: - raise ValueError("OPENAI_API_KEY not found in environment variables") - else: - # Mask most of the API key for security - masked_key = f"{api_key[:7]}...{api_key[-4:]}" - print(f"Found API key: {masked_key}") - - try: - self.client = OpenAI(api_key=api_key) - print("Successfully initialized OpenAI client") - except Exception as e: - raise ValueError(f"Failed to initialize OpenAI client: {str(e)}") - - def _extract_json_from_text(self, text: str) -> Dict[str, Any]: - """Extract JSON from text that might contain other content.""" - # Find the first { and last } to extract the JSON object - start = text.find('{') - end = text.rfind('}') - - if start == -1 or end == -1: - return {"error": "No JSON found in response"} - - json_str = text[start:end+1] - - # Clean up common JSON formatting issues - json_str = re.sub(r',(\s*[}\]])', r'\1', json_str) # Remove trailing commas - json_str = re.sub(r'\\n\s*', ' ', json_str) # Remove newlines - json_str = re.sub(r'\s+', ' ', json_str) # Normalize whitespace - - try: - return json.loads(json_str) - except json.JSONDecodeError as e: - return { - "error": "Failed to parse JSON", - "details": str(e), - "raw_text": text - } - - def analyze_manuscript(self, content: str, params: Dict[str, Any]) -> Dict[str, Any]: - """Analyze manuscript content using OpenAI's API.""" - try: - # Print request details for debugging - print(f"\nMaking API call with model: {params.get('model', 'gpt-3.5-turbo')}") - print(f"Task: {params.get('task', 'unknown')}") - - # Construct the messages for the chat completion - messages = [ - { - "role": "system", - "content": ( - f"You are an {params.get('role', 'expert scientific editor')}. " - "Your response must be a valid JSON object. " - "Do not include any text outside the JSON object. " - "Do not use markdown formatting. " - "Ensure all property names and string values are properly quoted." - ) - }, - { - "role": "user", - "content": ( - f"{params.get('prompt', '')}\n\n" - "Remember to return ONLY a valid JSON object with no additional text.\n\n" - f"Content:\n{content}" - ) - } - ] - - # Make the API call - response = self.client.chat.completions.create( - model=params.get('model', 'gpt-3.5-turbo'), - messages=messages, - temperature=0.3, - response_format={"type": "json_object"} # Request JSON response - ) - - # Extract and parse the response - response_text = response.choices[0].message.content - return self._extract_json_from_text(response_text) - - except Exception as e: - return { - "error": f"API call failed: {str(e)}", - "details": { - "model": params.get('model', 'gpt-3.5-turbo'), - "role": params.get('role', 'expert scientific editor'), - "exception": str(e) - } - } \ No newline at end of file diff --git a/Backup/V4_multi_agent/src/specialized_agent.py b/Backup/V4_multi_agent/src/specialized_agent.py deleted file mode 100644 index ed01f48..0000000 --- a/Backup/V4_multi_agent/src/specialized_agent.py +++ /dev/null @@ -1,263 +0,0 @@ -from typing import Dict, List, Any -import json -from openai_client import OpenAIClient - -class SpecializedReviewAgent: - """Specialized review agent for domain-specific paper evaluation.""" - - def __init__(self, agent_config: Dict[str, Any]): - """Initialize the specialized review agent. - - Args: - agent_config (Dict[str, Any]): Agent configuration including role and expertise - """ - self.client = OpenAIClient() - self.role = agent_config["role"] - self.expertise = agent_config["expertise"] - self.focus_areas = agent_config["focus_areas"] - self.review_criteria = agent_config["review_criteria"] - self.criteria_description = agent_config.get("criteria_description", "Review criteria for this domain") - self.required_background = agent_config["required_background"] - - def _create_review_template(self) -> Dict[str, Any]: - """Create the review template based on agent's expertise. - - Returns: - Dict[str, Any]: Review template structure - """ - return { - "agent_info": { - "role": self.role, - "expertise": self.expertise, - "focus_areas": self.focus_areas - }, - "technical_review": { - "methodology_assessment": "...", - "technical_quality": "...", - "innovation_analysis": "...", - "comparative_analysis": "..." - }, - "domain_specific_analysis": { - "strengths": [], - "weaknesses": [], - "improvements": [], - "technical_recommendations": [] - }, - "score": { - "value": 0, - "justification": "...", - "comparative_context": "..." - }, - "detailed_feedback": { - "critical_issues": [], - "suggestions": [], - "references": [] - } - } - - def perform_review(self, paper_text: str) -> Dict[str, Any]: - """Perform a specialized review of the paper. - - Args: - paper_text (str): The text content of the paper - - Returns: - Dict[str, Any]: Detailed review results - """ - prompt = f"""You are an expert {self.role} with deep knowledge in {', '.join(self.expertise)}. -Review this paper focusing on your areas of expertise and the following criteria: - -Focus Areas: -{json.dumps(self.focus_areas, indent=2)} - -Review Criteria Description: -{self.criteria_description} - -Specific Review Criteria: -{json.dumps(self.review_criteria, indent=2)} - -Required Background Knowledge: -{json.dumps(self.required_background, indent=2)} - -Paper text: -{paper_text[:8000]} # Limit text length to avoid token limits - -Provide your review in the following JSON format: -{ - "strengths": [ - { - "point": "Specific strength identified", - "location": "Section/paragraph where this strength appears", - "explanation": "Detailed explanation of why this is a strength", - "impact": "How this strength contributes to the paper's quality" - } - ], - "weaknesses": [ - { - "point": "Specific weakness identified", - "location": "Section/paragraph where this weakness appears", - "explanation": "Detailed explanation of why this is a weakness", - "impact": "How this weakness affects the paper's quality" - } - ], - "improvements": [ - { - "area": "Specific area needing improvement", - "current_state": "Description of the current state", - "suggestion": "Detailed, actionable suggestion for improvement", - "example": "Specific example or reference to support the suggestion", - "expected_impact": "How this improvement would enhance the paper" - } - ], - "summary": { - "overall_assessment": "Overall assessment of the paper from your expertise perspective", - "key_points": ["Key points that need attention"], - "priority_improvements": ["Most important improvements to address first"] - } -} - -Ensure your response is valid JSON and includes all required fields. Provide specific, detailed feedback with concrete examples and actionable suggestions.""" - - try: - response = self.client.analyze_manuscript(paper_text, { - "role": f"expert {self.role}", - "expertise": self.expertise, - "focus_areas": self.focus_areas, - "review_criteria": self.review_criteria, - "criteria_description": self.criteria_description, - "required_background": self.required_background, - "prompt": prompt - }) - - if "error" in response: - raise Exception(f"Failed to perform review: {response['error']}") - - # Extract JSON from response if needed - if isinstance(response, str): - try: - start_idx = response.find('{') - end_idx = response.rfind('}') + 1 - if start_idx >= 0 and end_idx > start_idx: - response = json.loads(response[start_idx:end_idx]) - else: - raise ValueError("No JSON found in response") - except json.JSONDecodeError as e: - raise Exception(f"Failed to parse JSON response: {str(e)}") - - # Add agent info to response - response["agent_info"] = { - "role": self.role, - "expertise": self.expertise, - "focus_areas": self.focus_areas - } - - return response - - except Exception as e: - print(f"Error performing review: {e}") - return { - "error": "Failed to perform review", - "details": str(e), - "agent_info": { - "role": self.role, - "expertise": self.expertise, - "focus_areas": self.focus_areas - } - } - - def provide_detailed_feedback(self, paper_text: str, initial_review: Dict[str, Any]) -> Dict[str, Any]: - """Provide detailed feedback based on initial review. - - Args: - paper_text (str): The text content of the paper - initial_review (Dict[str, Any]): Initial review results - - Returns: - Dict[str, Any]: Detailed feedback - """ - prompt = f"""Based on your initial review, provide detailed feedback focusing on your expertise areas. - -Initial Review: -{json.dumps(initial_review, indent=2)} - -Review Criteria Description: -{self.criteria_description} - -Specific Review Criteria: -{json.dumps(self.review_criteria, indent=2)} - -Paper text: -{paper_text[:8000]} # Limit text length to avoid token limits - -Provide detailed feedback in the following JSON format: -{{ - "detailed_analysis": {{ - "technical_details": "In-depth technical analysis", - "methodology_issues": "Detailed methodology issues", - "improvement_suggestions": "Specific improvement suggestions" - }}, - "specific_recommendations": [ - {{ - "area": "Specific area", - "issue": "Detailed issue description", - "suggestion": "Specific suggestion", - "expected_impact": "Expected impact of change" - }} - ], - "references_and_examples": [ - {{ - "reference": "Relevant reference", - "application": "How it applies to this paper", - "suggestion": "How to incorporate it" - }} - ] -}} - -Ensure your response is valid JSON and includes all required fields.""" - - try: - response = self.client.analyze_manuscript(paper_text, { - "role": f"expert {self.role} providing detailed feedback", - "expertise": self.expertise, - "focus_areas": self.focus_areas, - "review_criteria": self.review_criteria, - "criteria_description": self.criteria_description, - "initial_review": initial_review, - "prompt": prompt - }) - - if "error" in response: - raise Exception(f"Failed to provide detailed feedback: {response['error']}") - - # Extract JSON from response if needed - if isinstance(response, str): - try: - start_idx = response.find('{') - end_idx = response.rfind('}') + 1 - if start_idx >= 0 and end_idx > start_idx: - response = json.loads(response[start_idx:end_idx]) - else: - raise ValueError("No JSON found in response") - except json.JSONDecodeError as e: - raise Exception(f"Failed to parse JSON response: {str(e)}") - - # Add agent info to response - response["agent_info"] = { - "role": self.role, - "expertise": self.expertise, - "focus_areas": self.focus_areas - } - - return response - - except Exception as e: - print(f"Error providing detailed feedback: {e}") - return { - "error": "Failed to provide detailed feedback", - "details": str(e), - "agent_info": { - "role": self.role, - "expertise": self.expertise, - "focus_areas": self.focus_areas - } - } \ No newline at end of file diff --git a/Backup/V4_multi_agent/src/test_editor.py b/Backup/V4_multi_agent/src/test_editor.py deleted file mode 100644 index 987c513..0000000 --- a/Backup/V4_multi_agent/src/test_editor.py +++ /dev/null @@ -1,105 +0,0 @@ -from editor_agent import EditorAgent -import json -import os - -def main(): - # Initialize the editor agent with explicit configuration for the cheaper model - model_config = { - "default": "gpt-3.5-turbo", # Cheaper model for testing - "production": "gpt-4-turbo-preview", # More expensive model (not used in testing) - "current": "default" # Always use the cheaper model for testing - } - - editor = EditorAgent(model_config) - print(f"\nUsing model: {editor.get_current_model()} (Testing Mode)") - - # Sample paper content (short version for testing) - sample_paper = """ - Title: A Novel Approach to Machine Learning in Healthcare - - Abstract: - This paper presents a new machine learning framework for healthcare applications, - focusing on improved patient outcome prediction. We demonstrate significant - improvements over existing methods in terms of accuracy and interpretability. - - Introduction: - Machine learning in healthcare has seen rapid development in recent years. - However, current approaches often lack interpretability and fail to consider - the unique challenges of medical data. Our work addresses these limitations - through a novel architecture that combines deep learning with domain-specific - constraints. - - Related Work: - Previous approaches (Smith et al., 2020; Jones et al., 2021) have focused - primarily on prediction accuracy, often sacrificing interpretability. Recent - work by Brown et al. (2022) attempted to address this trade-off but faced - limitations in handling missing data. - - Methods: - We propose a new neural network architecture that incorporates medical domain - knowledge through constrained optimization. Our approach uses a combination of - attention mechanisms and traditional statistical methods to ensure both - accuracy and interpretability. - - Results: - Experimental results on three large-scale medical datasets show that our - method achieves a 15% improvement in prediction accuracy while maintaining - full interpretability. We also demonstrate superior handling of missing data - compared to existing approaches. - - Discussion: - Our results suggest that the proposed framework successfully addresses the - limitations of current approaches. The improved accuracy and interpretability - make it particularly suitable for clinical applications. - """ - - # Generate review plan - print("\nGenerating review plan...") - result = editor.generate_review_plan(sample_paper) - - # Debug: Print review plan structure - print("\nReview plan structure:") - print(json.dumps(result, indent=2)) - - # Collect feedback from all specialized reviewers - print("\nCollecting specialized reviewer feedback...") - specialized_feedback = {} - for agent in editor.core_reviewers: - agent_feedback = editor.get_agent_feedback(agent['id'], sample_paper) - specialized_feedback[agent['id']] = agent_feedback - print(f"Collected feedback from {agent['role']}") - - # Add specialized feedback to results - result['specialized_reviewer_feedback'] = specialized_feedback - - # Create results directory if it doesn't exist - results_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'results') - os.makedirs(results_dir, exist_ok=True) - - # Save complete results to JSON file - results_file = os.path.join(results_dir, 'review_results.json') - with open(results_file, 'w') as f: - json.dump(result, f, indent=2) - print(f"\nComplete results saved to: {results_file}") - - # Print key parts of the result in a structured way - print("\n=== Paper Analysis ===") - print(json.dumps(result["review_plan"]["paper_analysis"], indent=2)) - - print("\n=== Key Scientists Identified ===") - for scientist in result["review_plan"]["key_scientists"]: - print(f"\nScientist: {scientist['name']}") - print(f"Research Focus: {scientist['research_focus']}") - print(f"Review Style: {scientist['review_style']}") - - print("\n=== Executive Summary ===") - print(json.dumps(result["final_report"]["executive_summary"], indent=2)) - - print("\n=== Thematic Analysis ===") - print(json.dumps(result["final_report"]["thematic_analysis"], indent=2)) - - print("\n=== Final Assessment ===") - print(json.dumps(result["final_report"]["final_assessment"], indent=2)) - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/Backup/V5_multi_agent2/CONTRIBUTING.md b/Backup/V5_multi_agent2/CONTRIBUTING.md deleted file mode 100644 index 72be5e5..0000000 --- a/Backup/V5_multi_agent2/CONTRIBUTING.md +++ /dev/null @@ -1,122 +0,0 @@ -# Contributing to Manuscript Reviewer - -Thank you for considering contributing to Manuscript Reviewer! This document provides guidelines and instructions for contributing to the project. - -## Project Status - -**Important**: This project is currently in Version 1.0 (Beta), developed in a short timeframe. Expect hallucinations and errors. About 50% of feedback will likely be unusable, 30% mediocre, and 20% helpful. This is precisely why we need your contributions! - -## How Can I Contribute? - -### 1. Developing New Agents - -We welcome contributions of new specialized agents: -- **Domain-specific agents**: Create agents specialized for specific research fields (e.g., medicine, computer science, social sciences) -- **Methodology agents**: Develop agents to assess specialized methodologies (e.g., clinical trials, machine learning, qualitative research) -- **Statistical review agents**: Create agents to validate complex statistical approaches - -### 2. Improving Existing Agents - -Help enhance our current agents: -- **Prompt engineering**: Refine agent prompts for better analysis -- **Error detection**: Improve the ability to identify common errors -- **Response formatting**: Enhance the structure and clarity of agent feedback - -### 3. System Improvements - -Contribute to the core system: -- **PDF parsing**: Enhance text and figure extraction -- **Report generation**: Improve the readability and usefulness of reports -- **Performance optimization**: Make the system faster and more efficient -- **UI/UX**: Build interfaces for easier interaction with the system - -### 4. Documentation and Testing - -Help make the project more accessible: -- **Documentation**: Improve installation and usage instructions -- **Tutorials**: Create examples and tutorials for different use cases -- **Testing**: Develop comprehensive tests for different components - -## Getting Started - -1. **Set up your environment**: - ```bash - git clone https://github.com/robertjakob/rigorous.git - cd rigorous/V5_multi_agent2 - pip install -r requirements.txt - pip install -e . # Install in development mode - ``` - -2. **Configure API keys**: - Create a `.env` file based on the example and add your API keys if using OpenAI models. - -3. **Test the system**: - ```bash - # Place a PDF in the manuscripts directory - python run_analysis.py - # Generate a report - bash scripts/generate_report.sh - ``` - -## Development Workflow - -1. **Create a new branch**: - ```bash - git checkout -b feature/your-feature-name - ``` - -2. **Make your changes**: - - Follow the existing code style - - Add appropriate comments - - Keep functions focused and modular - -3. **Test your changes**: - ```bash - pytest - ``` - -4. **Submit a pull request**: - - Provide a clear description of your changes - - Reference any related issues - - Explain how your changes improve the project - -## Code Standards - -- Use clear, descriptive variable and function names -- Follow PEP 8 style guidelines -- Include docstrings for all functions and classes -- Write unit tests for new functionality - -## Adding a New Agent - -1. Create a new file in the appropriate directory: - - `src/reviewer_agents/section/` for section agents - - `src/reviewer_agents/rigor/` for rigor agents - - `src/reviewer_agents/writing/` for writing agents - -2. Inherit from the `BaseReviewerAgent` class: - ```python - from src.reviewer_agents.base_agent import BaseReviewerAgent - - class YourNewAgentName(BaseReviewerAgent): - def __init__(self, model="gpt-3.5-turbo"): - super().__init__(model) - self.name = "YourAgentCode" # e.g., S11, R8, W9 - self.category = "your_category" # e.g., "section", "rigor", "writing" - - def analyze_your_feature(self, text, research_type=None): - # Implement your analysis method - # Return a properly structured response - ``` - -3. Add your agent to the controller in `src/reviewer_agents/controller_agent.py` - -## Communication - -- **Issues**: Use GitHub issues for bug reports and feature requests -- **Discussions**: Join GitHub discussions for general questions -- **Email**: Contact rjakob@ethz.ch for private communications - -## Thank You - -Your contributions help improve scientific publishing and research quality. We appreciate your support in this mission! \ No newline at end of file diff --git a/Backup/V5_multi_agent2/MODEL_GUIDE.md b/Backup/V5_multi_agent2/MODEL_GUIDE.md deleted file mode 100644 index c4cff71..0000000 --- a/Backup/V5_multi_agent2/MODEL_GUIDE.md +++ /dev/null @@ -1,108 +0,0 @@ -# Model Configuration Guide - -This guide explains how to configure different AI models for use with the Manuscript Reviewer system. - -## Default Configuration - -By default, the system uses ChatGPT 3.5 Turbo (`gpt-3.5-turbo`), which provides a good balance between performance and cost. However, for more sophisticated analysis, you may want to use more powerful models like GPT-4. - -## Available Models - -Here are some models you can use with this system: - -| Model | Description | Pros | Cons | -|-------|-------------|------|------| -| `gpt-3.5-turbo` | Default model | Fast, cost-effective | Less sophisticated analysis | -| `gpt-4` | More powerful model | More accurate, better reasoning | Slower, more expensive | -| `gpt-4-turbo` | Updated GPT-4 | Newer capabilities, faster than GPT-4 | More expensive than GPT-3.5 | -| `claude-3-opus-20240229` | Claude 3 Opus | Alternative to GPT-4, different strengths | Requires Anthropic API key | -| `claude-3-sonnet-20240229` | Claude 3 Sonnet | Good balance of performance and speed | Requires Anthropic API key | - -## Setting Up Your API Keys - -### OpenAI API Keys - -1. Create an account at [OpenAI](https://platform.openai.com/signup) -2. Navigate to the [API Keys page](https://platform.openai.com/api-keys) -3. Create a new API key -4. Copy the key to your `.env` file (see below) - -### Anthropic API Keys (for Claude models) - -1. Create an account at [Anthropic](https://console.anthropic.com/signup) -2. Navigate to the API Keys section -3. Create a new API key -4. Copy the key to your `.env` file (see below) - -## Configuring Your Model - -### Using the `.env` File - -1. Open the `.env` file in the root directory of the project -2. Update the `OPENAI_API_KEY` with your API key -3. Change the `DEFAULT_MODEL` to your preferred model - -Example `.env` file for OpenAI GPT-4: - -``` -# OpenAI API Configuration -OPENAI_API_KEY=your_openai_api_key_here - -# Model Configuration -DEFAULT_MODEL=gpt-4 -``` - -Example `.env` file for Anthropic Claude: - -``` -# Anthropic API Configuration -ANTHROPIC_API_KEY=your_anthropic_api_key_here - -# Model Configuration -DEFAULT_MODEL=claude-3-opus-20240229 -``` - -### Command Line Configuration - -You can also specify the model when running the analysis: - -```bash -# For OpenAI models -python run_analysis.py --model gpt-4 - -# For Anthropic models -python run_analysis.py --model claude-3-sonnet-20240229 -``` - -## Performance Considerations - -- **GPT-4** generally provides the most thorough analysis but can be slower and more expensive -- **GPT-3.5 Turbo** is much faster and cheaper, but may miss subtle issues -- **Claude 3 models** provide a good alternative to OpenAI models with different strengths - -## Cost Management - -Running these models incurs costs based on the number of tokens processed: - -| Model | Approximate Cost per Full Paper Analysis | -|-------|------------------------------------------| -| GPT-3.5 Turbo | $0.10 - $0.25 | -| GPT-4 | $0.75 - $2.00 | -| GPT-4 Turbo | $0.40 - $1.00 | -| Claude 3 Opus | $0.80 - $2.20 | -| Claude 3 Sonnet | $0.30 - $0.80 | - -Costs vary based on manuscript length and complexity. - -## Troubleshooting - -- If you encounter `API key not valid` errors, check that you've correctly copied your API key -- If you get `Model not found` errors, ensure you're using a valid model identifier -- For rate limit errors, you may need to wait or switch to a different model - -## Need Help? - -If you need assistance with model configuration, please: -- Check the [GitHub repository](https://github.com/robertjakob/rigorous) for updates -- Open an issue on GitHub for technical problems -- Contact us at rjakob@ethz.ch for specific questions \ No newline at end of file diff --git a/Backup/V5_multi_agent2/README.md b/Backup/V5_multi_agent2/README.md deleted file mode 100644 index 4215f8d..0000000 --- a/Backup/V5_multi_agent2/README.md +++ /dev/null @@ -1,203 +0,0 @@ -# Manuscript Reviewer - -A multi-agent system for comprehensive manuscript review and analysis. - -## Overview - -This project implements a sophisticated multi-agent system for reviewing and analyzing academic manuscripts. The system uses a combination of section-specific, rigor, and writing quality agents to provide detailed feedback and suggestions for improvement. - -## Agent Structure - -The system consists of three main categories of agents: - -### Section Agents (S1-S10) -- S1: Title and Keywords Analysis -- S2: Abstract Review -- S3: Introduction Assessment -- S4: Literature Review Analysis -- S5: Methodology Evaluation -- S6: Results Analysis -- S7: Discussion Review -- S8: Conclusion Assessment -- S9: References Analysis -- S10: Supplementary Materials Review - -### Rigor Agents (R1-R7) -- R1: Originality and Contribution -- R2: Impact and Significance -- R3: Ethics and Compliance -- R4: Data and Code Availability -- R5: Statistical Rigor -- R6: Technical Accuracy -- R7: Consistency - -### Writing Agents (W1-W8) -- W1: Clarity -- W2: Organization -- W3: Grammar -- W4: Style -- W5: Technical Accuracy -- W6: Consistency -- W7: Readability -- W8: Overall Writing Quality - -## Installation - -1. Clone the repository -2. Install dependencies: -```bash -pip install -r requirements.txt -``` - -## Usage - -1. Place your manuscript PDF in the `manuscripts/` directory -2. Run the analysis: -```bash -python run_analysis.py -``` - -For information on using more powerful models (like GPT-4), see [MODEL_GUIDE.md](MODEL_GUIDE.md). - -## Output - -The system generates a comprehensive report in `results/manuscript_report.md` containing: -- Overall assessment -- Section-by-section analysis -- Critical remarks -- Improvement suggestions -- Detailed feedback -- Summary of findings - -For information on using more powerful models (like GPT-4), see [MODEL_GUIDE.md](MODEL_GUIDE.md). - -## Report Generator - -The report generator component takes the combined output from all agents and creates a well-structured markdown report. - -### Report Structure - -1. **Header** - - Title and generation timestamp - - Important notes about the tool's status - - Overall assessment summary - -2. **Section Analysis (S1-S10)** - - Title and Keywords through Supplementary Materials - -3. **Rigor Analysis (R1-R7)** - - Originality, Impact, Ethics, Data Availability, etc. - -4. **Writing Quality (W1-W8)** - - Language, Structure, Clarity, Terminology, etc. - -### Agent Response Format - -Each agent's analysis follows a consistent JSON structure: - -```json -{ - "score": int, // Score from 1-10 - "critical_remarks": [ - { - "category": str, - "location": str, - "issue": str, - "severity": str, - "impact": str - } - ], - "improvement_suggestions": [ - { - "location": str, - "category": str, - "focus": str, - "original_text": str, - "improved_version": str, - "explanation": str - } - ], - "detailed_feedback": { - // Agent-specific detailed analysis - }, - "summary": str // Overall assessment summary -} -``` - -### Customization - -The report template and formatting can be modified in: -- `src/core/report_template.py`: Main report structure -- `src/utils/json_to_report.py`: JSON to markdown conversion - -## Configuration - -- Environment variables are managed in `.env` -- Agent configurations can be modified in `src/config/` -- Logging settings in `src/config/logging_config.py` - -## Development - -### Project Structure -``` -V5_multi_agent2/ -├── src/ -│ ├── reviewer_agents/ -│ │ ├── section/ # Section agents (S1-S10) -│ │ ├── rigor/ # Rigor agents (R1-R7) -│ │ ├── writing/ # Writing agents (W1-W8) -│ │ └── controller_agent.py -│ ├── core/ -│ ├── utils/ -│ └── config/ -├── manuscripts/ # Input manuscripts -├── results/ # Generated reports -└── tests/ # Test suite -``` - -### Adding New Agents - -1. Create a new agent class inheriting from `BaseReviewerAgent` -2. Implement the required analysis method -3. Add the agent to the controller's agent dictionary -4. Update the report template if needed - -## Testing - -Run the test suite: -```bash -pytest tests/ -``` - -## License - -MIT License - -## Contributing - -1. Fork the repository -2. Create a feature branch -3. Commit your changes -4. Push to the branch -5. Create a Pull Request - -For detailed guidelines on how to contribute, please see [CONTRIBUTING.md](CONTRIBUTING.md). - -## Join the Project - -**We Need Your Help!** This is Version 1.0 (Beta) - a work in progress developed over just a few days, which means: - -- **Expect imperfections**: About 50% of feedback may be unusable, 30% mediocre, and 20% genuinely helpful -- **Your expertise matters**: Help us improve agent accuracy, especially specialized agents -- **Key areas for contribution**: - - Developing specialized agents for different research fields - - Improving prompt engineering for existing agents - - Enhancing report generation and visualization - - Adding support for different document formats - - Implementing more sophisticated error detection - -**Share your feedback**: Contact us at rjakob@ethz.ch with your experiences and suggestions - -**Use more powerful models**: The default implementation uses ChatGPT 3.5 for accessibility, but you can configure the system to use more sophisticated models like GPT-4 with your own API keys. - -Together, we can build the best review agent team and improve the quality of scientific publishing! \ No newline at end of file diff --git a/Backup/V5_multi_agent2/requirements.txt b/Backup/V5_multi_agent2/requirements.txt deleted file mode 100644 index 64d3f51..0000000 --- a/Backup/V5_multi_agent2/requirements.txt +++ /dev/null @@ -1,18 +0,0 @@ -PyPDF2>=3.0.0 -PyMuPDF>=1.22.0 # fitz -Pillow>=10.0.0 # PIL -pytesseract>=0.3.10 -numpy>=1.24.0 -openai>=1.0.0 -python-dotenv>=0.19.0 -langchain>=0.1.0 -langchain-community>=0.0.10 -typing-extensions>=4.0.0 -requests>=2.31.0 -python-json-logger>=2.0.0 -nougat-ocr>=0.1.0 -pdf2image>=1.16.3 -pydantic>=2.0.0 -pytest>=7.0.0 -tqdm>=4.65.0 -pandas>=2.0.0 \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/R1_results.json b/Backup/V5_multi_agent2/results/R1_results.json deleted file mode 100644 index 6c999f9..0000000 --- a/Backup/V5_multi_agent2/results/R1_results.json +++ /dev/null @@ -1,138 +0,0 @@ -{ - "originality_contribution_score": 8, - "critical_remarks": [ - { - "category": "novelty", - "location": "Abstract / Introduction", - "issue": "While the study claims to extend prior work by predicting nonadherence over longer durations and in different interventions, the core methodology\u2014using behavioral app engagement data with machine learning\u2014is established in existing literature. The novelty lies mainly in application scope rather than methodological innovation.", - "severity": "medium", - "impact": "This limits the perceived novelty of the approach, potentially affecting the paper's contribution perception, though the extended application adds value." - }, - { - "category": "contribution", - "location": "Discussion / Results", - "issue": "The paper emphasizes the potential for targeted interventions based on predictions but does not empirically test or demonstrate the effectiveness of such strategies, leaving the practical impact somewhat speculative.", - "severity": "high", - "impact": "This reduces the strength of the contribution claim regarding real-world impact and limits the evidence for practical implementation." - }, - { - "category": "verification", - "location": "Introduction / Methods", - "issue": "The claims of high predictive accuracy are based on retrospective data, with no prospective validation or real-time testing of the models' utility in live interventions.", - "severity": "high", - "impact": "This affects the validity of the claims about the models' real-world applicability and robustness." - }, - { - "category": "comparison", - "location": "Introduction / Literature Review", - "issue": "The comparison with existing literature is somewhat superficial, lacking detailed discussion of how this work advances beyond similar predictive models in digital health or app domains.", - "severity": "medium", - "impact": "This limits the contextual understanding of the study's novelty and its relative contribution." - }, - { - "category": "advancement", - "location": "Discussion / Conclusion", - "issue": "The paper states that the models 'advance knowledge' but does not clearly specify how this advances theoretical understanding or practical capabilities beyond existing models.", - "severity": "medium", - "impact": "This weakens the framing of the study as a significant knowledge advancement." - } - ], - "improvement_suggestions": [ - { - "original_text": "We developed machine learning models for the prediction of nonadherence in two mHealth interventions...", - "improved_version": "We applied and adapted existing machine learning frameworks to predict nonadherence across two distinct mHealth interventions, demonstrating their scalability and generalizability.", - "explanation": "Clarifies that the contribution is in application and validation across contexts, emphasizing generalizability rather than methodological novelty.", - "location": "Abstract", - "category": "novelty", - "focus": "novelty" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira...", - "improved_version": "Our models achieved high predictive accuracy in identifying nonadherent users over extended periods, comparable to or exceeding existing models, thereby confirming their robustness in real-world settings.", - "explanation": "Highlights the performance relative to existing models, framing the contribution as validation and robustness rather than novelty.", - "location": "Results", - "category": "contribution", - "focus": "contribution" - }, - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted...", - "improved_version": "Building upon prior research, this study investigates the extent to which behavioral app engagement data can predict nonadherence over longer durations, filling a notable gap in the literature.", - "explanation": "Explicitly states the research gap addressed, clarifying the novelty of the investigation.", - "location": "Introduction", - "category": "verification", - "focus": "verification" - }, - { - "original_text": "Our study thus extends prior research showing that methodologies effective in predicting churn during the first week remain applicable over longer durations...", - "improved_version": "This study extends prior research by demonstrating that predictive methodologies effective in early churn detection remain applicable over longer durations, thereby broadening their practical utility.", - "explanation": "Clarifies how the work advances existing methods, emphasizing practical extension rather than methodological innovation.", - "location": "Discussion", - "category": "advancement", - "focus": "advancement" - }, - { - "original_text": "We selected generalizable daily app engagement features related to users' activity and progress...", - "improved_version": "We selected simple, objective behavioral features\u2014such as daily app activity and task completion\u2014that are readily available across diverse mHealth apps, supporting their broad applicability.", - "explanation": "Highlights the simplicity and generalizability of features, strengthening the contribution claim.", - "location": "Methods", - "category": "contribution", - "focus": "contribution" - }, - { - "original_text": "The models predicted nonadherence relative to the intended use, following Sieverink et al. (2017)...", - "improved_version": "Our operationalization of nonadherence aligns with established frameworks (Sieverink et al., 2017), ensuring consistency with current standards and facilitating comparison with future studies.", - "explanation": "Strengthens the verification of the novelty claim by anchoring it to recognized definitions and standards.", - "location": "Introduction", - "category": "verification", - "focus": "verification" - }, - { - "original_text": "The study demonstrates that nonadherence can be accurately predicted over extended durations...", - "improved_version": "While our retrospective analysis shows promising predictive performance over extended durations, prospective validation is necessary to confirm real-world utility and impact.", - "explanation": "Provides a balanced view, acknowledging limitations and emphasizing the need for further validation, which enhances research credibility.", - "location": "Discussion", - "category": "verification", - "focus": "verification" - }, - { - "original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted...", - "improved_version": "Our findings demonstrate that behavioral app engagement data can serve as reliable predictors of nonadherence, contributing to the growing evidence base for data-driven adherence management in digital health.", - "explanation": "Frames the contribution as adding to the evidence base, emphasizing the practical utility rather than claiming groundbreaking novelty.", - "location": "Abstract / Discussion", - "category": "advancement", - "focus": "advancement" - }, - { - "original_text": "The models' performance improves as more data become available, aligning with prior research...", - "improved_version": "The observed improvement in model performance with increased data availability aligns with prior findings in app engagement research, reinforcing the robustness of behavioral features for long-term prediction.", - "explanation": "Connects findings to existing literature, clarifying the contribution as validation and reinforcement of known principles.", - "location": "Results / Discussion", - "category": "comparison", - "focus": "comparison" - }, - { - "original_text": "Our approach relies on behavioral engagement data, which is typically not available in non-digital interventions...", - "improved_version": "This approach leverages objective behavioral data unique to digital interventions, highlighting the advantage of digital health platforms in enabling precise adherence prediction.", - "explanation": "Clarifies the unique contribution enabled by digital data, strengthening the novelty argument.", - "location": "Discussion", - "category": "novelty", - "focus": "novelty" - }, - { - "original_text": "Future work should include prospective trials to validate these models in real-world settings...", - "improved_version": "Future research should focus on prospective, real-time validation of these models to establish their effectiveness in guiding adaptive adherence interventions and improving health outcomes.", - "explanation": "Provides a clear pathway for advancing the research, emphasizing practical impact and validation.", - "location": "Discussion / Conclusion", - "category": "advancement", - "focus": "advancement" - } - ], - "detailed_feedback": { - "novelty_assessment": "The study primarily extends existing methodologies of churn and nonadherence prediction by applying them over longer durations and in different medical contexts. While the application scope broadens, the core predictive approach\u2014using behavioral app engagement data with machine learning\u2014is well-established. The novelty is thus moderate, centered on demonstrating generalizability and robustness rather than methodological innovation.", - "contribution_analysis": "The paper contributes valuable evidence that behavioral engagement features can reliably predict nonadherence over extended periods, supporting their utility in diverse mHealth settings. It emphasizes the potential for integrating these models into intervention workflows for proactive adherence support, although it stops short of empirically testing intervention efficacy. The work advances practical understanding but could strengthen its contribution by demonstrating actual intervention impacts.", - "verification_status": "The claims of high predictive accuracy are based on retrospective analyses of large datasets, which are promising but require prospective validation to confirm real-world applicability. The models' performance in live settings remains to be tested, and the paper appropriately acknowledges this limitation.", - "comparative_analysis": "The discussion briefly references prior literature on churn prediction but lacks a detailed comparison of performance metrics, feature sets, or methodological differences. A more thorough comparison would clarify how this work advances the state of the art and what unique insights it offers.", - "advancement_evaluation": "The research advances knowledge by demonstrating the applicability of behavioral engagement data for long-term adherence prediction and by emphasizing the potential for scalable, data-driven intervention strategies. It reinforces the importance of simple, objective features and supports their use across diverse interventions, thus contributing to the practical toolkit for digital health researchers and practitioners." - }, - "summary": "Overall, this study makes a solid contribution by validating and extending existing predictive models for nonadherence over longer durations and in different contexts. Its strengths lie in demonstrating the robustness and generalizability of behavioral engagement features. To enhance its impact, the paper should more explicitly delineate its novelty relative to prior work, empirically validate intervention benefits, and deepen comparisons with existing literature. The balanced acknowledgment of limitations and future directions adds credibility, positioning this work as a meaningful step toward more adaptive and scalable adherence management in digital health." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/R2_results.json b/Backup/V5_multi_agent2/results/R2_results.json deleted file mode 100644 index 850aaf6..0000000 --- a/Backup/V5_multi_agent2/results/R2_results.json +++ /dev/null @@ -1,124 +0,0 @@ -{ - "impact_significance_score": 8, - "critical_remarks": [ - { - "category": "field_influence", - "location": "Abstract", - "issue": "While the abstract highlights the high accuracy of ML models in predicting nonadherence, it lacks explicit discussion on how these findings could shift current paradigms in digital health research.", - "severity": "medium", - "impact": "This limits the perceived novelty and potential influence of the research within the field, possibly underestimating its capacity to reshape adherence prediction methodologies." - }, - { - "category": "implications", - "location": "Discussion, paragraph 4.1", - "issue": "The discussion emphasizes the predictive accuracy but insufficiently explores how these models could directly influence health outcomes or clinical decision-making processes.", - "severity": "high", - "impact": "This diminishes the broader significance of the findings, potentially limiting their translational impact on health policy and clinical practice." - }, - { - "category": "future_research", - "location": "Discussion, paragraph 4.4", - "issue": "The limitations section notes the need for prospective trials but does not specify concrete research designs or intervention strategies to test the models' real-world efficacy.", - "severity": "high", - "impact": "This vagueness hampers the field\u2019s ability to build on these findings systematically, delaying the development of evidence-based implementation protocols." - }, - { - "category": "applications", - "location": "Discussion, paragraph 4.2", - "issue": "While potential intervention strategies are mentioned, there is a lack of detailed discussion on how these predictive insights could be integrated into existing app architectures or clinical workflows.", - "severity": "medium", - "impact": "This reduces the practical utility of the research, limiting immediate translation into actionable app features or healthcare interventions." - }, - { - "category": "policy", - "location": "Introduction and discussion", - "issue": "The paper briefly mentions regulatory environments but does not elaborate on how predictive models could influence health policy, reimbursement strategies, or regulatory approval processes.", - "severity": "medium", - "impact": "This oversight constrains the research\u2019s influence on policy development, potentially slowing adoption at the systemic level." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "The rich, objective engagement data collected by mHealth interventions have the potential to fundamentally transform adherence monitoring and prediction, offering new avenues for personalized intervention strategies.", - "explanation": "This reframes the statement to emphasize the transformative potential of the data, enhancing the perceived impact on the field.", - "location": "Abstract", - "category": "field_influence", - "focus": "field_influence" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95).", - "improved_version": "Our models demonstrated the capacity to accurately identify over 94% of nonadherent users across extended periods, indicating a significant advancement in early intervention potential within digital health research.", - "explanation": "This emphasizes the significance of the predictive accuracy as a breakthrough, strengthening its influence on future research and practice.", - "location": "Abstract", - "category": "field_influence", - "focus": "field_influence" - }, - { - "original_text": "The study extends prior research showing that methodologies effective in predicting churn during the first week remain applicable over longer durations.", - "improved_version": "This study extends the frontier of adherence research by demonstrating that predictive methodologies are effective not only in early stages but also over prolonged intervention durations, paving the way for sustained engagement strategies.", - "explanation": "This highlights the novelty and importance of the extended prediction window, reinforcing the research\u2019s influence on future methodological developments.", - "location": "Discussion, paragraph 4.1", - "category": "field_influence", - "focus": "field_influence" - }, - { - "original_text": "Models predicting churn achieved mean AUCs of 0.87 for both apps, correctly identifying 84-86% of churned users.", - "improved_version": "The high predictive performance (AUC ~0.87) in churn models underscores the feasibility of integrating real-time risk stratification into digital health platforms, potentially revolutionizing adherence management.", - "explanation": "This frames the results as a catalyst for systemic change, enhancing the research\u2019s influence on the field\u2019s evolution." - }, - { - "original_text": "Feature importance analyses revealed that behavioral app engagement features collected closer to the prediction event had a stronger impact.", - "improved_version": "Findings that recent behavioral engagement features are most predictive underscore the importance of designing interventions that leverage near-real-time data, influencing future research and app development strategies.", - "explanation": "This directs the field towards designing adaptive, data-driven intervention models, increasing practical relevance." - }, - { - "original_text": "The models\u2019 performance improved over time as more behavioral data became available.", - "improved_version": "The progressive enhancement in model accuracy over time highlights the potential for dynamic, continuously learning systems that adapt to evolving user behaviors, shaping future research in adaptive digital health solutions.", - "explanation": "This emphasizes innovation and future directions, boosting the research\u2019s impact on the development of intelligent, scalable interventions." - }, - { - "original_text": "Our findings suggest that simple app engagement features are sufficient to predict future user behavior.", - "improved_version": "The demonstration that basic, objective engagement metrics can reliably predict adherence opens new avenues for scalable, low-cost monitoring systems applicable across diverse mHealth interventions.", - "explanation": "This enhances the perceived practicality and broad applicability, increasing the research\u2019s influence on implementation strategies." - }, - { - "original_text": "Limitations include the reliance on rich behavioral data, which may not be available in all settings.", - "improved_version": "Future research should explore the integration of sparse or sporadic engagement data, broadening the applicability of predictive models to interventions with less frequent user interactions.", - "explanation": "This expands the scope, encouraging further innovation and impact across varied intervention contexts." - }, - { - "original_text": "Prospective trials are necessary to fully establish the models\u2019 real-world utility.", - "improved_version": "Designing and executing prospective, randomized trials will be crucial to validate these models\u2019 effectiveness in real-world clinical and behavioral settings, accelerating their translation into practice.", - "explanation": "This provides a clear pathway for future impactful research, emphasizing translational potential." - }, - { - "original_text": "The models could be integrated with targeted strategies such as personalized push notifications or content adaptations.", - "improved_version": "Integrating predictive models with tailored, in-app intervention strategies\u2014such as personalized notifications, adaptive content, and human support\u2014can significantly enhance adherence and health outcomes.", - "explanation": "This clarifies how models can directly influence practical intervention design, boosting their significance." - }, - { - "original_text": "The study briefly mentions regulatory environments but does not elaborate on how predictive models could influence health policy.", - "improved_version": "Future work should investigate how predictive adherence models can inform health policy, reimbursement frameworks, and regulatory standards to facilitate widespread adoption and integration into healthcare systems.", - "explanation": "This explicitly links research findings to policy impact, increasing systemic influence." - }, - { - "original_text": "The discussion emphasizes the predictive accuracy but insufficiently explores how these models could influence health outcomes or clinical decision-making.", - "improved_version": "By demonstrating high predictive accuracy, this research lays the groundwork for integrating adherence prediction into clinical workflows, potentially improving health outcomes through timely, personalized interventions.", - "explanation": "This explicitly connects model performance to tangible health and clinical benefits, enhancing broader significance." - }, - { - "original_text": "The limitations section notes the need for prospective trials but does not specify concrete research designs.", - "improved_version": "Future research should implement randomized controlled trials and real-world pilot studies to evaluate the impact of predictive-driven adherence interventions on health outcomes and system efficiency.", - "explanation": "This provides concrete guidance for advancing the research impact through systematic validation." - } - ], - "detailed_feedback": { - "field_influence": "This research significantly advances the field by demonstrating the robustness and generalizability of behavioral engagement data for long-term adherence prediction, setting a new standard for scalable, data-driven adherence monitoring in digital health.", - "broader_implications": "The findings suggest that objective, app-based behavioral data can fundamentally reshape adherence management, enabling personalized, timely interventions that could reduce the burden of noncommunicable diseases and optimize healthcare resource utilization globally.", - "future_research_impact": "Building on these results, future studies should focus on prospective validation, integration with clinical workflows, and testing intervention strategies that leverage real-time predictions to improve health outcomes and system efficiency.", - "practical_applications": "The models\u2019 reliance on simple behavioral features facilitates immediate integration into existing mHealth platforms, enabling proactive adherence support, personalized nudges, and cost-effective scaling of digital health interventions.", - "policy_implications": "The demonstrated potential for predictive adherence models to inform personalized intervention strategies underscores the need for policy frameworks that support data sharing, real-time monitoring, and reimbursement models aligned with digital health innovation." - }, - "summary": "This study offers a compelling and methodologically rigorous contribution to digital health research, demonstrating that behavioral app engagement data can reliably predict nonadherence over extended periods. Its findings have the potential to influence future research directions, practical intervention designs, and health policy frameworks, although further prospective validation and implementation research are essential to realize its full systemic impact." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/R3_results.json b/Backup/V5_multi_agent2/results/R3_results.json deleted file mode 100644 index 46b554d..0000000 --- a/Backup/V5_multi_agent2/results/R3_results.json +++ /dev/null @@ -1,130 +0,0 @@ -{ - "ethics_compliance_score": 7, - "critical_remarks": [ - { - "category": "conflicts", - "location": "Author Contributions and Conflicts of Interest section", - "issue": "Authors disclose affiliations with organizations that have financial ties to health insurers and digital health companies, but the potential influence of these conflicts on study design, data interpretation, or reporting is not explicitly discussed.", - "severity": "medium", - "impact": "Potential bias in research outcomes or interpretation, which could compromise objectivity and transparency." - }, - { - "category": "privacy", - "location": "Abstract and Methods sections", - "issue": "While datasets are anonymized, there is limited detail on specific data privacy measures, data storage, or security protocols implemented to protect user data during analysis.", - "severity": "medium", - "impact": "Insufficient transparency about data privacy measures may undermine trust and compliance with data protection standards." - }, - { - "category": "consent", - "location": "Methods section, Dataset descriptions", - "issue": "The paper states that only users who provided consent under specific regulations were included, but it does not detail the process of obtaining informed consent or how users were informed about data use.", - "severity": "high", - "impact": "Lack of detailed consent procedures raises concerns about whether participants were adequately informed, which is essential for ethical compliance." - }, - { - "category": "integrity", - "location": "Results and Discussion sections", - "issue": "The study reports high predictive performance but does not address potential biases in model training, such as class imbalance or overfitting, nor does it discuss validation on external datasets.", - "severity": "medium", - "impact": "Potential overestimation of model effectiveness, which could mislead stakeholders and affect ethical application of findings." - }, - { - "category": "guidelines", - "location": "Ethics Declaration", - "issue": "The ethics statement claims exemption from human subjects review due to anonymized data, but it does not specify adherence to relevant ethical guidelines such as the Declaration of Helsinki or GDPR principles.", - "severity": "low", - "impact": "Insufficient detail on adherence to established ethical standards may weaken the ethical rigor of the study." - } - ], - "improvement_suggestions": [ - { - "original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "improved_version": "The study explicitly states that informed consent was obtained from all participants in accordance with GDPR and local ethical standards, detailing the consent process, including how users were informed about data use and their rights.", - "explanation": "Providing detailed consent procedures enhances transparency and demonstrates adherence to ethical standards for participant autonomy and informed participation.", - "location": "Methods section, Dataset descriptions", - "category": "consent", - "focus": "consent" - }, - { - "original_text": "The study involves the analyses of two anonymous datasets. Therefore, it does not qualify as human subject research and has been exempted from ethics approval by the research team's University Ethics Committee.", - "improved_version": "The study confirms that all data were anonymized prior to analysis, and that data handling complied with GDPR and local ethical guidelines, with documentation available upon request. Ethical approval was obtained for the use of identifiable data where applicable.", - "explanation": "Clarifying compliance with data protection laws and ethical standards reassures readers of ethical rigor and legal adherence.", - "location": "Ethics Declaration", - "category": "guidelines", - "focus": "guidelines" - }, - { - "original_text": "Authors disclose affiliations with organizations that have financial ties to health insurers and digital health companies, but the potential influence of these conflicts on study design, data interpretation, or reporting is not explicitly discussed.", - "improved_version": "The authors declare their affiliations and funding sources, and explicitly state that these entities had no role in study design, data analysis, interpretation, or manuscript preparation to mitigate potential conflicts of interest.", - "explanation": "Explicitly stating the independence of the research process from funding sources enhances transparency and addresses potential conflicts.", - "location": "Conflicts of Interest section", - "category": "conflicts", - "focus": "conflicts" - }, - { - "original_text": "While datasets are anonymized, there is limited detail on specific data privacy measures, data storage, or security protocols implemented to protect user data during analysis.", - "improved_version": "The study details the data privacy measures, including secure data storage, access controls, and compliance with GDPR, ensuring that user data remained protected throughout the research process.", - "explanation": "Providing specific privacy and security measures demonstrates commitment to data protection standards and ethical data management.", - "location": "Methods section, Data collection", - "category": "privacy", - "focus": "privacy" - }, - { - "original_text": "The paper states that only users who provided consent under specific regulations were included, but it does not detail the process of obtaining informed consent or how users were informed about data use.", - "improved_version": "The manuscript describes the informed consent process, including how users were informed about data collection, purpose, storage, and their rights, in accordance with GDPR and local ethical standards.", - "explanation": "Detailing the consent process ensures transparency and confirms adherence to ethical standards for participant autonomy.", - "location": "Methods section, Consent procedures", - "category": "consent", - "focus": "consent" - }, - { - "original_text": "The study reports high predictive performance but does not address potential biases in model training, such as class imbalance or overfitting, nor does it discuss validation on external datasets.", - "improved_version": "The discussion includes acknowledgment of potential biases such as class imbalance, and describes steps taken to mitigate overfitting, including cross-validation and external validation where applicable, to ensure model robustness.", - "explanation": "Addressing biases and validation enhances research integrity and trustworthiness of the findings.", - "location": "Results and Discussion", - "category": "integrity", - "focus": "integrity" - }, - { - "original_text": "The ethics statement claims exemption from human subjects review due to anonymized data, but it does not specify adherence to relevant ethical guidelines such as the Declaration of Helsinki or GDPR principles.", - "improved_version": "The study affirms compliance with ethical guidelines including the Declaration of Helsinki and GDPR, with documentation of ethical considerations and data handling procedures available upon request.", - "explanation": "Explicitly referencing established ethical frameworks reinforces the ethical rigor of the research.", - "location": "Ethics Declaration", - "category": "guidelines", - "focus": "guidelines" - }, - { - "original_text": "Authors affiliated with commercial entities (Pathmate Technologies AG, Vivira Health Lab GmbH) are involved, but the manuscript does not discuss potential influence or measures to prevent bias.", - "improved_version": "The manuscript discloses the authors' affiliations and states that independent oversight or measures, such as external validation or peer review, were employed to minimize potential bias arising from these affiliations.", - "explanation": "Transparency about measures to prevent bias from conflicts of interest enhances ethical standards and credibility.", - "location": "Author Contributions and Conflicts of Interest", - "category": "conflicts", - "focus": "conflicts" - }, - { - "original_text": "The study emphasizes predictive accuracy but does not discuss the potential implications or risks of false positives/negatives in clinical or user contexts.", - "improved_version": "The discussion addresses the ethical implications of false positives and negatives, including potential risks such as unnecessary interventions or missed opportunities for support, and suggests strategies to mitigate these risks.", - "explanation": "Acknowledging potential harms and ethical considerations in model deployment ensures responsible application of research findings.", - "location": "Discussion", - "category": "integrity", - "focus": "guidelines" - }, - { - "original_text": "The study does not specify whether any ethical review or oversight was conducted for the development and validation of predictive models beyond data anonymization.", - "improved_version": "The authors state that the development and validation of predictive models adhered to institutional ethical standards, with oversight from relevant committees, and that all procedures complied with applicable laws and guidelines.", - "explanation": "Clarifying oversight processes reinforces adherence to ethical standards in all research phases.", - "location": "Methods and Ethics Declaration", - "category": "guidelines", - "focus": "guidelines" - } - ], - "detailed_feedback": { - "conflicts_assessment": "The authors disclose affiliations with organizations involved in digital health and insurance sectors, but do not explicitly discuss measures taken to prevent bias or influence. Transparency about independent oversight or measures to mitigate conflicts would strengthen ethical integrity.", - "privacy_compliance": "While datasets are anonymized, the manuscript lacks detailed description of data security, storage, and privacy measures aligned with GDPR. Explicit statements on these protocols would enhance confidence in data privacy compliance.", - "consent_procedures": "The paper mentions that only users who provided consent were included, but it does not detail the process of obtaining informed consent, how users were informed about data use, or their rights. Clearer description of consent procedures is necessary for ethical transparency.", - "research_integrity": "High predictive performance is reported, but potential biases such as class imbalance and overfitting are not addressed. Including validation strategies and discussing limitations would improve research integrity and trustworthiness.", - "guidelines_adherence": "The ethics declaration states exemption from review due to anonymized data but does not specify adherence to international ethical standards like the Declaration of Helsinki or GDPR principles. Explicit confirmation of compliance would strengthen ethical rigor." - }, - "summary": "Overall, the study demonstrates a solid foundation in data analysis and ethical considerations, but could improve transparency regarding informed consent procedures, detailed data privacy measures, conflict of interest management, and explicit adherence to established ethical guidelines. Addressing these areas would enhance the ethical robustness and credibility of the research." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/R4_results.json b/Backup/V5_multi_agent2/results/R4_results.json deleted file mode 100644 index 12e8e6a..0000000 --- a/Backup/V5_multi_agent2/results/R4_results.json +++ /dev/null @@ -1,130 +0,0 @@ -{ - "data_code_availability_score": 4, - "critical_remarks": [ - { - "category": "data_sharing", - "location": "Abstract / Data Description", - "issue": "The datasets are described in detail, but there is no mention of publicly available data repositories or links where the data can be accessed by other researchers.", - "severity": "high", - "impact": "Lack of accessible data hampers reproducibility and independent validation of results, reducing transparency." - }, - { - "category": "code_availability", - "location": "Methodology / Data Analysis", - "issue": "No mention of publicly available code repositories, scripts, or software used for analysis and modeling.", - "severity": "high", - "impact": "Absence of shared code limits reproducibility and impedes other researchers from verifying or building upon the work." - }, - { - "category": "documentation", - "location": "Appendix / Methodology", - "issue": "Details about software packages and hyperparameter grids are referenced but not provided within the main text or accessible supplementary materials.", - "severity": "medium", - "impact": "Insufficient documentation reduces clarity for replication efforts and understanding of the modeling process." - }, - { - "category": "restrictions", - "location": "Data / Ethics Declaration", - "issue": "Data use is limited to users who provided consent under specific regulations; no information on how others can access similar datasets or request data sharing.", - "severity": "medium", - "impact": "Restrictions limit external validation and broader reproducibility, especially for researchers outside the specific regulatory context." - }, - { - "category": "reproducibility", - "location": "Discussion / Limitations", - "issue": "The paper emphasizes the potential for generalizability but lacks details on how to reproduce the predictive models in different settings or with different data.", - "severity": "medium", - "impact": "Limited guidance on reproducibility reduces the ability of others to validate and extend the findings." - } - ], - "improvement_suggestions": [ - { - "original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "improved_version": "The datasets used in this study are not publicly available due to privacy and regulatory restrictions. However, de-identified versions or synthetic datasets mimicking the original data can be shared upon reasonable request and under data use agreements. Details are provided in the supplementary materials.", - "explanation": "Clarifies data sharing options within legal constraints, promoting transparency and enabling replication under controlled conditions.", - "location": "Data Description / Ethics Declaration", - "category": "data_sharing", - "focus": "data_sharing" - }, - { - "original_text": "The dataset included data from 8,372 users who activated their first prescription between January 1, 2023, and March 31, 2024.", - "improved_version": "The datasets, including anonymized user engagement and outcome data, are available in a public repository such as [Repository Name], under accession number [XYZ], or upon request from the corresponding author. Access is subject to data use agreements to ensure privacy compliance.", - "explanation": "Provides concrete access pathways, facilitating external validation and further research.", - "location": "Data Description", - "category": "data_sharing", - "focus": "data_sharing" - }, - { - "original_text": "No mention of publicly available code repositories.", - "improved_version": "All analysis scripts, preprocessing code, and modeling pipelines are openly available at [GitHub/Zenodo link], with detailed documentation on usage and dependencies.", - "explanation": "Ensures code transparency, enabling others to reproduce and build upon the analysis.", - "location": "Methodology / Data Analysis", - "category": "code_availability", - "focus": "code_availability" - }, - { - "original_text": "Detailed hyperparameter grids are provided in Appendix 6.1.", - "improved_version": "Complete code for data preprocessing, feature extraction, model training, hyperparameter tuning, and evaluation, along with hyperparameter grids, are hosted in the public repository [Link], with step-by-step instructions in the README file.", - "explanation": "Enhances reproducibility by providing executable code and clear instructions.", - "location": "Appendix / Methodology", - "category": "code_availability", - "focus": "code_availability" - }, - { - "original_text": "Details about software packages are referenced but not provided within the main text or accessible supplementary materials.", - "improved_version": "A comprehensive list of software packages, versions, and environment setup instructions are included in the repository's README and environment configuration files (e.g., environment.yml or requirements.txt).", - "explanation": "Facilitates environment replication, reducing setup barriers for other researchers.", - "location": "Appendix / Software environment", - "category": "documentation", - "focus": "documentation" - }, - { - "original_text": "No explicit mention of code documentation or usage instructions.", - "improved_version": "All code repositories include detailed documentation, including data input formats, step-by-step analysis workflows, and instructions for reproducing the figures and results.", - "explanation": "Improves usability and transparency of the codebase for external users.", - "location": "Code Documentation", - "category": "documentation", - "focus": "code_availability" - }, - { - "original_text": "The paper emphasizes the potential for generalizability but lacks details on how to reproduce models in different contexts.", - "improved_version": "We provide a reproducible pipeline with sample datasets, code, and detailed instructions in the supplementary materials, enabling researchers to adapt the models to new datasets or settings.", - "explanation": "Supports reproducibility and adaptation across different studies and populations.", - "location": "Discussion / Reproducibility", - "category": "reproducibility", - "focus": "reproducibility" - }, - { - "original_text": "The datasets are described in detail, but no links or access procedures are provided.", - "improved_version": "Data sharing details, including access procedures, repository links, and licensing conditions, are provided in the Data Availability Statement and supplementary materials.", - "explanation": "Ensures transparency about data accessibility, encouraging external validation.", - "location": "Data Description", - "category": "data_sharing", - "focus": "data_sharing" - }, - { - "original_text": "No mention of sharing analysis code or scripts.", - "improved_version": "All analysis scripts, including data preprocessing, feature engineering, and modeling code, are publicly available at [Repository Link], with comprehensive documentation and example workflows.", - "explanation": "Enables independent reproduction and validation of the results.", - "location": "Methodology / Data Analysis", - "category": "code_availability", - "focus": "code_availability" - }, - { - "original_text": "The paper does not specify how to access supplementary materials or code repositories.", - "improved_version": "All supplementary materials, including code, hyperparameter configurations, and detailed methodological descriptions, are hosted at [Repository/DOI link], accessible to all researchers.", - "explanation": "Provides clear access points, promoting transparency and reproducibility.", - "location": "Conclusion / Data and Code Availability", - "category": "documentation", - "focus": "code_availability" - } - ], - "detailed_feedback": { - "data_sharing_assessment": "While the datasets are described thoroughly, their availability is restricted due to privacy and regulatory considerations. To enhance transparency, the authors should specify whether de-identified or synthetic datasets are available and provide access procedures, such as data repositories or request protocols.", - "code_availability": "The manuscript lacks any mention of publicly accessible code repositories or scripts used for data analysis and modeling. Sharing code via platforms like GitHub or Zenodo, along with documentation, would significantly improve reproducibility and facilitate external validation.", - "documentation_completeness": "The current documentation references appendices and supplementary materials but does not include explicit instructions, software environment details, or step-by-step workflows. Providing comprehensive documentation, including environment setup files and usage instructions, would support reproducibility.", - "restrictions_justification": "Data access is limited by consent and regulatory restrictions, which are justified but should be clearly communicated. Providing pathways for data access under controlled conditions or sharing synthetic datasets can help balance privacy with transparency.", - "reproducibility_support": "The paper emphasizes the potential for generalizability but does not include executable code, detailed workflows, or environment specifications. Including these elements in public repositories would enable others to reproduce and adapt the models effectively." - }, - "summary": "Overall, the manuscript demonstrates strong methodological rigor but falls short in openly sharing data and code, which are critical for transparency and reproducibility. Addressing these gaps by providing access to datasets (where permissible), sharing analysis scripts, and including detailed documentation would substantially enhance the research's transparency and impact." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/R5_results.json b/Backup/V5_multi_agent2/results/R5_results.json deleted file mode 100644 index d5590b4..0000000 --- a/Backup/V5_multi_agent2/results/R5_results.json +++ /dev/null @@ -1,160 +0,0 @@ -{ - "statistical_rigor_score": 6, - "critical_remarks": [ - { - "category": "test_selection", - "location": "Methodology: 2.2 Feature Selection, Model Training, and Evaluation", - "issue": "While random forest models are used extensively, there is limited discussion on the justification for choosing this algorithm over others, especially given the class imbalance and the potential benefits of alternative methods like boosting or neural networks.", - "severity": "medium", - "impact": "This may limit the exploration of potentially more optimal models, affecting the robustness and generalizability of the findings." - }, - { - "category": "assumptions", - "location": "Methodology: 2.2 Data Preparation", - "issue": "There is no explicit mention of assumption checks for the machine learning models, such as feature independence, multicollinearity, or the distributional assumptions underlying the preprocessing steps.", - "severity": "medium", - "impact": "Unverified assumptions could lead to overfitting or biased models, reducing the validity of the predictive performance." - }, - { - "category": "sample_size", - "location": "Methodology: 2.2 Model Training and Evaluation", - "issue": "Although large sample sizes are used, there is no formal sample size justification or power analysis to confirm that the datasets are sufficient for the complexity of the models and the number of features.", - "severity": "low", - "impact": "This omission may affect the confidence in the stability and reliability of the model estimates." - }, - { - "category": "multiple_comparisons", - "location": "Results: 3 Prediction Results", - "issue": "Multiple performance metrics are reported across numerous weeks and days without correction for multiple testing or comparison, increasing the risk of Type I errors.", - "severity": "medium", - "impact": "This can lead to overstated significance and misinterpretation of the model's performance improvements over time." - }, - { - "category": "effect_size", - "location": "Results: 3.2 Prediction Results", - "issue": "While performance metrics like AUC, accuracy, and F1 are reported, there is limited discussion on the effect sizes or practical significance of the predictive improvements.", - "severity": "low", - "impact": "Without effect size context, it's difficult to assess the real-world impact of the models' predictive capabilities." - }, - { - "category": "confidence_intervals", - "location": "Results: 3.2 Prediction Results", - "issue": "Confidence intervals are not provided for key metrics such as AUC, accuracy, or F1 scores, which limits understanding of the precision of these estimates.", - "severity": "high", - "impact": "This omission hampers the assessment of statistical uncertainty and the robustness of the reported performance metrics." - }, - { - "category": "p_value", - "location": "Discussion: 4.1 Predicting Nonadherence", - "issue": "There is no mention of p-values or statistical significance testing for the differences in model performance metrics over time or between models.", - "severity": "low", - "impact": "This limits the ability to statistically validate observed performance trends or differences." - }, - { - "category": "power", - "location": "Methodology: 2.2 Model Training and Evaluation", - "issue": "No formal power analysis is conducted to determine whether the sample sizes and number of events are adequate for the model complexity and class imbalance.", - "severity": "low", - "impact": "Potential underpowering could lead to unstable estimates and reduced confidence in model performance." - }, - { - "category": "missing_data", - "location": "Methodology: 2.1 Datasets and Definitions of Nonadherence", - "issue": "Handling of missing data is not explicitly described; it is unclear whether missing engagement or demographic data were imputed, excluded, or otherwise managed.", - "severity": "high", - "impact": "Unaddressed missing data can bias results and compromise the validity of the models." - }, - { - "category": "outliers", - "location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence", - "issue": "There is no discussion on outlier detection or treatment for continuous variables such as number of exercises or sessions, which could skew model training.", - "severity": "medium", - "impact": "Unhandled outliers may distort model learning and reduce predictive accuracy." - } - ], - "improvement_suggestions": [ - { - "original_text": "While random forest models are used extensively, there is limited discussion on the justification for choosing this algorithm over others, especially given the class imbalance and the potential benefits of alternative methods like boosting or neural networks.", - "improved_version": "Include a rationale for selecting random forest algorithms, such as comparative performance analyses with other models like XGBoost, neural networks, or logistic regression, especially considering class imbalance and interpretability.", - "explanation": "Providing a comparative justification enhances the transparency and robustness of the model choice, ensuring that the selected method is optimal for the data characteristics.", - "location": "Methodology: 2.2 Model Training and Evaluation", - "category": "test_selection" - }, - { - "original_text": "There is no explicit mention of assumption checks for the machine learning models, such as feature independence, multicollinearity, or the distributional assumptions underlying the preprocessing steps.", - "improved_version": "Add a section detailing assumption checks, such as correlation analyses for multicollinearity, distribution assessments for features, and validation of preprocessing steps like normalization or scaling.", - "explanation": "Verifying assumptions ensures the validity of the preprocessing and modeling steps, reducing bias and overfitting risks.", - "location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence", - "category": "assumptions" - }, - { - "original_text": "Although large sample sizes are used, there is no formal sample size justification or power analysis to confirm that the datasets are sufficient for the complexity of the models and the number of features.", - "improved_version": "Incorporate a formal sample size and power analysis, considering the number of features, expected effect sizes, and class imbalance, to justify the adequacy of the datasets for model training.", - "explanation": "A formal justification enhances confidence that the study is adequately powered to detect meaningful differences and model performance levels.", - "location": "Methodology: 2.2 Model Training and Evaluation", - "category": "sample_size" - }, - { - "original_text": "Multiple performance metrics are reported across numerous weeks and days without correction for multiple testing or comparison, increasing the risk of Type I errors.", - "improved_version": "Apply statistical correction methods such as Bonferroni or Holm adjustments when comparing performance metrics across multiple weeks or days, and report adjusted p-values to control for multiple comparisons.", - "explanation": "Correcting for multiple comparisons reduces false-positive findings, increasing the validity of performance trend interpretations.", - "location": "Results: 3 Prediction Results", - "category": "multiple_comparisons" - }, - { - "original_text": "While performance metrics like AUC, accuracy, and F1 are reported, there is limited discussion on the effect sizes or practical significance of the predictive improvements.", - "improved_version": "Include effect size measures such as Cohen\u2019s d or differences in performance metrics with confidence intervals to contextualize the practical significance of improvements over time.", - "explanation": "Effect sizes provide a clearer understanding of the real-world impact of the predictive models beyond statistical significance alone.", - "location": "Results: 3.2 Prediction Results", - "category": "effect_size" - }, - { - "original_text": "Confidence intervals are not provided for key metrics such as AUC, accuracy, or F1 scores, which limits understanding of the precision of these estimates.", - "improved_version": "Calculate and report 95% confidence intervals for all key performance metrics (AUC, accuracy, F1, precision, recall) to quantify the uncertainty around these estimates.", - "explanation": "Confidence intervals allow for assessment of the statistical precision and reliability of the performance metrics, strengthening the interpretability of results.", - "location": "Results: 3.2 Prediction Results", - "category": "confidence_intervals" - }, - { - "original_text": "There is no mention of p-values or statistical significance testing for the differences in model performance metrics over time or between models.", - "improved_version": "Conduct formal statistical tests (e.g., paired t-tests, bootstrap comparisons) to evaluate whether observed differences in performance metrics across weeks or models are statistically significant, and report p-values accordingly.", - "explanation": "Statistical testing of performance differences enhances the rigor in claiming improvements or differences between models or time points.", - "location": "Discussion: 4.1 Predicting Nonadherence", - "category": "p_value" - }, - { - "original_text": "No formal power analysis is conducted to determine whether the sample sizes and number of events are adequate for the model complexity and class imbalance.", - "improved_version": "Include a formal power analysis or sample size calculation tailored to the expected effect sizes, class imbalance, and model complexity to justify dataset adequacy.", - "explanation": "This ensures the study is sufficiently powered to detect meaningful differences and reduces the risk of underpowered analyses.", - "location": "Methodology: 2.2 Model Training and Evaluation", - "category": "power" - }, - { - "original_text": "Handling of missing data is not explicitly described; it is unclear whether missing engagement or demographic data were imputed, excluded, or otherwise managed.", - "improved_version": "Describe the methods used for handling missing data, such as multiple imputation, exclusion criteria, or data augmentation, and justify their appropriateness.", - "explanation": "Explicit missing data handling prevents bias and ensures transparency in data preprocessing, improving the validity of the models.", - "location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence", - "category": "missing_data" - }, - { - "original_text": "There is no discussion on outlier detection or treatment for continuous variables such as number of exercises or sessions, which could skew model training.", - "improved_version": "Implement outlier detection procedures (e.g., z-scores, IQR-based methods) and specify how outliers are treated or excluded to prevent distortion of model training.", - "explanation": "Proper outlier management enhances model stability and accuracy by reducing the influence of extreme values.", - "location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence", - "category": "outliers" - } - ], - "detailed_feedback": { - "test_selection": "The study predominantly employs random forest algorithms for all predictions, citing prior success but not explicitly comparing alternative models. Incorporating a model comparison section, including simpler or more interpretable models like logistic regression or boosting algorithms, would strengthen the justification for the chosen approach and potentially improve predictive performance.", - "assumption_verification": "The preprocessing steps mention normalization and scaling but do not detail assumption checks such as multicollinearity assessments or feature independence. Explicitly conducting and reporting such checks ensures the data meet the assumptions underlying the preprocessing and modeling steps, reducing bias and overfitting risks.", - "sample_size_justification": "While large datasets are used, the absence of formal sample size calculations or power analyses limits confidence in the sufficiency of the data for complex ML models. Including such analyses would confirm that the study is adequately powered to detect meaningful differences and model performance levels.", - "multiple_comparison_handling": "Multiple performance metrics are reported across many time points without correction for multiple testing. Applying statistical correction methods like Bonferroni or Holm adjustments, and reporting adjusted p-values, would mitigate the risk of false-positive findings and enhance the robustness of performance trend claims.", - "effect_size_reporting": "The results focus on performance metrics without contextualizing their practical significance. Including effect size measures or confidence intervals for differences in performance over time would clarify the real-world impact of the models\u2019 improvements.", - "confidence_intervals": "Key metrics such as AUC, accuracy, and F1 scores are presented without confidence intervals. Calculating and reporting these intervals would provide insight into the precision and reliability of the estimates, strengthening the interpretability of the results.", - "p_value_interpretation": "The discussion does not include significance testing for performance differences. Conducting formal statistical tests and reporting p-values would allow for rigorous validation of observed trends and differences, reducing subjective interpretation.", - "statistical_power": "No formal power analysis is reported, which raises concerns about whether the sample sizes and number of events are sufficient for the complexity of the models, especially given class imbalance. Including power calculations would bolster confidence in the findings.", - "missing_data_handling": "The handling of missing data is not described, leaving uncertainty about potential biases introduced by data exclusion or imputation. Explicitly detailing missing data management strategies would improve transparency and validity.", - "outlier_treatment": "There is no discussion of outlier detection or treatment for continuous variables like exercise counts. Implementing outlier detection methods and describing their use would prevent skewed model training and improve accuracy." - }, - "summary": "Overall, the study demonstrates a commendable effort in applying machine learning to predict nonadherence in mHealth interventions, with extensive datasets and multiple performance evaluations. However, enhancements in statistical rigor\u2014such as explicit assumption checks, correction for multiple testing, confidence interval reporting, and formal power analyses\u2014are needed to strengthen the validity and interpretability of the findings. Addressing these aspects will improve the robustness, reproducibility, and practical relevance of the research outcomes." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/R6_results.json b/Backup/V5_multi_agent2/results/R6_results.json deleted file mode 100644 index 08d5ee0..0000000 --- a/Backup/V5_multi_agent2/results/R6_results.json +++ /dev/null @@ -1,178 +0,0 @@ -{ - "technical_accuracy_score": 8, - "critical_remarks": [ - { - "category": "derivations", - "location": "Mathematical Framework section, paragraphs describing feature normalization and model training", - "issue": "While the text mentions normalization techniques like square root scaling and standard scaling, it lacks detailed mathematical derivations or formulas explaining the exact transformations applied to features, which could lead to ambiguity in reproducibility.", - "severity": "medium", - "impact": "This omission may hinder precise replication and understanding of the preprocessing steps, affecting the technical rigor." - }, - { - "category": "algorithms", - "location": "Methodology, paragraph on model training and hyperparameter tuning", - "issue": "The choice of random forest is justified with references to prior studies, but there is no detailed discussion of hyperparameter settings, feature selection process, or validation procedures beyond stratified 10-fold cross-validation, which might impact the assessment of algorithm correctness and efficiency.", - "severity": "medium", - "impact": "Limited transparency on model tuning could obscure potential overfitting or underfitting issues, affecting the perceived robustness." - }, - { - "category": "terminology", - "location": "Throughout the document, especially in definitions of adherence and churn", - "issue": "The terms 'nonadherence,' 'churn,' and 'disengagement' are used with nuanced distinctions, but some definitions are inconsistent or lack precise operationalization, e.g., 'discontinuing use' vs 'not meeting weekly exercise thresholds.'", - "severity": "low", - "impact": "Inconsistent terminology may cause confusion in interpreting results and applying the models in practice." - }, - { - "category": "equations", - "location": "Mathematical Framework section, descriptions of feature normalization", - "issue": "The text mentions 'normalizing numerical data by performing square root scaling' but does not provide explicit formulas or equations illustrating the transformation, which could lead to ambiguity.", - "severity": "low", - "impact": "Lack of explicit equations reduces clarity and reproducibility." - }, - { - "category": "completeness", - "location": "Methodology, model evaluation", - "issue": "While multiple performance metrics are reported, there is limited discussion on how class imbalance was addressed beyond Tomek Links undersampling, and no mention of other techniques like SMOTE or cost-sensitive learning.", - "severity": "medium", - "impact": "Incomplete coverage of imbalance handling methods may affect the interpretation of model performance and generalizability." - }, - { - "category": "consistency", - "location": "Throughout the document, especially in definitions of prediction windows", - "issue": "Different definitions of prediction windows (e.g., 7-day, 31-day) are used across models and datasets without a unified framework or explicit rationale, which could lead to inconsistencies in interpretation.", - "severity": "low", - "impact": "This inconsistency can affect the logical comparability of results across models and datasets." - }, - { - "category": "implementation", - "location": "Methodology, software packages and hyperparameter tuning", - "issue": "The appendix mentions the use of 'freely available Python packages' but does not specify versions, specific libraries, or code snippets, which hampers reproducibility and assessment of implementation correctness.", - "severity": "medium", - "impact": "Limited implementation transparency diminishes confidence in the reproducibility and technical rigor." - }, - { - "category": "edge_cases", - "location": "Discussion, limitations", - "issue": "The text acknowledges that models rely on rich, continuous engagement data but does not explicitly address how the models perform with sparse or irregular data, or how they handle users with very short engagement periods.", - "severity": "high", - "impact": "Ignoring such edge cases could lead to overestimating model robustness and applicability in real-world, less ideal data scenarios." - }, - { - "category": "complexity", - "location": "Technical analysis, model evaluation", - "issue": "There is minimal discussion on the computational complexity, training time, or resource requirements of the random forest models, which is critical for practical deployment in large-scale mHealth systems.", - "severity": "low", - "impact": "Lack of complexity analysis limits understanding of model scalability and operational feasibility." - }, - { - "category": "documentation", - "location": "Overall, including appendices and references", - "issue": "The documentation of hyperparameter grids, feature importance analysis, and model evaluation procedures is limited to references to appendices, which are not fully detailed in the main text, reducing transparency.", - "severity": "medium", - "impact": "This hampers full understanding and independent validation of the technical methods used." - } - ], - "improvement_suggestions": [ - { - "original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "improved_version": "The manuscript should explicitly specify the exact inclusion criteria, including the consent process and data anonymization procedures, to enhance clarity and reproducibility.", - "explanation": "Clearer operationalization of inclusion criteria improves transparency and allows other researchers to replicate the sample selection process.", - "location": "Methodology, Dataset description", - "category": "documentation", - "focus": "clarity" - }, - { - "original_text": "We applied stratified 10-fold cross-validation and randomized search for hyperparameter tuning on the training sets, optimizing for F1 score for all models.", - "improved_version": "The hyperparameter tuning process should specify the parameter grid, number of iterations, and the criteria for selecting the best hyperparameters, along with details on how overfitting was prevented.", - "explanation": "Detailed hyperparameter procedures ensure reproducibility and allow assessment of the robustness of the tuning process.", - "location": "Methodology, model training", - "category": "implementation", - "focus": "clarity" - }, - { - "original_text": "Normalization techniques like square root scaling and standard scaling are mentioned but lack explicit formulas.", - "improved_version": "Include explicit formulas, e.g., for square root scaling: x' = sqrt(x + 1), and specify the parameters used for standard scaling (mean and standard deviation).", - "explanation": "Providing formulas enhances clarity, reproducibility, and allows others to verify the correctness of preprocessing steps.", - "location": "Mathematical Framework, preprocessing", - "category": "equations", - "focus": "equations" - }, - { - "original_text": "The models' performance metrics are reported, but the class imbalance handling methods are only briefly mentioned.", - "improved_version": "Describe in detail the class imbalance mitigation strategies employed, such as oversampling, undersampling, or cost-sensitive learning, and justify their choice.", - "explanation": "Comprehensive reporting of imbalance handling techniques clarifies how models maintain performance despite skewed classes.", - "location": "Methodology, evaluation", - "category": "completeness", - "focus": "completeness" - }, - { - "original_text": "The definitions of adherence and churn are given, but the operational thresholds (e.g., 'fewer than eight exercises') could be further justified.", - "improved_version": "Provide empirical or clinical justification for the chosen thresholds, referencing prior studies or clinical guidelines where applicable.", - "explanation": "Justification of thresholds strengthens the validity of the operational definitions and their relevance to health outcomes.", - "location": "Introduction, definitions", - "category": "terminology", - "focus": "terminology" - }, - { - "original_text": "The description of feature importance analyses is limited to appendix references.", - "improved_version": "Summarize key findings from feature importance analyses in the main text, including which features most strongly influence predictions and their potential clinical relevance.", - "explanation": "Enhances transparency and helps readers understand which behavioral factors drive model predictions.", - "location": "Results, feature importance", - "category": "documentation", - "focus": "clarity" - }, - { - "original_text": "The discussion mentions the reliance on continuous engagement data but does not specify how models perform with sparse or irregular data.", - "improved_version": "Include an analysis or discussion on model robustness in scenarios with sparse data, possibly with sensitivity analyses or simulation studies.", - "explanation": "Addressing edge cases improves understanding of model applicability in real-world, less-than-ideal data conditions.", - "location": "Discussion, limitations", - "category": "edge_cases", - "focus": "edge_cases" - }, - { - "original_text": "The complexity of the random forest models is not discussed.", - "improved_version": "Provide an analysis of computational complexity, including training time, inference time, and resource requirements, especially for large-scale deployment.", - "explanation": "Understanding computational demands is essential for practical implementation and scalability of the models.", - "location": "Technical analysis", - "category": "complexity", - "focus": "complexity" - }, - { - "original_text": "The hyperparameter grid details are only referenced as in Appendix 6.1 without main text elaboration.", - "improved_version": "Summarize key hyperparameter ranges and tuning procedures within the main manuscript to improve transparency and facilitate replication.", - "explanation": "Main text summaries aid comprehension and ensure critical methodological details are accessible without consulting appendices.", - "location": "Methodology, hyperparameter tuning", - "category": "documentation", - "focus": "clarity" - }, - { - "original_text": "The operational definitions of 'churned' and 'churning' users are based on last login dates, but potential ambiguities at program end are not discussed.", - "improved_version": "Clarify how the model distinguishes between users who stop using the app due to program completion versus disengagement, possibly by incorporating program end dates or additional engagement metrics.", - "explanation": "This clarification reduces misclassification and improves the validity of churn definitions in the context of program end versus disengagement.", - "location": "Discussion, limitations", - "category": "edge_cases", - "focus": "edge_cases" - }, - { - "original_text": "The models are primarily evaluated using metrics like AUC, F1, accuracy, etc., but the potential impact of false positives/negatives on clinical decision-making is not discussed.", - "improved_version": "Include an analysis of the clinical or operational implications of false positive and false negative predictions, possibly with threshold adjustments for optimal trade-offs.", - "explanation": "Understanding the practical impact of prediction errors informs deployment strategies and risk management in real-world settings.", - "location": "Discussion, implications", - "category": "technical_documentation", - "focus": "clarity" - } - ], - "detailed_feedback": { - "derivation_correctness": "The paper mentions normalization techniques like square root scaling and standard scaling but does not provide explicit formulas or mathematical derivations. Including these would clarify the preprocessing steps and facilitate exact replication. For example, specifying that 'for right-skewed features, x' = sqrt(x + 1)' was applied, and that standard scaling involved subtracting the mean and dividing by the standard deviation, would improve transparency.", - "algorithm_accuracy": "The use of random forest classifiers is justified with references to prior literature, and hyperparameter tuning via grid search and cross-validation is described. However, details such as the specific hyperparameter ranges, number of trees, maximum depth, and feature subset sizes are not provided. Including these would strengthen confidence in the model's correctness and allow others to replicate or evaluate the models' efficiency.", - "terminology_accuracy": "The paper distinguishes between 'nonadherence,' 'churn,' and 'disengagement,' but the operational definitions vary slightly across contexts. Clarifying that 'nonadherence' refers to not meeting the prescribed activity threshold, while 'churn' indicates complete discontinuation, and ensuring consistent use throughout, would improve terminological precision.", - "equation_clarity": "Explicit formulas for feature transformations, such as normalization, are absent. Including equations like 'x' = sqrt(x + 1) for skewed features or 'x' = (x - \u03bc) / \u03c3 for standard scaling, would enhance clarity and reproducibility.", - "content_completeness": "While performance metrics are comprehensively reported, the methodology for handling class imbalance (e.g., undersampling with Tomek Links) is only briefly mentioned. Providing detailed procedures, including the parameters and rationale, would improve completeness and allow critical assessment.", - "logical_consistency": "The paper maintains consistent definitions of prediction windows and aligns the operationalization of adherence and churn with prior literature. However, the varying thresholds for adherence (e.g., 8 exercises/week vs 1 blood pressure measurement/month) could be unified or justified more explicitly to ensure logical coherence across different conditions.", - "implementation_details": "The mention of Python packages and hyperparameter grids is limited to appendix references. Including key package versions, code snippets, or pseudocode in the main text would enhance transparency and facilitate independent validation.", - "edge_case_handling": "The models assume continuous, rich engagement data, but the paper does not explicitly address how sparse or irregular data are handled, especially for users with very short engagement periods or missing data. Discussing strategies like imputation, data augmentation, or model adjustments for such cases would improve robustness.", - "complexity_analysis": "The manuscript does not discuss the computational complexity or resource requirements of the random forest models. Including an analysis of training and inference times, especially for large datasets, would inform practical deployment considerations.", - "technical_documentation": "Hyperparameter tuning procedures, feature importance analyses, and evaluation metrics are referenced but not detailed in the main text. Providing summarized tables or descriptions of these processes would improve transparency and reproducibility." - }, - "summary": "Overall, the manuscript demonstrates a strong understanding of machine learning applications in mHealth adherence prediction, with robust performance metrics and thoughtful analysis. However, enhancing transparency in preprocessing formulas, hyperparameter details, and handling of edge cases would elevate the technical rigor. Addressing these areas will facilitate replication, practical deployment, and broader applicability of the proposed models." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/R7_results.json b/Backup/V5_multi_agent2/results/R7_results.json deleted file mode 100644 index 063d676..0000000 --- a/Backup/V5_multi_agent2/results/R7_results.json +++ /dev/null @@ -1,186 +0,0 @@ -{ - "consistency_score": 7, - "critical_remarks": [ - { - "category": "methods_results", - "location": "Section 2.2, paragraph starting with 'Aligning with previous churn prediction studies' ", - "issue": "The description of feature selection emphasizes behavioral engagement features but lacks clarity on how these features directly relate to the specific nonadherence definitions used in the results, especially for the weekly and monthly measures.", - "severity": "medium", - "impact": "This ambiguity may cause confusion about whether the features used are fully aligned with the operational definitions of nonadherence and churn, affecting the interpretability of the results." - }, - { - "category": "results_conclusions", - "location": "Section 3.2, paragraph starting 'Weekly nonadherence prediction models in Vivira demonstrated strong performances' ", - "issue": "The results show high performance metrics, but the discussion does not sufficiently address the potential impact of class imbalance on these metrics, especially since the adherence rates decline sharply over time.", - "severity": "high", - "impact": "This omission could lead to overestimating the models' robustness and their practical utility in real-world, highly imbalanced settings." - }, - { - "category": "logical_flow", - "location": "Section 4, paragraph starting 'Our findings show that nonadherence can be accurately predicted' ", - "issue": "The paragraph jumps from discussing model performance to implications for targeted strategies without clearly linking how the predictive accuracy translates into actionable interventions.", - "severity": "medium", - "impact": "This weakens the logical progression from results to practical applications, reducing clarity of the study's translational significance." - }, - { - "category": "terminology", - "location": "Throughout the manuscript, especially in Abstract and Introduction", - "issue": "The terms 'nonadherence', 'churn', and 'disengagement' are used somewhat interchangeably without explicit definitions or distinctions in some contexts.", - "severity": "low", - "impact": "Inconsistent terminology may cause confusion about what exactly is being predicted and how different concepts relate, affecting clarity." - }, - { - "category": "hypothesis", - "location": "Section 2, Introduction", - "issue": "The hypothesis that behavioral app engagement features can predict nonadherence over extended periods is implied but not explicitly stated or tested as a formal hypothesis.", - "severity": "low", - "impact": "Explicit hypotheses would strengthen the scientific framing and clarity of the research aims." - }, - { - "category": "interpretation", - "location": "Section 4, Discussion", - "issue": "The interpretation that models 'outperform' in early weeks without considering the influence of class imbalance or baseline rates may be overstated.", - "severity": "medium", - "impact": "This could lead to overconfidence in the models' early predictive utility, potentially misleading readers about their practical deployment." - }, - { - "category": "citations", - "location": "Throughout the manuscript, especially in the Introduction and Discussion", - "issue": "Some citations (e.g., [21], [5]) are used to support claims about prior work but are not always directly linked to the specific points made, and some references (e.g., [50]) are not consistently integrated with the discussion of adherence definitions.", - "severity": "low", - "impact": "Inconsistent citation integration may weaken the scholarly rigor and traceability of claims." - }, - { - "category": "figures", - "location": "Section 3, Figures 6.5 and 6.6", - "issue": "The figure captions describe performance trends but do not explicitly connect the visual data to the text's claims about model improvements over time or differences between user groups.", - "severity": "low", - "impact": "This reduces clarity and interpretability of the figures, limiting their effectiveness as supporting evidence." - }, - { - "category": "tables", - "location": "Section 3, Tables 6.1 to 6.4", - "issue": "Some tables present extensive data, but the alignment between the table content and the narrative summaries is sometimes weak, e.g., performance metrics are discussed in the text but not always explicitly linked to specific table rows or columns.", - "severity": "medium", - "impact": "This can cause confusion and reduce the ease of cross-referencing results, affecting overall coherence." - }, - { - "category": "supplementary", - "location": "Appendix 6.2", - "issue": "The appendix contains feature importance analyses, but the main text does not sufficiently highlight how these findings support the claims about feature relevance and model robustness.", - "severity": "low", - "impact": "Limited integration diminishes the clarity of how supplementary analyses underpin the main conclusions." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "The rich behavioral engagement data collected by mHealth interventions form the basis for predicting nonadherence, and explicitly testing this relationship is a core aim of this study.", - "explanation": "Clarifies the link between data and the research aim, strengthening the methodological rationale.", - "location": "Abstract", - "category": "abstract", - "focus": "methods_results" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.", - "improved_version": "Our models correctly identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira, where nonadherence was operationalized as completing fewer than eight exercises per week, with a mean AUC of 0.95.", - "explanation": "Ensures clarity by explicitly linking the performance metrics with the operational definition of nonadherence, improving transparency.", - "location": "Abstract", - "category": "abstract", - "focus": "results_conclusions" - }, - { - "original_text": "The description of feature selection emphasizes behavioral engagement features but lacks clarity on how these features directly relate to the specific nonadherence definitions used in the results.", - "improved_version": "Feature selection focused on behavioral engagement metrics such as daily app activity, session counts, and exercise completion, which directly correspond to the operational definitions of nonadherence (e.g., weekly exercise thresholds).", - "explanation": "Aligns features explicitly with the operational definitions, enhancing methodological transparency.", - "location": "Section 2.2", - "category": "methodology", - "focus": "methods_results" - }, - { - "original_text": "The results show high performance metrics, but the discussion does not sufficiently address the potential impact of class imbalance on these metrics.", - "improved_version": "While the models demonstrate high performance, it is important to consider that class imbalance\u2014particularly in adherence rates\u2014may inflate metrics like accuracy and F1-score; thus, metrics such as precision-recall curves should be interpreted with caution.", - "explanation": "Provides a nuanced interpretation, acknowledging limitations and preventing overstatement of results.", - "location": "Section 3.2", - "category": "results_conclusions", - "focus": "interpretation" - }, - { - "original_text": "The paragraph jumps from discussing model performance to implications for targeted strategies without clearly linking how the predictive accuracy translates into actionable interventions.", - "improved_version": "The high predictive accuracy suggests that these models could be integrated into intervention frameworks to identify at-risk users early, enabling timely, targeted strategies such as personalized notifications or system adaptations to prevent further disengagement.", - "explanation": "Strengthens the logical link between results and practical applications, clarifying translational relevance.", - "location": "Section 4, Discussion", - "category": "logical_flow", - "focus": "logical_flow" - }, - { - "original_text": "The terms 'nonadherence', 'churn', and 'disengagement' are used somewhat interchangeably without explicit definitions or distinctions.", - "improved_version": "Throughout the manuscript, 'nonadherence' refers to users not meeting the recommended engagement thresholds, while 'churn' specifically denotes complete discontinuation of app use, and 'disengagement' is used as a broader term encompassing both phenomena. Clear distinctions are maintained to avoid confusion.", - "explanation": "Clarifies terminology, improving consistency and reader understanding.", - "location": "Throughout", - "category": "terminology", - "focus": "terminology" - }, - { - "original_text": "The hypothesis that behavioral app engagement features can predict nonadherence over extended periods is implied but not explicitly stated or tested as a formal hypothesis.", - "improved_version": "This study hypothesizes that behavioral app engagement features, such as daily activity and exercise completion, can accurately predict nonadherence and churn over extended durations in mHealth interventions.", - "explanation": "Explicitly states the hypothesis, strengthening the scientific framing.", - "location": "Section 2, Introduction", - "category": "hypothesis", - "focus": "hypothesis" - }, - { - "original_text": "The interpretation that models 'outperform' in early weeks without considering the influence of class imbalance or baseline rates may be overstated.", - "improved_version": "Although early-week performance metrics are promising, they should be interpreted cautiously, considering the potential influence of class imbalance and baseline adherence rates, which may inflate performance indicators.", - "explanation": "Provides a balanced interpretation, acknowledging potential confounders.", - "location": "Section 4, Discussion", - "category": "interpretation", - "focus": "interpretation" - }, - { - "original_text": "Some citations (e.g., [21], [5]) are used to support claims about prior work but are not always directly linked to the specific points made.", - "improved_version": "Citations such as [21] and [5] are explicitly linked to prior studies on early user churn prediction, providing direct support for the claims about methodological foundations and performance benchmarks.", - "explanation": "Enhances scholarly rigor by ensuring citations directly support specific statements.", - "location": "Throughout", - "category": "citations", - "focus": "citations" - }, - { - "original_text": "The figure captions describe performance trends but do not explicitly connect the visual data to the text's claims about model improvements over time.", - "improved_version": "Figure 6.5 illustrates the steady increase in AUC and F1-score over Weeks 2 to 13, supporting the text's claim of improving model performance with accumulating data.", - "explanation": "Explicitly links figures to narrative claims, improving interpretability.", - "location": "Section 3", - "category": "figures", - "focus": "figures" - }, - { - "original_text": "Some tables present extensive data, but the alignment between the table content and the narrative summaries is sometimes weak.", - "improved_version": "Ensure that each key performance metric discussed in the text is directly cross-referenced with the corresponding table rows and columns, e.g., 'as shown in Table 6.1, the AUC increased from 0.89 in Week 2 to 0.99 in Week 13.'", - "explanation": "Facilitates easier cross-referencing and comprehension, enhancing coherence.", - "location": "Section 3", - "category": "tables", - "focus": "tables" - }, - { - "original_text": "The appendix contains feature importance analyses, but the main text does not sufficiently highlight how these findings support the claims about feature relevance and model robustness.", - "improved_version": "The main text references Appendix 6.2 to support claims that exercise-related features are most influential, emphasizing the robustness of behavioral features in predicting nonadherence across time.", - "explanation": "Strengthens the link between supplementary analyses and main conclusions, improving coherence.", - "location": "Section 4, Discussion", - "category": "supplementary", - "focus": "supplementary" - } - ], - "detailed_feedback": { - "methods_results_alignment": "The methodology section details the feature selection and modeling approach, focusing on behavioral engagement metrics like daily activity and exercise completion. The results demonstrate high predictive performance for nonadherence and churn, aligning well with these methods. However, explicit discussion on how these features directly operationalize the adherence definitions would improve clarity.", - "results_conclusions_alignment": "The results show strong predictive metrics, supporting the conclusion that nonadherence can be predicted over extended periods. Nonetheless, the discussion should more explicitly address how the performance metrics translate into real-world intervention potential, considering class imbalance and model limitations.", - "logical_flow": "The manuscript generally follows a logical sequence from background, methods, results, to discussion. Yet, transitions between model performance results and their practical implications could be smoother, explicitly linking predictive accuracy to intervention strategies.", - "terminology_consistency": "The manuscript uses 'nonadherence', 'churn', and 'disengagement' with some overlap but inconsistent distinctions. Clarifying and consistently applying these terms throughout would enhance clarity.", - "hypothesis_testing": "While the introduction implies that behavioral engagement features can predict nonadherence, the manuscript would benefit from explicitly stating and testing this as a formal hypothesis to strengthen scientific rigor.", - "interpretation_consistency": "The interpretation of high performance metrics, especially early in the program, should consider potential confounders like class imbalance. Overstating early predictive utility without this context could mislead readers.", - "citation_consistency": "Some references support broad claims but are not always directly linked to the specific points. More precise citation integration would improve scholarly rigor.", - "figure_text_alignment": "Figures effectively illustrate performance trends but lack explicit references in the captions to how they support the narrative claims. Enhancing this connection would improve interpretability.", - "table_text_alignment": "Tables contain comprehensive data, but the narrative sometimes lacks direct cross-referencing. Clearer linkage between text and table data would improve coherence.", - "supplementary_consistency": "Appendix analyses support main findings but are under-referenced in the text. Explicitly discussing how these analyses underpin key conclusions would strengthen the overall coherence." - }, - "summary": "Overall, the manuscript demonstrates a solid integration of methods and results, with clear evidence supporting the claim that behavioral app engagement features can predict nonadherence over extended periods. However, improvements in terminology clarity, explicit hypothesis framing, and stronger linking of figures, tables, and supplementary analyses to the main narrative would elevate the logical coherence and scholarly rigor. Addressing these aspects will enhance the clarity, transparency, and practical relevance of the research findings." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/S10_results.json b/Backup/V5_multi_agent2/results/S10_results.json deleted file mode 100644 index 26d4048..0000000 --- a/Backup/V5_multi_agent2/results/S10_results.json +++ /dev/null @@ -1,181 +0,0 @@ -{ - "score": 8, - "critical_remarks": [ - { - "category": "completeness", - "location": "Section 2.2, Feature Selection, Model Training, and Evaluation", - "issue": "While hyperparameter grids are mentioned to be in Appendix 6.1, the main text lacks a summary of key hyperparameters and their ranges, which would aid understanding without requiring immediate appendix access.", - "severity": "medium", - "impact": "This reduces transparency about model tuning and may hinder reproducibility or critical assessment of the modeling process." - }, - { - "category": "relevance", - "location": "Section 3.2.1, Vivira prediction results", - "issue": "The detailed tables and figures, while comprehensive, focus heavily on statistical metrics without explicitly linking how these translate into practical intervention strategies or clinical relevance.", - "severity": "medium", - "impact": "This limits the reader's ability to assess real-world applicability of the predictive performance." - }, - { - "category": "clarity", - "location": "Section 4.1, Summary of prediction results", - "issue": "The description of performance metrics over time is dense and could benefit from clearer summaries or visualizations to facilitate quick understanding.", - "severity": "low", - "impact": "This affects accessibility for readers seeking a quick grasp of model effectiveness." - }, - { - "category": "organization", - "location": "Section 4.2, Potential for targeted strategies", - "issue": "The discussion on reengagement after correct predictions is somewhat scattered, with multiple percentages and user categories presented without a clear structure.", - "severity": "low", - "impact": "This hampers the logical flow and makes it harder for readers to follow key insights." - }, - { - "category": "completeness", - "location": "Section 4.4, Limitations and future work", - "issue": "While limitations are acknowledged, there is limited discussion on potential biases introduced by the datasets or the impact of demographic variables on model performance.", - "severity": "medium", - "impact": "This omission affects the thoroughness of the critical evaluation and the generalizability of findings." - }, - { - "category": "clarity", - "location": "Appendix 1, Software packages and hyperparameter grids", - "issue": "The hyperparameter grids are only referenced as being in Appendix 6.1, but no summary or key examples are provided in the main text, which could aid understanding of model complexity.", - "severity": "low", - "impact": "This reduces transparency and may hinder replication or critical appraisal." - }, - { - "category": "relevance", - "location": "Section 4.3, Implications for researchers and providers", - "issue": "The discussion on expanding feature sets to include intervention-specific attributes is somewhat generic; specific examples tailored to the interventions studied could enhance relevance.", - "severity": "low", - "impact": "This would improve the practical applicability of recommendations." - }, - { - "category": "completeness", - "location": "Section 4.4, Limitations", - "issue": "The potential impact of data quality issues, such as missing data or measurement errors, is not discussed.", - "severity": "medium", - "impact": "This omission limits understanding of data robustness and model reliability." - }, - { - "category": "organization", - "location": "Overall structure", - "issue": "The supplementary materials include extensive tables and figures, but their integration into the narrative could be improved with more explicit cross-referencing and summaries.", - "severity": "low", - "impact": "This affects navigability and ease of extracting key insights." - }, - { - "category": "clarity", - "location": "Section 3.2.2, Manoa prediction results", - "issue": "The description of performance trends over months is detailed but could benefit from simplified summaries or visual aids for clarity.", - "severity": "low", - "impact": "This would enhance accessibility for diverse audiences." - } - ], - "improvement_suggestions": [ - { - "original_text": "Detailed hyperparameter grids are provided in Appendix 6.1.", - "improved_version": "Include a summary table or key hyperparameter ranges within the main text to clarify the model tuning process.", - "explanation": "Providing a concise overview enhances transparency and helps readers understand the model complexity without immediately consulting the appendix.", - "location": "Section 2.2, Appendix 6.1", - "category": "completeness", - "focus": "detail" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13.", - "improved_version": "Summarize the key performance metrics (e.g., AUC, F1) in a visual format like a line graph to illustrate trends over weeks.", - "explanation": "Visual summaries improve clarity and allow quick assessment of model performance evolution over time.", - "location": "Section 3.2.1", - "category": "clarity", - "focus": "presentation" - }, - { - "original_text": "The discussion on reengagement after correct predictions is somewhat scattered.", - "improved_version": "Reorganize this section into a clear subsection with bullet points or numbered lists highlighting the main findings: proportions reengaging, implications, and potential strategies.", - "explanation": "Structured presentation enhances readability and emphasizes key insights for practical application.", - "location": "Section 4.2", - "category": "organization", - "focus": "structure" - }, - { - "original_text": "While the datasets are described, the potential biases due to non-consenting users are not discussed.", - "improved_version": "Add a paragraph discussing possible selection bias introduced by excluding non-consenting users and how this might affect generalizability.", - "explanation": "Addressing dataset biases improves the thoroughness of the limitations section and informs interpretation of results.", - "location": "Section 4.4", - "category": "completeness", - "focus": "thoroughness" - }, - { - "original_text": "The performance metrics are dense and could be summarized more clearly.", - "improved_version": "Create a summary table that highlights the key performance metrics (AUC, accuracy, F1) across different weeks/months for each model, with color coding for performance levels.", - "explanation": "This makes complex data more accessible and facilitates comparison across models and time points.", - "location": "Section 3.2.1, 3.2.2", - "category": "clarity", - "focus": "presentation" - }, - { - "original_text": "The discussion on feature importance analyses is brief and references appendices.", - "improved_version": "Include a brief summary of the main findings from the feature importance analyses within the main text, highlighting which features were most predictive and how their importance changed over time.", - "explanation": "This enhances understanding of model interpretability and practical relevance without requiring appendix consultation.", - "location": "Section 4.1", - "category": "completeness", - "focus": "detail" - }, - { - "original_text": "The supplementary materials include extensive tables and figures, but their integration could be improved.", - "improved_version": "Add more explicit cross-references in the main text to specific tables and figures, and include brief interpretive captions or summaries for each visual aid.", - "explanation": "Improved linking and captions facilitate navigation and comprehension for readers reviewing supplementary data.", - "location": "Throughout", - "category": "organization", - "focus": "accessibility" - }, - { - "original_text": "The predictive utility during early days is limited, but this is not deeply discussed.", - "improved_version": "Expand the discussion to include potential strategies for improving early-day predictions, such as incorporating additional data sources or adjusting prediction windows.", - "explanation": "Addressing this limitation explicitly guides future research and model development efforts.", - "location": "Section 4.4", - "category": "completeness", - "focus": "thoroughness" - }, - { - "original_text": "The link between adherence and health outcomes is briefly mentioned as uncertain.", - "improved_version": "Include references to ongoing or planned studies that aim to evaluate the impact of adherence prediction and intervention on actual health outcomes, emphasizing the importance of this future work.", - "explanation": "This contextualizes the significance of the predictive models within broader health impact goals and highlights research gaps.", - "location": "Section 4.4", - "category": "relevance", - "focus": "connection" - }, - { - "original_text": "The discussion on intervention-specific features is generic.", - "improved_version": "Provide concrete examples tailored to the studied interventions, such as including features like 'number of exercise videos watched' for Vivira or 'number of blood pressure readings' for Manoa, and discuss how these could enhance model performance.", - "explanation": "Specific examples increase practical relevance and guide future feature engineering efforts.", - "location": "Section 4.3", - "category": "relevance", - "focus": "connection" - }, - { - "original_text": "The limitations section does not address potential biases from demographic variables.", - "improved_version": "Add a paragraph discussing how demographic factors like age, gender, or health status might influence model performance and the importance of stratified analyses or bias mitigation strategies.", - "explanation": "This enhances the thoroughness of limitations and informs future model fairness assessments.", - "location": "Section 4.4", - "category": "completeness", - "focus": "thoroughness" - }, - { - "original_text": "The supplementary materials are extensive but could benefit from a summary table of key findings.", - "improved_version": "Include a summary table at the end of the supplementary section that consolidates main performance metrics, sample sizes, and key insights for quick reference.", - "explanation": "This improves usability and allows readers to grasp core results without parsing all detailed data.", - "location": "Appendix", - "category": "organization", - "focus": "accessibility" - } - ], - "detailed_feedback": { - "relevance_analysis": "The supplementary materials are highly relevant to the main text, providing detailed data, tables, and figures that support the reported predictive performance and methodological approaches. They enhance transparency and allow for in-depth evaluation of the models and datasets, aligning well with the research objectives of predicting nonadherence and churn in mHealth interventions.", - "clarity_analysis": "While the materials are comprehensive, the presentation of complex data\u2014especially performance metrics over multiple weeks and months\u2014can be overwhelming. Incorporating visual summaries, clearer headings, and simplified language in some sections would improve accessibility and facilitate quicker understanding for diverse audiences.", - "consistency_analysis": "The supplementary data consistently align with the descriptions in the main text, with tables and figures matching the reported results. Cross-references are generally clear, although more explicit linking between figures, tables, and narrative explanations would reinforce coherence.", - "completeness_analysis": "The materials are detailed, including extensive tables, descriptive statistics, and performance metrics. However, some methodological details\u2014such as hyperparameter choices, data quality considerations, and potential biases\u2014are only briefly mentioned or relegated to appendices, which could be expanded for full transparency.", - "organization_analysis": "The supplementary files are logically structured into sections covering datasets, results, and appendices. Nonetheless, the integration could be improved by adding summary tables, clearer cross-references, and more consistent formatting to guide the reader through the extensive data efficiently." - }, - "summary": "Overall, the supplementary materials are of high quality, providing comprehensive and detailed data that support the main manuscript. The main areas for improvement involve enhancing clarity through visual summaries, explicitly linking data to practical implications, and expanding methodological transparency. Addressing these points would elevate the usability, interpretability, and impact of the supplementary content, making it more accessible and informative for a broad scientific audience." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/S1_results.json b/Backup/V5_multi_agent2/results/S1_results.json deleted file mode 100644 index 705c630..0000000 --- a/Backup/V5_multi_agent2/results/S1_results.json +++ /dev/null @@ -1,65 +0,0 @@ -{ - "title_keywords_score": 7, - "critical_remarks": [ - { - "category": "title_clarity", - "location": "Title", - "issue": "The current title clearly indicates the focus on predicting nonadherence in mobile health interventions but could benefit from more specificity regarding the methodology and scope.", - "severity": "medium", - "impact": "While understandable, the title may not fully convey the innovative aspect of the predictive modeling approach or the extended duration, potentially limiting immediate clarity for targeted audiences." - }, - { - "category": "title_length", - "location": "Title", - "issue": "The title is somewhat lengthy, which may reduce its impact and ease of discoverability in search results.", - "severity": "low", - "impact": "A more concise title could improve readability and search engine optimization without sacrificing essential information." - }, - { - "category": "keywords_relevance", - "location": "Keywords", - "issue": "No keywords section is explicitly present in the manuscript.", - "severity": "high", - "impact": "Lack of keywords hampers discoverability and indexing, reducing the manuscript\u2019s visibility in relevant searches." - }, - { - "category": "keywords_coverage", - "location": "Keywords", - "issue": "No keywords section is found; therefore, coverage cannot be assessed.", - "severity": "high", - "impact": "Absence of keywords limits the manuscript\u2019s reach to interested researchers and practitioners searching for related topics." - }, - { - "category": "guidelines", - "location": "Title", - "issue": "The title adheres to standard conventions in academic publishing, clearly stating the research focus without jargon.", - "severity": "low", - "impact": "Supports proper indexing and comprehension by the target academic audience." - }, - { - "category": "discoverability", - "location": "Title", - "issue": "The current title includes relevant keywords but could be optimized for SEO by incorporating more specific terms related to methodology and outcomes.", - "severity": "medium", - "impact": "Enhanced discoverability in digital searches, especially for keywords like 'machine learning,' 'predicting nonadherence,' and 'extended duration,' could attract a broader audience." - } - ], - "improvement_suggestions": [ - { - "original_text": "Predicting Nonadherence to Mobile Health Interventions", - "improved_version": "Machine Learning-Based Prediction of Nonadherence in Extended Mobile Health Interventions for Chronic Disease Management", - "explanation": "This revised title enhances clarity by specifying the use of machine learning, emphasizes the focus on nonadherence prediction, and highlights the extended duration of interventions. It balances impact and SEO by including relevant keywords such as 'machine learning,' 'prediction,' 'nonadherence,' 'mobile health,' and 'chronic disease management,' aligning with field standards for descriptive and discoverable titles.", - "location": "Title", - "category": "title", - "focus": "comprehensive_improvement" - } - ], - "detailed_feedback": { - "title_analysis": "The current title effectively communicates the core topic of predicting nonadherence in mobile health interventions, maintaining clarity and adherence to academic standards. However, it could be more impactful by including methodological keywords and scope details to improve searchability and audience engagement.", - "keywords_analysis": "No keywords section found", - "guidelines_compliance": "The title follows standard academic conventions, being concise and descriptive without jargon. It clearly states the research focus, aligning with typical scholarly standards for clarity and relevance.", - "discoverability_assessment": "The title contains relevant keywords but lacks optimization for search engines. Incorporating specific terms like 'machine learning,' 'chronic disease,' and 'extended duration' would improve visibility in digital searches and indexing, facilitating easier discovery by interested researchers.", - "audience_alignment": "The title appeals to researchers and practitioners interested in digital health, machine learning, and adherence prediction. Its clarity and specificity align with the audience's expectations for impactful, relevant research in the field." - }, - "summary": "Overall, the manuscript's title is clear and standards-compliant but can be significantly improved for discoverability and impact by including specific methodological and scope-related keywords. The absence of a dedicated keywords section limits searchability, which should be addressed to enhance the manuscript's visibility and reach within the academic community." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/S2_results.json b/Backup/V5_multi_agent2/results/S2_results.json deleted file mode 100644 index bf80a7f..0000000 --- a/Backup/V5_multi_agent2/results/S2_results.json +++ /dev/null @@ -1,138 +0,0 @@ -{ - "score": 6, - "critical_remarks": [ - { - "category": "structure", - "location": "Abstract", - "issue": "The abstract combines multiple complex results and methodological details without clear section demarcation, making it difficult to follow the logical flow of background, methods, results, and conclusions.", - "severity": "high", - "impact": "Reduces clarity and hampers quick comprehension of the study's purpose, approach, and key findings, which is essential for scientific communication." - }, - { - "category": "content", - "location": "Introduction and Methods", - "issue": "The abstract lacks explicit mention of the specific machine learning algorithms used, the rationale for their selection, and the detailed validation procedures, limiting understanding of methodological rigor.", - "severity": "high", - "impact": "Impairs assessment of technical validity and reproducibility of the predictive models, which are central to the study's contribution." - }, - { - "category": "clarity", - "location": "Results and Discussion", - "issue": "The dense presentation of performance metrics (AUC, accuracy, F1, etc.) across multiple weeks and models is overwhelming and poorly summarized, reducing readability.", - "severity": "medium", - "impact": "Hinders quick grasp of key findings and their implications, especially for readers less familiar with statistical metrics." - }, - { - "category": "standards", - "location": "Introduction and Methods", - "issue": "The abstract and main text do not consistently follow standard scientific reporting conventions, such as clearly separating background, methods, results, and discussion, and providing sufficient methodological detail.", - "severity": "medium", - "impact": "Weakens scientific rigor and reproducibility, which are crucial for peer evaluation and future research." - }, - { - "category": "impact", - "location": "Discussion", - "issue": "While the abstract emphasizes the predictive accuracy, it underrepresents the potential limitations and real-world applicability challenges, such as data sparsity or generalizability to broader populations.", - "severity": "medium", - "impact": "Limits the perceived significance and practical utility of the findings, reducing the manuscript's overall impact." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "This study investigates whether objective behavioral engagement data from mHealth interventions can accurately predict nonadherence and user churn over extended periods.", - "explanation": "Clarifies the research question and emphasizes the focus on predictive capability, improving clarity and framing.", - "location": "Abstract", - "category": "clarity", - "focus": "organization" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.", - "improved_version": "In Vivira, the models achieved an average recall of 94% in identifying nonadherent users (defined as completing fewer than 8 exercises per week) between Weeks 2 and 13, with a mean AUC of 0.95.", - "explanation": "Rephrases for clarity, explicitly linking the metric (recall) with the definition of nonadherence and highlighting model performance succinctly.", - "location": "Abstract", - "category": "clarity", - "focus": "results" - }, - { - "original_text": "The study also analyzed the number of users who reengage after a correct churn prediction, offering insights into the potential for targeted strategies to promote adherence.", - "improved_version": "Additionally, the study examined re-engagement rates following correct churn predictions, providing insights into how predictive models could inform targeted adherence-promoting interventions.", - "explanation": "Improves readability and emphasizes the practical relevance of re-engagement analysis for intervention strategies.", - "location": "Abstract", - "category": "clarity", - "focus": "results" - }, - { - "original_text": "The introduction provides extensive background but could benefit from a clearer statement of the study\u2019s specific aims and hypotheses.", - "improved_version": "The primary aim of this study is to evaluate the predictive accuracy of machine learning models in identifying nonadherence and user churn in two distinct mHealth interventions over extended durations, with the hypothesis that behavioral app engagement features can reliably forecast future nonadherence.", - "explanation": "Adds clarity by explicitly stating the aims and hypotheses, aligning background with research objectives.", - "location": "Introduction", - "category": "clarity", - "focus": "organization" - }, - { - "original_text": "We applied stratified 10-fold cross-validation and randomized search for hyperparameter tuning on the training sets, optimizing for F1 score for all models.", - "improved_version": "Model training involved stratified 10-fold cross-validation combined with randomized hyperparameter search, with the optimization criterion set to maximize the F1 score, ensuring balanced performance across classes.", - "explanation": "Clarifies the methodological approach and rationale, improving technical transparency.", - "location": "Methods", - "category": "methodology", - "focus": "methodology" - }, - { - "original_text": "The performance metrics showed a gradual improvement over time, but early predictions (Days 1-3) had limited utility.", - "improved_version": "Performance metrics indicated progressive improvement over the observation period, although predictions within the first three days showed limited accuracy, likely due to class imbalance and sparse early engagement data.", - "explanation": "Provides a more nuanced explanation of early prediction limitations, enhancing interpretability.", - "location": "Results", - "category": "results", - "focus": "results" - }, - { - "original_text": "The discussion emphasizes the predictive accuracy but should more thoroughly address limitations such as data sparsity, generalizability, and potential biases.", - "improved_version": "While the models demonstrate high predictive accuracy, limitations include potential biases due to the specific populations studied, the reliance on continuous engagement data, and challenges in generalizing to interventions with lower retention rates or different engagement patterns.", - "explanation": "Provides a balanced discussion of limitations, improving scientific rigor and transparency.", - "location": "Discussion", - "category": "standards", - "focus": "impact" - }, - { - "original_text": "The abstract and main text could better separate background, methods, results, and conclusions for clearer communication.", - "improved_version": "Reorganize the abstract into distinct sections: background (brief context), methods (study design, data, modeling approach), results (performance metrics), and conclusions (implications and future directions).", - "explanation": "Enhances clarity and adherence to standard scientific reporting formats.", - "location": "Abstract", - "category": "standards", - "focus": "organization" - }, - { - "original_text": "The models' performance metrics are presented in detail, but a summary table or figure highlighting key results across weeks would improve readability.", - "improved_version": "Include a summary table or figure that consolidates key performance metrics (AUC, accuracy, F1) across all prediction weeks, facilitating quick comparison and interpretation.", - "explanation": "Improves readability and allows readers to grasp performance trends at a glance.", - "location": "Results", - "category": "clarity", - "focus": "readability" - }, - { - "original_text": "The discussion should better connect the findings to practical implementation and potential challenges in real-world settings.", - "improved_version": "Strengthen the discussion by explicitly addressing how these predictive models could be integrated into clinical workflows, potential barriers such as data privacy, user acceptance, and the need for prospective validation studies.", - "explanation": "Enhances impact by linking findings to real-world applicability and challenges.", - "location": "Discussion", - "category": "impact", - "focus": "impact" - }, - { - "original_text": "The abstract contains extensive technical details that could be summarized more succinctly for broader accessibility.", - "improved_version": "Condense technical details by focusing on key performance metrics, main findings, and their implications, reserving detailed methodological descriptions for the main text.", - "explanation": "Improves readability and accessibility for a broader audience, including non-specialists.", - "location": "Abstract", - "category": "clarity", - "focus": "readability" - } - ], - "detailed_feedback": { - "structure_analysis": "The abstract attempts to cover background, methods, results, and implications but lacks clear segmentation into these sections, leading to a dense, unstructured flow. The main text is comprehensive but could benefit from clearer organization, especially in separating background, aims, methods, results, and discussion points for better readability.", - "content_analysis": "The abstract and main text provide extensive data on model performance, datasets, and analysis, but some methodological details\u2014such as the specific ML algorithms, validation procedures, and hyperparameter tuning\u2014are insufficiently described. The results are rich but presented in a manner that overwhelms the reader, with performance metrics scattered throughout the text rather than summarized effectively.", - "clarity_assessment": "The language is technical and precise but often overly dense, especially in the results sections. The presentation of multiple metrics across many weeks without summarization hampers quick understanding. Simplifying language, adding summaries, and using visual aids like tables or figures would greatly improve readability.", - "standards_compliance": "The manuscript generally follows scientific standards in reporting results, but the abstract could be better structured into conventional sections. The detailed methodological descriptions are appropriate but could be more concise and clearer in explaining the ML approaches and validation strategies. The references are comprehensive and relevant.", - "impact_evaluation": "The study addresses a significant gap in predicting nonadherence over extended periods, which has high practical relevance. The findings suggest broad applicability of behavioral engagement features for predictive modeling, potentially informing targeted interventions. However, the manuscript could better articulate the real-world challenges and next steps for implementation, enhancing its overall impact." - }, - "summary": "This manuscript presents a valuable and ambitious exploration of long-term nonadherence prediction in mHealth interventions using machine learning. While the technical rigor and extensive data are strengths, the overall clarity, structure, and presentation could be improved to make the findings more accessible and impactful. Addressing organizational issues, summarizing key results more effectively, and explicitly discussing practical implications and limitations would elevate the manuscript's quality and influence." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/S3_results.json b/Backup/V5_multi_agent2/results/S3_results.json deleted file mode 100644 index cbabb86..0000000 --- a/Backup/V5_multi_agent2/results/S3_results.json +++ /dev/null @@ -1,158 +0,0 @@ -{ - "score": 7, - "critical_remarks": [ - { - "category": "context", - "location": "Section 1 (Background and Context)", - "issue": "While the background discusses the importance of mHealth and NCDs, it lacks specific framing of the current challenges in adherence prediction and how existing gaps hinder progress.", - "severity": "medium", - "impact": "This limits the reader's understanding of the novelty and urgency of the research, reducing contextual clarity." - }, - { - "category": "problem", - "location": "Section 1 (Problem Statement)", - "issue": "The problem of high nonadherence is mentioned broadly, but the introduction does not explicitly specify the limitations of current prediction methods or why existing models are insufficient for longer-term or nuanced predictions.", - "severity": "high", - "impact": "This weakens the justification for the study, making the problem seem less pressing or novel." - }, - { - "category": "objectives", - "location": "Section 1 (Objectives Clarity)", - "issue": "The objectives are somewhat scattered; while the overall aim is to predict nonadherence, specific research questions or hypotheses are not clearly articulated upfront.", - "severity": "medium", - "impact": "This affects the clarity of the research scope and hampers the reader's ability to grasp precise aims." - }, - { - "category": "significance", - "location": "Section 1 (Justification of Significance)", - "issue": "Although the potential of predictive models is discussed, the introduction does not sufficiently emphasize how this research advances current knowledge or impacts clinical practice.", - "severity": "medium", - "impact": "This diminishes the perceived importance and innovative contribution of the study." - }, - { - "category": "literature integration", - "location": "Section 1 (Literature Review)", - "issue": "The review of prior studies is comprehensive but somewhat fragmented; it mentions many findings without synthesizing how these collectively inform the current research gap.", - "severity": "medium", - "impact": "This reduces coherence and makes it harder for readers to see the logical progression leading to the current study." - }, - { - "category": "flow and organization", - "location": "Section 1 (Flow and Organization)", - "issue": "The transition from background to problem and then to objectives is somewhat abrupt; the narrative jumps between topics without smooth linking sentences.", - "severity": "low", - "impact": "This affects readability and the logical flow, potentially confusing readers." - }, - { - "category": "technical accuracy", - "location": "Section 1 (Technical Content)", - "issue": "The definitions of adherence and churn are generally accurate but could benefit from clearer distinctions and more precise operationalization aligned with the study's focus.", - "severity": "low", - "impact": "This could lead to ambiguity in interpreting the results and their implications." - }, - { - "category": "research scope", - "location": "Section 1 (Scope Clarification)", - "issue": "The scope is broad, covering two interventions and multiple prediction tasks, but the introduction does not explicitly delineate the boundaries or limitations of this scope.", - "severity": "low", - "impact": "This may cause overgeneralization or misinterpretation of the study's applicability." - }, - { - "category": "hypotheses/questions", - "location": "Section 1 (Research Questions)", - "issue": "Explicit hypotheses or research questions are not clearly stated, which could guide the reader through the study's investigative focus.", - "severity": "medium", - "impact": "Lack of clear research questions diminishes the clarity of the study's aims and hinders focused evaluation." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rising prevalence and economic burden of noncommunicable diseases (NCDs) present a significant challenge to patients and healthcare systems, calling for innovative, scalable, and cost-effective solutions.", - "improved_version": "Noncommunicable diseases (NCDs) are increasingly prevalent worldwide, imposing substantial health and economic burdens on patients and healthcare systems. This escalating challenge underscores the urgent need for innovative, scalable, and cost-effective interventions.", - "explanation": "Enhances clarity by emphasizing the global scope and urgency, setting a stronger foundation for the importance of the research.", - "location": "Section 1", - "category": "context", - "focus": "background" - }, - { - "original_text": "Mobile health (mHealth) interventions, facilitated by the ubiquity of smartphones, have emerged as promising tools to support the prevention and management of NCDs.", - "improved_version": "The widespread adoption of smartphones has facilitated the development of mobile health (mHealth) interventions, which have shown promise in supporting the prevention and management of NCDs through accessible and scalable solutions.", - "explanation": "Provides a clearer link between smartphone ubiquity and the potential of mHealth, strengthening the background context.", - "location": "Section 1", - "category": "context", - "focus": "background" - }, - { - "original_text": "Yet, despite growing evidence and availability, mHealth interventions face high nonadherence, where users fail to use these tools as intended or discontinue use entirely before achieving desired outcomes.", - "improved_version": "Despite the increasing evidence supporting their efficacy, a major challenge remains: high rates of nonadherence, where users either fail to follow prescribed usage patterns or discontinue use prematurely, thereby limiting the interventions' effectiveness.", - "explanation": "Clarifies the problem by specifying the nature of nonadherence and its impact on effectiveness.", - "location": "Section 1", - "category": "problem", - "focus": "problem" - }, - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "The extensive behavioral data generated by mHealth interventions present an opportunity: can these data be leveraged to accurately predict nonadherence and enable timely interventions?", - "explanation": "Frames the problem as an opportunity, making the research gap more compelling and focused.", - "location": "Section 1", - "category": "problem", - "focus": "gap" - }, - { - "original_text": "We developed machine learning models for the prediction of nonadherence in two mHealth interventions, one for nonspecific and degenerative back pain over a program duration of 90 days (Vivira, n = 8,372), and another for hypertension self-management over 186 days (Manoa, n = 6,674).", - "improved_version": "This study aims to develop and evaluate machine learning models to predict nonadherence in two distinct mHealth interventions: Vivira, a 90-day back pain program, and Manoa, a 186-day hypertension self-management app, to assess the generalizability and robustness of predictive approaches across different conditions and durations.", - "explanation": "Clarifies the objectives and scope, emphasizing the study's focus on model development and evaluation across interventions.", - "location": "Section 1", - "category": "objectives", - "focus": "objectives" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.", - "improved_version": "Our models achieved high predictive accuracy, correctly identifying an average of 94% of nonadherent users in Vivira during Weeks 2 to 13, where nonadherence was operationalized as completing fewer than eight exercises per week.", - "explanation": "Adds clarity to the operational definition of nonadherence and emphasizes model performance.", - "location": "Section 1", - "category": "objectives", - "focus": "objectives" - }, - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "Given the richness of behavioral data from mHealth apps, this study investigates the extent to which nonadherence and churn can be accurately predicted over extended periods, enabling proactive intervention strategies.", - "explanation": "Aligns the research question with the data's potential, framing it as an investigation into predictive capacity.", - "location": "Section 1", - "category": "problem", - "focus": "gap" - }, - { - "original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).", - "improved_version": "The study demonstrates that machine learning models can accurately predict nonadherence, including complete discontinuation (churn), over long-term intervention periods, highlighting the potential for early, targeted support.", - "explanation": "Strengthens the significance by linking prediction accuracy to practical intervention benefits.", - "location": "Section 4", - "category": "significance", - "focus": "impact" - }, - { - "original_text": "Our descriptive analysis further emphasizes this relationship, showing that the decline in adherence over time in Vivira and Manoa is largely driven by churn (i.e., users discontinuing entirely).", - "improved_version": "Descriptive analyses reveal that the decline in adherence over time in both interventions is primarily attributable to user churn, underscoring the importance of early predictive detection.", - "explanation": "Clarifies the relationship and underscores the importance of early prediction for intervention planning.", - "location": "Section 4", - "category": "significance", - "focus": "impact" - }, - { - "original_text": "The potential of predictive models is discussed, but the introduction does not sufficiently emphasize how this research advances current knowledge or impacts clinical practice.", - "improved_version": "This research advances current understanding by demonstrating the feasibility of long-term adherence prediction using objective behavioral data, paving the way for integrating predictive analytics into routine clinical workflows to improve patient engagement and outcomes.", - "explanation": "Highlights the contribution and practical relevance, strengthening the justification of the study.", - "location": "Section 4", - "category": "significance", - "focus": "impact" - } - ], - "detailed_feedback": { - "context_analysis": "The introduction effectively outlines the global burden of NCDs and the promise of mHealth interventions, emphasizing their scalability and current adoption. However, it could better specify existing challenges in adherence prediction, especially over extended durations, and how current models fall short, to establish a clearer context for the study's necessity.", - "problem_analysis": "While high nonadherence rates are acknowledged, the introduction lacks a detailed critique of existing predictive approaches, their limitations in long-term or nuanced prediction, and the specific gaps this study aims to fill. Clarifying these points would strengthen the problem statement.", - "objectives_analysis": "The objectives are implied but not explicitly articulated as specific research questions or hypotheses. Clarifying whether the goal is to develop, validate, or compare models, and what specific aspects of nonadherence are targeted, would improve focus.", - "significance_assessment": "The potential impact on clinical practice and intervention strategies is mentioned but not deeply elaborated. Emphasizing how this research uniquely contributes to the field, advances predictive methodology, or influences health outcomes would enhance perceived importance.", - "structure_evaluation": "The introduction covers background, problem, and objectives but could benefit from clearer logical transitions. For example, explicitly linking the literature review to identified gaps and then to the study aims would improve flow and coherence." - }, - "summary": "Overall, the introduction presents a relevant and timely topic with a solid foundation in existing literature. However, it would benefit from clearer articulation of the research gap, explicit research questions, and a stronger emphasis on the study's innovative contribution. Improving the logical flow and explicitly framing the objectives and significance would elevate the manuscript's clarity and impact." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/S4_results.json b/Backup/V5_multi_agent2/results/S4_results.json deleted file mode 100644 index 841ef40..0000000 --- a/Backup/V5_multi_agent2/results/S4_results.json +++ /dev/null @@ -1,138 +0,0 @@ -{ - "score": 7, - "critical_remarks": [ - { - "category": "coverage", - "location": "Section 1 & 2", - "issue": "While the review covers a broad range of mHealth applications and related adherence issues, it predominantly emphasizes quantitative prediction models without sufficiently addressing the diversity of intervention types, populations, or contextual factors influencing adherence.", - "severity": "medium", - "impact": "Limits the comprehensiveness of the review, potentially overlooking important contextual or qualitative factors affecting nonadherence." - }, - { - "category": "analysis", - "location": "Section 4.1 & 4.4", - "issue": "The discussion on the limitations and future directions tends to be optimistic about model generalizability without critically examining potential biases, data quality issues, or the impact of unmeasured confounders.", - "severity": "high", - "impact": "Reduces the depth of critical evaluation, which is essential for understanding the robustness and applicability of the models." - }, - { - "category": "structure", - "location": "Throughout the review", - "issue": "The organization of sections, especially the results and discussion, is dense with detailed data but lacks clear thematic segmentation or summaries that guide the reader through key insights.", - "severity": "medium", - "impact": "Impairs readability and hampers the synthesis of complex findings into digestible conclusions." - }, - { - "category": "citations", - "location": "Section 2 & 4", - "issue": "While many references are relevant, there is a reliance on older or less recent studies for some foundational concepts, and some citations lack context or critical appraisal.", - "severity": "low", - "impact": "Slightly diminishes the currency and perceived rigor of the literature base." - }, - { - "category": "integration", - "location": "Section 4.1 & 4.3", - "issue": "The review discusses models and features primarily in technical terms, with limited integration of how these models translate into real-world clinical or behavioral outcomes.", - "severity": "high", - "impact": "Weakens the connection between predictive modeling and practical intervention or health impact, reducing translational relevance." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "While the review highlights the potential of rich behavioral data from mHealth interventions, it would benefit from explicitly discussing the variability in data quality, completeness, and the influence of contextual factors on prediction accuracy.", - "explanation": "Adding nuance about data variability enhances understanding of the limitations and applicability of predictive models.", - "location": "Section 1", - "category": "coverage", - "focus": "breadth" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95),", - "improved_version": "Our models demonstrated high predictive accuracy, correctly identifying approximately 94% of nonadherent users in Vivira, though further discussion on potential false positives and model calibration is necessary to assess clinical utility.", - "explanation": "This clarifies the performance metrics and encourages critical evaluation of model limitations.", - "location": "Section 3.2.1", - "category": "analysis", - "focus": "synthesis" - }, - { - "original_text": "The review predominantly emphasizes quantitative prediction models without sufficiently addressing the diversity of intervention types, populations, or contextual factors influencing adherence.", - "improved_version": "The review should incorporate a broader discussion of intervention diversity, including different health conditions, demographic groups, and settings, to better contextualize the applicability of the models.", - "explanation": "Expanding scope improves comprehensiveness and relevance across varied contexts.", - "location": "Section 2 & 4.4", - "category": "coverage", - "focus": "breadth" - }, - { - "original_text": "While many references are relevant, there is a reliance on older or less recent studies for some foundational concepts, and some citations lack context or critical appraisal.", - "improved_version": "Update the reference list with more recent studies where available, and include brief critical evaluations of key citations to highlight their strengths and limitations.", - "explanation": "Enhances the currency and critical depth of the literature base.", - "location": "Section 2 & 4", - "category": "citations", - "focus": "relevance" - }, - { - "original_text": "The organization of sections, especially the results and discussion, is dense with detailed data but lacks clear thematic segmentation or summaries that guide the reader through key insights.", - "improved_version": "Reorganize the results and discussion sections with clear subheadings and summary paragraphs that distill key findings and implications, improving readability and synthesis.", - "explanation": "Facilitates better comprehension and logical flow for readers.", - "location": "Section 3 & 4", - "category": "structure", - "focus": "organization" - }, - { - "original_text": "The review discusses models and features primarily in technical terms, with limited integration of how these models translate into real-world clinical or behavioral outcomes.", - "improved_version": "Incorporate a dedicated discussion on how predictive models can inform clinical decision-making, behavioral interventions, and health outcomes, bridging the gap between technical performance and practical impact.", - "explanation": "Strengthens the translational relevance of the review.", - "location": "Section 4.3", - "category": "integration", - "focus": "relevance" - }, - { - "original_text": "The discussion on the limitations and future directions tends to be optimistic about model generalizability without critically examining potential biases, data quality issues, or the impact of unmeasured confounders.", - "improved_version": "Expand the limitations section to critically evaluate potential biases, data quality concerns, and unmeasured confounders that may affect model validity and generalizability.", - "explanation": "Provides a more balanced and rigorous assessment of the models' robustness.", - "location": "Section 4.4", - "category": "analysis", - "focus": "depth" - }, - { - "original_text": "The review could benefit from explicitly discussing the variability in data quality, completeness, and the influence of contextual factors on prediction accuracy.", - "improved_version": "Explicitly address how data quality, completeness, and contextual variables influence model performance and the generalizability of findings across different populations and settings.", - "explanation": "Enhances understanding of factors affecting model robustness and transferability.", - "location": "Section 4.4", - "category": "coverage", - "focus": "depth" - }, - { - "original_text": "The review would be strengthened by including more discussion on how models could be integrated into clinical workflows or real-world settings.", - "improved_version": "Add a section discussing practical strategies for integrating predictive models into clinical workflows, including potential barriers, facilitators, and ethical considerations.", - "explanation": "Bridges research findings with implementation science, increasing translational impact.", - "location": "Section 4.3", - "category": "integration", - "focus": "relevance" - }, - { - "original_text": "The review discusses the importance of behavioral features but does not sufficiently explore the potential role of contextual, social, or environmental factors influencing adherence.", - "improved_version": "Incorporate a discussion on how contextual, social, and environmental factors might complement behavioral features in improving prediction accuracy and intervention design.", - "explanation": "Provides a more holistic view of adherence determinants, enriching the analysis.", - "location": "Section 2 & 4.1", - "category": "coverage", - "focus": "breadth" - }, - { - "original_text": "The review could include more critical appraisal of the limitations of machine learning approaches, such as overfitting, interpretability, and data bias.", - "improved_version": "Discuss potential ML limitations, including overfitting, interpretability challenges, and biases in training data, and suggest strategies to mitigate these issues.", - "explanation": "Enhances critical evaluation and guides future methodological improvements.", - "location": "Section 4.4", - "category": "analysis", - "focus": "depth" - } - ], - "detailed_feedback": { - "coverage_analysis": "The literature review provides a solid overview of quantitative models predicting nonadherence and churn in mHealth interventions, especially emphasizing behavioral engagement data. However, it underrepresents intervention diversity, including different health conditions, populations, and settings, which limits its comprehensiveness. Incorporating studies from varied contexts and qualitative insights would strengthen the breadth and applicability of the review.", - "analysis_quality": "The review demonstrates a good understanding of the technical aspects of ML models and their predictive performance. Nonetheless, it lacks critical appraisal of model limitations, biases, and the real-world challenges of implementation. A more nuanced discussion of these issues would deepen the analytical rigor and provide balanced insights into the translational potential.", - "structure_evaluation": "The manuscript is densely packed with detailed statistical results and technical descriptions, but the organization could be improved with clearer thematic segmentation. Summarizing key findings at the end of sections and using subheadings would enhance readability and guide the reader through complex data, facilitating better synthesis.", - "citation_assessment": "The citations are generally relevant and include recent studies; however, some foundational references are somewhat dated, and a few key recent publications are missing. Updating references and providing critical context for cited works would improve the scholarly rigor and currency of the review.", - "integration_review": "While the review discusses the technical performance of models extensively, it minimally addresses how these models translate into clinical or behavioral outcomes. Strengthening the discussion on practical implementation, ethical considerations, and health impact would improve the connection between research and real-world application." - }, - "summary": "Overall, this literature review offers a comprehensive and technically detailed overview of ML-based nonadherence prediction in mHealth interventions. It effectively highlights the predictive capabilities and potential applications but would benefit from deeper critical analysis, broader contextual coverage, clearer organization, and stronger integration with practical health outcomes. These enhancements would elevate its scholarly rigor and translational relevance, making it a more impactful resource for researchers and practitioners." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/S5_results.json b/Backup/V5_multi_agent2/results/S5_results.json deleted file mode 100644 index a00655d..0000000 --- a/Backup/V5_multi_agent2/results/S5_results.json +++ /dev/null @@ -1,145 +0,0 @@ -{ - "score": 7, - "critical_remarks": [ - { - "category": "design", - "location": "Section 2.2", - "issue": "The study employs a retrospective observational design using historical data, which limits causal inference and may introduce selection bias related to consent and data availability. The rationale for choosing this design over a prospective approach is not explicitly discussed.", - "severity": "high", - "impact": "This limits the ability to establish causality and may affect the generalizability of the findings. It also raises concerns about potential biases in the dataset." - }, - { - "category": "methods", - "location": "Section 2.2", - "issue": "The feature selection relies primarily on behavioral engagement metrics, with limited consideration of contextual or sociodemographic factors that could influence adherence, potentially reducing model robustness.", - "severity": "medium", - "impact": "This could lead to omitted variable bias, reducing the predictive accuracy and limiting insights into adherence drivers." - }, - { - "category": "analysis", - "location": "Section 2.2 and 3.2", - "issue": "The evaluation metrics focus heavily on AUC, accuracy, and F1-score, but do not sufficiently address calibration or the clinical utility of the models, such as decision thresholds or cost-benefit analyses.", - "severity": "medium", - "impact": "This limits understanding of how models would perform in real-world settings and their practical applicability for intervention." - }, - { - "category": "quality", - "location": "Section 4.4", - "issue": "The models are trained and validated on datasets with class imbalance, and while techniques like Tomek Links undersampling are used, there is limited discussion on how this imbalance might bias the results or affect model fairness.", - "severity": "high", - "impact": "Potential bias in model performance, especially in underrepresented groups, which could compromise validity and fairness." - }, - { - "category": "ethics", - "location": "Section 2.2 and 4.4", - "issue": "The datasets are anonymized and use consented data, but the manuscript does not detail how participant privacy is protected during data handling, nor does it discuss potential biases introduced by excluding non-consenting users.", - "severity": "medium", - "impact": "This could impact ethical transparency and the reproducibility of the study, as well as the representativeness of the sample." - }, - { - "category": "limitations handling", - "location": "Section 4.4", - "issue": "The discussion acknowledges limitations related to data scope and generalizability but lacks specific strategies for addressing these in future research, such as prospective validation or inclusion of diverse populations.", - "severity": "medium", - "impact": "This reduces the clarity on how future work could mitigate current limitations, affecting the study's translational potential." - } - ], - "improvement_suggestions": [ - { - "original_text": "The study employs a retrospective observational design using historical data, which limits causal inference and may introduce selection bias related to consent and data availability.", - "improved_version": "Consider designing a prospective study to validate the predictive models in real-time settings, which would enhance causal inference and reduce selection bias related to data consent and availability.", - "explanation": "A prospective approach allows for real-time validation and reduces biases inherent in retrospective data, strengthening causal claims and practical relevance.", - "location": "Section 2.1 and 4.4", - "category": "design", - "focus": "approach" - }, - { - "original_text": "The feature selection relies primarily on behavioral engagement metrics, with limited consideration of contextual or sociodemographic factors.", - "improved_version": "Incorporate additional features such as sociodemographic variables, health literacy, or baseline health status to enrich the models and improve robustness.", - "explanation": "Adding contextual variables can capture external influences on adherence, potentially increasing model accuracy and interpretability.", - "location": "Section 2.2", - "category": "methods", - "focus": "techniques" - }, - { - "original_text": "The evaluation metrics focus heavily on AUC, accuracy, and F1-score, but do not sufficiently address calibration or decision thresholds.", - "improved_version": "Include calibration plots and decision-analytic metrics such as net benefit or cost-effectiveness analyses to assess clinical utility.", - "explanation": "These additions help determine how well the predicted probabilities translate into actionable decisions, improving real-world applicability.", - "location": "Section 2.2 and 3.2", - "category": "analysis", - "focus": "validity" - }, - { - "original_text": "Models are trained on imbalanced datasets with techniques like undersampling, but there is limited discussion on bias or fairness across subgroups.", - "improved_version": "Implement fairness-aware modeling approaches and report subgroup performance metrics to assess bias and ensure equitable predictions.", - "explanation": "Addressing potential biases improves model fairness and validity, especially for vulnerable populations.", - "location": "Section 2.2 and 4.4", - "category": "quality", - "focus": "validity" - }, - { - "original_text": "The datasets are anonymized and use consented data, but the manuscript does not detail how participant privacy is protected during data handling.", - "improved_version": "Explicitly describe data security measures, anonymization procedures, and compliance with data protection regulations to enhance transparency.", - "explanation": "Clear ethical protocols reinforce trustworthiness and reproducibility of the research.", - "location": "Section 2.2 and 4.4", - "category": "ethics", - "focus": "procedures" - }, - { - "original_text": "The models are trained and validated on datasets with class imbalance, and while techniques like undersampling are used, there is limited discussion on how this might bias results.", - "improved_version": "Perform sensitivity analyses using alternative imbalance handling methods (e.g., SMOTE, cost-sensitive learning) and report subgroup performance to evaluate bias.", - "explanation": "This ensures the models are robust and fair across different user groups, improving validity and fairness.", - "location": "Section 2.2 and 4.4", - "category": "quality", - "focus": "validity" - }, - { - "original_text": "The study employs a retrospective observational design using historical data, which limits causal inference.", - "improved_version": "Plan future randomized controlled trials or intervention studies to test whether predictive models can effectively inform adherence-promoting strategies.", - "explanation": "Experimental validation would establish causality and practical effectiveness of the models in real-world interventions.", - "location": "Section 4.4", - "category": "design", - "focus": "approach" - }, - { - "original_text": "The study does not discuss how user privacy is protected during data collection and analysis.", - "improved_version": "Include detailed descriptions of data anonymization, encryption, and compliance with GDPR or relevant data protection standards to strengthen ethical rigor.", - "explanation": "Transparency about privacy safeguards enhances ethical credibility and reproducibility.", - "location": "Section 2.2 and 4.4", - "category": "ethics", - "focus": "procedures" - }, - { - "original_text": "The models are primarily based on behavioral app engagement features, with limited exploration of intervention-specific or social features.", - "improved_version": "Explore incorporating intervention-specific features such as social interactions, messaging frequency, or content engagement to potentially improve predictive performance.", - "explanation": "Adding diverse features may capture additional variance in adherence behavior, enhancing model robustness and utility.", - "location": "Section 2.2", - "category": "methods", - "focus": "techniques" - }, - { - "original_text": "The evaluation does not include calibration metrics or decision-analytic assessments.", - "improved_version": "Assess calibration using calibration plots and consider decision curve analysis to evaluate the clinical usefulness of the models.", - "explanation": "These metrics help determine how well predicted probabilities align with actual outcomes and their practical decision-making value.", - "location": "Section 2.2 and 3.2", - "category": "analysis", - "focus": "validity" - }, - { - "original_text": "The discussion of limitations does not specify strategies for prospective validation or addressing generalizability concerns.", - "improved_version": "Outline specific plans for prospective validation studies across diverse populations and settings to enhance generalizability and clinical translation.", - "explanation": "Proactive strategies for validation strengthen the evidence base and facilitate real-world implementation.", - "location": "Section 4.4", - "category": "limitations handling", - "focus": "future work" - } - ], - "detailed_feedback": { - "design_analysis": "The study adopts a retrospective observational design leveraging existing app engagement data to predict nonadherence and churn. While this approach allows for large-scale analysis and model development, it inherently limits causal inference and may introduce selection bias, especially given the consent-based data collection. The rationale for choosing this design over a prospective or experimental approach is not explicitly discussed, which could impact the interpretability and applicability of the findings.", - "methods_assessment": "The methodology employs machine learning models, primarily random forests, trained on behavioral engagement features such as daily app activity, session counts, and completed exercises. The feature selection is grounded in prior literature emphasizing app activity as a key predictor. Data preprocessing includes normalization and undersampling to address class imbalance. However, the approach omits potentially relevant contextual factors like sociodemographics or health status, which could enhance model robustness. The validation strategy uses stratified 10-fold cross-validation and a train-test split, but the handling of class imbalance, while addressed via undersampling, could benefit from additional techniques like SMOTE or cost-sensitive learning.", - "analysis_evaluation": "Model performance is evaluated using metrics such as AUC, accuracy, F1-score, precision, and recall, which are appropriate for classification tasks. The models demonstrate high predictive accuracy over extended periods, with performance improving as more data accrue. Nonetheless, the analysis lacks calibration assessments and decision-analytic evaluations, which are crucial for translating model outputs into clinical or intervention decisions. The focus on aggregate metrics may obscure subgroup disparities or potential biases in predictions across different user demographics.", - "quality_review": "The study reports on model validity through multiple performance metrics and addresses class imbalance with undersampling. However, there is limited discussion on potential biases introduced by data exclusion (non-consenting users) or imbalance in demographic groups. The models' fairness and generalizability could be compromised if these biases are not thoroughly examined. The reliance on behavioral app data is a strength, but the absence of external validation or prospective testing limits the robustness of the conclusions.", - "ethics_compliance": "The datasets used are anonymized and collected with participant consent, with ethical approval obtained from relevant bodies. The manuscript briefly mentions data privacy measures but does not detail specific procedures such as encryption, anonymization protocols, or compliance with data protection standards like GDPR. Transparency in these areas is essential for ethical rigor and reproducibility, especially given the sensitive nature of health data." - }, - "summary": "Overall, this manuscript presents a solid and methodologically sound approach to predicting nonadherence in mHealth interventions using behavioral engagement data and machine learning. The extensive analysis over long durations and across two distinct interventions demonstrates the potential for generalizable predictive models. However, the study's retrospective design, limited inclusion of contextual factors, and lack of calibration and fairness assessments temper the strength of its conclusions. Addressing these issues through prospective validation, broader feature inclusion, and detailed ethical protocols would significantly enhance the rigor, applicability, and impact of this research. Consequently, the manuscript merits a score of 7, reflecting its valuable contributions while acknowledging areas for methodological and ethical refinement." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/S6_results.json b/Backup/V5_multi_agent2/results/S6_results.json deleted file mode 100644 index ca12733..0000000 --- a/Backup/V5_multi_agent2/results/S6_results.json +++ /dev/null @@ -1,130 +0,0 @@ -{ - "score": 7, - "critical_remarks": [ - { - "category": "presentation", - "location": "Results section, Figures 6.5 and 6.6", - "issue": "Figures are described but not included or clearly labeled with axes, units, or legends, making interpretation difficult.", - "severity": "high", - "impact": "Reduces clarity and hampers the reader's ability to understand model performance trends visually." - }, - { - "category": "analysis", - "location": "Model performance metrics, Tables 6.1-6.4", - "issue": "While multiple metrics are reported, there is limited discussion on how these metrics interrelate or their practical significance, especially in the context of clinical or real-world utility.", - "severity": "medium", - "impact": "Weakens the interpretability of the statistical results and their implications for practice." - }, - { - "category": "interpretation", - "location": "Discussion, paragraph 4.1", - "issue": "The interpretation of model performance differences (e.g., between nonadherence and churn models) lacks depth regarding potential causes or implications for intervention strategies.", - "severity": "medium", - "impact": "Limits the reader\u2019s understanding of how these findings translate into actionable insights." - }, - { - "category": "quality", - "location": "Methods, hyperparameter tuning description", - "issue": "Details on hyperparameter tuning are referenced as in Appendix 6.1 but are not summarized or discussed in the main text, reducing transparency.", - "severity": "low", - "impact": "Slightly diminishes reproducibility and understanding of model optimization procedures." - }, - { - "category": "impact", - "location": "Results, predictive performance discussion", - "issue": "Although high performance metrics are reported, the potential for overfitting or model generalizability across different populations is not addressed.", - "severity": "high", - "impact": "Raises concerns about the practical applicability and robustness of the models in diverse real-world settings." - } - ], - "improvement_suggestions": [ - { - "original_text": "Figures are described but not included or clearly labeled with axes, units, or legends, making interpretation difficult.", - "improved_version": "Include the actual figures with clear labels, axes titles, units, and comprehensive legends. For example, in Figure 6.5, specify 'Model AUC over Weeks 2-13' with axes labeled as 'Week' and 'AUC'.", - "explanation": "Properly labeled and visible figures enhance visual comprehension and allow readers to interpret performance trends directly.", - "location": "Results section, Figures 6.5 and 6.6", - "category": "visualization", - "focus": "clarity" - }, - { - "original_text": "While multiple metrics are reported, there is limited discussion on how these metrics interrelate or their practical significance.", - "improved_version": "Add a brief interpretive paragraph explaining how metrics like AUC, F1, precision, and recall collectively inform the model's utility, especially emphasizing their relevance for clinical decision-making or intervention planning.", - "explanation": "Contextualizing statistical metrics helps readers understand their practical implications, making the results more meaningful.", - "location": "Results section, analysis discussion", - "category": "interpretation", - "focus": "clarity" - }, - { - "original_text": "The interpretation of performance differences between nonadherence and churn models lacks depth.", - "improved_version": "Expand the discussion to explore potential reasons for performance variations, such as differences in data quality, feature relevance, or inherent behavioral patterns, and discuss how these influence intervention strategies.", - "explanation": "Deeper analysis provides insights into model limitations and guides future improvements or application contexts.", - "location": "Discussion, paragraph 4.1", - "category": "interpretation", - "focus": "interpretation" - }, - { - "original_text": "Details on hyperparameter tuning are referenced as in Appendix 6.1 but are not summarized in the main text.", - "improved_version": "Provide a concise summary of hyperparameter tuning strategies in the main text, such as the range of parameters tested and the selection criteria, to improve transparency.", - "explanation": "Summarizing key methodological steps enhances reproducibility and reader understanding of model development.", - "location": "Methods section", - "category": "quality", - "focus": "clarity" - }, - { - "original_text": "High performance metrics are reported without addressing overfitting or generalizability.", - "improved_version": "Include a discussion on potential overfitting, such as validation strategies used, and comment on the models' expected performance in external or real-world datasets.", - "explanation": "Addressing model robustness increases confidence in the applicability of the findings and guides future validation efforts.", - "location": "Discussion, model robustness", - "category": "impact", - "focus": "significance" - }, - { - "original_text": "Results on model performance are extensive but lack a summary table or visual summary for quick reference.", - "improved_version": "Add a summary table or dashboard figure that consolidates key performance metrics across all prediction windows for easy comparison.", - "explanation": "Visual summaries facilitate quick understanding of overall model performance and trends.", - "location": "Results section, summary visualization", - "category": "visualization", - "focus": "clarity" - }, - { - "original_text": "The discussion of feature importance is limited to Appendix 6.2 without highlighting key features in the main text.", - "improved_version": "Summarize the most important features influencing predictions directly in the main text, such as 'daily exercise completion' and 'last login activity,' and discuss their practical relevance.", - "explanation": "Highlighting key features emphasizes their role and supports understanding of behavioral drivers of nonadherence.", - "location": "Results/discussion, feature importance", - "category": "interpretation", - "focus": "clarity" - }, - { - "original_text": "The manuscript does not explicitly address potential biases or limitations related to data collection or model applicability.", - "improved_version": "Add a paragraph discussing limitations such as selection bias (e.g., only consenting users), potential data sparsity in less engaged populations, and the need for external validation.", - "explanation": "Acknowledging limitations provides a balanced view and guides future research directions.", - "location": "Discussion, limitations", - "category": "quality", - "focus": "significance" - }, - { - "original_text": "The implications for practice are mentioned but could be expanded with concrete examples of how models could be integrated into intervention workflows.", - "improved_version": "Include specific examples, such as 'integrating real-time predictions into app notifications to prompt users at risk of nonadherence,' to illustrate practical application.", - "explanation": "Concrete examples make the potential impact more tangible and guide implementation efforts.", - "location": "Discussion, implications", - "category": "impact", - "focus": "significance" - }, - { - "original_text": "The overall presentation of results is dense, with extensive data tables and narrative, which could overwhelm readers.", - "improved_version": "Streamline the presentation by highlighting key findings in summary boxes or infographics and relegating detailed tables to supplementary materials, with clear cross-references.", - "explanation": "Simplified presentation improves readability and emphasizes main messages without sacrificing detail.", - "location": "Results section", - "category": "presentation", - "focus": "clarity" - } - ], - "detailed_feedback": { - "presentation_analysis": "The results are comprehensive, including multiple performance metrics, descriptive statistics, and detailed tables. However, the figures referenced are not included or well-labeled in the main text, which hampers visual interpretation. The dense data presentation could benefit from more visual summaries or simplified tables to enhance clarity and reader engagement.", - "analysis_quality": "The statistical analysis employs appropriate machine learning algorithms, cross-validation, and hyperparameter tuning. Performance metrics like AUC, accuracy, F1, precision, and recall are thoroughly reported across multiple prediction windows. Nonetheless, there is limited discussion on potential overfitting, model robustness, or external validation, which are critical for assessing real-world applicability.", - "interpretation_review": "The discussion provides a solid overview of model performance and potential applications. However, it lacks depth in interpreting differences between models, such as why nonadherence models outperform churn models in certain contexts, and how these insights translate into intervention strategies. Expanding on the behavioral significance of key features would strengthen the interpretive value.", - "visualization_assessment": "Figures are described but not included, and the tables are extensive but dense, making it difficult for readers to quickly grasp key trends. Incorporating visual summaries like performance trend graphs, feature importance charts, or consolidated performance dashboards would significantly improve comprehension.", - "significance_evaluation": "The high performance metrics suggest promising predictive capabilities, but the manuscript does not sufficiently address issues of overfitting, model generalizability, or applicability across diverse populations. Explicit discussion of these aspects would enhance the perceived significance and practical relevance of the findings." - }, - "summary": "Overall, this manuscript presents a valuable and methodologically sound exploration of nonadherence prediction in mHealth interventions. The extensive data and rigorous analysis support the potential utility of behavioral engagement features combined with machine learning. To elevate the manuscript, improvements in data visualization, deeper interpretive discussion, and explicit acknowledgment of limitations and generalizability are recommended. These enhancements would make the findings more accessible, actionable, and robust, aligning with the high standards expected for impactful research in digital health." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/S7_results.json b/Backup/V5_multi_agent2/results/S7_results.json deleted file mode 100644 index 0793b36..0000000 --- a/Backup/V5_multi_agent2/results/S7_results.json +++ /dev/null @@ -1,130 +0,0 @@ -{ - "score": 7, - "critical_remarks": [ - { - "category": "interpretation", - "location": "Section 4.1 - Result interpretation", - "issue": "While the discussion reports high prediction accuracy, it lacks a nuanced analysis of the clinical or practical significance of these metrics, such as how they translate into real-world intervention effectiveness.", - "severity": "medium", - "impact": "This limits the reader's understanding of how the predictive performance impacts actual adherence behavior change or health outcomes." - }, - { - "category": "context", - "location": "Section 4.1 - Literature comparison", - "issue": "The comparison with prior studies is somewhat superficial, often stating that results are 'consistent' without critically analyzing differences in methodology, populations, or settings that could influence the comparability.", - "severity": "medium", - "impact": "Reduces the depth of contextual understanding and may overstate the novelty or robustness of findings." - }, - { - "category": "reflection", - "location": "Section 4.4 - Limitations and future work", - "issue": "The limitations are acknowledged but lack specific strategies for addressing these issues in future research, such as how to handle sparse data or how to validate models prospectively.", - "severity": "high", - "impact": "Weakens the manuscript's guidance for subsequent research and practical implementation." - }, - { - "category": "impact", - "location": "Section 4.3 - Practical implications", - "issue": "While potential strategies are discussed, there is limited discussion on the actual feasibility, cost-effectiveness, or ethical considerations of implementing predictive interventions in real-world settings.", - "severity": "medium", - "impact": "Reduces the translational value and real-world relevance of the findings." - }, - { - "category": "quality", - "location": "Throughout the discussion", - "issue": "The discussion is dense with technical details and statistical results but often lacks coherence and clear synthesis, making it difficult for readers to grasp the overarching narrative or implications.", - "severity": "high", - "impact": "Impairs clarity and diminishes the overall readability and impact of the manuscript." - } - ], - "improvement_suggestions": [ - { - "original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).", - "improved_version": "Our results demonstrate that nonadherence, including complete discontinuation (churn), can be predicted with high accuracy over extended periods. This aligns with and extends prior definitions by Sieverink et al. (2017), emphasizing the potential for early intervention strategies.", - "explanation": "Clarifies the significance of the findings and explicitly links them to existing conceptual frameworks, enhancing interpretability.", - "location": "Section 4.1", - "category": "interpretation", - "focus": "significance" - }, - { - "original_text": "The comparison with prior studies is somewhat superficial, often stating that results are 'consistent' without critically analyzing differences in methodology, populations, or settings that could influence the comparability.", - "improved_version": "While our findings align with previous research on churn prediction, differences in intervention types, populations, and data collection methods should be considered. For example, prior studies often focus on shorter durations or different app domains, which may influence predictive performance.", - "explanation": "Provides a more nuanced and critical comparison, acknowledging contextual differences that affect generalizability.", - "location": "Section 4.1", - "category": "context", - "focus": "comparison" - }, - { - "original_text": "A shared characteristic of both analyzed mHealth interventions is that users must provide proof of medical need to access the apps and receive reimbursement. User behavior in these contexts may differ from that observed in mHealth interventions directly available in public app stores, where users can self-enroll.", - "improved_version": "The requirement for proof of medical need and reimbursement access may influence user engagement patterns, potentially leading to higher retention rates compared to freely available apps in public stores. Future studies should examine how onboarding and access mechanisms impact adherence trajectories.", - "explanation": "Highlights a specific contextual factor that could influence generalizability and suggests a direction for future research.", - "location": "Section 4.4", - "category": "reflection", - "focus": "limitations/future work" - }, - { - "original_text": "The models rely on rich, continuous behavioral app engagement data, which may not be available in all settings.", - "improved_version": "The predictive models depend on detailed, continuous engagement data, which may limit applicability in interventions with sporadic or sparse data collection. Developing models that incorporate less granular data or alternative indicators could broaden usability.", - "explanation": "Addresses a key limitation and proposes a constructive solution, enhancing the practical relevance of future work.", - "location": "Section 4.4", - "category": "reflection", - "focus": "limitations/future work" - }, - { - "original_text": "While the results are promising, prospective trials are necessary to fully establish the applicability of these models.", - "improved_version": "To validate the predictive utility and real-world impact of these models, prospective randomized trials incorporating intervention components based on model predictions are essential. Such studies should assess not only adherence but also health outcomes.", - "explanation": "Provides a clear, actionable future research direction that emphasizes validation and impact assessment.", - "location": "Section 4.4", - "category": "reflection", - "focus": "future work" - }, - { - "original_text": "Our discussion reports high prediction accuracy, but it lacks a nuanced analysis of the clinical or practical significance of these metrics.", - "improved_version": "Although the models demonstrate high accuracy, translating these metrics into meaningful clinical or behavioral improvements requires further investigation. For instance, understanding how early predictions influence adherence behaviors and health outcomes will be critical for implementation.", - "explanation": "Bridges statistical performance with practical significance, guiding future translational efforts.", - "location": "Section 4.1", - "category": "interpretation", - "focus": "implications" - }, - { - "original_text": "The comparison with prior studies is somewhat superficial, often stating that results are 'consistent' without critically analyzing differences in methodology, populations, or settings that could influence the comparability.", - "improved_version": "Future research should systematically compare models across diverse populations and intervention types to identify factors influencing predictive accuracy and generalizability, including demographic, behavioral, and contextual variables.", - "explanation": "Encourages comprehensive comparative analyses, strengthening the contextual understanding.", - "location": "Section 4.1", - "category": "context", - "focus": "comparison" - }, - { - "original_text": "The discussion is dense with technical details and statistical results but often lacks coherence and clear synthesis.", - "improved_version": "To enhance clarity, we recommend structuring the discussion around key themes: the predictive performance, practical implications, limitations, and future directions. Summarizing main points at the end of each subsection can improve readability.", - "explanation": "Improves overall coherence and helps readers synthesize complex information.", - "location": "Throughout the discussion", - "category": "quality", - "focus": "coherence" - }, - { - "original_text": "The models' performance metrics are high, but the discussion does not sufficiently address the potential for false positives and negatives in clinical decision-making.", - "improved_version": "While the models show high predictive accuracy, the implications of false positives and negatives\u2014such as unnecessary interventions or missed opportunities\u2014should be carefully considered. Future work should evaluate the cost-benefit balance of model deployment in practice.", - "explanation": "Adds critical reflection on the limitations of predictive accuracy and its impact on real-world decisions.", - "location": "Section 4.1", - "category": "impact", - "focus": "implications" - }, - { - "original_text": "The discussion could benefit from explicitly linking the findings to health outcomes, which remains an open question.", - "improved_version": "Ultimately, establishing a direct link between early adherence predictions and improved health outcomes remains an important avenue for future research. Integrating predictive models within adaptive intervention frameworks could facilitate this goal.", - "explanation": "Clarifies the ultimate translational goal and guides future research priorities.", - "location": "Section 4.4", - "category": "impact", - "focus": "significance" - } - ], - "detailed_feedback": { - "interpretation_analysis": "The discussion effectively highlights the high predictive accuracy of models over extended durations, emphasizing their potential for early intervention. However, it would benefit from a deeper analysis of how these metrics translate into meaningful behavioral or health improvements, including potential thresholds for clinical utility and the impact of false positives/negatives on users and providers.", - "context_review": "The manuscript references prior studies to support its findings but often treats these comparisons superficially. A more critical analysis considering differences in intervention types, populations, and data collection methods would strengthen claims of novelty and generalizability. Explicit discussion of how the current work advances or diverges from existing literature is needed.", - "reflection_assessment": "Limitations such as data dependency, generalizability, and the need for prospective validation are acknowledged but lack specific strategies for addressing them. Future research directions should include plans for validation in real-world settings, handling sparse data, and expanding to diverse populations. The discussion should also consider ethical and practical challenges of implementing predictive models in clinical workflows.", - "impact_evaluation": "The discussion appropriately discusses the potential for targeted strategies but underestimates practical challenges such as cost, user acceptance, and ethical considerations. Explicitly addressing these factors would enhance the translational relevance. Additionally, linking prediction performance to health outcomes would clarify the clinical significance of the models.", - "quality_analysis": "While comprehensive in technical detail, the discussion suffers from a lack of clear structure and synthesis. Organizing content around key themes\u2014performance, implications, limitations, future directions\u2014and summarizing main points would improve readability. Reducing jargon and providing concise takeaways would make the discussion more accessible." - }, - "summary": "Overall, the discussion demonstrates solid technical analysis and acknowledges key limitations, but it would benefit from deeper interpretation of results, critical comparison with existing literature, clearer structure, and explicit guidance for future research and practical application. These enhancements would elevate the manuscript's clarity, impact, and translational potential." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/S8_results.json b/Backup/V5_multi_agent2/results/S8_results.json deleted file mode 100644 index e5a891a..0000000 --- a/Backup/V5_multi_agent2/results/S8_results.json +++ /dev/null @@ -1,131 +0,0 @@ -{ - "score": 7, - "critical_remarks": [ - { - "category": "support", - "location": "Section 4.1", - "issue": "While the conclusion states that models can predict nonadherence accurately, it lacks explicit mention of how the results directly support the specific performance metrics reported, such as the high AUC and F1 scores, especially in the context of real-world applicability.", - "severity": "medium", - "impact": "This reduces confidence in the strength of the evidence backing the claims, making the support less transparent." - }, - { - "category": "objectives", - "location": "Section 4.1", - "issue": "The conclusion claims the study extends prior research but does not explicitly restate the original research objectives or how they were fulfilled beyond the general statement.", - "severity": "low", - "impact": "This diminishes clarity about the specific aims achieved, affecting the perceived completeness of the conclusion." - }, - { - "category": "implications", - "location": "Section 4.2", - "issue": "Implications for practice are discussed, but the potential limitations or challenges of implementing these predictive models in real-world settings are underexplored.", - "severity": "medium", - "impact": "This limits the practical relevance and may overstate the readiness for application." - }, - { - "category": "presentation", - "location": "Section 4.4", - "issue": "The conclusion contains dense, lengthy sentences that could be more concise and clearer, reducing overall readability.", - "severity": "low", - "impact": "This affects the clarity and impact of the final message." - } - ], - "improvement_suggestions": [ - { - "original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).", - "improved_version": "Our results demonstrate that nonadherence, including complete discontinuation (churn), can be reliably predicted over long-term intervention periods, aligning with the definitions by Sieverink et al. (2017).", - "explanation": "This clarification emphasizes the robustness of the findings and explicitly links the results to the operational definitions used, strengthening support clarity.", - "location": "Section 4.1", - "category": "support", - "focus": "clarity" - }, - { - "original_text": "Given the conceptual link between churn and nonadherence \u2013 where fully disengaged users are inherently nonadherent \u2013 these results are intuitive.", - "improved_version": "Since churn represents a complete disengagement, it logically aligns with nonadherence, making the high prediction accuracy expected and reinforcing the validity of our approach.", - "explanation": "This rephrasing explicitly states the conceptual link, enhancing the logical support for the results and their interpretation.", - "location": "Section 4.1", - "category": "support", - "focus": "support" - }, - { - "original_text": "Our descriptive analysis further emphasizes this relationship, showing that the decline in adherence over time in Vivira and Manoa is largely driven by churn (i.e., users discontinuing entire use).", - "improved_version": "Our descriptive data confirms that the decline in adherence over time is primarily due to user churn, supporting the predictive models' focus on identifying full discontinuation.", - "explanation": "This explicitly ties the descriptive findings to the predictive modeling, reinforcing the support from empirical data.", - "location": "Section 4.1", - "category": "support", - "focus": "support" - }, - { - "original_text": "In contrast, the performance of nonadherence prediction models in Manoa was comparatively lower, correctly identifying an average of 86% (SD = 7.6%, mean AUC = 0.82) of nonadherent users between Months 2 and 6 at a relatively higher false positive rate of 49.5% (SD = 12.9%).", - "improved_version": "In Manoa, nonadherence prediction accuracy was somewhat lower, with models correctly identifying 86% of nonadherent users (AUC = 0.82), though with a higher false positive rate of 49.5%, indicating room for improvement.", - "explanation": "This clarifies the performance limitations and contextualizes the results, providing a balanced view that supports a nuanced interpretation.", - "location": "Section 4.1", - "category": "support", - "focus": "clarity" - }, - { - "original_text": "Feature importance analyses across all prediction models showed that behavioral app engagement data collected closer to the prediction event had a stronger impact on model performance.", - "improved_version": "Analysis of feature importance revealed that behavioral engagement data obtained nearer to the prediction point significantly enhanced model performance.", - "explanation": "This concise rephrasing improves clarity and emphasizes the temporal relevance of data, supporting the methodological insights.", - "location": "Section 4.1", - "category": "support", - "focus": "clarity" - }, - { - "original_text": "Our findings further show that the predictive performance of daily churn prediction models improves over time as more behavioral app engagement data becomes available.", - "improved_version": "Our results indicate that daily churn prediction models become more accurate over time, as additional behavioral engagement data accumulates, supporting their robustness.", - "explanation": "This enhances clarity and explicitly states the causal relationship, strengthening support from the results.", - "location": "Section 4.1", - "category": "support", - "focus": "support" - }, - { - "original_text": "While nonadherence prediction models were more adept at identifying nonadherent users who had already churned, they also correctly identified a substantial proportion of nonadherent users before they recorded their last log in.", - "improved_version": "Although models predicted users who had already churned with higher accuracy, they also successfully identified many users at risk of nonadherence before complete disengagement.", - "explanation": "This clarifies the predictive capacity at different stages, emphasizing the practical utility for early intervention.", - "location": "Section 4.2", - "category": "support", - "focus": "clarity" - }, - { - "original_text": "In summary, churn prediction models demonstrated strong and improving performance throughout the observation period.", - "improved_version": "In conclusion, the churn prediction models showed strong, progressively improving performance over the entire observation window.", - "explanation": "This concise summary reinforces the key finding with emphasis on the temporal improvement, enhancing clarity and impact.", - "location": "Section 4.1", - "category": "presentation", - "focus": "strength" - }, - { - "original_text": "The results of our study demonstrate that ML algorithms, when paired with behavioral app engagement data, can generate actionable insights that may support targeted strategies for preventing nonadherence.", - "improved_version": "Our study confirms that machine learning algorithms utilizing behavioral engagement data can produce actionable insights to inform targeted adherence-promoting strategies.", - "explanation": "This improves clarity and emphasizes the practical relevance of the findings.", - "location": "Section 4.3", - "category": "implications", - "focus": "clarity" - }, - { - "original_text": "Previous research has consistently ranked features related to user app activity (e.g., daily logins) and app progress (e.g., completion of app-logged activities) as the most important for churn prediction in mHealth interventions [5, 21] and other app domains [7, 27, 29, 41, 56].", - "improved_version": "Consistent with prior studies [5, 21, 7, 27, 29, 41, 56], our findings highlight that user activity and progress features are the most predictive for churn in mHealth and other app domains.", - "explanation": "This consolidates evidence, making the statement more concise and emphasizing the generalizability of the feature importance.", - "location": "Section 4.3", - "category": "support", - "focus": "support" - }, - { - "original_text": "Future research should aim to replicate nonadherence prediction models in diverse mHealth contexts and evaluate their integration with targeted preventive strategies in prospective trials to assess the impact of these combined approaches on app usage, adherence, and health outcomes.", - "improved_version": "Future studies should validate these nonadherence prediction models across varied mHealth settings and test their integration with targeted interventions in prospective trials to evaluate effects on engagement and health outcomes.", - "explanation": "This streamlines the statement, making it more actionable and clearer about the next steps for research.", - "location": "Section 4.4", - "category": "future_directions", - "focus": "clarity" - } - ], - "detailed_feedback": { - "support_analysis": "The conclusion effectively summarizes the high predictive accuracy of the models, supported by reported metrics such as AUCs above 0.8 and F1 scores above 0.8. It also references the consistency of these results across two different interventions, reinforcing the robustness of the support. However, explicit links between specific results and their implications for real-world application could be strengthened to enhance transparency.", - "objective_fulfillment": "The conclusion addresses the primary objectives of demonstrating the predictive capacity of behavioral app engagement data for nonadherence and extending prior research to longer durations. It clearly states that the study fulfills these aims, although explicitly restating the original research questions would improve clarity on objective fulfillment.", - "implications_analysis": "The discussion of implications highlights the potential for targeted strategies and the generalizability of models. Nonetheless, it underplays the practical challenges, such as implementation barriers, user privacy concerns, and variability in real-world settings, which are critical for translating findings into practice.", - "presentation_analysis": "While comprehensive, the conclusion is somewhat verbose, with complex sentences that could be simplified for better readability. The overall structure could benefit from more concise summaries and clearer topic transitions to improve flow and impact.", - "contribution_analysis": "The manuscript clearly emphasizes that this is the first study to evaluate nonadherence prediction aligned with Sieverink et al.'s definition over extended periods, contributing novel insights. However, explicitly stating how this advances the field beyond prior models would further clarify its unique contribution." - }, - "summary": "Overall, the conclusion effectively summarizes key findings, supports claims with robust data, and discusses relevant implications. However, it would benefit from clearer linkage between results and claims, more balanced discussion of practical limitations, and improved conciseness. These enhancements would elevate the manuscript's clarity, support strength, and contribution clarity, making it more impactful and comprehensive." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/S9_results.json b/Backup/V5_multi_agent2/results/S9_results.json deleted file mode 100644 index 7e1637c..0000000 --- a/Backup/V5_multi_agent2/results/S9_results.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "accuracy_analysis": "", - "completeness_analysis": "", - "format_analysis": "", - "quality_analysis": "", - "organization_analysis": "" - }, - "summary": "Error in analysis: Error analyzing references: Unterminated string starting at: line 13 column 19 (char 600)", - "error": true -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/W1_results.json b/Backup/V5_multi_agent2/results/W1_results.json deleted file mode 100644 index c8cfaa1..0000000 --- a/Backup/V5_multi_agent2/results/W1_results.json +++ /dev/null @@ -1,162 +0,0 @@ -{ - "language_style_score": 7, - "critical_remarks": [ - { - "category": "grammar", - "location": "Abstract, paragraph 4", - "issue": "Inconsistent use of hyphenation in 'noncommunicable' and 'nonadherence'.", - "severity": "low", - "impact": "Minor inconsistency; standardization improves professionalism." - }, - { - "category": "spelling", - "location": "Introduction, paragraph 2", - "issue": "The term 'hyperglycemia' is spelled correctly, but in some instances, similar terms like 'hyperglycemia' are misspelled as 'hyperglycemia' (e.g., in references).", - "severity": "low", - "impact": "Ensures consistency and accuracy in terminology, maintaining credibility." - }, - { - "category": "punctuation", - "location": "Results, section 3.2.1", - "issue": "Numerous instances of missing commas after introductory phrases, e.g., 'Across the prediction windows from Week 2 to Week 13, the models achieved...'", - "severity": "medium", - "impact": "Reduces clarity and can cause reader confusion." - }, - { - "category": "sentence_structure", - "location": "Discussion, paragraph 4.1", - "issue": "Long, complex sentences that could be split for clarity, e.g., the sentence starting with 'Given the conceptual link...'", - "severity": "medium", - "impact": "Improves readability and comprehension." - }, - { - "category": "verb_tense", - "location": "Methodology, section 2.2", - "issue": "Inconsistent use of past and present tense, e.g., 'We selected features' (past) and 'we predict nonadherence' (present).", - "severity": "low", - "impact": "Consistency enhances professional tone and clarity." - }, - { - "category": "subject-verb", - "location": "Results, section 3.2.2", - "issue": "In several instances, plural subjects are paired with singular verbs, e.g., 'the models achieved a mean AUC...' (correct), but in some cases, agreement is inconsistent.", - "severity": "low", - "impact": "Maintains grammatical correctness and professionalism." - }, - { - "category": "articles", - "location": "Introduction, paragraph 2", - "issue": "Incorrect or missing articles, e.g., 'a systematic review of effectivity' should be 'a systematic review of effectiveness'.", - "severity": "low", - "impact": "Improves grammatical accuracy and clarity." - }, - { - "category": "prepositions", - "location": "Discussion, paragraph 4.2", - "issue": "Incorrect preposition usage, e.g., 'detect users in the early stages of disengagement' could be 'detect users at the early stages of disengagement'.", - "severity": "low", - "impact": "Enhances precision and correctness of expression." - }, - { - "category": "conjunctions", - "location": "Discussion, paragraph 4.4", - "issue": "Overuse of 'however' at the beginning of sentences, which can be stylistically repetitive.", - "severity": "low", - "impact": "Varying conjunctions improves flow and style." - }, - { - "category": "academic_conventions", - "location": "Throughout the document", - "issue": "Inconsistent formatting of references and in-text citations, e.g., some citations are bracketed, others are not.", - "severity": "medium", - "impact": "Reduces professionalism and adherence to academic standards." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "The rich data collected by mHealth interventions raise the question of whether\u2014and to what extent\u2014nonadherence can be predicted using these data.", - "explanation": "Replacing hyphens with em dashes and removing spaces improves punctuation consistency and formal style.", - "location": "Abstract, paragraph 1", - "category": "punctuation", - "focus": "punctuation" - }, - { - "original_text": "In the German SHI system [46].", - "improved_version": "In the German SHI system [46].", - "explanation": "Remove the period after the bracketed citation to adhere to standard referencing style.", - "location": "Methodology, section 2.1.1", - "category": "academic_conventions", - "focus": "citation format" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.", - "improved_version": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), where nonadherence was defined as completing fewer than eight therapeutic exercises per week.", - "explanation": "Clarifies the definition of nonadherence and improves sentence flow by restructuring.", - "location": "Results, section 3.2.1", - "category": "sentence_structure", - "focus": "clarity" - }, - { - "original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "improved_version": "The use of DiGA data is strictly limited; therefore, only users who provided consent under Article 4, Section 2, and 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "explanation": "Using a semicolon improves sentence connection; adding 'and' clarifies the list structure.", - "location": "Methodology, section 2.1.1", - "category": "punctuation", - "focus": "punctuation" - }, - { - "original_text": "In Vivira, we predicted nonadherence weekly from Weeks 2 to 13 based on users\u2019 daily app activity variables (active or inactive) and the daily number of completed exercises variables (continuous) of the preceding weeks.", - "improved_version": "In Vivira, we predicted nonadherence weekly from Weeks 2 to 13, based on users\u2019 daily app activity variables (active or inactive) and the daily number of completed exercises (continuous) from the preceding weeks.", - "explanation": "Adding a comma improves readability; removing 'variables' after 'exercises' avoids redundancy.", - "location": "Methodology, section 2.2", - "category": "sentence_structure", - "focus": "clarity" - }, - { - "original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).", - "improved_version": "Our findings demonstrate that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence\u2014relative to the intended use as defined by Sieverink et al. (2017)\u2014and in its most severe form: churn (i.e., complete discontinuation of use).", - "explanation": "Using em dashes and colons clarifies the structure and emphasizes key definitions, enhancing readability.", - "location": "Discussion, paragraph 4.1", - "category": "punctuation", - "focus": "punctuation" - }, - { - "original_text": "The article 'predicts nonadherence' (present tense) but elsewhere, the methodology is described in past tense.", - "improved_version": "Ensure consistent tense usage throughout; for example, use past tense ('predicted') when describing the methodology and results, and present tense ('predicts') only when discussing general truths or ongoing implications.", - "explanation": "Consistency in verb tense aligns with academic standards and clarifies temporal context.", - "location": "General, throughout the document", - "category": "verb_tense", - "focus": "tense consistency" - }, - { - "original_text": "The models achieved a mean AUC of 0.87 (SD = 0.06), a mean accuracy of 0.83 (SD = 0.05), a mean F1-score of 0.81 (SD = 0.13), a mean precision of 0.78 (SD = 0.12), and a mean recall of 0.86 (SD = 0.14) across all prediction points at the of Day 1 until 178, indicating a robust ability to discriminate between churned and non-churned users.", - "improved_version": "The models achieved a mean AUC of 0.87 (SD = 0.06), a mean accuracy of 0.83 (SD = 0.05), a mean F1-score of 0.81 (SD = 0.13), a mean precision of 0.78 (SD = 0.12), and a mean recall of 0.86 (SD = 0.14) across all prediction points from Day 1 until Day 178, indicating a robust ability to discriminate between churned and non-churned users.", - "explanation": "Corrects grammatical error ('at the of Day 1' to 'from Day 1') and improves clarity of time frame.", - "location": "Results, section 3.2.2", - "category": "grammar", - "focus": "grammar" - }, - { - "original_text": "The overall assessment paragraph.", - "improved_version": "Overall, the analysis indicates that the manuscript demonstrates solid methodological rigor, clear structure, and comprehensive coverage of the topic, with room for minor stylistic and grammatical improvements to enhance clarity and professionalism.", - "explanation": "Provides a concise, balanced summary of the overall quality and areas for improvement.", - "location": "Summary", - "category": "general", - "focus": "overall assessment" - } - ], - "detailed_feedback": { - "grammar_correctness": "The manuscript generally maintains grammatical correctness, but some complex sentences could be simplified for clarity. Minor issues include inconsistent tense usage and occasional awkward phrasing that can be smoothed for better flow.", - "spelling_accuracy": "Spelling is accurate throughout; however, consistency in hyphenation (e.g., 'noncommunicable') and terminology (e.g., 'hyperglycemia') should be maintained. Also, ensure all references to technical terms are spelled uniformly.", - "punctuation_usage": "Punctuation is mostly correct but can be improved by standardizing the use of em dashes, commas, and semicolons, especially in complex sentences. Proper punctuation enhances readability and formal tone.", - "sentence_structure": "Many sentences are lengthy and contain multiple ideas, which can be split into shorter, clearer sentences. This improves readability and reduces cognitive load for the reader.", - "verb_tense_consistency": "The manuscript shifts between past and present tense, particularly when describing methods and results. Maintaining consistent tense\u2014preferably past tense for completed actions\u2014would improve clarity and professionalism.", - "subject-verb_agreement": "Subject-verb agreement is generally correct; however, careful review is recommended in complex sentences, especially where collective nouns or multiple subjects are involved.", - "article_usage": "Articles are mostly used correctly, but some instances lack definite or indefinite articles where needed, affecting grammatical correctness and clarity.", - "preposition_usage": "Prepositions are generally appropriate, but some phrases could be more precise, e.g., 'predict users in the early stages' should be 'predict users at the early stages'.", - "conjunction_usage": "Conjunctions are used appropriately, but overuse of 'however' at sentence starts can be stylistically monotonous. Varying conjunctions can improve flow.", - "academic_conventions": "Citation formatting is inconsistent; some references lack proper punctuation or formatting. Standardizing reference style and citation placement will enhance professionalism." - }, - "summary": "The manuscript demonstrates strong academic writing with clear structure and comprehensive content. Minor language and stylistic improvements\u2014such as standardizing punctuation, tense, and reference formatting\u2014would elevate the clarity, professionalism, and readability of the work. Addressing these issues will ensure the manuscript aligns with high academic standards and enhances its impact." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/W2_results.json b/Backup/V5_multi_agent2/results/W2_results.json deleted file mode 100644 index a4ab2f0..0000000 --- a/Backup/V5_multi_agent2/results/W2_results.json +++ /dev/null @@ -1,202 +0,0 @@ -{ - "narrative_structure_score": 6, - "critical_remarks": [ - { - "category": "narrative_coherence", - "location": "Abstract and Introduction", - "issue": "The abstract provides a comprehensive overview but lacks explicit linkage to the specific research questions or hypotheses, making the narrative feel somewhat disconnected from the detailed objectives outlined later.", - "severity": "medium", - "impact": "This weakens the coherence between the abstract and the main research aims, potentially confusing readers about the core focus." - }, - { - "category": "logical_progression", - "location": "Results and Discussion", - "issue": "Results are detailed extensively but are not always clearly connected back to the hypotheses or research questions, leading to a fragmented understanding of how findings support or challenge initial assumptions.", - "severity": "high", - "impact": "This hampers the logical flow from data presentation to interpretation, reducing clarity of the overall narrative." - }, - { - "category": "transitions", - "location": "Between sections (e.g., Methods to Results, Results to Discussion)", - "issue": "Transitions are abrupt; for example, the shift from detailed prediction results to their implications in the discussion lacks smooth linking sentences.", - "severity": "medium", - "impact": "This disrupts the reader\u2019s flow, making the narrative feel disjointed and harder to follow." - }, - { - "category": "paragraph_organization", - "location": "Methods and Results sections", - "issue": "Some paragraphs, especially in the Methods, are overly dense with technical details, which could be better organized into subsections for clarity.", - "severity": "medium", - "impact": "This reduces readability and hampers quick comprehension of key methodological steps." - }, - { - "category": "topic_sentences", - "location": "Multiple sections", - "issue": "Many paragraphs lack clear topic sentences that outline their main point, especially in the Results and Discussion sections.", - "severity": "high", - "impact": "This diminishes the guiding thread for the reader, making it difficult to grasp the purpose of each paragraph." - }, - { - "category": "evidence_integration", - "location": "Results and Discussion", - "issue": "While extensive data are presented, there is limited integration of evidence with interpretative commentary, especially in linking statistical results to practical implications.", - "severity": "high", - "impact": "This weakens the persuasive power of the narrative and obscures the significance of findings." - }, - { - "category": "conclusion_alignment", - "location": "Conclusion", - "issue": "The conclusion summarizes findings but does not explicitly revisit the initial hypotheses or research questions, leading to a slight misalignment with the introduction.", - "severity": "medium", - "impact": "This reduces the narrative closure and clarity of how the study addresses its initial aims." - }, - { - "category": "hypothesis_tracking", - "location": "Throughout the paper", - "issue": "Explicit tracking of hypotheses or research questions is inconsistent; some sections discuss findings without clearly referencing the original aims.", - "severity": "high", - "impact": "This hampers the reader\u2019s ability to follow how evidence supports or refutes specific hypotheses." - }, - { - "category": "visual_integration", - "location": "Figures and Tables", - "issue": "Figures and tables are numerous and detailed but lack direct references or explanations within the text that clarify their relevance to the narrative.", - "severity": "medium", - "impact": "This diminishes their effectiveness as visual aids and can cause confusion about their purpose." - }, - { - "category": "reader_engagement", - "location": "Entire document", - "issue": "The dense technical language and extensive data presentation may overwhelm readers, reducing engagement.", - "severity": "medium", - "impact": "This could lead to decreased comprehension and interest, especially for non-specialist audiences." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "Building on the rich data collected by mHealth interventions, this study investigates whether and to what extent nonadherence can be accurately predicted, directly addressing this key question.", - "explanation": "Clarifies the purpose of the sentence, linking it explicitly to the research aim and enhancing narrative clarity.", - "location": "Abstract", - "category": "abstract", - "focus": "narrative_coherence" - }, - { - "original_text": "In the Introduction, the flow from the general context of NCDs to the specific focus on mHealth adherence is somewhat abrupt.", - "improved_version": "The rising prevalence and economic burden of noncommunicable diseases (NCDs) underscore the urgent need for scalable interventions. Among these, mobile health (mHealth) solutions have gained prominence due to their accessibility and potential to support disease management, yet adherence remains a critical challenge that this study aims to address.", - "explanation": "Creates a smoother logical progression from broad context to specific research focus, improving coherence.", - "location": "Introduction", - "category": "introduction", - "focus": "logical_progression" - }, - { - "original_text": "Results are detailed extensively but are not always clearly connected back to the hypotheses or research questions.", - "improved_version": "The results demonstrate that the machine learning models achieved high accuracy in predicting nonadherence, supporting the hypothesis that behavioral engagement data can serve as reliable predictors of user adherence patterns.", - "explanation": "Explicitly links findings to research hypotheses, strengthening the logical flow.", - "location": "Results", - "category": "results", - "focus": "hypothesis_tracking" - }, - { - "original_text": "The transition between the Results and Discussion sections is abrupt.", - "improved_version": "These predictive performance metrics highlight the models' robustness over extended periods. In the following discussion, we interpret these findings in the context of their practical implications for intervention strategies and future research directions.", - "explanation": "Provides a linking sentence that guides the reader smoothly from data to interpretation.", - "location": "End of Results / Beginning of Discussion", - "category": "transitions", - "focus": "transitions" - }, - { - "original_text": "Many paragraphs in Methods are overly dense with technical details.", - "improved_version": "The Methods section is organized into clear subsections: Data Collection, Feature Selection, Model Training, and Evaluation. Each subsection provides concise descriptions of the procedures, enhancing readability.", - "explanation": "Improves paragraph organization and readability by structuring technical content into subsections.", - "location": "Methods", - "category": "methodology", - "focus": "paragraph_organization" - }, - { - "original_text": "Many paragraphs lack clear topic sentences.", - "improved_version": "This paragraph introduces the primary features used for prediction, emphasizing their relevance to the intervention's objectives.", - "explanation": "Adding explicit topic sentences guides the reader through complex sections, improving clarity.", - "location": "Multiple sections", - "category": "topic_sentences", - "focus": "topic_sentence_effectiveness" - }, - { - "original_text": "Figures and tables are numerous but often lack references within the text.", - "improved_version": "Each figure and table is explicitly referenced in the text, e.g., 'As shown in Figure 6.5, the model performance improves steadily over weeks,' ensuring the visual data supports the narrative.", - "explanation": "Explicit references improve visual integration and help readers connect data with narrative points.", - "location": "Figures/Tables", - "category": "visual_integration", - "focus": "visual_element_integration" - }, - { - "original_text": "The discussion section summarizes findings but does not revisit initial hypotheses.", - "improved_version": "The discussion revisits the initial hypotheses, confirming that behavioral engagement data can reliably predict nonadherence and emphasizing the implications for targeted intervention strategies.", - "explanation": "Aligns conclusions with initial aims, reinforcing narrative closure.", - "location": "Discussion", - "category": "conclusion_alignment", - "focus": "conclusion_alignment" - }, - { - "original_text": "The narrative does not explicitly track the hypotheses throughout.", - "improved_version": "Throughout the Results and Discussion, we explicitly reference the hypotheses: (1) that behavioral data can predict nonadherence, and (2) that models can be effective over extended durations, providing a clear thread of hypothesis testing.", - "explanation": "Explicit hypothesis tracking enhances logical progression and clarity.", - "location": "Throughout the paper", - "category": "hypothesis_tracking", - "focus": "hypothesis_tracking" - }, - { - "original_text": "Figures and tables are detailed but could be better explained within the text.", - "improved_version": "In the Results section, we refer to Figure 6.5 to illustrate the trend of increasing AUC over weeks, and Table 6.1 to detail weekly model performance, providing interpretative commentary alongside the visuals.", - "explanation": "Enhances understanding by integrating visual data explanations into the narrative.", - "location": "Figures/Tables", - "category": "visual_integration", - "focus": "visual_element_integration" - }, - { - "original_text": "The dense technical language may reduce reader engagement.", - "improved_version": "To maintain engagement, technical details are summarized in supplementary materials, while the main text emphasizes key findings and their implications in accessible language.", - "explanation": "Balancing technical detail with readability improves overall engagement.", - "location": "Throughout the document", - "category": "reader_engagement", - "focus": "reader_engagement" - }, - { - "original_text": "The conclusion does not explicitly revisit the research questions.", - "improved_version": "The conclusion explicitly revisits the initial research questions, confirming that behavioral app engagement features can reliably predict nonadherence and discussing how these insights can inform future intervention strategies.", - "explanation": "Revisiting research questions provides narrative closure and reinforces the study's contributions.", - "location": "Conclusion", - "category": "conclusion_alignment", - "focus": "conclusion_alignment" - }, - { - "original_text": "The paper would benefit from clearer signposting of the hypotheses and research aims throughout.", - "improved_version": "Throughout the manuscript, we explicitly state and revisit our research aims and hypotheses, ensuring each section contributes to addressing these core questions, thereby strengthening the narrative coherence.", - "explanation": "Explicit signposting guides the reader through the research narrative, enhancing clarity.", - "location": "Entire document", - "category": "hypothesis_tracking", - "focus": "hypothesis_tracking" - }, - { - "original_text": "The extensive data presentation could be complemented with more interpretative commentary.", - "improved_version": "Alongside detailed tables and figures, we include interpretative summaries that highlight key trends and their implications for model performance and intervention design.", - "explanation": "Adding interpretative commentary makes data more accessible and meaningful, improving engagement.", - "location": "Results and Discussion", - "category": "evidence_integration", - "focus": "evidence_integration" - } - ], - "detailed_feedback": { - "narrative_coherence": "The overall narrative is somewhat fragmented due to dense technical descriptions and limited explicit links between sections. Improving coherence involves clearer signposting, summarizing key points, and ensuring each part logically flows into the next, especially between data presentation and interpretation.", - "logical_progression": "The manuscript generally follows a logical sequence from background to methods, results, and discussion. However, some sections lack explicit connections to the initial hypotheses, which could be strengthened by explicitly stating how each result supports or refutes the research aims.", - "section_transitions": "Transitions between sections, particularly from Results to Discussion, are abrupt. Incorporating bridging sentences that summarize findings and preview their implications would improve flow and reader comprehension.", - "paragraph_organization": "Many paragraphs are overly dense, especially in Methods, which hampers readability. Breaking complex paragraphs into smaller, thematic units with clear topic sentences would enhance clarity and engagement.", - "topic_sentence_effectiveness": "Several paragraphs lack clear topic sentences, making it difficult for readers to grasp their main purpose quickly. Adding explicit topic sentences at the start of each paragraph would guide the reader effectively.", - "supporting_evidence_integration": "While the data are extensive, the narrative often lacks interpretative commentary that connects evidence to broader implications. Integrating discussion of how specific results support hypotheses or practical applications would strengthen the argument.", - "conclusion_alignment": "The conclusion summarizes findings but does not explicitly revisit the initial research questions or hypotheses, weakening the narrative closure. Explicitly linking conclusions back to original aims would improve coherence.", - "hypothesis_tracking": "The manuscript does not consistently track hypotheses throughout. Explicit references to hypotheses when presenting results would clarify how findings relate to initial research aims.", - "visual_element_integration": "Figures and tables are numerous but often lack direct references or explanations within the text. Explicitly referencing and interpreting visuals within the narrative would enhance their utility.", - "reader_engagement": "The dense technical language and extensive data presentation may reduce engagement. Balancing technical detail with accessible summaries and visual aids would improve overall readability and interest." - }, - "summary": "This manuscript presents a comprehensive and technically detailed exploration of nonadherence prediction in mHealth interventions. Its strengths lie in extensive data analysis and model evaluation across multiple contexts. However, improvements in narrative coherence, explicit hypothesis tracking, section transitions, and visual integration are needed to enhance clarity and engagement. Structuring the content with clearer signposting, summarizing key points, and balancing technical detail with interpretative commentary will significantly strengthen the overall narrative flow and reader experience." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/W3_results.json b/Backup/V5_multi_agent2/results/W3_results.json deleted file mode 100644 index c20c201..0000000 --- a/Backup/V5_multi_agent2/results/W3_results.json +++ /dev/null @@ -1,202 +0,0 @@ -{ - "clarity_conciseness_score": 6, - "critical_remarks": [ - { - "category": "language_simplicity", - "location": "Abstract", - "issue": "Use of complex phrases like 'the rising prevalence and economic burden of noncommunicable diseases (NCDs) present a significant challenge' may hinder quick understanding.", - "severity": "medium", - "impact": "Reduces immediate clarity for readers unfamiliar with technical language." - }, - { - "category": "jargon", - "location": "Introduction", - "issue": "Terms like 'DiGA', 'PDT', 'stratified 10-fold cross-validation', and 'Tomek Links undersampling' are technical and may not be immediately clear to all readers.", - "severity": "high", - "impact": "Potentially alienates or confuses readers unfamiliar with specific technical terms." - }, - { - "category": "wordiness", - "location": "Literature Review", - "issue": "Several sentences are overly long and contain redundant phrases, e.g., 'A growing body of evidence suggests that mHealth interventions can effectively support the prevention and management of NCDs by addressing modifiable risk factors, including physical inactivity [52, 53], unhealthy diets [67], tobacco use [63], the harmful use of alcohol [12] and metabolic risk factors such as obesity [52], hypertension [1], and hyperglycemia [19].'", - "severity": "high", - "impact": "Obscures key points, making the text harder to scan and understand quickly." - }, - { - "category": "sentence_length", - "location": "Results", - "issue": "Many sentences, especially in the prediction results sections, are very long, containing multiple clauses and detailed data points.", - "severity": "high", - "impact": "Impairs readability and makes it difficult for readers to grasp main findings quickly." - }, - { - "category": "paragraph_length", - "location": "Discussion", - "issue": "Some paragraphs, especially in the discussion, are densely packed with information, reducing clarity.", - "severity": "medium", - "impact": "Overwhelms readers, making it harder to follow key arguments." - }, - { - "category": "voice", - "location": "Methodology", - "issue": "Use of passive voice in descriptions like 'Data was collected from...' and 'Models were trained...' can obscure agency and reduce engagement.", - "severity": "medium", - "impact": "Decreases immediacy and clarity of methodological descriptions." - }, - { - "category": "redundancy", - "location": "Results", - "issue": "Repeatedly mentioning model performance metrics (e.g., AUC, accuracy, F1) with similar values across sections adds unnecessary repetition.", - "severity": "low", - "impact": "Reduces conciseness without adding new information." - }, - { - "category": "ambiguity", - "location": "Introduction", - "issue": "Phrases like 'nonadherence can be accurately predicted' lack specificity about the degree or context of accuracy.", - "severity": "medium", - "impact": "Leaves some uncertainty about the practical significance of the findings." - }, - { - "category": "readability", - "location": "Discussion", - "issue": "Use of technical terms and complex sentences throughout hampers ease of reading for a broad audience.", - "severity": "high", - "impact": "Limits accessibility and quick comprehension." - }, - { - "category": "information_density", - "location": "Results & Discussion", - "issue": "High density of statistical data and model metrics in tables and text can overwhelm readers unfamiliar with statistical reporting.", - "severity": "medium", - "impact": "Reduces clarity of main findings and hampers quick understanding." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "The data collected by mHealth interventions raise the question: can we predict nonadherence using this data?", - "explanation": "Simplifies language and clarifies the research question, making it more direct and accessible.", - "location": "Abstract", - "category": "language_simplicity", - "focus": "language_simplicity" - }, - { - "original_text": "Terms like 'DiGA', 'PDT', 'stratified 10-fold cross-validation', and 'Tomek Links undersampling' are technical and may not be immediately clear to all readers.", - "improved_version": "Terms such as 'DiGA' (digital health application), 'PDT' (digital therapeutic), and 'Tomek Links undersampling' (a data balancing technique) are technical; consider briefly defining them when first introduced.", - "explanation": "Provides clarity for readers unfamiliar with specific jargon, improving comprehension.", - "location": "Introduction", - "category": "jargon", - "focus": "jargon" - }, - { - "original_text": "Several sentences are overly long and contain redundant phrases, e.g., 'A growing body of evidence suggests that mHealth interventions can effectively support the prevention and management of NCDs by addressing modifiable risk factors, including physical inactivity [52, 53], unhealthy diets [67], tobacco use [63], the harmful use of alcohol [12] and metabolic risk factors such as obesity [52], hypertension [1], and hyperglycemia [19].'", - "improved_version": "Evidence shows that mHealth interventions help prevent and manage NCDs by targeting risk factors like inactivity, poor diet, smoking, alcohol use, obesity, hypertension, and high blood sugar.", - "explanation": "Reduces wordiness and simplifies complex lists, making key points clearer and more concise.", - "location": "Literature Review", - "category": "wordiness", - "focus": "wordiness" - }, - { - "original_text": "Many sentences, especially in the prediction results sections, are very long, containing multiple clauses and detailed data points.", - "improved_version": "Break long sentences into shorter ones. For example, instead of 'AUC ranged from 0.89 in Week 2 to 0.99 in Week 13,' write 'The AUC ranged from 0.89 in Week 2 to 0.99 in Week 13.'", - "explanation": "Shorter sentences improve readability and help readers grasp key findings more easily.", - "location": "Results", - "category": "sentence_length", - "focus": "sentence_length" - }, - { - "original_text": "Some paragraphs, especially in the discussion, are densely packed with information, reducing clarity.", - "improved_version": "Divide lengthy paragraphs into smaller sections focused on single ideas. For example, separate discussion of model performance from implications for practice.", - "explanation": "Enhances readability by allowing readers to process one idea at a time.", - "location": "Discussion", - "category": "paragraph_length", - "focus": "paragraph_length" - }, - { - "original_text": "Use of passive voice in descriptions like 'Data was collected from...' and 'Models were trained...' can obscure agency and reduce engagement.", - "improved_version": "Use active voice: 'We collected data from...' and 'We trained the models...'.", - "explanation": "Active voice makes sentences clearer and more direct, increasing engagement.", - "location": "Methodology", - "category": "active_passive_voice", - "focus": "active_passive_voice" - }, - { - "original_text": "Repeatedly mentioning model performance metrics (e.g., AUC, accuracy, F1) with similar values across sections adds unnecessary repetition.", - "improved_version": "Summarize key performance metrics once in a table or summary paragraph instead of repeating similar details in multiple sections.", - "explanation": "Reduces redundancy, making the text more concise and focused.", - "location": "Results", - "category": "redundancy", - "focus": "redundancy" - }, - { - "original_text": "Phrases like 'nonadherence can be accurately predicted' lack specificity about the degree or context of accuracy.", - "improved_version": "Nonadherence was predicted with over 90% accuracy, indicating high reliability in our models.", - "explanation": "Provides concrete figures, clarifying the level of accuracy and strengthening the statement.", - "location": "Introduction", - "category": "ambiguity", - "focus": "ambiguity" - }, - { - "original_text": "Use of technical terms and complex sentences throughout hampers ease of reading for a broad audience.", - "improved_version": "Simplify technical language where possible and use shorter sentences to improve overall readability for diverse readers.", - "explanation": "Enhances accessibility, allowing a wider audience to understand key points more easily.", - "location": "Discussion", - "category": "readability", - "focus": "readability" - }, - { - "original_text": "High density of statistical data and model metrics in tables and text can overwhelm readers unfamiliar with statistical reporting.", - "improved_version": "Include only the most relevant statistics in the main text; move detailed tables to appendices or supplementary materials.", - "explanation": "Reduces cognitive load and helps highlight key findings without overwhelming the reader.", - "location": "Results & Discussion", - "category": "information_density", - "focus": "information_density" - }, - { - "original_text": "Many sentences are very long and contain multiple clauses, making them difficult to follow.", - "improved_version": "Shorten complex sentences into simpler, standalone sentences. For example, 'Models achieved high performance. They predicted nonadherence accurately.'", - "explanation": "Improves clarity and ease of reading by reducing sentence complexity.", - "location": "Results", - "category": "sentence_length", - "focus": "sentence_length" - }, - { - "original_text": "Some technical descriptions, such as 'stratified 10-fold cross-validation,' could benefit from brief explanations for clarity.", - "improved_version": "Use: 'We used stratified 10-fold cross-validation, a method that splits data into 10 parts while maintaining class proportions, to evaluate model performance.'", - "explanation": "Clarifies technical terms, making methods transparent to non-expert readers.", - "location": "Methodology", - "category": "jargon", - "focus": "jargon" - }, - { - "original_text": "The dense presentation of data and statistical results can be overwhelming.", - "improved_version": "Summarize key findings in text and present detailed data in well-organized tables or figures, with clear labels and legends.", - "explanation": "Enhances readability and helps readers focus on main insights without distraction.", - "location": "Results", - "category": "information_density", - "focus": "information_density" - }, - { - "original_text": "Some sections contain redundant information, such as repeatedly stating model performance metrics with similar values.", - "improved_version": "Consolidate performance metrics into summary statements, e.g., 'Models consistently achieved AUCs above 0.87, indicating strong predictive ability.'", - "explanation": "Reduces repetition, making the narrative more concise and focused.", - "location": "Results & Discussion", - "category": "redundancy", - "focus": "redundancy" - } - ], - "detailed_feedback": { - "language_simplicity": "The manuscript employs complex language and lengthy sentences, which can hinder quick understanding. Simplifying vocabulary and breaking long sentences into shorter, clearer ones will improve accessibility for a broader audience.", - "jargon_usage": "While technical terms are necessary for precision, many are introduced without explanation. Providing brief definitions or explanations upon first use will help non-expert readers grasp the concepts more easily.", - "wordiness": "Several sections contain verbose sentences with redundant phrases, which obscure key messages. Eliminating unnecessary words and focusing on core ideas will enhance clarity.", - "sentence_length": "Long, multi-clause sentences are prevalent, especially in data-heavy sections. Shortening sentences or splitting them into two will improve readability and comprehension.", - "paragraph_length": "Some paragraphs are densely packed with information, making it difficult to follow. Dividing lengthy paragraphs into smaller, focused sections will aid reader engagement.", - "active_passive_voice": "The frequent use of passive voice reduces immediacy and clarity. Rephrasing sentences to active voice will make descriptions more direct and engaging.", - "redundancy": "Repeated presentation of similar performance metrics and detailed data points can clutter the text. Summarizing key results and relocating detailed data to appendices will streamline the narrative.", - "ambiguity": "Certain statements lack specificity, such as 'nonadherence can be accurately predicted,' without quantifying accuracy. Including concrete metrics will clarify the strength of the findings.", - "readability": "Technical complexity and dense data presentation hinder ease of reading. Simplifying language, reducing jargon, and organizing data visually will improve overall readability.", - "information_density": "The manuscript contains a high volume of detailed statistical data, which can overwhelm readers. Focusing on main findings and relegating detailed tables to supplementary sections will enhance clarity." - }, - "summary": "Overall, the manuscript presents valuable findings on predicting nonadherence in mHealth interventions but would benefit from clearer, more concise language, reduced technical jargon, and better organization of data. Shortening sentences, clarifying technical terms, and dividing dense paragraphs will significantly improve readability and comprehension for a diverse audience." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/W4_results.json b/Backup/V5_multi_agent2/results/W4_results.json deleted file mode 100644 index b2fa263..0000000 --- a/Backup/V5_multi_agent2/results/W4_results.json +++ /dev/null @@ -1,178 +0,0 @@ -{ - "terminology_consistency_score": 7, - "critical_remarks": [ - { - "category": "term_usage", - "location": "Abstract, Paragraph 1", - "issue": "The term 'nonadherence' is introduced and used throughout, but sometimes it is contrasted with 'churn' without clear differentiation or consistent usage, leading to potential confusion.", - "severity": "medium", - "impact": "Inconsistent use of 'nonadherence' and 'churn' can cause ambiguity in understanding the scope of the study and the specific behaviors being predicted." - }, - { - "category": "notation", - "location": "Equations and Tables, e.g., Table 6.1", - "issue": "Performance metrics such as AUC, accuracy, F1-score are presented with inconsistent formatting and sometimes without units or clear explanation, e.g., SD values are sometimes in parentheses, sometimes not.", - "severity": "low", - "impact": "Inconsistent notation reduces clarity and may cause misinterpretation of statistical results." - }, - { - "category": "acronyms", - "location": "Introduction and Methods", - "issue": "Acronyms like NCDs, mHealth, DiGA, SHI, PHI are used before being explicitly defined, or their definitions are scattered, leading to potential confusion for readers unfamiliar with these terms.", - "severity": "high", - "impact": "Lack of clear, early definitions of acronyms hampers reader comprehension and consistency in terminology." - }, - { - "category": "variable_naming", - "location": "Results and Tables, e.g., Table 6.1", - "issue": "Variables such as 'nonadherent users', 'churned users', 'active users' are used, but sometimes the criteria for these categories (e.g., thresholds) are described differently in different sections, risking inconsistency.", - "severity": "medium", - "impact": "Inconsistent variable definitions can lead to confusion about what exactly is being measured and predicted." - }, - { - "category": "unit_notation", - "location": "Methodology, Descriptive Statistics", - "issue": "Time units such as days, weeks, and months are used variably with inconsistent formatting (e.g., 'Days 1-7', 'Week 1', 'Month 2') without uniform notation or clarification.", - "severity": "low", - "impact": "Inconsistent notation of time units can cause misinterpretation of the temporal scope of data and analysis." - }, - { - "category": "abbreviations", - "location": "Throughout the document", - "issue": "Some abbreviations like 'SD', 'IQR', 'n' are used without initial expansion or clear explanation, and sometimes the same abbreviation is used for different concepts.", - "severity": "medium", - "impact": "Inconsistent abbreviation usage reduces clarity and may lead to misunderstandings." - }, - { - "category": "technical_terms", - "location": "Discussion, Paragraph 1", - "issue": "Terms such as 'predictive performance', 'classification', 'model accuracy' are used interchangeably without precise definitions or distinctions, which could cause confusion.", - "severity": "low", - "impact": "Ambiguous technical terminology can impair precise understanding of the results." - }, - { - "category": "field_terminology", - "location": "Introduction, Paragraph 2", - "issue": "Terms like 'adherence', 'nonadherence', 'churn', 'dropout' are used, but their definitions vary slightly across literature and are sometimes used interchangeably without clarification.", - "severity": "high", - "impact": "Inconsistent field-specific terminology affects the clarity of the conceptual framework." - }, - { - "category": "cross_references", - "location": "Throughout the document", - "issue": "References to figures, tables, and appendices (e.g., 'Figure 6.1', 'Appendix 6.2') are sometimes inconsistent in formatting or missing, which can hinder navigation.", - "severity": "low", - "impact": "Inconsistent cross-referencing impacts document usability." - }, - { - "category": "definition_consistency", - "location": "Methodology, Dataset descriptions", - "issue": "Definitions of adherence (e.g., 'completing eight exercises per week') are provided, but similar definitions for 'churn' or 'nonadherence' are sometimes described differently in various sections, risking inconsistency.", - "severity": "medium", - "impact": "Inconsistent definitions compromise the clarity of outcome measures." - } - ], - "improvement_suggestions": [ - { - "original_text": "nonadherence was measured weekly, defined as completing eight or more exercises per week.", - "improved_version": "Nonadherence was measured weekly, defined as completing fewer than eight exercises per week.", - "explanation": "Clarifies the threshold for nonadherence, ensuring consistent understanding of the criterion.", - "location": "Methodology, Dataset descriptions", - "category": "term_usage", - "focus": "term_usage" - }, - { - "original_text": "churn (users\u2019 last login within program duration)", - "improved_version": "churn, defined as the user's last login within the program duration, indicating complete discontinuation.", - "explanation": "Provides a clear, consistent definition of churn aligned with the literature and the context, reducing ambiguity.", - "location": "Introduction, Methods", - "category": "field_terminology", - "focus": "definitions" - }, - { - "original_text": "The performance metrics such as AUC, accuracy, F1-score are presented with inconsistent formatting.", - "improved_version": "Performance metrics such as Area Under the Curve (AUC), accuracy, and F1-score are consistently formatted, with abbreviations defined at first use and values presented with standard decimal notation.", - "explanation": "Enhances clarity and uniformity in reporting statistical results.", - "location": "Results, Tables", - "category": "notation", - "focus": "notation" - }, - { - "original_text": "Acronyms like NCDs, mHealth, DiGA, SHI, PHI are used before being explicitly defined.", - "improved_version": "Define all acronyms upon first mention, e.g., 'noncommunicable diseases (NCDs)', 'mobile health (mHealth)', 'Digital Healthcare Act (DiGA)', 'Statutory Health Insurance (SHI)', 'Private Health Insurance (PHI)'.", - "explanation": "Ensures all readers understand the acronyms from the first occurrence, improving clarity.", - "location": "Introduction, Methods", - "category": "acronyms", - "focus": "definition" - }, - { - "original_text": "Variables such as 'nonadherent users', 'churned users' are used with thresholds described in different sections.", - "improved_version": "Standardize variable definitions throughout the manuscript, explicitly stating thresholds (e.g., 'nonadherent users: those completing fewer than 8 exercises per week') and maintaining consistent terminology.", - "explanation": "Prevents confusion by ensuring uniform understanding of key variables.", - "location": "Results, Discussion", - "category": "variable_naming", - "focus": "definition" - }, - { - "original_text": "Time units such as days, weeks, and months are used variably with inconsistent formatting.", - "improved_version": "Adopt a uniform format for time units, e.g., always write 'Days 1-7', 'Week 1', 'Month 2', and clarify the time frame when first introduced.", - "explanation": "Reduces ambiguity and improves readability regarding temporal data.", - "location": "Methodology, Results", - "category": "unit_notation", - "focus": "notation" - }, - { - "original_text": "Some abbreviations like 'SD', 'IQR', 'n' are used without initial expansion.", - "improved_version": "Initially define abbreviations such as 'Standard Deviation (SD)', 'Interquartile Range (IQR)', and 'sample size (n)' at their first appearance.", - "explanation": "Ensures clarity for readers unfamiliar with statistical abbreviations.", - "location": "Appendix, Descriptive statistics", - "category": "abbreviations", - "focus": "definition" - }, - { - "original_text": "Terms like 'predictive performance', 'classification', 'model accuracy' are used interchangeably.", - "improved_version": "Use precise terminology: 'predictive performance' for overall assessment, 'classification accuracy' for specific metric, and clearly distinguish between 'model accuracy', 'precision', 'recall', etc., with definitions if necessary.", - "explanation": "Reduces ambiguity by clarifying technical terms and their specific meanings.", - "location": "Discussion, Results", - "category": "technical_terms", - "focus": "term_usage" - }, - { - "original_text": "Terms like 'adherence', 'nonadherence', 'churn', 'dropout' are used interchangeably.", - "improved_version": "Differentiate clearly: 'adherence' refers to following the prescribed use, 'nonadherence' to failing to meet the threshold, 'churn' to complete discontinuation, and 'dropout' as a specific form of churn, with consistent definitions provided early.", - "explanation": "Ensures precise understanding of each concept and their relationships.", - "location": "Introduction, Methods", - "category": "field_terminology", - "focus": "definitions" - }, - { - "original_text": "References to figures and tables (e.g., 'Figure 6.1') are sometimes inconsistent.", - "improved_version": "Ensure all cross-references follow a uniform format, e.g., 'Figure 6.1', 'Table 6.1', and verify that all references are correctly numbered and linked.", - "explanation": "Improves navigation and consistency in referencing visual aids.", - "location": "Throughout the document", - "category": "cross_references", - "focus": "formatting" - }, - { - "original_text": "Definitions of adherence and nonadherence are scattered and sometimes inconsistent.", - "improved_version": "Provide a comprehensive, consistent definition of adherence and nonadherence early in the Methods section, and use these definitions uniformly throughout the manuscript.", - "explanation": "Maintains clarity and consistency in outcome measures and key concepts.", - "location": "Methods, Results, Discussion", - "category": "definition_consistency", - "focus": "definitions" - } - ], - "detailed_feedback": { - "term_usage_consistency": "The manuscript generally uses 'nonadherence' and 'churn' appropriately, but occasionally conflates or contrasts them without clear distinction. Consistent terminology should be maintained, with explicit definitions and consistent application across sections to avoid confusion.", - "notation_consistency": "Statistical metrics such as AUC, accuracy, and F1-score are presented with parentheses or without, and sometimes with SD values. Standardizing formatting\u2014such as always writing 'mean (SD)'\u2014will improve clarity. Also, ensure all units are explicitly stated and consistently formatted.", - "acronym_usage": "Many acronyms are introduced without initial expansion, which can hinder understanding for readers unfamiliar with the terms. All acronyms should be spelled out at first use, with the abbreviation in parentheses, and used consistently thereafter.", - "variable_naming_consistency": "Variables like 'nonadherent users' and 'churned users' are sometimes defined with thresholds in one section but not in others. Establish clear, uniform definitions early and apply them consistently throughout to avoid ambiguity.", - "unit_notation_consistency": "Time-related data are expressed as 'Days 1-7', 'Week 1', 'Month 2' with varying formats. Adopting a uniform notation and explicitly defining the time frames at first mention will enhance clarity.", - "abbreviation_consistency": "Abbreviations such as 'SD', 'IQR', 'n' are used without initial definitions or inconsistent formatting. All abbreviations should be defined upon first mention and used uniformly.", - "technical_term_consistency": "Terms like 'predictive performance' and 'classification' are used broadly without precise definitions. Clarify these terms when first introduced and maintain consistent usage to improve technical clarity.", - "field_terminology": "Key concepts like 'adherence', 'nonadherence', 'churn', and 'dropout' are sometimes used interchangeably or without clear distinctions. Explicit, consistent definitions aligned with the literature will strengthen conceptual clarity.", - "cross_reference_consistency": "References to figures, tables, and appendices should follow a uniform format and be checked for correctness. Proper cross-referencing improves navigation and reduces confusion.", - "definition_consistency": "Definitions of core concepts such as 'adherence' and 'nonadherence' are scattered and sometimes inconsistent. Providing comprehensive, early, and uniform definitions will improve overall clarity and understanding." - }, - "summary": "Overall, the manuscript demonstrates good use of terminology but would benefit from systematic standardization across all sections. Clarifying definitions, maintaining consistent notation, and explicitly defining acronyms and technical terms will significantly enhance clarity, coherence, and the scientific rigor of the presentation." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/W5_results.json b/Backup/V5_multi_agent2/results/W5_results.json deleted file mode 100644 index 352a224..0000000 --- a/Backup/V5_multi_agent2/results/W5_results.json +++ /dev/null @@ -1,194 +0,0 @@ -{ - "inclusive_language_score": 4, - "critical_remarks": [ - { - "category": "gender_neutrality", - "location": "Abstract", - "issue": "The abstract primarily uses gendered pronouns like 'users' without gender-specific language, but the presentation is neutral; however, the gender distribution is reported with '68.3% female' and '31.6% male,' which could be more inclusive by emphasizing diversity beyond binary categories.", - "severity": "low", - "impact": "Limited inclusivity in gender reporting; could overlook non-binary identities." - }, - { - "category": "cultural_sensitivity", - "location": "Introduction", - "issue": "The description of health systems and interventions is Germany-centric, with limited acknowledgment of diverse global contexts, which may reduce cultural sensitivity and inclusivity for international audiences.", - "severity": "medium", - "impact": "Restricts the perceived applicability of findings across different cultural and healthcare settings." - }, - { - "category": "age_terminology", - "location": "Participant descriptions (Vivira and Manoa datasets)", - "issue": "Age categories are described with ranges like '18\u201335 years' and 'over 75 years,' which are appropriate, but the language could be more inclusive by explicitly stating that age is a variable and avoiding implying age-related stereotypes.", - "severity": "low", - "impact": "Minimal, but more explicit acknowledgment of age diversity would improve inclusivity." - }, - { - "category": "disability_inclusion", - "location": "Methodology", - "issue": "The language assumes all users are able-bodied and does not explicitly consider users with disabilities or impairments, which could marginalize those with disabilities.", - "severity": "high", - "impact": "Excludes or unintentionally marginalizes users with disabilities, reducing the inclusivity of the research." - }, - { - "category": "socioeconomic_sensitivity", - "location": "Introduction and methodology", - "issue": "The socioeconomic background of users is not discussed; the focus on smartphone access and healthcare systems may overlook socioeconomic barriers faced by marginalized populations.", - "severity": "medium", - "impact": "Limits understanding of how socioeconomic factors influence adherence and engagement, reducing overall inclusivity." - }, - { - "category": "geographic_inclusivity", - "location": "Introduction and datasets description", - "issue": "The study is Germany-centric, with references to German healthcare systems and regulations, which may limit applicability and inclusivity for populations in other geographic regions.", - "severity": "medium", - "impact": "May reduce relevance for international or non-European populations, limiting global inclusivity." - }, - { - "category": "professional_titles", - "location": "Methodology and author contributions", - "issue": "The text uses titles like 'Jakob R,' 'Benning L,' etc., but does not specify professional titles or roles, which could be more respectful and precise.", - "severity": "low", - "impact": "Minor; clearer professional titles could enhance clarity and respect." - }, - { - "category": "stereotype_avoidance", - "location": "Introduction and discussion", - "issue": "The language does not reinforce stereotypes; however, the focus on age and gender distributions without contextualizing diversity may inadvertently reinforce stereotypes about certain groups' engagement levels.", - "severity": "low", - "impact": "Minimal, but explicit acknowledgment of diversity would prevent stereotypes." - }, - { - "category": "identity_language", - "location": "Participant descriptions", - "issue": "Participants are described with binary gender categories ('female,' 'male') and a small 'non-binary' label, but the language could be more inclusive by emphasizing that gender identity is diverse and fluid.", - "severity": "medium", - "impact": "Reduces recognition of gender diversity beyond binary categories." - }, - { - "category": "historical_context", - "location": "Introduction and discussion", - "issue": "The discussion of digital health systems is historical and Germany-specific, with limited acknowledgment of global developments or the evolving nature of digital health, which could be more inclusive of diverse historical contexts.", - "severity": "low", - "impact": "Limits the understanding of global and historical diversity in digital health evolution." - } - ], - "improvement_suggestions": [ - { - "original_text": "68.3% were female, 31.6% were male, and 0.1% non-binary.", - "improved_version": "68.3% identified as women, 31.6% as men, and 0.1% as non-binary or other gender identities.", - "explanation": "Using 'identified as' and inclusive categories acknowledges gender diversity beyond binary labels, promoting greater inclusivity.", - "location": "Participant descriptions (Vivira dataset)", - "category": "gender_neutrality", - "focus": "gender_neutrality" - }, - { - "original_text": "The description of health systems and interventions is Germany-centric.", - "improved_version": "While this study focuses on the German healthcare context, the methodologies and findings may be adaptable to diverse international healthcare systems, emphasizing the importance of cultural and systemic differences.", - "explanation": "Acknowledging the context's specificity while highlighting potential for broader application enhances cultural sensitivity and inclusivity.", - "location": "Introduction", - "category": "cultural_sensitivity", - "focus": "cultural_sensitivity" - }, - { - "original_text": "Participants are described with age ranges such as 18\u201335 years.", - "improved_version": "Participants' ages are categorized into ranges such as 18\u201335 years, with an emphasis that age is a variable that encompasses diverse life stages and experiences, avoiding stereotypes about engagement or health behaviors based on age.", - "explanation": "Explicitly framing age as a broad, inclusive variable helps prevent age-related stereotypes and promotes age diversity acknowledgment.", - "location": "Participant descriptions", - "category": "age_terminology", - "focus": "age_terminology" - }, - { - "original_text": "The language assumes all users are able-bodied.", - "improved_version": "Future research should consider including users with disabilities or impairments, ensuring accessibility features are evaluated and that the language and design are inclusive of diverse physical abilities.", - "explanation": "Explicitly recognizing users with disabilities promotes inclusivity and encourages accessible design and research practices.", - "location": "Methodology", - "category": "disability_inclusion", - "focus": "disability_inclusion" - }, - { - "original_text": "The socioeconomic background of users is not discussed.", - "improved_version": "Future studies should explore socioeconomic factors influencing engagement, such as access to smartphones, internet connectivity, and health literacy, to better understand barriers faced by marginalized populations.", - "explanation": "Addressing socioeconomic barriers broadens the inclusivity of the research and highlights disparities that may affect adherence.", - "location": "Introduction and methodology", - "category": "socioeconomic_sensitivity", - "focus": "socioeconomic_sensitivity" - }, - { - "original_text": "The study is Germany-centric, with references to German healthcare systems.", - "improved_version": "While this study is based on the German healthcare context, the methodologies and insights are intended to inform international applications, with adaptations made for different healthcare systems and cultural settings.", - "explanation": "Explicitly stating potential for international relevance promotes geographic inclusivity and acknowledges diversity in healthcare contexts.", - "location": "Introduction", - "category": "geographic_inclusivity", - "focus": "geographic_inclusivity" - }, - { - "original_text": "Author contributions list names without titles.", - "improved_version": "Author contributions should include professional titles or roles (e.g., researcher, clinician, data scientist) to clarify expertise and foster respect.", - "explanation": "Using professional titles clarifies roles and promotes respectful acknowledgment of diverse expertise.", - "location": "Author contributions", - "category": "professional_titles", - "focus": "professional_titles" - }, - { - "original_text": "Participants are described with binary gender categories.", - "improved_version": "Participants' gender identities are acknowledged as diverse, with options beyond binary categories, such as 'woman,' 'man,' 'non-binary,' 'prefer to self-describe,' or 'prefer not to say.'", - "explanation": "This approach respects gender diversity and reduces binary stereotypes, fostering inclusivity.", - "location": "Participant descriptions", - "category": "stereotypes", - "focus": "stereotypes" - }, - { - "original_text": "Participants are described as 'users' without explicit mention of their gender identity.", - "improved_version": "Participants are described as 'individuals' or 'people' to emphasize personhood over labels, and gender identity is acknowledged as diverse and fluid where relevant.", - "explanation": "Using person-centered language avoids stereotypes and emphasizes individual diversity.", - "location": "Participant descriptions", - "category": "identity_language", - "focus": "identity_language" - }, - { - "original_text": "Descriptions of health systems focus on Germany-specific policies.", - "improved_version": "The discussion of health systems includes a brief overview of global developments in digital health, recognizing the evolving and diverse nature of digital therapeutics worldwide.", - "explanation": "This broadens the historical and contextual perspective, making the content more globally inclusive.", - "location": "Introduction and discussion", - "category": "historical_context", - "focus": "historical_context" - }, - { - "original_text": "The language in the discussion emphasizes technological and healthcare system specifics without addressing cultural diversity.", - "improved_version": "Future research should consider cultural differences in health behaviors, technology acceptance, and healthcare access, ensuring that models and interventions are adaptable across diverse populations.", - "explanation": "Acknowledging cultural diversity enhances the cultural sensitivity and global applicability of the research.", - "location": "Discussion", - "category": "cultural_sensitivity", - "focus": "cultural_sensitivity" - }, - { - "original_text": "The participant demographics are primarily binary gender categories with minimal diversity acknowledgment.", - "improved_version": "Participant demographics should include a broader spectrum of gender identities, cultural backgrounds, and socioeconomic statuses, with explicit efforts to recruit diverse populations where possible.", - "explanation": "This promotes inclusivity and reduces marginalization of underrepresented groups.", - "location": "Participant descriptions", - "category": "gender_neutrality", - "focus": "gender_neutrality" - }, - { - "original_text": "The language assumes all participants are able to engage with mobile technology equally.", - "improved_version": "Researchers should recognize and address barriers faced by users with disabilities or limited access to technology, designing interventions that are accessible and inclusive for all users.", - "explanation": "This promotes disability inclusion and broadens the reach of interventions.", - "location": "Methodology", - "category": "disability_inclusion", - "focus": "disability_inclusion" - } - ], - "detailed_feedback": { - "gender_neutral_language": "The current text often reports gender data in binary terms, which can unintentionally reinforce gender stereotypes or exclude non-binary identities. Using inclusive language such as 'individuals identifying as women, men, non-binary, or other gender identities' respects gender diversity and promotes inclusivity. Additionally, emphasizing that gender is a spectrum and that data collection methods can be expanded to include more options enhances the research's sensitivity.", - "cultural_sensitivity": "The focus on German healthcare systems and regulations limits the cultural scope of the research. To improve cultural sensitivity, the manuscript should acknowledge that health behaviors, system structures, and acceptance of digital health vary globally. Including a discussion on how findings could be adapted or interpreted in different cultural contexts fosters inclusivity of diverse populations and international applicability.", - "age_appropriate_terminology": "The age ranges used are appropriate; however, the language could be more inclusive by explicitly stating that age is a variable representing diverse life stages and experiences. Avoiding implicit stereotypes about engagement or health behaviors based on age helps foster a more inclusive perspective. For example, recognizing that older adults may have different needs and capacities encourages broader participation.", - "disability_inclusive_language": "The current description assumes all users are able-bodied and does not explicitly consider users with disabilities. Incorporating language that emphasizes accessibility and invites inclusion of users with impairments\u2014such as 'designed to be accessible for users with diverse physical abilities'\u2014would promote disability inclusion and ensure the research considers a broader user base.", - "socioeconomic_sensitivity": "The manuscript does not address socioeconomic barriers, such as access to smartphones, internet, or digital literacy, which can significantly impact engagement. Including a discussion on how socioeconomic factors influence adherence and how interventions can be tailored or made accessible to marginalized groups enhances socioeconomic sensitivity and equity.", - "geographic_inclusivity": "The study's focus on Germany limits its geographic inclusivity. To broaden this, the authors should acknowledge that healthcare systems, cultural attitudes, and technology acceptance differ worldwide. Suggesting that future research validate models in diverse settings promotes a more inclusive and globally relevant approach.", - "professional_titles": "Author contributions list names without titles or roles. Including professional titles (e.g., 'Dr.', 'Professor', 'Data Scientist') clarifies expertise and fosters respect for diverse roles, enhancing transparency and inclusivity in acknowledgment.", - "stereotype_avoidance": "While the language does not overtly reinforce stereotypes, the demographic reporting could inadvertently imply stereotypes about engagement levels based on age or gender. Explicitly emphasizing diversity and avoiding assumptions about behavior based on demographic categories helps prevent stereotypes.", - "identity_language": "Participants are described with binary gender labels and a small 'non-binary' mention. Using inclusive language such as 'participants' with self-identified gender labels or open options like 'prefer to self-describe' respects gender fluidity and diversity, promoting a more inclusive approach.", - "historical_context": "The focus on German digital health policies and systems is specific and may overlook the broader evolution of digital therapeutics globally. Including a brief overview of international developments and acknowledging the dynamic, evolving nature of digital health ensures sensitivity to different historical and cultural contexts, making the research more inclusive." - }, - "summary": "Overall, the manuscript demonstrates a generally neutral and objective tone but could significantly benefit from more explicit inclusive language and broader cultural, gender, and disability considerations. Addressing these areas will enhance the research's accessibility, relevance, and respect for diverse populations, aligning with best practices in inclusive and unbiased scientific communication. Implementing specific suggestions across sections will improve the manuscript's sensitivity and global applicability, fostering a more equitable scientific discourse." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/W6_results.json b/Backup/V5_multi_agent2/results/W6_results.json deleted file mode 100644 index c63782d..0000000 --- a/Backup/V5_multi_agent2/results/W6_results.json +++ /dev/null @@ -1,242 +0,0 @@ -{ - "citation_formatting_score": 4, - "critical_remarks": [ - { - "category": "in_text_format", - "location": "Introduction paragraph, lines 16-44", - "issue": "In-text citations are inconsistently formatted; some are bracketed with brackets and numbers, others are embedded with author names and years, and some lack proper punctuation or formatting.", - "severity": "high", - "impact": "Reduces readability and undermines the scholarly credibility of the manuscript, making it difficult to verify sources and follow citation conventions." - }, - { - "category": "reference_format", - "location": "Bibliography section", - "issue": "References are inconsistently formatted: some entries include DOIs, URLs, and journal details properly, others omit critical information like volume, issue, or page numbers, and some have inconsistent punctuation and line breaks.", - "severity": "high", - "impact": "Impairs the professional appearance and hampers accurate indexing, retrieval, and verification of sources." - }, - { - "category": "style_consistency", - "location": "Throughout the references list", - "issue": "Multiple citation styles are used interchangeably, including numbered brackets, author-year formats, and varying punctuation styles, leading to a lack of uniformity.", - "severity": "high", - "impact": "Creates confusion and diminishes the manuscript's scholarly rigor, potentially violating journal submission guidelines." - }, - { - "category": "reference_completeness", - "location": "Multiple references, e.g., [1], [2], [3]", - "issue": "Several references lack complete bibliographic details, such as missing volume, issue, page numbers, or publication year, especially for online-only sources.", - "severity": "high", - "impact": "Prevents proper citation tracking and reduces the credibility of the references." - }, - { - "category": "doi_format", - "location": "References, e.g., [1], [2], [3]", - "issue": "DOIs are inconsistently formatted; some are complete URLs (https://doi.org/...), others are just numeric, and some are missing entirely.", - "severity": "medium", - "impact": "Hinders direct access to sources and violates standard DOI formatting guidelines." - }, - { - "category": "author_name_formatting", - "location": "References, e.g., [1], [2], [3]", - "issue": "Author names are inconsistently formatted; some use full names, others initials, and some have inconsistent ordering or punctuation.", - "severity": "medium", - "impact": "Reduces clarity and professionalism, complicates author attribution, and affects citation indexing." - }, - { - "category": "publication_date_formatting", - "location": "References, e.g., [1], [2], [3]", - "issue": "Publication dates are inconsistently formatted; some include month and year, others only year, and some have extraneous information.", - "severity": "medium", - "impact": "Impairs chronological clarity and consistency across references." - }, - { - "category": "journal_format", - "location": "References, e.g., [1], [2], [3]", - "issue": "Journal names are variably formatted; some are italicized, others are plain text, and some abbreviations are inconsistent or missing.", - "severity": "medium", - "impact": "Affects professional presentation and adherence to journal style guides." - }, - { - "category": "volume_format", - "location": "References, e.g., [1], [2], [3]", - "issue": "Volume, issue, and page numbers are inconsistently formatted; some include parentheses, others omit issue numbers, and page ranges are sometimes missing or improperly formatted.", - "severity": "medium", - "impact": "Reduces clarity and hampers accurate source identification." - }, - { - "category": "cross_reference", - "location": "Throughout the manuscript", - "issue": "In-text citations do not consistently match reference list entries; some citations are missing, and numbering or author-year references are mismatched or inconsistent.", - "severity": "high", - "impact": "Undermines the integrity of scholarly referencing and impairs verification of sources." - } - ], - "improvement_suggestions": [ - { - "original_text": "In: (2025) (In preparation)", - "improved_version": "In: Jakob R, Benning L, S\u00e9vin CM, Von Wangenheim F, Fleisch E, Kowatsch T. (2025). Predicting Nonadherence to Mobile Health Interventions. (In preparation).", - "explanation": "Standardizes the in-text citation format for a forthcoming publication, aligning with common scholarly conventions.", - "location": "Reference entry for the planned publication", - "category": "references", - "focus": "reference_format" - }, - { - "original_text": "[16, 26, 44, 64]", - "improved_version": "(Author et al., 2016; Author et al., 2020; Author et al., 2022; Author et al., 2015)", - "explanation": "Converts numeric citations to author-year format for clarity and consistency, especially in-text, aligning with common styles like APA or Vancouver with author-year.", - "location": "Introduction paragraph, lines 16-44", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[1]", - "improved_version": "(Alessa et al., 2018)", - "explanation": "Provides author name and year for clarity, making it easier for readers to identify sources without cross-referencing numbers.", - "location": "Introduction, paragraph 2, lines 16-44", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[54, 55]", - "improved_version": "Bl\u00fcmel et al., 2020; Teepe et al., 2022", - "explanation": "Replaces numeric references with author-year citations for consistency and readability.", - "location": "Introduction, paragraph 2, lines 16-44", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "https://doi.org/10.2196/10723", - "improved_version": "https://doi.org/10.2196/10723", - "explanation": "Ensure all DOIs are formatted as complete URLs starting with https://doi.org/ for uniformity and easy access.", - "location": "References, e.g., [1]", - "category": "doi_format", - "focus": "doi_format" - }, - { - "original_text": "[2]", - "improved_version": "Ganju et al., 2020", - "explanation": "Replace numeric citation with author-year format for clarity and consistency in the references list.", - "location": "Throughout the manuscript, e.g., in-text citations", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[3]", - "improved_version": "Beger et al., 2023", - "explanation": "Standardizes citation style to author-year for consistency and easier source identification.", - "location": "Throughout the manuscript", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[4]", - "improved_version": "Bl\u00fcmel et al., 2020", - "explanation": "Provides full author names and year, aligning with common citation styles and improving clarity.", - "location": "Introduction, paragraph 2", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[5]", - "improved_version": "Bricker et al., 2023", - "explanation": "Ensures consistent author-year citation style for all references, enhancing uniformity.", - "location": "Results section, paragraph 3", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[6]", - "improved_version": "Donkin et al., 2011", - "explanation": "Aligns with the author-year citation style, facilitating easier cross-referencing and readability.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[7]", - "improved_version": "Drachen et al., 2016", - "explanation": "Standardizes citation style, improving consistency across the manuscript.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[8]", - "improved_version": "Ernsting et al., 2017", - "explanation": "Provides clarity and uniformity in referencing, aiding reader comprehension.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[9]", - "improved_version": "Eysenbach, 2005", - "explanation": "Ensures consistent author-year citation style, improving scholarly presentation.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[10]", - "improved_version": "Firth et al., 2017", - "explanation": "Aligns with the author-year citation style, aiding clarity and uniformity.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[11]", - "improved_version": "Fleming et al., 2018", - "explanation": "Provides consistent citation style, enhancing professional appearance.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[12]", - "improved_version": "Fowler et al., 2016", - "explanation": "Standardizes citation style for clarity and consistency.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[13]", - "improved_version": "F\u00fcrstenau et al., 2023", - "explanation": "Ensures uniform author-year citation style, improving readability.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[14]", - "improved_version": "Ganju et al., 2020", - "explanation": "Provides clarity and consistency in citations, aiding verification.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[15]", - "improved_version": "Geiler et al., 2022", - "explanation": "Standardizes citation style for uniformity and professionalism.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - } - ], - "detailed_feedback": { - "in_text_citation_format": "The manuscript employs a mixture of numeric bracketed citations and author-year formats, leading to inconsistency. For clarity and adherence to common academic standards, all in-text citations should follow a uniform style, preferably author-year, especially in the main text. This enhances readability and allows easier cross-referencing with the reference list.", - "reference_list_format": "The reference list exhibits inconsistent formatting: some entries include full journal titles, volume, issue, page numbers, and DOIs, while others omit these details. Standardizing the format according to a recognized style guide (e.g., APA, Vancouver) and ensuring all references contain complete bibliographic information will improve professionalism and facilitate source verification.", - "citation_style_consistency": "Currently, the manuscript switches between numbered citations and author-year formats, which can confuse readers and violate journal guidelines. Adopting a single, consistent citation style throughout the document is essential for scholarly rigor.", - "reference_completeness": "Many references lack complete details such as volume, issue, page ranges, or publication dates. Ensuring each reference includes all necessary bibliographic elements will improve accuracy and traceability.", - "doi_url_formatting": "DOIs are inconsistently formatted; some are presented as URLs (https://doi.org/...), others as plain numbers or missing entirely. All DOIs should be formatted as full URLs to enable direct access, following current standards.", - "author_name_formatting": "Author names are inconsistently formatted, with some entries using full names, others initials, and varying punctuation. Standardizing author name formats (e.g., Lastname, First Initials) across all references will enhance clarity.", - "publication_date_formatting": "Publication dates are variably formatted, with some including month and year, others only year. Consistent date formatting (e.g., Year, Month) across all references will improve chronological clarity.", - "journal_name_formatting": "Journal names are inconsistently italicized or abbreviated; some are fully spelled out, others abbreviated. Consistent formatting\u2014either full journal names italicized or standardized abbreviations\u2014will improve uniformity.", - "volume_issue_page_formatting": "Volume, issue, and page numbers are inconsistently formatted; some entries include parentheses, others omit issue numbers or page ranges. Uniform formatting following a style guide (e.g., Volume(Issue), pages) is recommended.", - "cross_reference_accuracy": "In-text citations do not always match reference list entries, with some missing references or mismatched numbering. Cross-checking all citations to ensure they correctly correspond to the reference list is crucial for scholarly integrity." - }, - "summary": "Overall, the manuscript demonstrates a significant inconsistency in citation formatting and style. Addressing these issues by standardizing in-text citation formats, completing reference details, uniformly formatting journal titles, DOIs, author names, and publication dates, and ensuring accurate cross-referencing will markedly improve the scholarly quality, professionalism, and readability of the document. Implementing a consistent citation style aligned with journal guidelines is strongly recommended." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/W7_results.json b/Backup/V5_multi_agent2/results/W7_results.json deleted file mode 100644 index a50cd68..0000000 --- a/Backup/V5_multi_agent2/results/W7_results.json +++ /dev/null @@ -1,193 +0,0 @@ -{ - "audience_alignment_score": 8, - "critical_remarks": [ - { - "category": "methodology", - "location": "Section 2.2", - "issue": "While the methodology is detailed, some technical descriptions, such as hyperparameter tuning and feature preprocessing, assume prior knowledge and lack explicit explanations for non-expert readers.", - "severity": "medium", - "impact": "Could hinder understanding for readers unfamiliar with machine learning procedures, limiting accessibility to a broader academic audience." - }, - { - "category": "visuals", - "location": "Throughout the Results section", - "issue": "The figures and tables are referenced with minimal descriptive context, and the visual complexity may overwhelm readers without accompanying interpretative summaries.", - "severity": "medium", - "impact": "May reduce clarity of key findings, affecting audience comprehension and engagement." - }, - { - "category": "references", - "location": "Bibliography", - "issue": "While extensive, the references predominantly cite technical and methodological papers; there is limited integration of recent clinical or behavioral science literature that contextualizes the findings.", - "severity": "low", - "impact": "Slightly limits the interdisciplinary appeal and depth of contextual understanding for clinicians or behavioral scientists." - }, - { - "category": "results", - "location": "Section 3.2", - "issue": "Results are densely presented with numerous metrics and statistical details, which may obscure the overarching narrative of model performance.", - "severity": "high", - "impact": "Could challenge readers' ability to grasp the main implications, reducing overall engagement." - }, - { - "category": "discussion", - "location": "Section 4", - "issue": "While the discussion covers technical performance, it lacks sufficient exploration of practical implications, limitations, or potential real-world applications beyond technical metrics.", - "severity": "medium", - "impact": "May limit the relevance for practitioners seeking actionable insights, affecting broader applicability." - }, - { - "category": "conclusion", - "location": "Section 4.4", - "issue": "The conclusion summarizes findings but does not clearly articulate future directions or specific recommendations for implementation.", - "severity": "low", - "impact": "Reduces the perceived utility of the research for advancing practice or policy." - }, - { - "category": "terminology", - "location": "Throughout the document", - "issue": "Use of technical terms like 'AUC', 'F1-score', 'stratified 10-fold cross-validation' assumes familiarity; some terms could benefit from brief definitions or explanations.", - "severity": "medium", - "impact": "May alienate or confuse readers outside the machine learning or digital health research communities." - }, - { - "category": "writing style", - "location": "Abstract and sections", - "issue": "The writing is formal and technical but occasionally verbose, which could hinder readability for a broader academic audience.", - "severity": "low", - "impact": "Potentially reduces accessibility and engagement for interdisciplinary readers." - }, - { - "category": "section organization", - "location": "Entire document", - "issue": "While sections are logically ordered, some subsections (e.g., detailed tables and appendices) could be better integrated with main text to improve flow and contextual understanding.", - "severity": "medium", - "impact": "May cause readers to overlook key insights or struggle to connect detailed data with overarching themes." - }, - { - "category": "visual elements", - "location": "Figures 1-8 and tables", - "issue": "Visuals are data-rich but lack sufficient interpretative captions or summaries that highlight key takeaways.", - "severity": "medium", - "impact": "Could diminish the clarity and immediate understanding of complex data, affecting engagement." - }, - { - "category": "references", - "location": "Bibliography", - "issue": "The referencing style is consistent but predominantly includes journal articles; inclusion of more gray literature or recent reviews could enhance comprehensiveness.", - "severity": "low", - "impact": "Minimal impact but could improve depth for readers seeking broader context." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "Given the extensive behavioral data collected by mHealth interventions, it is pertinent to investigate the extent to which nonadherence can be accurately predicted using these data.", - "explanation": "Clarifies the research question with more precise language, enhancing clarity and focus for the audience.", - "location": "Abstract", - "category": "organization", - "focus": "clarity" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.", - "improved_version": "In Vivira, our models successfully identified approximately 94% of nonadherent users\u2014those completing fewer than eight exercises weekly\u2014between Weeks 2 and 13, with an average AUC of 0.95.", - "explanation": "Rephrases for clarity, explicitly linking the metric to the adherence definition, aiding comprehension.", - "location": "Abstract", - "category": "results", - "focus": "clarity" - }, - { - "original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "improved_version": "Due to regulatory restrictions, only users who explicitly consented under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included in the analysis.", - "explanation": "Simplifies and clarifies regulatory context, making it more accessible to readers unfamiliar with legal details.", - "location": "Section 2.1.1", - "category": "terminology", - "focus": "clarity" - }, - { - "original_text": "Figures and tables are referenced with minimal descriptive context, which may overwhelm readers without accompanying interpretative summaries.", - "improved_version": "Each figure and table should be accompanied by a concise interpretative caption that highlights key findings, such as trends or significant differences, to guide reader understanding.", - "explanation": "Enhances visual comprehension by providing context, improving engagement and clarity.", - "location": "Throughout Results", - "category": "visuals", - "focus": "organization" - }, - { - "original_text": "Results are densely presented with numerous metrics and statistical details, which may obscure the overarching narrative of model performance.", - "improved_version": "Summarize key performance metrics in a narrative that emphasizes overall trends and implications, reserving detailed statistics for supplementary tables or appendices.", - "explanation": "Improves readability by focusing on main messages, making complex data more digestible.", - "location": "Section 3.2", - "category": "results", - "focus": "organization" - }, - { - "original_text": "While the discussion covers technical performance, it lacks sufficient exploration of practical implications, limitations, or potential real-world applications beyond technical metrics.", - "improved_version": "Expand the discussion to include practical implications, potential implementation strategies, and limitations, thereby bridging the gap between technical performance and real-world applicability.", - "explanation": "Enhances relevance for practitioners and policymakers, increasing audience engagement.", - "location": "Section 4", - "category": "discussion", - "focus": "application" - }, - { - "original_text": "The conclusion summarizes findings but does not clearly articulate future directions or specific recommendations for implementation.", - "improved_version": "Conclude with specific recommendations for future research, potential clinical integration, and policy considerations to maximize the impact of these predictive models.", - "explanation": "Provides actionable guidance, increasing the utility of the research for stakeholders.", - "location": "Section 4.4", - "category": "conclusion", - "focus": "organization" - }, - { - "original_text": "Use of technical terms like 'AUC', 'F1-score', 'stratified 10-fold cross-validation' assumes prior knowledge; some terms could benefit from brief definitions or explanations.", - "improved_version": "Include brief explanations of key technical terms (e.g., 'AUC' as the area under the receiver operating characteristic curve) when first introduced to enhance accessibility.", - "explanation": "Makes the content more approachable for interdisciplinary audiences, broadening readership.", - "location": "Throughout technical sections", - "category": "terminology", - "focus": "clarity" - }, - { - "original_text": "The writing is formal and technical but occasionally verbose, which could hinder readability for a broader academic audience.", - "improved_version": "Simplify complex sentences and reduce jargon where possible, aiming for concise, clear language that maintains technical accuracy.", - "explanation": "Improves readability and engagement across diverse academic backgrounds.", - "location": "Abstract and main sections", - "category": "writing style", - "focus": "clarity" - }, - { - "original_text": "While sections are logically ordered, some subsections (e.g., detailed tables and appendices) could be better integrated with main text to improve flow and contextual understanding.", - "improved_version": "Integrate key findings from tables and appendices into the main narrative with explicit references, ensuring smooth flow and contextual clarity.", - "explanation": "Enhances coherence and helps readers connect detailed data with overarching themes.", - "location": "Section organization", - "category": "organization", - "focus": "organization" - }, - { - "original_text": "Visuals are data-rich but lack sufficient interpretative captions or summaries that highlight key takeaways.", - "improved_version": "Add descriptive captions and brief summaries for each figure and table that explicitly state the main insights and their relevance to the research questions.", - "explanation": "Facilitates quicker understanding and emphasizes the significance of visual data.", - "location": "Figures 1-8 and tables", - "category": "visuals", - "focus": "organization" - }, - { - "original_text": "The referencing style is consistent but predominantly includes journal articles; inclusion of more gray literature or recent reviews could enhance comprehensiveness.", - "improved_version": "Incorporate recent review articles, gray literature, and policy documents where relevant to provide a more comprehensive and current literature context.", - "explanation": "Broadens the scope and relevance, appealing to a wider academic and practitioner audience.", - "location": "Bibliography", - "category": "references", - "focus": "depth" - } - ], - "detailed_feedback": { - "technical_depth": "The manuscript demonstrates a high level of technical depth, appropriate for researchers familiar with machine learning and digital health. However, some methodological details, such as hyperparameter tuning and feature preprocessing, could be elaborated to enhance transparency and reproducibility for advanced readers.", - "terminology_usage": "The use of field-specific terms like 'AUC', 'F1-score', and 'stratified 10-fold cross-validation' aligns with standard scientific conventions. Nonetheless, providing brief definitions or context for these terms upon first mention would improve accessibility for interdisciplinary audiences or newcomers to ML methods.", - "writing_formality": "The writing maintains a formal, academic tone suitable for scholarly publication. To improve readability for a broader audience, consider reducing verbosity and simplifying complex sentences without sacrificing technical accuracy.", - "section_organization": "The overall structure follows a logical progression, with clear separation of Introduction, Methods, Results, and Discussion. However, integrating key findings from detailed tables into the main narrative and ensuring smooth transitions between sections would enhance flow and comprehension.", - "visual_integration": "Figures and tables are comprehensive but could benefit from more interpretative captions that highlight main insights. Embedding summaries or key takeaways alongside visuals would aid reader understanding and engagement.", - "reference_style": "References are consistently formatted and relevant, but expanding to include recent reviews and gray literature would provide a more comprehensive background and contextual foundation.", - "methodology_detail": "The methodology is described with sufficient technical detail for replication but could include more explanation of specific ML procedures, such as hyperparameter selection rationale and feature engineering steps, to improve transparency.", - "results_presentation": "Results are detailed with numerous metrics, which may overwhelm readers. Summarizing key performance trends and implications in the main text, with detailed statistics in appendices, would improve clarity.", - "discussion_depth": "The discussion addresses technical performance well but could be expanded to explore practical implications, limitations, and potential real-world applications, making the findings more relevant for practitioners.", - "conclusion_format": "The conclusion summarizes main findings but would benefit from explicit future directions, recommendations for implementation, and broader impact statements to enhance utility and engagement." - }, - "summary": "Overall, the manuscript exhibits strong technical rigor and is well-suited for an academic audience familiar with digital health and machine learning. To broaden its accessibility and impact, targeted improvements in clarity, visual integration, and contextual explanations are recommended. These enhancements would facilitate better engagement across interdisciplinary audiences, including clinicians, behavioral scientists, and policymakers, thereby maximizing the research's translational potential." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/W8_results.json b/Backup/V5_multi_agent2/results/W8_results.json deleted file mode 100644 index c3a43a9..0000000 --- a/Backup/V5_multi_agent2/results/W8_results.json +++ /dev/null @@ -1,127 +0,0 @@ -{ - "visual_presentation_score": 2, - "critical_remarks": [ - { - "category": "Figures", - "location": "Figures 1, 3, 5, 6, 8", - "issue": "All figures are missing visual content; only placeholder captions and descriptions are provided, with no actual images or diagrams included.", - "severity": "high", - "impact": "Severely limits the reader's ability to interpret data visually, reducing comprehension and engagement. Visuals are crucial for understanding trends, patterns, and results." - }, - { - "category": "Tables", - "location": "Tables 5-11", - "issue": "Tables are densely packed with numerical data but lack clear formatting cues such as alternating row colors, bold headers, or spacing to enhance readability.", - "severity": "medium", - "impact": "Hinders quick scanning and comparison of data, potentially leading to misinterpretation or oversight of key statistics." - }, - { - "category": "Visual element placement", - "location": "Throughout the manuscript", - "issue": "Figures and tables are embedded within dense text blocks without strategic placement or referencing, making navigation cumbersome.", - "severity": "medium", - "impact": "Reduces flow and readability, forcing readers to search for visual aids and disrupting comprehension." - }, - { - "category": "Caption completeness", - "location": "All figures and tables", - "issue": "Captions are lengthy but often lack concise summaries of key insights or explanations of axes, units, or significance.", - "severity": "low", - "impact": "Limits the utility of visuals for quick understanding; captions should clarify what the visual demonstrates." - }, - { - "category": "Color scheme", - "location": "Figures 6-8", - "issue": "Color schemes are not specified, and given the absence of actual visuals, it's unclear if contrast and accessibility considerations (e.g., for color-blind readers) are addressed.", - "severity": "low", - "impact": "Potential accessibility issues if color is used without contrast considerations; proper color choices enhance clarity." - }, - { - "category": "Data visualization effectiveness", - "location": "Figures 6-8", - "issue": "Figures intended to show performance metrics are placeholders with no actual plots or charts, rendering them ineffective.", - "severity": "high", - "impact": "Prevents visual comparison of model performance over time, which is critical for understanding trends and results." - }, - { - "category": "Visual hierarchy", - "location": "Throughout the manuscript", - "issue": "Lack of visual hierarchy; no use of font size, bolding, or spacing to differentiate headings, subheadings, and key data points.", - "severity": "medium", - "impact": "Makes navigation and comprehension more difficult, especially when scanning for key results." - }, - { - "category": "Accessibility considerations", - "location": "Figures and tables", - "issue": "No evidence of accessibility features such as alt text, high contrast, or font size considerations.", - "severity": "low", - "impact": "Limits accessibility for readers with visual impairments or color vision deficiencies." - }, - { - "category": "Consistency in visual style", - "location": "Throughout the manuscript", - "issue": "No consistent style for tables and figure captions; formatting varies, and visual elements are absent.", - "severity": "low", - "impact": "Reduces professional appearance and coherence of the manuscript." - }, - { - "category": "Integration with text", - "location": "Throughout the manuscript", - "issue": "Visual elements are missing or poorly integrated; references to figures/tables are frequent but visuals are absent, making it hard to connect data with narrative.", - "severity": "high", - "impact": "Hinders understanding of key points and weakens the support visuals could provide." - } - ], - "improvement_suggestions": [ - { - "original_text": "Figure 1: Percentages of daily active users and cumulative percentage of users\u2019 last login across 90-Day program duration in Vivira (n = 8,372).", - "improved_version": "Figure 1: Line chart illustrating daily active user retention and cumulative last login percentage over the 90-day Vivira program. Include clear axes labels, legend, and high-resolution image.", - "explanation": "Visuals should clearly depict trends with labels and legends for quick interpretation, enhancing comprehension.", - "location": "Figures 1, 3, 5, 6, 8", - "category": "Figures", - "focus": "Clarity and effectiveness" - }, - { - "original_text": "Tables 5-11: Descriptive statistics of users\u2019 number of active days and completed exercises per week in Vivira and Manoa.", - "improved_version": "Tables 5-11: Reformat tables with alternating row shading, bold headers, and consistent decimal places. Add summary notes highlighting key statistics at the top of each table.", - "explanation": "Improved formatting enhances readability, allows quick comparison, and emphasizes important data points.", - "location": "Tables 5-11", - "category": "Tables", - "focus": "Readability and clarity" - }, - { - "original_text": "Throughout the manuscript", - "improved_version": "Embed visual elements close to relevant text sections, with clear references and consistent style. Use subheadings and spacing to delineate sections clearly.", - "explanation": "Strategic placement and consistent style improve flow and help readers connect visuals with narrative.", - "location": "Entire manuscript", - "category": "Visual placement", - "focus": "Flow and integration" - }, - { - "original_text": "Captions are lengthy but lack concise summaries.", - "improved_version": "Revise captions to be concise yet informative, e.g., 'Figure 1: Retention and last login trends over 90 days in Vivira.' Include key insights or data points if space allows.", - "explanation": "Clear, concise captions facilitate quick understanding and contextualize visuals effectively.", - "location": "Figures and tables", - "category": "Caption completeness", - "focus": "Clarity and utility" - }, - { - "original_text": "Color schemes are unspecified.", - "improved_version": "Apply high-contrast color schemes suitable for color-blind readers, such as color palettes from ColorBrewer. Use distinct colors for different data series and ensure all are distinguishable in grayscale.", - "explanation": "Enhances accessibility and ensures visuals are interpretable by all readers." - } - ], - "detailed_feedback": { - "figure_quality": "Figures are absent; placeholder captions do not provide visual content. When included, should be high-resolution, clear, and well-labeled.", - "table_formatting": "Tables are dense with data, lacking visual cues for readability. Formatting improvements needed for clarity.", - "visual_placement": "Visuals are poorly integrated, often missing or placed without clear reference, disrupting flow.", - "caption_completeness": "Captions are lengthy but lack concise summaries and clarity about axes, units, and significance.", - "color_scheme": "Not specified; should use accessible, high-contrast palettes for clarity and accessibility.", - "data_visualization": "Effective data visualization is missing; inclusion of well-designed charts would significantly improve comprehension.", - "visual_hierarchy": "Lacking; visual elements do not differentiate headings, subheadings, or key data points, reducing clarity.", - "accessibility": "No explicit accessibility features; improvements needed for color contrast, font size, and descriptive alt text.", - "visual_consistency": "Inconsistent style and formatting across visuals; standardization would improve professionalism.", - "text_integration": "Visuals are not well integrated with the text; better referencing and placement are necessary for coherence." - }, - "summary": "Overall, the manuscript's visual presentation is severely lacking, with missing figures, poorly formatted tables, and inadequate integration of visuals with the narrative. To enhance clarity, readability, and impact, the authors should incorporate high-quality, well-designed visual elements, improve formatting and placement, and ensure visuals support and complement the textual content effectively." -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/combined_results.json b/Backup/V5_multi_agent2/results/combined_results.json deleted file mode 100644 index 60ce316..0000000 --- a/Backup/V5_multi_agent2/results/combined_results.json +++ /dev/null @@ -1,3778 +0,0 @@ -{ - "S1": { - "title_keywords_score": 7, - "critical_remarks": [ - { - "category": "title_clarity", - "location": "Title", - "issue": "The current title clearly indicates the focus on predicting nonadherence in mobile health interventions but could benefit from more specificity regarding the methodology and scope.", - "severity": "medium", - "impact": "While understandable, the title may not fully convey the innovative aspect of the predictive modeling approach or the extended duration, potentially limiting immediate clarity for targeted audiences." - }, - { - "category": "title_length", - "location": "Title", - "issue": "The title is somewhat lengthy, which may reduce its impact and ease of discoverability in search results.", - "severity": "low", - "impact": "A more concise title could improve readability and search engine optimization without sacrificing essential information." - }, - { - "category": "keywords_relevance", - "location": "Keywords", - "issue": "No keywords section is explicitly present in the manuscript.", - "severity": "high", - "impact": "Lack of keywords hampers discoverability and indexing, reducing the manuscript\u2019s visibility in relevant searches." - }, - { - "category": "keywords_coverage", - "location": "Keywords", - "issue": "No keywords section is found; therefore, coverage cannot be assessed.", - "severity": "high", - "impact": "Absence of keywords limits the manuscript\u2019s reach to interested researchers and practitioners searching for related topics." - }, - { - "category": "guidelines", - "location": "Title", - "issue": "The title adheres to standard conventions in academic publishing, clearly stating the research focus without jargon.", - "severity": "low", - "impact": "Supports proper indexing and comprehension by the target academic audience." - }, - { - "category": "discoverability", - "location": "Title", - "issue": "The current title includes relevant keywords but could be optimized for SEO by incorporating more specific terms related to methodology and outcomes.", - "severity": "medium", - "impact": "Enhanced discoverability in digital searches, especially for keywords like 'machine learning,' 'predicting nonadherence,' and 'extended duration,' could attract a broader audience." - } - ], - "improvement_suggestions": [ - { - "original_text": "Predicting Nonadherence to Mobile Health Interventions", - "improved_version": "Machine Learning-Based Prediction of Nonadherence in Extended Mobile Health Interventions for Chronic Disease Management", - "explanation": "This revised title enhances clarity by specifying the use of machine learning, emphasizes the focus on nonadherence prediction, and highlights the extended duration of interventions. It balances impact and SEO by including relevant keywords such as 'machine learning,' 'prediction,' 'nonadherence,' 'mobile health,' and 'chronic disease management,' aligning with field standards for descriptive and discoverable titles.", - "location": "Title", - "category": "title", - "focus": "comprehensive_improvement" - } - ], - "detailed_feedback": { - "title_analysis": "The current title effectively communicates the core topic of predicting nonadherence in mobile health interventions, maintaining clarity and adherence to academic standards. However, it could be more impactful by including methodological keywords and scope details to improve searchability and audience engagement.", - "keywords_analysis": "No keywords section found", - "guidelines_compliance": "The title follows standard academic conventions, being concise and descriptive without jargon. It clearly states the research focus, aligning with typical scholarly standards for clarity and relevance.", - "discoverability_assessment": "The title contains relevant keywords but lacks optimization for search engines. Incorporating specific terms like 'machine learning,' 'chronic disease,' and 'extended duration' would improve visibility in digital searches and indexing, facilitating easier discovery by interested researchers.", - "audience_alignment": "The title appeals to researchers and practitioners interested in digital health, machine learning, and adherence prediction. Its clarity and specificity align with the audience's expectations for impactful, relevant research in the field." - }, - "summary": "Overall, the manuscript's title is clear and standards-compliant but can be significantly improved for discoverability and impact by including specific methodological and scope-related keywords. The absence of a dedicated keywords section limits searchability, which should be addressed to enhance the manuscript's visibility and reach within the academic community." - }, - "S2": { - "score": 6, - "critical_remarks": [ - { - "category": "structure", - "location": "Abstract", - "issue": "The abstract combines multiple complex results and methodological details without clear section demarcation, making it difficult to follow the logical flow of background, methods, results, and conclusions.", - "severity": "high", - "impact": "Reduces clarity and hampers quick comprehension of the study's purpose, approach, and key findings, which is essential for scientific communication." - }, - { - "category": "content", - "location": "Introduction and Methods", - "issue": "The abstract lacks explicit mention of the specific machine learning algorithms used, the rationale for their selection, and the detailed validation procedures, limiting understanding of methodological rigor.", - "severity": "high", - "impact": "Impairs assessment of technical validity and reproducibility of the predictive models, which are central to the study's contribution." - }, - { - "category": "clarity", - "location": "Results and Discussion", - "issue": "The dense presentation of performance metrics (AUC, accuracy, F1, etc.) across multiple weeks and models is overwhelming and poorly summarized, reducing readability.", - "severity": "medium", - "impact": "Hinders quick grasp of key findings and their implications, especially for readers less familiar with statistical metrics." - }, - { - "category": "standards", - "location": "Introduction and Methods", - "issue": "The abstract and main text do not consistently follow standard scientific reporting conventions, such as clearly separating background, methods, results, and discussion, and providing sufficient methodological detail.", - "severity": "medium", - "impact": "Weakens scientific rigor and reproducibility, which are crucial for peer evaluation and future research." - }, - { - "category": "impact", - "location": "Discussion", - "issue": "While the abstract emphasizes the predictive accuracy, it underrepresents the potential limitations and real-world applicability challenges, such as data sparsity or generalizability to broader populations.", - "severity": "medium", - "impact": "Limits the perceived significance and practical utility of the findings, reducing the manuscript's overall impact." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "This study investigates whether objective behavioral engagement data from mHealth interventions can accurately predict nonadherence and user churn over extended periods.", - "explanation": "Clarifies the research question and emphasizes the focus on predictive capability, improving clarity and framing.", - "location": "Abstract", - "category": "clarity", - "focus": "organization" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.", - "improved_version": "In Vivira, the models achieved an average recall of 94% in identifying nonadherent users (defined as completing fewer than 8 exercises per week) between Weeks 2 and 13, with a mean AUC of 0.95.", - "explanation": "Rephrases for clarity, explicitly linking the metric (recall) with the definition of nonadherence and highlighting model performance succinctly.", - "location": "Abstract", - "category": "clarity", - "focus": "results" - }, - { - "original_text": "The study also analyzed the number of users who reengage after a correct churn prediction, offering insights into the potential for targeted strategies to promote adherence.", - "improved_version": "Additionally, the study examined re-engagement rates following correct churn predictions, providing insights into how predictive models could inform targeted adherence-promoting interventions.", - "explanation": "Improves readability and emphasizes the practical relevance of re-engagement analysis for intervention strategies.", - "location": "Abstract", - "category": "clarity", - "focus": "results" - }, - { - "original_text": "The introduction provides extensive background but could benefit from a clearer statement of the study\u2019s specific aims and hypotheses.", - "improved_version": "The primary aim of this study is to evaluate the predictive accuracy of machine learning models in identifying nonadherence and user churn in two distinct mHealth interventions over extended durations, with the hypothesis that behavioral app engagement features can reliably forecast future nonadherence.", - "explanation": "Adds clarity by explicitly stating the aims and hypotheses, aligning background with research objectives.", - "location": "Introduction", - "category": "clarity", - "focus": "organization" - }, - { - "original_text": "We applied stratified 10-fold cross-validation and randomized search for hyperparameter tuning on the training sets, optimizing for F1 score for all models.", - "improved_version": "Model training involved stratified 10-fold cross-validation combined with randomized hyperparameter search, with the optimization criterion set to maximize the F1 score, ensuring balanced performance across classes.", - "explanation": "Clarifies the methodological approach and rationale, improving technical transparency.", - "location": "Methods", - "category": "methodology", - "focus": "methodology" - }, - { - "original_text": "The performance metrics showed a gradual improvement over time, but early predictions (Days 1-3) had limited utility.", - "improved_version": "Performance metrics indicated progressive improvement over the observation period, although predictions within the first three days showed limited accuracy, likely due to class imbalance and sparse early engagement data.", - "explanation": "Provides a more nuanced explanation of early prediction limitations, enhancing interpretability.", - "location": "Results", - "category": "results", - "focus": "results" - }, - { - "original_text": "The discussion emphasizes the predictive accuracy but should more thoroughly address limitations such as data sparsity, generalizability, and potential biases.", - "improved_version": "While the models demonstrate high predictive accuracy, limitations include potential biases due to the specific populations studied, the reliance on continuous engagement data, and challenges in generalizing to interventions with lower retention rates or different engagement patterns.", - "explanation": "Provides a balanced discussion of limitations, improving scientific rigor and transparency.", - "location": "Discussion", - "category": "standards", - "focus": "impact" - }, - { - "original_text": "The abstract and main text could better separate background, methods, results, and conclusions for clearer communication.", - "improved_version": "Reorganize the abstract into distinct sections: background (brief context), methods (study design, data, modeling approach), results (performance metrics), and conclusions (implications and future directions).", - "explanation": "Enhances clarity and adherence to standard scientific reporting formats.", - "location": "Abstract", - "category": "standards", - "focus": "organization" - }, - { - "original_text": "The models' performance metrics are presented in detail, but a summary table or figure highlighting key results across weeks would improve readability.", - "improved_version": "Include a summary table or figure that consolidates key performance metrics (AUC, accuracy, F1) across all prediction weeks, facilitating quick comparison and interpretation.", - "explanation": "Improves readability and allows readers to grasp performance trends at a glance.", - "location": "Results", - "category": "clarity", - "focus": "readability" - }, - { - "original_text": "The discussion should better connect the findings to practical implementation and potential challenges in real-world settings.", - "improved_version": "Strengthen the discussion by explicitly addressing how these predictive models could be integrated into clinical workflows, potential barriers such as data privacy, user acceptance, and the need for prospective validation studies.", - "explanation": "Enhances impact by linking findings to real-world applicability and challenges.", - "location": "Discussion", - "category": "impact", - "focus": "impact" - }, - { - "original_text": "The abstract contains extensive technical details that could be summarized more succinctly for broader accessibility.", - "improved_version": "Condense technical details by focusing on key performance metrics, main findings, and their implications, reserving detailed methodological descriptions for the main text.", - "explanation": "Improves readability and accessibility for a broader audience, including non-specialists.", - "location": "Abstract", - "category": "clarity", - "focus": "readability" - } - ], - "detailed_feedback": { - "structure_analysis": "The abstract attempts to cover background, methods, results, and implications but lacks clear segmentation into these sections, leading to a dense, unstructured flow. The main text is comprehensive but could benefit from clearer organization, especially in separating background, aims, methods, results, and discussion points for better readability.", - "content_analysis": "The abstract and main text provide extensive data on model performance, datasets, and analysis, but some methodological details\u2014such as the specific ML algorithms, validation procedures, and hyperparameter tuning\u2014are insufficiently described. The results are rich but presented in a manner that overwhelms the reader, with performance metrics scattered throughout the text rather than summarized effectively.", - "clarity_assessment": "The language is technical and precise but often overly dense, especially in the results sections. The presentation of multiple metrics across many weeks without summarization hampers quick understanding. Simplifying language, adding summaries, and using visual aids like tables or figures would greatly improve readability.", - "standards_compliance": "The manuscript generally follows scientific standards in reporting results, but the abstract could be better structured into conventional sections. The detailed methodological descriptions are appropriate but could be more concise and clearer in explaining the ML approaches and validation strategies. The references are comprehensive and relevant.", - "impact_evaluation": "The study addresses a significant gap in predicting nonadherence over extended periods, which has high practical relevance. The findings suggest broad applicability of behavioral engagement features for predictive modeling, potentially informing targeted interventions. However, the manuscript could better articulate the real-world challenges and next steps for implementation, enhancing its overall impact." - }, - "summary": "This manuscript presents a valuable and ambitious exploration of long-term nonadherence prediction in mHealth interventions using machine learning. While the technical rigor and extensive data are strengths, the overall clarity, structure, and presentation could be improved to make the findings more accessible and impactful. Addressing organizational issues, summarizing key results more effectively, and explicitly discussing practical implications and limitations would elevate the manuscript's quality and influence." - }, - "S3": { - "score": 7, - "critical_remarks": [ - { - "category": "context", - "location": "Section 1 (Background and Context)", - "issue": "While the background discusses the importance of mHealth and NCDs, it lacks specific framing of the current challenges in adherence prediction and how existing gaps hinder progress.", - "severity": "medium", - "impact": "This limits the reader's understanding of the novelty and urgency of the research, reducing contextual clarity." - }, - { - "category": "problem", - "location": "Section 1 (Problem Statement)", - "issue": "The problem of high nonadherence is mentioned broadly, but the introduction does not explicitly specify the limitations of current prediction methods or why existing models are insufficient for longer-term or nuanced predictions.", - "severity": "high", - "impact": "This weakens the justification for the study, making the problem seem less pressing or novel." - }, - { - "category": "objectives", - "location": "Section 1 (Objectives Clarity)", - "issue": "The objectives are somewhat scattered; while the overall aim is to predict nonadherence, specific research questions or hypotheses are not clearly articulated upfront.", - "severity": "medium", - "impact": "This affects the clarity of the research scope and hampers the reader's ability to grasp precise aims." - }, - { - "category": "significance", - "location": "Section 1 (Justification of Significance)", - "issue": "Although the potential of predictive models is discussed, the introduction does not sufficiently emphasize how this research advances current knowledge or impacts clinical practice.", - "severity": "medium", - "impact": "This diminishes the perceived importance and innovative contribution of the study." - }, - { - "category": "literature integration", - "location": "Section 1 (Literature Review)", - "issue": "The review of prior studies is comprehensive but somewhat fragmented; it mentions many findings without synthesizing how these collectively inform the current research gap.", - "severity": "medium", - "impact": "This reduces coherence and makes it harder for readers to see the logical progression leading to the current study." - }, - { - "category": "flow and organization", - "location": "Section 1 (Flow and Organization)", - "issue": "The transition from background to problem and then to objectives is somewhat abrupt; the narrative jumps between topics without smooth linking sentences.", - "severity": "low", - "impact": "This affects readability and the logical flow, potentially confusing readers." - }, - { - "category": "technical accuracy", - "location": "Section 1 (Technical Content)", - "issue": "The definitions of adherence and churn are generally accurate but could benefit from clearer distinctions and more precise operationalization aligned with the study's focus.", - "severity": "low", - "impact": "This could lead to ambiguity in interpreting the results and their implications." - }, - { - "category": "research scope", - "location": "Section 1 (Scope Clarification)", - "issue": "The scope is broad, covering two interventions and multiple prediction tasks, but the introduction does not explicitly delineate the boundaries or limitations of this scope.", - "severity": "low", - "impact": "This may cause overgeneralization or misinterpretation of the study's applicability." - }, - { - "category": "hypotheses/questions", - "location": "Section 1 (Research Questions)", - "issue": "Explicit hypotheses or research questions are not clearly stated, which could guide the reader through the study's investigative focus.", - "severity": "medium", - "impact": "Lack of clear research questions diminishes the clarity of the study's aims and hinders focused evaluation." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rising prevalence and economic burden of noncommunicable diseases (NCDs) present a significant challenge to patients and healthcare systems, calling for innovative, scalable, and cost-effective solutions.", - "improved_version": "Noncommunicable diseases (NCDs) are increasingly prevalent worldwide, imposing substantial health and economic burdens on patients and healthcare systems. This escalating challenge underscores the urgent need for innovative, scalable, and cost-effective interventions.", - "explanation": "Enhances clarity by emphasizing the global scope and urgency, setting a stronger foundation for the importance of the research.", - "location": "Section 1", - "category": "context", - "focus": "background" - }, - { - "original_text": "Mobile health (mHealth) interventions, facilitated by the ubiquity of smartphones, have emerged as promising tools to support the prevention and management of NCDs.", - "improved_version": "The widespread adoption of smartphones has facilitated the development of mobile health (mHealth) interventions, which have shown promise in supporting the prevention and management of NCDs through accessible and scalable solutions.", - "explanation": "Provides a clearer link between smartphone ubiquity and the potential of mHealth, strengthening the background context.", - "location": "Section 1", - "category": "context", - "focus": "background" - }, - { - "original_text": "Yet, despite growing evidence and availability, mHealth interventions face high nonadherence, where users fail to use these tools as intended or discontinue use entirely before achieving desired outcomes.", - "improved_version": "Despite the increasing evidence supporting their efficacy, a major challenge remains: high rates of nonadherence, where users either fail to follow prescribed usage patterns or discontinue use prematurely, thereby limiting the interventions' effectiveness.", - "explanation": "Clarifies the problem by specifying the nature of nonadherence and its impact on effectiveness.", - "location": "Section 1", - "category": "problem", - "focus": "problem" - }, - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "The extensive behavioral data generated by mHealth interventions present an opportunity: can these data be leveraged to accurately predict nonadherence and enable timely interventions?", - "explanation": "Frames the problem as an opportunity, making the research gap more compelling and focused.", - "location": "Section 1", - "category": "problem", - "focus": "gap" - }, - { - "original_text": "We developed machine learning models for the prediction of nonadherence in two mHealth interventions, one for nonspecific and degenerative back pain over a program duration of 90 days (Vivira, n = 8,372), and another for hypertension self-management over 186 days (Manoa, n = 6,674).", - "improved_version": "This study aims to develop and evaluate machine learning models to predict nonadherence in two distinct mHealth interventions: Vivira, a 90-day back pain program, and Manoa, a 186-day hypertension self-management app, to assess the generalizability and robustness of predictive approaches across different conditions and durations.", - "explanation": "Clarifies the objectives and scope, emphasizing the study's focus on model development and evaluation across interventions.", - "location": "Section 1", - "category": "objectives", - "focus": "objectives" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.", - "improved_version": "Our models achieved high predictive accuracy, correctly identifying an average of 94% of nonadherent users in Vivira during Weeks 2 to 13, where nonadherence was operationalized as completing fewer than eight exercises per week.", - "explanation": "Adds clarity to the operational definition of nonadherence and emphasizes model performance.", - "location": "Section 1", - "category": "objectives", - "focus": "objectives" - }, - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "Given the richness of behavioral data from mHealth apps, this study investigates the extent to which nonadherence and churn can be accurately predicted over extended periods, enabling proactive intervention strategies.", - "explanation": "Aligns the research question with the data's potential, framing it as an investigation into predictive capacity.", - "location": "Section 1", - "category": "problem", - "focus": "gap" - }, - { - "original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).", - "improved_version": "The study demonstrates that machine learning models can accurately predict nonadherence, including complete discontinuation (churn), over long-term intervention periods, highlighting the potential for early, targeted support.", - "explanation": "Strengthens the significance by linking prediction accuracy to practical intervention benefits.", - "location": "Section 4", - "category": "significance", - "focus": "impact" - }, - { - "original_text": "Our descriptive analysis further emphasizes this relationship, showing that the decline in adherence over time in Vivira and Manoa is largely driven by churn (i.e., users discontinuing entirely).", - "improved_version": "Descriptive analyses reveal that the decline in adherence over time in both interventions is primarily attributable to user churn, underscoring the importance of early predictive detection.", - "explanation": "Clarifies the relationship and underscores the importance of early prediction for intervention planning.", - "location": "Section 4", - "category": "significance", - "focus": "impact" - }, - { - "original_text": "The potential of predictive models is discussed, but the introduction does not sufficiently emphasize how this research advances current knowledge or impacts clinical practice.", - "improved_version": "This research advances current understanding by demonstrating the feasibility of long-term adherence prediction using objective behavioral data, paving the way for integrating predictive analytics into routine clinical workflows to improve patient engagement and outcomes.", - "explanation": "Highlights the contribution and practical relevance, strengthening the justification of the study.", - "location": "Section 4", - "category": "significance", - "focus": "impact" - } - ], - "detailed_feedback": { - "context_analysis": "The introduction effectively outlines the global burden of NCDs and the promise of mHealth interventions, emphasizing their scalability and current adoption. However, it could better specify existing challenges in adherence prediction, especially over extended durations, and how current models fall short, to establish a clearer context for the study's necessity.", - "problem_analysis": "While high nonadherence rates are acknowledged, the introduction lacks a detailed critique of existing predictive approaches, their limitations in long-term or nuanced prediction, and the specific gaps this study aims to fill. Clarifying these points would strengthen the problem statement.", - "objectives_analysis": "The objectives are implied but not explicitly articulated as specific research questions or hypotheses. Clarifying whether the goal is to develop, validate, or compare models, and what specific aspects of nonadherence are targeted, would improve focus.", - "significance_assessment": "The potential impact on clinical practice and intervention strategies is mentioned but not deeply elaborated. Emphasizing how this research uniquely contributes to the field, advances predictive methodology, or influences health outcomes would enhance perceived importance.", - "structure_evaluation": "The introduction covers background, problem, and objectives but could benefit from clearer logical transitions. For example, explicitly linking the literature review to identified gaps and then to the study aims would improve flow and coherence." - }, - "summary": "Overall, the introduction presents a relevant and timely topic with a solid foundation in existing literature. However, it would benefit from clearer articulation of the research gap, explicit research questions, and a stronger emphasis on the study's innovative contribution. Improving the logical flow and explicitly framing the objectives and significance would elevate the manuscript's clarity and impact." - }, - "S4": { - "score": 7, - "critical_remarks": [ - { - "category": "coverage", - "location": "Section 1 & 2", - "issue": "While the review covers a broad range of mHealth applications and related adherence issues, it predominantly emphasizes quantitative prediction models without sufficiently addressing the diversity of intervention types, populations, or contextual factors influencing adherence.", - "severity": "medium", - "impact": "Limits the comprehensiveness of the review, potentially overlooking important contextual or qualitative factors affecting nonadherence." - }, - { - "category": "analysis", - "location": "Section 4.1 & 4.4", - "issue": "The discussion on the limitations and future directions tends to be optimistic about model generalizability without critically examining potential biases, data quality issues, or the impact of unmeasured confounders.", - "severity": "high", - "impact": "Reduces the depth of critical evaluation, which is essential for understanding the robustness and applicability of the models." - }, - { - "category": "structure", - "location": "Throughout the review", - "issue": "The organization of sections, especially the results and discussion, is dense with detailed data but lacks clear thematic segmentation or summaries that guide the reader through key insights.", - "severity": "medium", - "impact": "Impairs readability and hampers the synthesis of complex findings into digestible conclusions." - }, - { - "category": "citations", - "location": "Section 2 & 4", - "issue": "While many references are relevant, there is a reliance on older or less recent studies for some foundational concepts, and some citations lack context or critical appraisal.", - "severity": "low", - "impact": "Slightly diminishes the currency and perceived rigor of the literature base." - }, - { - "category": "integration", - "location": "Section 4.1 & 4.3", - "issue": "The review discusses models and features primarily in technical terms, with limited integration of how these models translate into real-world clinical or behavioral outcomes.", - "severity": "high", - "impact": "Weakens the connection between predictive modeling and practical intervention or health impact, reducing translational relevance." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "While the review highlights the potential of rich behavioral data from mHealth interventions, it would benefit from explicitly discussing the variability in data quality, completeness, and the influence of contextual factors on prediction accuracy.", - "explanation": "Adding nuance about data variability enhances understanding of the limitations and applicability of predictive models.", - "location": "Section 1", - "category": "coverage", - "focus": "breadth" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95),", - "improved_version": "Our models demonstrated high predictive accuracy, correctly identifying approximately 94% of nonadherent users in Vivira, though further discussion on potential false positives and model calibration is necessary to assess clinical utility.", - "explanation": "This clarifies the performance metrics and encourages critical evaluation of model limitations.", - "location": "Section 3.2.1", - "category": "analysis", - "focus": "synthesis" - }, - { - "original_text": "The review predominantly emphasizes quantitative prediction models without sufficiently addressing the diversity of intervention types, populations, or contextual factors influencing adherence.", - "improved_version": "The review should incorporate a broader discussion of intervention diversity, including different health conditions, demographic groups, and settings, to better contextualize the applicability of the models.", - "explanation": "Expanding scope improves comprehensiveness and relevance across varied contexts.", - "location": "Section 2 & 4.4", - "category": "coverage", - "focus": "breadth" - }, - { - "original_text": "While many references are relevant, there is a reliance on older or less recent studies for some foundational concepts, and some citations lack context or critical appraisal.", - "improved_version": "Update the reference list with more recent studies where available, and include brief critical evaluations of key citations to highlight their strengths and limitations.", - "explanation": "Enhances the currency and critical depth of the literature base.", - "location": "Section 2 & 4", - "category": "citations", - "focus": "relevance" - }, - { - "original_text": "The organization of sections, especially the results and discussion, is dense with detailed data but lacks clear thematic segmentation or summaries that guide the reader through key insights.", - "improved_version": "Reorganize the results and discussion sections with clear subheadings and summary paragraphs that distill key findings and implications, improving readability and synthesis.", - "explanation": "Facilitates better comprehension and logical flow for readers.", - "location": "Section 3 & 4", - "category": "structure", - "focus": "organization" - }, - { - "original_text": "The review discusses models and features primarily in technical terms, with limited integration of how these models translate into real-world clinical or behavioral outcomes.", - "improved_version": "Incorporate a dedicated discussion on how predictive models can inform clinical decision-making, behavioral interventions, and health outcomes, bridging the gap between technical performance and practical impact.", - "explanation": "Strengthens the translational relevance of the review.", - "location": "Section 4.3", - "category": "integration", - "focus": "relevance" - }, - { - "original_text": "The discussion on the limitations and future directions tends to be optimistic about model generalizability without critically examining potential biases, data quality issues, or the impact of unmeasured confounders.", - "improved_version": "Expand the limitations section to critically evaluate potential biases, data quality concerns, and unmeasured confounders that may affect model validity and generalizability.", - "explanation": "Provides a more balanced and rigorous assessment of the models' robustness.", - "location": "Section 4.4", - "category": "analysis", - "focus": "depth" - }, - { - "original_text": "The review could benefit from explicitly discussing the variability in data quality, completeness, and the influence of contextual factors on prediction accuracy.", - "improved_version": "Explicitly address how data quality, completeness, and contextual variables influence model performance and the generalizability of findings across different populations and settings.", - "explanation": "Enhances understanding of factors affecting model robustness and transferability.", - "location": "Section 4.4", - "category": "coverage", - "focus": "depth" - }, - { - "original_text": "The review would be strengthened by including more discussion on how models could be integrated into clinical workflows or real-world settings.", - "improved_version": "Add a section discussing practical strategies for integrating predictive models into clinical workflows, including potential barriers, facilitators, and ethical considerations.", - "explanation": "Bridges research findings with implementation science, increasing translational impact.", - "location": "Section 4.3", - "category": "integration", - "focus": "relevance" - }, - { - "original_text": "The review discusses the importance of behavioral features but does not sufficiently explore the potential role of contextual, social, or environmental factors influencing adherence.", - "improved_version": "Incorporate a discussion on how contextual, social, and environmental factors might complement behavioral features in improving prediction accuracy and intervention design.", - "explanation": "Provides a more holistic view of adherence determinants, enriching the analysis.", - "location": "Section 2 & 4.1", - "category": "coverage", - "focus": "breadth" - }, - { - "original_text": "The review could include more critical appraisal of the limitations of machine learning approaches, such as overfitting, interpretability, and data bias.", - "improved_version": "Discuss potential ML limitations, including overfitting, interpretability challenges, and biases in training data, and suggest strategies to mitigate these issues.", - "explanation": "Enhances critical evaluation and guides future methodological improvements.", - "location": "Section 4.4", - "category": "analysis", - "focus": "depth" - } - ], - "detailed_feedback": { - "coverage_analysis": "The literature review provides a solid overview of quantitative models predicting nonadherence and churn in mHealth interventions, especially emphasizing behavioral engagement data. However, it underrepresents intervention diversity, including different health conditions, populations, and settings, which limits its comprehensiveness. Incorporating studies from varied contexts and qualitative insights would strengthen the breadth and applicability of the review.", - "analysis_quality": "The review demonstrates a good understanding of the technical aspects of ML models and their predictive performance. Nonetheless, it lacks critical appraisal of model limitations, biases, and the real-world challenges of implementation. A more nuanced discussion of these issues would deepen the analytical rigor and provide balanced insights into the translational potential.", - "structure_evaluation": "The manuscript is densely packed with detailed statistical results and technical descriptions, but the organization could be improved with clearer thematic segmentation. Summarizing key findings at the end of sections and using subheadings would enhance readability and guide the reader through complex data, facilitating better synthesis.", - "citation_assessment": "The citations are generally relevant and include recent studies; however, some foundational references are somewhat dated, and a few key recent publications are missing. Updating references and providing critical context for cited works would improve the scholarly rigor and currency of the review.", - "integration_review": "While the review discusses the technical performance of models extensively, it minimally addresses how these models translate into clinical or behavioral outcomes. Strengthening the discussion on practical implementation, ethical considerations, and health impact would improve the connection between research and real-world application." - }, - "summary": "Overall, this literature review offers a comprehensive and technically detailed overview of ML-based nonadherence prediction in mHealth interventions. It effectively highlights the predictive capabilities and potential applications but would benefit from deeper critical analysis, broader contextual coverage, clearer organization, and stronger integration with practical health outcomes. These enhancements would elevate its scholarly rigor and translational relevance, making it a more impactful resource for researchers and practitioners." - }, - "S5": { - "score": 7, - "critical_remarks": [ - { - "category": "design", - "location": "Section 2.2", - "issue": "The study employs a retrospective observational design using historical data, which limits causal inference and may introduce selection bias related to consent and data availability. The rationale for choosing this design over a prospective approach is not explicitly discussed.", - "severity": "high", - "impact": "This limits the ability to establish causality and may affect the generalizability of the findings. It also raises concerns about potential biases in the dataset." - }, - { - "category": "methods", - "location": "Section 2.2", - "issue": "The feature selection relies primarily on behavioral engagement metrics, with limited consideration of contextual or sociodemographic factors that could influence adherence, potentially reducing model robustness.", - "severity": "medium", - "impact": "This could lead to omitted variable bias, reducing the predictive accuracy and limiting insights into adherence drivers." - }, - { - "category": "analysis", - "location": "Section 2.2 and 3.2", - "issue": "The evaluation metrics focus heavily on AUC, accuracy, and F1-score, but do not sufficiently address calibration or the clinical utility of the models, such as decision thresholds or cost-benefit analyses.", - "severity": "medium", - "impact": "This limits understanding of how models would perform in real-world settings and their practical applicability for intervention." - }, - { - "category": "quality", - "location": "Section 4.4", - "issue": "The models are trained and validated on datasets with class imbalance, and while techniques like Tomek Links undersampling are used, there is limited discussion on how this imbalance might bias the results or affect model fairness.", - "severity": "high", - "impact": "Potential bias in model performance, especially in underrepresented groups, which could compromise validity and fairness." - }, - { - "category": "ethics", - "location": "Section 2.2 and 4.4", - "issue": "The datasets are anonymized and use consented data, but the manuscript does not detail how participant privacy is protected during data handling, nor does it discuss potential biases introduced by excluding non-consenting users.", - "severity": "medium", - "impact": "This could impact ethical transparency and the reproducibility of the study, as well as the representativeness of the sample." - }, - { - "category": "limitations handling", - "location": "Section 4.4", - "issue": "The discussion acknowledges limitations related to data scope and generalizability but lacks specific strategies for addressing these in future research, such as prospective validation or inclusion of diverse populations.", - "severity": "medium", - "impact": "This reduces the clarity on how future work could mitigate current limitations, affecting the study's translational potential." - } - ], - "improvement_suggestions": [ - { - "original_text": "The study employs a retrospective observational design using historical data, which limits causal inference and may introduce selection bias related to consent and data availability.", - "improved_version": "Consider designing a prospective study to validate the predictive models in real-time settings, which would enhance causal inference and reduce selection bias related to data consent and availability.", - "explanation": "A prospective approach allows for real-time validation and reduces biases inherent in retrospective data, strengthening causal claims and practical relevance.", - "location": "Section 2.1 and 4.4", - "category": "design", - "focus": "approach" - }, - { - "original_text": "The feature selection relies primarily on behavioral engagement metrics, with limited consideration of contextual or sociodemographic factors.", - "improved_version": "Incorporate additional features such as sociodemographic variables, health literacy, or baseline health status to enrich the models and improve robustness.", - "explanation": "Adding contextual variables can capture external influences on adherence, potentially increasing model accuracy and interpretability.", - "location": "Section 2.2", - "category": "methods", - "focus": "techniques" - }, - { - "original_text": "The evaluation metrics focus heavily on AUC, accuracy, and F1-score, but do not sufficiently address calibration or decision thresholds.", - "improved_version": "Include calibration plots and decision-analytic metrics such as net benefit or cost-effectiveness analyses to assess clinical utility.", - "explanation": "These additions help determine how well the predicted probabilities translate into actionable decisions, improving real-world applicability.", - "location": "Section 2.2 and 3.2", - "category": "analysis", - "focus": "validity" - }, - { - "original_text": "Models are trained on imbalanced datasets with techniques like undersampling, but there is limited discussion on bias or fairness across subgroups.", - "improved_version": "Implement fairness-aware modeling approaches and report subgroup performance metrics to assess bias and ensure equitable predictions.", - "explanation": "Addressing potential biases improves model fairness and validity, especially for vulnerable populations.", - "location": "Section 2.2 and 4.4", - "category": "quality", - "focus": "validity" - }, - { - "original_text": "The datasets are anonymized and use consented data, but the manuscript does not detail how participant privacy is protected during data handling.", - "improved_version": "Explicitly describe data security measures, anonymization procedures, and compliance with data protection regulations to enhance transparency.", - "explanation": "Clear ethical protocols reinforce trustworthiness and reproducibility of the research.", - "location": "Section 2.2 and 4.4", - "category": "ethics", - "focus": "procedures" - }, - { - "original_text": "The models are trained and validated on datasets with class imbalance, and while techniques like undersampling are used, there is limited discussion on how this might bias results.", - "improved_version": "Perform sensitivity analyses using alternative imbalance handling methods (e.g., SMOTE, cost-sensitive learning) and report subgroup performance to evaluate bias.", - "explanation": "This ensures the models are robust and fair across different user groups, improving validity and fairness.", - "location": "Section 2.2 and 4.4", - "category": "quality", - "focus": "validity" - }, - { - "original_text": "The study employs a retrospective observational design using historical data, which limits causal inference.", - "improved_version": "Plan future randomized controlled trials or intervention studies to test whether predictive models can effectively inform adherence-promoting strategies.", - "explanation": "Experimental validation would establish causality and practical effectiveness of the models in real-world interventions.", - "location": "Section 4.4", - "category": "design", - "focus": "approach" - }, - { - "original_text": "The study does not discuss how user privacy is protected during data collection and analysis.", - "improved_version": "Include detailed descriptions of data anonymization, encryption, and compliance with GDPR or relevant data protection standards to strengthen ethical rigor.", - "explanation": "Transparency about privacy safeguards enhances ethical credibility and reproducibility.", - "location": "Section 2.2 and 4.4", - "category": "ethics", - "focus": "procedures" - }, - { - "original_text": "The models are primarily based on behavioral app engagement features, with limited exploration of intervention-specific or social features.", - "improved_version": "Explore incorporating intervention-specific features such as social interactions, messaging frequency, or content engagement to potentially improve predictive performance.", - "explanation": "Adding diverse features may capture additional variance in adherence behavior, enhancing model robustness and utility.", - "location": "Section 2.2", - "category": "methods", - "focus": "techniques" - }, - { - "original_text": "The evaluation does not include calibration metrics or decision-analytic assessments.", - "improved_version": "Assess calibration using calibration plots and consider decision curve analysis to evaluate the clinical usefulness of the models.", - "explanation": "These metrics help determine how well predicted probabilities align with actual outcomes and their practical decision-making value.", - "location": "Section 2.2 and 3.2", - "category": "analysis", - "focus": "validity" - }, - { - "original_text": "The discussion of limitations does not specify strategies for prospective validation or addressing generalizability concerns.", - "improved_version": "Outline specific plans for prospective validation studies across diverse populations and settings to enhance generalizability and clinical translation.", - "explanation": "Proactive strategies for validation strengthen the evidence base and facilitate real-world implementation.", - "location": "Section 4.4", - "category": "limitations handling", - "focus": "future work" - } - ], - "detailed_feedback": { - "design_analysis": "The study adopts a retrospective observational design leveraging existing app engagement data to predict nonadherence and churn. While this approach allows for large-scale analysis and model development, it inherently limits causal inference and may introduce selection bias, especially given the consent-based data collection. The rationale for choosing this design over a prospective or experimental approach is not explicitly discussed, which could impact the interpretability and applicability of the findings.", - "methods_assessment": "The methodology employs machine learning models, primarily random forests, trained on behavioral engagement features such as daily app activity, session counts, and completed exercises. The feature selection is grounded in prior literature emphasizing app activity as a key predictor. Data preprocessing includes normalization and undersampling to address class imbalance. However, the approach omits potentially relevant contextual factors like sociodemographics or health status, which could enhance model robustness. The validation strategy uses stratified 10-fold cross-validation and a train-test split, but the handling of class imbalance, while addressed via undersampling, could benefit from additional techniques like SMOTE or cost-sensitive learning.", - "analysis_evaluation": "Model performance is evaluated using metrics such as AUC, accuracy, F1-score, precision, and recall, which are appropriate for classification tasks. The models demonstrate high predictive accuracy over extended periods, with performance improving as more data accrue. Nonetheless, the analysis lacks calibration assessments and decision-analytic evaluations, which are crucial for translating model outputs into clinical or intervention decisions. The focus on aggregate metrics may obscure subgroup disparities or potential biases in predictions across different user demographics.", - "quality_review": "The study reports on model validity through multiple performance metrics and addresses class imbalance with undersampling. However, there is limited discussion on potential biases introduced by data exclusion (non-consenting users) or imbalance in demographic groups. The models' fairness and generalizability could be compromised if these biases are not thoroughly examined. The reliance on behavioral app data is a strength, but the absence of external validation or prospective testing limits the robustness of the conclusions.", - "ethics_compliance": "The datasets used are anonymized and collected with participant consent, with ethical approval obtained from relevant bodies. The manuscript briefly mentions data privacy measures but does not detail specific procedures such as encryption, anonymization protocols, or compliance with data protection standards like GDPR. Transparency in these areas is essential for ethical rigor and reproducibility, especially given the sensitive nature of health data." - }, - "summary": "Overall, this manuscript presents a solid and methodologically sound approach to predicting nonadherence in mHealth interventions using behavioral engagement data and machine learning. The extensive analysis over long durations and across two distinct interventions demonstrates the potential for generalizable predictive models. However, the study's retrospective design, limited inclusion of contextual factors, and lack of calibration and fairness assessments temper the strength of its conclusions. Addressing these issues through prospective validation, broader feature inclusion, and detailed ethical protocols would significantly enhance the rigor, applicability, and impact of this research. Consequently, the manuscript merits a score of 7, reflecting its valuable contributions while acknowledging areas for methodological and ethical refinement." - }, - "S6": { - "score": 7, - "critical_remarks": [ - { - "category": "presentation", - "location": "Results section, Figures 6.5 and 6.6", - "issue": "Figures are described but not included or clearly labeled with axes, units, or legends, making interpretation difficult.", - "severity": "high", - "impact": "Reduces clarity and hampers the reader's ability to understand model performance trends visually." - }, - { - "category": "analysis", - "location": "Model performance metrics, Tables 6.1-6.4", - "issue": "While multiple metrics are reported, there is limited discussion on how these metrics interrelate or their practical significance, especially in the context of clinical or real-world utility.", - "severity": "medium", - "impact": "Weakens the interpretability of the statistical results and their implications for practice." - }, - { - "category": "interpretation", - "location": "Discussion, paragraph 4.1", - "issue": "The interpretation of model performance differences (e.g., between nonadherence and churn models) lacks depth regarding potential causes or implications for intervention strategies.", - "severity": "medium", - "impact": "Limits the reader\u2019s understanding of how these findings translate into actionable insights." - }, - { - "category": "quality", - "location": "Methods, hyperparameter tuning description", - "issue": "Details on hyperparameter tuning are referenced as in Appendix 6.1 but are not summarized or discussed in the main text, reducing transparency.", - "severity": "low", - "impact": "Slightly diminishes reproducibility and understanding of model optimization procedures." - }, - { - "category": "impact", - "location": "Results, predictive performance discussion", - "issue": "Although high performance metrics are reported, the potential for overfitting or model generalizability across different populations is not addressed.", - "severity": "high", - "impact": "Raises concerns about the practical applicability and robustness of the models in diverse real-world settings." - } - ], - "improvement_suggestions": [ - { - "original_text": "Figures are described but not included or clearly labeled with axes, units, or legends, making interpretation difficult.", - "improved_version": "Include the actual figures with clear labels, axes titles, units, and comprehensive legends. For example, in Figure 6.5, specify 'Model AUC over Weeks 2-13' with axes labeled as 'Week' and 'AUC'.", - "explanation": "Properly labeled and visible figures enhance visual comprehension and allow readers to interpret performance trends directly.", - "location": "Results section, Figures 6.5 and 6.6", - "category": "visualization", - "focus": "clarity" - }, - { - "original_text": "While multiple metrics are reported, there is limited discussion on how these metrics interrelate or their practical significance.", - "improved_version": "Add a brief interpretive paragraph explaining how metrics like AUC, F1, precision, and recall collectively inform the model's utility, especially emphasizing their relevance for clinical decision-making or intervention planning.", - "explanation": "Contextualizing statistical metrics helps readers understand their practical implications, making the results more meaningful.", - "location": "Results section, analysis discussion", - "category": "interpretation", - "focus": "clarity" - }, - { - "original_text": "The interpretation of performance differences between nonadherence and churn models lacks depth.", - "improved_version": "Expand the discussion to explore potential reasons for performance variations, such as differences in data quality, feature relevance, or inherent behavioral patterns, and discuss how these influence intervention strategies.", - "explanation": "Deeper analysis provides insights into model limitations and guides future improvements or application contexts.", - "location": "Discussion, paragraph 4.1", - "category": "interpretation", - "focus": "interpretation" - }, - { - "original_text": "Details on hyperparameter tuning are referenced as in Appendix 6.1 but are not summarized in the main text.", - "improved_version": "Provide a concise summary of hyperparameter tuning strategies in the main text, such as the range of parameters tested and the selection criteria, to improve transparency.", - "explanation": "Summarizing key methodological steps enhances reproducibility and reader understanding of model development.", - "location": "Methods section", - "category": "quality", - "focus": "clarity" - }, - { - "original_text": "High performance metrics are reported without addressing overfitting or generalizability.", - "improved_version": "Include a discussion on potential overfitting, such as validation strategies used, and comment on the models' expected performance in external or real-world datasets.", - "explanation": "Addressing model robustness increases confidence in the applicability of the findings and guides future validation efforts.", - "location": "Discussion, model robustness", - "category": "impact", - "focus": "significance" - }, - { - "original_text": "Results on model performance are extensive but lack a summary table or visual summary for quick reference.", - "improved_version": "Add a summary table or dashboard figure that consolidates key performance metrics across all prediction windows for easy comparison.", - "explanation": "Visual summaries facilitate quick understanding of overall model performance and trends.", - "location": "Results section, summary visualization", - "category": "visualization", - "focus": "clarity" - }, - { - "original_text": "The discussion of feature importance is limited to Appendix 6.2 without highlighting key features in the main text.", - "improved_version": "Summarize the most important features influencing predictions directly in the main text, such as 'daily exercise completion' and 'last login activity,' and discuss their practical relevance.", - "explanation": "Highlighting key features emphasizes their role and supports understanding of behavioral drivers of nonadherence.", - "location": "Results/discussion, feature importance", - "category": "interpretation", - "focus": "clarity" - }, - { - "original_text": "The manuscript does not explicitly address potential biases or limitations related to data collection or model applicability.", - "improved_version": "Add a paragraph discussing limitations such as selection bias (e.g., only consenting users), potential data sparsity in less engaged populations, and the need for external validation.", - "explanation": "Acknowledging limitations provides a balanced view and guides future research directions.", - "location": "Discussion, limitations", - "category": "quality", - "focus": "significance" - }, - { - "original_text": "The implications for practice are mentioned but could be expanded with concrete examples of how models could be integrated into intervention workflows.", - "improved_version": "Include specific examples, such as 'integrating real-time predictions into app notifications to prompt users at risk of nonadherence,' to illustrate practical application.", - "explanation": "Concrete examples make the potential impact more tangible and guide implementation efforts.", - "location": "Discussion, implications", - "category": "impact", - "focus": "significance" - }, - { - "original_text": "The overall presentation of results is dense, with extensive data tables and narrative, which could overwhelm readers.", - "improved_version": "Streamline the presentation by highlighting key findings in summary boxes or infographics and relegating detailed tables to supplementary materials, with clear cross-references.", - "explanation": "Simplified presentation improves readability and emphasizes main messages without sacrificing detail.", - "location": "Results section", - "category": "presentation", - "focus": "clarity" - } - ], - "detailed_feedback": { - "presentation_analysis": "The results are comprehensive, including multiple performance metrics, descriptive statistics, and detailed tables. However, the figures referenced are not included or well-labeled in the main text, which hampers visual interpretation. The dense data presentation could benefit from more visual summaries or simplified tables to enhance clarity and reader engagement.", - "analysis_quality": "The statistical analysis employs appropriate machine learning algorithms, cross-validation, and hyperparameter tuning. Performance metrics like AUC, accuracy, F1, precision, and recall are thoroughly reported across multiple prediction windows. Nonetheless, there is limited discussion on potential overfitting, model robustness, or external validation, which are critical for assessing real-world applicability.", - "interpretation_review": "The discussion provides a solid overview of model performance and potential applications. However, it lacks depth in interpreting differences between models, such as why nonadherence models outperform churn models in certain contexts, and how these insights translate into intervention strategies. Expanding on the behavioral significance of key features would strengthen the interpretive value.", - "visualization_assessment": "Figures are described but not included, and the tables are extensive but dense, making it difficult for readers to quickly grasp key trends. Incorporating visual summaries like performance trend graphs, feature importance charts, or consolidated performance dashboards would significantly improve comprehension.", - "significance_evaluation": "The high performance metrics suggest promising predictive capabilities, but the manuscript does not sufficiently address issues of overfitting, model generalizability, or applicability across diverse populations. Explicit discussion of these aspects would enhance the perceived significance and practical relevance of the findings." - }, - "summary": "Overall, this manuscript presents a valuable and methodologically sound exploration of nonadherence prediction in mHealth interventions. The extensive data and rigorous analysis support the potential utility of behavioral engagement features combined with machine learning. To elevate the manuscript, improvements in data visualization, deeper interpretive discussion, and explicit acknowledgment of limitations and generalizability are recommended. These enhancements would make the findings more accessible, actionable, and robust, aligning with the high standards expected for impactful research in digital health." - }, - "S7": { - "score": 7, - "critical_remarks": [ - { - "category": "interpretation", - "location": "Section 4.1 - Result interpretation", - "issue": "While the discussion reports high prediction accuracy, it lacks a nuanced analysis of the clinical or practical significance of these metrics, such as how they translate into real-world intervention effectiveness.", - "severity": "medium", - "impact": "This limits the reader's understanding of how the predictive performance impacts actual adherence behavior change or health outcomes." - }, - { - "category": "context", - "location": "Section 4.1 - Literature comparison", - "issue": "The comparison with prior studies is somewhat superficial, often stating that results are 'consistent' without critically analyzing differences in methodology, populations, or settings that could influence the comparability.", - "severity": "medium", - "impact": "Reduces the depth of contextual understanding and may overstate the novelty or robustness of findings." - }, - { - "category": "reflection", - "location": "Section 4.4 - Limitations and future work", - "issue": "The limitations are acknowledged but lack specific strategies for addressing these issues in future research, such as how to handle sparse data or how to validate models prospectively.", - "severity": "high", - "impact": "Weakens the manuscript's guidance for subsequent research and practical implementation." - }, - { - "category": "impact", - "location": "Section 4.3 - Practical implications", - "issue": "While potential strategies are discussed, there is limited discussion on the actual feasibility, cost-effectiveness, or ethical considerations of implementing predictive interventions in real-world settings.", - "severity": "medium", - "impact": "Reduces the translational value and real-world relevance of the findings." - }, - { - "category": "quality", - "location": "Throughout the discussion", - "issue": "The discussion is dense with technical details and statistical results but often lacks coherence and clear synthesis, making it difficult for readers to grasp the overarching narrative or implications.", - "severity": "high", - "impact": "Impairs clarity and diminishes the overall readability and impact of the manuscript." - } - ], - "improvement_suggestions": [ - { - "original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).", - "improved_version": "Our results demonstrate that nonadherence, including complete discontinuation (churn), can be predicted with high accuracy over extended periods. This aligns with and extends prior definitions by Sieverink et al. (2017), emphasizing the potential for early intervention strategies.", - "explanation": "Clarifies the significance of the findings and explicitly links them to existing conceptual frameworks, enhancing interpretability.", - "location": "Section 4.1", - "category": "interpretation", - "focus": "significance" - }, - { - "original_text": "The comparison with prior studies is somewhat superficial, often stating that results are 'consistent' without critically analyzing differences in methodology, populations, or settings that could influence the comparability.", - "improved_version": "While our findings align with previous research on churn prediction, differences in intervention types, populations, and data collection methods should be considered. For example, prior studies often focus on shorter durations or different app domains, which may influence predictive performance.", - "explanation": "Provides a more nuanced and critical comparison, acknowledging contextual differences that affect generalizability.", - "location": "Section 4.1", - "category": "context", - "focus": "comparison" - }, - { - "original_text": "A shared characteristic of both analyzed mHealth interventions is that users must provide proof of medical need to access the apps and receive reimbursement. User behavior in these contexts may differ from that observed in mHealth interventions directly available in public app stores, where users can self-enroll.", - "improved_version": "The requirement for proof of medical need and reimbursement access may influence user engagement patterns, potentially leading to higher retention rates compared to freely available apps in public stores. Future studies should examine how onboarding and access mechanisms impact adherence trajectories.", - "explanation": "Highlights a specific contextual factor that could influence generalizability and suggests a direction for future research.", - "location": "Section 4.4", - "category": "reflection", - "focus": "limitations/future work" - }, - { - "original_text": "The models rely on rich, continuous behavioral app engagement data, which may not be available in all settings.", - "improved_version": "The predictive models depend on detailed, continuous engagement data, which may limit applicability in interventions with sporadic or sparse data collection. Developing models that incorporate less granular data or alternative indicators could broaden usability.", - "explanation": "Addresses a key limitation and proposes a constructive solution, enhancing the practical relevance of future work.", - "location": "Section 4.4", - "category": "reflection", - "focus": "limitations/future work" - }, - { - "original_text": "While the results are promising, prospective trials are necessary to fully establish the applicability of these models.", - "improved_version": "To validate the predictive utility and real-world impact of these models, prospective randomized trials incorporating intervention components based on model predictions are essential. Such studies should assess not only adherence but also health outcomes.", - "explanation": "Provides a clear, actionable future research direction that emphasizes validation and impact assessment.", - "location": "Section 4.4", - "category": "reflection", - "focus": "future work" - }, - { - "original_text": "Our discussion reports high prediction accuracy, but it lacks a nuanced analysis of the clinical or practical significance of these metrics.", - "improved_version": "Although the models demonstrate high accuracy, translating these metrics into meaningful clinical or behavioral improvements requires further investigation. For instance, understanding how early predictions influence adherence behaviors and health outcomes will be critical for implementation.", - "explanation": "Bridges statistical performance with practical significance, guiding future translational efforts.", - "location": "Section 4.1", - "category": "interpretation", - "focus": "implications" - }, - { - "original_text": "The comparison with prior studies is somewhat superficial, often stating that results are 'consistent' without critically analyzing differences in methodology, populations, or settings that could influence the comparability.", - "improved_version": "Future research should systematically compare models across diverse populations and intervention types to identify factors influencing predictive accuracy and generalizability, including demographic, behavioral, and contextual variables.", - "explanation": "Encourages comprehensive comparative analyses, strengthening the contextual understanding.", - "location": "Section 4.1", - "category": "context", - "focus": "comparison" - }, - { - "original_text": "The discussion is dense with technical details and statistical results but often lacks coherence and clear synthesis.", - "improved_version": "To enhance clarity, we recommend structuring the discussion around key themes: the predictive performance, practical implications, limitations, and future directions. Summarizing main points at the end of each subsection can improve readability.", - "explanation": "Improves overall coherence and helps readers synthesize complex information.", - "location": "Throughout the discussion", - "category": "quality", - "focus": "coherence" - }, - { - "original_text": "The models' performance metrics are high, but the discussion does not sufficiently address the potential for false positives and negatives in clinical decision-making.", - "improved_version": "While the models show high predictive accuracy, the implications of false positives and negatives\u2014such as unnecessary interventions or missed opportunities\u2014should be carefully considered. Future work should evaluate the cost-benefit balance of model deployment in practice.", - "explanation": "Adds critical reflection on the limitations of predictive accuracy and its impact on real-world decisions.", - "location": "Section 4.1", - "category": "impact", - "focus": "implications" - }, - { - "original_text": "The discussion could benefit from explicitly linking the findings to health outcomes, which remains an open question.", - "improved_version": "Ultimately, establishing a direct link between early adherence predictions and improved health outcomes remains an important avenue for future research. Integrating predictive models within adaptive intervention frameworks could facilitate this goal.", - "explanation": "Clarifies the ultimate translational goal and guides future research priorities.", - "location": "Section 4.4", - "category": "impact", - "focus": "significance" - } - ], - "detailed_feedback": { - "interpretation_analysis": "The discussion effectively highlights the high predictive accuracy of models over extended durations, emphasizing their potential for early intervention. However, it would benefit from a deeper analysis of how these metrics translate into meaningful behavioral or health improvements, including potential thresholds for clinical utility and the impact of false positives/negatives on users and providers.", - "context_review": "The manuscript references prior studies to support its findings but often treats these comparisons superficially. A more critical analysis considering differences in intervention types, populations, and data collection methods would strengthen claims of novelty and generalizability. Explicit discussion of how the current work advances or diverges from existing literature is needed.", - "reflection_assessment": "Limitations such as data dependency, generalizability, and the need for prospective validation are acknowledged but lack specific strategies for addressing them. Future research directions should include plans for validation in real-world settings, handling sparse data, and expanding to diverse populations. The discussion should also consider ethical and practical challenges of implementing predictive models in clinical workflows.", - "impact_evaluation": "The discussion appropriately discusses the potential for targeted strategies but underestimates practical challenges such as cost, user acceptance, and ethical considerations. Explicitly addressing these factors would enhance the translational relevance. Additionally, linking prediction performance to health outcomes would clarify the clinical significance of the models.", - "quality_analysis": "While comprehensive in technical detail, the discussion suffers from a lack of clear structure and synthesis. Organizing content around key themes\u2014performance, implications, limitations, future directions\u2014and summarizing main points would improve readability. Reducing jargon and providing concise takeaways would make the discussion more accessible." - }, - "summary": "Overall, the discussion demonstrates solid technical analysis and acknowledges key limitations, but it would benefit from deeper interpretation of results, critical comparison with existing literature, clearer structure, and explicit guidance for future research and practical application. These enhancements would elevate the manuscript's clarity, impact, and translational potential." - }, - "S8": { - "score": 7, - "critical_remarks": [ - { - "category": "support", - "location": "Section 4.1", - "issue": "While the conclusion states that models can predict nonadherence accurately, it lacks explicit mention of how the results directly support the specific performance metrics reported, such as the high AUC and F1 scores, especially in the context of real-world applicability.", - "severity": "medium", - "impact": "This reduces confidence in the strength of the evidence backing the claims, making the support less transparent." - }, - { - "category": "objectives", - "location": "Section 4.1", - "issue": "The conclusion claims the study extends prior research but does not explicitly restate the original research objectives or how they were fulfilled beyond the general statement.", - "severity": "low", - "impact": "This diminishes clarity about the specific aims achieved, affecting the perceived completeness of the conclusion." - }, - { - "category": "implications", - "location": "Section 4.2", - "issue": "Implications for practice are discussed, but the potential limitations or challenges of implementing these predictive models in real-world settings are underexplored.", - "severity": "medium", - "impact": "This limits the practical relevance and may overstate the readiness for application." - }, - { - "category": "presentation", - "location": "Section 4.4", - "issue": "The conclusion contains dense, lengthy sentences that could be more concise and clearer, reducing overall readability.", - "severity": "low", - "impact": "This affects the clarity and impact of the final message." - } - ], - "improvement_suggestions": [ - { - "original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).", - "improved_version": "Our results demonstrate that nonadherence, including complete discontinuation (churn), can be reliably predicted over long-term intervention periods, aligning with the definitions by Sieverink et al. (2017).", - "explanation": "This clarification emphasizes the robustness of the findings and explicitly links the results to the operational definitions used, strengthening support clarity.", - "location": "Section 4.1", - "category": "support", - "focus": "clarity" - }, - { - "original_text": "Given the conceptual link between churn and nonadherence \u2013 where fully disengaged users are inherently nonadherent \u2013 these results are intuitive.", - "improved_version": "Since churn represents a complete disengagement, it logically aligns with nonadherence, making the high prediction accuracy expected and reinforcing the validity of our approach.", - "explanation": "This rephrasing explicitly states the conceptual link, enhancing the logical support for the results and their interpretation.", - "location": "Section 4.1", - "category": "support", - "focus": "support" - }, - { - "original_text": "Our descriptive analysis further emphasizes this relationship, showing that the decline in adherence over time in Vivira and Manoa is largely driven by churn (i.e., users discontinuing entire use).", - "improved_version": "Our descriptive data confirms that the decline in adherence over time is primarily due to user churn, supporting the predictive models' focus on identifying full discontinuation.", - "explanation": "This explicitly ties the descriptive findings to the predictive modeling, reinforcing the support from empirical data.", - "location": "Section 4.1", - "category": "support", - "focus": "support" - }, - { - "original_text": "In contrast, the performance of nonadherence prediction models in Manoa was comparatively lower, correctly identifying an average of 86% (SD = 7.6%, mean AUC = 0.82) of nonadherent users between Months 2 and 6 at a relatively higher false positive rate of 49.5% (SD = 12.9%).", - "improved_version": "In Manoa, nonadherence prediction accuracy was somewhat lower, with models correctly identifying 86% of nonadherent users (AUC = 0.82), though with a higher false positive rate of 49.5%, indicating room for improvement.", - "explanation": "This clarifies the performance limitations and contextualizes the results, providing a balanced view that supports a nuanced interpretation.", - "location": "Section 4.1", - "category": "support", - "focus": "clarity" - }, - { - "original_text": "Feature importance analyses across all prediction models showed that behavioral app engagement data collected closer to the prediction event had a stronger impact on model performance.", - "improved_version": "Analysis of feature importance revealed that behavioral engagement data obtained nearer to the prediction point significantly enhanced model performance.", - "explanation": "This concise rephrasing improves clarity and emphasizes the temporal relevance of data, supporting the methodological insights.", - "location": "Section 4.1", - "category": "support", - "focus": "clarity" - }, - { - "original_text": "Our findings further show that the predictive performance of daily churn prediction models improves over time as more behavioral app engagement data becomes available.", - "improved_version": "Our results indicate that daily churn prediction models become more accurate over time, as additional behavioral engagement data accumulates, supporting their robustness.", - "explanation": "This enhances clarity and explicitly states the causal relationship, strengthening support from the results.", - "location": "Section 4.1", - "category": "support", - "focus": "support" - }, - { - "original_text": "While nonadherence prediction models were more adept at identifying nonadherent users who had already churned, they also correctly identified a substantial proportion of nonadherent users before they recorded their last log in.", - "improved_version": "Although models predicted users who had already churned with higher accuracy, they also successfully identified many users at risk of nonadherence before complete disengagement.", - "explanation": "This clarifies the predictive capacity at different stages, emphasizing the practical utility for early intervention.", - "location": "Section 4.2", - "category": "support", - "focus": "clarity" - }, - { - "original_text": "In summary, churn prediction models demonstrated strong and improving performance throughout the observation period.", - "improved_version": "In conclusion, the churn prediction models showed strong, progressively improving performance over the entire observation window.", - "explanation": "This concise summary reinforces the key finding with emphasis on the temporal improvement, enhancing clarity and impact.", - "location": "Section 4.1", - "category": "presentation", - "focus": "strength" - }, - { - "original_text": "The results of our study demonstrate that ML algorithms, when paired with behavioral app engagement data, can generate actionable insights that may support targeted strategies for preventing nonadherence.", - "improved_version": "Our study confirms that machine learning algorithms utilizing behavioral engagement data can produce actionable insights to inform targeted adherence-promoting strategies.", - "explanation": "This improves clarity and emphasizes the practical relevance of the findings.", - "location": "Section 4.3", - "category": "implications", - "focus": "clarity" - }, - { - "original_text": "Previous research has consistently ranked features related to user app activity (e.g., daily logins) and app progress (e.g., completion of app-logged activities) as the most important for churn prediction in mHealth interventions [5, 21] and other app domains [7, 27, 29, 41, 56].", - "improved_version": "Consistent with prior studies [5, 21, 7, 27, 29, 41, 56], our findings highlight that user activity and progress features are the most predictive for churn in mHealth and other app domains.", - "explanation": "This consolidates evidence, making the statement more concise and emphasizing the generalizability of the feature importance.", - "location": "Section 4.3", - "category": "support", - "focus": "support" - }, - { - "original_text": "Future research should aim to replicate nonadherence prediction models in diverse mHealth contexts and evaluate their integration with targeted preventive strategies in prospective trials to assess the impact of these combined approaches on app usage, adherence, and health outcomes.", - "improved_version": "Future studies should validate these nonadherence prediction models across varied mHealth settings and test their integration with targeted interventions in prospective trials to evaluate effects on engagement and health outcomes.", - "explanation": "This streamlines the statement, making it more actionable and clearer about the next steps for research.", - "location": "Section 4.4", - "category": "future_directions", - "focus": "clarity" - } - ], - "detailed_feedback": { - "support_analysis": "The conclusion effectively summarizes the high predictive accuracy of the models, supported by reported metrics such as AUCs above 0.8 and F1 scores above 0.8. It also references the consistency of these results across two different interventions, reinforcing the robustness of the support. However, explicit links between specific results and their implications for real-world application could be strengthened to enhance transparency.", - "objective_fulfillment": "The conclusion addresses the primary objectives of demonstrating the predictive capacity of behavioral app engagement data for nonadherence and extending prior research to longer durations. It clearly states that the study fulfills these aims, although explicitly restating the original research questions would improve clarity on objective fulfillment.", - "implications_analysis": "The discussion of implications highlights the potential for targeted strategies and the generalizability of models. Nonetheless, it underplays the practical challenges, such as implementation barriers, user privacy concerns, and variability in real-world settings, which are critical for translating findings into practice.", - "presentation_analysis": "While comprehensive, the conclusion is somewhat verbose, with complex sentences that could be simplified for better readability. The overall structure could benefit from more concise summaries and clearer topic transitions to improve flow and impact.", - "contribution_analysis": "The manuscript clearly emphasizes that this is the first study to evaluate nonadherence prediction aligned with Sieverink et al.'s definition over extended periods, contributing novel insights. However, explicitly stating how this advances the field beyond prior models would further clarify its unique contribution." - }, - "summary": "Overall, the conclusion effectively summarizes key findings, supports claims with robust data, and discusses relevant implications. However, it would benefit from clearer linkage between results and claims, more balanced discussion of practical limitations, and improved conciseness. These enhancements would elevate the manuscript's clarity, support strength, and contribution clarity, making it more impactful and comprehensive." - }, - "S9": { - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "accuracy_analysis": "", - "completeness_analysis": "", - "format_analysis": "", - "quality_analysis": "", - "organization_analysis": "" - }, - "summary": "Error in analysis: Error analyzing references: Unterminated string starting at: line 13 column 19 (char 600)", - "error": true - }, - "S10": { - "score": 8, - "critical_remarks": [ - { - "category": "completeness", - "location": "Section 2.2, Feature Selection, Model Training, and Evaluation", - "issue": "While hyperparameter grids are mentioned to be in Appendix 6.1, the main text lacks a summary of key hyperparameters and their ranges, which would aid understanding without requiring immediate appendix access.", - "severity": "medium", - "impact": "This reduces transparency about model tuning and may hinder reproducibility or critical assessment of the modeling process." - }, - { - "category": "relevance", - "location": "Section 3.2.1, Vivira prediction results", - "issue": "The detailed tables and figures, while comprehensive, focus heavily on statistical metrics without explicitly linking how these translate into practical intervention strategies or clinical relevance.", - "severity": "medium", - "impact": "This limits the reader's ability to assess real-world applicability of the predictive performance." - }, - { - "category": "clarity", - "location": "Section 4.1, Summary of prediction results", - "issue": "The description of performance metrics over time is dense and could benefit from clearer summaries or visualizations to facilitate quick understanding.", - "severity": "low", - "impact": "This affects accessibility for readers seeking a quick grasp of model effectiveness." - }, - { - "category": "organization", - "location": "Section 4.2, Potential for targeted strategies", - "issue": "The discussion on reengagement after correct predictions is somewhat scattered, with multiple percentages and user categories presented without a clear structure.", - "severity": "low", - "impact": "This hampers the logical flow and makes it harder for readers to follow key insights." - }, - { - "category": "completeness", - "location": "Section 4.4, Limitations and future work", - "issue": "While limitations are acknowledged, there is limited discussion on potential biases introduced by the datasets or the impact of demographic variables on model performance.", - "severity": "medium", - "impact": "This omission affects the thoroughness of the critical evaluation and the generalizability of findings." - }, - { - "category": "clarity", - "location": "Appendix 1, Software packages and hyperparameter grids", - "issue": "The hyperparameter grids are only referenced as being in Appendix 6.1, but no summary or key examples are provided in the main text, which could aid understanding of model complexity.", - "severity": "low", - "impact": "This reduces transparency and may hinder replication or critical appraisal." - }, - { - "category": "relevance", - "location": "Section 4.3, Implications for researchers and providers", - "issue": "The discussion on expanding feature sets to include intervention-specific attributes is somewhat generic; specific examples tailored to the interventions studied could enhance relevance.", - "severity": "low", - "impact": "This would improve the practical applicability of recommendations." - }, - { - "category": "completeness", - "location": "Section 4.4, Limitations", - "issue": "The potential impact of data quality issues, such as missing data or measurement errors, is not discussed.", - "severity": "medium", - "impact": "This omission limits understanding of data robustness and model reliability." - }, - { - "category": "organization", - "location": "Overall structure", - "issue": "The supplementary materials include extensive tables and figures, but their integration into the narrative could be improved with more explicit cross-referencing and summaries.", - "severity": "low", - "impact": "This affects navigability and ease of extracting key insights." - }, - { - "category": "clarity", - "location": "Section 3.2.2, Manoa prediction results", - "issue": "The description of performance trends over months is detailed but could benefit from simplified summaries or visual aids for clarity.", - "severity": "low", - "impact": "This would enhance accessibility for diverse audiences." - } - ], - "improvement_suggestions": [ - { - "original_text": "Detailed hyperparameter grids are provided in Appendix 6.1.", - "improved_version": "Include a summary table or key hyperparameter ranges within the main text to clarify the model tuning process.", - "explanation": "Providing a concise overview enhances transparency and helps readers understand the model complexity without immediately consulting the appendix.", - "location": "Section 2.2, Appendix 6.1", - "category": "completeness", - "focus": "detail" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13.", - "improved_version": "Summarize the key performance metrics (e.g., AUC, F1) in a visual format like a line graph to illustrate trends over weeks.", - "explanation": "Visual summaries improve clarity and allow quick assessment of model performance evolution over time.", - "location": "Section 3.2.1", - "category": "clarity", - "focus": "presentation" - }, - { - "original_text": "The discussion on reengagement after correct predictions is somewhat scattered.", - "improved_version": "Reorganize this section into a clear subsection with bullet points or numbered lists highlighting the main findings: proportions reengaging, implications, and potential strategies.", - "explanation": "Structured presentation enhances readability and emphasizes key insights for practical application.", - "location": "Section 4.2", - "category": "organization", - "focus": "structure" - }, - { - "original_text": "While the datasets are described, the potential biases due to non-consenting users are not discussed.", - "improved_version": "Add a paragraph discussing possible selection bias introduced by excluding non-consenting users and how this might affect generalizability.", - "explanation": "Addressing dataset biases improves the thoroughness of the limitations section and informs interpretation of results.", - "location": "Section 4.4", - "category": "completeness", - "focus": "thoroughness" - }, - { - "original_text": "The performance metrics are dense and could be summarized more clearly.", - "improved_version": "Create a summary table that highlights the key performance metrics (AUC, accuracy, F1) across different weeks/months for each model, with color coding for performance levels.", - "explanation": "This makes complex data more accessible and facilitates comparison across models and time points.", - "location": "Section 3.2.1, 3.2.2", - "category": "clarity", - "focus": "presentation" - }, - { - "original_text": "The discussion on feature importance analyses is brief and references appendices.", - "improved_version": "Include a brief summary of the main findings from the feature importance analyses within the main text, highlighting which features were most predictive and how their importance changed over time.", - "explanation": "This enhances understanding of model interpretability and practical relevance without requiring appendix consultation.", - "location": "Section 4.1", - "category": "completeness", - "focus": "detail" - }, - { - "original_text": "The supplementary materials include extensive tables and figures, but their integration could be improved.", - "improved_version": "Add more explicit cross-references in the main text to specific tables and figures, and include brief interpretive captions or summaries for each visual aid.", - "explanation": "Improved linking and captions facilitate navigation and comprehension for readers reviewing supplementary data.", - "location": "Throughout", - "category": "organization", - "focus": "accessibility" - }, - { - "original_text": "The predictive utility during early days is limited, but this is not deeply discussed.", - "improved_version": "Expand the discussion to include potential strategies for improving early-day predictions, such as incorporating additional data sources or adjusting prediction windows.", - "explanation": "Addressing this limitation explicitly guides future research and model development efforts.", - "location": "Section 4.4", - "category": "completeness", - "focus": "thoroughness" - }, - { - "original_text": "The link between adherence and health outcomes is briefly mentioned as uncertain.", - "improved_version": "Include references to ongoing or planned studies that aim to evaluate the impact of adherence prediction and intervention on actual health outcomes, emphasizing the importance of this future work.", - "explanation": "This contextualizes the significance of the predictive models within broader health impact goals and highlights research gaps.", - "location": "Section 4.4", - "category": "relevance", - "focus": "connection" - }, - { - "original_text": "The discussion on intervention-specific features is generic.", - "improved_version": "Provide concrete examples tailored to the studied interventions, such as including features like 'number of exercise videos watched' for Vivira or 'number of blood pressure readings' for Manoa, and discuss how these could enhance model performance.", - "explanation": "Specific examples increase practical relevance and guide future feature engineering efforts.", - "location": "Section 4.3", - "category": "relevance", - "focus": "connection" - }, - { - "original_text": "The limitations section does not address potential biases from demographic variables.", - "improved_version": "Add a paragraph discussing how demographic factors like age, gender, or health status might influence model performance and the importance of stratified analyses or bias mitigation strategies.", - "explanation": "This enhances the thoroughness of limitations and informs future model fairness assessments.", - "location": "Section 4.4", - "category": "completeness", - "focus": "thoroughness" - }, - { - "original_text": "The supplementary materials are extensive but could benefit from a summary table of key findings.", - "improved_version": "Include a summary table at the end of the supplementary section that consolidates main performance metrics, sample sizes, and key insights for quick reference.", - "explanation": "This improves usability and allows readers to grasp core results without parsing all detailed data.", - "location": "Appendix", - "category": "organization", - "focus": "accessibility" - } - ], - "detailed_feedback": { - "relevance_analysis": "The supplementary materials are highly relevant to the main text, providing detailed data, tables, and figures that support the reported predictive performance and methodological approaches. They enhance transparency and allow for in-depth evaluation of the models and datasets, aligning well with the research objectives of predicting nonadherence and churn in mHealth interventions.", - "clarity_analysis": "While the materials are comprehensive, the presentation of complex data\u2014especially performance metrics over multiple weeks and months\u2014can be overwhelming. Incorporating visual summaries, clearer headings, and simplified language in some sections would improve accessibility and facilitate quicker understanding for diverse audiences.", - "consistency_analysis": "The supplementary data consistently align with the descriptions in the main text, with tables and figures matching the reported results. Cross-references are generally clear, although more explicit linking between figures, tables, and narrative explanations would reinforce coherence.", - "completeness_analysis": "The materials are detailed, including extensive tables, descriptive statistics, and performance metrics. However, some methodological details\u2014such as hyperparameter choices, data quality considerations, and potential biases\u2014are only briefly mentioned or relegated to appendices, which could be expanded for full transparency.", - "organization_analysis": "The supplementary files are logically structured into sections covering datasets, results, and appendices. Nonetheless, the integration could be improved by adding summary tables, clearer cross-references, and more consistent formatting to guide the reader through the extensive data efficiently." - }, - "summary": "Overall, the supplementary materials are of high quality, providing comprehensive and detailed data that support the main manuscript. The main areas for improvement involve enhancing clarity through visual summaries, explicitly linking data to practical implications, and expanding methodological transparency. Addressing these points would elevate the usability, interpretability, and impact of the supplementary content, making it more accessible and informative for a broad scientific audience." - }, - "R1": { - "originality_contribution_score": 8, - "critical_remarks": [ - { - "category": "novelty", - "location": "Abstract / Introduction", - "issue": "While the study claims to extend prior work by predicting nonadherence over longer durations and in different interventions, the core methodology\u2014using behavioral app engagement data with machine learning\u2014is established in existing literature. The novelty lies mainly in application scope rather than methodological innovation.", - "severity": "medium", - "impact": "This limits the perceived novelty of the approach, potentially affecting the paper's contribution perception, though the extended application adds value." - }, - { - "category": "contribution", - "location": "Discussion / Results", - "issue": "The paper emphasizes the potential for targeted interventions based on predictions but does not empirically test or demonstrate the effectiveness of such strategies, leaving the practical impact somewhat speculative.", - "severity": "high", - "impact": "This reduces the strength of the contribution claim regarding real-world impact and limits the evidence for practical implementation." - }, - { - "category": "verification", - "location": "Introduction / Methods", - "issue": "The claims of high predictive accuracy are based on retrospective data, with no prospective validation or real-time testing of the models' utility in live interventions.", - "severity": "high", - "impact": "This affects the validity of the claims about the models' real-world applicability and robustness." - }, - { - "category": "comparison", - "location": "Introduction / Literature Review", - "issue": "The comparison with existing literature is somewhat superficial, lacking detailed discussion of how this work advances beyond similar predictive models in digital health or app domains.", - "severity": "medium", - "impact": "This limits the contextual understanding of the study's novelty and its relative contribution." - }, - { - "category": "advancement", - "location": "Discussion / Conclusion", - "issue": "The paper states that the models 'advance knowledge' but does not clearly specify how this advances theoretical understanding or practical capabilities beyond existing models.", - "severity": "medium", - "impact": "This weakens the framing of the study as a significant knowledge advancement." - } - ], - "improvement_suggestions": [ - { - "original_text": "We developed machine learning models for the prediction of nonadherence in two mHealth interventions...", - "improved_version": "We applied and adapted existing machine learning frameworks to predict nonadherence across two distinct mHealth interventions, demonstrating their scalability and generalizability.", - "explanation": "Clarifies that the contribution is in application and validation across contexts, emphasizing generalizability rather than methodological novelty.", - "location": "Abstract", - "category": "novelty", - "focus": "novelty" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira...", - "improved_version": "Our models achieved high predictive accuracy in identifying nonadherent users over extended periods, comparable to or exceeding existing models, thereby confirming their robustness in real-world settings.", - "explanation": "Highlights the performance relative to existing models, framing the contribution as validation and robustness rather than novelty.", - "location": "Results", - "category": "contribution", - "focus": "contribution" - }, - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted...", - "improved_version": "Building upon prior research, this study investigates the extent to which behavioral app engagement data can predict nonadherence over longer durations, filling a notable gap in the literature.", - "explanation": "Explicitly states the research gap addressed, clarifying the novelty of the investigation.", - "location": "Introduction", - "category": "verification", - "focus": "verification" - }, - { - "original_text": "Our study thus extends prior research showing that methodologies effective in predicting churn during the first week remain applicable over longer durations...", - "improved_version": "This study extends prior research by demonstrating that predictive methodologies effective in early churn detection remain applicable over longer durations, thereby broadening their practical utility.", - "explanation": "Clarifies how the work advances existing methods, emphasizing practical extension rather than methodological innovation.", - "location": "Discussion", - "category": "advancement", - "focus": "advancement" - }, - { - "original_text": "We selected generalizable daily app engagement features related to users' activity and progress...", - "improved_version": "We selected simple, objective behavioral features\u2014such as daily app activity and task completion\u2014that are readily available across diverse mHealth apps, supporting their broad applicability.", - "explanation": "Highlights the simplicity and generalizability of features, strengthening the contribution claim.", - "location": "Methods", - "category": "contribution", - "focus": "contribution" - }, - { - "original_text": "The models predicted nonadherence relative to the intended use, following Sieverink et al. (2017)...", - "improved_version": "Our operationalization of nonadherence aligns with established frameworks (Sieverink et al., 2017), ensuring consistency with current standards and facilitating comparison with future studies.", - "explanation": "Strengthens the verification of the novelty claim by anchoring it to recognized definitions and standards.", - "location": "Introduction", - "category": "verification", - "focus": "verification" - }, - { - "original_text": "The study demonstrates that nonadherence can be accurately predicted over extended durations...", - "improved_version": "While our retrospective analysis shows promising predictive performance over extended durations, prospective validation is necessary to confirm real-world utility and impact.", - "explanation": "Provides a balanced view, acknowledging limitations and emphasizing the need for further validation, which enhances research credibility.", - "location": "Discussion", - "category": "verification", - "focus": "verification" - }, - { - "original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted...", - "improved_version": "Our findings demonstrate that behavioral app engagement data can serve as reliable predictors of nonadherence, contributing to the growing evidence base for data-driven adherence management in digital health.", - "explanation": "Frames the contribution as adding to the evidence base, emphasizing the practical utility rather than claiming groundbreaking novelty.", - "location": "Abstract / Discussion", - "category": "advancement", - "focus": "advancement" - }, - { - "original_text": "The models' performance improves as more data become available, aligning with prior research...", - "improved_version": "The observed improvement in model performance with increased data availability aligns with prior findings in app engagement research, reinforcing the robustness of behavioral features for long-term prediction.", - "explanation": "Connects findings to existing literature, clarifying the contribution as validation and reinforcement of known principles.", - "location": "Results / Discussion", - "category": "comparison", - "focus": "comparison" - }, - { - "original_text": "Our approach relies on behavioral engagement data, which is typically not available in non-digital interventions...", - "improved_version": "This approach leverages objective behavioral data unique to digital interventions, highlighting the advantage of digital health platforms in enabling precise adherence prediction.", - "explanation": "Clarifies the unique contribution enabled by digital data, strengthening the novelty argument.", - "location": "Discussion", - "category": "novelty", - "focus": "novelty" - }, - { - "original_text": "Future work should include prospective trials to validate these models in real-world settings...", - "improved_version": "Future research should focus on prospective, real-time validation of these models to establish their effectiveness in guiding adaptive adherence interventions and improving health outcomes.", - "explanation": "Provides a clear pathway for advancing the research, emphasizing practical impact and validation.", - "location": "Discussion / Conclusion", - "category": "advancement", - "focus": "advancement" - } - ], - "detailed_feedback": { - "novelty_assessment": "The study primarily extends existing methodologies of churn and nonadherence prediction by applying them over longer durations and in different medical contexts. While the application scope broadens, the core predictive approach\u2014using behavioral app engagement data with machine learning\u2014is well-established. The novelty is thus moderate, centered on demonstrating generalizability and robustness rather than methodological innovation.", - "contribution_analysis": "The paper contributes valuable evidence that behavioral engagement features can reliably predict nonadherence over extended periods, supporting their utility in diverse mHealth settings. It emphasizes the potential for integrating these models into intervention workflows for proactive adherence support, although it stops short of empirically testing intervention efficacy. The work advances practical understanding but could strengthen its contribution by demonstrating actual intervention impacts.", - "verification_status": "The claims of high predictive accuracy are based on retrospective analyses of large datasets, which are promising but require prospective validation to confirm real-world applicability. The models' performance in live settings remains to be tested, and the paper appropriately acknowledges this limitation.", - "comparative_analysis": "The discussion briefly references prior literature on churn prediction but lacks a detailed comparison of performance metrics, feature sets, or methodological differences. A more thorough comparison would clarify how this work advances the state of the art and what unique insights it offers.", - "advancement_evaluation": "The research advances knowledge by demonstrating the applicability of behavioral engagement data for long-term adherence prediction and by emphasizing the potential for scalable, data-driven intervention strategies. It reinforces the importance of simple, objective features and supports their use across diverse interventions, thus contributing to the practical toolkit for digital health researchers and practitioners." - }, - "summary": "Overall, this study makes a solid contribution by validating and extending existing predictive models for nonadherence over longer durations and in different contexts. Its strengths lie in demonstrating the robustness and generalizability of behavioral engagement features. To enhance its impact, the paper should more explicitly delineate its novelty relative to prior work, empirically validate intervention benefits, and deepen comparisons with existing literature. The balanced acknowledgment of limitations and future directions adds credibility, positioning this work as a meaningful step toward more adaptive and scalable adherence management in digital health." - }, - "R2": { - "impact_significance_score": 8, - "critical_remarks": [ - { - "category": "field_influence", - "location": "Abstract", - "issue": "While the abstract highlights the high accuracy of ML models in predicting nonadherence, it lacks explicit discussion on how these findings could shift current paradigms in digital health research.", - "severity": "medium", - "impact": "This limits the perceived novelty and potential influence of the research within the field, possibly underestimating its capacity to reshape adherence prediction methodologies." - }, - { - "category": "implications", - "location": "Discussion, paragraph 4.1", - "issue": "The discussion emphasizes the predictive accuracy but insufficiently explores how these models could directly influence health outcomes or clinical decision-making processes.", - "severity": "high", - "impact": "This diminishes the broader significance of the findings, potentially limiting their translational impact on health policy and clinical practice." - }, - { - "category": "future_research", - "location": "Discussion, paragraph 4.4", - "issue": "The limitations section notes the need for prospective trials but does not specify concrete research designs or intervention strategies to test the models' real-world efficacy.", - "severity": "high", - "impact": "This vagueness hampers the field\u2019s ability to build on these findings systematically, delaying the development of evidence-based implementation protocols." - }, - { - "category": "applications", - "location": "Discussion, paragraph 4.2", - "issue": "While potential intervention strategies are mentioned, there is a lack of detailed discussion on how these predictive insights could be integrated into existing app architectures or clinical workflows.", - "severity": "medium", - "impact": "This reduces the practical utility of the research, limiting immediate translation into actionable app features or healthcare interventions." - }, - { - "category": "policy", - "location": "Introduction and discussion", - "issue": "The paper briefly mentions regulatory environments but does not elaborate on how predictive models could influence health policy, reimbursement strategies, or regulatory approval processes.", - "severity": "medium", - "impact": "This oversight constrains the research\u2019s influence on policy development, potentially slowing adoption at the systemic level." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "The rich, objective engagement data collected by mHealth interventions have the potential to fundamentally transform adherence monitoring and prediction, offering new avenues for personalized intervention strategies.", - "explanation": "This reframes the statement to emphasize the transformative potential of the data, enhancing the perceived impact on the field.", - "location": "Abstract", - "category": "field_influence", - "focus": "field_influence" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95).", - "improved_version": "Our models demonstrated the capacity to accurately identify over 94% of nonadherent users across extended periods, indicating a significant advancement in early intervention potential within digital health research.", - "explanation": "This emphasizes the significance of the predictive accuracy as a breakthrough, strengthening its influence on future research and practice.", - "location": "Abstract", - "category": "field_influence", - "focus": "field_influence" - }, - { - "original_text": "The study extends prior research showing that methodologies effective in predicting churn during the first week remain applicable over longer durations.", - "improved_version": "This study extends the frontier of adherence research by demonstrating that predictive methodologies are effective not only in early stages but also over prolonged intervention durations, paving the way for sustained engagement strategies.", - "explanation": "This highlights the novelty and importance of the extended prediction window, reinforcing the research\u2019s influence on future methodological developments.", - "location": "Discussion, paragraph 4.1", - "category": "field_influence", - "focus": "field_influence" - }, - { - "original_text": "Models predicting churn achieved mean AUCs of 0.87 for both apps, correctly identifying 84-86% of churned users.", - "improved_version": "The high predictive performance (AUC ~0.87) in churn models underscores the feasibility of integrating real-time risk stratification into digital health platforms, potentially revolutionizing adherence management.", - "explanation": "This frames the results as a catalyst for systemic change, enhancing the research\u2019s influence on the field\u2019s evolution." - }, - { - "original_text": "Feature importance analyses revealed that behavioral app engagement features collected closer to the prediction event had a stronger impact.", - "improved_version": "Findings that recent behavioral engagement features are most predictive underscore the importance of designing interventions that leverage near-real-time data, influencing future research and app development strategies.", - "explanation": "This directs the field towards designing adaptive, data-driven intervention models, increasing practical relevance." - }, - { - "original_text": "The models\u2019 performance improved over time as more behavioral data became available.", - "improved_version": "The progressive enhancement in model accuracy over time highlights the potential for dynamic, continuously learning systems that adapt to evolving user behaviors, shaping future research in adaptive digital health solutions.", - "explanation": "This emphasizes innovation and future directions, boosting the research\u2019s impact on the development of intelligent, scalable interventions." - }, - { - "original_text": "Our findings suggest that simple app engagement features are sufficient to predict future user behavior.", - "improved_version": "The demonstration that basic, objective engagement metrics can reliably predict adherence opens new avenues for scalable, low-cost monitoring systems applicable across diverse mHealth interventions.", - "explanation": "This enhances the perceived practicality and broad applicability, increasing the research\u2019s influence on implementation strategies." - }, - { - "original_text": "Limitations include the reliance on rich behavioral data, which may not be available in all settings.", - "improved_version": "Future research should explore the integration of sparse or sporadic engagement data, broadening the applicability of predictive models to interventions with less frequent user interactions.", - "explanation": "This expands the scope, encouraging further innovation and impact across varied intervention contexts." - }, - { - "original_text": "Prospective trials are necessary to fully establish the models\u2019 real-world utility.", - "improved_version": "Designing and executing prospective, randomized trials will be crucial to validate these models\u2019 effectiveness in real-world clinical and behavioral settings, accelerating their translation into practice.", - "explanation": "This provides a clear pathway for future impactful research, emphasizing translational potential." - }, - { - "original_text": "The models could be integrated with targeted strategies such as personalized push notifications or content adaptations.", - "improved_version": "Integrating predictive models with tailored, in-app intervention strategies\u2014such as personalized notifications, adaptive content, and human support\u2014can significantly enhance adherence and health outcomes.", - "explanation": "This clarifies how models can directly influence practical intervention design, boosting their significance." - }, - { - "original_text": "The study briefly mentions regulatory environments but does not elaborate on how predictive models could influence health policy.", - "improved_version": "Future work should investigate how predictive adherence models can inform health policy, reimbursement frameworks, and regulatory standards to facilitate widespread adoption and integration into healthcare systems.", - "explanation": "This explicitly links research findings to policy impact, increasing systemic influence." - }, - { - "original_text": "The discussion emphasizes the predictive accuracy but insufficiently explores how these models could influence health outcomes or clinical decision-making.", - "improved_version": "By demonstrating high predictive accuracy, this research lays the groundwork for integrating adherence prediction into clinical workflows, potentially improving health outcomes through timely, personalized interventions.", - "explanation": "This explicitly connects model performance to tangible health and clinical benefits, enhancing broader significance." - }, - { - "original_text": "The limitations section notes the need for prospective trials but does not specify concrete research designs.", - "improved_version": "Future research should implement randomized controlled trials and real-world pilot studies to evaluate the impact of predictive-driven adherence interventions on health outcomes and system efficiency.", - "explanation": "This provides concrete guidance for advancing the research impact through systematic validation." - } - ], - "detailed_feedback": { - "field_influence": "This research significantly advances the field by demonstrating the robustness and generalizability of behavioral engagement data for long-term adherence prediction, setting a new standard for scalable, data-driven adherence monitoring in digital health.", - "broader_implications": "The findings suggest that objective, app-based behavioral data can fundamentally reshape adherence management, enabling personalized, timely interventions that could reduce the burden of noncommunicable diseases and optimize healthcare resource utilization globally.", - "future_research_impact": "Building on these results, future studies should focus on prospective validation, integration with clinical workflows, and testing intervention strategies that leverage real-time predictions to improve health outcomes and system efficiency.", - "practical_applications": "The models\u2019 reliance on simple behavioral features facilitates immediate integration into existing mHealth platforms, enabling proactive adherence support, personalized nudges, and cost-effective scaling of digital health interventions.", - "policy_implications": "The demonstrated potential for predictive adherence models to inform personalized intervention strategies underscores the need for policy frameworks that support data sharing, real-time monitoring, and reimbursement models aligned with digital health innovation." - }, - "summary": "This study offers a compelling and methodologically rigorous contribution to digital health research, demonstrating that behavioral app engagement data can reliably predict nonadherence over extended periods. Its findings have the potential to influence future research directions, practical intervention designs, and health policy frameworks, although further prospective validation and implementation research are essential to realize its full systemic impact." - }, - "R3": { - "ethics_compliance_score": 7, - "critical_remarks": [ - { - "category": "conflicts", - "location": "Author Contributions and Conflicts of Interest section", - "issue": "Authors disclose affiliations with organizations that have financial ties to health insurers and digital health companies, but the potential influence of these conflicts on study design, data interpretation, or reporting is not explicitly discussed.", - "severity": "medium", - "impact": "Potential bias in research outcomes or interpretation, which could compromise objectivity and transparency." - }, - { - "category": "privacy", - "location": "Abstract and Methods sections", - "issue": "While datasets are anonymized, there is limited detail on specific data privacy measures, data storage, or security protocols implemented to protect user data during analysis.", - "severity": "medium", - "impact": "Insufficient transparency about data privacy measures may undermine trust and compliance with data protection standards." - }, - { - "category": "consent", - "location": "Methods section, Dataset descriptions", - "issue": "The paper states that only users who provided consent under specific regulations were included, but it does not detail the process of obtaining informed consent or how users were informed about data use.", - "severity": "high", - "impact": "Lack of detailed consent procedures raises concerns about whether participants were adequately informed, which is essential for ethical compliance." - }, - { - "category": "integrity", - "location": "Results and Discussion sections", - "issue": "The study reports high predictive performance but does not address potential biases in model training, such as class imbalance or overfitting, nor does it discuss validation on external datasets.", - "severity": "medium", - "impact": "Potential overestimation of model effectiveness, which could mislead stakeholders and affect ethical application of findings." - }, - { - "category": "guidelines", - "location": "Ethics Declaration", - "issue": "The ethics statement claims exemption from human subjects review due to anonymized data, but it does not specify adherence to relevant ethical guidelines such as the Declaration of Helsinki or GDPR principles.", - "severity": "low", - "impact": "Insufficient detail on adherence to established ethical standards may weaken the ethical rigor of the study." - } - ], - "improvement_suggestions": [ - { - "original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "improved_version": "The study explicitly states that informed consent was obtained from all participants in accordance with GDPR and local ethical standards, detailing the consent process, including how users were informed about data use and their rights.", - "explanation": "Providing detailed consent procedures enhances transparency and demonstrates adherence to ethical standards for participant autonomy and informed participation.", - "location": "Methods section, Dataset descriptions", - "category": "consent", - "focus": "consent" - }, - { - "original_text": "The study involves the analyses of two anonymous datasets. Therefore, it does not qualify as human subject research and has been exempted from ethics approval by the research team's University Ethics Committee.", - "improved_version": "The study confirms that all data were anonymized prior to analysis, and that data handling complied with GDPR and local ethical guidelines, with documentation available upon request. Ethical approval was obtained for the use of identifiable data where applicable.", - "explanation": "Clarifying compliance with data protection laws and ethical standards reassures readers of ethical rigor and legal adherence.", - "location": "Ethics Declaration", - "category": "guidelines", - "focus": "guidelines" - }, - { - "original_text": "Authors disclose affiliations with organizations that have financial ties to health insurers and digital health companies, but the potential influence of these conflicts on study design, data interpretation, or reporting is not explicitly discussed.", - "improved_version": "The authors declare their affiliations and funding sources, and explicitly state that these entities had no role in study design, data analysis, interpretation, or manuscript preparation to mitigate potential conflicts of interest.", - "explanation": "Explicitly stating the independence of the research process from funding sources enhances transparency and addresses potential conflicts.", - "location": "Conflicts of Interest section", - "category": "conflicts", - "focus": "conflicts" - }, - { - "original_text": "While datasets are anonymized, there is limited detail on specific data privacy measures, data storage, or security protocols implemented to protect user data during analysis.", - "improved_version": "The study details the data privacy measures, including secure data storage, access controls, and compliance with GDPR, ensuring that user data remained protected throughout the research process.", - "explanation": "Providing specific privacy and security measures demonstrates commitment to data protection standards and ethical data management.", - "location": "Methods section, Data collection", - "category": "privacy", - "focus": "privacy" - }, - { - "original_text": "The paper states that only users who provided consent under specific regulations were included, but it does not detail the process of obtaining informed consent or how users were informed about data use.", - "improved_version": "The manuscript describes the informed consent process, including how users were informed about data collection, purpose, storage, and their rights, in accordance with GDPR and local ethical standards.", - "explanation": "Detailing the consent process ensures transparency and confirms adherence to ethical standards for participant autonomy.", - "location": "Methods section, Consent procedures", - "category": "consent", - "focus": "consent" - }, - { - "original_text": "The study reports high predictive performance but does not address potential biases in model training, such as class imbalance or overfitting, nor does it discuss validation on external datasets.", - "improved_version": "The discussion includes acknowledgment of potential biases such as class imbalance, and describes steps taken to mitigate overfitting, including cross-validation and external validation where applicable, to ensure model robustness.", - "explanation": "Addressing biases and validation enhances research integrity and trustworthiness of the findings.", - "location": "Results and Discussion", - "category": "integrity", - "focus": "integrity" - }, - { - "original_text": "The ethics statement claims exemption from human subjects review due to anonymized data, but it does not specify adherence to relevant ethical guidelines such as the Declaration of Helsinki or GDPR principles.", - "improved_version": "The study affirms compliance with ethical guidelines including the Declaration of Helsinki and GDPR, with documentation of ethical considerations and data handling procedures available upon request.", - "explanation": "Explicitly referencing established ethical frameworks reinforces the ethical rigor of the research.", - "location": "Ethics Declaration", - "category": "guidelines", - "focus": "guidelines" - }, - { - "original_text": "Authors affiliated with commercial entities (Pathmate Technologies AG, Vivira Health Lab GmbH) are involved, but the manuscript does not discuss potential influence or measures to prevent bias.", - "improved_version": "The manuscript discloses the authors' affiliations and states that independent oversight or measures, such as external validation or peer review, were employed to minimize potential bias arising from these affiliations.", - "explanation": "Transparency about measures to prevent bias from conflicts of interest enhances ethical standards and credibility.", - "location": "Author Contributions and Conflicts of Interest", - "category": "conflicts", - "focus": "conflicts" - }, - { - "original_text": "The study emphasizes predictive accuracy but does not discuss the potential implications or risks of false positives/negatives in clinical or user contexts.", - "improved_version": "The discussion addresses the ethical implications of false positives and negatives, including potential risks such as unnecessary interventions or missed opportunities for support, and suggests strategies to mitigate these risks.", - "explanation": "Acknowledging potential harms and ethical considerations in model deployment ensures responsible application of research findings.", - "location": "Discussion", - "category": "integrity", - "focus": "guidelines" - }, - { - "original_text": "The study does not specify whether any ethical review or oversight was conducted for the development and validation of predictive models beyond data anonymization.", - "improved_version": "The authors state that the development and validation of predictive models adhered to institutional ethical standards, with oversight from relevant committees, and that all procedures complied with applicable laws and guidelines.", - "explanation": "Clarifying oversight processes reinforces adherence to ethical standards in all research phases.", - "location": "Methods and Ethics Declaration", - "category": "guidelines", - "focus": "guidelines" - } - ], - "detailed_feedback": { - "conflicts_assessment": "The authors disclose affiliations with organizations involved in digital health and insurance sectors, but do not explicitly discuss measures taken to prevent bias or influence. Transparency about independent oversight or measures to mitigate conflicts would strengthen ethical integrity.", - "privacy_compliance": "While datasets are anonymized, the manuscript lacks detailed description of data security, storage, and privacy measures aligned with GDPR. Explicit statements on these protocols would enhance confidence in data privacy compliance.", - "consent_procedures": "The paper mentions that only users who provided consent were included, but it does not detail the process of obtaining informed consent, how users were informed about data use, or their rights. Clearer description of consent procedures is necessary for ethical transparency.", - "research_integrity": "High predictive performance is reported, but potential biases such as class imbalance and overfitting are not addressed. Including validation strategies and discussing limitations would improve research integrity and trustworthiness.", - "guidelines_adherence": "The ethics declaration states exemption from review due to anonymized data but does not specify adherence to international ethical standards like the Declaration of Helsinki or GDPR principles. Explicit confirmation of compliance would strengthen ethical rigor." - }, - "summary": "Overall, the study demonstrates a solid foundation in data analysis and ethical considerations, but could improve transparency regarding informed consent procedures, detailed data privacy measures, conflict of interest management, and explicit adherence to established ethical guidelines. Addressing these areas would enhance the ethical robustness and credibility of the research." - }, - "R4": { - "data_code_availability_score": 4, - "critical_remarks": [ - { - "category": "data_sharing", - "location": "Abstract / Data Description", - "issue": "The datasets are described in detail, but there is no mention of publicly available data repositories or links where the data can be accessed by other researchers.", - "severity": "high", - "impact": "Lack of accessible data hampers reproducibility and independent validation of results, reducing transparency." - }, - { - "category": "code_availability", - "location": "Methodology / Data Analysis", - "issue": "No mention of publicly available code repositories, scripts, or software used for analysis and modeling.", - "severity": "high", - "impact": "Absence of shared code limits reproducibility and impedes other researchers from verifying or building upon the work." - }, - { - "category": "documentation", - "location": "Appendix / Methodology", - "issue": "Details about software packages and hyperparameter grids are referenced but not provided within the main text or accessible supplementary materials.", - "severity": "medium", - "impact": "Insufficient documentation reduces clarity for replication efforts and understanding of the modeling process." - }, - { - "category": "restrictions", - "location": "Data / Ethics Declaration", - "issue": "Data use is limited to users who provided consent under specific regulations; no information on how others can access similar datasets or request data sharing.", - "severity": "medium", - "impact": "Restrictions limit external validation and broader reproducibility, especially for researchers outside the specific regulatory context." - }, - { - "category": "reproducibility", - "location": "Discussion / Limitations", - "issue": "The paper emphasizes the potential for generalizability but lacks details on how to reproduce the predictive models in different settings or with different data.", - "severity": "medium", - "impact": "Limited guidance on reproducibility reduces the ability of others to validate and extend the findings." - } - ], - "improvement_suggestions": [ - { - "original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "improved_version": "The datasets used in this study are not publicly available due to privacy and regulatory restrictions. However, de-identified versions or synthetic datasets mimicking the original data can be shared upon reasonable request and under data use agreements. Details are provided in the supplementary materials.", - "explanation": "Clarifies data sharing options within legal constraints, promoting transparency and enabling replication under controlled conditions.", - "location": "Data Description / Ethics Declaration", - "category": "data_sharing", - "focus": "data_sharing" - }, - { - "original_text": "The dataset included data from 8,372 users who activated their first prescription between January 1, 2023, and March 31, 2024.", - "improved_version": "The datasets, including anonymized user engagement and outcome data, are available in a public repository such as [Repository Name], under accession number [XYZ], or upon request from the corresponding author. Access is subject to data use agreements to ensure privacy compliance.", - "explanation": "Provides concrete access pathways, facilitating external validation and further research.", - "location": "Data Description", - "category": "data_sharing", - "focus": "data_sharing" - }, - { - "original_text": "No mention of publicly available code repositories.", - "improved_version": "All analysis scripts, preprocessing code, and modeling pipelines are openly available at [GitHub/Zenodo link], with detailed documentation on usage and dependencies.", - "explanation": "Ensures code transparency, enabling others to reproduce and build upon the analysis.", - "location": "Methodology / Data Analysis", - "category": "code_availability", - "focus": "code_availability" - }, - { - "original_text": "Detailed hyperparameter grids are provided in Appendix 6.1.", - "improved_version": "Complete code for data preprocessing, feature extraction, model training, hyperparameter tuning, and evaluation, along with hyperparameter grids, are hosted in the public repository [Link], with step-by-step instructions in the README file.", - "explanation": "Enhances reproducibility by providing executable code and clear instructions.", - "location": "Appendix / Methodology", - "category": "code_availability", - "focus": "code_availability" - }, - { - "original_text": "Details about software packages are referenced but not provided within the main text or accessible supplementary materials.", - "improved_version": "A comprehensive list of software packages, versions, and environment setup instructions are included in the repository's README and environment configuration files (e.g., environment.yml or requirements.txt).", - "explanation": "Facilitates environment replication, reducing setup barriers for other researchers.", - "location": "Appendix / Software environment", - "category": "documentation", - "focus": "documentation" - }, - { - "original_text": "No explicit mention of code documentation or usage instructions.", - "improved_version": "All code repositories include detailed documentation, including data input formats, step-by-step analysis workflows, and instructions for reproducing the figures and results.", - "explanation": "Improves usability and transparency of the codebase for external users.", - "location": "Code Documentation", - "category": "documentation", - "focus": "code_availability" - }, - { - "original_text": "The paper emphasizes the potential for generalizability but lacks details on how to reproduce models in different contexts.", - "improved_version": "We provide a reproducible pipeline with sample datasets, code, and detailed instructions in the supplementary materials, enabling researchers to adapt the models to new datasets or settings.", - "explanation": "Supports reproducibility and adaptation across different studies and populations.", - "location": "Discussion / Reproducibility", - "category": "reproducibility", - "focus": "reproducibility" - }, - { - "original_text": "The datasets are described in detail, but no links or access procedures are provided.", - "improved_version": "Data sharing details, including access procedures, repository links, and licensing conditions, are provided in the Data Availability Statement and supplementary materials.", - "explanation": "Ensures transparency about data accessibility, encouraging external validation.", - "location": "Data Description", - "category": "data_sharing", - "focus": "data_sharing" - }, - { - "original_text": "No mention of sharing analysis code or scripts.", - "improved_version": "All analysis scripts, including data preprocessing, feature engineering, and modeling code, are publicly available at [Repository Link], with comprehensive documentation and example workflows.", - "explanation": "Enables independent reproduction and validation of the results.", - "location": "Methodology / Data Analysis", - "category": "code_availability", - "focus": "code_availability" - }, - { - "original_text": "The paper does not specify how to access supplementary materials or code repositories.", - "improved_version": "All supplementary materials, including code, hyperparameter configurations, and detailed methodological descriptions, are hosted at [Repository/DOI link], accessible to all researchers.", - "explanation": "Provides clear access points, promoting transparency and reproducibility.", - "location": "Conclusion / Data and Code Availability", - "category": "documentation", - "focus": "code_availability" - } - ], - "detailed_feedback": { - "data_sharing_assessment": "While the datasets are described thoroughly, their availability is restricted due to privacy and regulatory considerations. To enhance transparency, the authors should specify whether de-identified or synthetic datasets are available and provide access procedures, such as data repositories or request protocols.", - "code_availability": "The manuscript lacks any mention of publicly accessible code repositories or scripts used for data analysis and modeling. Sharing code via platforms like GitHub or Zenodo, along with documentation, would significantly improve reproducibility and facilitate external validation.", - "documentation_completeness": "The current documentation references appendices and supplementary materials but does not include explicit instructions, software environment details, or step-by-step workflows. Providing comprehensive documentation, including environment setup files and usage instructions, would support reproducibility.", - "restrictions_justification": "Data access is limited by consent and regulatory restrictions, which are justified but should be clearly communicated. Providing pathways for data access under controlled conditions or sharing synthetic datasets can help balance privacy with transparency.", - "reproducibility_support": "The paper emphasizes the potential for generalizability but does not include executable code, detailed workflows, or environment specifications. Including these elements in public repositories would enable others to reproduce and adapt the models effectively." - }, - "summary": "Overall, the manuscript demonstrates strong methodological rigor but falls short in openly sharing data and code, which are critical for transparency and reproducibility. Addressing these gaps by providing access to datasets (where permissible), sharing analysis scripts, and including detailed documentation would substantially enhance the research's transparency and impact." - }, - "R5": { - "statistical_rigor_score": 6, - "critical_remarks": [ - { - "category": "test_selection", - "location": "Methodology: 2.2 Feature Selection, Model Training, and Evaluation", - "issue": "While random forest models are used extensively, there is limited discussion on the justification for choosing this algorithm over others, especially given the class imbalance and the potential benefits of alternative methods like boosting or neural networks.", - "severity": "medium", - "impact": "This may limit the exploration of potentially more optimal models, affecting the robustness and generalizability of the findings." - }, - { - "category": "assumptions", - "location": "Methodology: 2.2 Data Preparation", - "issue": "There is no explicit mention of assumption checks for the machine learning models, such as feature independence, multicollinearity, or the distributional assumptions underlying the preprocessing steps.", - "severity": "medium", - "impact": "Unverified assumptions could lead to overfitting or biased models, reducing the validity of the predictive performance." - }, - { - "category": "sample_size", - "location": "Methodology: 2.2 Model Training and Evaluation", - "issue": "Although large sample sizes are used, there is no formal sample size justification or power analysis to confirm that the datasets are sufficient for the complexity of the models and the number of features.", - "severity": "low", - "impact": "This omission may affect the confidence in the stability and reliability of the model estimates." - }, - { - "category": "multiple_comparisons", - "location": "Results: 3 Prediction Results", - "issue": "Multiple performance metrics are reported across numerous weeks and days without correction for multiple testing or comparison, increasing the risk of Type I errors.", - "severity": "medium", - "impact": "This can lead to overstated significance and misinterpretation of the model's performance improvements over time." - }, - { - "category": "effect_size", - "location": "Results: 3.2 Prediction Results", - "issue": "While performance metrics like AUC, accuracy, and F1 are reported, there is limited discussion on the effect sizes or practical significance of the predictive improvements.", - "severity": "low", - "impact": "Without effect size context, it's difficult to assess the real-world impact of the models' predictive capabilities." - }, - { - "category": "confidence_intervals", - "location": "Results: 3.2 Prediction Results", - "issue": "Confidence intervals are not provided for key metrics such as AUC, accuracy, or F1 scores, which limits understanding of the precision of these estimates.", - "severity": "high", - "impact": "This omission hampers the assessment of statistical uncertainty and the robustness of the reported performance metrics." - }, - { - "category": "p_value", - "location": "Discussion: 4.1 Predicting Nonadherence", - "issue": "There is no mention of p-values or statistical significance testing for the differences in model performance metrics over time or between models.", - "severity": "low", - "impact": "This limits the ability to statistically validate observed performance trends or differences." - }, - { - "category": "power", - "location": "Methodology: 2.2 Model Training and Evaluation", - "issue": "No formal power analysis is conducted to determine whether the sample sizes and number of events are adequate for the model complexity and class imbalance.", - "severity": "low", - "impact": "Potential underpowering could lead to unstable estimates and reduced confidence in model performance." - }, - { - "category": "missing_data", - "location": "Methodology: 2.1 Datasets and Definitions of Nonadherence", - "issue": "Handling of missing data is not explicitly described; it is unclear whether missing engagement or demographic data were imputed, excluded, or otherwise managed.", - "severity": "high", - "impact": "Unaddressed missing data can bias results and compromise the validity of the models." - }, - { - "category": "outliers", - "location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence", - "issue": "There is no discussion on outlier detection or treatment for continuous variables such as number of exercises or sessions, which could skew model training.", - "severity": "medium", - "impact": "Unhandled outliers may distort model learning and reduce predictive accuracy." - } - ], - "improvement_suggestions": [ - { - "original_text": "While random forest models are used extensively, there is limited discussion on the justification for choosing this algorithm over others, especially given the class imbalance and the potential benefits of alternative methods like boosting or neural networks.", - "improved_version": "Include a rationale for selecting random forest algorithms, such as comparative performance analyses with other models like XGBoost, neural networks, or logistic regression, especially considering class imbalance and interpretability.", - "explanation": "Providing a comparative justification enhances the transparency and robustness of the model choice, ensuring that the selected method is optimal for the data characteristics.", - "location": "Methodology: 2.2 Model Training and Evaluation", - "category": "test_selection" - }, - { - "original_text": "There is no explicit mention of assumption checks for the machine learning models, such as feature independence, multicollinearity, or the distributional assumptions underlying the preprocessing steps.", - "improved_version": "Add a section detailing assumption checks, such as correlation analyses for multicollinearity, distribution assessments for features, and validation of preprocessing steps like normalization or scaling.", - "explanation": "Verifying assumptions ensures the validity of the preprocessing and modeling steps, reducing bias and overfitting risks.", - "location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence", - "category": "assumptions" - }, - { - "original_text": "Although large sample sizes are used, there is no formal sample size justification or power analysis to confirm that the datasets are sufficient for the complexity of the models and the number of features.", - "improved_version": "Incorporate a formal sample size and power analysis, considering the number of features, expected effect sizes, and class imbalance, to justify the adequacy of the datasets for model training.", - "explanation": "A formal justification enhances confidence that the study is adequately powered to detect meaningful differences and model performance levels.", - "location": "Methodology: 2.2 Model Training and Evaluation", - "category": "sample_size" - }, - { - "original_text": "Multiple performance metrics are reported across numerous weeks and days without correction for multiple testing or comparison, increasing the risk of Type I errors.", - "improved_version": "Apply statistical correction methods such as Bonferroni or Holm adjustments when comparing performance metrics across multiple weeks or days, and report adjusted p-values to control for multiple comparisons.", - "explanation": "Correcting for multiple comparisons reduces false-positive findings, increasing the validity of performance trend interpretations.", - "location": "Results: 3 Prediction Results", - "category": "multiple_comparisons" - }, - { - "original_text": "While performance metrics like AUC, accuracy, and F1 are reported, there is limited discussion on the effect sizes or practical significance of the predictive improvements.", - "improved_version": "Include effect size measures such as Cohen\u2019s d or differences in performance metrics with confidence intervals to contextualize the practical significance of improvements over time.", - "explanation": "Effect sizes provide a clearer understanding of the real-world impact of the predictive models beyond statistical significance alone.", - "location": "Results: 3.2 Prediction Results", - "category": "effect_size" - }, - { - "original_text": "Confidence intervals are not provided for key metrics such as AUC, accuracy, or F1 scores, which limits understanding of the precision of these estimates.", - "improved_version": "Calculate and report 95% confidence intervals for all key performance metrics (AUC, accuracy, F1, precision, recall) to quantify the uncertainty around these estimates.", - "explanation": "Confidence intervals allow for assessment of the statistical precision and reliability of the performance metrics, strengthening the interpretability of results.", - "location": "Results: 3.2 Prediction Results", - "category": "confidence_intervals" - }, - { - "original_text": "There is no mention of p-values or statistical significance testing for the differences in model performance metrics over time or between models.", - "improved_version": "Conduct formal statistical tests (e.g., paired t-tests, bootstrap comparisons) to evaluate whether observed differences in performance metrics across weeks or models are statistically significant, and report p-values accordingly.", - "explanation": "Statistical testing of performance differences enhances the rigor in claiming improvements or differences between models or time points.", - "location": "Discussion: 4.1 Predicting Nonadherence", - "category": "p_value" - }, - { - "original_text": "No formal power analysis is conducted to determine whether the sample sizes and number of events are adequate for the model complexity and class imbalance.", - "improved_version": "Include a formal power analysis or sample size calculation tailored to the expected effect sizes, class imbalance, and model complexity to justify dataset adequacy.", - "explanation": "This ensures the study is sufficiently powered to detect meaningful differences and reduces the risk of underpowered analyses.", - "location": "Methodology: 2.2 Model Training and Evaluation", - "category": "power" - }, - { - "original_text": "Handling of missing data is not explicitly described; it is unclear whether missing engagement or demographic data were imputed, excluded, or otherwise managed.", - "improved_version": "Describe the methods used for handling missing data, such as multiple imputation, exclusion criteria, or data augmentation, and justify their appropriateness.", - "explanation": "Explicit missing data handling prevents bias and ensures transparency in data preprocessing, improving the validity of the models.", - "location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence", - "category": "missing_data" - }, - { - "original_text": "There is no discussion on outlier detection or treatment for continuous variables such as number of exercises or sessions, which could skew model training.", - "improved_version": "Implement outlier detection procedures (e.g., z-scores, IQR-based methods) and specify how outliers are treated or excluded to prevent distortion of model training.", - "explanation": "Proper outlier management enhances model stability and accuracy by reducing the influence of extreme values.", - "location": "Data Preparation: 2.1 Datasets and Definitions of Nonadherence", - "category": "outliers" - } - ], - "detailed_feedback": { - "test_selection": "The study predominantly employs random forest algorithms for all predictions, citing prior success but not explicitly comparing alternative models. Incorporating a model comparison section, including simpler or more interpretable models like logistic regression or boosting algorithms, would strengthen the justification for the chosen approach and potentially improve predictive performance.", - "assumption_verification": "The preprocessing steps mention normalization and scaling but do not detail assumption checks such as multicollinearity assessments or feature independence. Explicitly conducting and reporting such checks ensures the data meet the assumptions underlying the preprocessing and modeling steps, reducing bias and overfitting risks.", - "sample_size_justification": "While large datasets are used, the absence of formal sample size calculations or power analyses limits confidence in the sufficiency of the data for complex ML models. Including such analyses would confirm that the study is adequately powered to detect meaningful differences and model performance levels.", - "multiple_comparison_handling": "Multiple performance metrics are reported across many time points without correction for multiple testing. Applying statistical correction methods like Bonferroni or Holm adjustments, and reporting adjusted p-values, would mitigate the risk of false-positive findings and enhance the robustness of performance trend claims.", - "effect_size_reporting": "The results focus on performance metrics without contextualizing their practical significance. Including effect size measures or confidence intervals for differences in performance over time would clarify the real-world impact of the models\u2019 improvements.", - "confidence_intervals": "Key metrics such as AUC, accuracy, and F1 scores are presented without confidence intervals. Calculating and reporting these intervals would provide insight into the precision and reliability of the estimates, strengthening the interpretability of the results.", - "p_value_interpretation": "The discussion does not include significance testing for performance differences. Conducting formal statistical tests and reporting p-values would allow for rigorous validation of observed trends and differences, reducing subjective interpretation.", - "statistical_power": "No formal power analysis is reported, which raises concerns about whether the sample sizes and number of events are sufficient for the complexity of the models, especially given class imbalance. Including power calculations would bolster confidence in the findings.", - "missing_data_handling": "The handling of missing data is not described, leaving uncertainty about potential biases introduced by data exclusion or imputation. Explicitly detailing missing data management strategies would improve transparency and validity.", - "outlier_treatment": "There is no discussion of outlier detection or treatment for continuous variables like exercise counts. Implementing outlier detection methods and describing their use would prevent skewed model training and improve accuracy." - }, - "summary": "Overall, the study demonstrates a commendable effort in applying machine learning to predict nonadherence in mHealth interventions, with extensive datasets and multiple performance evaluations. However, enhancements in statistical rigor\u2014such as explicit assumption checks, correction for multiple testing, confidence interval reporting, and formal power analyses\u2014are needed to strengthen the validity and interpretability of the findings. Addressing these aspects will improve the robustness, reproducibility, and practical relevance of the research outcomes." - }, - "R6": { - "technical_accuracy_score": 8, - "critical_remarks": [ - { - "category": "derivations", - "location": "Mathematical Framework section, paragraphs describing feature normalization and model training", - "issue": "While the text mentions normalization techniques like square root scaling and standard scaling, it lacks detailed mathematical derivations or formulas explaining the exact transformations applied to features, which could lead to ambiguity in reproducibility.", - "severity": "medium", - "impact": "This omission may hinder precise replication and understanding of the preprocessing steps, affecting the technical rigor." - }, - { - "category": "algorithms", - "location": "Methodology, paragraph on model training and hyperparameter tuning", - "issue": "The choice of random forest is justified with references to prior studies, but there is no detailed discussion of hyperparameter settings, feature selection process, or validation procedures beyond stratified 10-fold cross-validation, which might impact the assessment of algorithm correctness and efficiency.", - "severity": "medium", - "impact": "Limited transparency on model tuning could obscure potential overfitting or underfitting issues, affecting the perceived robustness." - }, - { - "category": "terminology", - "location": "Throughout the document, especially in definitions of adherence and churn", - "issue": "The terms 'nonadherence,' 'churn,' and 'disengagement' are used with nuanced distinctions, but some definitions are inconsistent or lack precise operationalization, e.g., 'discontinuing use' vs 'not meeting weekly exercise thresholds.'", - "severity": "low", - "impact": "Inconsistent terminology may cause confusion in interpreting results and applying the models in practice." - }, - { - "category": "equations", - "location": "Mathematical Framework section, descriptions of feature normalization", - "issue": "The text mentions 'normalizing numerical data by performing square root scaling' but does not provide explicit formulas or equations illustrating the transformation, which could lead to ambiguity.", - "severity": "low", - "impact": "Lack of explicit equations reduces clarity and reproducibility." - }, - { - "category": "completeness", - "location": "Methodology, model evaluation", - "issue": "While multiple performance metrics are reported, there is limited discussion on how class imbalance was addressed beyond Tomek Links undersampling, and no mention of other techniques like SMOTE or cost-sensitive learning.", - "severity": "medium", - "impact": "Incomplete coverage of imbalance handling methods may affect the interpretation of model performance and generalizability." - }, - { - "category": "consistency", - "location": "Throughout the document, especially in definitions of prediction windows", - "issue": "Different definitions of prediction windows (e.g., 7-day, 31-day) are used across models and datasets without a unified framework or explicit rationale, which could lead to inconsistencies in interpretation.", - "severity": "low", - "impact": "This inconsistency can affect the logical comparability of results across models and datasets." - }, - { - "category": "implementation", - "location": "Methodology, software packages and hyperparameter tuning", - "issue": "The appendix mentions the use of 'freely available Python packages' but does not specify versions, specific libraries, or code snippets, which hampers reproducibility and assessment of implementation correctness.", - "severity": "medium", - "impact": "Limited implementation transparency diminishes confidence in the reproducibility and technical rigor." - }, - { - "category": "edge_cases", - "location": "Discussion, limitations", - "issue": "The text acknowledges that models rely on rich, continuous engagement data but does not explicitly address how the models perform with sparse or irregular data, or how they handle users with very short engagement periods.", - "severity": "high", - "impact": "Ignoring such edge cases could lead to overestimating model robustness and applicability in real-world, less ideal data scenarios." - }, - { - "category": "complexity", - "location": "Technical analysis, model evaluation", - "issue": "There is minimal discussion on the computational complexity, training time, or resource requirements of the random forest models, which is critical for practical deployment in large-scale mHealth systems.", - "severity": "low", - "impact": "Lack of complexity analysis limits understanding of model scalability and operational feasibility." - }, - { - "category": "documentation", - "location": "Overall, including appendices and references", - "issue": "The documentation of hyperparameter grids, feature importance analysis, and model evaluation procedures is limited to references to appendices, which are not fully detailed in the main text, reducing transparency.", - "severity": "medium", - "impact": "This hampers full understanding and independent validation of the technical methods used." - } - ], - "improvement_suggestions": [ - { - "original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "improved_version": "The manuscript should explicitly specify the exact inclusion criteria, including the consent process and data anonymization procedures, to enhance clarity and reproducibility.", - "explanation": "Clearer operationalization of inclusion criteria improves transparency and allows other researchers to replicate the sample selection process.", - "location": "Methodology, Dataset description", - "category": "documentation", - "focus": "clarity" - }, - { - "original_text": "We applied stratified 10-fold cross-validation and randomized search for hyperparameter tuning on the training sets, optimizing for F1 score for all models.", - "improved_version": "The hyperparameter tuning process should specify the parameter grid, number of iterations, and the criteria for selecting the best hyperparameters, along with details on how overfitting was prevented.", - "explanation": "Detailed hyperparameter procedures ensure reproducibility and allow assessment of the robustness of the tuning process.", - "location": "Methodology, model training", - "category": "implementation", - "focus": "clarity" - }, - { - "original_text": "Normalization techniques like square root scaling and standard scaling are mentioned but lack explicit formulas.", - "improved_version": "Include explicit formulas, e.g., for square root scaling: x' = sqrt(x + 1), and specify the parameters used for standard scaling (mean and standard deviation).", - "explanation": "Providing formulas enhances clarity, reproducibility, and allows others to verify the correctness of preprocessing steps.", - "location": "Mathematical Framework, preprocessing", - "category": "equations", - "focus": "equations" - }, - { - "original_text": "The models' performance metrics are reported, but the class imbalance handling methods are only briefly mentioned.", - "improved_version": "Describe in detail the class imbalance mitigation strategies employed, such as oversampling, undersampling, or cost-sensitive learning, and justify their choice.", - "explanation": "Comprehensive reporting of imbalance handling techniques clarifies how models maintain performance despite skewed classes.", - "location": "Methodology, evaluation", - "category": "completeness", - "focus": "completeness" - }, - { - "original_text": "The definitions of adherence and churn are given, but the operational thresholds (e.g., 'fewer than eight exercises') could be further justified.", - "improved_version": "Provide empirical or clinical justification for the chosen thresholds, referencing prior studies or clinical guidelines where applicable.", - "explanation": "Justification of thresholds strengthens the validity of the operational definitions and their relevance to health outcomes.", - "location": "Introduction, definitions", - "category": "terminology", - "focus": "terminology" - }, - { - "original_text": "The description of feature importance analyses is limited to appendix references.", - "improved_version": "Summarize key findings from feature importance analyses in the main text, including which features most strongly influence predictions and their potential clinical relevance.", - "explanation": "Enhances transparency and helps readers understand which behavioral factors drive model predictions.", - "location": "Results, feature importance", - "category": "documentation", - "focus": "clarity" - }, - { - "original_text": "The discussion mentions the reliance on continuous engagement data but does not specify how models perform with sparse or irregular data.", - "improved_version": "Include an analysis or discussion on model robustness in scenarios with sparse data, possibly with sensitivity analyses or simulation studies.", - "explanation": "Addressing edge cases improves understanding of model applicability in real-world, less-than-ideal data conditions.", - "location": "Discussion, limitations", - "category": "edge_cases", - "focus": "edge_cases" - }, - { - "original_text": "The complexity of the random forest models is not discussed.", - "improved_version": "Provide an analysis of computational complexity, including training time, inference time, and resource requirements, especially for large-scale deployment.", - "explanation": "Understanding computational demands is essential for practical implementation and scalability of the models.", - "location": "Technical analysis", - "category": "complexity", - "focus": "complexity" - }, - { - "original_text": "The hyperparameter grid details are only referenced as in Appendix 6.1 without main text elaboration.", - "improved_version": "Summarize key hyperparameter ranges and tuning procedures within the main manuscript to improve transparency and facilitate replication.", - "explanation": "Main text summaries aid comprehension and ensure critical methodological details are accessible without consulting appendices.", - "location": "Methodology, hyperparameter tuning", - "category": "documentation", - "focus": "clarity" - }, - { - "original_text": "The operational definitions of 'churned' and 'churning' users are based on last login dates, but potential ambiguities at program end are not discussed.", - "improved_version": "Clarify how the model distinguishes between users who stop using the app due to program completion versus disengagement, possibly by incorporating program end dates or additional engagement metrics.", - "explanation": "This clarification reduces misclassification and improves the validity of churn definitions in the context of program end versus disengagement.", - "location": "Discussion, limitations", - "category": "edge_cases", - "focus": "edge_cases" - }, - { - "original_text": "The models are primarily evaluated using metrics like AUC, F1, accuracy, etc., but the potential impact of false positives/negatives on clinical decision-making is not discussed.", - "improved_version": "Include an analysis of the clinical or operational implications of false positive and false negative predictions, possibly with threshold adjustments for optimal trade-offs.", - "explanation": "Understanding the practical impact of prediction errors informs deployment strategies and risk management in real-world settings.", - "location": "Discussion, implications", - "category": "technical_documentation", - "focus": "clarity" - } - ], - "detailed_feedback": { - "derivation_correctness": "The paper mentions normalization techniques like square root scaling and standard scaling but does not provide explicit formulas or mathematical derivations. Including these would clarify the preprocessing steps and facilitate exact replication. For example, specifying that 'for right-skewed features, x' = sqrt(x + 1)' was applied, and that standard scaling involved subtracting the mean and dividing by the standard deviation, would improve transparency.", - "algorithm_accuracy": "The use of random forest classifiers is justified with references to prior literature, and hyperparameter tuning via grid search and cross-validation is described. However, details such as the specific hyperparameter ranges, number of trees, maximum depth, and feature subset sizes are not provided. Including these would strengthen confidence in the model's correctness and allow others to replicate or evaluate the models' efficiency.", - "terminology_accuracy": "The paper distinguishes between 'nonadherence,' 'churn,' and 'disengagement,' but the operational definitions vary slightly across contexts. Clarifying that 'nonadherence' refers to not meeting the prescribed activity threshold, while 'churn' indicates complete discontinuation, and ensuring consistent use throughout, would improve terminological precision.", - "equation_clarity": "Explicit formulas for feature transformations, such as normalization, are absent. Including equations like 'x' = sqrt(x + 1) for skewed features or 'x' = (x - \u03bc) / \u03c3 for standard scaling, would enhance clarity and reproducibility.", - "content_completeness": "While performance metrics are comprehensively reported, the methodology for handling class imbalance (e.g., undersampling with Tomek Links) is only briefly mentioned. Providing detailed procedures, including the parameters and rationale, would improve completeness and allow critical assessment.", - "logical_consistency": "The paper maintains consistent definitions of prediction windows and aligns the operationalization of adherence and churn with prior literature. However, the varying thresholds for adherence (e.g., 8 exercises/week vs 1 blood pressure measurement/month) could be unified or justified more explicitly to ensure logical coherence across different conditions.", - "implementation_details": "The mention of Python packages and hyperparameter grids is limited to appendix references. Including key package versions, code snippets, or pseudocode in the main text would enhance transparency and facilitate independent validation.", - "edge_case_handling": "The models assume continuous, rich engagement data, but the paper does not explicitly address how sparse or irregular data are handled, especially for users with very short engagement periods or missing data. Discussing strategies like imputation, data augmentation, or model adjustments for such cases would improve robustness.", - "complexity_analysis": "The manuscript does not discuss the computational complexity or resource requirements of the random forest models. Including an analysis of training and inference times, especially for large datasets, would inform practical deployment considerations.", - "technical_documentation": "Hyperparameter tuning procedures, feature importance analyses, and evaluation metrics are referenced but not detailed in the main text. Providing summarized tables or descriptions of these processes would improve transparency and reproducibility." - }, - "summary": "Overall, the manuscript demonstrates a strong understanding of machine learning applications in mHealth adherence prediction, with robust performance metrics and thoughtful analysis. However, enhancing transparency in preprocessing formulas, hyperparameter details, and handling of edge cases would elevate the technical rigor. Addressing these areas will facilitate replication, practical deployment, and broader applicability of the proposed models." - }, - "R7": { - "consistency_score": 7, - "critical_remarks": [ - { - "category": "methods_results", - "location": "Section 2.2, paragraph starting with 'Aligning with previous churn prediction studies' ", - "issue": "The description of feature selection emphasizes behavioral engagement features but lacks clarity on how these features directly relate to the specific nonadherence definitions used in the results, especially for the weekly and monthly measures.", - "severity": "medium", - "impact": "This ambiguity may cause confusion about whether the features used are fully aligned with the operational definitions of nonadherence and churn, affecting the interpretability of the results." - }, - { - "category": "results_conclusions", - "location": "Section 3.2, paragraph starting 'Weekly nonadherence prediction models in Vivira demonstrated strong performances' ", - "issue": "The results show high performance metrics, but the discussion does not sufficiently address the potential impact of class imbalance on these metrics, especially since the adherence rates decline sharply over time.", - "severity": "high", - "impact": "This omission could lead to overestimating the models' robustness and their practical utility in real-world, highly imbalanced settings." - }, - { - "category": "logical_flow", - "location": "Section 4, paragraph starting 'Our findings show that nonadherence can be accurately predicted' ", - "issue": "The paragraph jumps from discussing model performance to implications for targeted strategies without clearly linking how the predictive accuracy translates into actionable interventions.", - "severity": "medium", - "impact": "This weakens the logical progression from results to practical applications, reducing clarity of the study's translational significance." - }, - { - "category": "terminology", - "location": "Throughout the manuscript, especially in Abstract and Introduction", - "issue": "The terms 'nonadherence', 'churn', and 'disengagement' are used somewhat interchangeably without explicit definitions or distinctions in some contexts.", - "severity": "low", - "impact": "Inconsistent terminology may cause confusion about what exactly is being predicted and how different concepts relate, affecting clarity." - }, - { - "category": "hypothesis", - "location": "Section 2, Introduction", - "issue": "The hypothesis that behavioral app engagement features can predict nonadherence over extended periods is implied but not explicitly stated or tested as a formal hypothesis.", - "severity": "low", - "impact": "Explicit hypotheses would strengthen the scientific framing and clarity of the research aims." - }, - { - "category": "interpretation", - "location": "Section 4, Discussion", - "issue": "The interpretation that models 'outperform' in early weeks without considering the influence of class imbalance or baseline rates may be overstated.", - "severity": "medium", - "impact": "This could lead to overconfidence in the models' early predictive utility, potentially misleading readers about their practical deployment." - }, - { - "category": "citations", - "location": "Throughout the manuscript, especially in the Introduction and Discussion", - "issue": "Some citations (e.g., [21], [5]) are used to support claims about prior work but are not always directly linked to the specific points made, and some references (e.g., [50]) are not consistently integrated with the discussion of adherence definitions.", - "severity": "low", - "impact": "Inconsistent citation integration may weaken the scholarly rigor and traceability of claims." - }, - { - "category": "figures", - "location": "Section 3, Figures 6.5 and 6.6", - "issue": "The figure captions describe performance trends but do not explicitly connect the visual data to the text's claims about model improvements over time or differences between user groups.", - "severity": "low", - "impact": "This reduces clarity and interpretability of the figures, limiting their effectiveness as supporting evidence." - }, - { - "category": "tables", - "location": "Section 3, Tables 6.1 to 6.4", - "issue": "Some tables present extensive data, but the alignment between the table content and the narrative summaries is sometimes weak, e.g., performance metrics are discussed in the text but not always explicitly linked to specific table rows or columns.", - "severity": "medium", - "impact": "This can cause confusion and reduce the ease of cross-referencing results, affecting overall coherence." - }, - { - "category": "supplementary", - "location": "Appendix 6.2", - "issue": "The appendix contains feature importance analyses, but the main text does not sufficiently highlight how these findings support the claims about feature relevance and model robustness.", - "severity": "low", - "impact": "Limited integration diminishes the clarity of how supplementary analyses underpin the main conclusions." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "The rich behavioral engagement data collected by mHealth interventions form the basis for predicting nonadherence, and explicitly testing this relationship is a core aim of this study.", - "explanation": "Clarifies the link between data and the research aim, strengthening the methodological rationale.", - "location": "Abstract", - "category": "abstract", - "focus": "methods_results" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.", - "improved_version": "Our models correctly identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira, where nonadherence was operationalized as completing fewer than eight exercises per week, with a mean AUC of 0.95.", - "explanation": "Ensures clarity by explicitly linking the performance metrics with the operational definition of nonadherence, improving transparency.", - "location": "Abstract", - "category": "abstract", - "focus": "results_conclusions" - }, - { - "original_text": "The description of feature selection emphasizes behavioral engagement features but lacks clarity on how these features directly relate to the specific nonadherence definitions used in the results.", - "improved_version": "Feature selection focused on behavioral engagement metrics such as daily app activity, session counts, and exercise completion, which directly correspond to the operational definitions of nonadherence (e.g., weekly exercise thresholds).", - "explanation": "Aligns features explicitly with the operational definitions, enhancing methodological transparency.", - "location": "Section 2.2", - "category": "methodology", - "focus": "methods_results" - }, - { - "original_text": "The results show high performance metrics, but the discussion does not sufficiently address the potential impact of class imbalance on these metrics.", - "improved_version": "While the models demonstrate high performance, it is important to consider that class imbalance\u2014particularly in adherence rates\u2014may inflate metrics like accuracy and F1-score; thus, metrics such as precision-recall curves should be interpreted with caution.", - "explanation": "Provides a nuanced interpretation, acknowledging limitations and preventing overstatement of results.", - "location": "Section 3.2", - "category": "results_conclusions", - "focus": "interpretation" - }, - { - "original_text": "The paragraph jumps from discussing model performance to implications for targeted strategies without clearly linking how the predictive accuracy translates into actionable interventions.", - "improved_version": "The high predictive accuracy suggests that these models could be integrated into intervention frameworks to identify at-risk users early, enabling timely, targeted strategies such as personalized notifications or system adaptations to prevent further disengagement.", - "explanation": "Strengthens the logical link between results and practical applications, clarifying translational relevance.", - "location": "Section 4, Discussion", - "category": "logical_flow", - "focus": "logical_flow" - }, - { - "original_text": "The terms 'nonadherence', 'churn', and 'disengagement' are used somewhat interchangeably without explicit definitions or distinctions.", - "improved_version": "Throughout the manuscript, 'nonadherence' refers to users not meeting the recommended engagement thresholds, while 'churn' specifically denotes complete discontinuation of app use, and 'disengagement' is used as a broader term encompassing both phenomena. Clear distinctions are maintained to avoid confusion.", - "explanation": "Clarifies terminology, improving consistency and reader understanding.", - "location": "Throughout", - "category": "terminology", - "focus": "terminology" - }, - { - "original_text": "The hypothesis that behavioral app engagement features can predict nonadherence over extended periods is implied but not explicitly stated or tested as a formal hypothesis.", - "improved_version": "This study hypothesizes that behavioral app engagement features, such as daily activity and exercise completion, can accurately predict nonadherence and churn over extended durations in mHealth interventions.", - "explanation": "Explicitly states the hypothesis, strengthening the scientific framing.", - "location": "Section 2, Introduction", - "category": "hypothesis", - "focus": "hypothesis" - }, - { - "original_text": "The interpretation that models 'outperform' in early weeks without considering the influence of class imbalance or baseline rates may be overstated.", - "improved_version": "Although early-week performance metrics are promising, they should be interpreted cautiously, considering the potential influence of class imbalance and baseline adherence rates, which may inflate performance indicators.", - "explanation": "Provides a balanced interpretation, acknowledging potential confounders.", - "location": "Section 4, Discussion", - "category": "interpretation", - "focus": "interpretation" - }, - { - "original_text": "Some citations (e.g., [21], [5]) are used to support claims about prior work but are not always directly linked to the specific points made.", - "improved_version": "Citations such as [21] and [5] are explicitly linked to prior studies on early user churn prediction, providing direct support for the claims about methodological foundations and performance benchmarks.", - "explanation": "Enhances scholarly rigor by ensuring citations directly support specific statements.", - "location": "Throughout", - "category": "citations", - "focus": "citations" - }, - { - "original_text": "The figure captions describe performance trends but do not explicitly connect the visual data to the text's claims about model improvements over time.", - "improved_version": "Figure 6.5 illustrates the steady increase in AUC and F1-score over Weeks 2 to 13, supporting the text's claim of improving model performance with accumulating data.", - "explanation": "Explicitly links figures to narrative claims, improving interpretability.", - "location": "Section 3", - "category": "figures", - "focus": "figures" - }, - { - "original_text": "Some tables present extensive data, but the alignment between the table content and the narrative summaries is sometimes weak.", - "improved_version": "Ensure that each key performance metric discussed in the text is directly cross-referenced with the corresponding table rows and columns, e.g., 'as shown in Table 6.1, the AUC increased from 0.89 in Week 2 to 0.99 in Week 13.'", - "explanation": "Facilitates easier cross-referencing and comprehension, enhancing coherence.", - "location": "Section 3", - "category": "tables", - "focus": "tables" - }, - { - "original_text": "The appendix contains feature importance analyses, but the main text does not sufficiently highlight how these findings support the claims about feature relevance and model robustness.", - "improved_version": "The main text references Appendix 6.2 to support claims that exercise-related features are most influential, emphasizing the robustness of behavioral features in predicting nonadherence across time.", - "explanation": "Strengthens the link between supplementary analyses and main conclusions, improving coherence.", - "location": "Section 4, Discussion", - "category": "supplementary", - "focus": "supplementary" - } - ], - "detailed_feedback": { - "methods_results_alignment": "The methodology section details the feature selection and modeling approach, focusing on behavioral engagement metrics like daily activity and exercise completion. The results demonstrate high predictive performance for nonadherence and churn, aligning well with these methods. However, explicit discussion on how these features directly operationalize the adherence definitions would improve clarity.", - "results_conclusions_alignment": "The results show strong predictive metrics, supporting the conclusion that nonadherence can be predicted over extended periods. Nonetheless, the discussion should more explicitly address how the performance metrics translate into real-world intervention potential, considering class imbalance and model limitations.", - "logical_flow": "The manuscript generally follows a logical sequence from background, methods, results, to discussion. Yet, transitions between model performance results and their practical implications could be smoother, explicitly linking predictive accuracy to intervention strategies.", - "terminology_consistency": "The manuscript uses 'nonadherence', 'churn', and 'disengagement' with some overlap but inconsistent distinctions. Clarifying and consistently applying these terms throughout would enhance clarity.", - "hypothesis_testing": "While the introduction implies that behavioral engagement features can predict nonadherence, the manuscript would benefit from explicitly stating and testing this as a formal hypothesis to strengthen scientific rigor.", - "interpretation_consistency": "The interpretation of high performance metrics, especially early in the program, should consider potential confounders like class imbalance. Overstating early predictive utility without this context could mislead readers.", - "citation_consistency": "Some references support broad claims but are not always directly linked to the specific points. More precise citation integration would improve scholarly rigor.", - "figure_text_alignment": "Figures effectively illustrate performance trends but lack explicit references in the captions to how they support the narrative claims. Enhancing this connection would improve interpretability.", - "table_text_alignment": "Tables contain comprehensive data, but the narrative sometimes lacks direct cross-referencing. Clearer linkage between text and table data would improve coherence.", - "supplementary_consistency": "Appendix analyses support main findings but are under-referenced in the text. Explicitly discussing how these analyses underpin key conclusions would strengthen the overall coherence." - }, - "summary": "Overall, the manuscript demonstrates a solid integration of methods and results, with clear evidence supporting the claim that behavioral app engagement features can predict nonadherence over extended periods. However, improvements in terminology clarity, explicit hypothesis framing, and stronger linking of figures, tables, and supplementary analyses to the main narrative would elevate the logical coherence and scholarly rigor. Addressing these aspects will enhance the clarity, transparency, and practical relevance of the research findings." - }, - "W1": { - "language_style_score": 7, - "critical_remarks": [ - { - "category": "grammar", - "location": "Abstract, paragraph 4", - "issue": "Inconsistent use of hyphenation in 'noncommunicable' and 'nonadherence'.", - "severity": "low", - "impact": "Minor inconsistency; standardization improves professionalism." - }, - { - "category": "spelling", - "location": "Introduction, paragraph 2", - "issue": "The term 'hyperglycemia' is spelled correctly, but in some instances, similar terms like 'hyperglycemia' are misspelled as 'hyperglycemia' (e.g., in references).", - "severity": "low", - "impact": "Ensures consistency and accuracy in terminology, maintaining credibility." - }, - { - "category": "punctuation", - "location": "Results, section 3.2.1", - "issue": "Numerous instances of missing commas after introductory phrases, e.g., 'Across the prediction windows from Week 2 to Week 13, the models achieved...'", - "severity": "medium", - "impact": "Reduces clarity and can cause reader confusion." - }, - { - "category": "sentence_structure", - "location": "Discussion, paragraph 4.1", - "issue": "Long, complex sentences that could be split for clarity, e.g., the sentence starting with 'Given the conceptual link...'", - "severity": "medium", - "impact": "Improves readability and comprehension." - }, - { - "category": "verb_tense", - "location": "Methodology, section 2.2", - "issue": "Inconsistent use of past and present tense, e.g., 'We selected features' (past) and 'we predict nonadherence' (present).", - "severity": "low", - "impact": "Consistency enhances professional tone and clarity." - }, - { - "category": "subject-verb", - "location": "Results, section 3.2.2", - "issue": "In several instances, plural subjects are paired with singular verbs, e.g., 'the models achieved a mean AUC...' (correct), but in some cases, agreement is inconsistent.", - "severity": "low", - "impact": "Maintains grammatical correctness and professionalism." - }, - { - "category": "articles", - "location": "Introduction, paragraph 2", - "issue": "Incorrect or missing articles, e.g., 'a systematic review of effectivity' should be 'a systematic review of effectiveness'.", - "severity": "low", - "impact": "Improves grammatical accuracy and clarity." - }, - { - "category": "prepositions", - "location": "Discussion, paragraph 4.2", - "issue": "Incorrect preposition usage, e.g., 'detect users in the early stages of disengagement' could be 'detect users at the early stages of disengagement'.", - "severity": "low", - "impact": "Enhances precision and correctness of expression." - }, - { - "category": "conjunctions", - "location": "Discussion, paragraph 4.4", - "issue": "Overuse of 'however' at the beginning of sentences, which can be stylistically repetitive.", - "severity": "low", - "impact": "Varying conjunctions improves flow and style." - }, - { - "category": "academic_conventions", - "location": "Throughout the document", - "issue": "Inconsistent formatting of references and in-text citations, e.g., some citations are bracketed, others are not.", - "severity": "medium", - "impact": "Reduces professionalism and adherence to academic standards." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "The rich data collected by mHealth interventions raise the question of whether\u2014and to what extent\u2014nonadherence can be predicted using these data.", - "explanation": "Replacing hyphens with em dashes and removing spaces improves punctuation consistency and formal style.", - "location": "Abstract, paragraph 1", - "category": "punctuation", - "focus": "punctuation" - }, - { - "original_text": "In the German SHI system [46].", - "improved_version": "In the German SHI system [46].", - "explanation": "Remove the period after the bracketed citation to adhere to standard referencing style.", - "location": "Methodology, section 2.1.1", - "category": "academic_conventions", - "focus": "citation format" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.", - "improved_version": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), where nonadherence was defined as completing fewer than eight therapeutic exercises per week.", - "explanation": "Clarifies the definition of nonadherence and improves sentence flow by restructuring.", - "location": "Results, section 3.2.1", - "category": "sentence_structure", - "focus": "clarity" - }, - { - "original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "improved_version": "The use of DiGA data is strictly limited; therefore, only users who provided consent under Article 4, Section 2, and 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "explanation": "Using a semicolon improves sentence connection; adding 'and' clarifies the list structure.", - "location": "Methodology, section 2.1.1", - "category": "punctuation", - "focus": "punctuation" - }, - { - "original_text": "In Vivira, we predicted nonadherence weekly from Weeks 2 to 13 based on users\u2019 daily app activity variables (active or inactive) and the daily number of completed exercises variables (continuous) of the preceding weeks.", - "improved_version": "In Vivira, we predicted nonadherence weekly from Weeks 2 to 13, based on users\u2019 daily app activity variables (active or inactive) and the daily number of completed exercises (continuous) from the preceding weeks.", - "explanation": "Adding a comma improves readability; removing 'variables' after 'exercises' avoids redundancy.", - "location": "Methodology, section 2.2", - "category": "sentence_structure", - "focus": "clarity" - }, - { - "original_text": "Our findings show that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence relative to intended use as defined by Sieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use).", - "improved_version": "Our findings demonstrate that nonadherence to mHealth interventions can be accurately predicted over extended program durations, both in terms of adherence\u2014relative to the intended use as defined by Sieverink et al. (2017)\u2014and in its most severe form: churn (i.e., complete discontinuation of use).", - "explanation": "Using em dashes and colons clarifies the structure and emphasizes key definitions, enhancing readability.", - "location": "Discussion, paragraph 4.1", - "category": "punctuation", - "focus": "punctuation" - }, - { - "original_text": "The article 'predicts nonadherence' (present tense) but elsewhere, the methodology is described in past tense.", - "improved_version": "Ensure consistent tense usage throughout; for example, use past tense ('predicted') when describing the methodology and results, and present tense ('predicts') only when discussing general truths or ongoing implications.", - "explanation": "Consistency in verb tense aligns with academic standards and clarifies temporal context.", - "location": "General, throughout the document", - "category": "verb_tense", - "focus": "tense consistency" - }, - { - "original_text": "The models achieved a mean AUC of 0.87 (SD = 0.06), a mean accuracy of 0.83 (SD = 0.05), a mean F1-score of 0.81 (SD = 0.13), a mean precision of 0.78 (SD = 0.12), and a mean recall of 0.86 (SD = 0.14) across all prediction points at the of Day 1 until 178, indicating a robust ability to discriminate between churned and non-churned users.", - "improved_version": "The models achieved a mean AUC of 0.87 (SD = 0.06), a mean accuracy of 0.83 (SD = 0.05), a mean F1-score of 0.81 (SD = 0.13), a mean precision of 0.78 (SD = 0.12), and a mean recall of 0.86 (SD = 0.14) across all prediction points from Day 1 until Day 178, indicating a robust ability to discriminate between churned and non-churned users.", - "explanation": "Corrects grammatical error ('at the of Day 1' to 'from Day 1') and improves clarity of time frame.", - "location": "Results, section 3.2.2", - "category": "grammar", - "focus": "grammar" - }, - { - "original_text": "The overall assessment paragraph.", - "improved_version": "Overall, the analysis indicates that the manuscript demonstrates solid methodological rigor, clear structure, and comprehensive coverage of the topic, with room for minor stylistic and grammatical improvements to enhance clarity and professionalism.", - "explanation": "Provides a concise, balanced summary of the overall quality and areas for improvement.", - "location": "Summary", - "category": "general", - "focus": "overall assessment" - } - ], - "detailed_feedback": { - "grammar_correctness": "The manuscript generally maintains grammatical correctness, but some complex sentences could be simplified for clarity. Minor issues include inconsistent tense usage and occasional awkward phrasing that can be smoothed for better flow.", - "spelling_accuracy": "Spelling is accurate throughout; however, consistency in hyphenation (e.g., 'noncommunicable') and terminology (e.g., 'hyperglycemia') should be maintained. Also, ensure all references to technical terms are spelled uniformly.", - "punctuation_usage": "Punctuation is mostly correct but can be improved by standardizing the use of em dashes, commas, and semicolons, especially in complex sentences. Proper punctuation enhances readability and formal tone.", - "sentence_structure": "Many sentences are lengthy and contain multiple ideas, which can be split into shorter, clearer sentences. This improves readability and reduces cognitive load for the reader.", - "verb_tense_consistency": "The manuscript shifts between past and present tense, particularly when describing methods and results. Maintaining consistent tense\u2014preferably past tense for completed actions\u2014would improve clarity and professionalism.", - "subject-verb_agreement": "Subject-verb agreement is generally correct; however, careful review is recommended in complex sentences, especially where collective nouns or multiple subjects are involved.", - "article_usage": "Articles are mostly used correctly, but some instances lack definite or indefinite articles where needed, affecting grammatical correctness and clarity.", - "preposition_usage": "Prepositions are generally appropriate, but some phrases could be more precise, e.g., 'predict users in the early stages' should be 'predict users at the early stages'.", - "conjunction_usage": "Conjunctions are used appropriately, but overuse of 'however' at sentence starts can be stylistically monotonous. Varying conjunctions can improve flow.", - "academic_conventions": "Citation formatting is inconsistent; some references lack proper punctuation or formatting. Standardizing reference style and citation placement will enhance professionalism." - }, - "summary": "The manuscript demonstrates strong academic writing with clear structure and comprehensive content. Minor language and stylistic improvements\u2014such as standardizing punctuation, tense, and reference formatting\u2014would elevate the clarity, professionalism, and readability of the work. Addressing these issues will ensure the manuscript aligns with high academic standards and enhances its impact." - }, - "W2": { - "narrative_structure_score": 6, - "critical_remarks": [ - { - "category": "narrative_coherence", - "location": "Abstract and Introduction", - "issue": "The abstract provides a comprehensive overview but lacks explicit linkage to the specific research questions or hypotheses, making the narrative feel somewhat disconnected from the detailed objectives outlined later.", - "severity": "medium", - "impact": "This weakens the coherence between the abstract and the main research aims, potentially confusing readers about the core focus." - }, - { - "category": "logical_progression", - "location": "Results and Discussion", - "issue": "Results are detailed extensively but are not always clearly connected back to the hypotheses or research questions, leading to a fragmented understanding of how findings support or challenge initial assumptions.", - "severity": "high", - "impact": "This hampers the logical flow from data presentation to interpretation, reducing clarity of the overall narrative." - }, - { - "category": "transitions", - "location": "Between sections (e.g., Methods to Results, Results to Discussion)", - "issue": "Transitions are abrupt; for example, the shift from detailed prediction results to their implications in the discussion lacks smooth linking sentences.", - "severity": "medium", - "impact": "This disrupts the reader\u2019s flow, making the narrative feel disjointed and harder to follow." - }, - { - "category": "paragraph_organization", - "location": "Methods and Results sections", - "issue": "Some paragraphs, especially in the Methods, are overly dense with technical details, which could be better organized into subsections for clarity.", - "severity": "medium", - "impact": "This reduces readability and hampers quick comprehension of key methodological steps." - }, - { - "category": "topic_sentences", - "location": "Multiple sections", - "issue": "Many paragraphs lack clear topic sentences that outline their main point, especially in the Results and Discussion sections.", - "severity": "high", - "impact": "This diminishes the guiding thread for the reader, making it difficult to grasp the purpose of each paragraph." - }, - { - "category": "evidence_integration", - "location": "Results and Discussion", - "issue": "While extensive data are presented, there is limited integration of evidence with interpretative commentary, especially in linking statistical results to practical implications.", - "severity": "high", - "impact": "This weakens the persuasive power of the narrative and obscures the significance of findings." - }, - { - "category": "conclusion_alignment", - "location": "Conclusion", - "issue": "The conclusion summarizes findings but does not explicitly revisit the initial hypotheses or research questions, leading to a slight misalignment with the introduction.", - "severity": "medium", - "impact": "This reduces the narrative closure and clarity of how the study addresses its initial aims." - }, - { - "category": "hypothesis_tracking", - "location": "Throughout the paper", - "issue": "Explicit tracking of hypotheses or research questions is inconsistent; some sections discuss findings without clearly referencing the original aims.", - "severity": "high", - "impact": "This hampers the reader\u2019s ability to follow how evidence supports or refutes specific hypotheses." - }, - { - "category": "visual_integration", - "location": "Figures and Tables", - "issue": "Figures and tables are numerous and detailed but lack direct references or explanations within the text that clarify their relevance to the narrative.", - "severity": "medium", - "impact": "This diminishes their effectiveness as visual aids and can cause confusion about their purpose." - }, - { - "category": "reader_engagement", - "location": "Entire document", - "issue": "The dense technical language and extensive data presentation may overwhelm readers, reducing engagement.", - "severity": "medium", - "impact": "This could lead to decreased comprehension and interest, especially for non-specialist audiences." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "Building on the rich data collected by mHealth interventions, this study investigates whether and to what extent nonadherence can be accurately predicted, directly addressing this key question.", - "explanation": "Clarifies the purpose of the sentence, linking it explicitly to the research aim and enhancing narrative clarity.", - "location": "Abstract", - "category": "abstract", - "focus": "narrative_coherence" - }, - { - "original_text": "In the Introduction, the flow from the general context of NCDs to the specific focus on mHealth adherence is somewhat abrupt.", - "improved_version": "The rising prevalence and economic burden of noncommunicable diseases (NCDs) underscore the urgent need for scalable interventions. Among these, mobile health (mHealth) solutions have gained prominence due to their accessibility and potential to support disease management, yet adherence remains a critical challenge that this study aims to address.", - "explanation": "Creates a smoother logical progression from broad context to specific research focus, improving coherence.", - "location": "Introduction", - "category": "introduction", - "focus": "logical_progression" - }, - { - "original_text": "Results are detailed extensively but are not always clearly connected back to the hypotheses or research questions.", - "improved_version": "The results demonstrate that the machine learning models achieved high accuracy in predicting nonadherence, supporting the hypothesis that behavioral engagement data can serve as reliable predictors of user adherence patterns.", - "explanation": "Explicitly links findings to research hypotheses, strengthening the logical flow.", - "location": "Results", - "category": "results", - "focus": "hypothesis_tracking" - }, - { - "original_text": "The transition between the Results and Discussion sections is abrupt.", - "improved_version": "These predictive performance metrics highlight the models' robustness over extended periods. In the following discussion, we interpret these findings in the context of their practical implications for intervention strategies and future research directions.", - "explanation": "Provides a linking sentence that guides the reader smoothly from data to interpretation.", - "location": "End of Results / Beginning of Discussion", - "category": "transitions", - "focus": "transitions" - }, - { - "original_text": "Many paragraphs in Methods are overly dense with technical details.", - "improved_version": "The Methods section is organized into clear subsections: Data Collection, Feature Selection, Model Training, and Evaluation. Each subsection provides concise descriptions of the procedures, enhancing readability.", - "explanation": "Improves paragraph organization and readability by structuring technical content into subsections.", - "location": "Methods", - "category": "methodology", - "focus": "paragraph_organization" - }, - { - "original_text": "Many paragraphs lack clear topic sentences.", - "improved_version": "This paragraph introduces the primary features used for prediction, emphasizing their relevance to the intervention's objectives.", - "explanation": "Adding explicit topic sentences guides the reader through complex sections, improving clarity.", - "location": "Multiple sections", - "category": "topic_sentences", - "focus": "topic_sentence_effectiveness" - }, - { - "original_text": "Figures and tables are numerous but often lack references within the text.", - "improved_version": "Each figure and table is explicitly referenced in the text, e.g., 'As shown in Figure 6.5, the model performance improves steadily over weeks,' ensuring the visual data supports the narrative.", - "explanation": "Explicit references improve visual integration and help readers connect data with narrative points.", - "location": "Figures/Tables", - "category": "visual_integration", - "focus": "visual_element_integration" - }, - { - "original_text": "The discussion section summarizes findings but does not revisit initial hypotheses.", - "improved_version": "The discussion revisits the initial hypotheses, confirming that behavioral engagement data can reliably predict nonadherence and emphasizing the implications for targeted intervention strategies.", - "explanation": "Aligns conclusions with initial aims, reinforcing narrative closure.", - "location": "Discussion", - "category": "conclusion_alignment", - "focus": "conclusion_alignment" - }, - { - "original_text": "The narrative does not explicitly track the hypotheses throughout.", - "improved_version": "Throughout the Results and Discussion, we explicitly reference the hypotheses: (1) that behavioral data can predict nonadherence, and (2) that models can be effective over extended durations, providing a clear thread of hypothesis testing.", - "explanation": "Explicit hypothesis tracking enhances logical progression and clarity.", - "location": "Throughout the paper", - "category": "hypothesis_tracking", - "focus": "hypothesis_tracking" - }, - { - "original_text": "Figures and tables are detailed but could be better explained within the text.", - "improved_version": "In the Results section, we refer to Figure 6.5 to illustrate the trend of increasing AUC over weeks, and Table 6.1 to detail weekly model performance, providing interpretative commentary alongside the visuals.", - "explanation": "Enhances understanding by integrating visual data explanations into the narrative.", - "location": "Figures/Tables", - "category": "visual_integration", - "focus": "visual_element_integration" - }, - { - "original_text": "The dense technical language may reduce reader engagement.", - "improved_version": "To maintain engagement, technical details are summarized in supplementary materials, while the main text emphasizes key findings and their implications in accessible language.", - "explanation": "Balancing technical detail with readability improves overall engagement.", - "location": "Throughout the document", - "category": "reader_engagement", - "focus": "reader_engagement" - }, - { - "original_text": "The conclusion does not explicitly revisit the research questions.", - "improved_version": "The conclusion explicitly revisits the initial research questions, confirming that behavioral app engagement features can reliably predict nonadherence and discussing how these insights can inform future intervention strategies.", - "explanation": "Revisiting research questions provides narrative closure and reinforces the study's contributions.", - "location": "Conclusion", - "category": "conclusion_alignment", - "focus": "conclusion_alignment" - }, - { - "original_text": "The paper would benefit from clearer signposting of the hypotheses and research aims throughout.", - "improved_version": "Throughout the manuscript, we explicitly state and revisit our research aims and hypotheses, ensuring each section contributes to addressing these core questions, thereby strengthening the narrative coherence.", - "explanation": "Explicit signposting guides the reader through the research narrative, enhancing clarity.", - "location": "Entire document", - "category": "hypothesis_tracking", - "focus": "hypothesis_tracking" - }, - { - "original_text": "The extensive data presentation could be complemented with more interpretative commentary.", - "improved_version": "Alongside detailed tables and figures, we include interpretative summaries that highlight key trends and their implications for model performance and intervention design.", - "explanation": "Adding interpretative commentary makes data more accessible and meaningful, improving engagement.", - "location": "Results and Discussion", - "category": "evidence_integration", - "focus": "evidence_integration" - } - ], - "detailed_feedback": { - "narrative_coherence": "The overall narrative is somewhat fragmented due to dense technical descriptions and limited explicit links between sections. Improving coherence involves clearer signposting, summarizing key points, and ensuring each part logically flows into the next, especially between data presentation and interpretation.", - "logical_progression": "The manuscript generally follows a logical sequence from background to methods, results, and discussion. However, some sections lack explicit connections to the initial hypotheses, which could be strengthened by explicitly stating how each result supports or refutes the research aims.", - "section_transitions": "Transitions between sections, particularly from Results to Discussion, are abrupt. Incorporating bridging sentences that summarize findings and preview their implications would improve flow and reader comprehension.", - "paragraph_organization": "Many paragraphs are overly dense, especially in Methods, which hampers readability. Breaking complex paragraphs into smaller, thematic units with clear topic sentences would enhance clarity and engagement.", - "topic_sentence_effectiveness": "Several paragraphs lack clear topic sentences, making it difficult for readers to grasp their main purpose quickly. Adding explicit topic sentences at the start of each paragraph would guide the reader effectively.", - "supporting_evidence_integration": "While the data are extensive, the narrative often lacks interpretative commentary that connects evidence to broader implications. Integrating discussion of how specific results support hypotheses or practical applications would strengthen the argument.", - "conclusion_alignment": "The conclusion summarizes findings but does not explicitly revisit the initial research questions or hypotheses, weakening the narrative closure. Explicitly linking conclusions back to original aims would improve coherence.", - "hypothesis_tracking": "The manuscript does not consistently track hypotheses throughout. Explicit references to hypotheses when presenting results would clarify how findings relate to initial research aims.", - "visual_element_integration": "Figures and tables are numerous but often lack direct references or explanations within the text. Explicitly referencing and interpreting visuals within the narrative would enhance their utility.", - "reader_engagement": "The dense technical language and extensive data presentation may reduce engagement. Balancing technical detail with accessible summaries and visual aids would improve overall readability and interest." - }, - "summary": "This manuscript presents a comprehensive and technically detailed exploration of nonadherence prediction in mHealth interventions. Its strengths lie in extensive data analysis and model evaluation across multiple contexts. However, improvements in narrative coherence, explicit hypothesis tracking, section transitions, and visual integration are needed to enhance clarity and engagement. Structuring the content with clearer signposting, summarizing key points, and balancing technical detail with interpretative commentary will significantly strengthen the overall narrative flow and reader experience." - }, - "W3": { - "clarity_conciseness_score": 6, - "critical_remarks": [ - { - "category": "language_simplicity", - "location": "Abstract", - "issue": "Use of complex phrases like 'the rising prevalence and economic burden of noncommunicable diseases (NCDs) present a significant challenge' may hinder quick understanding.", - "severity": "medium", - "impact": "Reduces immediate clarity for readers unfamiliar with technical language." - }, - { - "category": "jargon", - "location": "Introduction", - "issue": "Terms like 'DiGA', 'PDT', 'stratified 10-fold cross-validation', and 'Tomek Links undersampling' are technical and may not be immediately clear to all readers.", - "severity": "high", - "impact": "Potentially alienates or confuses readers unfamiliar with specific technical terms." - }, - { - "category": "wordiness", - "location": "Literature Review", - "issue": "Several sentences are overly long and contain redundant phrases, e.g., 'A growing body of evidence suggests that mHealth interventions can effectively support the prevention and management of NCDs by addressing modifiable risk factors, including physical inactivity [52, 53], unhealthy diets [67], tobacco use [63], the harmful use of alcohol [12] and metabolic risk factors such as obesity [52], hypertension [1], and hyperglycemia [19].'", - "severity": "high", - "impact": "Obscures key points, making the text harder to scan and understand quickly." - }, - { - "category": "sentence_length", - "location": "Results", - "issue": "Many sentences, especially in the prediction results sections, are very long, containing multiple clauses and detailed data points.", - "severity": "high", - "impact": "Impairs readability and makes it difficult for readers to grasp main findings quickly." - }, - { - "category": "paragraph_length", - "location": "Discussion", - "issue": "Some paragraphs, especially in the discussion, are densely packed with information, reducing clarity.", - "severity": "medium", - "impact": "Overwhelms readers, making it harder to follow key arguments." - }, - { - "category": "voice", - "location": "Methodology", - "issue": "Use of passive voice in descriptions like 'Data was collected from...' and 'Models were trained...' can obscure agency and reduce engagement.", - "severity": "medium", - "impact": "Decreases immediacy and clarity of methodological descriptions." - }, - { - "category": "redundancy", - "location": "Results", - "issue": "Repeatedly mentioning model performance metrics (e.g., AUC, accuracy, F1) with similar values across sections adds unnecessary repetition.", - "severity": "low", - "impact": "Reduces conciseness without adding new information." - }, - { - "category": "ambiguity", - "location": "Introduction", - "issue": "Phrases like 'nonadherence can be accurately predicted' lack specificity about the degree or context of accuracy.", - "severity": "medium", - "impact": "Leaves some uncertainty about the practical significance of the findings." - }, - { - "category": "readability", - "location": "Discussion", - "issue": "Use of technical terms and complex sentences throughout hampers ease of reading for a broad audience.", - "severity": "high", - "impact": "Limits accessibility and quick comprehension." - }, - { - "category": "information_density", - "location": "Results & Discussion", - "issue": "High density of statistical data and model metrics in tables and text can overwhelm readers unfamiliar with statistical reporting.", - "severity": "medium", - "impact": "Reduces clarity of main findings and hampers quick understanding." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "The data collected by mHealth interventions raise the question: can we predict nonadherence using this data?", - "explanation": "Simplifies language and clarifies the research question, making it more direct and accessible.", - "location": "Abstract", - "category": "language_simplicity", - "focus": "language_simplicity" - }, - { - "original_text": "Terms like 'DiGA', 'PDT', 'stratified 10-fold cross-validation', and 'Tomek Links undersampling' are technical and may not be immediately clear to all readers.", - "improved_version": "Terms such as 'DiGA' (digital health application), 'PDT' (digital therapeutic), and 'Tomek Links undersampling' (a data balancing technique) are technical; consider briefly defining them when first introduced.", - "explanation": "Provides clarity for readers unfamiliar with specific jargon, improving comprehension.", - "location": "Introduction", - "category": "jargon", - "focus": "jargon" - }, - { - "original_text": "Several sentences are overly long and contain redundant phrases, e.g., 'A growing body of evidence suggests that mHealth interventions can effectively support the prevention and management of NCDs by addressing modifiable risk factors, including physical inactivity [52, 53], unhealthy diets [67], tobacco use [63], the harmful use of alcohol [12] and metabolic risk factors such as obesity [52], hypertension [1], and hyperglycemia [19].'", - "improved_version": "Evidence shows that mHealth interventions help prevent and manage NCDs by targeting risk factors like inactivity, poor diet, smoking, alcohol use, obesity, hypertension, and high blood sugar.", - "explanation": "Reduces wordiness and simplifies complex lists, making key points clearer and more concise.", - "location": "Literature Review", - "category": "wordiness", - "focus": "wordiness" - }, - { - "original_text": "Many sentences, especially in the prediction results sections, are very long, containing multiple clauses and detailed data points.", - "improved_version": "Break long sentences into shorter ones. For example, instead of 'AUC ranged from 0.89 in Week 2 to 0.99 in Week 13,' write 'The AUC ranged from 0.89 in Week 2 to 0.99 in Week 13.'", - "explanation": "Shorter sentences improve readability and help readers grasp key findings more easily.", - "location": "Results", - "category": "sentence_length", - "focus": "sentence_length" - }, - { - "original_text": "Some paragraphs, especially in the discussion, are densely packed with information, reducing clarity.", - "improved_version": "Divide lengthy paragraphs into smaller sections focused on single ideas. For example, separate discussion of model performance from implications for practice.", - "explanation": "Enhances readability by allowing readers to process one idea at a time.", - "location": "Discussion", - "category": "paragraph_length", - "focus": "paragraph_length" - }, - { - "original_text": "Use of passive voice in descriptions like 'Data was collected from...' and 'Models were trained...' can obscure agency and reduce engagement.", - "improved_version": "Use active voice: 'We collected data from...' and 'We trained the models...'.", - "explanation": "Active voice makes sentences clearer and more direct, increasing engagement.", - "location": "Methodology", - "category": "active_passive_voice", - "focus": "active_passive_voice" - }, - { - "original_text": "Repeatedly mentioning model performance metrics (e.g., AUC, accuracy, F1) with similar values across sections adds unnecessary repetition.", - "improved_version": "Summarize key performance metrics once in a table or summary paragraph instead of repeating similar details in multiple sections.", - "explanation": "Reduces redundancy, making the text more concise and focused.", - "location": "Results", - "category": "redundancy", - "focus": "redundancy" - }, - { - "original_text": "Phrases like 'nonadherence can be accurately predicted' lack specificity about the degree or context of accuracy.", - "improved_version": "Nonadherence was predicted with over 90% accuracy, indicating high reliability in our models.", - "explanation": "Provides concrete figures, clarifying the level of accuracy and strengthening the statement.", - "location": "Introduction", - "category": "ambiguity", - "focus": "ambiguity" - }, - { - "original_text": "Use of technical terms and complex sentences throughout hampers ease of reading for a broad audience.", - "improved_version": "Simplify technical language where possible and use shorter sentences to improve overall readability for diverse readers.", - "explanation": "Enhances accessibility, allowing a wider audience to understand key points more easily.", - "location": "Discussion", - "category": "readability", - "focus": "readability" - }, - { - "original_text": "High density of statistical data and model metrics in tables and text can overwhelm readers unfamiliar with statistical reporting.", - "improved_version": "Include only the most relevant statistics in the main text; move detailed tables to appendices or supplementary materials.", - "explanation": "Reduces cognitive load and helps highlight key findings without overwhelming the reader.", - "location": "Results & Discussion", - "category": "information_density", - "focus": "information_density" - }, - { - "original_text": "Many sentences are very long and contain multiple clauses, making them difficult to follow.", - "improved_version": "Shorten complex sentences into simpler, standalone sentences. For example, 'Models achieved high performance. They predicted nonadherence accurately.'", - "explanation": "Improves clarity and ease of reading by reducing sentence complexity.", - "location": "Results", - "category": "sentence_length", - "focus": "sentence_length" - }, - { - "original_text": "Some technical descriptions, such as 'stratified 10-fold cross-validation,' could benefit from brief explanations for clarity.", - "improved_version": "Use: 'We used stratified 10-fold cross-validation, a method that splits data into 10 parts while maintaining class proportions, to evaluate model performance.'", - "explanation": "Clarifies technical terms, making methods transparent to non-expert readers.", - "location": "Methodology", - "category": "jargon", - "focus": "jargon" - }, - { - "original_text": "The dense presentation of data and statistical results can be overwhelming.", - "improved_version": "Summarize key findings in text and present detailed data in well-organized tables or figures, with clear labels and legends.", - "explanation": "Enhances readability and helps readers focus on main insights without distraction.", - "location": "Results", - "category": "information_density", - "focus": "information_density" - }, - { - "original_text": "Some sections contain redundant information, such as repeatedly stating model performance metrics with similar values.", - "improved_version": "Consolidate performance metrics into summary statements, e.g., 'Models consistently achieved AUCs above 0.87, indicating strong predictive ability.'", - "explanation": "Reduces repetition, making the narrative more concise and focused.", - "location": "Results & Discussion", - "category": "redundancy", - "focus": "redundancy" - } - ], - "detailed_feedback": { - "language_simplicity": "The manuscript employs complex language and lengthy sentences, which can hinder quick understanding. Simplifying vocabulary and breaking long sentences into shorter, clearer ones will improve accessibility for a broader audience.", - "jargon_usage": "While technical terms are necessary for precision, many are introduced without explanation. Providing brief definitions or explanations upon first use will help non-expert readers grasp the concepts more easily.", - "wordiness": "Several sections contain verbose sentences with redundant phrases, which obscure key messages. Eliminating unnecessary words and focusing on core ideas will enhance clarity.", - "sentence_length": "Long, multi-clause sentences are prevalent, especially in data-heavy sections. Shortening sentences or splitting them into two will improve readability and comprehension.", - "paragraph_length": "Some paragraphs are densely packed with information, making it difficult to follow. Dividing lengthy paragraphs into smaller, focused sections will aid reader engagement.", - "active_passive_voice": "The frequent use of passive voice reduces immediacy and clarity. Rephrasing sentences to active voice will make descriptions more direct and engaging.", - "redundancy": "Repeated presentation of similar performance metrics and detailed data points can clutter the text. Summarizing key results and relocating detailed data to appendices will streamline the narrative.", - "ambiguity": "Certain statements lack specificity, such as 'nonadherence can be accurately predicted,' without quantifying accuracy. Including concrete metrics will clarify the strength of the findings.", - "readability": "Technical complexity and dense data presentation hinder ease of reading. Simplifying language, reducing jargon, and organizing data visually will improve overall readability.", - "information_density": "The manuscript contains a high volume of detailed statistical data, which can overwhelm readers. Focusing on main findings and relegating detailed tables to supplementary sections will enhance clarity." - }, - "summary": "Overall, the manuscript presents valuable findings on predicting nonadherence in mHealth interventions but would benefit from clearer, more concise language, reduced technical jargon, and better organization of data. Shortening sentences, clarifying technical terms, and dividing dense paragraphs will significantly improve readability and comprehension for a diverse audience." - }, - "W4": { - "terminology_consistency_score": 7, - "critical_remarks": [ - { - "category": "term_usage", - "location": "Abstract, Paragraph 1", - "issue": "The term 'nonadherence' is introduced and used throughout, but sometimes it is contrasted with 'churn' without clear differentiation or consistent usage, leading to potential confusion.", - "severity": "medium", - "impact": "Inconsistent use of 'nonadherence' and 'churn' can cause ambiguity in understanding the scope of the study and the specific behaviors being predicted." - }, - { - "category": "notation", - "location": "Equations and Tables, e.g., Table 6.1", - "issue": "Performance metrics such as AUC, accuracy, F1-score are presented with inconsistent formatting and sometimes without units or clear explanation, e.g., SD values are sometimes in parentheses, sometimes not.", - "severity": "low", - "impact": "Inconsistent notation reduces clarity and may cause misinterpretation of statistical results." - }, - { - "category": "acronyms", - "location": "Introduction and Methods", - "issue": "Acronyms like NCDs, mHealth, DiGA, SHI, PHI are used before being explicitly defined, or their definitions are scattered, leading to potential confusion for readers unfamiliar with these terms.", - "severity": "high", - "impact": "Lack of clear, early definitions of acronyms hampers reader comprehension and consistency in terminology." - }, - { - "category": "variable_naming", - "location": "Results and Tables, e.g., Table 6.1", - "issue": "Variables such as 'nonadherent users', 'churned users', 'active users' are used, but sometimes the criteria for these categories (e.g., thresholds) are described differently in different sections, risking inconsistency.", - "severity": "medium", - "impact": "Inconsistent variable definitions can lead to confusion about what exactly is being measured and predicted." - }, - { - "category": "unit_notation", - "location": "Methodology, Descriptive Statistics", - "issue": "Time units such as days, weeks, and months are used variably with inconsistent formatting (e.g., 'Days 1-7', 'Week 1', 'Month 2') without uniform notation or clarification.", - "severity": "low", - "impact": "Inconsistent notation of time units can cause misinterpretation of the temporal scope of data and analysis." - }, - { - "category": "abbreviations", - "location": "Throughout the document", - "issue": "Some abbreviations like 'SD', 'IQR', 'n' are used without initial expansion or clear explanation, and sometimes the same abbreviation is used for different concepts.", - "severity": "medium", - "impact": "Inconsistent abbreviation usage reduces clarity and may lead to misunderstandings." - }, - { - "category": "technical_terms", - "location": "Discussion, Paragraph 1", - "issue": "Terms such as 'predictive performance', 'classification', 'model accuracy' are used interchangeably without precise definitions or distinctions, which could cause confusion.", - "severity": "low", - "impact": "Ambiguous technical terminology can impair precise understanding of the results." - }, - { - "category": "field_terminology", - "location": "Introduction, Paragraph 2", - "issue": "Terms like 'adherence', 'nonadherence', 'churn', 'dropout' are used, but their definitions vary slightly across literature and are sometimes used interchangeably without clarification.", - "severity": "high", - "impact": "Inconsistent field-specific terminology affects the clarity of the conceptual framework." - }, - { - "category": "cross_references", - "location": "Throughout the document", - "issue": "References to figures, tables, and appendices (e.g., 'Figure 6.1', 'Appendix 6.2') are sometimes inconsistent in formatting or missing, which can hinder navigation.", - "severity": "low", - "impact": "Inconsistent cross-referencing impacts document usability." - }, - { - "category": "definition_consistency", - "location": "Methodology, Dataset descriptions", - "issue": "Definitions of adherence (e.g., 'completing eight exercises per week') are provided, but similar definitions for 'churn' or 'nonadherence' are sometimes described differently in various sections, risking inconsistency.", - "severity": "medium", - "impact": "Inconsistent definitions compromise the clarity of outcome measures." - } - ], - "improvement_suggestions": [ - { - "original_text": "nonadherence was measured weekly, defined as completing eight or more exercises per week.", - "improved_version": "Nonadherence was measured weekly, defined as completing fewer than eight exercises per week.", - "explanation": "Clarifies the threshold for nonadherence, ensuring consistent understanding of the criterion.", - "location": "Methodology, Dataset descriptions", - "category": "term_usage", - "focus": "term_usage" - }, - { - "original_text": "churn (users\u2019 last login within program duration)", - "improved_version": "churn, defined as the user's last login within the program duration, indicating complete discontinuation.", - "explanation": "Provides a clear, consistent definition of churn aligned with the literature and the context, reducing ambiguity.", - "location": "Introduction, Methods", - "category": "field_terminology", - "focus": "definitions" - }, - { - "original_text": "The performance metrics such as AUC, accuracy, F1-score are presented with inconsistent formatting.", - "improved_version": "Performance metrics such as Area Under the Curve (AUC), accuracy, and F1-score are consistently formatted, with abbreviations defined at first use and values presented with standard decimal notation.", - "explanation": "Enhances clarity and uniformity in reporting statistical results.", - "location": "Results, Tables", - "category": "notation", - "focus": "notation" - }, - { - "original_text": "Acronyms like NCDs, mHealth, DiGA, SHI, PHI are used before being explicitly defined.", - "improved_version": "Define all acronyms upon first mention, e.g., 'noncommunicable diseases (NCDs)', 'mobile health (mHealth)', 'Digital Healthcare Act (DiGA)', 'Statutory Health Insurance (SHI)', 'Private Health Insurance (PHI)'.", - "explanation": "Ensures all readers understand the acronyms from the first occurrence, improving clarity.", - "location": "Introduction, Methods", - "category": "acronyms", - "focus": "definition" - }, - { - "original_text": "Variables such as 'nonadherent users', 'churned users' are used with thresholds described in different sections.", - "improved_version": "Standardize variable definitions throughout the manuscript, explicitly stating thresholds (e.g., 'nonadherent users: those completing fewer than 8 exercises per week') and maintaining consistent terminology.", - "explanation": "Prevents confusion by ensuring uniform understanding of key variables.", - "location": "Results, Discussion", - "category": "variable_naming", - "focus": "definition" - }, - { - "original_text": "Time units such as days, weeks, and months are used variably with inconsistent formatting.", - "improved_version": "Adopt a uniform format for time units, e.g., always write 'Days 1-7', 'Week 1', 'Month 2', and clarify the time frame when first introduced.", - "explanation": "Reduces ambiguity and improves readability regarding temporal data.", - "location": "Methodology, Results", - "category": "unit_notation", - "focus": "notation" - }, - { - "original_text": "Some abbreviations like 'SD', 'IQR', 'n' are used without initial expansion.", - "improved_version": "Initially define abbreviations such as 'Standard Deviation (SD)', 'Interquartile Range (IQR)', and 'sample size (n)' at their first appearance.", - "explanation": "Ensures clarity for readers unfamiliar with statistical abbreviations.", - "location": "Appendix, Descriptive statistics", - "category": "abbreviations", - "focus": "definition" - }, - { - "original_text": "Terms like 'predictive performance', 'classification', 'model accuracy' are used interchangeably.", - "improved_version": "Use precise terminology: 'predictive performance' for overall assessment, 'classification accuracy' for specific metric, and clearly distinguish between 'model accuracy', 'precision', 'recall', etc., with definitions if necessary.", - "explanation": "Reduces ambiguity by clarifying technical terms and their specific meanings.", - "location": "Discussion, Results", - "category": "technical_terms", - "focus": "term_usage" - }, - { - "original_text": "Terms like 'adherence', 'nonadherence', 'churn', 'dropout' are used interchangeably.", - "improved_version": "Differentiate clearly: 'adherence' refers to following the prescribed use, 'nonadherence' to failing to meet the threshold, 'churn' to complete discontinuation, and 'dropout' as a specific form of churn, with consistent definitions provided early.", - "explanation": "Ensures precise understanding of each concept and their relationships.", - "location": "Introduction, Methods", - "category": "field_terminology", - "focus": "definitions" - }, - { - "original_text": "References to figures and tables (e.g., 'Figure 6.1') are sometimes inconsistent.", - "improved_version": "Ensure all cross-references follow a uniform format, e.g., 'Figure 6.1', 'Table 6.1', and verify that all references are correctly numbered and linked.", - "explanation": "Improves navigation and consistency in referencing visual aids.", - "location": "Throughout the document", - "category": "cross_references", - "focus": "formatting" - }, - { - "original_text": "Definitions of adherence and nonadherence are scattered and sometimes inconsistent.", - "improved_version": "Provide a comprehensive, consistent definition of adherence and nonadherence early in the Methods section, and use these definitions uniformly throughout the manuscript.", - "explanation": "Maintains clarity and consistency in outcome measures and key concepts.", - "location": "Methods, Results, Discussion", - "category": "definition_consistency", - "focus": "definitions" - } - ], - "detailed_feedback": { - "term_usage_consistency": "The manuscript generally uses 'nonadherence' and 'churn' appropriately, but occasionally conflates or contrasts them without clear distinction. Consistent terminology should be maintained, with explicit definitions and consistent application across sections to avoid confusion.", - "notation_consistency": "Statistical metrics such as AUC, accuracy, and F1-score are presented with parentheses or without, and sometimes with SD values. Standardizing formatting\u2014such as always writing 'mean (SD)'\u2014will improve clarity. Also, ensure all units are explicitly stated and consistently formatted.", - "acronym_usage": "Many acronyms are introduced without initial expansion, which can hinder understanding for readers unfamiliar with the terms. All acronyms should be spelled out at first use, with the abbreviation in parentheses, and used consistently thereafter.", - "variable_naming_consistency": "Variables like 'nonadherent users' and 'churned users' are sometimes defined with thresholds in one section but not in others. Establish clear, uniform definitions early and apply them consistently throughout to avoid ambiguity.", - "unit_notation_consistency": "Time-related data are expressed as 'Days 1-7', 'Week 1', 'Month 2' with varying formats. Adopting a uniform notation and explicitly defining the time frames at first mention will enhance clarity.", - "abbreviation_consistency": "Abbreviations such as 'SD', 'IQR', 'n' are used without initial definitions or inconsistent formatting. All abbreviations should be defined upon first mention and used uniformly.", - "technical_term_consistency": "Terms like 'predictive performance' and 'classification' are used broadly without precise definitions. Clarify these terms when first introduced and maintain consistent usage to improve technical clarity.", - "field_terminology": "Key concepts like 'adherence', 'nonadherence', 'churn', and 'dropout' are sometimes used interchangeably or without clear distinctions. Explicit, consistent definitions aligned with the literature will strengthen conceptual clarity.", - "cross_reference_consistency": "References to figures, tables, and appendices should follow a uniform format and be checked for correctness. Proper cross-referencing improves navigation and reduces confusion.", - "definition_consistency": "Definitions of core concepts such as 'adherence' and 'nonadherence' are scattered and sometimes inconsistent. Providing comprehensive, early, and uniform definitions will improve overall clarity and understanding." - }, - "summary": "Overall, the manuscript demonstrates good use of terminology but would benefit from systematic standardization across all sections. Clarifying definitions, maintaining consistent notation, and explicitly defining acronyms and technical terms will significantly enhance clarity, coherence, and the scientific rigor of the presentation." - }, - "W5": { - "inclusive_language_score": 4, - "critical_remarks": [ - { - "category": "gender_neutrality", - "location": "Abstract", - "issue": "The abstract primarily uses gendered pronouns like 'users' without gender-specific language, but the presentation is neutral; however, the gender distribution is reported with '68.3% female' and '31.6% male,' which could be more inclusive by emphasizing diversity beyond binary categories.", - "severity": "low", - "impact": "Limited inclusivity in gender reporting; could overlook non-binary identities." - }, - { - "category": "cultural_sensitivity", - "location": "Introduction", - "issue": "The description of health systems and interventions is Germany-centric, with limited acknowledgment of diverse global contexts, which may reduce cultural sensitivity and inclusivity for international audiences.", - "severity": "medium", - "impact": "Restricts the perceived applicability of findings across different cultural and healthcare settings." - }, - { - "category": "age_terminology", - "location": "Participant descriptions (Vivira and Manoa datasets)", - "issue": "Age categories are described with ranges like '18\u201335 years' and 'over 75 years,' which are appropriate, but the language could be more inclusive by explicitly stating that age is a variable and avoiding implying age-related stereotypes.", - "severity": "low", - "impact": "Minimal, but more explicit acknowledgment of age diversity would improve inclusivity." - }, - { - "category": "disability_inclusion", - "location": "Methodology", - "issue": "The language assumes all users are able-bodied and does not explicitly consider users with disabilities or impairments, which could marginalize those with disabilities.", - "severity": "high", - "impact": "Excludes or unintentionally marginalizes users with disabilities, reducing the inclusivity of the research." - }, - { - "category": "socioeconomic_sensitivity", - "location": "Introduction and methodology", - "issue": "The socioeconomic background of users is not discussed; the focus on smartphone access and healthcare systems may overlook socioeconomic barriers faced by marginalized populations.", - "severity": "medium", - "impact": "Limits understanding of how socioeconomic factors influence adherence and engagement, reducing overall inclusivity." - }, - { - "category": "geographic_inclusivity", - "location": "Introduction and datasets description", - "issue": "The study is Germany-centric, with references to German healthcare systems and regulations, which may limit applicability and inclusivity for populations in other geographic regions.", - "severity": "medium", - "impact": "May reduce relevance for international or non-European populations, limiting global inclusivity." - }, - { - "category": "professional_titles", - "location": "Methodology and author contributions", - "issue": "The text uses titles like 'Jakob R,' 'Benning L,' etc., but does not specify professional titles or roles, which could be more respectful and precise.", - "severity": "low", - "impact": "Minor; clearer professional titles could enhance clarity and respect." - }, - { - "category": "stereotype_avoidance", - "location": "Introduction and discussion", - "issue": "The language does not reinforce stereotypes; however, the focus on age and gender distributions without contextualizing diversity may inadvertently reinforce stereotypes about certain groups' engagement levels.", - "severity": "low", - "impact": "Minimal, but explicit acknowledgment of diversity would prevent stereotypes." - }, - { - "category": "identity_language", - "location": "Participant descriptions", - "issue": "Participants are described with binary gender categories ('female,' 'male') and a small 'non-binary' label, but the language could be more inclusive by emphasizing that gender identity is diverse and fluid.", - "severity": "medium", - "impact": "Reduces recognition of gender diversity beyond binary categories." - }, - { - "category": "historical_context", - "location": "Introduction and discussion", - "issue": "The discussion of digital health systems is historical and Germany-specific, with limited acknowledgment of global developments or the evolving nature of digital health, which could be more inclusive of diverse historical contexts.", - "severity": "low", - "impact": "Limits the understanding of global and historical diversity in digital health evolution." - } - ], - "improvement_suggestions": [ - { - "original_text": "68.3% were female, 31.6% were male, and 0.1% non-binary.", - "improved_version": "68.3% identified as women, 31.6% as men, and 0.1% as non-binary or other gender identities.", - "explanation": "Using 'identified as' and inclusive categories acknowledges gender diversity beyond binary labels, promoting greater inclusivity.", - "location": "Participant descriptions (Vivira dataset)", - "category": "gender_neutrality", - "focus": "gender_neutrality" - }, - { - "original_text": "The description of health systems and interventions is Germany-centric.", - "improved_version": "While this study focuses on the German healthcare context, the methodologies and findings may be adaptable to diverse international healthcare systems, emphasizing the importance of cultural and systemic differences.", - "explanation": "Acknowledging the context's specificity while highlighting potential for broader application enhances cultural sensitivity and inclusivity.", - "location": "Introduction", - "category": "cultural_sensitivity", - "focus": "cultural_sensitivity" - }, - { - "original_text": "Participants are described with age ranges such as 18\u201335 years.", - "improved_version": "Participants' ages are categorized into ranges such as 18\u201335 years, with an emphasis that age is a variable that encompasses diverse life stages and experiences, avoiding stereotypes about engagement or health behaviors based on age.", - "explanation": "Explicitly framing age as a broad, inclusive variable helps prevent age-related stereotypes and promotes age diversity acknowledgment.", - "location": "Participant descriptions", - "category": "age_terminology", - "focus": "age_terminology" - }, - { - "original_text": "The language assumes all users are able-bodied.", - "improved_version": "Future research should consider including users with disabilities or impairments, ensuring accessibility features are evaluated and that the language and design are inclusive of diverse physical abilities.", - "explanation": "Explicitly recognizing users with disabilities promotes inclusivity and encourages accessible design and research practices.", - "location": "Methodology", - "category": "disability_inclusion", - "focus": "disability_inclusion" - }, - { - "original_text": "The socioeconomic background of users is not discussed.", - "improved_version": "Future studies should explore socioeconomic factors influencing engagement, such as access to smartphones, internet connectivity, and health literacy, to better understand barriers faced by marginalized populations.", - "explanation": "Addressing socioeconomic barriers broadens the inclusivity of the research and highlights disparities that may affect adherence.", - "location": "Introduction and methodology", - "category": "socioeconomic_sensitivity", - "focus": "socioeconomic_sensitivity" - }, - { - "original_text": "The study is Germany-centric, with references to German healthcare systems.", - "improved_version": "While this study is based on the German healthcare context, the methodologies and insights are intended to inform international applications, with adaptations made for different healthcare systems and cultural settings.", - "explanation": "Explicitly stating potential for international relevance promotes geographic inclusivity and acknowledges diversity in healthcare contexts.", - "location": "Introduction", - "category": "geographic_inclusivity", - "focus": "geographic_inclusivity" - }, - { - "original_text": "Author contributions list names without titles.", - "improved_version": "Author contributions should include professional titles or roles (e.g., researcher, clinician, data scientist) to clarify expertise and foster respect.", - "explanation": "Using professional titles clarifies roles and promotes respectful acknowledgment of diverse expertise.", - "location": "Author contributions", - "category": "professional_titles", - "focus": "professional_titles" - }, - { - "original_text": "Participants are described with binary gender categories.", - "improved_version": "Participants' gender identities are acknowledged as diverse, with options beyond binary categories, such as 'woman,' 'man,' 'non-binary,' 'prefer to self-describe,' or 'prefer not to say.'", - "explanation": "This approach respects gender diversity and reduces binary stereotypes, fostering inclusivity.", - "location": "Participant descriptions", - "category": "stereotypes", - "focus": "stereotypes" - }, - { - "original_text": "Participants are described as 'users' without explicit mention of their gender identity.", - "improved_version": "Participants are described as 'individuals' or 'people' to emphasize personhood over labels, and gender identity is acknowledged as diverse and fluid where relevant.", - "explanation": "Using person-centered language avoids stereotypes and emphasizes individual diversity.", - "location": "Participant descriptions", - "category": "identity_language", - "focus": "identity_language" - }, - { - "original_text": "Descriptions of health systems focus on Germany-specific policies.", - "improved_version": "The discussion of health systems includes a brief overview of global developments in digital health, recognizing the evolving and diverse nature of digital therapeutics worldwide.", - "explanation": "This broadens the historical and contextual perspective, making the content more globally inclusive.", - "location": "Introduction and discussion", - "category": "historical_context", - "focus": "historical_context" - }, - { - "original_text": "The language in the discussion emphasizes technological and healthcare system specifics without addressing cultural diversity.", - "improved_version": "Future research should consider cultural differences in health behaviors, technology acceptance, and healthcare access, ensuring that models and interventions are adaptable across diverse populations.", - "explanation": "Acknowledging cultural diversity enhances the cultural sensitivity and global applicability of the research.", - "location": "Discussion", - "category": "cultural_sensitivity", - "focus": "cultural_sensitivity" - }, - { - "original_text": "The participant demographics are primarily binary gender categories with minimal diversity acknowledgment.", - "improved_version": "Participant demographics should include a broader spectrum of gender identities, cultural backgrounds, and socioeconomic statuses, with explicit efforts to recruit diverse populations where possible.", - "explanation": "This promotes inclusivity and reduces marginalization of underrepresented groups.", - "location": "Participant descriptions", - "category": "gender_neutrality", - "focus": "gender_neutrality" - }, - { - "original_text": "The language assumes all participants are able to engage with mobile technology equally.", - "improved_version": "Researchers should recognize and address barriers faced by users with disabilities or limited access to technology, designing interventions that are accessible and inclusive for all users.", - "explanation": "This promotes disability inclusion and broadens the reach of interventions.", - "location": "Methodology", - "category": "disability_inclusion", - "focus": "disability_inclusion" - } - ], - "detailed_feedback": { - "gender_neutral_language": "The current text often reports gender data in binary terms, which can unintentionally reinforce gender stereotypes or exclude non-binary identities. Using inclusive language such as 'individuals identifying as women, men, non-binary, or other gender identities' respects gender diversity and promotes inclusivity. Additionally, emphasizing that gender is a spectrum and that data collection methods can be expanded to include more options enhances the research's sensitivity.", - "cultural_sensitivity": "The focus on German healthcare systems and regulations limits the cultural scope of the research. To improve cultural sensitivity, the manuscript should acknowledge that health behaviors, system structures, and acceptance of digital health vary globally. Including a discussion on how findings could be adapted or interpreted in different cultural contexts fosters inclusivity of diverse populations and international applicability.", - "age_appropriate_terminology": "The age ranges used are appropriate; however, the language could be more inclusive by explicitly stating that age is a variable representing diverse life stages and experiences. Avoiding implicit stereotypes about engagement or health behaviors based on age helps foster a more inclusive perspective. For example, recognizing that older adults may have different needs and capacities encourages broader participation.", - "disability_inclusive_language": "The current description assumes all users are able-bodied and does not explicitly consider users with disabilities. Incorporating language that emphasizes accessibility and invites inclusion of users with impairments\u2014such as 'designed to be accessible for users with diverse physical abilities'\u2014would promote disability inclusion and ensure the research considers a broader user base.", - "socioeconomic_sensitivity": "The manuscript does not address socioeconomic barriers, such as access to smartphones, internet, or digital literacy, which can significantly impact engagement. Including a discussion on how socioeconomic factors influence adherence and how interventions can be tailored or made accessible to marginalized groups enhances socioeconomic sensitivity and equity.", - "geographic_inclusivity": "The study's focus on Germany limits its geographic inclusivity. To broaden this, the authors should acknowledge that healthcare systems, cultural attitudes, and technology acceptance differ worldwide. Suggesting that future research validate models in diverse settings promotes a more inclusive and globally relevant approach.", - "professional_titles": "Author contributions list names without titles or roles. Including professional titles (e.g., 'Dr.', 'Professor', 'Data Scientist') clarifies expertise and fosters respect for diverse roles, enhancing transparency and inclusivity in acknowledgment.", - "stereotype_avoidance": "While the language does not overtly reinforce stereotypes, the demographic reporting could inadvertently imply stereotypes about engagement levels based on age or gender. Explicitly emphasizing diversity and avoiding assumptions about behavior based on demographic categories helps prevent stereotypes.", - "identity_language": "Participants are described with binary gender labels and a small 'non-binary' mention. Using inclusive language such as 'participants' with self-identified gender labels or open options like 'prefer to self-describe' respects gender fluidity and diversity, promoting a more inclusive approach.", - "historical_context": "The focus on German digital health policies and systems is specific and may overlook the broader evolution of digital therapeutics globally. Including a brief overview of international developments and acknowledging the dynamic, evolving nature of digital health ensures sensitivity to different historical and cultural contexts, making the research more inclusive." - }, - "summary": "Overall, the manuscript demonstrates a generally neutral and objective tone but could significantly benefit from more explicit inclusive language and broader cultural, gender, and disability considerations. Addressing these areas will enhance the research's accessibility, relevance, and respect for diverse populations, aligning with best practices in inclusive and unbiased scientific communication. Implementing specific suggestions across sections will improve the manuscript's sensitivity and global applicability, fostering a more equitable scientific discourse." - }, - "W6": { - "citation_formatting_score": 4, - "critical_remarks": [ - { - "category": "in_text_format", - "location": "Introduction paragraph, lines 16-44", - "issue": "In-text citations are inconsistently formatted; some are bracketed with brackets and numbers, others are embedded with author names and years, and some lack proper punctuation or formatting.", - "severity": "high", - "impact": "Reduces readability and undermines the scholarly credibility of the manuscript, making it difficult to verify sources and follow citation conventions." - }, - { - "category": "reference_format", - "location": "Bibliography section", - "issue": "References are inconsistently formatted: some entries include DOIs, URLs, and journal details properly, others omit critical information like volume, issue, or page numbers, and some have inconsistent punctuation and line breaks.", - "severity": "high", - "impact": "Impairs the professional appearance and hampers accurate indexing, retrieval, and verification of sources." - }, - { - "category": "style_consistency", - "location": "Throughout the references list", - "issue": "Multiple citation styles are used interchangeably, including numbered brackets, author-year formats, and varying punctuation styles, leading to a lack of uniformity.", - "severity": "high", - "impact": "Creates confusion and diminishes the manuscript's scholarly rigor, potentially violating journal submission guidelines." - }, - { - "category": "reference_completeness", - "location": "Multiple references, e.g., [1], [2], [3]", - "issue": "Several references lack complete bibliographic details, such as missing volume, issue, page numbers, or publication year, especially for online-only sources.", - "severity": "high", - "impact": "Prevents proper citation tracking and reduces the credibility of the references." - }, - { - "category": "doi_format", - "location": "References, e.g., [1], [2], [3]", - "issue": "DOIs are inconsistently formatted; some are complete URLs (https://doi.org/...), others are just numeric, and some are missing entirely.", - "severity": "medium", - "impact": "Hinders direct access to sources and violates standard DOI formatting guidelines." - }, - { - "category": "author_name_formatting", - "location": "References, e.g., [1], [2], [3]", - "issue": "Author names are inconsistently formatted; some use full names, others initials, and some have inconsistent ordering or punctuation.", - "severity": "medium", - "impact": "Reduces clarity and professionalism, complicates author attribution, and affects citation indexing." - }, - { - "category": "publication_date_formatting", - "location": "References, e.g., [1], [2], [3]", - "issue": "Publication dates are inconsistently formatted; some include month and year, others only year, and some have extraneous information.", - "severity": "medium", - "impact": "Impairs chronological clarity and consistency across references." - }, - { - "category": "journal_format", - "location": "References, e.g., [1], [2], [3]", - "issue": "Journal names are variably formatted; some are italicized, others are plain text, and some abbreviations are inconsistent or missing.", - "severity": "medium", - "impact": "Affects professional presentation and adherence to journal style guides." - }, - { - "category": "volume_format", - "location": "References, e.g., [1], [2], [3]", - "issue": "Volume, issue, and page numbers are inconsistently formatted; some include parentheses, others omit issue numbers, and page ranges are sometimes missing or improperly formatted.", - "severity": "medium", - "impact": "Reduces clarity and hampers accurate source identification." - }, - { - "category": "cross_reference", - "location": "Throughout the manuscript", - "issue": "In-text citations do not consistently match reference list entries; some citations are missing, and numbering or author-year references are mismatched or inconsistent.", - "severity": "high", - "impact": "Undermines the integrity of scholarly referencing and impairs verification of sources." - } - ], - "improvement_suggestions": [ - { - "original_text": "In: (2025) (In preparation)", - "improved_version": "In: Jakob R, Benning L, S\u00e9vin CM, Von Wangenheim F, Fleisch E, Kowatsch T. (2025). Predicting Nonadherence to Mobile Health Interventions. (In preparation).", - "explanation": "Standardizes the in-text citation format for a forthcoming publication, aligning with common scholarly conventions.", - "location": "Reference entry for the planned publication", - "category": "references", - "focus": "reference_format" - }, - { - "original_text": "[16, 26, 44, 64]", - "improved_version": "(Author et al., 2016; Author et al., 2020; Author et al., 2022; Author et al., 2015)", - "explanation": "Converts numeric citations to author-year format for clarity and consistency, especially in-text, aligning with common styles like APA or Vancouver with author-year.", - "location": "Introduction paragraph, lines 16-44", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[1]", - "improved_version": "(Alessa et al., 2018)", - "explanation": "Provides author name and year for clarity, making it easier for readers to identify sources without cross-referencing numbers.", - "location": "Introduction, paragraph 2, lines 16-44", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[54, 55]", - "improved_version": "Bl\u00fcmel et al., 2020; Teepe et al., 2022", - "explanation": "Replaces numeric references with author-year citations for consistency and readability.", - "location": "Introduction, paragraph 2, lines 16-44", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "https://doi.org/10.2196/10723", - "improved_version": "https://doi.org/10.2196/10723", - "explanation": "Ensure all DOIs are formatted as complete URLs starting with https://doi.org/ for uniformity and easy access.", - "location": "References, e.g., [1]", - "category": "doi_format", - "focus": "doi_format" - }, - { - "original_text": "[2]", - "improved_version": "Ganju et al., 2020", - "explanation": "Replace numeric citation with author-year format for clarity and consistency in the references list.", - "location": "Throughout the manuscript, e.g., in-text citations", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[3]", - "improved_version": "Beger et al., 2023", - "explanation": "Standardizes citation style to author-year for consistency and easier source identification.", - "location": "Throughout the manuscript", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[4]", - "improved_version": "Bl\u00fcmel et al., 2020", - "explanation": "Provides full author names and year, aligning with common citation styles and improving clarity.", - "location": "Introduction, paragraph 2", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[5]", - "improved_version": "Bricker et al., 2023", - "explanation": "Ensures consistent author-year citation style for all references, enhancing uniformity.", - "location": "Results section, paragraph 3", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[6]", - "improved_version": "Donkin et al., 2011", - "explanation": "Aligns with the author-year citation style, facilitating easier cross-referencing and readability.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[7]", - "improved_version": "Drachen et al., 2016", - "explanation": "Standardizes citation style, improving consistency across the manuscript.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[8]", - "improved_version": "Ernsting et al., 2017", - "explanation": "Provides clarity and uniformity in referencing, aiding reader comprehension.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[9]", - "improved_version": "Eysenbach, 2005", - "explanation": "Ensures consistent author-year citation style, improving scholarly presentation.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[10]", - "improved_version": "Firth et al., 2017", - "explanation": "Aligns with the author-year citation style, aiding clarity and uniformity.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[11]", - "improved_version": "Fleming et al., 2018", - "explanation": "Provides consistent citation style, enhancing professional appearance.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[12]", - "improved_version": "Fowler et al., 2016", - "explanation": "Standardizes citation style for clarity and consistency.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[13]", - "improved_version": "F\u00fcrstenau et al., 2023", - "explanation": "Ensures uniform author-year citation style, improving readability.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[14]", - "improved_version": "Ganju et al., 2020", - "explanation": "Provides clarity and consistency in citations, aiding verification.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - }, - { - "original_text": "[15]", - "improved_version": "Geiler et al., 2022", - "explanation": "Standardizes citation style for uniformity and professionalism.", - "location": "Discussion, paragraph 4", - "category": "in_text_format", - "focus": "in_text_format" - } - ], - "detailed_feedback": { - "in_text_citation_format": "The manuscript employs a mixture of numeric bracketed citations and author-year formats, leading to inconsistency. For clarity and adherence to common academic standards, all in-text citations should follow a uniform style, preferably author-year, especially in the main text. This enhances readability and allows easier cross-referencing with the reference list.", - "reference_list_format": "The reference list exhibits inconsistent formatting: some entries include full journal titles, volume, issue, page numbers, and DOIs, while others omit these details. Standardizing the format according to a recognized style guide (e.g., APA, Vancouver) and ensuring all references contain complete bibliographic information will improve professionalism and facilitate source verification.", - "citation_style_consistency": "Currently, the manuscript switches between numbered citations and author-year formats, which can confuse readers and violate journal guidelines. Adopting a single, consistent citation style throughout the document is essential for scholarly rigor.", - "reference_completeness": "Many references lack complete details such as volume, issue, page ranges, or publication dates. Ensuring each reference includes all necessary bibliographic elements will improve accuracy and traceability.", - "doi_url_formatting": "DOIs are inconsistently formatted; some are presented as URLs (https://doi.org/...), others as plain numbers or missing entirely. All DOIs should be formatted as full URLs to enable direct access, following current standards.", - "author_name_formatting": "Author names are inconsistently formatted, with some entries using full names, others initials, and varying punctuation. Standardizing author name formats (e.g., Lastname, First Initials) across all references will enhance clarity.", - "publication_date_formatting": "Publication dates are variably formatted, with some including month and year, others only year. Consistent date formatting (e.g., Year, Month) across all references will improve chronological clarity.", - "journal_name_formatting": "Journal names are inconsistently italicized or abbreviated; some are fully spelled out, others abbreviated. Consistent formatting\u2014either full journal names italicized or standardized abbreviations\u2014will improve uniformity.", - "volume_issue_page_formatting": "Volume, issue, and page numbers are inconsistently formatted; some entries include parentheses, others omit issue numbers or page ranges. Uniform formatting following a style guide (e.g., Volume(Issue), pages) is recommended.", - "cross_reference_accuracy": "In-text citations do not always match reference list entries, with some missing references or mismatched numbering. Cross-checking all citations to ensure they correctly correspond to the reference list is crucial for scholarly integrity." - }, - "summary": "Overall, the manuscript demonstrates a significant inconsistency in citation formatting and style. Addressing these issues by standardizing in-text citation formats, completing reference details, uniformly formatting journal titles, DOIs, author names, and publication dates, and ensuring accurate cross-referencing will markedly improve the scholarly quality, professionalism, and readability of the document. Implementing a consistent citation style aligned with journal guidelines is strongly recommended." - }, - "W7": { - "audience_alignment_score": 8, - "critical_remarks": [ - { - "category": "methodology", - "location": "Section 2.2", - "issue": "While the methodology is detailed, some technical descriptions, such as hyperparameter tuning and feature preprocessing, assume prior knowledge and lack explicit explanations for non-expert readers.", - "severity": "medium", - "impact": "Could hinder understanding for readers unfamiliar with machine learning procedures, limiting accessibility to a broader academic audience." - }, - { - "category": "visuals", - "location": "Throughout the Results section", - "issue": "The figures and tables are referenced with minimal descriptive context, and the visual complexity may overwhelm readers without accompanying interpretative summaries.", - "severity": "medium", - "impact": "May reduce clarity of key findings, affecting audience comprehension and engagement." - }, - { - "category": "references", - "location": "Bibliography", - "issue": "While extensive, the references predominantly cite technical and methodological papers; there is limited integration of recent clinical or behavioral science literature that contextualizes the findings.", - "severity": "low", - "impact": "Slightly limits the interdisciplinary appeal and depth of contextual understanding for clinicians or behavioral scientists." - }, - { - "category": "results", - "location": "Section 3.2", - "issue": "Results are densely presented with numerous metrics and statistical details, which may obscure the overarching narrative of model performance.", - "severity": "high", - "impact": "Could challenge readers' ability to grasp the main implications, reducing overall engagement." - }, - { - "category": "discussion", - "location": "Section 4", - "issue": "While the discussion covers technical performance, it lacks sufficient exploration of practical implications, limitations, or potential real-world applications beyond technical metrics.", - "severity": "medium", - "impact": "May limit the relevance for practitioners seeking actionable insights, affecting broader applicability." - }, - { - "category": "conclusion", - "location": "Section 4.4", - "issue": "The conclusion summarizes findings but does not clearly articulate future directions or specific recommendations for implementation.", - "severity": "low", - "impact": "Reduces the perceived utility of the research for advancing practice or policy." - }, - { - "category": "terminology", - "location": "Throughout the document", - "issue": "Use of technical terms like 'AUC', 'F1-score', 'stratified 10-fold cross-validation' assumes familiarity; some terms could benefit from brief definitions or explanations.", - "severity": "medium", - "impact": "May alienate or confuse readers outside the machine learning or digital health research communities." - }, - { - "category": "writing style", - "location": "Abstract and sections", - "issue": "The writing is formal and technical but occasionally verbose, which could hinder readability for a broader academic audience.", - "severity": "low", - "impact": "Potentially reduces accessibility and engagement for interdisciplinary readers." - }, - { - "category": "section organization", - "location": "Entire document", - "issue": "While sections are logically ordered, some subsections (e.g., detailed tables and appendices) could be better integrated with main text to improve flow and contextual understanding.", - "severity": "medium", - "impact": "May cause readers to overlook key insights or struggle to connect detailed data with overarching themes." - }, - { - "category": "visual elements", - "location": "Figures 1-8 and tables", - "issue": "Visuals are data-rich but lack sufficient interpretative captions or summaries that highlight key takeaways.", - "severity": "medium", - "impact": "Could diminish the clarity and immediate understanding of complex data, affecting engagement." - }, - { - "category": "references", - "location": "Bibliography", - "issue": "The referencing style is consistent but predominantly includes journal articles; inclusion of more gray literature or recent reviews could enhance comprehensiveness.", - "severity": "low", - "impact": "Minimal impact but could improve depth for readers seeking broader context." - } - ], - "improvement_suggestions": [ - { - "original_text": "The rich data collected by mHealth interventions raise the question of whether \u2013 and to what extent \u2013 nonadherence can be predicted using these data.", - "improved_version": "Given the extensive behavioral data collected by mHealth interventions, it is pertinent to investigate the extent to which nonadherence can be accurately predicted using these data.", - "explanation": "Clarifies the research question with more precise language, enhancing clarity and focus for the audience.", - "location": "Abstract", - "category": "organization", - "focus": "clarity" - }, - { - "original_text": "Our models identified an average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), defined as completing fewer than eight therapeutic exercises per week.", - "improved_version": "In Vivira, our models successfully identified approximately 94% of nonadherent users\u2014those completing fewer than eight exercises weekly\u2014between Weeks 2 and 13, with an average AUC of 0.95.", - "explanation": "Rephrases for clarity, explicitly linking the metric to the adherence definition, aiding comprehension.", - "location": "Abstract", - "category": "results", - "focus": "clarity" - }, - { - "original_text": "The use of DiGA data is strictly limited. Therefore, only users who provided consent under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included.", - "improved_version": "Due to regulatory restrictions, only users who explicitly consented under Article 4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included in the analysis.", - "explanation": "Simplifies and clarifies regulatory context, making it more accessible to readers unfamiliar with legal details.", - "location": "Section 2.1.1", - "category": "terminology", - "focus": "clarity" - }, - { - "original_text": "Figures and tables are referenced with minimal descriptive context, which may overwhelm readers without accompanying interpretative summaries.", - "improved_version": "Each figure and table should be accompanied by a concise interpretative caption that highlights key findings, such as trends or significant differences, to guide reader understanding.", - "explanation": "Enhances visual comprehension by providing context, improving engagement and clarity.", - "location": "Throughout Results", - "category": "visuals", - "focus": "organization" - }, - { - "original_text": "Results are densely presented with numerous metrics and statistical details, which may obscure the overarching narrative of model performance.", - "improved_version": "Summarize key performance metrics in a narrative that emphasizes overall trends and implications, reserving detailed statistics for supplementary tables or appendices.", - "explanation": "Improves readability by focusing on main messages, making complex data more digestible.", - "location": "Section 3.2", - "category": "results", - "focus": "organization" - }, - { - "original_text": "While the discussion covers technical performance, it lacks sufficient exploration of practical implications, limitations, or potential real-world applications beyond technical metrics.", - "improved_version": "Expand the discussion to include practical implications, potential implementation strategies, and limitations, thereby bridging the gap between technical performance and real-world applicability.", - "explanation": "Enhances relevance for practitioners and policymakers, increasing audience engagement.", - "location": "Section 4", - "category": "discussion", - "focus": "application" - }, - { - "original_text": "The conclusion summarizes findings but does not clearly articulate future directions or specific recommendations for implementation.", - "improved_version": "Conclude with specific recommendations for future research, potential clinical integration, and policy considerations to maximize the impact of these predictive models.", - "explanation": "Provides actionable guidance, increasing the utility of the research for stakeholders.", - "location": "Section 4.4", - "category": "conclusion", - "focus": "organization" - }, - { - "original_text": "Use of technical terms like 'AUC', 'F1-score', 'stratified 10-fold cross-validation' assumes prior knowledge; some terms could benefit from brief definitions or explanations.", - "improved_version": "Include brief explanations of key technical terms (e.g., 'AUC' as the area under the receiver operating characteristic curve) when first introduced to enhance accessibility.", - "explanation": "Makes the content more approachable for interdisciplinary audiences, broadening readership.", - "location": "Throughout technical sections", - "category": "terminology", - "focus": "clarity" - }, - { - "original_text": "The writing is formal and technical but occasionally verbose, which could hinder readability for a broader academic audience.", - "improved_version": "Simplify complex sentences and reduce jargon where possible, aiming for concise, clear language that maintains technical accuracy.", - "explanation": "Improves readability and engagement across diverse academic backgrounds.", - "location": "Abstract and main sections", - "category": "writing style", - "focus": "clarity" - }, - { - "original_text": "While sections are logically ordered, some subsections (e.g., detailed tables and appendices) could be better integrated with main text to improve flow and contextual understanding.", - "improved_version": "Integrate key findings from tables and appendices into the main narrative with explicit references, ensuring smooth flow and contextual clarity.", - "explanation": "Enhances coherence and helps readers connect detailed data with overarching themes.", - "location": "Section organization", - "category": "organization", - "focus": "organization" - }, - { - "original_text": "Visuals are data-rich but lack sufficient interpretative captions or summaries that highlight key takeaways.", - "improved_version": "Add descriptive captions and brief summaries for each figure and table that explicitly state the main insights and their relevance to the research questions.", - "explanation": "Facilitates quicker understanding and emphasizes the significance of visual data.", - "location": "Figures 1-8 and tables", - "category": "visuals", - "focus": "organization" - }, - { - "original_text": "The referencing style is consistent but predominantly includes journal articles; inclusion of more gray literature or recent reviews could enhance comprehensiveness.", - "improved_version": "Incorporate recent review articles, gray literature, and policy documents where relevant to provide a more comprehensive and current literature context.", - "explanation": "Broadens the scope and relevance, appealing to a wider academic and practitioner audience.", - "location": "Bibliography", - "category": "references", - "focus": "depth" - } - ], - "detailed_feedback": { - "technical_depth": "The manuscript demonstrates a high level of technical depth, appropriate for researchers familiar with machine learning and digital health. However, some methodological details, such as hyperparameter tuning and feature preprocessing, could be elaborated to enhance transparency and reproducibility for advanced readers.", - "terminology_usage": "The use of field-specific terms like 'AUC', 'F1-score', and 'stratified 10-fold cross-validation' aligns with standard scientific conventions. Nonetheless, providing brief definitions or context for these terms upon first mention would improve accessibility for interdisciplinary audiences or newcomers to ML methods.", - "writing_formality": "The writing maintains a formal, academic tone suitable for scholarly publication. To improve readability for a broader audience, consider reducing verbosity and simplifying complex sentences without sacrificing technical accuracy.", - "section_organization": "The overall structure follows a logical progression, with clear separation of Introduction, Methods, Results, and Discussion. However, integrating key findings from detailed tables into the main narrative and ensuring smooth transitions between sections would enhance flow and comprehension.", - "visual_integration": "Figures and tables are comprehensive but could benefit from more interpretative captions that highlight main insights. Embedding summaries or key takeaways alongside visuals would aid reader understanding and engagement.", - "reference_style": "References are consistently formatted and relevant, but expanding to include recent reviews and gray literature would provide a more comprehensive background and contextual foundation.", - "methodology_detail": "The methodology is described with sufficient technical detail for replication but could include more explanation of specific ML procedures, such as hyperparameter selection rationale and feature engineering steps, to improve transparency.", - "results_presentation": "Results are detailed with numerous metrics, which may overwhelm readers. Summarizing key performance trends and implications in the main text, with detailed statistics in appendices, would improve clarity.", - "discussion_depth": "The discussion addresses technical performance well but could be expanded to explore practical implications, limitations, and potential real-world applications, making the findings more relevant for practitioners.", - "conclusion_format": "The conclusion summarizes main findings but would benefit from explicit future directions, recommendations for implementation, and broader impact statements to enhance utility and engagement." - }, - "summary": "Overall, the manuscript exhibits strong technical rigor and is well-suited for an academic audience familiar with digital health and machine learning. To broaden its accessibility and impact, targeted improvements in clarity, visual integration, and contextual explanations are recommended. These enhancements would facilitate better engagement across interdisciplinary audiences, including clinicians, behavioral scientists, and policymakers, thereby maximizing the research's translational potential." - }, - "W8": { - "visual_presentation_score": 2, - "critical_remarks": [ - { - "category": "Figures", - "location": "Figures 1, 3, 5, 6, 8", - "issue": "All figures are missing visual content; only placeholder captions and descriptions are provided, with no actual images or diagrams included.", - "severity": "high", - "impact": "Severely limits the reader's ability to interpret data visually, reducing comprehension and engagement. Visuals are crucial for understanding trends, patterns, and results." - }, - { - "category": "Tables", - "location": "Tables 5-11", - "issue": "Tables are densely packed with numerical data but lack clear formatting cues such as alternating row colors, bold headers, or spacing to enhance readability.", - "severity": "medium", - "impact": "Hinders quick scanning and comparison of data, potentially leading to misinterpretation or oversight of key statistics." - }, - { - "category": "Visual element placement", - "location": "Throughout the manuscript", - "issue": "Figures and tables are embedded within dense text blocks without strategic placement or referencing, making navigation cumbersome.", - "severity": "medium", - "impact": "Reduces flow and readability, forcing readers to search for visual aids and disrupting comprehension." - }, - { - "category": "Caption completeness", - "location": "All figures and tables", - "issue": "Captions are lengthy but often lack concise summaries of key insights or explanations of axes, units, or significance.", - "severity": "low", - "impact": "Limits the utility of visuals for quick understanding; captions should clarify what the visual demonstrates." - }, - { - "category": "Color scheme", - "location": "Figures 6-8", - "issue": "Color schemes are not specified, and given the absence of actual visuals, it's unclear if contrast and accessibility considerations (e.g., for color-blind readers) are addressed.", - "severity": "low", - "impact": "Potential accessibility issues if color is used without contrast considerations; proper color choices enhance clarity." - }, - { - "category": "Data visualization effectiveness", - "location": "Figures 6-8", - "issue": "Figures intended to show performance metrics are placeholders with no actual plots or charts, rendering them ineffective.", - "severity": "high", - "impact": "Prevents visual comparison of model performance over time, which is critical for understanding trends and results." - }, - { - "category": "Visual hierarchy", - "location": "Throughout the manuscript", - "issue": "Lack of visual hierarchy; no use of font size, bolding, or spacing to differentiate headings, subheadings, and key data points.", - "severity": "medium", - "impact": "Makes navigation and comprehension more difficult, especially when scanning for key results." - }, - { - "category": "Accessibility considerations", - "location": "Figures and tables", - "issue": "No evidence of accessibility features such as alt text, high contrast, or font size considerations.", - "severity": "low", - "impact": "Limits accessibility for readers with visual impairments or color vision deficiencies." - }, - { - "category": "Consistency in visual style", - "location": "Throughout the manuscript", - "issue": "No consistent style for tables and figure captions; formatting varies, and visual elements are absent.", - "severity": "low", - "impact": "Reduces professional appearance and coherence of the manuscript." - }, - { - "category": "Integration with text", - "location": "Throughout the manuscript", - "issue": "Visual elements are missing or poorly integrated; references to figures/tables are frequent but visuals are absent, making it hard to connect data with narrative.", - "severity": "high", - "impact": "Hinders understanding of key points and weakens the support visuals could provide." - } - ], - "improvement_suggestions": [ - { - "original_text": "Figure 1: Percentages of daily active users and cumulative percentage of users\u2019 last login across 90-Day program duration in Vivira (n = 8,372).", - "improved_version": "Figure 1: Line chart illustrating daily active user retention and cumulative last login percentage over the 90-day Vivira program. Include clear axes labels, legend, and high-resolution image.", - "explanation": "Visuals should clearly depict trends with labels and legends for quick interpretation, enhancing comprehension.", - "location": "Figures 1, 3, 5, 6, 8", - "category": "Figures", - "focus": "Clarity and effectiveness" - }, - { - "original_text": "Tables 5-11: Descriptive statistics of users\u2019 number of active days and completed exercises per week in Vivira and Manoa.", - "improved_version": "Tables 5-11: Reformat tables with alternating row shading, bold headers, and consistent decimal places. Add summary notes highlighting key statistics at the top of each table.", - "explanation": "Improved formatting enhances readability, allows quick comparison, and emphasizes important data points.", - "location": "Tables 5-11", - "category": "Tables", - "focus": "Readability and clarity" - }, - { - "original_text": "Throughout the manuscript", - "improved_version": "Embed visual elements close to relevant text sections, with clear references and consistent style. Use subheadings and spacing to delineate sections clearly.", - "explanation": "Strategic placement and consistent style improve flow and help readers connect visuals with narrative.", - "location": "Entire manuscript", - "category": "Visual placement", - "focus": "Flow and integration" - }, - { - "original_text": "Captions are lengthy but lack concise summaries.", - "improved_version": "Revise captions to be concise yet informative, e.g., 'Figure 1: Retention and last login trends over 90 days in Vivira.' Include key insights or data points if space allows.", - "explanation": "Clear, concise captions facilitate quick understanding and contextualize visuals effectively.", - "location": "Figures and tables", - "category": "Caption completeness", - "focus": "Clarity and utility" - }, - { - "original_text": "Color schemes are unspecified.", - "improved_version": "Apply high-contrast color schemes suitable for color-blind readers, such as color palettes from ColorBrewer. Use distinct colors for different data series and ensure all are distinguishable in grayscale.", - "explanation": "Enhances accessibility and ensures visuals are interpretable by all readers." - } - ], - "detailed_feedback": { - "figure_quality": "Figures are absent; placeholder captions do not provide visual content. When included, should be high-resolution, clear, and well-labeled.", - "table_formatting": "Tables are dense with data, lacking visual cues for readability. Formatting improvements needed for clarity.", - "visual_placement": "Visuals are poorly integrated, often missing or placed without clear reference, disrupting flow.", - "caption_completeness": "Captions are lengthy but lack concise summaries and clarity about axes, units, and significance.", - "color_scheme": "Not specified; should use accessible, high-contrast palettes for clarity and accessibility.", - "data_visualization": "Effective data visualization is missing; inclusion of well-designed charts would significantly improve comprehension.", - "visual_hierarchy": "Lacking; visual elements do not differentiate headings, subheadings, or key data points, reducing clarity.", - "accessibility": "No explicit accessibility features; improvements needed for color contrast, font size, and descriptive alt text.", - "visual_consistency": "Inconsistent style and formatting across visuals; standardization would improve professionalism.", - "text_integration": "Visuals are not well integrated with the text; better referencing and placement are necessary for coherence." - }, - "summary": "Overall, the manuscript's visual presentation is severely lacking, with missing figures, poorly formatted tables, and inadequate integration of visuals with the narrative. To enhance clarity, readability, and impact, the authors should incorporate high-quality, well-designed visual elements, improve formatting and placement, and ensure visuals support and complement the textual content effectively." - } -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/combined_results_20250422_235313.json b/Backup/V5_multi_agent2/results/combined_results_20250422_235313.json deleted file mode 100644 index 16b841f..0000000 --- a/Backup/V5_multi_agent2/results/combined_results_20250422_235313.json +++ /dev/null @@ -1,1297 +0,0 @@ -{ - "R1": { - "originality_contribution_score": 8, - "critical_remarks": [ - { - "category": "novelty", - "location": "Introduction", - "issue": "Lack of clear identification of the specific novel aspects of the research approach.", - "severity": "medium", - "impact": "May lead to ambiguity regarding the unique contributions of the study." - }, - { - "category": "contribution", - "location": "Results", - "issue": "Limited discussion on the practical implications and real-world applications of the findings.", - "severity": "medium", - "impact": "Reduces the clarity on how the research can be applied in telehealth or emergency scenarios." - } - ], - "improvement_suggestions": [ - { - "original_text": "Recent computer vision-based approaches for BMI estimation have predominantly been limited to datasets of up to 7,211 full-body images or 14,500 facial images.", - "improved_version": "Existing computer vision-based approaches for BMI estimation have primarily utilized datasets with a maximum of 7,211 full-body images or 14,500 facial images.", - "explanation": "Improves clarity and readability by rephrasing the sentence for better flow.", - "location": "Abstract", - "category": "abstract", - "focus": "novelty" - }, - { - "original_text": "Our model demonstrates strong generalizability to unseen data, as demonstrated by our evaluation on the full-body Visual-Body-To-BMI dataset, achieving a MAPE of 13.38%, despite not being trained on this dataset at all.", - "improved_version": "Our model showcases robust generalizability to unseen data, as evidenced by achieving a MAPE of 13.38% on the full-body Visual-Body-To-BMI dataset, despite no prior training on this specific dataset.", - "explanation": "Enhances clarity and precision in conveying the model's generalizability.", - "location": "Discussion", - "category": "discussion", - "focus": "advancement" - } - ], - "detailed_feedback": { - "novelty_assessment": "The research introduces a novel approach of BMI estimation from smartphone camera images using a significantly larger real-world dataset, which surpasses the limitations of previous studies with smaller datasets.", - "contribution_analysis": "The study contributes by providing a comprehensive evaluation of BMI estimation from smartphone images, introducing an automatic image filtering strategy, and demonstrating the real-world applicability of the approach.", - "verification_status": "The claims of novelty are well-supported through the detailed methodology description, results presentation, and comparison with existing literature, showcasing advancements over previous studies.", - "comparative_analysis": "The study effectively compares different image perspectives for BMI estimation within a homogeneous dataset, providing valuable insights into the impact of various views on weight predictions.", - "advancement_evaluation": "The research significantly advances the field by demonstrating the feasibility of BMI estimation from smartphone images, showcasing strong generalizability, and providing open-source contributions for further research and application development." - }, - "summary": "The research demonstrates a high level of originality and contribution to the field through a novel approach to BMI estimation from smartphone images. While some sections could benefit from clearer identification of novelty and more in-depth discussion on practical implications, the study effectively verifies its claims, compares well with existing literature, and significantly advances knowledge in the field." - }, - "R2": { - "impact_significance_score": 8, - "critical_remarks": [ - { - "category": "field_influence", - "location": "Introduction", - "issue": "The introduction lacks a clear statement on how this research will influence the field of digital health interventions.", - "severity": "medium", - "impact": "This affects the clarity of the research's contribution to the field." - }, - { - "category": "implications", - "location": "Discussion", - "issue": "The discussion section does not thoroughly explore the broader implications of the findings beyond BMI estimation.", - "severity": "medium", - "impact": "This limits the understanding of the potential impact of the research." - }, - { - "category": "applications", - "location": "Results", - "issue": "The practical applications of the research could be more explicitly linked to real-world scenarios and benefits.", - "severity": "medium", - "impact": "This reduces the clarity on how the research can be practically implemented." - } - ], - "improvement_suggestions": [ - { - "original_text": "Recent computer vision-based approaches for BMI estimation have predominantly been limited to datasets of up to 7,211 full-body images or 14,500 facial images.", - "improved_version": "Current computer vision-based BMI estimation approaches have primarily utilized datasets with a maximum of 7,211 full-body images or 14,500 facial images.", - "explanation": "Clarifies the sentence structure for better readability and comprehension.", - "location": "Abstract", - "category": "abstract", - "focus": "field_influence" - }, - { - "original_text": "We achieve a Mean Absolute Percentage Error (MAPE) of 8% on our hold-out test dataset (real-world data only), using full-torso images.", - "improved_version": "Our model achieves an 8% Mean Absolute Percentage Error (MAPE) on the hold-out test dataset, utilizing full-torso images.", - "explanation": "Enhances clarity and conciseness in presenting the research findings.", - "location": "Results", - "category": "results", - "focus": "field_influence" - }, - { - "original_text": "To address the gap in datasets of limited size, we train deep learning models on a large-scale, real-world dataset of over 84,000 full-body images of 25,353 individuals.", - "improved_version": "To bridge the gap in small datasets, we utilize deep learning models trained on a comprehensive real-world dataset comprising over 84,000 full-body images from 25,353 individuals.", - "explanation": "Provides a more descriptive and engaging presentation of the dataset size and research approach.", - "location": "Introduction", - "category": "introduction", - "focus": "field_influence" - }, - { - "original_text": "Our model demonstrates strong generalizability to unseen data, as demonstrated by our evaluation on the full-body Visual-Body-To-BMI dataset, achieving a MAPE of 13.38%, despite not being trained on this dataset at all.", - "improved_version": "Our model exhibits robust generalizability to unseen data, exemplified by achieving a MAPE of 13.38% on the Visual-Body-To-BMI dataset, despite not being trained on this specific dataset.", - "explanation": "Enhances the emphasis on the model's generalizability and performance on unseen data.", - "location": "Discussion", - "category": "discussion", - "focus": "future_research" - }, - { - "original_text": "Our work also provides insights for remote weight estimation during telemedicine consultations.", - "improved_version": "Additionally, our research offers valuable insights for remote weight estimation in telemedicine consultations.", - "explanation": "Strengthens the statement on the practical implications of the research.", - "location": "Conclusion", - "category": "conclusion", - "focus": "applications" - } - ], - "detailed_feedback": { - "field_influence": "The research presents a significant contribution to the field of digital health interventions by introducing a novel approach to BMI estimation from smartphone images using deep learning on a large-scale real-world dataset.", - "broader_implications": "The findings have broader implications for telehealth services, emergency medical scenarios, and remote health monitoring, offering a digital, real-time solution for weight assessment.", - "future_research_impact": "The study's focus on different image perspectives and the evaluation on external datasets pave the way for future research on optimizing BMI estimation models for diverse applications and datasets.", - "practical_applications": "The research showcases practical applications in image filtering, BMI estimation, and mobile deployment, demonstrating the potential for real-world implementation in healthcare settings.", - "policy_implications": "The study's findings can inform policies related to digital health interventions, telemedicine, and remote monitoring, highlighting the importance of accurate and efficient weight estimation methods." - }, - "summary": "The research demonstrates a significant contribution to the field of digital health interventions through innovative BMI estimation from smartphone images. While the study presents valuable insights and practical applications, improvements are needed in clearly articulating the field influence, broader implications, and practical relevance of the findings." - }, - "R3": { - "ethics_compliance_score": 7, - "critical_remarks": [ - { - "category": "privacy", - "location": "Methodology - Image filtering", - "issue": "Self-reported height and weight labels introduce potential inaccuracies and biases in the data.", - "severity": "medium", - "impact": "This could affect the accuracy and reliability of the BMI estimation model." - }, - { - "category": "consent", - "location": "Abstract", - "issue": "The consent process for using the dataset is not clearly outlined.", - "severity": "medium", - "impact": "Lack of transparency regarding data usage and consent may raise ethical concerns." - }, - { - "category": "integrity", - "location": "Results - Results on external datasets", - "issue": "Comparison with original authors' results lacks detailed explanation for discrepancies.", - "severity": "low", - "impact": "Transparency in reporting and discussing differences is crucial for research integrity." - } - ], - "improvement_suggestions": [ - { - "original_text": "Images were collected from users of a fitness app over the course of 10+ years.", - "improved_version": "Images were collected from users of a fitness app over a period of more than 10 years.", - "explanation": "Clarity in the duration helps in understanding the data collection timeline.", - "location": "Abstract", - "category": "abstract", - "focus": "guidelines" - }, - { - "original_text": "Participants were instructed to take a front-facing photo of themselves.", - "improved_version": "Participants were explicitly instructed to take a front-facing photo of themselves.", - "explanation": "Explicit instructions ensure clarity and informed participation.", - "location": "Methodology - Image filtering", - "category": "methodology", - "focus": "consent" - }, - { - "original_text": "We split the dataset into training, test, and validation sets of 75%, 15%, and 10% respectively.", - "improved_version": "The dataset was divided into training, test, and validation sets comprising 75%, 15%, and 10% of the data respectively.", - "explanation": "Using consistent terminology enhances clarity and understanding.", - "location": "Methodology", - "category": "methodology", - "focus": "guidelines" - }, - { - "original_text": "We report the processing speed for filtering and inference on Android and iOS devices.", - "improved_version": "The processing speed for filtering and inference on Android and iOS devices is reported.", - "explanation": "Clear reporting of results enhances transparency and reproducibility.", - "location": "Results", - "category": "results", - "focus": "guidelines" - }, - { - "original_text": "The height of each user was self-reported at the beginning of the fitness program.", - "improved_version": "Users self-reported their height at the start of the fitness program.", - "explanation": "Using active voice and clear language improves readability and engagement.", - "location": "Methodology", - "category": "methodology", - "focus": "consent" - } - ], - "detailed_feedback": { - "conflicts_assessment": "The text does not explicitly mention any conflicts of interest, which should be clearly stated to ensure transparency.", - "privacy_compliance": "Data privacy measures are mentioned, but the reliance on self-reported height and weight labels raises concerns about data accuracy and privacy.", - "consent_procedures": "The consent process for using the dataset is not detailed, potentially leading to ethical issues regarding data usage.", - "research_integrity": "The study demonstrates robust generalization to unseen data, but lacks detailed discussion on discrepancies with original authors' results.", - "guidelines_adherence": "While the study adheres to some ethical guidelines, improvements in transparency, consent procedures, and reporting are needed for full compliance." - }, - "summary": "The study demonstrates a strong effort towards ethical compliance and research standards, with notable improvements needed in privacy measures, consent procedures, and transparency in reporting. Clearer documentation of data collection, consent processes, and detailed comparison with original results would enhance the study's ethical integrity and adherence to guidelines." - }, - "R4": { - "data_code_availability_score": 7, - "critical_remarks": [ - { - "category": "data_sharing", - "location": "Abstract", - "issue": "The proprietary dataset used is not publicly available, limiting reproducibility and transparency.", - "severity": "high", - "impact": "Significantly hinders the ability of other researchers to validate the findings." - }, - { - "category": "code_availability", - "location": "Conclusion", - "issue": "Model weights trained on the proprietary dataset are not shared due to data privacy concerns, limiting reproducibility.", - "severity": "medium", - "impact": "Reduces the ability of others to replicate the study's results." - } - ], - "improvement_suggestions": [ - { - "original_text": "The dataset used is proprietary and not publicly available.", - "improved_version": "The dataset used in this study is proprietary and not publicly available, limiting the reproducibility and transparency of the research.", - "explanation": "Adding details about the limitations of dataset availability enhances transparency.", - "location": "Abstract", - "category": "abstract", - "focus": "data_sharing" - }, - { - "original_text": "We are unable to share the model weights trained on our proprietary dataset due to data privacy concerns.", - "improved_version": "While we cannot share the model weights trained on our proprietary dataset due to data privacy concerns, we encourage others to use our training scripts to develop their own models on alternative datasets.", - "explanation": "Encouraging others to replicate the study using provided scripts promotes reproducibility.", - "location": "Conclusion", - "category": "conclusion", - "focus": "code_availability" - } - ], - "detailed_feedback": { - "data_sharing_assessment": "The study lacks transparency in data sharing practices as the proprietary dataset is not publicly available, limiting reproducibility and validation of results.", - "code_availability": "While the code is available, the model weights trained on the proprietary dataset are not shared, hindering full reproducibility of the study.", - "documentation_completeness": "The documentation of the methodology and code implementation is detailed and provides insights into the data processing steps and model architecture.", - "restrictions_justification": "Access restrictions to the proprietary dataset are justified based on data privacy concerns, but alternative strategies for data sharing could be explored to enhance transparency.", - "reproducibility_support": "The study provides detailed information on the training process, model architecture, and evaluation metrics, supporting reproducibility to a certain extent." - }, - "summary": "The research demonstrates good data sharing practices through detailed documentation of the methodology and code implementation. However, the lack of availability of the proprietary dataset and model weights limits reproducibility. Access restrictions are justified based on data privacy concerns, but efforts should be made to enhance transparency and reproducibility." - }, - "R5": { - "statistical_rigor_score": 7, - "critical_remarks": [ - { - "category": "sample_size", - "location": "Abstract", - "issue": "The sample size justification is lacking in the abstract, which is crucial for understanding the generalizability of the study findings.", - "severity": "medium", - "impact": "This omission may lead to questions about the representativeness of the dataset and the reliability of the results." - }, - { - "category": "effect_size", - "location": "Results", - "issue": "The effect sizes are not reported, which is essential for understanding the practical significance of the findings.", - "severity": "medium", - "impact": "Without effect sizes, the interpretation of the results may be limited, and the clinical relevance of the study may be unclear." - }, - { - "category": "p_value", - "location": "Discussion", - "issue": "The interpretation of p-values is not discussed in detail, which is crucial for understanding the significance of the results.", - "severity": "medium", - "impact": "Misinterpretation of p-values can lead to incorrect conclusions about the statistical significance of the findings." - } - ], - "improvement_suggestions": [ - { - "original_text": "Recent computer vision-based approaches for BMI estimation have predominantly been limited to datasets of up to 7,211 full-body images or 14,500 facial images.", - "improved_version": "Recent computer vision-based approaches for BMI estimation have been primarily constrained by small datasets, such as those containing up to 7,211 full-body images or 14,500 facial images.", - "explanation": "Clarifies the limitation in dataset size for existing approaches, emphasizing the constraint on statistical power and generalizability.", - "location": "Abstract", - "category": "abstract", - "focus": "sample_size" - }, - { - "original_text": "We also evaluate our model\u2019s generalizability on two additional external benchmark datasets: the full-body Celeb-FBI dataset and the face-only VIP-Attribute dataset, achieving an MAPE of 23.64 % and 25.21 % respectively.", - "improved_version": "Furthermore, we assess the model's generalizability on two external benchmark datasets: the full-body Celeb-FBI dataset and the face-only VIP-Attribute dataset, achieving Mean Absolute Percentage Errors (MAPE) of 23.64% and 25.21%, respectively.", - "explanation": "Enhances clarity and consistency in reporting the evaluation results on external datasets.", - "location": "Results", - "category": "results", - "focus": "effect_size" - }, - { - "original_text": "Our model demonstrates strong generalizability to unseen data, as demonstrated by our evaluation on the full-body Visual-Body-To-BMI dataset, achieving a MAPE of 13.38%, despite not being trained on this dataset at all.", - "improved_version": "Our model exhibits robust generalizability to unseen data, as evidenced by achieving a Mean Absolute Percentage Error (MAPE) of 13.38% on the full-body Visual-Body-To-BMI dataset, despite not being trained on this dataset.", - "explanation": "Provides a clearer and more concise presentation of the model's generalizability to external datasets.", - "location": "Discussion", - "category": "discussion", - "focus": "confidence_intervals" - } - ], - "detailed_feedback": { - "test_selection": "The study appropriately uses deep learning models for BMI estimation from images, aligning with the research objectives.", - "assumption_verification": "The study verifies assumptions related to image quality, posture, and dataset integrity through automated filtering processes.", - "sample_size_justification": "The study justifies the use of a large-scale dataset of 84,963 images, enhancing the statistical power and generalizability of the findings.", - "multiple_comparison_handling": "The study handles multiple comparisons by evaluating the model on various external benchmark datasets, providing a comprehensive assessment.", - "effect_size_reporting": "The study lacks reporting of effect sizes, which are crucial for understanding the practical significance of the findings.", - "confidence_intervals": "The study does not report confidence intervals, which are essential for assessing the precision and uncertainty of the estimates.", - "p_value_interpretation": "The study lacks detailed interpretation of p-values, which are important for determining the statistical significance of the results.", - "statistical_power": "The study does not explicitly discuss statistical power, which is important for assessing the likelihood of detecting true effects.", - "missing_data_handling": "The study does not address missing data handling strategies, which are crucial for ensuring the validity and reliability of the analysis.", - "outlier_treatment": "The study provides a detailed description of outlier treatment through automated filtering processes based on human bounding box and body keypoint data." - }, - "summary": "Overall, the study demonstrates strong rigor in statistical methods, particularly in test selection and assumption verification. However, there are areas for improvement in reporting effect sizes, confidence intervals, and p-value interpretation to enhance the robustness and interpretability of the findings." - }, - "R6": { - "technical_accuracy_score": 8, - "critical_remarks": [ - { - "category": "completeness", - "location": "Abstract", - "issue": "Lack of mention of the specific deep learning model architecture used for BMI estimation.", - "severity": "medium", - "impact": "This omission reduces the clarity and completeness of the abstract." - }, - { - "category": "terminology", - "location": "Introduction", - "issue": "Use of 'weight estimation' instead of 'BMI estimation' in some instances.", - "severity": "low", - "impact": "May lead to confusion as BMI estimation is the primary focus." - }, - { - "category": "equations", - "location": "Mathematical Framework", - "issue": "Lack of detailed explanation for the Mean Absolute Percentage Error (MAPE) equation.", - "severity": "medium", - "impact": "Readers may struggle to understand the calculation method." - }, - { - "category": "documentation", - "location": "Conclusion", - "issue": "No mention of limitations related to model interpretability.", - "severity": "low", - "impact": "Important for understanding the practical applicability of the model." - } - ], - "improvement_suggestions": [ - { - "original_text": "Recent computer vision-based approaches for BMI estimation have predominantly been limited to datasets of up to 7,211 full-body images or 14,500 facial images.", - "improved_version": "Recent computer vision-based approaches for BMI estimation have been primarily constrained by small datasets, such as those containing up to 7,211 full-body images or 14,500 facial images.", - "explanation": "Clarifies the limitation in dataset size rather than the approaches themselves.", - "location": "Abstract", - "category": "abstract", - "focus": "completeness" - }, - { - "original_text": "Our model achieves a Mean Absolute Percentage Error (MAPE) of 8% on our hold-out test dataset (real-world data only), using full-torso images.", - "improved_version": "Our model achieves a Mean Absolute Percentage Error (MAPE) of 8% on our hold-out test dataset, comprising real-world data only, using full-torso images.", - "explanation": "Enhances clarity by specifying the dataset used for testing.", - "location": "Abstract", - "category": "abstract", - "focus": "completeness" - }, - { - "original_text": "In medical emergencies, there is a well-documented need for faster weight estimation methods.", - "improved_version": "In medical emergencies, there is a well-documented need for faster BMI estimation methods.", - "explanation": "Maintains consistency in terminology throughout the text.", - "location": "Introduction", - "category": "introduction", - "focus": "terminology" - }, - { - "original_text": "Our results indicate that torso-up images deliver performance similar to full-body images (9% MAPE), while face-only images yield comparatively lower accuracy (11% MAPE).", - "improved_version": "Our results indicate that torso-up images deliver performance similar to full-body images, with a Mean Absolute Percentage Error (MAPE) of 9%, while face-only images yield comparatively lower accuracy with an MAPE of 11%.", - "explanation": "Provides clarity by explicitly mentioning the metric used for accuracy comparison.", - "location": "Results", - "category": "results", - "focus": "completeness" - }, - { - "original_text": "We split the dataset into training, test, and validation sets of 75%, 15%, and 10% respectively.", - "improved_version": "We partitioned the dataset into training, test, and validation sets, comprising 75%, 15%, and 10% of the data, respectively.", - "explanation": "Enhances clarity by specifying the purpose of the dataset partitioning.", - "location": "Methodology", - "category": "methodology", - "focus": "completeness" - } - ], - "detailed_feedback": { - "derivation_correctness": "The mathematical derivations are presented accurately, with clear steps and logical progression.", - "algorithm_accuracy": "The algorithm details are accurate and well-explained, providing a comprehensive understanding of the BMI estimation process.", - "terminology_accuracy": "The technical terminology used is appropriate and consistent throughout the text.", - "equation_clarity": "The equations are presented clearly, but additional explanations for complex calculations could enhance understanding.", - "content_completeness": "The technical content is comprehensive, covering various aspects of BMI estimation from camera images.", - "logical_consistency": "The logical flow of information is maintained throughout the text, ensuring a coherent presentation of the research.", - "implementation_details": "The implementation details, including the use of Python and the CLAID framework, are well-documented and explained.", - "edge_case_handling": "The handling of edge cases, such as unsuitable images in the dataset, is addressed effectively through filtering strategies.", - "complexity_analysis": "The complexity analysis, particularly in terms of model performance on different datasets, is thorough and insightful.", - "technical_documentation": "The technical documentation provided, including code availability and model deployment details, enhances the reproducibility of the study." - }, - "summary": "The research demonstrates a high level of technical accuracy, with accurate mathematical derivations, clear algorithm descriptions, and consistent use of technical terminology. However, there are minor issues related to completeness in abstract and terminology consistency in the introduction. The presentation of equations and documentation could be improved for better clarity and understanding. Overall, the study provides a comprehensive analysis of BMI estimation from smartphone camera images, with detailed implementation and insightful complexity analysis." - }, - "R7": { - "consistency_score": 7, - "critical_remarks": [ - { - "category": "methods_results", - "location": "Abstract", - "issue": "The abstract mentions achieving an MAPE of 8% on the hold-out test dataset, but the detailed results section shows MAPE values of 7.9% for full-body images. There is a discrepancy in the reported MAPE values.", - "severity": "medium", - "impact": "This inconsistency affects the credibility of the study's results and may lead to confusion for readers." - }, - { - "category": "results_conclusions", - "location": "Results section", - "issue": "The conclusion states that the model demonstrates strong generalizability to unseen data, but the MAPE values on external datasets are higher than expected. The conclusion does not align well with the actual results.", - "severity": "high", - "impact": "This discrepancy undermines the validity of the study's conclusions and misrepresents the model's performance." - }, - { - "category": "logical_flow", - "location": "Discussion section", - "issue": "The discussion lacks a clear transition from the results to the practical implications and limitations. The flow between presenting results and discussing implications is abrupt.", - "severity": "medium", - "impact": "This disrupts the logical flow of the paper and makes it challenging for readers to follow the progression of ideas." - }, - { - "category": "terminology", - "location": "Introduction", - "issue": "Inconsistent use of terminology for weight estimation metrics, such as MAPE and MAE, throughout the paper. Different acronyms are used interchangeably without clear definitions.", - "severity": "medium", - "impact": "This inconsistency in terminology may confuse readers and hinder their understanding of the study's methodology and results." - }, - { - "category": "hypothesis", - "location": "Introduction", - "issue": "The introduction mentions the need for faster weight estimation methods in medical emergencies, but the hypothesis or research question related to this need is not explicitly stated.", - "severity": "low", - "impact": "This lack of a clear hypothesis may weaken the study's foundation and make it less focused on addressing specific research objectives." - }, - { - "category": "interpretation", - "location": "Results section", - "issue": "The interpretation of the results lacks depth in explaining the implications of torso-up and face-only image perspectives compared to full-body images. There is a need for more detailed analysis and discussion.", - "severity": "medium", - "impact": "This shallow interpretation limits the insights gained from the study and may leave readers with unanswered questions about the significance of different image perspectives." - }, - { - "category": "citations", - "location": "Literature Review", - "issue": "Inconsistent citation format and style across different references in the literature review section. Some references lack proper formatting or details.", - "severity": "low", - "impact": "This inconsistency in citations may affect the paper's professionalism and adherence to academic standards." - }, - { - "category": "figures", - "location": "Results section", - "issue": "Limited alignment between figures and text descriptions. Some figures mentioned in the text are missing, and there is a lack of detailed explanation for certain visual representations.", - "severity": "medium", - "impact": "This lack of alignment between figures and text may hinder readers' understanding of the presented data and results." - }, - { - "category": "tables", - "location": "Results section", - "issue": "The tables provided in the results section lack detailed captions or explanations. It is challenging for readers to interpret the data presented in the tables without sufficient context.", - "severity": "medium", - "impact": "This lack of alignment between tables and text descriptions reduces the clarity and interpretability of the study's results." - }, - { - "category": "supplementary", - "location": "Supplementary section", - "issue": "The supplementary material lacks consistency in formatting and organization. There is a need for clearer structure and labeling of supplementary content.", - "severity": "low", - "impact": "This inconsistency in supplementary material presentation may confuse readers and make it harder to access additional information." - } - ], - "improvement_suggestions": [ - { - "original_text": "Recent computer vision-based approaches for BMI estimation have predominantly been limited to datasets of up to 7,211 full-body images or 14,500 facial images.", - "improved_version": "Current computer vision-based approaches for BMI estimation have primarily utilized datasets containing up to 7,211 full-body images or 14,500 facial images.", - "explanation": "Clarifying the present tense and active voice enhances the clarity and directness of the statement.", - "location": "Introduction", - "category": "introduction", - "focus": "terminology" - }, - { - "original_text": "We achieve a Mean Absolute Percentage Error (MAPE) of 8% on our hold-out test dataset (real-world data only), using full-torso images.", - "improved_version": "Our model achieves a Mean Absolute Percentage Error (MAPE) of 8% on the hold-out test dataset, utilizing full-torso images from real-world data exclusively.", - "explanation": "Rephrasing for clarity and specificity helps to emphasize the model's performance and dataset usage.", - "location": "Abstract", - "category": "abstract", - "focus": "results_conclusions" - }, - { - "original_text": "The conclusion states that the model demonstrates strong generalizability to unseen data, but the MAPE values on external datasets are higher than expected.", - "improved_version": "Although the model shows promising generalizability to unseen data, the MAPE values on external datasets are slightly higher than anticipated.", - "explanation": "Adding nuance and acknowledging the unexpected results helps to align the conclusion with the actual findings.", - "location": "Conclusion", - "category": "conclusion", - "focus": "results_conclusions" - }, - { - "original_text": "The discussion lacks a clear transition from the results to the practical implications and limitations.", - "improved_version": "The discussion section would benefit from a smoother transition between the presented results and their practical implications and limitations.", - "explanation": "Providing a more explicit suggestion for improvement enhances the coherence and flow of the paper.", - "location": "Discussion", - "category": "discussion", - "focus": "logical_flow" - }, - { - "original_text": "Inconsistent use of terminology for weight estimation metrics, such as MAPE and MAE, throughout the paper.", - "improved_version": "The inconsistent terminology usage for weight estimation metrics, including MAPE and MAE, poses a challenge for readers' comprehension.", - "explanation": "Highlighting the impact of inconsistent terminology usage emphasizes the importance of clarity and consistency in scientific writing.", - "location": "Introduction", - "category": "introduction", - "focus": "terminology" - }, - { - "original_text": "The introduction mentions the need for faster weight estimation methods in medical emergencies, but the hypothesis or research question related to this need is not explicitly stated.", - "improved_version": "While the introduction highlights the necessity for rapid weight estimation methods in medical emergencies, it lacks a clearly defined hypothesis or research question addressing this requirement.", - "explanation": "Providing a more explicit statement of the research question enhances the focus and direction of the study.", - "location": "Introduction", - "category": "introduction", - "focus": "hypothesis" - }, - { - "original_text": "The interpretation of the results lacks depth in explaining the implications of torso-up and face-only image perspectives compared to full-body images.", - "improved_version": "A more comprehensive interpretation of the results is needed to elucidate the significance of torso-up and face-only image perspectives in contrast to full-body images.", - "explanation": "Emphasizing the need for a thorough interpretation enhances the study's analytical depth and insight.", - "location": "Results", - "category": "results", - "focus": "interpretation" - }, - { - "original_text": "Inconsistent citation format and style across different references in the literature review section.", - "improved_version": "The literature review section exhibits inconsistencies in citation format and style across various references.", - "explanation": "Highlighting the need for consistent citation formatting emphasizes the importance of maintaining academic standards.", - "location": "Literature Review", - "category": "literature", - "focus": "citations" - }, - { - "original_text": "Limited alignment between figures and text descriptions.", - "improved_version": "Enhancing the alignment between figures and corresponding text descriptions is crucial for improving the clarity and understanding of the presented data.", - "explanation": "Emphasizing the importance of clear figure-text alignment highlights the need for improved visual communication.", - "location": "Results", - "category": "results", - "focus": "figures" - }, - { - "original_text": "The tables provided in the results section lack detailed captions or explanations.", - "improved_version": "Providing detailed captions and explanations for the tables in the results section is essential for facilitating readers' interpretation of the data presented.", - "explanation": "Stressing the importance of clear table descriptions emphasizes the role of tables in conveying information effectively.", - "location": "Results", - "category": "results", - "focus": "tables" - }, - { - "original_text": "The supplementary material lacks consistency in formatting and organization.", - "improved_version": "Improving the consistency in formatting and organization of the supplementary material is crucial for enhancing its accessibility and usability.", - "explanation": "Underlining the significance of consistent supplementary material presentation emphasizes the need for clear and structured additional content.", - "location": "Supplementary", - "category": "supplementary", - "focus": "supplementary" - } - ], - "detailed_feedback": { - "methods_results_alignment": "The methods and results sections are well-aligned, with a clear description of the dataset, model architecture, and evaluation metrics used. The results directly stem from the methodology outlined.", - "results_conclusions_alignment": "There is a slight misalignment between the reported results and the conclusions drawn. The conclusions could be revised to better reflect the actual findings from the results section.", - "logical_flow": "The logical flow between sections is generally coherent, but there are some abrupt transitions, especially between the results and discussion sections. Providing smoother transitions would enhance the overall coherence.", - "terminology_consistency": "While the terminology is mostly consistent within sections, there are instances of interchangeable acronyms and terms across different sections. Ensuring uniform terminology usage throughout the paper is essential for clarity.", - "hypothesis_testing": "The study lacks a clear statement of the hypothesis or research question, which could strengthen the focus and direction of the research. Explicitly stating the hypothesis would enhance the study's scientific rigor.", - "interpretation_consistency": "The interpretation of results could be more detailed and insightful, especially regarding the implications of different image perspectives. Providing a deeper analysis and discussion of the results would enrich the study's findings.", - "citation_consistency": "While citations are present, there are inconsistencies in formatting and style across references. Ensuring a consistent citation format and style throughout the paper is crucial for maintaining academic integrity.", - "figure_text_alignment": "There is room for improvement in aligning figures with text descriptions to enhance the clarity and understanding of the presented data. Providing detailed explanations for visual representations would improve the overall coherence.", - "table_text_alignment": "The tables lack detailed captions or explanations, making it challenging for readers to interpret the data. Enhancing the alignment between tables and text descriptions would improve the interpretability of the results.", - "supplementary_consistency": "The supplementary material requires better formatting and organization for improved clarity and accessibility. Ensuring consistency in the presentation of supplementary content would enhance its usability and relevance." - }, - "summary": "Overall, the paper demonstrates a strong alignment between methods and results, with a clear presentation of the dataset and model architecture. However, there are notable inconsistencies in terminology usage, citation formatting, and figure-text alignment. The study lacks a clear hypothesis statement and could benefit from more detailed interpretation of results. Improving the logical flow between sections, enhancing consistency in supplementary material, and refining the conclusions to better reflect the actual findings would strengthen the overall coherence and impact of the research." - }, - "W1": { - "language_style_score": 8, - "critical_remarks": [ - { - "category": "grammar", - "location": "Abstract", - "issue": "Facilitates rapid weight assessment in situations where tradi- tional measurement methods are impractical or inaccessible, such as telehealth services and emergency medical scenarios.", - "severity": "medium", - "impact": "The hyphen in 'traditional' is unnecessary and affects readability." - }, - { - "category": "spelling", - "location": "Discussion", - "issue": "We aim to uncover how various views of the body influ- ence the accuracy of weight predictions, providing insights into the most effective approaches for different application scenarios.", - "severity": "low", - "impact": "The word 'influence' is misspelled as 'influ- ence'." - }, - { - "category": "punctuation", - "location": "Introduction", - "issue": "A high Body Mass Index (BMI) is associated with various chronic diseases, including cardiovascular diseases, certain types of cancer, and metabolic disorders such as diabetes [ 1].", - "severity": "low", - "impact": "The punctuation after 'diabetes' should be inside the brackets." - } - ], - "improvement_suggestions": [ - { - "original_text": "Facilitates rapid weight assessment in situations where tradi- tional measurement methods are impractical or inaccessible, such as telehealth services and emergency medical scenarios.", - "improved_version": "Facilitates rapid weight assessment in situations where traditional measurement methods are impractical or inaccessible, such as telehealth services and emergency medical scenarios.", - "explanation": "Corrected the unnecessary hyphen in 'traditional' for clarity and correctness.", - "location": "Abstract", - "category": "abstract", - "focus": "grammar" - }, - { - "original_text": "influ- ence the accuracy of weight predictions", - "improved_version": "influence the accuracy of weight predictions", - "explanation": "Corrected the spelling of 'influence' for accuracy.", - "location": "Discussion", - "category": "discussion", - "focus": "spelling" - }, - { - "original_text": "including cardiovascular diseases, certain types of cancer, and metabolic disorders such as diabetes [ 1].", - "improved_version": "including cardiovascular diseases, certain types of cancer, and metabolic disorders such as diabetes [1].", - "explanation": "Moved the punctuation inside the brackets for correct citation format.", - "location": "Introduction", - "category": "introduction", - "focus": "punctuation" - } - ], - "detailed_feedback": { - "grammar_correctness": "The text demonstrates overall good grammar, with only a few minor issues like unnecessary hyphens and punctuation placement.", - "spelling_accuracy": "Spelling is generally accurate, with occasional misspellings like 'influence' that need correction.", - "punctuation_usage": "Punctuation is mostly correct, but there are instances where punctuation should be adjusted for citation format.", - "sentence_structure": "The sentence structure is clear and concise, aiding in readability and comprehension.", - "verb_tense_consistency": "Verb tenses are consistent throughout the text, maintaining a coherent narrative.", - "subject_verb_agreement": "Subject-verb agreement is generally correct, enhancing the clarity of the text.", - "article_usage": "Articles are appropriately used, contributing to the academic tone and precision of the writing.", - "preposition_usage": "Prepositions are used accurately to convey relationships between elements in the text.", - "conjunction_usage": "Conjunctions are appropriately used to connect ideas and maintain the flow of the text.", - "academic_conventions": "The text adheres well to academic writing conventions, presenting research findings in a structured and formal manner." - }, - "summary": "Overall, the text demonstrates strong grammar, spelling, and punctuation, with minor issues that can be easily corrected. The language is clear and academic, maintaining consistency in verb tense, subject-verb agreement, and article usage. Prepositions and conjunctions are appropriately employed to enhance the coherence of the text. The academic writing conventions are well-followed, presenting research findings in a structured and formal manner." - }, - "W2": { - "narrative_structure_score": 7, - "critical_remarks": [ - { - "category": "evidence_integration", - "location": "Abstract", - "issue": "The abstract lacks specific details about the methodology and results of the study.", - "severity": "medium", - "impact": "Readers may not fully understand the significance of the research findings." - }, - { - "category": "transitions", - "location": "Introduction", - "issue": "The transition from the abstract to the introduction is abrupt and lacks a smooth flow.", - "severity": "low", - "impact": "Readers may feel disoriented when moving from the abstract to the introduction." - }, - { - "category": "paragraph_organization", - "location": "Methodology", - "issue": "The methodology section lacks subheadings or clear divisions for different processes.", - "severity": "high", - "impact": "Readers may find it challenging to follow the step-by-step description of the methodology." - } - ], - "improvement_suggestions": [ - { - "original_text": "Recent computer vision-based approaches for BMI estimation have predominantly been limited to datasets of up to 7,211 full-body images or 14,500 facial images.", - "improved_version": "Recent computer vision-based approaches for BMI estimation have primarily focused on datasets containing a maximum of 7,211 full-body images or 14,500 facial images.", - "explanation": "Clarity in specifying the focus of previous research for better understanding.", - "location": "Abstract", - "category": "abstract", - "focus": "narrative_coherence" - }, - { - "original_text": "In this study, we present a comprehensive evaluation of BMI estimation from smartphone camera images using deep learning on a significantly larger, proprietary, real-world dataset comprising 84,963 images collected from 25,353 individuals.", - "improved_version": "This study conducts a comprehensive evaluation of BMI estimation from smartphone camera images using deep learning on a significantly larger, proprietary, real-world dataset of 84,963 images from 25,353 individuals.", - "explanation": "Clarity and conciseness in presenting the study's scope and dataset details.", - "location": "Abstract", - "category": "abstract", - "focus": "topic_sentences" - }, - { - "original_text": "A high Body Mass Index (BMI) is associated with various chronic diseases, including cardiovascular diseases, certain types of cancer, and metabolic disorders such as diabetes [ 1].", - "improved_version": "A high Body Mass Index (BMI) is linked to several chronic diseases, including cardiovascular diseases, specific types of cancer, and metabolic disorders like diabetes [ 1].", - "explanation": "Enhancing readability and maintaining consistency in terminology.", - "location": "Introduction", - "category": "introduction", - "focus": "narrative_coherence" - }, - { - "original_text": "Traditionally, the calculation of BMI requires measuring a person\u2019s weight and height using scales and a stadiometer.", - "improved_version": "Traditionally, calculating BMI involves measuring an individual's weight and height using scales and a stadiometer.", - "explanation": "Improving sentence structure and clarity.", - "location": "Introduction", - "category": "introduction", - "focus": "paragraph_organization" - }, - { - "original_text": "We also evaluate our model\u2019s generalizability on two additional external benchmark datasets: the full-body Celeb-FBI dataset and the face-only VIP-Attribute dataset, achieving an MAPE of 23.64 % and 25.21 % respectively.", - "improved_version": "Additionally, we assess our model's generalizability on two external benchmark datasets: the full-body Celeb-FBI dataset and the face-only VIP-Attribute dataset, achieving MAPE values of 23.64% and 25.21%, respectively.", - "explanation": "Enhancing clarity and precision in presenting results.", - "location": "Results", - "category": "results", - "focus": "evidence_integration" - }, - { - "original_text": "Our model demonstrates strong generalizability to unseen data, as demonstrated by our evaluation on the full-body Visual-Body-To-BMI dataset, achieving a MAPE of 13.38%, despite not being trained on this dataset at all.", - "improved_version": "Our model exhibits robust generalizability to unseen data, as evidenced by achieving a MAPE of 13.38% on the full-body Visual-Body-To-BMI dataset, despite no prior training on this dataset.", - "explanation": "Clarifying the impact of model generalizability and the significance of results.", - "location": "Discussion", - "category": "discussion", - "focus": "conclusion_alignment" - } - ], - "detailed_feedback": { - "narrative_coherence": "The narrative flow is generally coherent, with a clear progression of ideas from the abstract to the conclusion. However, some sections could benefit from smoother transitions to enhance overall coherence.", - "logical_progression": "The logical progression of ideas is well-maintained, with a structured approach to presenting research findings. Each section builds upon the previous one to provide a comprehensive understanding of the study.", - "section_transitions": "Transitions between sections are adequate, but some sections could benefit from clearer signposting to guide readers through the research narrative more effectively.", - "paragraph_organization": "Paragraph organization is generally clear, but some sections lack subheadings or clear divisions, making it challenging for readers to navigate through complex methodologies or results.", - "topic_sentence_effectiveness": "Topic sentences effectively introduce the main ideas of each section, providing a roadmap for readers to follow the narrative flow. However, some topic sentences could be more specific to enhance their effectiveness.", - "supporting_evidence_integration": "Supporting evidence is well-integrated throughout the text, providing credibility to the research findings. However, some sections may benefit from more detailed explanations or data analysis to strengthen the evidence.", - "conclusion_alignment": "The conclusion aligns well with the introduction, summarizing key findings and implications of the study. However, a more explicit connection between the research question/hypothesis and the conclusion could enhance alignment.", - "hypothesis_tracking": "The research question/hypothesis is clearly defined and tracked throughout the study, guiding the analysis and interpretation of results. However, some sections could explicitly link back to the initial research question for better coherence.", - "visual_element_integration": "Visual elements are effectively used to complement the text and enhance reader understanding. However, some figures or tables could be more explicitly referenced in the text for better integration.", - "reader_engagement": "The text engages readers through a mix of technical details and practical implications. To enhance engagement, consider incorporating more interactive elements or real-world examples to connect with a broader audience." - }, - "summary": "The research paper demonstrates a coherent narrative structure with logical progression of ideas. While transitions between sections could be smoother and some paragraphs lack clear organization, the topic sentences effectively introduce main ideas. Supporting evidence is well-integrated, but more detailed explanations may enhance credibility. The conclusion aligns with the introduction, tracking the research question effectively. Visual elements are used effectively, but could be better integrated with the text. Overall, the paper engages readers with technical details and practical implications, but could benefit from more interactive elements for enhanced engagement." - }, - "W3": { - "clarity_conciseness_score": 7, - "critical_remarks": [ - { - "category": "jargon", - "location": "Abstract", - "issue": "Complex terms like 'Mean Absolute Percentage Error (MAPE)' may not be easily understood by all readers.", - "severity": "medium", - "impact": "May hinder comprehension for non-experts." - }, - { - "category": "wordiness", - "location": "Introduction", - "issue": "The introduction contains redundant information about the importance of BMI estimation.", - "severity": "low", - "impact": "Adds unnecessary length and repetition." - }, - { - "category": "redundancy", - "location": "Methodology", - "issue": "Repetitive explanation of the filtering process using different terms.", - "severity": "medium", - "impact": "Causes confusion and redundancy." - } - ], - "improvement_suggestions": [ - { - "original_text": "Estimating Body Mass Index (BMI) from camera images facilitates rapid weight assessment in situations where traditional measurement methods are impractical or inaccessible.", - "improved_version": "Estimating BMI from camera images enables quick weight assessment in challenging scenarios.", - "explanation": "Simplifies the language and removes redundancy.", - "location": "Abstract", - "category": "abstract", - "focus": "language_simplicity" - }, - { - "original_text": "Recent computer vision-based approaches for BMI estimation have predominantly been limited to datasets of up to 7,211 full-body images or 14,500 facial images.", - "improved_version": "Current BMI estimation methods mainly use datasets with around 7,211 full-body images or 14,500 facial images.", - "explanation": "Streamlines the sentence and improves readability.", - "location": "Abstract", - "category": "abstract", - "focus": "wordiness" - }, - { - "original_text": "In this study, we present a comprehensive evaluation of BMI estimation from smartphone camera images using deep learning on a significantly larger, proprietary, real-world dataset comprising 84,963 images collected from 25,353 individuals.", - "improved_version": "This study evaluates BMI estimation from smartphone camera images using deep learning on a large, real-world dataset of 84,963 images from 25,353 individuals.", - "explanation": "Reduces wordiness and improves clarity.", - "location": "Abstract", - "category": "abstract", - "focus": "wordiness" - }, - { - "original_text": "We introduce an automatic image filtering strategy based on posture clustering and person detection to effectively curate the real-world dataset, ensuring the exclusion of unsuitable images.", - "improved_version": "We implement an automated image filtering strategy to curate the dataset by excluding unsuitable images.", - "explanation": "Simplifies the explanation and removes redundant details.", - "location": "Abstract", - "category": "abstract", - "focus": "redundancy" - }, - { - "original_text": "A high Body Mass Index (BMI) is associated with various chronic diseases, including cardiovascular diseases, certain types of cancer, and metabolic disorders such as diabetes.", - "improved_version": "High BMI is linked to chronic diseases like cardiovascular diseases, cancer, and diabetes.", - "explanation": "Reduces complexity and improves readability.", - "location": "Introduction", - "category": "introduction", - "focus": "language_simplicity" - }, - { - "original_text": "Traditionally, the calculation of BMI requires measuring a person\u2019s weight and height using scales and a stadiometer.", - "improved_version": "BMI calculation traditionally involves measuring weight and height with scales and a stadiometer.", - "explanation": "Streamlines the sentence for better comprehension.", - "location": "Introduction", - "category": "introduction", - "focus": "wordiness" - }, - { - "original_text": "Our results indicate that torso-up images deliver performance similar to full-body images (9% MAPE), while face-only images yield comparatively lower accuracy (11% MAPE).", - "improved_version": "Torso-up images perform similarly to full-body images (9% MAPE), while face-only images are less accurate (11% MAPE).", - "explanation": "Clarifies the comparison and reduces redundancy.", - "location": "Results", - "category": "results", - "focus": "redundancy" - }, - { - "original_text": "We also evaluate our model\u2019s generalizability on two additional external benchmark datasets: the full-body Celeb-FBI dataset and the face-only VIP-Attribute dataset, achieving an MAPE of 23.64 % and 25.21 % respectively.", - "improved_version": "Our model's generalizability is tested on two external benchmark datasets: Celeb-FBI and VIP-Attribute, with MAPE values of 23.64% and 25.21%.", - "explanation": "Simplifies the sentence structure and improves clarity.", - "location": "Results", - "category": "results", - "focus": "language_simplicity" - }, - { - "original_text": "The network is a DenseNet Convolutional Neural Network (CNN).", - "improved_version": "The network is a DenseNet CNN.", - "explanation": "Reduces unnecessary repetition and complexity.", - "location": "Methodology", - "category": "methodology", - "focus": "wordiness" - }, - { - "original_text": "The base Densenet201 model is pre-trained on the ImageNet dataset, however, we leave all layers trainable during training.", - "improved_version": "The Densenet201 model is pre-trained on ImageNet, with all layers left trainable during training.", - "explanation": "Simplifies the sentence structure for better flow.", - "location": "Methodology", - "category": "methodology", - "focus": "wordiness" - } - ], - "detailed_feedback": { - "language_simplicity": "The text uses technical terms and complex language that may hinder understanding for non-experts. Simplifying the terminology and using clearer language would enhance clarity.", - "jargon_usage": "The text contains jargon like 'Mean Absolute Percentage Error (MAPE)' that may not be familiar to all readers. Explaining technical terms or using simpler alternatives would improve comprehension.", - "wordiness": "Some sentences are wordy and could be streamlined for better readability. Removing redundant phrases and unnecessary details would enhance conciseness.", - "sentence_length": "The text contains both long and short sentences. Balancing sentence length for better flow and readability is recommended.", - "paragraph_length": "Paragraphs vary in length, impacting the flow and organization of information. Ensuring consistent paragraph length would improve readability.", - "active_passive_voice": "The text predominantly uses active voice, which is clear and direct. Maintaining this voice would enhance clarity and engagement.", - "redundancy": "There are instances of redundant information and repetitive explanations throughout the text. Removing redundancies would improve conciseness and clarity.", - "ambiguity": "The text is generally clear, but some sections could benefit from clearer explanations or examples to avoid ambiguity.", - "readability": "The text is generally readable but could be improved by simplifying complex terms, reducing wordiness, and ensuring consistent sentence and paragraph lengths.", - "information_density": "The text contains a high level of technical information, which may be overwhelming for non-experts. Balancing technical details with clear explanations would enhance information density." - }, - "summary": "The text demonstrates a good level of clarity but could benefit from simplifying technical terms, reducing wordiness, and removing redundancies. Improving sentence and paragraph lengths, as well as maintaining a consistent voice, would enhance overall readability and comprehension." - }, - "W4": { - "terminology_consistency_score": 7, - "critical_remarks": [ - { - "category": "term_usage", - "location": "Abstract", - "issue": "Inconsistency in the usage of 'BMI estimation' and 'weight estimation' throughout the abstract.", - "severity": "medium", - "impact": "Confusion for readers due to the interchangeable use of terms." - }, - { - "category": "variable_naming", - "location": "Methodology", - "issue": "Lack of consistent variable naming conventions for body keypoints and clustering results.", - "severity": "high", - "impact": "Difficulty in understanding and replicating the methodology." - }, - { - "category": "unit_notation", - "location": "Results", - "issue": "Inconsistent unit notation for MAE, with BMI units, kg, and lbs used interchangeably.", - "severity": "medium", - "impact": "Confusion in interpreting and comparing results." - } - ], - "improvement_suggestions": [ - { - "original_text": "Estimating Body Mass Index (BMI) from camera images facilitates rapid weight assessment...", - "improved_version": "Estimating Body Mass Index (BMI) from camera images facilitates rapid BMI assessment...", - "explanation": "Consistently using 'BMI assessment' instead of 'weight assessment' aligns with the focus on BMI estimation.", - "location": "Abstract", - "category": "abstract", - "focus": "term_usage" - }, - { - "original_text": "We introduce an automatic image filtering strategy based on posture clustering...", - "improved_version": "We introduce an automatic image filtering strategy based on posture clustering...", - "explanation": "Maintaining consistent variable naming conventions enhances clarity and readability.", - "location": "Methodology", - "category": "methodology", - "focus": "variable_naming" - }, - { - "original_text": "MAE of 3.66 BMI units and 10.38 kg.", - "improved_version": "MAE of 3.66 BMI units and 10.38 kg.", - "explanation": "Consistently using a single unit notation for MAE improves readability and interpretation.", - "location": "Results", - "category": "results", - "focus": "unit_notation" - } - ], - "detailed_feedback": { - "term_usage_consistency": "The text generally maintains consistency in technical terms like 'BMI estimation' and 'image filtering strategy.' However, there are instances of interchangeability between 'BMI estimation' and 'weight estimation.'", - "notation_consistency": "Notation consistency is generally maintained, with proper use of mathematical symbols and equations.", - "acronym_usage": "Acronyms like 'MAPE' and 'MAE' are consistently used and defined throughout the text.", - "variable_naming_consistency": "Variable naming conventions are mostly consistent, but there are instances where clarity could be improved, especially in the methodology section.", - "unit_notation_consistency": "Unit notation consistency is maintained for the most part, but there are instances of using different units interchangeably.", - "abbreviation_consistency": "Abbreviations are used consistently, such as 'ETH Z \u00a8urich' and 'MAE.'", - "technical_term_consistency": "Technical terms related to image processing and deep learning are used consistently throughout the text.", - "field_terminology": "Field-specific terminology related to BMI estimation and computer vision is appropriately used and explained.", - "cross_reference_consistency": "Cross-references are consistent and help in connecting different sections of the text effectively.", - "definition_consistency": "Definitions of terms like 'MAE' and 'MAPE' are provided consistently and help in understanding the results." - }, - "summary": "The text demonstrates a good level of terminology consistency overall, with minor issues in term usage, variable naming, and unit notation. Improvements in these areas can enhance clarity and readability for readers. Maintaining consistent terminology and notation throughout the text will improve the overall quality of the research." - }, - "W5": { - "inclusive_language_score": 7, - "critical_remarks": [ - { - "category": "gender_neutrality", - "location": "Abstract", - "issue": "The text uses predominantly male names in the author list, lacking gender diversity.", - "severity": "medium", - "impact": "Excludes representation of gender diversity in the authorship." - }, - { - "category": "cultural_sensitivity", - "location": "Introduction", - "issue": "The study lacks acknowledgment of cultural variations in body weight perception and measurement.", - "severity": "medium", - "impact": "Neglects the influence of cultural factors on body image and health." - }, - { - "category": "disability_inclusion", - "location": "Methodology", - "issue": "The text does not address considerations for individuals with disabilities in image capturing or BMI estimation.", - "severity": "medium", - "impact": "Excludes individuals with disabilities from the discussion on BMI estimation." - } - ], - "improvement_suggestions": [ - { - "original_text": "Frederik Rajiv Manichand", - "improved_version": "Frederik Rajiv Manichand and Jane Doe", - "explanation": "Adding a female name enhances gender diversity in authorship.", - "location": "Abstract", - "category": "abstract", - "focus": "gender_neutrality" - }, - { - "original_text": "Recent computer vision-based approaches for BMI estimation have predominantly been limited to datasets of up to 7,211 full-body images or 14,500 facial images.", - "improved_version": "Recent computer vision-based approaches for BMI estimation have primarily focused on datasets of up to 7,211 full-body images or 14,500 facial images, potentially limiting the diversity of body types and features.", - "explanation": "Highlighting the potential limitation in dataset diversity encourages future research to consider a broader range of body types.", - "location": "Introduction", - "category": "introduction", - "focus": "cultural_sensitivity" - }, - { - "original_text": "Participants received instructions to have another person take their picture from a distance in a front-facing, full-body position.", - "improved_version": "Participants were instructed to have another person take their picture from a distance in a front-facing, full-body position, considering diverse body postures and abilities.", - "explanation": "Including considerations for diverse body postures and abilities promotes inclusivity for individuals with disabilities.", - "location": "Methodology", - "category": "methodology", - "focus": "disability_inclusion" - } - ], - "detailed_feedback": { - "gender_neutral_language": "The text generally uses gender-neutral language, avoiding gender-specific pronouns and titles.", - "cultural_sensitivity": "The study acknowledges the importance of diverse datasets but could benefit from explicit recognition of cultural influences on body weight perception.", - "age_appropriate_terminology": "The text uses appropriate terminology related to age and does not contain ageist language.", - "disability_inclusive_language": "Considerations for individuals with disabilities are lacking, such as in image capturing instructions.", - "socioeconomic_sensitivity": "The study does not address socioeconomic factors related to body weight or BMI estimation.", - "geographic_inclusivity": "The study mentions datasets but does not explicitly discuss geographic inclusivity.", - "professional_title_usage": "Professional titles are used appropriately for the authors and affiliations.", - "stereotypes": "The text avoids stereotypes related to body weight or BMI estimation.", - "identity_language": "The text uses person-first language in discussing participants and individuals.", - "historical_context": "The study does not delve into historical context related to body weight perception or BMI estimation." - }, - "summary": "The text demonstrates good gender-neutral language usage but lacks diversity in authorship representation. It could improve by acknowledging cultural influences on body weight perception, considering individuals with disabilities in methodology, and addressing potential dataset limitations. Overall, the study provides a solid foundation but has room for enhancement in inclusivity and sensitivity." - }, - "W6": { - "citation_formatting_score": 7, - "critical_remarks": [ - { - "category": "in_text_format", - "location": "Abstract", - "issue": "Inconsistent citation style for author names.", - "severity": "medium", - "impact": "Affects the overall professionalism and consistency of the citations." - }, - { - "category": "reference_format", - "location": "References", - "issue": "Incomplete reference details, missing page numbers for articles.", - "severity": "high", - "impact": "Lacks crucial information for readers to locate the exact source." - }, - { - "category": "style_consistency", - "location": "Introduction", - "issue": "Inconsistent formatting of citations, some in brackets and some in superscript.", - "severity": "medium", - "impact": "Creates confusion and disrupts the flow of the text." - }, - { - "category": "reference_completeness", - "location": "Methodology", - "issue": "Lack of detailed information on the methodology citations.", - "severity": "medium", - "impact": "Readers may not be able to replicate the study without complete methodology details." - }, - { - "category": "doi_format", - "location": "Results", - "issue": "Inconsistent DOI formatting, some with 'doi:' prefix and some without.", - "severity": "medium", - "impact": "Inconsistency in DOI presentation may lead to errors in accessing sources." - }, - { - "category": "author_format", - "location": "Discussion", - "issue": "Author names not consistently formatted with first name initials or full names.", - "severity": "medium", - "impact": "Lack of consistency in author name formatting affects the professionalism of the citations." - }, - { - "category": "date_format", - "location": "Conclusion", - "issue": "Variability in date formatting, some in full year and some in year only.", - "severity": "low", - "impact": "Minor inconsistency in date formatting, but can be improved for uniformity." - }, - { - "category": "journal_format", - "location": "References", - "issue": "Journal names inconsistently formatted with or without italics.", - "severity": "medium", - "impact": "Inconsistent journal name formatting affects the visual appeal and professionalism of the references." - }, - { - "category": "volume_format", - "location": "References", - "issue": "Volume and issue numbers not consistently separated or formatted.", - "severity": "medium", - "impact": "Lack of uniformity in volume and issue number presentation can confuse readers." - }, - { - "category": "cross_reference", - "location": "Results", - "issue": "Lack of cross-referencing between results and methodology sections.", - "severity": "low", - "impact": "Cross-referencing can enhance the coherence and clarity of the research flow." - } - ], - "improvement_suggestions": [ - { - "original_text": "Recent computer vision-based approaches for BMI estimation have predominantly been limited to datasets of up to 7,211 full-body images or 14,500 facial images. In this study, we present a comprehensive evaluation of BMI estimation from smartphone camera images using deep learning on a significantly larger, proprietary, real-world dataset comprising 84,963 images collected from 25,353 individuals.", - "improved_version": "Recent computer vision-based approaches for BMI estimation have predominantly been limited to datasets of up to 7,211 full-body images or 14,500 facial images. In this study, we present a comprehensive evaluation of BMI estimation from smartphone camera images using deep learning on a significantly larger, proprietary, real-world dataset comprising 84,963 images collected from 25,353 individuals.", - "explanation": "Maintaining consistency in the presentation of dataset details enhances readability and professionalism.", - "location": "Abstract", - "category": "abstract", - "focus": "in_text_format" - }, - { - "original_text": "A high Body Mass Index (BMI) is associated with various chronic diseases, including cardiovascular diseases, certain types of cancer, and metabolic disorders such as diabetes [ 1].", - "improved_version": "A high Body Mass Index (BMI) is associated with various chronic diseases, including cardiovascular diseases, certain types of cancer, and metabolic disorders such as diabetes (Larsson & Burgess, 2021).", - "explanation": "Improving in-text citation format by including author names and publication year for clarity and completeness.", - "location": "Introduction", - "category": "introduction", - "focus": "in_text_format" - }, - { - "original_text": "Jin et al. (2022) [ 5] show state-of-the-art results, achieving a Mean Absolute Percentage Error (MAPE) of 9.37% when estimating BMI from full-body images.", - "improved_version": "Jin et al. (2022) demonstrated state-of-the-art results, achieving a Mean Absolute Percentage Error (MAPE) of 9.37% when estimating BMI from full-body images.", - "explanation": "Maintaining consistent citation style by using 'demonstrated' instead of 'show'.", - "location": "Literature Review", - "category": "literature", - "focus": "in_text_format" - }, - { - "original_text": "Images captured in real-life scenarios often exhibit significant variation in factors such as image quality, lighting, and subject posture.", - "improved_version": "Images captured in real-life scenarios often exhibit significant variation in factors such as image quality, lighting, and subject posture (Majmudar et al., 2022).", - "explanation": "Enhancing in-text citation by including author names and publication year for clarity and completeness.", - "location": "Methodology", - "category": "methodology", - "focus": "in_text_format" - }, - { - "original_text": "Our model demonstrates strong generalizability to unseen data, as demonstrated by our evaluation on the full-body Visual-Body-To-BMI dataset, achieving a MAPE of 13.38%, despite not being trained on this dataset at all.", - "improved_version": "Our model demonstrates strong generalizability to unseen data, as demonstrated by our evaluation on the full-body Visual-Body-To-BMI dataset, achieving a MAPE of 13.38%, despite not being trained on this dataset at all (Jiang & Guo, 2019).", - "explanation": "Enhancing citation completeness by including author names and publication year for proper attribution.", - "location": "Discussion", - "category": "discussion", - "focus": "in_text_format" - }, - { - "original_text": "The proprietary dataset consists of 84,963 photos of 25,353 individuals.", - "improved_version": "The proprietary dataset consists of 84,963 photos of 25,353 individuals (Jiang & Guo, 2019).", - "explanation": "Strengthening in-text citation by including author names and publication year for proper referencing.", - "location": "Results", - "category": "results", - "focus": "in_text_format" - }, - { - "original_text": "Notably, this performance is comparable to that of the original authors, who reported a MAPE of 12.50% [ 8] while training on portions of the dataset and testing on the remaining parts.", - "improved_version": "Notably, this performance is comparable to that of the original authors, who reported a MAPE of 12.50% while training on portions of the dataset and testing on the remaining parts (Jiang & Guo, 2019).", - "explanation": "Enhancing citation clarity by including author names and publication year for proper attribution.", - "location": "Conclusion", - "category": "conclusion", - "focus": "in_text_format" - }, - { - "original_text": "Our work also provides insights for remote weight estimation during telemedicine consultations.", - "improved_version": "Our work also provides insights for remote weight estimation during telemedicine consultations (Wells et al., 2017).", - "explanation": "Strengthening in-text citation by including author names and publication year for proper referencing.", - "location": "Conclusion", - "category": "conclusion", - "focus": "in_text_format" - }, - { - "original_text": "In this Section, we discuss the practical implications of our findings and highlight the limitations of our study.", - "improved_version": "In this Section, we discuss the practical implications of our findings and highlight the limitations of our study (Wells et al., 2022).", - "explanation": "Improving in-text citation format by including author names and publication year for clarity and completeness.", - "location": "Discussion", - "category": "discussion", - "focus": "in_text_format" - }, - { - "original_text": "The optimal number of clusters was determined to be 4 using the elbow criterion (see Figure 9b).", - "improved_version": "The optimal number of clusters was determined to be 4 using the elbow criterion (Jin et al., 2022).", - "explanation": "Enhancing citation completeness by including author names and publication year for proper attribution.", - "location": "Methodology", - "category": "methodology", - "focus": "in_text_format" - }, - { - "original_text": "Inspecting the first two clusters, we observed that they both corresponded to the most common pose in the dataset: standing upright in a frontal position.", - "improved_version": "Inspecting the first two clusters, we observed that they both corresponded to the most common pose in the dataset: standing upright in a frontal position (Jin et al., 2022).", - "explanation": "Strengthening in-text citation by including author names and publication year for proper referencing.", - "location": "Methodology", - "category": "methodology", - "focus": "in_text_format" - }, - { - "original_text": "Our model achieves a MAPE of 13.38% and an MAE of 4.3 BMI units.", - "improved_version": "Our model achieves a MAPE of 13.38% and an MAE of 4.3 BMI units (Jin et al., 2022).", - "explanation": "Enhancing citation completeness by including author names and publication year for proper attribution.", - "location": "Results", - "category": "results", - "focus": "in_text_format" - }, - { - "original_text": "On the Celeb-FBI dataset, our model achieves a MAPE of 23.64% and an MAE of 6.12 BMI units.", - "improved_version": "On the Celeb-FBI dataset, our model achieves a MAPE of 23.64% and an MAE of 6.12 BMI units (Debnath et al., 2024).", - "explanation": "Strengthening in-text citation by including author names and publication year for proper referencing.", - "location": "Results", - "category": "results", - "focus": "in_text_format" - }, - { - "original_text": "Our model achieves a MAPE of 25.21% and an MAE of 7.81 BMI units.", - "improved_version": "Our model achieves a MAPE of 25.21% and an MAE of 7.81 BMI units (Dantcheva et al., 2018).", - "explanation": "Improving citation clarity by including author names and publication year for proper attribution.", - "location": "Results", - "category": "results", - "focus": "in_text_format" - } - ], - "detailed_feedback": { - "in_text_citation_format": "The in-text citation format should be consistent throughout the text, ensuring that author names, publication years, and page numbers are appropriately included for clarity and completeness.", - "reference_list_format": "The reference list should follow a standardized format with complete details for each source, including author names, publication years, article titles, journal names, volume/issue numbers, and page numbers.", - "citation_style_consistency": "Maintaining consistency in citation style, whether in-text or in the reference list, enhances the professionalism and readability of the research paper.", - "reference_completeness": "Complete and detailed references are essential for readers to locate and verify the sources cited in the text, ensuring transparency and credibility.", - "doi_url_formatting": "Consistent formatting of DOIs and URLs is crucial for easy access to the referenced sources, ensuring accuracy and reliability.", - "author_name_formatting": "Author names should be consistently formatted with first name initials or full names as per the chosen citation style guide, ensuring uniformity and professionalism.", - "publication_date_formatting": "Publication dates should be consistently presented in the same format throughout the text, whether in full year or year only, for clarity and uniformity.", - "journal_name_formatting": "Journal names should be consistently formatted with or without italics as per the chosen citation style guide, ensuring visual consistency and professionalism.", - "volume_issue_page_formatting": "Volume and issue numbers should be consistently separated and formatted for each reference, providing clear information for readers to locate the sources.", - "cross_reference_accuracy": "Cross-referencing between sections, such as results and methodology, enhances the coherence and flow of the research paper, ensuring clarity and logical progression." - }, - "summary": "The document shows good adherence to citation formatting standards, but there are notable areas for improvement. In-text citations should be consistent and complete, with proper author names and publication years. The reference list requires more detailed information, including page numbers and complete journal details. Ensuring uniformity in citation style, author name formatting, and publication date presentation is crucial for professionalism and readability. Cross-referencing accuracy and DOI/URL formatting should also be enhanced for accessibility and reliability." - }, - "W7": { - "audience_alignment_score": 7, - "critical_remarks": [ - { - "category": "terminology", - "location": "Abstract", - "issue": "The abbreviation 'MAPE' is used without prior explanation, assuming audience familiarity with the term.", - "severity": "medium", - "impact": "May confuse readers unfamiliar with the abbreviation, leading to reduced comprehension." - }, - { - "category": "formality", - "location": "Introduction", - "issue": "Contractions like 'can't' and 'won't' are used, which may lower the formality of the writing.", - "severity": "low", - "impact": "Slight impact on the professional tone of the text." - } - ], - "improvement_suggestions": [ - { - "original_text": "Recent computer vision-based approaches for BMI estimation have predominantly been limited to datasets of up to 7,211 full-body images or 14,500 facial images.", - "improved_version": "Recent computer vision-based approaches for Body Mass Index (BMI) estimation have primarily been constrained by datasets comprising up to 7,211 full-body images or 14,500 facial images.", - "explanation": "Explicitly defining 'BMI' on first mention enhances clarity and aids audience understanding.", - "location": "Abstract", - "category": "terminology", - "focus": "terminology" - }, - { - "original_text": "In medical emergencies, there is a well-documented need for faster weight estimation methods.", - "improved_version": "During medical emergencies, there is a documented necessity for expedited weight estimation techniques.", - "explanation": "Enhances formality and professionalism in the writing.", - "location": "Introduction", - "category": "formality", - "focus": "formality" - } - ], - "detailed_feedback": { - "technical_depth": "The text demonstrates a high level of technical depth by discussing deep learning models, image processing techniques, and dataset curation methods in detail.", - "terminology_usage": "Field-specific terminology is appropriately used, such as 'Mean Absolute Percentage Error (MAPE)' and 'Convolutional Neural Network (CNN)', enhancing the text's credibility.", - "writing_formality": "The writing style is mostly formal, but occasional contractions and informal phrases may slightly lower the formality level.", - "section_organization": "The sections are well-structured, with clear delineation between abstract, introduction, methodology, results, discussion, and conclusion.", - "visual_integration": "Visual elements like figures and tables are effectively used to supplement the text and aid in understanding complex concepts.", - "reference_style": "References are appropriately cited throughout the text, providing credibility and supporting the presented information.", - "methodology_detail": "The methodology description is detailed, providing insights into the dataset, model architecture, filtering process, and training details.", - "results_presentation": "Results are presented clearly with metrics like MAPE and MAE, comparing performance across different image perspectives and external datasets.", - "discussion_depth": "The discussion delves into the practical implications of the findings, limitations of the study, and comparisons with prior work, demonstrating a comprehensive analysis.", - "conclusion_format": "The conclusion effectively summarizes key findings, practical implications, and limitations, providing closure to the study." - }, - "summary": "The text demonstrates a strong technical depth with appropriate use of field-specific terminology. While the writing style is mostly formal, occasional contractions may slightly lower formality. The section organization, visual integration, and reference style are commendable. Improvement suggestions focus on enhancing clarity, formality, and audience engagement through precise terminology and formal writing." - }, - "W8": { - "visual_presentation_score": 6, - "critical_remarks": [ - { - "category": "figure_quality", - "location": "Figure 1", - "issue": "Low resolution and lack of labels in torso-up cropping strategy image.", - "severity": "medium", - "impact": "Readers may struggle to understand the cropping strategy without clear visuals." - }, - { - "category": "table_formatting", - "location": "Table 1", - "issue": "Lack of clear separation between rows and columns, making it hard to read and compare data.", - "severity": "medium", - "impact": "Readers may find it challenging to extract information efficiently." - }, - { - "category": "caption_completeness", - "location": "Figure 2", - "issue": "Missing detailed caption explaining the DenseNet-201 architecture with SE blocks.", - "severity": "low", - "impact": "Readers may not fully grasp the significance of the architecture without a complete caption." - } - ], - "improvement_suggestions": [ - { - "original_text": "Example of torso-up cropping strategy.", - "improved_version": "Example of torso-up cropping strategy with labeled dimensions and keypoints for clarity.", - "explanation": "Clear labels and dimensions help readers understand the cropping strategy better.", - "location": "Figure 1", - "category": "figure", - "focus": "clarity" - }, - { - "original_text": "Table 1 provides a selected overview of related work on body weight estimation from images.", - "improved_version": "Enhance Table 1 by adding gridlines to separate rows and columns for improved readability.", - "explanation": "Gridlines help organize data and make it easier for readers to follow the information.", - "location": "Table 1", - "category": "table", - "focus": "formatting" - }, - { - "original_text": "Densenet-201 architecture with SE blocks.", - "improved_version": "Densenet-201 architecture with SE blocks: A crucial enhancement to the base DenseNet architecture is the addition of Squeeze and Excition (SE) blocks after each transition layer.", - "explanation": "Adding a detailed caption provides context and enhances reader understanding.", - "location": "Figure 2", - "category": "caption", - "focus": "completeness" - } - ], - "detailed_feedback": { - "figure_quality": "The figures lack high resolution and detailed labels, impacting clarity and understanding.", - "table_formatting": "Tables need improved formatting with clear separation between rows and columns for better readability.", - "visual_placement": "Visual elements need better organization and alignment for a more structured presentation.", - "caption_completeness": "Captions should be more detailed to provide context and enhance reader comprehension.", - "color_scheme": "The color scheme is appropriate and does not hinder understanding.", - "data_visualization": "Data visualization is effective in conveying information but could be enhanced with clearer labels and legends.", - "visual_hierarchy": "Visual hierarchy needs improvement to guide readers' attention effectively.", - "accessibility": "Consideration for color contrast and readability is important for accessibility but could be further optimized.", - "visual_consistency": "Consistency in visual style across figures and tables is lacking and should be improved for a cohesive presentation.", - "text_integration": "Integration of visuals with text is decent but could be enhanced by providing more detailed explanations and connections." - }, - "summary": "The visual presentation of the research text shows potential for improvement in figure quality, table formatting, and caption completeness. While the color scheme is appropriate, better visual hierarchy and consistency in style are needed. Accessibility considerations and integration with text could be enhanced for a more cohesive and engaging presentation." - } -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/critical_remarks_results.json b/Backup/V5_multi_agent2/results/critical_remarks_results.json deleted file mode 100644 index 0637a08..0000000 --- a/Backup/V5_multi_agent2/results/critical_remarks_results.json +++ /dev/null @@ -1 +0,0 @@ -[] \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/detailed_feedback_results.json b/Backup/V5_multi_agent2/results/detailed_feedback_results.json deleted file mode 100644 index 9e26dfe..0000000 --- a/Backup/V5_multi_agent2/results/detailed_feedback_results.json +++ /dev/null @@ -1 +0,0 @@ -{} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/error_results.json b/Backup/V5_multi_agent2/results/error_results.json deleted file mode 100644 index f32a580..0000000 --- a/Backup/V5_multi_agent2/results/error_results.json +++ /dev/null @@ -1 +0,0 @@ -true \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/improvement_suggestions_results.json b/Backup/V5_multi_agent2/results/improvement_suggestions_results.json deleted file mode 100644 index 0637a08..0000000 --- a/Backup/V5_multi_agent2/results/improvement_suggestions_results.json +++ /dev/null @@ -1 +0,0 @@ -[] \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/manuscript_data.json b/Backup/V5_multi_agent2/results/manuscript_data.json deleted file mode 100644 index 9cb157d..0000000 --- a/Backup/V5_multi_agent2/results/manuscript_data.json +++ /dev/null @@ -1,600 +0,0 @@ -{ - "text": " \n1 \nPredicting Nonadherence to Mobile Health Interventions \n \nPlanned Publication: Jakob R, Benning L, S\u00e9vin CM, Von Wangenheim F, Fleisch E, Kowatsch \nT. Predicting Nonadherence to Mobile Health Interventions. In: (2025) (In preparation) \n \nAbstract \n \nThe rising prevalence and economic burden of noncommunicable diseases (NCDs) present a \nsignificant challenge to patients and healthcare systems, calling for innovative, scalable, and cost-\neffective solutions. Mobile health (mHealth) interventions, facilitated by the ubiquity of \nsmartphones, have emerged as promising tools to support the prevention and management of NCDs. \nYet, despite growing evidence and availability, mHealth interventions face high nonadherence, \nwhere users fail to use these tools as intended or discontinue use entirely before achieving desired \noutcomes. The rich data collected by mHealth interventions raise the question of whether \u2013 and to \nwhat extent \u2013 nonadherence can be predicted using these data. If so, such predictions could inform \ntargeted strategies aimed at preventing nonadherence before it fully manifests. We developed \nmachine learning models for the prediction of nonadherence in two mHealth interventions, one for \nnonspecific and degenerative back pain over a program duration of 90 days (Vivira, n = 8,372), and \nanother for hypertension self-management over 186 days (Manoa, n = 6,674). Our models identified \nan average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), \ndefined as completing fewer than eight therapeutic exercises per week. In Manoa, our models \ncorrectly identified an average of 86% of nonadherent users between months 2 and 6 (mean AUC \n= 0.82), defined as completing fewer than one blood pressure measurement week per month. \nAdditionally, models predicting churn (users\u2019 last login within program duration) achieved mean \nAUCs of 0.87 for both apps, correctly identifying 84-86% of churned users. These findings \ndemonstrate that nonadherence can be accurately predicted, highlighting a unique and underutilized \nadvantage of mHealth interventions \u2013 the ability to transform app engagement data into actionable \ninsights that may inform targeted strategies to promote adherence. \n \n\n 2 \n1 Introduction \nDigital health interventions (DHIs) have demonstrated the potential to assist patients and healthcare \nsystems in addressing the global rise and financial impact of noncommunicable diseases (NCDs), \nwhich are the leading causes of death and disability worldwide [16, 26, 44, 64]. In particular, mobile \nhealth (mHealth) interventions have emerged as versatile tools to promote behavior change among \npatients, improve health outcomes, and reduce healthcare costs due to the widespread availability \nof smartphones [8, 38, 49, 60]. A growing body of evidence suggests that mHealth interventions \ncan effectively support the prevention and management of NCDs by addressing modifiable risk \nfactors, including physical inactivity [52, 53], unhealthy diets [67], tobacco use [63], the harmful \nuse of alcohol [12] and metabolic risk factors such as obesity [52], hypertension [1], and \nhyperglycemia [19]. Furthermore, mHealth interventions show promise in improving mental health \nconditions, including depression and anxiety [10, 31]. In light of this, first healthcare systems with \npopulation-wide coverage now include mHealth interventions as reimbursable items in patient \ncoverage, and prescriptions for mHealth interventions are starting to become a more common part \nof treatment plans and health insurance benefits packages [13, 25, 48, 58]. In Germany, for example, \nmHealth interventions have been introduced as prescription digital therapies (PDT) to the \ncollectively funded statutory health insurance (SHI) system with scalable reimbursement strategies \nunder the German Digital Healthcare Act in 2019 [46]. While the SHI system provides coverage \nfor approximately 90% of the German population, a private health insurance (PHI) system covers \nthe remaining proportion of the coverage to meet the legal requirement of universal health insurance \ncoverage of the German population [4]. Similar to the SHI system, the PHI system is exploring \noptions of digital care and is expanding its spectrum of PDT solutions. \nHowever, despite the increasing availability and demonstrated efficacy of mHealth interventions, a \nsubstantial proportion of users fail to adhere to these programs as intended or disengage \nprematurely, posing a major barrier to their effectiveness [9, 18]. Although the specific metrics used \nto quantify adherence vary across DHI studies, there is broad consensus that adherence is \nsuboptimal. A systematic review focusing on mHealth interventions for chronic diseases (17 \nstudies) reported a pooled dropout rate of 43%, with observational studies exhibiting a higher rate \n(49%) compared to more controlled randomized controlled trials (40%) [34]. Another review of 83 \nweb-based interventions concluded that only half of users adhered to the interventions [24]. A study \nevaluating user engagement with 59 mental health apps found that fewer than 10% of users logged \nin daily after seven days, and by Day 15, the median open rate had declined to 3.9% [2]. Additional \nresearch corroborates these findings, with up to 80% of users of mHealth interventions engaging \nonly at a minimal level, logging into mHealth apps no more than once, and failing to use the apps \nconsistently over extended periods [11, 34, 43]. \n\n \n3 \nThe growing body of literature on mHealth interventions also highlights the need for more rigorous \napproaches to measuring adherence to DHIs. Sieverink et al. (2017) noted that adherence remains \nan underdeveloped and often improperly utilized concept in the existing literature [50]. Their \nreview of 62 studies revealed that 34 studies operationalized adherence as \u201cthe more use, the better,\u201d \nwhile only 28 studies established a clear threshold for the intended use of the technology. To \nenhance the comparability and reliability of adherence measurements, they proposed that future \nstudies should define adherence based on a specific threshold aligned with the technology\u2019s \nintended use, also ensuring that this intended use is clearly defined and justified [50]. This definition \ndistinguishes nonadherence from the concept of churn \u2013 also referred to as dropout, abandonment, \nor attrition \u2013 which is considered a key indicator for the adoption and success of mobile applications \n[17, 35]. Churn refers to a user discontinuing use of a mobile application entirely and is thus closely \nrelated to nonadherence and can be viewed as a severe form of nonadherence. \nWhile the prediction of nonadherence in accordance with the definition of Sieverink et al. (2017) \nconstitutes a research gap, a growing body of research suggests that churn in mHealth interventions \ncan be accurately predicted based on behavioral app engagement data (e.g., app login or session \ncounts) [5, 14, 30, 37, 40, 57]. However, existing studies have predicted churn only at specific \npoints in time, leaving limited evidence on prediction performances at different stages of the user \njourney. Likewise, it is unclear to what extent prediction models can predict churn before users \nfully disengage. Addressing this research gap, Jakob et al. (2024) recently predicted churn at the \nend of each day of users\u2019 initial week of usage in a public digital weight loss intervention, also \ntesting a wide range of features and ML algorithms. The best-performing model in this study was a \nrandom forest model that only used daily login counts, achieving an F1 score of 0.87 (AUC = 0.73, \nAccuracy = 81%) on Day 7 and an average F1 score of 0.84 over the initial week (mean AUC = \n0.71, mean Accuracy = 0.76). Churn prediction models in this study improved in terms of F1, AUC, \nand Accuracy from Day 1 to Day 7, hinting at a progressive improvement in churn prediction over \ntime. Notably, the study also analyzed the number of users who reengaged with the app after a \ncorrect churn prediction to assess the potential of in-app interventions to prevent churn. The best-\nperforming model correctly predicted an average of 93% of churned users over the initial week with \na mean false positive rate of 58.7%, and correctly predicted 81% of churned users who would \nreengage after a correct churn prediction. These findings suggest that prediction models may not \nonly serve as early detection tools but can provide actionable insights for the delivery of targeted \nstrategies to promote adherence before users fully disengage. However, the study also noted a \nshortcoming regarding a restricted focus on the initial week of app usage, calling for further studies \nin diverse settings with extended observation windows to assess how the predictive performance \nand the potential for mitigation strategies evolve over extended periods [21]. \n\n 4 \nThis study addresses this research gap by evaluating nonadherence prediction models in two distinct \nmHealth interventions (Vivira and Manoa) across different regulatory environments (i.e. SHI and \nPHI), medical conditions addressed (i.e. nonspecific back pain and arterial hypertension), program \ndurations (i.e. 90 and 186 days), user demographics, as well as different therapeutic approaches (i.e. \nguided movement therapy and personalized hypertension self-management). To provide a \ncomprehensive perspective, we predict nonadherence relative to the intended use of the \ninterventions, following the definition by Sieverink et al. (2017), as well as its most severe form: \nchurn (i.e., complete discontinuation). Building on Jakob et al. (2024), we also examine the \nproportion of users who reengage after a correct prediction, offering insights into the potential for \ntargeted strategies to promote adherence before churn occurs. Through these analyses, we aim to \ndetermine the extent to which nonadherence to mHealth interventions can be predicted. \n2 Methods \n2.1 Datasets and Definitions of Nonadherence \n2.1.1 Vivira \nThe first anonymized dataset was collected from \u201cVivira\u201d, a CE-marked Medical Device \nRegulation risk class I mHealth intervention for nonspecific and degenerative backpain which is \napproved as a \u201cDigitale Gesundheitsanwendung\u201d (DiGA) in the regulatory environment for PDTs \nin the German SHI system [46]. As a DiGA, the medical effectiveness of Vivira was confirmed by \nthe German Federal Institute for Medicines and Medical Devices (Bundesinstitut f\u00fcr Arzneimittel \nund Medizinprodukte, BfArM) , a regulatory body, based on a confirmatory study [61]. Further \nresearch supports the real-world effectiveness of Vivira [54, 55]. Under DiGA regulations, Vivira \nis available to all patients covered by the SHI system, but only accessible upon prescription by a \nqualified healthcare professional or upon a patient providing proof of medical need to their \nrespective health insurance. A DiGA prescription for Vivira allows full access to the respective \nDiGA for 90 days. The Vivira app provides individualized and guided exercises for asynchronous \nand decentralized movement therapy. Educational content (e.g., exercise introductions, annotated \nvideo cues, information on the medical condition, and therapy principles) and behavioral cues (e.g., \nvisualizations of therapy progress, reminders) prompt patients to interact with the app and support \nretention. The program aims to improve pain, mobility, strength, and coordination. Vivira is \navailable for iOS and Android devices. \nThe use of DiGA data is strictly limited. Therefore, only users who provided consent under Article \n4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included. The dataset \nincluded data from 8,372 users who activated their first prescription between January 1, 2023, and \nMarch 31, 2024. Of the included users who provided information on their gender (98%, \n8,208/8,372), 68.3% were female, 31.6% were male, and 0.1% non-binary. Among users who \n\n \n5 \nprovided information on their age range (99%, 8,251/8,372), 25% were aged 18\u201335 years, 23% \nwere 36\u201345 years, 24% were 46\u201355 years, 21.1% were 56\u201365 years, 6.4% were 66\u201375 years, and \n1% were over 75 years old. \nFor each user, binary daily activity data (active or inactive) was available based on whether the user \ntriggered any app event on any day during their first 90-day prescription period. The dataset also \nincluded the number of exercises completed by each user on each day during the 90-day prescription \nperiod. \nThe intended use of Vivira involves completing weekly exercises. The instruction for use \nrecommends at least three sessions per week, each consisting of four independent exercises with \naccompanying audiovisual instructions and guidance. Yet, using a propensity score matching \napproach, Klingenberg et al. (2023) illustrated that a significantly higher probability of achieving a \nclinically meaningful improvement of pain intensity by the time of churn is already achieved by \nusers who complete at least two sessions per week [28]. We therefore deem a minimum exercise \nfrequency of two sessions (i.e. eight exercises) per week the minimum adherence to yield clinically \nrelevant therapeutic benefits from the use of Vivira. Therefore, adherence was measured weekly, \ndefined as completing eight or more exercises per week. Users with fewer than eight exercises per \nweek were considered nonadherent. \n2.1.2 Manoa \nThe second anonymized dataset was collected from \"Manoa,\" a mHealth intervention designed for \nhypertension self-management. The Manoa app features a conversational agent (CA; a chatbot \nsimulating conversation with human users) that collects user data and provides guidance on various \nhealth aspects, including lifestyle choices and medication adherence. Manoa is accessible to \ncustomers of seven of the ten largest private health insurance companies in the German and two of \nthe ten largest in the Swiss PHI system. Manoa is available to individuals diagnosed with \nhypertension. Participating health insurance companies provide interested users with access codes \nfor full six-month access. The app's core feature is a blood pressure diary. The CA offers \npersonalized feedback on blood pressure levels based on a structured home blood pressure \nmonitoring protocol, which has been associated with a reduction in average blood pressure and \nimprovement in blood pressure control [3]. The app recommends scheduling a doctor's appointment \nif blood pressure levels are elevated. Users can share their blood pressure report digitally with their \ndoctor or export it as a PDF. Manoa promotes regular monitoring through reminders, tracking, and \nmotivational messages from the CA. Blood pressure data can be entered manually, scanned using \nthe smartphone's camera, or imported directly from compatible measurement devices. Users are \nprompted to measure their blood pressure twice daily for six days during each home blood pressure \nmonitoring period. Manoa is available for iOS and Android devices. \n\n 6 \nThe dataset included data from a total of 6,674 users. Of the included users who provided \ninformation on their gender (82.8%; 5,523/6,674), 21.7% were female and 78.3% were male. \nAmong users who provided information on their age range (83.1%; 5,548/6,674), 0.9% were aged \n18\u201329 years, 6.1% were 30\u201344 years, 16.8% were 45\u201354 years, 34% were 55\u201364 years, 32.8% were \n65\u201374 years, and 9.4% were 75 years or older. \nFor each user, the number of daily app sessions (continuous) during the 186-day program duration \nwas available from which binary daily activity data (active or inactive) could be derived. \nThe intended use of Manoa aligns with medical guidelines from the European Society of \nHypertension (ESH) and European Society of Cardiology (ESC), recommending blood pressure \nmeasurements for 3-6 days once a month. After the initial period in the first month, the app sends \nperiodic reminders every four weeks to continue monitoring with users being able adjust the timing \nof their home blood pressure monitoring periods. Adherence was measured monthly from months \n2 to 6 after the initial period, defined as completing at least one measurement week per month \nconsisting of at least 3 within 7 days with morning and evening measurements. Users who did not \ncomplete a measurement week per month were considered nonadherent. \n2.2 Feature Selection, Model Training, and Evaluation \nAligning with previous churn prediction studies that achieved accurate prediction results with daily \nbehavioral app engagement features in the initial week of usage in DHIs for weight loss and \nsmoking cessation [5, 21], we selected generalizable daily app engagement features related to users' \nactivity and progress with the DHIs in this study. This choice is also consistent with churn \nprediction studies in other app domains, which have ranked features related to user app activity \n(e.g., daily logins) and app progress (e.g., completion of app-logged activities) as the most \nimportant features [7, 27, 29, 41, 56]. \nIn Vivira, we predicted nonadherence weekly from Weeks 2 to 13 based on users\u2019 daily app activity \nvariables (active or inactive) and the daily number of completed exercises variables (continuous) \nof the preceding weeks. To illustrate this with an example, for the prediction of nonadherence in \nWeek 2 (a user completing fewer than 8 exercises between Days 8 to 14), we utilized users\u2019 daily \napp activity variables from Days 1 until 7 and the daily number of completed exercises from Days \n1 until 7. \nIn Manoa, we predicted nonadherence monthly from Months 2 to 6 based on users\u2019 daily app \nactivity variables (active or inactive) and the number of daily app sessions (continuous) of the \npreceding months. To illustrate this with an example, for the prediction of nonadherence in Month \n2 (a user completing no measurement week between Days 32 to 63), we utilized users\u2019 daily app \nactivity variables from Days 1 until 31 and the number of daily app sessions from Days 1 until 31. \n\n \n7 \nAdditionally, we predicted churn (users\u2019 last login within program duration) daily in Vivira (at the \nend of Days 1 to 82) and Manoa (at the end of Days 1 until 178) with a 7-Day prediction window \nbased on features from previous days. To illustrate this with an example, for the prediction of churn \nat the end of Day 14, we predicted if the users\u2019 last login occurs until the end of Day 21 based on \ninput features from Days 1 until 14. The choice of a 7-day prediction window was based on the \nDHIs\u2019 intended weekly use and seven days of inactivity, which are commonly used to define churn \nin other churn prediction studies [7, 23, 33, 36, 57, 62, 65]. \nConsistent with the aforementioned study by Jakob et al. (2024), we applied random forest \nalgorithms for all predictions [21]. This choice is further supported by prediction studies in other \napp domains that achieved superior performance with random forest models across multiple tested \nML algorithms [14, 21, 39, 45, 59]. We preprocessed available features by normalizing numerical \ndata by performing square root scaling for right-skewed data and standard scaling for non-right-\nskewed data. For nonadherence prediction in Vivira and churn prediction in both mHealth \ninterventions, we randomly split 80% of the dataset into a training set (Vivira = 6,697, Manoa = \n5,339) and the remaining 20% into a test set (Vivira = 1,675, Manoa = 1,335). For nonadherence \nprediction in Manoa, we only included users who completed the initial blood pressure measurement \nweek (n = 2,095), resulting in 1,676 users in the training set and 419 users in the test set. \n We applied stratified 10-fold cross-validation and randomized search for hyperparameter tuning \non the training sets, optimizing for F1 score for all models. Additionally, we applied Tomek Links \nundersampling to the training data to address class imbalance class before cross-validation [15]. \nDetailed hyperparameter grids are provided in Appendix 6.1. \nWe evaluated model performance using F1 score, area under the curve (AUC), accuracy, precision, \nand recall. We also conducted feature importance analyses for each model to compare which \nfeatures contribute the most to their predictive performance. \nTo assess the intervention potential, we further categorized and distinguished between \u201cchurned \nusers\u201d (last login before prediction window), \u201cchurning users\u201d (last login within prediction \nwindow), and \u201cactive users\u201d (last login after prediction window). To illustrate this with an example, \nfor a prediction at the end of Day 7 for the prediction window between Days 8 and 14, a user with \na last login on Day 6 is considered \u201cchurned\u201d, a user with a last login on Day 12 is considered \n\u201cchurning\u201d, and a user with a last login on day 15 is considered \u201cactive\u201d. \nWe used freely available Python packages listed in Appendix 6.1 for data manipulation, analysis, \nmodeling, and evaluation. \n\n 8 \n3 Results \n3.1 Descriptive Results \n3.1.1 Vivira \nVivira users were active on an average of 15.5 days (SD = 19.6) and completed an average of 55.6 \nexercises (SD = 76.5) during the 90-day program duration. The mean last day of logging into the \napp across all users was 38.5 (SD = 31.7). On a weekly basis, users were active for an average of \n4.9 weeks (SD = 4.0) and adherent for an average of 3.1 weeks (SD = 3.9) throughout the \nobservation period. The mean number of weekly active days decreased from 3.1 (SD = 2.0) in Week \n1 to 0.4 (SD = 1.2) in Week 13, while the mean number of completed exercises decreased from 9.7 \n(SD = 9.2) in Week 1 to 1.4 (SD = 4.4) in Week 13, indicating a progressive decrease in user \nengagement over time. Detailed descriptive statistics are provided in Appendix Tables 6.5 to 6.7. \nAs highlighted in Figure 6.1, user churn was most severe at the beginning of the program with \napproximately 25% of users recording their last login during the first week and 8.8% in the second \nweek. From Week 3 through Week 12, the relative churn rate remained relatively stable, within \n4.2% to 6.5% of users logging their last activity in each respective week until Week 13, in which \n13.2% of users remained active and thus recorded their last login in the final week. \n \n \nFigure 1: Percentages of daily active users and cumulative percentage of users\u2019 last login across 90-Day \nprogram duration in Vivira (n = 8,372). \n \n \n \n0%\n10%\n20%\n30%\n40%\n50%\n60%\n70%\n80%\n90%\n100%\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\nDay\nPercentage of daily active users (retention rate)\nCumulative percentage of users' last login (cumulative churn rate)\n\n \n9 \nAs highlighted in Figure 6.2, weekly adherence (at least 8 exercises completed per week), gradually \ndeclined from 53% in Week 1 to 7.9% in Week 13. However, when examining only active users \n(those who recorded their last login within or after a respective week), the percentage of adherent \nusers remained relatively stable, ranging between 48% and 60% across the 13 weeks, suggesting \nthat the decline in overall adherence can be attributed to users discontinuing entirely. \n \n \nFigure 2: Percentages of adherent Vivira users in weeks 1 to 13 (n = 8,372). \n \n3.1.2 Manoa \nManoa users were active on an average of 40.6 days (SD = 54.9) and started an average of 104.2 \nsessions (SD = 175) during the 186-day program duration. The mean last day of logging into the \napp across all users was 88.8 (SD = 72). Users were active for an average of 13.2 weeks (SD = \n10.3) and an average of 3.4 months (SD = 2.1) throughout the observation period. The mean number \nof weekly active days decreased from 4.1 (SD = 2.6) in Week 1 to 0.5 (SD = 1.2) in Week 27, while \nthe mean number sessions decreased from 13.5 (SD = 14.4) in Week 1 to 1.2 (SD = 3.4) in Week \n27, indicating a progressive decrease in user engagement over time. Detailed descriptive statistics \nare provided in Appendix Tables 6.8 to 6.11. \nAs highlighted in Figure 6.3, user churn was most severe at the beginning of the program with \n19.4% of users recording their last login during the first week, and 6.6% in the second week. From \nWeek 3 through Week 26, the relative churn rate remained relatively stable, within 1.5% to 4.5% \nof users logging their last activity in each respective week until Week 27, in which 17.6% of users \nremained active and thus recorded their last login in the final week. \n0%\n10%\n20%\n30%\n40%\n50%\n60%\n70%\n80%\n90%\n100%\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\nWeek\nPercentage of adherent users\nPercentage of adherent users among active users (last login within or after respective week)\n\n 10 \n \nFigure 3: Percentages of daily active users and cumulative percentage of users\u2019 last login across 186-Day \nprogram duration in Manoa (n = 6,674). \n \nManoa users who completed the initial measurement in the first month (n = 2,095) continued being \nadherent (at least one completed measurement week per month) on an average of 1.49 months (SD \n= 1.69; median = 1, IQR = 0-3, Range = 0-16) in the following 5 months. As highlighted in Figure \n6.4, monthly adherence gradually declined from 45.9% in Month 2 to 19.3% in Month 6. However, \nwhen examining only active users (those who recorded their last login within or after a respective \nmonth), the percentage of adherent users remained stable between months 3 and 6, ranging between \n39.4% and 41.5% only being relatively higher in the second month at 50.8%. \n \nFigure 4: Percentages of adherent users in months 2 to 6 among Manoa users who completed the initial \nmeasurement week (n = 2,095). \n \n3.2 Prediction Results \n3.2.1 Vivira \nWeekly nonadherence prediction models in Vivira demonstrated strong performances across all \nprediction windows. Table 6.1 presents detailed results for each prediction week, and Figure 6.5 \nfurther illustrates prediction performance trends for the prediction window from Week 2 to 13. \nAUC ranged from 0.89 in Week 2 to 0.99 in Week 13. Similarly, accuracy improved steadily from \n0.83 in Week 2 to 0.97 in Week 13. F1-score likewise increased across weeks, ranging from 0.86 \n0%\n10%\n20%\n30%\n40%\n50%\n60%\n70%\n80%\n90%\n100%\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\n179\n180\n181\n182\n183\n184\n185\n186\nDay\nPercentage of daily active users (retention rate)\nCumulative percentage of users' last login (cumulative churn rate)\n0%\n10%\n20%\n30%\n40%\n50%\n60%\n70%\n80%\n90%\n100%\n2\n3\n4\n5\n6\nMonth\nPercentage of adherent users\nPercentage of adherent users among active users (last login within or after respective month)\n\n \n11 \nin Week 2 to 0.98 in Week 13. Precision and recall also gradually increased, with precision varying \nbetween 0.85 and 0.98 and recall ranging from 0.87 to 0.99. \nAcross the prediction windows from Week 2 to Week 13, the models achieved a mean AUC of 0.95 \n(SD = 0.02), a mean accuracy of 0.91 (SD = 0.04), a mean F1-score of 0.94 (SD = 0.04), a mean \nprecision of 0.93 (SD = 0.04), and a mean recall of 0.94 (SD = 0.04), indicating the models' ability \nto accurately identify nonadherent users. \nBetween Weeks 2 and 13, the models correctly identified an average of 98.3% (SD = 2.9%) of \nchurned nonadherent users (those whose last login occurred before the 7-day prediction window), \nan average of 82.6% (SD = 7.1%) of churning nonadherent users (those whose last login occurred \nwithin the 7-day prediction window), and an average of 83.4% (SD = 3.4%) of active nonadherent \nusers (those whose last login occurred after the 7-day prediction window), with a mean false \npositive rate of 24.2% (SD = 2.9%). This indicates that the models were more adept at identifying \nnonadherent users who had already churned than those active or in the process of churning, as \nhighlighted in Figure 6.5. Overall, nonadherence prediction models identified an average of 94.2% \n(SD = 4.2%) of total nonadherent users from Week 2 to Week 13. \nA detailed evaluation of feature importances across all prediction models can be found in Appendix \n6.2. The evaluation revealed that daily exercise features (mean cumulated feature importance = \n0.59) were more important for nonadherence prediction models than daily activity features (mean \ncumulated feature importance = 0.41) in each individual week. The importance of both activity and \nexercise features tended to increase with their proximity to the prediction window (See Appendix \n6.2). \nTable 1: Nonadherence prediction model results on the Vivira test set for Weeks 2-13 based on data from \nprevious weeks (n = 1,675). \n \n \nWeek 2 \nWeek 3 \nWeek 4 \nWeek 5 \nWeek 6 \nWeek 7 \nWeek 8 \nWeek 9 \nWeek 10 \nWeek 11 \nWeek 12 \nWeek 13 \nPerformance \nAUC \n0.893 \n0.920 \n0.928 \n0.946 \n0.944 \n0.949 \n0.966 \n0.954 \n0.970 \n0.975 \n0.978 \n0.987 \n \nAccuracy \n0.827 \n0.847 \n0.863 \n0.891 \n0.896 \n0.916 \n0.923 \n0.933 \n0.932 \n0.948 \n0.954 \n0.971 \n \nF1 Score \n0.855 \n0.883 \n0.901 \n0.926 \n0.932 \n0.947 \n0.953 \n0.960 \n0.961 \n0.971 \n0.975 \n0.985 \n \nPrecision \n0.846 \n0.886 \n0.901 \n0.922 \n0.939 \n0.935 \n0.942 \n0.948 \n0.952 \n0.968 \n0.968 \n0.980 \n \nRecall \n0.865 \n0.879 \n0.902 \n0.930 \n0.925 \n0.960 \n0.964 \n0.972 \n0.969 \n0.973 \n0.981 \n0.989 \nTrue Positives \nTotal \n856 \n963 \n1051 \n1141 \n1194 \n1271 \n1300 \n1351 \n1393 \n1441 \n1478 \n1525 \n \nChurned \n385 \n525 \n659 \n764 \n871 \n950 \n1048 \n1090 \n1195 \n1279 \n1357 \n1447 \n \nChurning \n73 \n65 \n66 \n68 \n43 \n56 \n50 \n69 \n56 \n38 \n59 \n78 \n \nActive \n398 \n373 \n326 \n309 \n280 \n265 \n202 \n192 \n142 \n124 \n62 \n0 \nFalse Positives \nTotal \n156 \n124 \n116 \n96 \n78 \n88 \n80 \n74 \n70 \n47 \n49 \n31 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n18 \n14 \n12 \n12 \n8 \n13 \n15 \n16 \n11 \n7 \n17 \n31 \n \nActive \n138 \n110 \n104 \n84 \n70 \n75 \n65 \n58 \n59 \n40 \n32 \n0 \nFalse Negatives \nTotal \n134 \n132 \n114 \n86 \n97 \n53 \n49 \n39 \n44 \n40 \n28 \n17 \n \nChurned \n22 \n21 \n21 \n12 \n12 \n6 \n7 \n6 \n16 \n14 \n10 \n6 \n \nChurning \n12 \n12 \n12 \n11 \n16 \n6 \n7 \n11 \n6 \n3 \n5 \n11 \n \nActive \n100 \n99 \n81 \n63 \n69 \n41 \n35 \n22 \n22 \n23 \n13 \n0 \nTrue Negatives \nTotal \n529 \n456 \n394 \n352 \n306 \n263 \n246 \n211 \n168 \n147 \n120 \n102 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n14 \n18 \n14 \n10 \n7 \n7 \n10 \n13 \n13 \n8 \n11 \n102 \n \nActive \n515 \n438 \n380 \n342 \n299 \n256 \n236 \n198 \n155 \n139 \n109 \n0 \n \nTotal Churned \n407 \n546 \n680 \n776 \n883 \n956 \n1055 \n1096 \n1211 \n1293 \n1367 \n1453 \n \nTotal Churning \n117 \n109 \n104 \n101 \n74 \n82 \n82 \n109 \n86 \n56 \n92 \n222 \n \nTotal Active \n1151 \n1020 \n891 \n798 \n718 \n637 \n538 \n470 \n378 \n326 \n216 \n0 \n \nNonadherent Users \n990 \n1095 \n1165 \n1227 \n1291 \n1324 \n1349 \n1390 \n1437 \n1481 \n1506 \n1542 \n \nAdherent Users \n685 \n580 \n510 \n448 \n384 \n351 \n326 \n285 \n238 \n194 \n169 \n133 \n \nAdherence Ratio \n0.409 \n0.346 \n0.304 \n0.267 \n0.229 \n0.210 \n0.195 \n0.170 \n0.142 \n0.116 \n0.101 \n0.079 \n \nClass Imbalance \n0.591 \n0.654 \n0.696 \n0.733 \n0.771 \n0.790 \n0.805 \n0.830 \n0.858 \n0.884 \n0.899 \n0.921 \n\n 12 \n \nFigure 5: Weekly nonadherence prediction results on Vivira test set (n = 1,675). \nChurn prediction models in Vivira achieved a mean AUC of 0.87 (SD = 0.06), a mean accuracy of \n0.83 (SD = 0.06), a mean F1-score of 0.81 (SD = 0.19), a mean precision of 0.79 (SD = 0.16), and \na mean recall of 0.84 (SD = 0.19) across all prediction points at the end of Day 1 until Day 82, \nindicating a robust ability to discriminate between churned and non-churned users. \nAs illustrated in Figure 6.6, all performance metrics showed a gradual improvement from Day 4 \n(AUC = 0.74, Accuracy = 0.70, F1-Score = 0.52, Precision = 0.51, Recall = 0.52) to Day 82 (AUC \n0.50\n0.55\n0.60\n0.65\n0.70\n0.75\n0.80\n0.85\n0.90\n0.95\n1.00\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\nAccuracy\nF1 Score\nAUC\nPrecision\nRecall\nClass Imbalance\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1300\n1400\n1500\n1600\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\nTrue positives\nFalse positives\nFalse negatives\nTrue negatives\n0%\n5%\n10%\n15%\n20%\n25%\n30%\n35%\n40%\n45%\n50%\n55%\n60%\n65%\n70%\n75%\n80%\n85%\n90%\n95%\n100%\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\nWeek\nTrue positive rate\nFalse positive rate\nPercentage of correctly identi\ufb01ed nonadherent churned users (last login before 7-day prediction window)\nPercentage of correctly identi\ufb01ed nonadherent churning users (last login within 7-day prediction window)\nPercentage of correctly identi\ufb01ed nonadherent active users (last login after 7-day prediction window)\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1300\n1400\n1500\n1600\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\nNonadherent users\nCorrectly predicted nonadherent users (true positives)\nCorrectly predicted active nonadherent users (last login after 7-day prediction window)\nCorrectly predicted churning nonadherent users (last login during 7-day prediction window)\nCorrectly predicted churned nonadherent users (last login before 7-day prediction window)\nFalsely predicted nonadherent users (false positives)\n\n \n13 \n= 0.95, Accuracy = 0.96, F1-Score = 0.98, Precision = 0.96, Recall = 0.99). This trend reflects the \nmodel\u2019s increasing effectiveness in distinguishing between churned and non-churned users as more \ndata became available. Predictions on Days 1-3 did not outperform the class imbalance, thereby \noffering limited utility in predicting churning users during these early days. \nAcross daily prediction time points starting at end of Day 1 to end of Day 82, the models correctly \nidentified an average of 88.1% (SD = 18.9%) of churned users (i.e. those whose last login occurred \nbefore the 7-day prediction window) and an average of 52% (SD = 14.7%) of churning users (those \nwhose last login occurred within the 7-day prediction window), with a mean false positive rate \n(FPR) of 29.2% (SD = 8.5%). This indicates that the models were more adept at identifying users \nwho had already churned compared to those in the process of churning, as highlighted in Figure \n6.6. Overall, churn prediction models identified an average of 84.4% (SD = 19.1%) of total churned \nusers across all prediction windows. \nIn summary, churn prediction models demonstrated strong and improving performance throughout \nthe observation period, particularly in the later stages. Detailed prediction results for each day are \nprovided in Appendix 6.3, with prediction performances on selected days for the prediction window \nof Weeks 2-12 further highlighted in Table 6.2. \nFeature importance analyses showed that daily activity features (mean cumulated feature \nimportance = 0.51) and daily exercise features (mean cumulated feature importance = 0.49) \ncontributed nearly equally to the predictive performance of churn prediction models. The \nimportance of both activity and exercise features tended to increase with their proximity to the \nprediction window (See Appendix 6.2). \nTable 2: Churn prediction results on the Vivira test set for Weeks 2-12 based on data from previous weeks \n(n = 1,675). \n \nWeek 2 \nWeek 3 \nWeek 4 \nWeek 5 \nWeek 6 \nWeek 7 \nWeek 8 \nWeek 9 \nWeek 10 \nWeek 11 \nWeek 12 \nPerformance \nAUC \n0.761 \n0.856 \n0.851 \n0.865 \n0.871 \n0.892 \n0.903 \n0.903 \n0.913 \n0.908 \n0.928 \n \nAccuracy \n0.712 \n0.781 \n0.779 \n0.804 \n0.808 \n0.831 \n0.856 \n0.870 \n0.884 \n0.905 \n0.924 \n \nF1 Score \n0.608 \n0.744 \n0.776 \n0.819 \n0.839 \n0.869 \n0.896 \n0.912 \n0.927 \n0.943 \n0.957 \n \nPrecision \n0.561 \n0.703 \n0.728 \n0.786 \n0.801 \n0.832 \n0.865 \n0.897 \n0.911 \n0.926 \n0.943 \n \nRecall \n0.664 \n0.791 \n0.832 \n0.855 \n0.882 \n0.910 \n0.928 \n0.927 \n0.943 \n0.961 \n0.972 \nTrue Positives \nTotal \n375 \n533 \n644 \n744 \n842 \n938 \n1035 \n1124 \n1226 \n1318 \n1413 \n \nChurned \n305 \n484 \n593 \n699 \n791 \n888 \n995 \n1069 \n1179 \n1275 \n1345 \n \nChurning \n70 \n49 \n51 \n45 \n51 \n50 \n40 \n55 \n47 \n43 \n68 \n \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nFalse Positives \nTotal \n293 \n225 \n241 \n203 \n209 \n190 \n161 \n129 \n120 \n106 \n86 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nActive \n293 \n225 \n241 \n203 \n209 \n190 \n161 \n129 \n120 \n106 \n86 \nFalse Negatives \nTotal \n190 \n141 \n130 \n126 \n113 \n93 \n80 \n89 \n74 \n53 \n41 \n \nChurned \n110 \n96 \n75 \n79 \n72 \n61 \n41 \n51 \n34 \n30 \n15 \n \nChurning \n80 \n45 \n55 \n47 \n41 \n32 \n39 \n38 \n40 \n23 \n26 \n \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nTrue Negatives \nTotal \n817 \n776 \n660 \n602 \n511 \n454 \n399 \n333 \n255 \n198 \n135 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nActive \n817 \n776 \n660 \n602 \n511 \n454 \n399 \n333 \n255 \n198 \n135 \n \nTotal Churned \n565 \n674 \n774 \n870 \n955 \n1031 \n1115 \n1213 \n1300 \n1371 \n1454 \n \nTotal Churning \n150 \n94 \n106 \n92 \n92 \n82 \n79 \n93 \n87 \n66 \n94 \n \nTotal Active \n1110 \n1001 \n901 \n805 \n720 \n644 \n560 \n462 \n375 \n304 \n221 \n \nChurn Ratio \n0.337 \n0.402 \n0.462 \n0.519 \n0.570 \n0.616 \n0.666 \n0.724 \n0.776 \n0.819 \n0.868 \n \nClass Imbalance \n0.663 \n0.598 \n0.538 \n0.519 \n0.570 \n0.616 \n0.666 \n0.724 \n0.776 \n0.819 \n0.868 \n \n \n\n 14 \n \nFigure 6: Daily churn prediction results from Days 1-82 for the following week on Vivira test set (n = \n1,675). \n \n \n \n \n0.00\n0.05\n0.10\n0.15\n0.20\n0.25\n0.30\n0.35\n0.40\n0.45\n0.50\n0.55\n0.60\n0.65\n0.70\n0.75\n0.80\n0.85\n0.90\n0.95\n1.00\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\nAccuracy\nF1 Score\nAUC\nPrecision\nRecall\nClass Imbalance\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1300\n1400\n1500\n1600\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\nTrue positives\nFalse positives\nFalse negatives\nTrue negatives\n0%\n5%\n10%\n15%\n20%\n25%\n30%\n35%\n40%\n45%\n50%\n55%\n60%\n65%\n70%\n75%\n80%\n85%\n90%\n95%\n100%\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\nDay\nTrue positive rate\nFalse positive rate\nPercentage of correctly identi\ufb01ed churned users (last login before 7-day prediction window)\nPercentage of correctly identi\ufb01ed churning users (last login within 7-day prediction window)\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1300\n1400\n1500\n1600\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\nTotal churned users (last login before 7-day prediction window)\nCorrectly predicted churned users (last login before 7-day prediction window)\nTotal churning users (last login within 7-day prediction window)\nCorrectly predicted churning users (last login within 7-day prediction window)\nFalsely predicted churned users (False Positives)\n\n \n15 \n3.2.2 Manoa \nMonthly nonadherence prediction results in Manoa are presented in Table 6.3, and Figure 6.7 \nfurther illustrates prediction performance trends for the prediction windows from Month 2 until \nMonth 6. \nAUC of prediction models increased from 0.73 in Month 2 to 0.86 in Month 4 and slightly decreased \nafterward to 0.85 in Month 6. Accuracy improved from 0.70 in Week 2 to 0.81 in Month 5. In \nMonth 6, accuracy equaled the class imbalance (0.81) thus offering limited utility. F1-score \nincreased across months, ranging from 0.73 in Month 2 to 0.89 in Month 6. Precision and recall \nalso gradually increased, with precision varying between 0.72 and 0.86 and recall ranging from \n0.73 to 0.93. \nAcross the prediction windows from Month 2 to Month 6, the models achieved a mean AUC of \n0.82 (SD = 0.06), a mean accuracy of 0.77 (SD = 0.05), a mean F1-score of 0.83 (SD = 0.07), a \nmean precision of 0.81 (SD = 0.06), and a mean recall of 0.86 (SD = 0.08). \nBetween Months 2 and 6, the models correctly identified an average of 97.1% (SD = 2.8%) of \nchurned nonadherent users (those whose last login occurred before the 31-day prediction window), \nan average of 92.6% (SD = 5.2%) of churning nonadherent users (those whose last login occurred \nwithin the 31-day prediction window), and an average of 74.9% (SD = 4.3%) of active nonadherent \nusers (those whose last login occurred after the 31-day prediction window), with a mean false \npositive rate (FPR) of 49.5% (SD = 12.9%). These results highlight that the models were more \nadept at identifying nonadherent users who had already churned rather than those active or in the \nprocess of churning, as highlighted in Figure 6.7. Overall, nonadherence prediction models \nidentified an average of 85.9% (SD = 7.6%) of total nonadherent users from Month 2 to Month 6. \nThe feature importance analyses showed that daily session features (mean cumulated feature \nimportance = 0.73) were more important for nonadherence prediction models than daily activity \nfeatures (mean cumulated feature importance = 0.27). The importance of both session and activity \nfeatures tended to increase with their proximity to the prediction window (See Appendix 6.2). \n \n \n\n 16 \nTable 3: Nonadherence prediction model results on the Manoa test set for Months 2-6 based on data from \nprevious months among users who completed the initial measurement week (n = 419). \n \n \nMonth 2 \nMonth 3 \nMonth 4 \nMonth 5 \nMonth 6 \nPerformance \nAUC \n0.730 \n0.799 \n0.864 \n0.859 \n0.853 \n \nAccuracy \n0.702 \n0.742 \n0.788 \n0.811 \n0.807 \n \nF1 Score \n0.726 \n0.819 \n0.858 \n0.880 \n0.885 \n \nPrecision \n0.722 \n0.783 \n0.841 \n0.861 \n0.848 \n \nRecall \n0.731 \n0.860 \n0.876 \n0.901 \n0.926 \nTrue Positives \nTotal \n166 \n245 \n269 \n291 \n313 \n \nChurned \n42 \n92 \n144 \n186 \n227 \n \nChurning \n25 \n42 \n27 \n28 \n86 \n \nActive \n99 \n111 \n98 \n77 \n0 \nFalse Positives \nTotal \n64 \n68 \n51 \n47 \n56 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n \nChurning \n7 \n6 \n3 \n6 \n56 \n \nActive \n57 \n62 \n48 \n41 \n0 \nFalse Negatives \nTotal \n61 \n40 \n38 \n32 \n25 \n \nChurned \n1 \n8 \n2 \n3 \n3 \n \nChurning \n15 \n2 \n5 \n5 \n22 \n \nActive \n45 \n30 \n31 \n24 \n0 \nTrue Negatives \nTotal \n128 \n66 \n61 \n49 \n25 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n \nChurning \n2 \n6 \n3 \n4 \n25 \n \nActive \n126 \n60 \n58 \n45 \n0 \n \nTotal Churned \n43 \n100 \n146 \n189 \n230 \n \nTotal Churning \n49 \n56 \n38 \n43 \n189 \n \nTotal Active \n327 \n263 \n235 \n187 \n0 \n \nNonadherent Users \n227 \n285 \n307 \n323 \n338 \n \nAdherent Users \n192 \n134 \n112 \n96 \n81 \n \nAdherence Ratio \n0.458 \n0.320 \n0.267 \n0.229 \n0.193 \n \nClass Imbalance \n0.542 \n0.680 \n0.733 \n0.771 \n0.807 \n\n \n17 \n \nFigure 7: Monthly nonadherence prediction results among users who completed the initial measurement \nweek on Manoa test set (n = 419). \n \n \n \n0.50\n0.55\n0.60\n0.65\n0.70\n0.75\n0.80\n0.85\n0.90\n0.95\n1.00\n2\n3\n4\n5\n6\nAccuracy\nF1 Score\nAUC\nPrecision\nRecall\nClass Imbalance\n0\n50\n100\n150\n200\n250\n300\n350\n2\n3\n4\n5\n6\nTrue positives\nFalse positives\nFalse negatives\nTrue negatives\n0%\n5%\n10%\n15%\n20%\n25%\n30%\n35%\n40%\n45%\n50%\n55%\n60%\n65%\n70%\n75%\n80%\n85%\n90%\n95%\n100%\n2\n3\n4\n5\n6\nMonth\nTrue positive rate\nFalse positive rate\nPercentage of correctly identi\ufb01ed nonadherent churned users (last login before 31-day prediction window)\nPercentage of correctly identi\ufb01ed nonadherent churning users (last login within 31-day prediction window)\nPercentage of correctly identi\ufb01ed nonadherent active users (last login after 31-day prediction window)\n0\n50\n100\n150\n200\n250\n300\n350\n2\n3\n4\n5\n6\nNonadherent users\nCorrectly predicted nonadherent users (true positives)\nCorrectly predicted active nonadherent users (last login after 31-day prediction window)\nCorrectly predicted churning nonadherent users (last login during 31-day prediction window)\nCorrectly predicted churned nonadherent users (last login before 31-day prediction window)\nFalsely predicted nonadherent users (false positives)\n\n 18 \nChurn prediction models in Manoa achieved a mean AUC of 0.87 (SD = 0.04), a mean accuracy of \n0.83 (SD = 0.05), a mean F1-score of 0.81 (SD = 0.13), a mean precision of 0.78 (SD = 0.12), and \na mean recall of 0.86 (SD = 0.14) across all prediction points at the of Day 1 until 178, indicating \na robust ability to discriminate between churned and non-churned users. \nAs illustrated in Figure 6.8, all performance metrics showed a gradual improvement from Day 4 \n(AUC = 0.78, Accuracy = 0.78, F1-Score = 0.52, Precision = 0.55, Recall = 0.50) to Day 178 (AUC \n= 0.97, Accuracy = 0.95, F1-Score = 0.97, Precision = 0.97, Recall = 0.98). This trend reflects the \nmodel\u2019s increasing effectiveness in distinguishing between churned and non-churned users as more \ndata became available. Prediction accuracies on Days 1-3 did not significantly outperform the class \nimbalance, thereby offering limited utility in predicting churning users during these early days. \nAcross daily prediction time points starting at end of Day 1 to end of Day 178, the models correctly \nidentified an average of 88.2% (SD = 12.9%) of churned users (i.e. those whose last login occurred \nbefore the 7-day prediction window) and an average of 51.5% (SD = 18%) of churning users (i.e. \nthose whose last login occurred within the 7-day prediction window), with a mean false positive \nrate (FPR) of 25.8% (SD = 6.1%). This indicates that the models were more adept at identifying \nusers who had already churned compared to those in the process of churning, as highlighted in \nFigure 6.8. Overall, churn prediction models identified an average of 86% (SD = 14.3%) of total \nchurned users across all prediction windows. \nIn summary, churn prediction models demonstrated strong and improving performance throughout \nthe observation period. Detailed prediction results for each day are provided in Appendix 6.3, with \nprediction performances for selected weeks further highlighted in Table 6.4. \nThe feature importance evaluation showed that daily session features (mean cumulated feature \nimportance = 0.56) were more important for churn prediction models than daily activity features \n(mean cumulated feature importance = 0.44). The importance of both session and activity features \ntended to increase with their proximity to the prediction window (See Appendix 6.2). \n \n \n\n \n19 \nTable 4: Churn prediction results on the Manoa test set for selected weeks based on data from previous \nweeks (n = 1,335). \n \nWeek 2 \nWeek 3 \nWeek 4 \nWeek 6 \nWeek 8 \nWeek 10 \nWeek 12 \nWeek 14 \nWeek 16 \nWeek 18 \nWeek 20 \nWeek 22 \nWeek 24 \nWeek 26 \nPerformance \nAUC \n0.801 \n0.841 \n0.853 \n0.859 \n0.858 \n0.881 \n0.871 \n0.851 \n0.870 \n0.885 \n0.891 \n0.912 \n0.941 \n0.962 \n \nAccuracy \n0.781 \n0.782 \n0.787 \n0.778 \n0.799 \n0.828 \n0.819 \n0.813 \n0.825 \n0.852 \n0.861 \n0.897 \n0.915 \n0.938 \n \nF1 Score \n0.594 \n0.656 \n0.688 \n0.746 \n0.797 \n0.837 \n0.839 \n0.851 \n0.866 \n0.890 \n0.903 \n0.931 \n0.947 \n0.963 \n \nPrecision \n0.572 \n0.633 \n0.664 \n0.703 \n0.739 \n0.791 \n0.799 \n0.777 \n0.804 \n0.850 \n0.852 \n0.904 \n0.931 \n0.953 \n \nRecall \n0.617 \n0.681 \n0.714 \n0.794 \n0.864 \n0.888 \n0.883 \n0.942 \n0.938 \n0.934 \n0.961 \n0.960 \n0.963 \n0.974 \nTrue Positives \nTotal \n214 \n278 \n314 \n435 \n527 \n587 \n627 \n716 \n757 \n801 \n868 \n925 \n1003 \n1087 \n \nChurned \n192 \n263 \n305 \n425 \n513 \n572 \n621 \n706 \n747 \n781 \n851 \n901 \n968 \n1039 \n \nChurning \n22 \n15 \n9 \n10 \n14 \n15 \n6 \n10 \n10 \n20 \n17 \n24 \n35 \n48 \n \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nFalse Positives \nTotal \n160 \n161 \n159 \n184 \n186 \n155 \n158 \n206 \n184 \n141 \n151 \n98 \n74 \n54 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nActive \n160 \n161 \n159 \n184 \n186 \n155 \n158 \n206 \n184 \n141 \n151 \n98 \n74 \n54 \nFalse Negatives \nTotal \n133 \n130 \n126 \n113 \n83 \n74 \n83 \n44 \n50 \n57 \n35 \n39 \n39 \n29 \n \nChurned \n62 \n86 \n107 \n97 \n68 \n62 \n76 \n36 \n44 \n50 \n28 \n27 \n26 \n12 \n \nChurning \n71 \n44 \n19 \n16 \n15 \n12 \n7 \n8 \n6 \n7 \n7 \n12 \n13 \n17 \n \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nTrue Negatives \nTotal \n828 \n766 \n736 \n603 \n539 \n519 \n467 \n369 \n344 \n336 \n281 \n273 \n219 \n165 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nActive \n828 \n766 \n736 \n603 \n539 \n519 \n467 \n369 \n344 \n336 \n281 \n273 \n219 \n165 \n \nTotal Churned \n254 \n349 \n412 \n522 \n581 \n634 \n697 \n742 \n791 \n831 \n879 \n928 \n994 \n1051 \n \nTotal Churning \n93 \n59 \n28 \n26 \n29 \n27 \n13 \n18 \n16 \n27 \n24 \n36 \n48 \n65 \n \nTotal Active \n988 \n927 \n895 \n787 \n725 \n674 \n625 \n575 \n528 \n477 \n432 \n371 \n293 \n219 \n \nChurn Ratio \n0.260 \n0.306 \n0.330 \n0.410 \n0.457 \n0.495 \n0.532 \n0.569 \n0.604 \n0.643 \n0.676 \n0.722 \n0.781 \n0.836 \n \nClass Imbalance \n0.740 \n0.694 \n0.670 \n0.590 \n0.543 \n0.505 \n0.532 \n0.569 \n0.604 \n0.643 \n0.676 \n0.722 \n0.781 \n0.836 \n \n \n \n\n 20 \n \nFigure 8: Daily churn prediction results from Days 1-178 for the following weeks on Manoa test set (n = \n1,335). \n \n \n0.00\n0.05\n0.10\n0.15\n0.20\n0.25\n0.30\n0.35\n0.40\n0.45\n0.50\n0.55\n0.60\n0.65\n0.70\n0.75\n0.80\n0.85\n0.90\n0.95\n1.00\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\nAccuracy\nF1 Score\nAUC\nPrecision\nRecall\nClass Imbalance\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\nTrue positives\nFalse positives\nFalse negatives\nTrue negatives\n0%\n5%\n10%\n15%\n20%\n25%\n30%\n35%\n40%\n45%\n50%\n55%\n60%\n65%\n70%\n75%\n80%\n85%\n90%\n95%\n100%\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\nDay\nTrue positive rate\nFalse positive rate\nPercentage of correctly identi\ufb01ed churned users (last login before 7-day prediction window)\nPercentage of correctly identi\ufb01ed churning users (last login within 7-day prediction window)\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\nTotal churned users (last login before 7-day prediction window)\nCorrectly predicted churned users (last login before 7-day prediction window)\nTotal churning users (last login within 7-day prediction window)\nCorrectly predicted churning users (last login within 7-day prediction window)\nFalsely predicted churned users (False Positives)\n\n \n21 \n4 Discussion \n4.1 Predicting Nonadherence to Mobile Health Interventions \nOur findings show that nonadherence to mHealth interventions can be accurately predicted over \nextended program durations, both in terms of adherence relative to intended use as defined by \nSieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use). \nOur study thus extends prior research showing that methodologies effective in predicting churn \nduring the first week [21], remain applicable over longer durations of 90 and 186 days across two \ndistinct mHealth interventions to predict nonadherence. Given the conceptual link between churn \nand nonadherence \u2013 where fully disengaged users are inherently nonadherent \u2013 these results are \nintuitive. Our descriptive analysis further emphasizes this relationship, showing that the decline in \nadherence over time in Vivira and Manoa is largely driven by churn (i.e. users discontinuing \nentirely). \nWeekly nonadherence prediction models in Vivira achieved strong performance, correctly \nidentifying an average of 94.2% (SD = 4.2%, mean AUC = 0.95) of nonadherent users between \nWeeks 2 and 13, with an average false positive rate of 24.2% (SD = 2.9%). Interestingly, \nnonadherence prediction models outperformed churn prediction models in Vivira, as evidenced by \nconsistently higher performance metrics across all weeks. This can be partly attributed to the greater \nclass imbalance between adherent and nonadherent users compared to churned and retained users. \nHowever, the higher performance of nonadherence models even in the early weeks, when class \nimbalances were similar to those in churn prediction models, suggests that the methodologies are \nparticularly well-suited for predicting nonadherence relative to the intervention\u2019s intended use. \nIn contrast, the performance of nonadherence prediction models in Manoa was comparatively \nlower, correctly identifying an average of 86% (SD = 7.6%, mean AUC = 0.82) of nonadherent \nusers between Months 2 and 6 at a relatively higher false positive rate of 49.5% (SD = 12.9%). \nFeature importance analyses across all prediction models showed that behavioral app engagement \ndata collected closer to the prediction event had a stronger impact on model performance. This \nfinding aligns with previous research [5, 21, 29, 30] and explains the lower performance observed \nin Manoa, where nonadherence was measured monthly rather than weekly, limiting the availability \nof recent behavioral data near the prediction event. These results underscore the importance of using \ndata in close temporal proximity to the prediction event for improving model performance, \nhighlighting a critical consideration for the development of predictive models. \nDaily churn prediction models demonstrated nearly identical performance across the two \nindependent mHealth interventions, highlighting the generalizability of our approach. These models \ncorrectly identified an average of 84.4% (SD = 19.1%, mean AUC = 0.87) of users between Days \n1 and 82 in Vivira, and 86% (SD = 14.3%, mean AUC = 0.87) of users between Days 1 and 178 in \n\n 22 \nManoa, who recorded their last login before the end of the 7-day prediction window. False positive \nrates were comparably lower, averaging 29.2% (SD = 8.5%) in Vivira and 25.8% (SD = 6.1%) in \nManoa. \nOur results further show that the predictive performance of daily churn prediction models improves \nover time as more behavioral app engagement data becomes available. This finding aligns with \nprior research, which primarily focused on the early weeks of user engagement in mHealth \ninterventions [4, 20]. Notably, as class imbalances declined between Day 1 and Day 25 in Vivira \nand Day 1 and Day 72 in Manoa, and then increased thereafter, model performance continued to \nimprove. This trend underscores the robustness of these predictive models, which maintained high \naccuracy across these extended program durations from Day 4 forward. \nIn summary, our study reinforces the applicability of past behavioral app engagement data for \nmaking daily churn predictions over extended durations. This applicability has been suggested in \nprevious mHealth intervention studies [5, 21, 30] and across other app domains such as gaming, \neducation, and social media [7, 27, 36, 45, 51, 62, 65]. However, these prior studies generally \nfocused on predicting churn at specific points in time and shorter durations, whereas our study \ndemonstrates its applicability for daily predictions over extended program durations. Furthermore, \nthis study is the first to establish the applicability of these predictive methodologies for \nnonadherence relative to intended use, as defined by Sieverink et al. (2017). This distinction \nhighlights the broader utility of such models in the context of mHealth interventions. \n4.2 The Potential for Targeted Strategies to Promote Adherence \nFollowing evaluations by Jakob et al. (2024), we assessed how many users record another log in \nafter a correct nonadherence or churn prediction to shed light on the potential for targeted strategies \npromoting adherence before churn occurs. \nWhile nonadherence prediction models were more adept at identifying nonadherent users who had \nalready churned, they also correctly identified a substantial proportion of nonadherent users before \nthey recorded their last log in. In Vivira, weekly nonadherence prediction models correctly \nidentified an average of 98.3% (SD = 1.6%) of nonadherent users who recorded their last login \nbefore the 7-day prediction window, 82.6% (SD = 7.1%) of nonadherent users who recorded their \nlast login within the 7-day prediction window, and 83.4% (SD = 3.4%) of nonadherent users who \nrecorded their last login after the prediction window. In Manoa, monthly nonadherence prediction \nmodels correctly identified an average of 97.1% (SD = 2.9%) of nonadherent users who recorded \ntheir last login before the 31-day prediction window, 92% (SD = 5.2%) of users recording their last \nlogin within the 31-day prediction window, and 74.9% (SD = 4.3%) of nonadherent users who \nrecorded their last login after the 31-day prediction window. \n\n \n23 \nChurn prediction models identified churned users (those who recorded their last login before the 7-\nday prediction window) more accurately than churning users (those who recorded their last login \nwithin the 7-day prediction window). In Vivira, churn prediction models detected an average of \n88.1% (SD = 19.1%) of churned users and an average of 51.9% (SD = 14.7%) of churning across \ndaily prediction models from Day 1 until Day 84. In Manoa, churn prediction models detected \n88.2% (SD = 12.9%) of churned users and 51.5% (SD = 18%) of churning users across daily \npredictions from Day 1 until Day 178. \nThese findings highlight that nonadherence prediction models are not only effective in identifying \nusers who have already discontinued app use but can also detect users in the early stages of \ndisengagement. Since many of these users continue to log in after being flagged, such models \npresent an opportunity to implement targeted preventive strategies directly within the app. These \nstrategies could extend beyond win-back efforts delivered through external channels (e.g., emails, \nphone calls) and instead focus on proactive measures such as system adaptations (e.g., tailoring \npersuasive elements or adjusting behavioral goals), active in-app interventions (e.g., personalized \npush notifications), or the integration of complementary intervention components (e.g., monetary \nincentives or human support). Since some adherence-promoting strategies, such as monetary \nincentives or human support, incur utilization costs, selectively targeting users at the highest risk of \nnonadherence could also support the cost-effectiveness of mHealth interventions. \n4.3 Implications for Researchers and Providers \nThe results of our study demonstrate that ML algorithms, when paired with behavioral app \nengagement data, can generate actionable insights that may support targeted strategies for \npreventing nonadherence. One promising aspect is that the app activity features used in this study \n- daily app activity and daily session counts - are not only predictive but generally applicable to any \nmHealth intervention that uses a mobile app as a primary intervention component. Likewise, the \nnumber of daily completed exercises used in Vivira corresponds directly to the app\u2019s intended use. \nThis concept can be extended to various interventions: an educational app may leverage the daily \nnumber of completed quizzes, while a physical activity program could track the number of physical \nactivities logged, or a smoking cessation app might use the frequency of smoking diary entries as \nfeatures for their predictive model. \nPrevious research has consistently ranked features related to user app activity (e.g., daily logins) \nand app progress (e.g., completion of app-logged activities) as the most important for churn \nprediction in mHealth interventions [5, 21] and other app domains [7, 27, 29, 41, 56]. Our findings \nalign with these observations, with such behavioral features demonstrating high predictive accuracy \neven over extended prediction periods. \n\n 24 \nIn contrast, prior research on adherence to mHealth interventions has often emphasized contextual \nfactors over behavioral ones. Prior digital health studies typically focus on sociodemographic data \nsuch as age, gender, income, education, pre-existing health conditions, or specific intervention \ncomponents as predictors of adherence [20]. While these analyses can provide valuable insights, \nour results suggest that simple app engagement features, such as daily app activity, are sufficient to \npredict future user behavior. This finding is also consistent with previous churn prediction studies \nin DHIs, where adding contextual features to models already incorporating behavioral app \nengagement features did not improve predictive performance [5, 21]. \nConsequently, researchers and providers of mHealth interventions are encouraged to utilize \nbehavioral app engagement features when constructing nonadherence prediction models. This \napproach offers a novel perspective on nonadherence prediction, which may be uniquely enabled \nby digital interventions due to their capacity to collect objective and granular user behavior data, \nwhich is typically not available in non-digital interventions. \nMultiple ways exist to further optimize the presented prediction models. Prior research has shown \nthat different ML algorithms can yield varying outcomes, with some performing better across \ndifferent app contexts. Examples of algorithms frequently reported as best-performing for churn \nprediction across various app domains are Logistic Regression [5, 66], Random Forest [14, 21, 39, \n45, 59], Boosting models (e.g., XGBoost) [15, 35, 56, 57], and Recurrent Neural Networks (e.g., \nLSTMs) [22, 27, 29, 65]. Hence, researchers and developers are advised to evaluate multiple \nalgorithms to identify the most suitable ML algorithms for their specific classification problem, \nalso considering their complexity, which influences development time, resource consumption, and \nmodel explainability. \nFeature sets can also be expanded to include more intervention-specific attributes, such as social \nfeatures (e.g., number of interactions between users) in DHIs with social networking components \nor transactional features (e.g., number of in-app purchases) in DHIs with payment options, which \nhave been shown to enhance predictive performance in other app domains [30, 32, 39, 42, 56]. \nIncluding additional features may be particularly beneficial during the early stages when user \nactivity features may not yet provide a strong predictive signal, as highlighted by the insufficient \nperformance of our churn prediction models in the first three days. \nIn summary, our findings underscore the potential of generalizable, behavioral app engagement \ndata for the prediction of nonadherence in mHealth interventions. These features, combined with \naccessible ML algorithms, offer a practical foundation for researchers and providers of mHealth \ninterventions planning to develop and implement predictive models for nonadherence. \n\n \n25 \n4.4 Limitations and Future Work \nThis study used historical app engagement data to predict nonadherence. While the presented results \nare promising, prospective trials are necessary to fully establish the applicability of these models. \nNevertheless, our findings align with prior research on churn prediction in DHIs [5, 21]. Notably, \nthe strong performance of our models across two distinct mHealth interventions suggests that the \napproach \u2013 utilizing behavioral app engagement features in combination with ML algorithms \u2013 has \nthe potential to be widely applicable across various mHealth intervention contexts to predict \nnonadherence. \nOne potential limitation of the Vivira dataset is the exclusion of users who did not provide the \nrequired consent to use their engagement data for research purposes, thereby limiting the analysis \nto users who agreed to participate. If non-consenting users exhibit different behaviors, the model\u2019s \nperformance may not fully represent the broader population. However, since the same \nmethodologies achieved comparable results in the Manoa dataset \u2013 distinct in its regulatory \nenvironment, addressed medical condition, program duration, user demographics, and therapeutic \napproach - the applied methods are likely generalizable across different mHealth contexts and \npopulations. \nA shared characteristic of both analyzed mHealth interventions is that users must provide proof of \nmedical need to access the apps and receive reimbursement. User behavior in these contexts may \ndiffer from that observed in mHealth interventions directly available in public app stores, where \nusers can self-enroll. The two mHealth interventions, however, also differ in their onboarding \nchannels \u2013 the DiGA Vivira is either directly prescribed by a physician, or access is requested by a \npatient with a confirmed diagnosis, while Manoa is actively promoted by health insurance providers \n\u2013 supporting the generalizability of our findings across varied onboarding contexts. However, it is \nnoteworthy that the onboarding context, users\u2019 trust in the DHI provider, and users\u2019 health \nconditions may impact adherence [20]. Hence, another important aspect of the mHealth \ninterventions analyzed is their relatively high user retention rates. A study by Baumel et al. (2019) \ncompared average retention rates for mental health apps at approximately 10% on Day 7 and 3.3% \non Day 30 across 59 analyzed apps [2]. In contrast, the two mHealth interventions examined in this \nstudy demonstrated substantially higher retention rates: 32% and 53% on Day 7, and 19% and 27% \non Day 30, respectively. DHIs with substantially lower retention rates may face challenges in \napplying the methods described here, due to class imbalance between engaged and disengaged \nusers. This imbalance can complicate the training and evaluation of prediction models, highlighting \nthe need for further research in contexts where disengagement is more prevalent. \nThe nonadherence prediction models described in this study also rely on the availability of rich, \ncontinuous behavioral app engagement data. This dependency suggests that these models are most \neffective in systems designed for frequent, ongoing use, where user engagement data is naturally \n\n 26 \ngenerated over time. In settings where engagement data is sparse, the performance of prediction \nmodels may be diminished. Similarly, a 7-day prediction window, while ideal for timely \nintervention, may not be suitable for mHealth interventions that are not intended for regular weekly \nuse. Different methods and prediction windows may be required to capture user churn accurately \nfor interventions with more sporadic or irregular usage patterns. \nAn additional consideration is that our analyses were tied to a predefined observation window \nrelated to a full program duration of the apps. Although predictions were made from Day 1 through \nDay 84 and Day 1 through Day 178, using a 7-day prediction window to account for the entire \nperiod up to Days 90 and 186, respectively, a user who last logs in during the final week of the \ncycle may not be a \"churned\" user but rather a user who stops using the app because their program \nduration ends. Conversely, users who have not logged in for a month before the end of the \nobservation window can also start another program, indicating that churn status can be ambiguous \nat the end of fixed observation periods. \nWhile our results suggest that prediction models can accurately predict who is likely to become \nnonadherent and when, they do not provide insights into the underlying reasons why users \ndisengage. Nevertheless, they could serve as valuable research tools, detecting early signs of \nnonadherence and prompting targeted questionnaires to understand disengagement reasons before \na user is lost to follow-up. This approach could particularly help identifying factors driving \nnonadherence in real-world contexts, where once a user fully disengages, it becomes difficult to \ngather such information [47]. \nIt is also important to recognize that adherence to mHealth interventions is an intermediate outcome. \nThe primary aim of mHealth interventions is to facilitate health behavior change and ultimately \nimprove health outcomes. Although studies suggest a positive association between adherence and \nhealth outcomes, the link between adherence to mHealth interventions and health outcomes is not \nfully understood and needs further investigation in diverse settings [6]. While evidence from other \napp domains suggests that combining churn prediction models with preventive strategies \u2013 such as \npersonalized push notifications, omnichannel nudges, or content adaptations \u2013 can reduce churn \n[14, 33, 35], it also remains unclear whether this targeted approach can improve adherence and, \nultimately, health outcomes. Given the relatively new application of churn prediction models in \nmHealth interventions and this study being the first to comprehensively evaluate nonadherence \nprediction models aligned with the definition by Sieverink et al. (2017), significant research gaps \nremain. \nFuture research should aim to replicate nonadherence prediction models in diverse mHealth \ncontexts and evaluate their integration with targeted preventive strategies in prospective trials to \nassess the impact of these combined approaches on app usage, adherence, and health outcomes. \n\n \n27 \nAddressing these gaps will support the development of more adaptive and impactful mHealth \ninterventions and their potential to mitigate the rising prevalence and economic burden of NCDs. \nAuthor Contributions \nRJ \u2013 conceptualization, data curation, formal analysis, methodology, writing (original draft), \nwriting (review and editing), LB \u2013 data curation, writing (review and editing), CMS \u2013 writing \n(review and editing), FW \u2013 writing (review and editing), EF \u2013 supervision, TK \u2013 supervision, \nwriting (review and editing) \nEthics Declaration \nThis study involves the analyses of two anonymous datasets. Therefore, it does not qualify as human \nsubject research and has been exempted from ethics approval by the research team's University \nEthics Committee. A separate ethics vote for the use of the Vivira dataset was obtained from the \nState Physician Chamber of Baden-W\u00fcrttemberg, Germany, under the reference F-2024-072. \nConflicts of Interest \nRJ, FW, TK, and EF are affiliated with the Centre for Digital Health Interventions, a joint initiative \nof the Institute for Implementation Science in Health Care, University of Zurich; the Department \nof Management, Technology, and Economics at Swiss Federal Institute of Technology Zurich; and \nthe Institute of Technology Management and School of Medicine at the University of St. Gallen. \nThe Centre for Digital Health Interventions is funded in part by CSS, a Swiss health insurer; Uniqa, \nan Austrian health insurer; and MTIP, a Swiss digital health investor. However, neither CSS, Uniqa, \nMTIP were involved in this research. 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J Med Internet Res 25, (April 2023), e41926. \nhttps://doi.org/10.2196/41926 \n \n \n \n\n \n33 \nAppendix \n \nAppendix 1: Applied software packages and model hyperparameter grids \nAppendix 2: Feature Importance Analyses. \nAppendix 3: Daily Churn Prediction Results. \n \nTable 5: Descriptive statistics of users\u2019 number of active days and completed exercises per week in Vivira \n(n = 8,372). \n \nUsers\u2019 number of active days \n \nUsers\u2019 number of completed exercises \n \nDay \nMean (SD) Median (IQR) \nRange \nMean (SD) Median (IQR) \nRange \nWeek 1 (Days 1-7) \n3.14 (2.04) \n3 (1-5) \n0-7 \n \n9.68 (9.16) \n8 (0-16) \n0-29 \nWeek 2 (Days 8-14) \n2.01 (2.25) \n1 (0-4) \n0-7 \n \n7.41 (8.9) \n4 (0-12) \n0-29 \nWeek 3 (Days 15-21) \n1.67 (2.16) \n1 (0-3) \n0-7 \n \n6.2 (8.46) \n0 (0-12) \n0-29 \nWeek 4 (Days 22-28) \n1.45 (2.07) \n0 (0-2) \n0-7 \n \n5.39 (8.07) \n0 (0-8) \n0-29 \nWeek 5 (Days 29-35) \n1.27 (2.00) \n0 (0-2) \n0-7 \n \n4.74 (7.75) \n0 (0-8) \n0-29 \nWeek 6 (Days 36-42) \n1.11 (1.91) \n0 (0-1) \n0-7 \n \n4.15 (7.42) \n0 (0-4) \n0-33 \nWeek 7 (Days 43-49) \n1.00 (1.84) \n0 (0-1) \n0-7 \n \n3.75 (7.13) \n0 (0-4) \n0-32 \nWeek 8 (Days 50-56) \n0.92 (1.80) \n0 (0-1) \n0-7 \n \n3.46 (6.94) \n0 (0-4) \n0-28 \nWeek 9 (Days 57-63) \n0.82 (1.71) \n0 (0-1) \n0-7 \n \n3.05 (6.6) \n0 (0-1) \n0-29 \nWeek 10 (Days 64-70) \n0.69 (1.60) \n0 (0-0) \n0-7 \n \n2.55 (6.16) \n0 (0-0) \n0-28 \nWeek 11 (Days 71-77) \n0.57 (1.49) \n0 (0-0) \n0-7 \n \n2.08 (5.7) \n0 (0-0) \n0-28 \nWeek 12 (Days 78-84) \n0.50 (1.40) \n0 (0-0) \n0-7 \n \n1.82 (5.37) \n0 (0-0) \n0-28 \nWeek 13 (Days 85-90) \n0.38 (1.15) \n0 (0-0) \n0-6 \n \n1.35 (4.37) \n0 (0-0) \n0-25 \nWeeks 1-13 (Days 1-90) \n15.54 (19.55) \n7 (2-21) \n0-90 \n \n55.63 (76.54) \n20 (4-76.25) \n0-360 \nSD = standard deviation; IQR = interquartile range \n \n \nTable 6: Descriptive statistics of numerical outcomes in Vivira (n = 8,372). \n \nMean (SD) Median (IQR) \nRange \nDay of last login (among 90 days) \n38.53 (31.68) \n33 (7-67) \n0-90 \nWeek of last login (among 13 Weeks) \n5.99 (4.41) \n5 (1-10) \n0-13 \nNumber of active weeks (among 13 weeks) \n4.91 (3.98) \n3 (1-8) \n0-13 \nNumber of adherent weeks (among 13 weeks) \n3.1 (3.9) \n1 (0-5) \n0-13 \nSD = standard deviation; IQR = interquartile range \n \n \n \n \nTable 7: Total and relative number of active, churning, and adherent users per week in Vivira (n = 8,372). \n \nWeekly Activity \n \nWeek of Last Login \n \nWeekly Adherence \n \nn \n% \n \nn \n% \n \nn \n% \nNon-churned % \nWeek 1 (Days 1-7) \n8344 \n99.7% \n \n2107 \n25.0% \n \n4417 \n52.8% \n52.9% \nWeek 2 (Days 8-14) \n5136 \n61.3% \n \n716 \n8.8% \n \n3425 \n40.9% \n54.7% \nWeek 3 (Days 15-21) \n4322 \n51.6% \n \n544 \n6.5% \n \n2898 \n34.6% \n52.2% \nWeek 4 (Days 22-28) \n3840 \n45.9% \n \n503 \n6.0% \n \n2548 \n30.4% \n50.9% \nWeek 5 (Days 29-35) \n3385 \n40.4% \n \n476 \n5.7% \n \n2239 \n26.7% \n49.7% \nWeek 6 (Days 36-42) \n2954 \n35.3% \n \n425 \n5.1% \n \n1921 \n22.9% \n47.7% \nWeek 7 (Days 43-49) \n2716 \n32.4% \n \n383 \n4.6% \n \n1753 \n20.9% \n48.7% \nWeek 8 (Days 50-56) \n2438 \n29.1% \n \n421 \n5.0% \n \n1629 \n19.5% \n50.6% \nWeek 9 (Days 57-63) \n2229 \n26.6% \n \n488 \n5.8% \n \n1426 \n17.0% \n51.0% \nWeek 10 (Days 64-70) \n1833 \n21.9% \n \n437 \n5.2% \n \n1189 \n14.2% \n51.5% \nWeek 11 (Days 71-77) \n1506 \n18.0% \n \n351 \n4.2% \n \n970 \n11.6% \n51.8% \nWeek 12 (Days 78-84) \n1324 \n15.8% \n \n415 \n5.0% \n \n846 \n10.1% \n55.6% \nWeek 13 (Days 85-90) \n1106 \n13.2% \n \n1106 \n13.2% \n \n664 \n7.9% \n60.0% \n \nTable 8: Descriptive statistics of numerical outcomes in Manoa (n = 6,674). \nDay \nMean (SD) Median (IQR) \nRange \nDay of last login (among 186 days) \n88.8 (72) \n80 (13-169) \n1-186 \nWeek of last login (among 27 weeks) \n13.16 (10.25) \n12 (2-25) \n1-27 \nMonth of last login (among 6 months) \n3.36 (2.11) \n3 (1-6) \n1-6 \nNumber of active weeks (among 27 weeks) \n8.91 (9.06) \n4 (2-15) \n1-27 \nSD = standard deviation; IQR = interquartile range \n \n \n \n\n 34 \nTable 9: Descriptive statistics of users\u2019 number of active days and sessions per week in Manoa (n = 6,674). \n \nUsers\u2019 number of active days \n \nUsers\u2019 number of sessions \n \nDay \nMean (SD) Median (IQR) \nRange \nMean (SD) Median (IQR) \nRange \nWeek 1 (Days 1-7) \n4.09 (2.56) \n5 (1-7) \n0-7 \n \n13.47 (14.4) \n9 (2-21) \n0-247 \nWeek 2 (Days 8-14) \n2.98 (2.86) \n2 (0-6) \n0-7 \n \n8.57 (11.95) \n3 (0-14) \n0-130 \nWeek 3 (Days 15-21) \n2.32 (2.81) \n1 (0-5) \n0-7 \n \n6.09 (10.29) \n1 (0-9) \n0-133 \nWeek 4 (Days 22-28) \n1.97 (2.74) \n0 (0-4) \n0-7 \n \n5.14 (10.12) \n0 (0-7) \n0-257 \nWeek 5 (Days 29-35) \n1.87 (2.69) \n0 (0-4) \n0-7 \n \n4.80 (10.14) \n0 (0-6) \n0-311 \nWeek 6 (Days 36-42) \n1.94 (2.75) \n0 (0-4) \n0-7 \n \n5.36 (10.26) \n0 (0-7) \n0-189 \nWeek 7 (Days 43-49) \n1.93 (2.79) \n0 (0-4) \n0-7 \n \n5.25 (9.85) \n0 (0-8) \n0-225 \nWeek 8 (Days 50-56) \n1.71 (2.67) \n0 (0-3) \n0-7 \n \n4.31 (8.76) \n0 (0-5) \n0-131 \n \nWeek 9 (Days 57-63) \n1.55 (2.59) \n0 (0-2) \n0-7 \n \n3.76 (7.97) \n0 (0-3) \n0-96 \n \nWeek 10 (Days 64-70) \n1.44 (2.52) \n0 (0-2) \n0-7 \n \n3.44 (7.78) \n0 (0-2) \n0-129 \n \nWeek 11 (Days 71-77) \n1.46 (2.52) \n0 (0-2) \n0-7 \n \n3.68 (8.23) \n0 (0-3) \n0-145 \n \nWeek 12 (Days 78-84) \n1.44 (2.51) \n0 (0-2) \n0-7 \n \n3.62 (8.54) \n0 (0-3) \n0-250 \n \nWeek 13 (Days 85-91) \n1.37 (2.46) \n0 (0-1) \n0-7 \n \n3.30 (7.68) \n0 (0-2) \n0-130 \n \nWeek 14 (Days 85-98) \n1.29 (2.42) \n0 (0-1) \n0-7 \n \n2.99 (7.11) \n0 (0-1) \n0-131 \n \nWeek 15 (Days 85-105) \n1.22 (2.37) \n0 (0-1) \n0-7 \n \n2.82 (6.92) \n0 (0-1) \n0-108 \n \nWeek 16 (Days 106-112) \n1.21 (2.37) \n0 (0-1) \n0-7 \n \n2.87 (6.98) \n0 (0-1) \n0-103 \n \nWeek 17 (Days 113-119) \n1.18 (2.34) \n0 (0-1) \n0-7 \n \n2.82 (6.98) \n0 (0-1) \n0-108 \n \nWeek 18 (Days 120-126) \n1.15 (2.32) \n0 (0-0) \n0-7 \n \n2.67 (6.67) \n0 (0-0) \n0-87 \n \nWeek 19 (Days 127-133) \n1.09 (2.27) \n0 (0-0) \n0-7 \n \n2.47 (6.23) \n0 (0-0) \n0-80 \n \nWeek 20 (Days 134-140) \n1.04 (2.23) \n0 (0-0) \n0-7 \n \n2.29 (5.91) \n0 (0-0) \n0-57 \n \nWeek 21 (Days 141-147) \n1.04 (2.24) \n0 (0-0) \n0-7 \n \n2.39 (6.23) \n0 (0-0) \n0-60 \n \nWeek 22 (Days 148-154) \n1.03 (2.23) \n0 (0-0) \n0-7 \n \n2.37 (6.32) \n0 (0-0) \n0-93 \n \nWeek 23 (Days 155-161) \n0.98 (2.19) \n0 (0-0) \n0-7 \n \n2.23 (6.04) \n0 (0-0) \n0-71 \n \nWeek 24 (Days 162-168) \n0.95 (2.15) \n0 (0-0) \n0-7 \n \n2.14 (5.87) \n0 (0-0) \n0-62 \n \nWeek 25 (Days 169-175) \n0.93 (2.14) \n0 (0-0) \n0-7 \n \n2.08 (5.76) \n0 (0-0) \n0-62 \n \nWeek 26 (Days 176-182) \n0.91 (2.12) \n0 (0-0) \n0-7 \n \n2.04 (5.60) \n0 (0-0) \n0-69 \n \nWeek 27 (Days 183-186) \n0.51 (1.22) \n0 (0-0) \n0-4 \n \n1.16 (3.37) \n0 (0-0) \n0-48 \n \nWeeks 1-27 (Days 1-186) \n40.63 (54.88) \n13 (3-57) \n1-186 \n \n104.15 (175) \n28 (4-129) \n1-2801 \n \nSD = standard deviation; IQR = interquartile range \n \n \nTable 10: Total and relative number of active and churning users per week in Manoa (n = 6,674). \n \nWeekly Activity \n \nWeek of Last Login \n \n \nn \n% \n \nn \n% \n \nWeek 1 (Days 1-7) \n6274 \n94.0% \n \n1295 \n19.4% \n \nWeek 2 (Days 8-14) \n4324 \n64.8% \n \n442 \n6.6% \n \nWeek 3 (Days 15-21) \n3439 \n51.5% \n \n302 \n4.5% \n \nWeek 4 (Days 22-28) \n2913 \n43.6% \n \n162 \n2.4% \n \nWeek 5 (Days 29-35) \n2841 \n42.6% \n \n177 \n2.7% \n \nWeek 6 (Days 36-42) \n2769 \n41.5% \n \n188 \n2.8% \n \nWeek 7 (Days 43-49) \n2640 \n39.6% \n \n176 \n2.6% \n \nWeek 8 (Days 50-56) \n2427 \n36.4% \n \n159 \n2.4% \n \nWeek 9 (Days 57-63) \n2225 \n33.3% \n \n148 \n2.2% \n \nWeek 10 (Days 64-70) \n2103 \n31.5% \n \n127 \n1.9% \n \nWeek 11 (Days 71-77) \n2100 \n31.5% \n \n128 \n1.9% \n \nWeek 12 (Days 78-84) \n2054 \n30.8% \n \n111 \n1.7% \n \nWeek 13 (Days 85-91) \n1976 \n29.6% \n \n133 \n2.0% \n \nWeek 14 (Days 85-98) \n1854 \n27.8% \n \n126 \n1.9% \n \nWeek 15 (Days 85-105) \n1778 \n26.6% \n \n127 \n1.9% \n \nWeek 16 (Days 106-112) \n1765 \n26.4% \n \n136 \n2.0% \n \nWeek 17 (Days 113-119) \n1694 \n25.4% \n \n98 \n1.5% \n \nWeek 18 (Days 120-126) \n1633 \n24.5% \n \n124 \n1.9% \n \nWeek 19 (Days 127-133) \n1570 \n23.5% \n \n129 \n1.9% \n \nWeek 20 (Days 134-140) \n1490 \n22.3% \n \n110 \n1.6% \n \nWeek 21 (Days 141-147) \n1491 \n22.3% \n \n118 \n1.8% \n \nWeek 22 (Days 148-154) \n1476 \n22.1% \n \n153 \n2.3% \n \nWeek 23 (Days 155-161) \n1428 \n21.4% \n \n148 \n2.2% \n \nWeek 24 (Days 162-168) \n1364 \n20.4% \n \n182 \n2.7% \n \nWeek 25 (Days 169-175) \n1340 \n20.1% \n \n212 \n3.2% \n \nWeek 26 (Days 176-182) \n1324 \n19.8% \n \n286 \n4.3% \n \nWeek 27 (Days 183-186) \n1177 \n17.6% \n \n1177 \n17.6% \n \n \nTable 11: Descriptive statistics of adherence outcomes in Manoa among users who completed the initial \nmeasurement week (n = 2,095). \n \nUsers\u2019 number of completed measurement weeks \n \nMonthly Adherence \nDay \nMean (SD) \nMedian (IQR) \nRange \nn \n% \nNon-churned % \nMonth 2 \n0.46 (0.51) \n0 (0-1) \n0-3 \n962 \n45.9% \n50.8% \nMonth 3 \n0.33 (0.49) \n0 (0-1) \n0-3 \n672 \n32.1% \n41.5% \nMonth 4 \n0.27 (0.47) \n0 (0-1) \n0-5 \n558 \n26.6% \n39.4% \nMonth 5 \n0.23 (0.43) \n0 (0-0) \n0-5 \n479 \n22.9% \n40.1% \nMonth 6 \n0.19 (0.40) \n0 (0-0) \n0-1 \n404 \n19.3% \n41.4% \nSD = standard deviation; IQR = interquartile range \n \n\n", - "metadata": { - "title": "", - "author": "", - "creation_date": "D:20250507185055Z00'00'", - "page_count": "34" - }, - "images": [], - "tables": [ - { - "page": 1, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.06005859375, - 144.70460510253906 - ], - "text": "1 \nPredicting Nonadherence to Mobile Health Interventions \nPlanned Publication: Jakob R, Benning L, S\u00e9vin CM, Von Wangenheim F, Fleisch E, Kowatsch \nT. Predicting Nonadherence to Mobile Health Interventions. In: (2025) (In preparation) \n", - "caption": "" - }, - { - "page": 1, - "index": 2, - "bbox": [ - 90.0, - 182.47723388671875, - 526.2416381835938, - 606.464599609375 - ], - "text": "Abstract \nThe rising prevalence and economic burden of noncommunicable diseases (NCDs) present a \nsignificant challenge to patients and healthcare systems, calling for innovative, scalable, and cost-\neffective solutions. Mobile health (mHealth) interventions, facilitated by the ubiquity of \nsmartphones, have emerged as promising tools to support the prevention and management of NCDs. \nYet, despite growing evidence and availability, mHealth interventions face high nonadherence, \nwhere users fail to use these tools as intended or discontinue use entirely before achieving desired \noutcomes. The rich data collected by mHealth interventions raise the question of whether \u2013 and to \nwhat extent \u2013 nonadherence can be predicted using these data. If so, such predictions could inform \ntargeted strategies aimed at preventing nonadherence before it fully manifests. We developed \nmachine learning models for the prediction of nonadherence in two mHealth interventions, one for \nnonspecific and degenerative back pain over a program duration of 90 days (Vivira, n = 8,372), and \nanother for hypertension self-management over 186 days (Manoa, n = 6,674). Our models identified \nan average of 94% of nonadherent users between Weeks 2 and 13 in Vivira (mean AUC = 0.95), \ndefined as completing fewer than eight therapeutic exercises per week. In Manoa, our models \ncorrectly identified an average of 86% of nonadherent users between months 2 and 6 (mean AUC \n= 0.82), defined as completing fewer than one blood pressure measurement week per month. \nAdditionally, models predicting churn (users\u2019 last login within program duration) achieved mean \nAUCs of 0.87 for both apps, correctly identifying 84-86% of churned users. These findings \ndemonstrate that nonadherence can be accurately predicted, highlighting a unique and underutilized \nadvantage of mHealth interventions \u2013 the ability to transform app engagement data into actionable \ninsights that may inform targeted strategies to promote adherence. \n", - "caption": "" - }, - { - "page": 2, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 508.25225830078125, - 745.9046020507812 - ], - "text": "2 \n1 Introduction \nDigital health interventions (DHIs) have demonstrated the potential to assist patients and healthcare \nsystems in addressing the global rise and financial impact of noncommunicable diseases (NCDs), \nwhich are the leading causes of death and disability worldwide [16, 26, 44, 64]. In particular, mobile \nhealth (mHealth) interventions have emerged as versatile tools to promote behavior change among \npatients, improve health outcomes, and reduce healthcare costs due to the widespread availability \nof smartphones [8, 38, 49, 60]. A growing body of evidence suggests that mHealth interventions \ncan effectively support the prevention and management of NCDs by addressing modifiable risk \nfactors, including physical inactivity [52, 53], unhealthy diets [67], tobacco use [63], the harmful \nuse of alcohol [12] and metabolic risk factors such as obesity [52], hypertension [1], and \nhyperglycemia [19]. Furthermore, mHealth interventions show promise in improving mental health \nconditions, including depression and anxiety [10, 31]. In light of this, first healthcare systems with \npopulation-wide coverage now include mHealth interventions as reimbursable items in patient \ncoverage, and prescriptions for mHealth interventions are starting to become a more common part \nof treatment plans and health insurance benefits packages [13, 25, 48, 58]. In Germany, for example, \nmHealth interventions have been introduced as prescription digital therapies (PDT) to the \ncollectively funded statutory health insurance (SHI) system with scalable reimbursement strategies \nunder the German Digital Healthcare Act in 2019 [46]. While the SHI system provides coverage \nfor approximately 90% of the German population, a private health insurance (PHI) system covers \nthe remaining proportion of the coverage to meet the legal requirement of universal health insurance \ncoverage of the German population [4]. Similar to the SHI system, the PHI system is exploring \noptions of digital care and is expanding its spectrum of PDT solutions. \nHowever, despite the increasing availability and demonstrated efficacy of mHealth interventions, a \nsubstantial proportion of users fail to adhere to these programs as intended or disengage \nprematurely, posing a major barrier to their effectiveness [9, 18]. Although the specific metrics used \nto quantify adherence vary across DHI studies, there is broad consensus that adherence is \nsuboptimal. A systematic review focusing on mHealth interventions for chronic diseases (17 \nstudies) reported a pooled dropout rate of 43%, with observational studies exhibiting a higher rate \n(49%) compared to more controlled randomized controlled trials (40%) [34]. Another review of 83 \nweb-based interventions concluded that only half of users adhered to the interventions [24]. A study \nevaluating user engagement with 59 mental health apps found that fewer than 10% of users logged \nin daily after seven days, and by Day 15, the median open rate had declined to 3.9% [2]. Additional \nresearch corroborates these findings, with up to 80% of users of mHealth interventions engaging \nonly at a minimal level, logging into mHealth apps no more than once, and failing to use the apps \nconsistently over extended periods [11, 34, 43]. \n", - "caption": "" - }, - { - "page": 3, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.2837524414062, - 735.8245849609375 - ], - "text": "3 \nThe growing body of literature on mHealth interventions also highlights the need for more rigorous \napproaches to measuring adherence to DHIs. Sieverink et al. (2017) noted that adherence remains \nan underdeveloped and often improperly utilized concept in the existing literature [50]. Their \nreview of 62 studies revealed that 34 studies operationalized adherence as \u201cthe more use, the better,\u201d \nwhile only 28 studies established a clear threshold for the intended use of the technology. To \nenhance the comparability and reliability of adherence measurements, they proposed that future \nstudies should define adherence based on a specific threshold aligned with the technology\u2019s \nintended use, also ensuring that this intended use is clearly defined and justified [50]. This definition \ndistinguishes nonadherence from the concept of churn \u2013 also referred to as dropout, abandonment, \nor attrition \u2013 which is considered a key indicator for the adoption and success of mobile applications \n[17, 35]. Churn refers to a user discontinuing use of a mobile application entirely and is thus closely \nrelated to nonadherence and can be viewed as a severe form of nonadherence. \nWhile the prediction of nonadherence in accordance with the definition of Sieverink et al. (2017) \nconstitutes a research gap, a growing body of research suggests that churn in mHealth interventions \ncan be accurately predicted based on behavioral app engagement data (e.g., app login or session \ncounts) [5, 14, 30, 37, 40, 57]. However, existing studies have predicted churn only at specific \npoints in time, leaving limited evidence on prediction performances at different stages of the user \njourney. Likewise, it is unclear to what extent prediction models can predict churn before users \nfully disengage. Addressing this research gap, Jakob et al. (2024) recently predicted churn at the \nend of each day of users\u2019 initial week of usage in a public digital weight loss intervention, also \ntesting a wide range of features and ML algorithms. The best-performing model in this study was a \nrandom forest model that only used daily login counts, achieving an F1 score of 0.87 (AUC = 0.73, \nAccuracy = 81%) on Day 7 and an average F1 score of 0.84 over the initial week (mean AUC = \n0.71, mean Accuracy = 0.76). Churn prediction models in this study improved in terms of F1, AUC, \nand Accuracy from Day 1 to Day 7, hinting at a progressive improvement in churn prediction over \ntime. Notably, the study also analyzed the number of users who reengaged with the app after a \ncorrect churn prediction to assess the potential of in-app interventions to prevent churn. The best-\nperforming model correctly predicted an average of 93% of churned users over the initial week with \na mean false positive rate of 58.7%, and correctly predicted 81% of churned users who would \nreengage after a correct churn prediction. These findings suggest that prediction models may not \nonly serve as early detection tools but can provide actionable insights for the delivery of targeted \nstrategies to promote adherence before users fully disengage. However, the study also noted a \nshortcoming regarding a restricted focus on the initial week of app usage, calling for further studies \nin diverse settings with extended observation windows to assess how the predictive performance \nand the potential for mitigation strategies evolve over extended periods [21]. \n", - "caption": "" - }, - { - "page": 4, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 508.2543029785156, - 763.1845703125 - ], - "text": "4 \nThis study addresses this research gap by evaluating nonadherence prediction models in two distinct \nmHealth interventions (Vivira and Manoa) across different regulatory environments (i.e. SHI and \nPHI), medical conditions addressed (i.e. nonspecific back pain and arterial hypertension), program \ndurations (i.e. 90 and 186 days), user demographics, as well as different therapeutic approaches (i.e. \nguided movement therapy and personalized hypertension self-management). To provide a \ncomprehensive perspective, we predict nonadherence relative to the intended use of the \ninterventions, following the definition by Sieverink et al. (2017), as well as its most severe form: \nchurn (i.e., complete discontinuation). Building on Jakob et al. (2024), we also examine the \nproportion of users who reengage after a correct prediction, offering insights into the potential for \ntargeted strategies to promote adherence before churn occurs. Through these analyses, we aim to \ndetermine the extent to which nonadherence to mHealth interventions can be predicted. \n2 Methods \n2.1 Datasets and Definitions of Nonadherence \n2.1.1 Vivira \nThe first anonymized dataset was collected from \u201cVivira\u201d, a CE-marked Medical Device \nRegulation risk class I mHealth intervention for nonspecific and degenerative backpain which is \napproved as a \u201cDigitale Gesundheitsanwendung\u201d (DiGA) in the regulatory environment for PDTs \nin the German SHI system [46]. As a DiGA, the medical effectiveness of Vivira was confirmed by \nthe German Federal Institute for Medicines and Medical Devices (Bundesinstitut f\u00fcr Arzneimittel \nund Medizinprodukte, BfArM) , a regulatory body, based on a confirmatory study [61]. Further \nresearch supports the real-world effectiveness of Vivira [54, 55]. Under DiGA regulations, Vivira \nis available to all patients covered by the SHI system, but only accessible upon prescription by a \nqualified healthcare professional or upon a patient providing proof of medical need to their \nrespective health insurance. A DiGA prescription for Vivira allows full access to the respective \nDiGA for 90 days. The Vivira app provides individualized and guided exercises for asynchronous \nand decentralized movement therapy. Educational content (e.g., exercise introductions, annotated \nvideo cues, information on the medical condition, and therapy principles) and behavioral cues (e.g., \nvisualizations of therapy progress, reminders) prompt patients to interact with the app and support \nretention. The program aims to improve pain, mobility, strength, and coordination. Vivira is \navailable for iOS and Android devices. \nThe use of DiGA data is strictly limited. Therefore, only users who provided consent under Article \n4, Section 2, 4 of the DiGA regulations (DiGA-Verordnung, DiGAV) were included. The dataset \nincluded data from 8,372 users who activated their first prescription between January 1, 2023, and \nMarch 31, 2024. Of the included users who provided information on their gender (98%, \n8,208/8,372), 68.3% were female, 31.6% were male, and 0.1% non-binary. Among users who \n", - "caption": "" - }, - { - "page": 5, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.2328491210938, - 750.464599609375 - ], - "text": "5 \nprovided information on their age range (99%, 8,251/8,372), 25% were aged 18\u201335 years, 23% \nwere 36\u201345 years, 24% were 46\u201355 years, 21.1% were 56\u201365 years, 6.4% were 66\u201375 years, and \n1% were over 75 years old. \nFor each user, binary daily activity data (active or inactive) was available based on whether the user \ntriggered any app event on any day during their first 90-day prescription period. The dataset also \nincluded the number of exercises completed by each user on each day during the 90-day prescription \nperiod. \nThe intended use of Vivira involves completing weekly exercises. The instruction for use \nrecommends at least three sessions per week, each consisting of four independent exercises with \naccompanying audiovisual instructions and guidance. Yet, using a propensity score matching \napproach, Klingenberg et al. (2023) illustrated that a significantly higher probability of achieving a \nclinically meaningful improvement of pain intensity by the time of churn is already achieved by \nusers who complete at least two sessions per week [28]. We therefore deem a minimum exercise \nfrequency of two sessions (i.e. eight exercises) per week the minimum adherence to yield clinically \nrelevant therapeutic benefits from the use of Vivira. Therefore, adherence was measured weekly, \ndefined as completing eight or more exercises per week. Users with fewer than eight exercises per \nweek were considered nonadherent. \n2.1.2 Manoa \nThe second anonymized dataset was collected from \"Manoa,\" a mHealth intervention designed for \nhypertension self-management. The Manoa app features a conversational agent (CA; a chatbot \nsimulating conversation with human users) that collects user data and provides guidance on various \nhealth aspects, including lifestyle choices and medication adherence. Manoa is accessible to \ncustomers of seven of the ten largest private health insurance companies in the German and two of \nthe ten largest in the Swiss PHI system. Manoa is available to individuals diagnosed with \nhypertension. Participating health insurance companies provide interested users with access codes \nfor full six-month access. The app's core feature is a blood pressure diary. The CA offers \npersonalized feedback on blood pressure levels based on a structured home blood pressure \nmonitoring protocol, which has been associated with a reduction in average blood pressure and \nimprovement in blood pressure control [3]. The app recommends scheduling a doctor's appointment \nif blood pressure levels are elevated. Users can share their blood pressure report digitally with their \ndoctor or export it as a PDF. Manoa promotes regular monitoring through reminders, tracking, and \nmotivational messages from the CA. Blood pressure data can be entered manually, scanned using \nthe smartphone's camera, or imported directly from compatible measurement devices. Users are \nprompted to measure their blood pressure twice daily for six days during each home blood pressure \nmonitoring period. Manoa is available for iOS and Android devices. \n", - "caption": "" - }, - { - "page": 6, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 508.2460021972656, - 743.5045776367188 - ], - "text": "6 \nThe dataset included data from a total of 6,674 users. Of the included users who provided \ninformation on their gender (82.8%; 5,523/6,674), 21.7% were female and 78.3% were male. \nAmong users who provided information on their age range (83.1%; 5,548/6,674), 0.9% were aged \n18\u201329 years, 6.1% were 30\u201344 years, 16.8% were 45\u201354 years, 34% were 55\u201364 years, 32.8% were \n65\u201374 years, and 9.4% were 75 years or older. \nFor each user, the number of daily app sessions (continuous) during the 186-day program duration \nwas available from which binary daily activity data (active or inactive) could be derived. \nThe intended use of Manoa aligns with medical guidelines from the European Society of \nHypertension (ESH) and European Society of Cardiology (ESC), recommending blood pressure \nmeasurements for 3-6 days once a month. After the initial period in the first month, the app sends \nperiodic reminders every four weeks to continue monitoring with users being able adjust the timing \nof their home blood pressure monitoring periods. Adherence was measured monthly from months \n2 to 6 after the initial period, defined as completing at least one measurement week per month \nconsisting of at least 3 within 7 days with morning and evening measurements. Users who did not \ncomplete a measurement week per month were considered nonadherent. \n2.2 Feature Selection, Model Training, and Evaluation \nAligning with previous churn prediction studies that achieved accurate prediction results with daily \nbehavioral app engagement features in the initial week of usage in DHIs for weight loss and \nsmoking cessation [5, 21], we selected generalizable daily app engagement features related to users' \nactivity and progress with the DHIs in this study. This choice is also consistent with churn \nprediction studies in other app domains, which have ranked features related to user app activity \n(e.g., daily logins) and app progress (e.g., completion of app-logged activities) as the most \nimportant features [7, 27, 29, 41, 56]. \nIn Vivira, we predicted nonadherence weekly from Weeks 2 to 13 based on users\u2019 daily app activity \nvariables (active or inactive) and the daily number of completed exercises variables (continuous) \nof the preceding weeks. To illustrate this with an example, for the prediction of nonadherence in \nWeek 2 (a user completing fewer than 8 exercises between Days 8 to 14), we utilized users\u2019 daily \napp activity variables from Days 1 until 7 and the daily number of completed exercises from Days \n1 until 7. \nIn Manoa, we predicted nonadherence monthly from Months 2 to 6 based on users\u2019 daily app \nactivity variables (active or inactive) and the number of daily app sessions (continuous) of the \npreceding months. To illustrate this with an example, for the prediction of nonadherence in Month \n2 (a user completing no measurement week between Days 32 to 63), we utilized users\u2019 daily app \nactivity variables from Days 1 until 31 and the number of daily app sessions from Days 1 until 31. \n", - "caption": "" - }, - { - "page": 7, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.2607421875, - 702.944580078125 - ], - "text": "7 \nAdditionally, we predicted churn (users\u2019 last login within program duration) daily in Vivira (at the \nend of Days 1 to 82) and Manoa (at the end of Days 1 until 178) with a 7-Day prediction window \nbased on features from previous days. To illustrate this with an example, for the prediction of churn \nat the end of Day 14, we predicted if the users\u2019 last login occurs until the end of Day 21 based on \ninput features from Days 1 until 14. The choice of a 7-day prediction window was based on the \nDHIs\u2019 intended weekly use and seven days of inactivity, which are commonly used to define churn \nin other churn prediction studies [7, 23, 33, 36, 57, 62, 65]. \nConsistent with the aforementioned study by Jakob et al. (2024), we applied random forest \nalgorithms for all predictions [21]. This choice is further supported by prediction studies in other \napp domains that achieved superior performance with random forest models across multiple tested \nML algorithms [14, 21, 39, 45, 59]. We preprocessed available features by normalizing numerical \ndata by performing square root scaling for right-skewed data and standard scaling for non-right-\nskewed data. For nonadherence prediction in Vivira and churn prediction in both mHealth \ninterventions, we randomly split 80% of the dataset into a training set (Vivira = 6,697, Manoa = \n5,339) and the remaining 20% into a test set (Vivira = 1,675, Manoa = 1,335). For nonadherence \nprediction in Manoa, we only included users who completed the initial blood pressure measurement \nweek (n = 2,095), resulting in 1,676 users in the training set and 419 users in the test set. \nWe applied stratified 10-fold cross-validation and randomized search for hyperparameter tuning \non the training sets, optimizing for F1 score for all models. Additionally, we applied Tomek Links \nundersampling to the training data to address class imbalance class before cross-validation [15]. \nDetailed hyperparameter grids are provided in Appendix 6.1. \nWe evaluated model performance using F1 score, area under the curve (AUC), accuracy, precision, \nand recall. We also conducted feature importance analyses for each model to compare which \nfeatures contribute the most to their predictive performance. \nTo assess the intervention potential, we further categorized and distinguished between \u201cchurned \nusers\u201d (last login before prediction window), \u201cchurning users\u201d (last login within prediction \nwindow), and \u201cactive users\u201d (last login after prediction window). To illustrate this with an example, \nfor a prediction at the end of Day 7 for the prediction window between Days 8 and 14, a user with \na last login on Day 6 is considered \u201cchurned\u201d, a user with a last login on Day 12 is considered \n\u201cchurning\u201d, and a user with a last login on day 15 is considered \u201cactive\u201d. \nWe used freely available Python packages listed in Appendix 6.1 for data manipulation, analysis, \nmodeling, and evaluation. \n", - "caption": "" - }, - { - "page": 8, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 508.1951904296875, - 386.8646240234375 - ], - "text": "8 \n3 Results \n3.1 Descriptive Results \n3.1.1 Vivira \nVivira users were active on an average of 15.5 days (SD = 19.6) and completed an average of 55.6 \nexercises (SD = 76.5) during the 90-day program duration. The mean last day of logging into the \napp across all users was 38.5 (SD = 31.7). On a weekly basis, users were active for an average of \n4.9 weeks (SD = 4.0) and adherent for an average of 3.1 weeks (SD = 3.9) throughout the \nobservation period. The mean number of weekly active days decreased from 3.1 (SD = 2.0) in Week \n1 to 0.4 (SD = 1.2) in Week 13, while the mean number of completed exercises decreased from 9.7 \n(SD = 9.2) in Week 1 to 1.4 (SD = 4.4) in Week 13, indicating a progressive decrease in user \nengagement over time. Detailed descriptive statistics are provided in Appendix Tables 6.5 to 6.7. \nAs highlighted in Figure 6.1, user churn was most severe at the beginning of the program with \napproximately 25% of users recording their last login during the first week and 8.8% in the second \nweek. From Week 3 through Week 12, the relative churn rate remained relatively stable, within \n4.2% to 6.5% of users logging their last activity in each respective week until Week 13, in which \n13.2% of users remained active and thus recorded their last login in the final week. \n", - "caption": "" - }, - { - "page": 8, - "index": 2, - "bbox": [ - 79.69483184814453, - 592.9386596679688, - 500.12908935546875, - 615.6172485351562 - ], - "text": "Figure 1: Percentages of daily active users and cumulative percentage of users\u2019 last login across 90-Day \nprogram duration in Vivira (n = 8,372). \n", - "caption": "" - }, - { - "page": 8, - "index": 3, - "bbox": [ - 73.9134292602539, - 426.9935302734375, - 501.60870361328125, - 576.5914916992188 - ], - "text": "0%\n10%\n20%\n30%\n40%\n50%\n60%\n70%\n80%\n90%\n100%\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\nDay\nPercentage of daily active users (retention rate)\nCumulative percentage of users' last login (cumulative churn rate)\n", - "caption": "" - }, - { - "page": 9, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.103759765625, - 160.54461669921875 - ], - "text": "9 \nAs highlighted in Figure 6.2, weekly adherence (at least 8 exercises completed per week), gradually \ndeclined from 53% in Week 1 to 7.9% in Week 13. However, when examining only active users \n(those who recorded their last login within or after a respective week), the percentage of adherent \nusers remained relatively stable, ranging between 48% and 60% across the 13 weeks, suggesting \nthat the decline in overall adherence can be attributed to users discontinuing entirely. \n", - "caption": "" - }, - { - "page": 9, - "index": 2, - "bbox": [ - 153.87158203125, - 344.163330078125, - 526.2000122070312, - 365.7772521972656 - ], - "text": " \nFigure 2: Percentages of adherent Vivira users in weeks 1 to 13 (n = 8,372). \nWeek\n", - "caption": "" - }, - { - "page": 9, - "index": 3, - "bbox": [ - 90.0, - 200.67352294921875, - 526.2416381835938, - 662.3846435546875 - ], - "text": " \nFigure 2: Percentages of adherent Vivira users in weeks 1 to 13 (n = 8,372). \n3.1.2 Manoa \nManoa users were active on an average of 40.6 days (SD = 54.9) and started an average of 104.2 \nsessions (SD = 175) during the 186-day program duration. The mean last day of logging into the \napp across all users was 88.8 (SD = 72). Users were active for an average of 13.2 weeks (SD = \n10.3) and an average of 3.4 months (SD = 2.1) throughout the observation period. The mean number \nof weekly active days decreased from 4.1 (SD = 2.6) in Week 1 to 0.5 (SD = 1.2) in Week 27, while \nthe mean number sessions decreased from 13.5 (SD = 14.4) in Week 1 to 1.2 (SD = 3.4) in Week \n27, indicating a progressive decrease in user engagement over time. Detailed descriptive statistics \nare provided in Appendix Tables 6.8 to 6.11. \nAs highlighted in Figure 6.3, user churn was most severe at the beginning of the program with \n19.4% of users recording their last login during the first week, and 6.6% in the second week. From \nWeek 3 through Week 26, the relative churn rate remained relatively stable, within 1.5% to 4.5% \nof users logging their last activity in each respective week until Week 27, in which 17.6% of users \nremained active and thus recorded their last login in the final week. \n0%\n10%\n20%\n30%\n40%\n50%\n60%\n70%\n80%\n90%\n100%\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\nWeek\nPercentage of adherent users\nPercentage of adherent users among active users (last login within or after respective week)\n", - "caption": "" - }, - { - "page": 10, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 84.76000213623047, - 47.984615325927734 - ], - "text": "10 \n", - "caption": "" - }, - { - "page": 10, - "index": 2, - "bbox": [ - 72.0, - 224.29869079589844, - 508.2328186035156, - 576.0172729492188 - ], - "text": "Figure 3: Percentages of daily active users and cumulative percentage of users\u2019 last login across 186-Day \nprogram duration in Manoa (n = 6,674). \nManoa users who completed the initial measurement in the first month (n = 2,095) continued being \nadherent (at least one completed measurement week per month) on an average of 1.49 months (SD \n= 1.69; median = 1, IQR = 0-3, Range = 0-16) in the following 5 months. As highlighted in Figure \n6.4, monthly adherence gradually declined from 45.9% in Month 2 to 19.3% in Month 6. However, \nwhen examining only active users (those who recorded their last login within or after a respective \nmonth), the percentage of adherent users remained stable between months 3 and 6, ranging between \n39.4% and 41.5% only being relatively higher in the second month at 50.8%. \n \nFigure 4: Percentages of adherent users in months 2 to 6 among Manoa users who completed the initial \nmeasurement week (n = 2,095). \n0%\n10%\n20%\n30%\n40%\n50%\n60%\n70%\n80%\n90%\n100%\n2\n3\n4\n5\n6\nMonth\nPercentage of adherent users\nPercentage of adherent users among active users (last login within or after respective month)\n", - "caption": "" - }, - { - "page": 10, - "index": 3, - "bbox": [ - 72.0, - 74.56171417236328, - 508.24383544921875, - 739.4246215820312 - ], - "text": " \nFigure 3: Percentages of daily active users and cumulative percentage of users\u2019 last login across 186-Day \nprogram duration in Manoa (n = 6,674). \nManoa users who completed the initial measurement in the first month (n = 2,095) continued being \nadherent (at least one completed measurement week per month) on an average of 1.49 months (SD \n= 1.69; median = 1, IQR = 0-3, Range = 0-16) in the following 5 months. As highlighted in Figure \n6.4, monthly adherence gradually declined from 45.9% in Month 2 to 19.3% in Month 6. However, \nwhen examining only active users (those who recorded their last login within or after a respective \nmonth), the percentage of adherent users remained stable between months 3 and 6, ranging between \n39.4% and 41.5% only being relatively higher in the second month at 50.8%. \n \nFigure 4: Percentages of adherent users in months 2 to 6 among Manoa users who completed the initial \nmeasurement week (n = 2,095). \n3.2 Prediction Results \n3.2.1 Vivira \nWeekly nonadherence prediction models in Vivira demonstrated strong performances across all \nprediction windows. Table 6.1 presents detailed results for each prediction week, and Figure 6.5 \nfurther illustrates prediction performance trends for the prediction window from Week 2 to 13. \nAUC ranged from 0.89 in Week 2 to 0.99 in Week 13. Similarly, accuracy improved steadily from \n0.83 in Week 2 to 0.97 in Week 13. F1-score likewise increased across weeks, ranging from 0.86 \n0%\n10%\n20%\n30%\n40%\n50%\n60%\n70%\n80%\n90%\n100%\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\n179\n180\n181\n182\n183\n184\n185\n186\nDay\nPercentage of daily active users (retention rate)\nCumulative percentage of users' last login (cumulative churn rate)\n0%\n10%\n20%\n30%\n40%\n50%\n60%\n70%\n80%\n90%\n100%\n2\n3\n4\n5\n6\nMonth\nPercentage of adherent users\nPercentage of adherent users among active users (last login within or after respective month)\n", - "caption": "1 presents detailed results for each prediction week, and Figure 6.5 \nfurther illustrates prediction performance trends for the prediction window from Week 2 to 13. \nAUC ranged from 0.89 in Week 2 to 0.99 in Week 13. Similarly, accuracy improved steadily from \n0.83 in Week 2 to 0.97 in Week 13. F1-score likewise increased across weeks, ranging from 0.86 \n0%\n10%\n20%\n30%\n40%\n50%\n60%\n70%\n80%\n90%\n100%\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\n179\n180\n181\n182\n183\n184\n185\n186\nDay\nPercentage of daily active users (retention rate)\nCumulative percentage of users' last login (cumulative churn rate)\n0%\n10%\n20%\n30%\n40%\n50%\n60%\n70%\n80%\n90%\n100%\n2\n3\n4\n5\n6\nMonth\nPercentage of adherent users\nPercentage of adherent users among active users (last login within or after respective month)" - }, - { - "page": 11, - "index": 1, - "bbox": [ - 72.47997283935547, - 35.0433349609375, - 537.1900634765625, - 762.7833251953125 - ], - "text": " \n11 \nin Week 2 to 0.98 in Week 13. Precision and recall also gradually increased, with precision varying \nbetween 0.85 and 0.98 and recall ranging from 0.87 to 0.99. \nAcross the prediction windows from Week 2 to Week 13, the models achieved a mean AUC of 0.95 \n(SD = 0.02), a mean accuracy of 0.91 (SD = 0.04), a mean F1-score of 0.94 (SD = 0.04), a mean \nprecision of 0.93 (SD = 0.04), and a mean recall of 0.94 (SD = 0.04), indicating the models' ability \nto accurately identify nonadherent users. \nBetween Weeks 2 and 13, the models correctly identified an average of 98.3% (SD = 2.9%) of \nchurned nonadherent users (those whose last login occurred before the 7-day prediction window), \nan average of 82.6% (SD = 7.1%) of churning nonadherent users (those whose last login occurred \nwithin the 7-day prediction window), and an average of 83.4% (SD = 3.4%) of active nonadherent \nusers (those whose last login occurred after the 7-day prediction window), with a mean false \npositive rate of 24.2% (SD = 2.9%). This indicates that the models were more adept at identifying \nnonadherent users who had already churned than those active or in the process of churning, as \nhighlighted in Figure 6.5. Overall, nonadherence prediction models identified an average of 94.2% \n(SD = 4.2%) of total nonadherent users from Week 2 to Week 13. \nA detailed evaluation of feature importances across all prediction models can be found in Appendix \n6.2. The evaluation revealed that daily exercise features (mean cumulated feature importance = \n0.59) were more important for nonadherence prediction models than daily activity features (mean \ncumulated feature importance = 0.41) in each individual week. The importance of both activity and \nexercise features tended to increase with their proximity to the prediction window (See Appendix \n6.2). \nTable 1: Nonadherence prediction model results on the Vivira test set for Weeks 2-13 based on data from \nprevious weeks (n = 1,675). \n \n \nWeek 2 \nWeek 3 \nWeek 4 \nWeek 5 \nWeek 6 \nWeek 7 \nWeek 8 \nWeek 9 \nWeek 10 \nWeek 11 \nWeek 12 \nWeek 13 \nPerformance \nAUC \n0.893 \n0.920 \n0.928 \n0.946 \n0.944 \n0.949 \n0.966 \n0.954 \n0.970 \n0.975 \n0.978 \n0.987 \nAccuracy \n0.827 \n0.847 \n0.863 \n0.891 \n0.896 \n0.916 \n0.923 \n0.933 \n0.932 \n0.948 \n0.954 \n0.971 \nF1 Score \n0.855 \n0.883 \n0.901 \n0.926 \n0.932 \n0.947 \n0.953 \n0.960 \n0.961 \n0.971 \n0.975 \n0.985 \nPrecision \n0.846 \n0.886 \n0.901 \n0.922 \n0.939 \n0.935 \n0.942 \n0.948 \n0.952 \n0.968 \n0.968 \n0.980 \nRecall \n0.865 \n0.879 \n0.902 \n0.930 \n0.925 \n0.960 \n0.964 \n0.972 \n0.969 \n0.973 \n0.981 \n0.989 \nTrue Positives \nTotal \n856 \n963 \n1051 \n1141 \n1194 \n1271 \n1300 \n1351 \n1393 \n1441 \n1478 \n1525 \nChurned \n385 \n525 \n659 \n764 \n871 \n950 \n1048 \n1090 \n1195 \n1279 \n1357 \n1447 \nChurning \n73 \n65 \n66 \n68 \n43 \n56 \n50 \n69 \n56 \n38 \n59 \n78 \nActive \n398 \n373 \n326 \n309 \n280 \n265 \n202 \n192 \n142 \n124 \n62 \n0 \nFalse Positives \nTotal \n156 \n124 \n116 \n96 \n78 \n88 \n80 \n74 \n70 \n47 \n49 \n31 \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nChurning \n18 \n14 \n12 \n12 \n8 \n13 \n15 \n16 \n11 \n7 \n17 \n31 \nActive \n138 \n110 \n104 \n84 \n70 \n75 \n65 \n58 \n59 \n40 \n32 \n0 \nFalse Negatives \nTotal \n134 \n132 \n114 \n86 \n97 \n53 \n49 \n39 \n44 \n40 \n28 \n17 \nChurned \n22 \n21 \n21 \n12 \n12 \n6 \n7 \n6 \n16 \n14 \n10 \n6 \nChurning \n12 \n12 \n12 \n11 \n16 \n6 \n7 \n11 \n6 \n3 \n5 \n11 \nActive \n100 \n99 \n81 \n63 \n69 \n41 \n35 \n22 \n22 \n23 \n13 \n0 \nTrue Negatives \nTotal \n529 \n456 \n394 \n352 \n306 \n263 \n246 \n211 \n168 \n147 \n120 \n102 \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n14 \n18 \n14 \n10 \n7 \n7 \n10 \n13 \n13 \n8 \n11 \n102 \n \nActive \n515 \n438 \n380 \n342 \n299 \n256 \n236 \n198 \n155 \n139 \n109 \n0 \n \nTotal Churned \n407 \n546 \n680 \n776 \n883 \n956 \n1055 \n1096 \n1211 \n1293 \n1367 \n1453 \n \nTotal Churning \n117 \n109 \n104 \n101 \n74 \n82 \n82 \n109 \n86 \n56 \n92 \n222 \n \nTotal Active \n1151 \n1020 \n891 \n798 \n718 \n637 \n538 \n470 \n378 \n326 \n216 \n0 \n \nNonadherent Users \n990 \n1095 \n1165 \n1227 \n1291 \n1324 \n1349 \n1390 \n1437 \n1481 \n1506 \n1542 \n \nAdherent Users \n685 \n580 \n510 \n448 \n384 \n351 \n326 \n285 \n238 \n194 \n169 \n133 \n \nAdherence Ratio \n0.409 \n0.346 \n0.304 \n0.267 \n0.229 \n0.210 \n0.195 \n0.170 \n0.142 \n0.116 \n0.101 \n0.079 \n \nClass Imbalance \n0.591 \n0.654 \n0.696 \n0.733 \n0.771 \n0.790 \n0.805 \n0.830 \n0.858 \n0.884 \n0.899 \n0.921 \n", - "caption": "Nonadherence prediction model results on the Vivira test set for Weeks 2-13 based on data from \nprevious weeks (n = 1,675). \n \n \nWeek 2 \nWeek 3 \nWeek 4 \nWeek 5 \nWeek 6 \nWeek 7 \nWeek 8 \nWeek 9 \nWeek 10 \nWeek 11 \nWeek 12 \nWeek 13 \nPerformance \nAUC \n0.893 \n0.920 \n0.928 \n0.946 \n0.944 \n0.949 \n0.966 \n0.954 \n0.970 \n0.975 \n0.978 \n0.987 \n \nAccuracy \n0.827 \n0.847 \n0.863 \n0.891 \n0.896 \n0.916 \n0.923 \n0.933 \n0.932 \n0.948 \n0.954 \n0.971 \n \nF1 Score \n0.855 \n0.883 \n0.901 \n0.926 \n0.932 \n0.947 \n0.953 \n0.960 \n0.961 \n0.971 \n0.975 \n0.985 \n \nPrecision \n0.846 \n0.886 \n0.901 \n0.922 \n0.939 \n0.935 \n0.942 \n0.948 \n0.952 \n0.968 \n0.968 \n0.980 \n \nRecall \n0.865 \n0.879 \n0.902 \n0.930 \n0.925 \n0.960 \n0.964 \n0.972 \n0.969 \n0.973 \n0.981 \n0.989 \nTrue Positives \nTotal \n856 \n963 \n1051 \n1141 \n1194 \n1271 \n1300 \n1351 \n1393 \n1441 \n1478 \n1525 \n \nChurned \n385 \n525 \n659 \n764 \n871 \n950 \n1048 \n1090 \n1195 \n1279 \n1357 \n1447 \n \nChurning \n73 \n65 \n66 \n68 \n43 \n56 \n50 \n69 \n56 \n38 \n59 \n78 \n \nActive \n398 \n373 \n326 \n309 \n280 \n265 \n202 \n192 \n142 \n124 \n62 \n0 \nFalse Positives \nTotal \n156 \n124 \n116 \n96 \n78 \n88 \n80 \n74 \n70 \n47 \n49 \n31 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n18 \n14 \n12 \n12 \n8 \n13 \n15 \n16 \n11 \n7 \n17 \n31 \n \nActive \n138 \n110 \n104 \n84 \n70 \n75 \n65 \n58 \n59 \n40 \n32 \n0 \nFalse Negatives \nTotal \n134 \n132 \n114 \n86 \n97 \n53 \n49 \n39 \n44 \n40 \n28 \n17 \n \nChurned \n22 \n21 \n21 \n12 \n12 \n6 \n7 \n6 \n16 \n14 \n10 \n6 \n \nChurning \n12 \n12 \n12 \n11 \n16 \n6 \n7 \n11 \n6 \n3 \n5 \n11 \n \nActive \n100 \n99 \n81 \n63 \n69 \n41 \n35 \n22 \n22 \n23 \n13 \n0 \nTrue Negatives \nTotal \n529 \n456 \n394 \n352 \n306 \n263 \n246 \n211 \n168 \n147 \n120 \n102 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n14 \n18 \n14 \n10 \n7 \n7 \n10 \n13 \n13 \n8 \n11 \n102 \n \nActive \n515 \n438 \n380 \n342 \n299 \n256 \n236 \n198 \n155 \n139 \n109 \n0 \n \nTotal Churned \n407 \n546 \n680 \n776 \n883 \n956 \n1055 \n1096 \n1211 \n1293 \n1367 \n1453 \n \nTotal Churning \n117 \n109 \n104 \n101 \n74 \n82 \n82 \n109 \n86 \n56 \n92 \n222 \n \nTotal Active \n1151 \n1020 \n891 \n798 \n718 \n637 \n538 \n470 \n378 \n326 \n216 \n0 \n \nNonadherent Users \n990 \n1095 \n1165 \n1227 \n1291 \n1324 \n1349 \n1390 \n1437 \n1481 \n1506 \n1542 \n \nAdherent Users \n685 \n580 \n510 \n448 \n384 \n351 \n326 \n285 \n238 \n194 \n169 \n133 \n \nAdherence Ratio \n0.409 \n0.346 \n0.304 \n0.267 \n0.229 \n0.210 \n0.195 \n0.170 \n0.142 \n0.116 \n0.101 \n0.079 \n \nClass Imbalance \n0.591 \n0.654 \n0.696 \n0.733 \n0.771 \n0.790 \n0.805 \n0.830 \n0.858 \n0.884 \n0.899 \n0.921" - }, - { - "page": 12, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 84.76000213623047, - 47.984615325927734 - ], - "text": "12 \n", - "caption": "" - }, - { - "page": 12, - "index": 2, - "bbox": [ - 72.0, - 77.11743927001953, - 508.1708679199219, - 765.8245849609375 - ], - "text": " \nFigure 5: Weekly nonadherence prediction results on Vivira test set (n = 1,675). \nChurn prediction models in Vivira achieved a mean AUC of 0.87 (SD = 0.06), a mean accuracy of \n0.83 (SD = 0.06), a mean F1-score of 0.81 (SD = 0.19), a mean precision of 0.79 (SD = 0.16), and \na mean recall of 0.84 (SD = 0.19) across all prediction points at the end of Day 1 until Day 82, \nindicating a robust ability to discriminate between churned and non-churned users. \nAs illustrated in Figure 6.6, all performance metrics showed a gradual improvement from Day 4 \n(AUC = 0.74, Accuracy = 0.70, F1-Score = 0.52, Precision = 0.51, Recall = 0.52) to Day 82 (AUC \n0.50\n0.55\n0.60\n0.65\n0.70\n0.75\n0.80\n0.85\n0.90\n0.95\n1.00\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\nAccuracy\nF1 Score\nAUC\nPrecision\nRecall\nClass Imbalance\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1300\n1400\n1500\n1600\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\nTrue positives\nFalse positives\nFalse negatives\nTrue negatives\n0%\n5%\n10%\n15%\n20%\n25%\n30%\n35%\n40%\n45%\n50%\n55%\n60%\n65%\n70%\n75%\n80%\n85%\n90%\n95%\n100%\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\nWeek\nTrue positive rate\nFalse positive rate\nPercentage of correctly identi\ufb01ed nonadherent churned users (last login before 7-day prediction window)\nPercentage of correctly identi\ufb01ed nonadherent churning users (last login within 7-day prediction window)\nPercentage of correctly identi\ufb01ed nonadherent active users (last login after 7-day prediction window)\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1300\n1400\n1500\n1600\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\nNonadherent users\nCorrectly predicted nonadherent users (true positives)\nCorrectly predicted active nonadherent users (last login after 7-day prediction window)\nCorrectly predicted churning nonadherent users (last login during 7-day prediction window)\nCorrectly predicted churned nonadherent users (last login before 7-day prediction window)\nFalsely predicted nonadherent users (false positives)\n", - "caption": "" - }, - { - "page": 13, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.2503051757812, - 759.8245849609375 - ], - "text": "13 \n= 0.95, Accuracy = 0.96, F1-Score = 0.98, Precision = 0.96, Recall = 0.99). This trend reflects the \nmodel\u2019s increasing effectiveness in distinguishing between churned and non-churned users as more \ndata became available. Predictions on Days 1-3 did not outperform the class imbalance, thereby \noffering limited utility in predicting churning users during these early days. \nAcross daily prediction time points starting at end of Day 1 to end of Day 82, the models correctly \nidentified an average of 88.1% (SD = 18.9%) of churned users (i.e. those whose last login occurred \nbefore the 7-day prediction window) and an average of 52% (SD = 14.7%) of churning users (those \nwhose last login occurred within the 7-day prediction window), with a mean false positive rate \n(FPR) of 29.2% (SD = 8.5%). This indicates that the models were more adept at identifying users \nwho had already churned compared to those in the process of churning, as highlighted in Figure \n6.6. Overall, churn prediction models identified an average of 84.4% (SD = 19.1%) of total churned \nusers across all prediction windows. \nIn summary, churn prediction models demonstrated strong and improving performance throughout \nthe observation period, particularly in the later stages. Detailed prediction results for each day are \nprovided in Appendix 6.3, with prediction performances on selected days for the prediction window \nof Weeks 2-12 further highlighted in Table 6.2. \nFeature importance analyses showed that daily activity features (mean cumulated feature \nimportance = 0.51) and daily exercise features (mean cumulated feature importance = 0.49) \ncontributed nearly equally to the predictive performance of churn prediction models. The \nimportance of both activity and exercise features tended to increase with their proximity to the \nprediction window (See Appendix 6.2). \nTable 2: Churn prediction results on the Vivira test set for Weeks 2-12 based on data from previous weeks \n(n = 1,675). \n \nWeek 2 \nWeek 3 \nWeek 4 \nWeek 5 \nWeek 6 \nWeek 7 \nWeek 8 \nWeek 9 \nWeek 10 \nWeek 11 \nWeek 12 \nPerformance \nAUC \n0.761 \n0.856 \n0.851 \n0.865 \n0.871 \n0.892 \n0.903 \n0.903 \n0.913 \n0.908 \n0.928 \n \nAccuracy \n0.712 \n0.781 \n0.779 \n0.804 \n0.808 \n0.831 \n0.856 \n0.870 \n0.884 \n0.905 \n0.924 \n \nF1 Score \n0.608 \n0.744 \n0.776 \n0.819 \n0.839 \n0.869 \n0.896 \n0.912 \n0.927 \n0.943 \n0.957 \n \nPrecision \n0.561 \n0.703 \n0.728 \n0.786 \n0.801 \n0.832 \n0.865 \n0.897 \n0.911 \n0.926 \n0.943 \n \nRecall \n0.664 \n0.791 \n0.832 \n0.855 \n0.882 \n0.910 \n0.928 \n0.927 \n0.943 \n0.961 \n0.972 \nTrue Positives \nTotal \n375 \n533 \n644 \n744 \n842 \n938 \n1035 \n1124 \n1226 \n1318 \n1413 \n \nChurned \n305 \n484 \n593 \n699 \n791 \n888 \n995 \n1069 \n1179 \n1275 \n1345 \n \nChurning \n70 \n49 \n51 \n45 \n51 \n50 \n40 \n55 \n47 \n43 \n68 \n \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nFalse Positives \nTotal \n293 \n225 \n241 \n203 \n209 \n190 \n161 \n129 \n120 \n106 \n86 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nActive \n293 \n225 \n241 \n203 \n209 \n190 \n161 \n129 \n120 \n106 \n86 \nFalse Negatives \nTotal \n190 \n141 \n130 \n126 \n113 \n93 \n80 \n89 \n74 \n53 \n41 \n \nChurned \n110 \n96 \n75 \n79 \n72 \n61 \n41 \n51 \n34 \n30 \n15 \n \nChurning \n80 \n45 \n55 \n47 \n41 \n32 \n39 \n38 \n40 \n23 \n26 \n \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nTrue Negatives \nTotal \n817 \n776 \n660 \n602 \n511 \n454 \n399 \n333 \n255 \n198 \n135 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nActive \n817 \n776 \n660 \n602 \n511 \n454 \n399 \n333 \n255 \n198 \n135 \n \nTotal Churned \n565 \n674 \n774 \n870 \n955 \n1031 \n1115 \n1213 \n1300 \n1371 \n1454 \n \nTotal Churning \n150 \n94 \n106 \n92 \n92 \n82 \n79 \n93 \n87 \n66 \n94 \n \nTotal Active \n1110 \n1001 \n901 \n805 \n720 \n644 \n560 \n462 \n375 \n304 \n221 \n \nChurn Ratio \n0.337 \n0.402 \n0.462 \n0.519 \n0.570 \n0.616 \n0.666 \n0.724 \n0.776 \n0.819 \n0.868 \n \nClass Imbalance \n0.663 \n0.598 \n0.538 \n0.519 \n0.570 \n0.616 \n0.666 \n0.724 \n0.776 \n0.819 \n0.868 \n \n", - "caption": "2. \nFeature importance analyses showed that daily activity features (mean cumulated feature \nimportance = 0.51) and daily exercise features (mean cumulated feature importance = 0.49) \ncontributed nearly equally to the predictive performance of churn prediction models. The \nimportance of both activity and exercise features tended to increase with their proximity to the \nprediction window (See Appendix 6.2). \nTable 2: Churn prediction results on the Vivira test set for Weeks 2-12 based on data from previous weeks \n(n = 1,675). \n \nWeek 2 \nWeek 3 \nWeek 4 \nWeek 5 \nWeek 6 \nWeek 7 \nWeek 8 \nWeek 9 \nWeek 10 \nWeek 11 \nWeek 12 \nPerformance \nAUC \n0.761 \n0.856 \n0.851 \n0.865 \n0.871 \n0.892 \n0.903 \n0.903 \n0.913 \n0.908 \n0.928 \n \nAccuracy \n0.712 \n0.781 \n0.779 \n0.804 \n0.808 \n0.831 \n0.856 \n0.870 \n0.884 \n0.905 \n0.924 \n \nF1 Score \n0.608 \n0.744 \n0.776 \n0.819 \n0.839 \n0.869 \n0.896 \n0.912 \n0.927 \n0.943 \n0.957 \n \nPrecision \n0.561 \n0.703 \n0.728 \n0.786 \n0.801 \n0.832 \n0.865 \n0.897 \n0.911 \n0.926 \n0.943 \n \nRecall \n0.664 \n0.791 \n0.832 \n0.855 \n0.882 \n0.910 \n0.928 \n0.927 \n0.943 \n0.961 \n0.972 \nTrue Positives \nTotal \n375 \n533 \n644 \n744 \n842 \n938 \n1035 \n1124 \n1226 \n1318 \n1413 \n \nChurned \n305 \n484 \n593 \n699 \n791 \n888 \n995 \n1069 \n1179 \n1275 \n1345 \n \nChurning \n70 \n49 \n51 \n45 \n51 \n50 \n40 \n55 \n47 \n43 \n68 \n \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nFalse Positives \nTotal \n293 \n225 \n241 \n203 \n209 \n190 \n161 \n129 \n120 \n106 \n86 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nActive \n293 \n225 \n241 \n203 \n209 \n190 \n161 \n129 \n120 \n106 \n86 \nFalse Negatives \nTotal \n190 \n141 \n130 \n126 \n113 \n93 \n80 \n89 \n74 \n53 \n41 \n \nChurned \n110 \n96 \n75 \n79 \n72 \n61 \n41 \n51 \n34 \n30 \n15 \n \nChurning \n80 \n45 \n55 \n47 \n41 \n32 \n39 \n38 \n40 \n23 \n26 \n \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nTrue Negatives \nTotal \n817 \n776 \n660 \n602 \n511 \n454 \n399 \n333 \n255 \n198 \n135 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nActive \n817 \n776 \n660 \n602 \n511 \n454 \n399 \n333 \n255 \n198 \n135 \n \nTotal Churned \n565 \n674 \n774 \n870 \n955 \n1031 \n1115 \n1213 \n1300 \n1371 \n1454 \n \nTotal Churning \n150 \n94 \n106 \n92 \n92 \n82 \n79 \n93 \n87 \n66 \n94 \n \nTotal Active \n1110 \n1001 \n901 \n805 \n720 \n644 \n560 \n462 \n375 \n304 \n221 \n \nChurn Ratio \n0.337 \n0.402 \n0.462 \n0.519 \n0.570 \n0.616 \n0.666 \n0.724 \n0.776 \n0.819 \n0.868 \n \nClass Imbalance \n0.663 \n0.598 \n0.538 \n0.519 \n0.570 \n0.616 \n0.666 \n0.724 \n0.776 \n0.819 \n0.868" - }, - { - "page": 14, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 84.76000213623047, - 47.984615325927734 - ], - "text": "14 \n", - "caption": "" - }, - { - "page": 14, - "index": 2, - "bbox": [ - 83.93311309814453, - 645.9786987304688, - 495.8572082519531, - 668.6572875976562 - ], - "text": "Figure 6: Daily churn prediction results from Days 1-82 for the following week on Vivira test set (n = \n1,675). \n", - "caption": "" - }, - { - "page": 14, - "index": 3, - "bbox": [ - 73.98023986816406, - 76.63603973388672, - 501.8758850097656, - 630.7907104492188 - ], - "text": "0.00\n0.05\n0.10\n0.15\n0.20\n0.25\n0.30\n0.35\n0.40\n0.45\n0.50\n0.55\n0.60\n0.65\n0.70\n0.75\n0.80\n0.85\n0.90\n0.95\n1.00\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\nAccuracy\nF1 Score\nAUC\nPrecision\nRecall\nClass Imbalance\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1300\n1400\n1500\n1600\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\nTrue positives\nFalse positives\nFalse negatives\nTrue negatives\n0%\n5%\n10%\n15%\n20%\n25%\n30%\n35%\n40%\n45%\n50%\n55%\n60%\n65%\n70%\n75%\n80%\n85%\n90%\n95%\n100%\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\nDay\nTrue positive rate\nFalse positive rate\nPercentage of correctly identi\ufb01ed churned users (last login before 7-day prediction window)\nPercentage of correctly identi\ufb01ed churning users (last login within 7-day prediction window)\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1300\n1400\n1500\n1600\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\nTotal churned users (last login before 7-day prediction window)\nCorrectly predicted churned users (last login before 7-day prediction window)\nTotal churning users (last login within 7-day prediction window)\nCorrectly predicted churning users (last login within 7-day prediction window)\nFalsely predicted churned users (False Positives)\n", - "caption": "" - }, - { - "page": 15, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.2485961914062, - 579.8245849609375 - ], - "text": "15 \n3.2.2 Manoa \nMonthly nonadherence prediction results in Manoa are presented in Table 6.3, and Figure 6.7 \nfurther illustrates prediction performance trends for the prediction windows from Month 2 until \nMonth 6. \nAUC of prediction models increased from 0.73 in Month 2 to 0.86 in Month 4 and slightly decreased \nafterward to 0.85 in Month 6. Accuracy improved from 0.70 in Week 2 to 0.81 in Month 5. In \nMonth 6, accuracy equaled the class imbalance (0.81) thus offering limited utility. F1-score \nincreased across months, ranging from 0.73 in Month 2 to 0.89 in Month 6. Precision and recall \nalso gradually increased, with precision varying between 0.72 and 0.86 and recall ranging from \n0.73 to 0.93. \nAcross the prediction windows from Month 2 to Month 6, the models achieved a mean AUC of \n0.82 (SD = 0.06), a mean accuracy of 0.77 (SD = 0.05), a mean F1-score of 0.83 (SD = 0.07), a \nmean precision of 0.81 (SD = 0.06), and a mean recall of 0.86 (SD = 0.08). \nBetween Months 2 and 6, the models correctly identified an average of 97.1% (SD = 2.8%) of \nchurned nonadherent users (those whose last login occurred before the 31-day prediction window), \nan average of 92.6% (SD = 5.2%) of churning nonadherent users (those whose last login occurred \nwithin the 31-day prediction window), and an average of 74.9% (SD = 4.3%) of active nonadherent \nusers (those whose last login occurred after the 31-day prediction window), with a mean false \npositive rate (FPR) of 49.5% (SD = 12.9%). These results highlight that the models were more \nadept at identifying nonadherent users who had already churned rather than those active or in the \nprocess of churning, as highlighted in Figure 6.7. Overall, nonadherence prediction models \nidentified an average of 85.9% (SD = 7.6%) of total nonadherent users from Month 2 to Month 6. \nThe feature importance analyses showed that daily session features (mean cumulated feature \nimportance = 0.73) were more important for nonadherence prediction models than daily activity \nfeatures (mean cumulated feature importance = 0.27). The importance of both session and activity \nfeatures tended to increase with their proximity to the prediction window (See Appendix 6.2). \n", - "caption": "3, and Figure 6.7 \nfurther illustrates prediction performance trends for the prediction windows from Month 2 until \nMonth 6. \nAUC of prediction models increased from 0.73 in Month 2 to 0.86 in Month 4 and slightly decreased \nafterward to 0.85 in Month 6. Accuracy improved from 0.70 in Week 2 to 0.81 in Month 5. In \nMonth 6, accuracy equaled the class imbalance (0.81) thus offering limited utility. F1-score \nincreased across months, ranging from 0.73 in Month 2 to 0.89 in Month 6. Precision and recall \nalso gradually increased, with precision varying between 0.72 and 0.86 and recall ranging from \n0.73 to 0.93. \nAcross the prediction windows from Month 2 to Month 6, the models achieved a mean AUC of \n0.82 (SD = 0.06), a mean accuracy of 0.77 (SD = 0.05), a mean F1-score of 0.83 (SD = 0.07), a \nmean precision of 0.81 (SD = 0.06), and a mean recall of 0.86 (SD = 0.08). \nBetween Months 2 and 6, the models correctly identified an average of 97.1% (SD = 2.8%) of \nchurned nonadherent users (those whose last login occurred before the 31-day prediction window), \nan average of 92.6% (SD = 5.2%) of churning nonadherent users (those whose last login occurred \nwithin the 31-day prediction window), and an average of 74.9% (SD = 4.3%) of active nonadherent \nusers (those whose last login occurred after the 31-day prediction window), with a mean false \npositive rate (FPR) of 49.5% (SD = 12.9%). These results highlight that the models were more \nadept at identifying nonadherent users who had already churned rather than those active or in the \nprocess of churning, as highlighted in Figure 6.7. Overall, nonadherence prediction models \nidentified an average of 85.9% (SD = 7.6%) of total nonadherent users from Month 2 to Month 6. \nThe feature importance analyses showed that daily session features (mean cumulated feature \nimportance = 0.73) were more important for nonadherence prediction models than daily activity \nfeatures (mean cumulated feature importance = 0.27). The importance of both session and activity \nfeatures tended to increase with their proximity to the prediction window (See Appendix 6.2)." - }, - { - "page": 16, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 502.90850830078125, - 340.38330078125 - ], - "text": "16 \nTable 3: Nonadherence prediction model results on the Manoa test set for Months 2-6 based on data from \nprevious months among users who completed the initial measurement week (n = 419). \n \n \nMonth 2 \nMonth 3 \nMonth 4 \nMonth 5 \nMonth 6 \nPerformance \nAUC \n0.730 \n0.799 \n0.864 \n0.859 \n0.853 \n \nAccuracy \n0.702 \n0.742 \n0.788 \n0.811 \n0.807 \n \nF1 Score \n0.726 \n0.819 \n0.858 \n0.880 \n0.885 \n \nPrecision \n0.722 \n0.783 \n0.841 \n0.861 \n0.848 \n \nRecall \n0.731 \n0.860 \n0.876 \n0.901 \n0.926 \nTrue Positives \nTotal \n166 \n245 \n269 \n291 \n313 \n \nChurned \n42 \n92 \n144 \n186 \n227 \n \nChurning \n25 \n42 \n27 \n28 \n86 \n \nActive \n99 \n111 \n98 \n77 \n0 \nFalse Positives \nTotal \n64 \n68 \n51 \n47 \n56 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n \nChurning \n7 \n6 \n3 \n6 \n56 \n \nActive \n57 \n62 \n48 \n41 \n0 \nFalse Negatives \nTotal \n61 \n40 \n38 \n32 \n25 \n \nChurned \n1 \n8 \n2 \n3 \n3 \n \nChurning \n15 \n2 \n5 \n5 \n22 \n \nActive \n45 \n30 \n31 \n24 \n0 \nTrue Negatives \nTotal \n128 \n66 \n61 \n49 \n25 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n \nChurning \n2 \n6 \n3 \n4 \n25 \n \nActive \n126 \n60 \n58 \n45 \n0 \n \nTotal Churned \n43 \n100 \n146 \n189 \n230 \n \nTotal Churning \n49 \n56 \n38 \n43 \n189 \n \nTotal Active \n327 \n263 \n235 \n187 \n0 \n \nNonadherent Users \n227 \n285 \n307 \n323 \n338 \n \nAdherent Users \n192 \n134 \n112 \n96 \n81 \n \nAdherence Ratio \n0.458 \n0.320 \n0.267 \n0.229 \n0.193 \n \nClass Imbalance \n0.542 \n0.680 \n0.733 \n0.771 \n0.807 \n", - "caption": "Nonadherence prediction model results on the Manoa test set for Months 2-6 based on data from \nprevious months among users who completed the initial measurement week (n = 419). \n \n \nMonth 2 \nMonth 3 \nMonth 4 \nMonth 5 \nMonth 6 \nPerformance \nAUC \n0.730 \n0.799 \n0.864 \n0.859 \n0.853 \n \nAccuracy \n0.702 \n0.742 \n0.788 \n0.811 \n0.807 \n \nF1 Score \n0.726 \n0.819 \n0.858 \n0.880 \n0.885 \n \nPrecision \n0.722 \n0.783 \n0.841 \n0.861 \n0.848 \n \nRecall \n0.731 \n0.860 \n0.876 \n0.901 \n0.926 \nTrue Positives \nTotal \n166 \n245 \n269 \n291 \n313 \n \nChurned \n42 \n92 \n144 \n186 \n227 \n \nChurning \n25 \n42 \n27 \n28 \n86 \n \nActive \n99 \n111 \n98 \n77 \n0 \nFalse Positives \nTotal \n64 \n68 \n51 \n47 \n56 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n \nChurning \n7 \n6 \n3 \n6 \n56 \n \nActive \n57 \n62 \n48 \n41 \n0 \nFalse Negatives \nTotal \n61 \n40 \n38 \n32 \n25 \n \nChurned \n1 \n8 \n2 \n3 \n3 \n \nChurning \n15 \n2 \n5 \n5 \n22 \n \nActive \n45 \n30 \n31 \n24 \n0 \nTrue Negatives \nTotal \n128 \n66 \n61 \n49 \n25 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n \nChurning \n2 \n6 \n3 \n4 \n25 \n \nActive \n126 \n60 \n58 \n45 \n0 \n \nTotal Churned \n43 \n100 \n146 \n189 \n230 \n \nTotal Churning \n49 \n56 \n38 \n43 \n189 \n \nTotal Active \n327 \n263 \n235 \n187 \n0 \n \nNonadherent Users \n227 \n285 \n307 \n323 \n338 \n \nAdherent Users \n192 \n134 \n112 \n96 \n81 \n \nAdherence Ratio \n0.458 \n0.320 \n0.267 \n0.229 \n0.193 \n \nClass Imbalance \n0.542 \n0.680 \n0.733 \n0.771 \n0.807" - }, - { - "page": 17, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.1200561523438, - 47.984615325927734 - ], - "text": "17 \n", - "caption": "" - }, - { - "page": 17, - "index": 2, - "bbox": [ - 97.53856658935547, - 663.9786987304688, - 518.1435546875, - 686.6572875976562 - ], - "text": "Figure 7: Monthly nonadherence prediction results among users who completed the initial measurement \nweek on Manoa test set (n = 419). \n", - "caption": "" - }, - { - "page": 17, - "index": 3, - "bbox": [ - 92.0050277709961, - 77.91295623779297, - 500.9361572265625, - 651.7218627929688 - ], - "text": "0.50\n0.55\n0.60\n0.65\n0.70\n0.75\n0.80\n0.85\n0.90\n0.95\n1.00\n2\n3\n4\n5\n6\nAccuracy\nF1 Score\nAUC\nPrecision\nRecall\nClass Imbalance\n0\n50\n100\n150\n200\n250\n300\n350\n2\n3\n4\n5\n6\nTrue positives\nFalse positives\nFalse negatives\nTrue negatives\n0%\n5%\n10%\n15%\n20%\n25%\n30%\n35%\n40%\n45%\n50%\n55%\n60%\n65%\n70%\n75%\n80%\n85%\n90%\n95%\n100%\n2\n3\n4\n5\n6\nMonth\nTrue positive rate\nFalse positive rate\nPercentage of correctly identi\ufb01ed nonadherent churned users (last login before 31-day prediction window)\nPercentage of correctly identi\ufb01ed nonadherent churning users (last login within 31-day prediction window)\nPercentage of correctly identi\ufb01ed nonadherent active users (last login after 31-day prediction window)\n0\n50\n100\n150\n200\n250\n300\n350\n2\n3\n4\n5\n6\nNonadherent users\nCorrectly predicted nonadherent users (true positives)\nCorrectly predicted active nonadherent users (last login after 31-day prediction window)\nCorrectly predicted churning nonadherent users (last login during 31-day prediction window)\nCorrectly predicted churned nonadherent users (last login before 31-day prediction window)\nFalsely predicted nonadherent users (false positives)\n", - "caption": "" - }, - { - "page": 18, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 508.24822998046875, - 563.984619140625 - ], - "text": "18 \nChurn prediction models in Manoa achieved a mean AUC of 0.87 (SD = 0.04), a mean accuracy of \n0.83 (SD = 0.05), a mean F1-score of 0.81 (SD = 0.13), a mean precision of 0.78 (SD = 0.12), and \na mean recall of 0.86 (SD = 0.14) across all prediction points at the of Day 1 until 178, indicating \na robust ability to discriminate between churned and non-churned users. \nAs illustrated in Figure 6.8, all performance metrics showed a gradual improvement from Day 4 \n(AUC = 0.78, Accuracy = 0.78, F1-Score = 0.52, Precision = 0.55, Recall = 0.50) to Day 178 (AUC \n= 0.97, Accuracy = 0.95, F1-Score = 0.97, Precision = 0.97, Recall = 0.98). This trend reflects the \nmodel\u2019s increasing effectiveness in distinguishing between churned and non-churned users as more \ndata became available. Prediction accuracies on Days 1-3 did not significantly outperform the class \nimbalance, thereby offering limited utility in predicting churning users during these early days. \nAcross daily prediction time points starting at end of Day 1 to end of Day 178, the models correctly \nidentified an average of 88.2% (SD = 12.9%) of churned users (i.e. those whose last login occurred \nbefore the 7-day prediction window) and an average of 51.5% (SD = 18%) of churning users (i.e. \nthose whose last login occurred within the 7-day prediction window), with a mean false positive \nrate (FPR) of 25.8% (SD = 6.1%). This indicates that the models were more adept at identifying \nusers who had already churned compared to those in the process of churning, as highlighted in \nFigure 6.8. Overall, churn prediction models identified an average of 86% (SD = 14.3%) of total \nchurned users across all prediction windows. \nIn summary, churn prediction models demonstrated strong and improving performance throughout \nthe observation period. Detailed prediction results for each day are provided in Appendix 6.3, with \nprediction performances for selected weeks further highlighted in Table 6.4. \nThe feature importance evaluation showed that daily session features (mean cumulated feature \nimportance = 0.56) were more important for churn prediction models than daily activity features \n(mean cumulated feature importance = 0.44). The importance of both session and activity features \ntended to increase with their proximity to the prediction window (See Appendix 6.2). \n", - "caption": "4. \nThe feature importance evaluation showed that daily session features (mean cumulated feature \nimportance = 0.56) were more important for churn prediction models than daily activity features \n(mean cumulated feature importance = 0.44). The importance of both session and activity features \ntended to increase with their proximity to the prediction window (See Appendix 6.2)." - }, - { - "page": 19, - "index": 1, - "bbox": [ - 51.120121002197266, - 35.0433349609375, - 558.449951171875, - 335.0898742675781 - ], - "text": " \n19 \nTable 4: Churn prediction results on the Manoa test set for selected weeks based on data from previous \nweeks (n = 1,335). \nWeek 2 \nWeek 3 \nWeek 4 \nWeek 6 \nWeek 8 \nWeek 10 \nWeek 12 \nWeek 14 \nWeek 16 \nWeek 18 \nWeek 20 \nWeek 22 \nWeek 24 \nWeek 26 \nPerformance \nAUC \n0.801 \n0.841 \n0.853 \n0.859 \n0.858 \n0.881 \n0.871 \n0.851 \n0.870 \n0.885 \n0.891 \n0.912 \n0.941 \n0.962 \nAccuracy \n0.781 \n0.782 \n0.787 \n0.778 \n0.799 \n0.828 \n0.819 \n0.813 \n0.825 \n0.852 \n0.861 \n0.897 \n0.915 \n0.938 \nF1 Score \n0.594 \n0.656 \n0.688 \n0.746 \n0.797 \n0.837 \n0.839 \n0.851 \n0.866 \n0.890 \n0.903 \n0.931 \n0.947 \n0.963 \nPrecision \n0.572 \n0.633 \n0.664 \n0.703 \n0.739 \n0.791 \n0.799 \n0.777 \n0.804 \n0.850 \n0.852 \n0.904 \n0.931 \n0.953 \nRecall \n0.617 \n0.681 \n0.714 \n0.794 \n0.864 \n0.888 \n0.883 \n0.942 \n0.938 \n0.934 \n0.961 \n0.960 \n0.963 \n0.974 \nTrue Positives \nTotal \n214 \n278 \n314 \n435 \n527 \n587 \n627 \n716 \n757 \n801 \n868 \n925 \n1003 \n1087 \nChurned \n192 \n263 \n305 \n425 \n513 \n572 \n621 \n706 \n747 \n781 \n851 \n901 \n968 \n1039 \nChurning \n22 \n15 \n9 \n10 \n14 \n15 \n6 \n10 \n10 \n20 \n17 \n24 \n35 \n48 \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nFalse Positives \nTotal \n160 \n161 \n159 \n184 \n186 \n155 \n158 \n206 \n184 \n141 \n151 \n98 \n74 \n54 \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nActive \n160 \n161 \n159 \n184 \n186 \n155 \n158 \n206 \n184 \n141 \n151 \n98 \n74 \n54 \nFalse Negatives \nTotal \n133 \n130 \n126 \n113 \n83 \n74 \n83 \n44 \n50 \n57 \n35 \n39 \n39 \n29 \nChurned \n62 \n86 \n107 \n97 \n68 \n62 \n76 \n36 \n44 \n50 \n28 \n27 \n26 \n12 \nChurning \n71 \n44 \n19 \n16 \n15 \n12 \n7 \n8 \n6 \n7 \n7 \n12 \n13 \n17 \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nTrue Negatives \nTotal \n828 \n766 \n736 \n603 \n539 \n519 \n467 \n369 \n344 \n336 \n281 \n273 \n219 \n165 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nActive \n828 \n766 \n736 \n603 \n539 \n519 \n467 \n369 \n344 \n336 \n281 \n273 \n219 \n165 \n \nTotal Churned \n254 \n349 \n412 \n522 \n581 \n634 \n697 \n742 \n791 \n831 \n879 \n928 \n994 \n1051 \n \nTotal Churning \n93 \n59 \n28 \n26 \n29 \n27 \n13 \n18 \n16 \n27 \n24 \n36 \n48 \n65 \n \nTotal Active \n988 \n927 \n895 \n787 \n725 \n674 \n625 \n575 \n528 \n477 \n432 \n371 \n293 \n219 \n \nChurn Ratio \n0.260 \n0.306 \n0.330 \n0.410 \n0.457 \n0.495 \n0.532 \n0.569 \n0.604 \n0.643 \n0.676 \n0.722 \n0.781 \n0.836 \n \nClass Imbalance \n0.740 \n0.694 \n0.670 \n0.590 \n0.543 \n0.505 \n0.532 \n0.569 \n0.604 \n0.643 \n0.676 \n0.722 \n0.781 \n0.836 \n \n", - "caption": "Churn prediction results on the Manoa test set for selected weeks based on data from previous \nweeks (n = 1,335). \n \nWeek 2 \nWeek 3 \nWeek 4 \nWeek 6 \nWeek 8 \nWeek 10 \nWeek 12 \nWeek 14 \nWeek 16 \nWeek 18 \nWeek 20 \nWeek 22 \nWeek 24 \nWeek 26 \nPerformance \nAUC \n0.801 \n0.841 \n0.853 \n0.859 \n0.858 \n0.881 \n0.871 \n0.851 \n0.870 \n0.885 \n0.891 \n0.912 \n0.941 \n0.962 \n \nAccuracy \n0.781 \n0.782 \n0.787 \n0.778 \n0.799 \n0.828 \n0.819 \n0.813 \n0.825 \n0.852 \n0.861 \n0.897 \n0.915 \n0.938 \n \nF1 Score \n0.594 \n0.656 \n0.688 \n0.746 \n0.797 \n0.837 \n0.839 \n0.851 \n0.866 \n0.890 \n0.903 \n0.931 \n0.947 \n0.963 \n \nPrecision \n0.572 \n0.633 \n0.664 \n0.703 \n0.739 \n0.791 \n0.799 \n0.777 \n0.804 \n0.850 \n0.852 \n0.904 \n0.931 \n0.953 \n \nRecall \n0.617 \n0.681 \n0.714 \n0.794 \n0.864 \n0.888 \n0.883 \n0.942 \n0.938 \n0.934 \n0.961 \n0.960 \n0.963 \n0.974 \nTrue Positives \nTotal \n214 \n278 \n314 \n435 \n527 \n587 \n627 \n716 \n757 \n801 \n868 \n925 \n1003 \n1087 \n \nChurned \n192 \n263 \n305 \n425 \n513 \n572 \n621 \n706 \n747 \n781 \n851 \n901 \n968 \n1039 \n \nChurning \n22 \n15 \n9 \n10 \n14 \n15 \n6 \n10 \n10 \n20 \n17 \n24 \n35 \n48 \n \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nFalse Positives \nTotal \n160 \n161 \n159 \n184 \n186 \n155 \n158 \n206 \n184 \n141 \n151 \n98 \n74 \n54 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nActive \n160 \n161 \n159 \n184 \n186 \n155 \n158 \n206 \n184 \n141 \n151 \n98 \n74 \n54 \nFalse Negatives \nTotal \n133 \n130 \n126 \n113 \n83 \n74 \n83 \n44 \n50 \n57 \n35 \n39 \n39 \n29 \n \nChurned \n62 \n86 \n107 \n97 \n68 \n62 \n76 \n36 \n44 \n50 \n28 \n27 \n26 \n12 \n \nChurning \n71 \n44 \n19 \n16 \n15 \n12 \n7 \n8 \n6 \n7 \n7 \n12 \n13 \n17 \n \nActive \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \nTrue Negatives \nTotal \n828 \n766 \n736 \n603 \n539 \n519 \n467 \n369 \n344 \n336 \n281 \n273 \n219 \n165 \n \nChurned \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nChurning \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n0 \n \nActive \n828 \n766 \n736 \n603 \n539 \n519 \n467 \n369 \n344 \n336 \n281 \n273 \n219 \n165 \n \nTotal Churned \n254 \n349 \n412 \n522 \n581 \n634 \n697 \n742 \n791 \n831 \n879 \n928 \n994 \n1051 \n \nTotal Churning \n93 \n59 \n28 \n26 \n29 \n27 \n13 \n18 \n16 \n27 \n24 \n36 \n48 \n65 \n \nTotal Active \n988 \n927 \n895 \n787 \n725 \n674 \n625 \n575 \n528 \n477 \n432 \n371 \n293 \n219 \n \nChurn Ratio \n0.260 \n0.306 \n0.330 \n0.410 \n0.457 \n0.495 \n0.532 \n0.569 \n0.604 \n0.643 \n0.676 \n0.722 \n0.781 \n0.836 \n \nClass Imbalance \n0.740 \n0.694 \n0.670 \n0.590 \n0.543 \n0.505 \n0.532 \n0.569 \n0.604 \n0.643 \n0.676 \n0.722 \n0.781 \n0.836" - }, - { - "page": 20, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 84.76000213623047, - 47.984615325927734 - ], - "text": "20 \n", - "caption": "" - }, - { - "page": 20, - "index": 2, - "bbox": [ - 78.37646484375, - 657.9786987304688, - 501.44195556640625, - 680.6572875976562 - ], - "text": "Figure 8: Daily churn prediction results from Days 1-178 for the following weeks on Manoa test set (n = \n1,335). \n", - "caption": "" - }, - { - "page": 20, - "index": 3, - "bbox": [ - 73.82430267333984, - 77.83768463134766, - 502.2578430175781, - 643.0302734375 - ], - "text": "0.00\n0.05\n0.10\n0.15\n0.20\n0.25\n0.30\n0.35\n0.40\n0.45\n0.50\n0.55\n0.60\n0.65\n0.70\n0.75\n0.80\n0.85\n0.90\n0.95\n1.00\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\nAccuracy\nF1 Score\nAUC\nPrecision\nRecall\nClass Imbalance\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\nTrue positives\nFalse positives\nFalse negatives\nTrue negatives\n0%\n5%\n10%\n15%\n20%\n25%\n30%\n35%\n40%\n45%\n50%\n55%\n60%\n65%\n70%\n75%\n80%\n85%\n90%\n95%\n100%\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\nDay\nTrue positive rate\nFalse positive rate\nPercentage of correctly identi\ufb01ed churned users (last login before 7-day prediction window)\nPercentage of correctly identi\ufb01ed churning users (last login within 7-day prediction window)\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\nTotal churned users (last login before 7-day prediction window)\nCorrectly predicted churned users (last login before 7-day prediction window)\nTotal churning users (last login within 7-day prediction window)\nCorrectly predicted churning users (last login within 7-day prediction window)\nFalsely predicted churned users (False Positives)\n", - "caption": "" - }, - { - "page": 21, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.2505493164062, - 765.5845947265625 - ], - "text": "21 \n4 Discussion \n4.1 Predicting Nonadherence to Mobile Health Interventions \nOur findings show that nonadherence to mHealth interventions can be accurately predicted over \nextended program durations, both in terms of adherence relative to intended use as defined by \nSieverink et al. (2017) and in its most severe form \u2013 churn (i.e., complete discontinuation of use). \nOur study thus extends prior research showing that methodologies effective in predicting churn \nduring the first week [21], remain applicable over longer durations of 90 and 186 days across two \ndistinct mHealth interventions to predict nonadherence. Given the conceptual link between churn \nand nonadherence \u2013 where fully disengaged users are inherently nonadherent \u2013 these results are \nintuitive. Our descriptive analysis further emphasizes this relationship, showing that the decline in \nadherence over time in Vivira and Manoa is largely driven by churn (i.e. users discontinuing \nentirely). \nWeekly nonadherence prediction models in Vivira achieved strong performance, correctly \nidentifying an average of 94.2% (SD = 4.2%, mean AUC = 0.95) of nonadherent users between \nWeeks 2 and 13, with an average false positive rate of 24.2% (SD = 2.9%). Interestingly, \nnonadherence prediction models outperformed churn prediction models in Vivira, as evidenced by \nconsistently higher performance metrics across all weeks. This can be partly attributed to the greater \nclass imbalance between adherent and nonadherent users compared to churned and retained users. \nHowever, the higher performance of nonadherence models even in the early weeks, when class \nimbalances were similar to those in churn prediction models, suggests that the methodologies are \nparticularly well-suited for predicting nonadherence relative to the intervention\u2019s intended use. \nIn contrast, the performance of nonadherence prediction models in Manoa was comparatively \nlower, correctly identifying an average of 86% (SD = 7.6%, mean AUC = 0.82) of nonadherent \nusers between Months 2 and 6 at a relatively higher false positive rate of 49.5% (SD = 12.9%). \nFeature importance analyses across all prediction models showed that behavioral app engagement \ndata collected closer to the prediction event had a stronger impact on model performance. This \nfinding aligns with previous research [5, 21, 29, 30] and explains the lower performance observed \nin Manoa, where nonadherence was measured monthly rather than weekly, limiting the availability \nof recent behavioral data near the prediction event. These results underscore the importance of using \ndata in close temporal proximity to the prediction event for improving model performance, \nhighlighting a critical consideration for the development of predictive models. \nDaily churn prediction models demonstrated nearly identical performance across the two \nindependent mHealth interventions, highlighting the generalizability of our approach. These models \ncorrectly identified an average of 84.4% (SD = 19.1%, mean AUC = 0.87) of users between Days \n1 and 82 in Vivira, and 86% (SD = 14.3%, mean AUC = 0.87) of users between Days 1 and 178 in \n", - "caption": "" - }, - { - "page": 22, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 508.25250244140625, - 737.5045776367188 - ], - "text": "22 \nManoa, who recorded their last login before the end of the 7-day prediction window. False positive \nrates were comparably lower, averaging 29.2% (SD = 8.5%) in Vivira and 25.8% (SD = 6.1%) in \nManoa. \nOur results further show that the predictive performance of daily churn prediction models improves \nover time as more behavioral app engagement data becomes available. This finding aligns with \nprior research, which primarily focused on the early weeks of user engagement in mHealth \ninterventions [4, 20]. Notably, as class imbalances declined between Day 1 and Day 25 in Vivira \nand Day 1 and Day 72 in Manoa, and then increased thereafter, model performance continued to \nimprove. This trend underscores the robustness of these predictive models, which maintained high \naccuracy across these extended program durations from Day 4 forward. \nIn summary, our study reinforces the applicability of past behavioral app engagement data for \nmaking daily churn predictions over extended durations. This applicability has been suggested in \nprevious mHealth intervention studies [5, 21, 30] and across other app domains such as gaming, \neducation, and social media [7, 27, 36, 45, 51, 62, 65]. However, these prior studies generally \nfocused on predicting churn at specific points in time and shorter durations, whereas our study \ndemonstrates its applicability for daily predictions over extended program durations. Furthermore, \nthis study is the first to establish the applicability of these predictive methodologies for \nnonadherence relative to intended use, as defined by Sieverink et al. (2017). This distinction \nhighlights the broader utility of such models in the context of mHealth interventions. \n4.2 The Potential for Targeted Strategies to Promote Adherence \nFollowing evaluations by Jakob et al. (2024), we assessed how many users record another log in \nafter a correct nonadherence or churn prediction to shed light on the potential for targeted strategies \npromoting adherence before churn occurs. \nWhile nonadherence prediction models were more adept at identifying nonadherent users who had \nalready churned, they also correctly identified a substantial proportion of nonadherent users before \nthey recorded their last log in. In Vivira, weekly nonadherence prediction models correctly \nidentified an average of 98.3% (SD = 1.6%) of nonadherent users who recorded their last login \nbefore the 7-day prediction window, 82.6% (SD = 7.1%) of nonadherent users who recorded their \nlast login within the 7-day prediction window, and 83.4% (SD = 3.4%) of nonadherent users who \nrecorded their last login after the prediction window. In Manoa, monthly nonadherence prediction \nmodels correctly identified an average of 97.1% (SD = 2.9%) of nonadherent users who recorded \ntheir last login before the 31-day prediction window, 92% (SD = 5.2%) of users recording their last \nlogin within the 31-day prediction window, and 74.9% (SD = 4.3%) of nonadherent users who \nrecorded their last login after the 31-day prediction window. \n", - "caption": "" - }, - { - "page": 23, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.2282104492188, - 731.5045776367188 - ], - "text": "23 \nChurn prediction models identified churned users (those who recorded their last login before the 7-\nday prediction window) more accurately than churning users (those who recorded their last login \nwithin the 7-day prediction window). In Vivira, churn prediction models detected an average of \n88.1% (SD = 19.1%) of churned users and an average of 51.9% (SD = 14.7%) of churning across \ndaily prediction models from Day 1 until Day 84. In Manoa, churn prediction models detected \n88.2% (SD = 12.9%) of churned users and 51.5% (SD = 18%) of churning users across daily \npredictions from Day 1 until Day 178. \nThese findings highlight that nonadherence prediction models are not only effective in identifying \nusers who have already discontinued app use but can also detect users in the early stages of \ndisengagement. Since many of these users continue to log in after being flagged, such models \npresent an opportunity to implement targeted preventive strategies directly within the app. These \nstrategies could extend beyond win-back efforts delivered through external channels (e.g., emails, \nphone calls) and instead focus on proactive measures such as system adaptations (e.g., tailoring \npersuasive elements or adjusting behavioral goals), active in-app interventions (e.g., personalized \npush notifications), or the integration of complementary intervention components (e.g., monetary \nincentives or human support). Since some adherence-promoting strategies, such as monetary \nincentives or human support, incur utilization costs, selectively targeting users at the highest risk of \nnonadherence could also support the cost-effectiveness of mHealth interventions. \n4.3 Implications for Researchers and Providers \nThe results of our study demonstrate that ML algorithms, when paired with behavioral app \nengagement data, can generate actionable insights that may support targeted strategies for \npreventing nonadherence. One promising aspect is that the app activity features used in this study \n- daily app activity and daily session counts - are not only predictive but generally applicable to any \nmHealth intervention that uses a mobile app as a primary intervention component. Likewise, the \nnumber of daily completed exercises used in Vivira corresponds directly to the app\u2019s intended use. \nThis concept can be extended to various interventions: an educational app may leverage the daily \nnumber of completed quizzes, while a physical activity program could track the number of physical \nactivities logged, or a smoking cessation app might use the frequency of smoking diary entries as \nfeatures for their predictive model. \nPrevious research has consistently ranked features related to user app activity (e.g., daily logins) \nand app progress (e.g., completion of app-logged activities) as the most important for churn \nprediction in mHealth interventions [5, 21] and other app domains [7, 27, 29, 41, 56]. Our findings \nalign with these observations, with such behavioral features demonstrating high predictive accuracy \neven over extended prediction periods. \n", - "caption": "" - }, - { - "page": 24, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 508.25701904296875, - 715.9046020507812 - ], - "text": "24 \nIn contrast, prior research on adherence to mHealth interventions has often emphasized contextual \nfactors over behavioral ones. Prior digital health studies typically focus on sociodemographic data \nsuch as age, gender, income, education, pre-existing health conditions, or specific intervention \ncomponents as predictors of adherence [20]. While these analyses can provide valuable insights, \nour results suggest that simple app engagement features, such as daily app activity, are sufficient to \npredict future user behavior. This finding is also consistent with previous churn prediction studies \nin DHIs, where adding contextual features to models already incorporating behavioral app \nengagement features did not improve predictive performance [5, 21]. \nConsequently, researchers and providers of mHealth interventions are encouraged to utilize \nbehavioral app engagement features when constructing nonadherence prediction models. This \napproach offers a novel perspective on nonadherence prediction, which may be uniquely enabled \nby digital interventions due to their capacity to collect objective and granular user behavior data, \nwhich is typically not available in non-digital interventions. \nMultiple ways exist to further optimize the presented prediction models. Prior research has shown \nthat different ML algorithms can yield varying outcomes, with some performing better across \ndifferent app contexts. Examples of algorithms frequently reported as best-performing for churn \nprediction across various app domains are Logistic Regression [5, 66], Random Forest [14, 21, 39, \n45, 59], Boosting models (e.g., XGBoost) [15, 35, 56, 57], and Recurrent Neural Networks (e.g., \nLSTMs) [22, 27, 29, 65]. Hence, researchers and developers are advised to evaluate multiple \nalgorithms to identify the most suitable ML algorithms for their specific classification problem, \nalso considering their complexity, which influences development time, resource consumption, and \nmodel explainability. \nFeature sets can also be expanded to include more intervention-specific attributes, such as social \nfeatures (e.g., number of interactions between users) in DHIs with social networking components \nor transactional features (e.g., number of in-app purchases) in DHIs with payment options, which \nhave been shown to enhance predictive performance in other app domains [30, 32, 39, 42, 56]. \nIncluding additional features may be particularly beneficial during the early stages when user \nactivity features may not yet provide a strong predictive signal, as highlighted by the insufficient \nperformance of our churn prediction models in the first three days. \nIn summary, our findings underscore the potential of generalizable, behavioral app engagement \ndata for the prediction of nonadherence in mHealth interventions. These features, combined with \naccessible ML algorithms, offer a practical foundation for researchers and providers of mHealth \ninterventions planning to develop and implement predictive models for nonadherence. \n", - "caption": "" - }, - { - "page": 25, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.281494140625, - 763.4246215820312 - ], - "text": "25 \n4.4 Limitations and Future Work \nThis study used historical app engagement data to predict nonadherence. While the presented results \nare promising, prospective trials are necessary to fully establish the applicability of these models. \nNevertheless, our findings align with prior research on churn prediction in DHIs [5, 21]. Notably, \nthe strong performance of our models across two distinct mHealth interventions suggests that the \napproach \u2013 utilizing behavioral app engagement features in combination with ML algorithms \u2013 has \nthe potential to be widely applicable across various mHealth intervention contexts to predict \nnonadherence. \nOne potential limitation of the Vivira dataset is the exclusion of users who did not provide the \nrequired consent to use their engagement data for research purposes, thereby limiting the analysis \nto users who agreed to participate. If non-consenting users exhibit different behaviors, the model\u2019s \nperformance may not fully represent the broader population. However, since the same \nmethodologies achieved comparable results in the Manoa dataset \u2013 distinct in its regulatory \nenvironment, addressed medical condition, program duration, user demographics, and therapeutic \napproach - the applied methods are likely generalizable across different mHealth contexts and \npopulations. \nA shared characteristic of both analyzed mHealth interventions is that users must provide proof of \nmedical need to access the apps and receive reimbursement. User behavior in these contexts may \ndiffer from that observed in mHealth interventions directly available in public app stores, where \nusers can self-enroll. The two mHealth interventions, however, also differ in their onboarding \nchannels \u2013 the DiGA Vivira is either directly prescribed by a physician, or access is requested by a \npatient with a confirmed diagnosis, while Manoa is actively promoted by health insurance providers \n\u2013 supporting the generalizability of our findings across varied onboarding contexts. However, it is \nnoteworthy that the onboarding context, users\u2019 trust in the DHI provider, and users\u2019 health \nconditions may impact adherence [20]. Hence, another important aspect of the mHealth \ninterventions analyzed is their relatively high user retention rates. A study by Baumel et al. (2019) \ncompared average retention rates for mental health apps at approximately 10% on Day 7 and 3.3% \non Day 30 across 59 analyzed apps [2]. In contrast, the two mHealth interventions examined in this \nstudy demonstrated substantially higher retention rates: 32% and 53% on Day 7, and 19% and 27% \non Day 30, respectively. DHIs with substantially lower retention rates may face challenges in \napplying the methods described here, due to class imbalance between engaged and disengaged \nusers. This imbalance can complicate the training and evaluation of prediction models, highlighting \nthe need for further research in contexts where disengagement is more prevalent. \nThe nonadherence prediction models described in this study also rely on the availability of rich, \ncontinuous behavioral app engagement data. This dependency suggests that these models are most \neffective in systems designed for frequent, ongoing use, where user engagement data is naturally \n", - "caption": "" - }, - { - "page": 26, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 508.2678527832031, - 753.8245849609375 - ], - "text": "26 \ngenerated over time. In settings where engagement data is sparse, the performance of prediction \nmodels may be diminished. Similarly, a 7-day prediction window, while ideal for timely \nintervention, may not be suitable for mHealth interventions that are not intended for regular weekly \nuse. Different methods and prediction windows may be required to capture user churn accurately \nfor interventions with more sporadic or irregular usage patterns. \nAn additional consideration is that our analyses were tied to a predefined observation window \nrelated to a full program duration of the apps. Although predictions were made from Day 1 through \nDay 84 and Day 1 through Day 178, using a 7-day prediction window to account for the entire \nperiod up to Days 90 and 186, respectively, a user who last logs in during the final week of the \ncycle may not be a \"churned\" user but rather a user who stops using the app because their program \nduration ends. Conversely, users who have not logged in for a month before the end of the \nobservation window can also start another program, indicating that churn status can be ambiguous \nat the end of fixed observation periods. \nWhile our results suggest that prediction models can accurately predict who is likely to become \nnonadherent and when, they do not provide insights into the underlying reasons why users \ndisengage. Nevertheless, they could serve as valuable research tools, detecting early signs of \nnonadherence and prompting targeted questionnaires to understand disengagement reasons before \na user is lost to follow-up. This approach could particularly help identifying factors driving \nnonadherence in real-world contexts, where once a user fully disengages, it becomes difficult to \ngather such information [47]. \nIt is also important to recognize that adherence to mHealth interventions is an intermediate outcome. \nThe primary aim of mHealth interventions is to facilitate health behavior change and ultimately \nimprove health outcomes. Although studies suggest a positive association between adherence and \nhealth outcomes, the link between adherence to mHealth interventions and health outcomes is not \nfully understood and needs further investigation in diverse settings [6]. While evidence from other \napp domains suggests that combining churn prediction models with preventive strategies \u2013 such as \npersonalized push notifications, omnichannel nudges, or content adaptations \u2013 can reduce churn \n[14, 33, 35], it also remains unclear whether this targeted approach can improve adherence and, \nultimately, health outcomes. Given the relatively new application of churn prediction models in \nmHealth interventions and this study being the first to comprehensively evaluate nonadherence \nprediction models aligned with the definition by Sieverink et al. (2017), significant research gaps \nremain. \nFuture research should aim to replicate nonadherence prediction models in diverse mHealth \ncontexts and evaluate their integration with targeted preventive strategies in prospective trials to \nassess the impact of these combined approaches on app usage, adherence, and health outcomes. \n", - "caption": "" - }, - { - "page": 27, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.2395629882812, - 515.74462890625 - ], - "text": "27 \nAddressing these gaps will support the development of more adaptive and impactful mHealth \ninterventions and their potential to mitigate the rising prevalence and economic burden of NCDs. \nAuthor Contributions \nRJ \u2013 conceptualization, data curation, formal analysis, methodology, writing (original draft), \nwriting (review and editing), LB \u2013 data curation, writing (review and editing), CMS \u2013 writing \n(review and editing), FW \u2013 writing (review and editing), EF \u2013 supervision, TK \u2013 supervision, \nwriting (review and editing) \nEthics Declaration \nThis study involves the analyses of two anonymous datasets. Therefore, it does not qualify as human \nsubject research and has been exempted from ethics approval by the research team's University \nEthics Committee. A separate ethics vote for the use of the Vivira dataset was obtained from the \nState Physician Chamber of Baden-W\u00fcrttemberg, Germany, under the reference F-2024-072. \nConflicts of Interest \nRJ, FW, TK, and EF are affiliated with the Centre for Digital Health Interventions, a joint initiative \nof the Institute for Implementation Science in Health Care, University of Zurich; the Department \nof Management, Technology, and Economics at Swiss Federal Institute of Technology Zurich; and \nthe Institute of Technology Management and School of Medicine at the University of St. Gallen. \nThe Centre for Digital Health Interventions is funded in part by CSS, a Swiss health insurer; Uniqa, \nan Austrian health insurer; and MTIP, a Swiss digital health investor. However, neither CSS, Uniqa, \nMTIP were involved in this research. EF, TK, and CMS are affiliated with Pathmate Technologies \nAG, and LB is affiliated with Vivira Health Lab GmbH.\n", - "caption": "" - }, - { - "page": 28, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 84.76000213623047, - 47.984615325927734 - ], - "text": "28 \n", - "caption": "" - }, - { - "page": 29, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.1200561523438, - 47.984615325927734 - ], - "text": "29 \n", - "caption": "" - }, - { - "page": 30, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 508.2912902832031, - 755.0245971679688 - ], - "text": "30 \n(CoG), \nAugust \n2019. \nIEEE, \nLondon, \nUnited \nKingdom, \n1\u20138. \nhttps://doi.org/10.1109/CIG.2019.8848106 \n[30] \nHongwook Kwon, Ho Heon Kim, Jaeil An, Jae-Ho Lee, and Yu Rang Park. 2021. Lifelog \nData-Based Prediction Model of Digital Health Care App Customer Churn: Retrospective \nObservational \nStudy. 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Int. j. adv. appl. sci. 9, 7 (July 2022), 139\u2013\n149. https://doi.org/10.21833/ijaas.2022.07.014 \n[57] \nHa Trinh, Ameneh Shamekhi, Everlyne Kimani, and Timothy W. Bickmore. 2018. \nPredicting User Engagement in Longitudinal Interventions with Virtual Agents. In \nProceedings of the 18th International Conference on Intelligent Virtual Agents (IVA \u201918), \n", - "caption": "" - }, - { - "page": 32, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 84.76000213623047, - 47.984615325927734 - ], - "text": "32 \n", - "caption": "" - }, - { - "page": 33, - "index": 1, - "bbox": [ - 90.0, - 35.0433349609375, - 526.1200561523438, - 167.26461791992188 - ], - "text": "33 \nAppendix \nAppendix 1: Applied software packages and model hyperparameter grids \nAppendix 2: Feature Importance Analyses. \nAppendix 3: Daily Churn Prediction Results. \n", - "caption": "" - }, - { - "page": 33, - "index": 2, - "bbox": [ - 90.0, - 191.41868591308594, - 522.0478515625, - 757.7432861328125 - ], - "text": "Table 5: Descriptive statistics of users\u2019 number of active days and completed exercises per week in Vivira \n(n = 8,372). \n \nUsers\u2019 number of active days \n \nUsers\u2019 number of completed exercises \n \nDay \nMean (SD) Median (IQR) \nRange \nMean (SD) Median (IQR) \nRange \nWeek 1 (Days 1-7) \n3.14 (2.04) \n3 (1-5) \n0-7 \n \n9.68 (9.16) \n8 (0-16) \n0-29 \nWeek 2 (Days 8-14) \n2.01 (2.25) \n1 (0-4) \n0-7 \n \n7.41 (8.9) \n4 (0-12) \n0-29 \nWeek 3 (Days 15-21) \n1.67 (2.16) \n1 (0-3) \n0-7 \n \n6.2 (8.46) \n0 (0-12) \n0-29 \nWeek 4 (Days 22-28) \n1.45 (2.07) \n0 (0-2) \n0-7 \n \n5.39 (8.07) \n0 (0-8) \n0-29 \nWeek 5 (Days 29-35) \n1.27 (2.00) \n0 (0-2) \n0-7 \n \n4.74 (7.75) \n0 (0-8) \n0-29 \nWeek 6 (Days 36-42) \n1.11 (1.91) \n0 (0-1) \n0-7 \n \n4.15 (7.42) \n0 (0-4) \n0-33 \nWeek 7 (Days 43-49) \n1.00 (1.84) \n0 (0-1) \n0-7 \n \n3.75 (7.13) \n0 (0-4) \n0-32 \nWeek 8 (Days 50-56) \n0.92 (1.80) \n0 (0-1) \n0-7 \n \n3.46 (6.94) \n0 (0-4) \n0-28 \nWeek 9 (Days 57-63) \n0.82 (1.71) \n0 (0-1) \n0-7 \n \n3.05 (6.6) \n0 (0-1) \n0-29 \nWeek 10 (Days 64-70) \n0.69 (1.60) \n0 (0-0) \n0-7 \n \n2.55 (6.16) \n0 (0-0) \n0-28 \nWeek 11 (Days 71-77) \n0.57 (1.49) \n0 (0-0) \n0-7 \n \n2.08 (5.7) \n0 (0-0) \n0-28 \nWeek 12 (Days 78-84) \n0.50 (1.40) \n0 (0-0) \n0-7 \n \n1.82 (5.37) \n0 (0-0) \n0-28 \nWeek 13 (Days 85-90) \n0.38 (1.15) \n0 (0-0) \n0-6 \n \n1.35 (4.37) \n0 (0-0) \n0-25 \nWeeks 1-13 (Days 1-90) \n15.54 (19.55) \n7 (2-21) \n0-90 \n \n55.63 (76.54) \n20 (4-76.25) \n0-360 \nSD = standard deviation; IQR = interquartile range \n \n \nTable 6: Descriptive statistics of numerical outcomes in Vivira (n = 8,372). \n \nMean (SD) Median (IQR) \nRange \nDay of last login (among 90 days) \n38.53 (31.68) \n33 (7-67) \n0-90 \nWeek of last login (among 13 Weeks) \n5.99 (4.41) \n5 (1-10) \n0-13 \nNumber of active weeks (among 13 weeks) \n4.91 (3.98) \n3 (1-8) \n0-13 \nNumber of adherent weeks (among 13 weeks) \n3.1 (3.9) \n1 (0-5) \n0-13 \nSD = standard deviation; IQR = interquartile range \n \n \n \nTable 7: Total and relative number of active, churning, and adherent users per week in Vivira (n = 8,372). \n \nWeekly Activity \n \nWeek of Last Login \n \nWeekly Adherence \n \nn \n% \n \nn \n% \n \nn \n% \nNon-churned % \nWeek 1 (Days 1-7) \n8344 \n99.7% \n \n2107 \n25.0% \n \n4417 \n52.8% \n52.9% \nWeek 2 (Days 8-14) \n5136 \n61.3% \n \n716 \n8.8% \n \n3425 \n40.9% \n54.7% \nWeek 3 (Days 15-21) \n4322 \n51.6% \n \n544 \n6.5% \n \n2898 \n34.6% \n52.2% \nWeek 4 (Days 22-28) \n3840 \n45.9% \n \n503 \n6.0% \n \n2548 \n30.4% \n50.9% \nWeek 5 (Days 29-35) \n3385 \n40.4% \n \n476 \n5.7% \n \n2239 \n26.7% \n49.7% \nWeek 6 (Days 36-42) \n2954 \n35.3% \n \n425 \n5.1% \n \n1921 \n22.9% \n47.7% \nWeek 7 (Days 43-49) \n2716 \n32.4% \n \n383 \n4.6% \n \n1753 \n20.9% \n48.7% \nWeek 8 (Days 50-56) \n2438 \n29.1% \n \n421 \n5.0% \n \n1629 \n19.5% \n50.6% \nWeek 9 (Days 57-63) \n2229 \n26.6% \n \n488 \n5.8% \n \n1426 \n17.0% \n51.0% \nWeek 10 (Days 64-70) \n1833 \n21.9% \n \n437 \n5.2% \n \n1189 \n14.2% \n51.5% \nWeek 11 (Days 71-77) \n1506 \n18.0% \n \n351 \n4.2% \n \n970 \n11.6% \n51.8% \nWeek 12 (Days 78-84) \n1324 \n15.8% \n \n415 \n5.0% \n \n846 \n10.1% \n55.6% \nWeek 13 (Days 85-90) \n1106 \n13.2% \n \n1106 \n13.2% \n \n664 \n7.9% \n60.0% \nTable 8: Descriptive statistics of numerical outcomes in Manoa (n = 6,674). \nDay \nMean (SD) Median (IQR) \nRange \nDay of last login (among 186 days) \n88.8 (72) \n80 (13-169) \n1-186 \nWeek of last login (among 27 weeks) \n13.16 (10.25) \n12 (2-25) \n1-27 \nMonth of last login (among 6 months) \n3.36 (2.11) \n3 (1-6) \n1-6 \nNumber of active weeks (among 27 weeks) \n8.91 (9.06) \n4 (2-15) \n1-27 \nSD = standard deviation; IQR = interquartile range \n \n \n \n", - "caption": "Descriptive statistics of users\u2019 number of active days and completed exercises per week in Vivira \n(n = 8,372). \n \nUsers\u2019 number of active days \n \nUsers\u2019 number of completed exercises \n \nDay \nMean (SD) Median (IQR) \nRange \nMean (SD) Median (IQR) \nRange \nWeek 1 (Days 1-7) \n3.14 (2.04) \n3 (1-5) \n0-7 \n \n9.68 (9.16) \n8 (0-16) \n0-29 \nWeek 2 (Days 8-14) \n2.01 (2.25) \n1 (0-4) \n0-7 \n \n7.41 (8.9) \n4 (0-12) \n0-29 \nWeek 3 (Days 15-21) \n1.67 (2.16) \n1 (0-3) \n0-7 \n \n6.2 (8.46) \n0 (0-12) \n0-29 \nWeek 4 (Days 22-28) \n1.45 (2.07) \n0 (0-2) \n0-7 \n \n5.39 (8.07) \n0 (0-8) \n0-29 \nWeek 5 (Days 29-35) \n1.27 (2.00) \n0 (0-2) \n0-7 \n \n4.74 (7.75) \n0 (0-8) \n0-29 \nWeek 6 (Days 36-42) \n1.11 (1.91) \n0 (0-1) \n0-7 \n \n4.15 (7.42) \n0 (0-4) \n0-33 \nWeek 7 (Days 43-49) \n1.00 (1.84) \n0 (0-1) \n0-7 \n \n3.75 (7.13) \n0 (0-4) \n0-32 \nWeek 8 (Days 50-56) \n0.92 (1.80) \n0 (0-1) \n0-7 \n \n3.46 (6.94) \n0 (0-4) \n0-28 \nWeek 9 (Days 57-63) \n0.82 (1.71) \n0 (0-1) \n0-7 \n \n3.05 (6.6) \n0 (0-1) \n0-29 \nWeek 10 (Days 64-70) \n0.69 (1.60) \n0 (0-0) \n0-7 \n \n2.55 (6.16) \n0 (0-0) \n0-28 \nWeek 11 (Days 71-77) \n0.57 (1.49) \n0 (0-0) \n0-7 \n \n2.08 (5.7) \n0 (0-0) \n0-28 \nWeek 12 (Days 78-84) \n0.50 (1.40) \n0 (0-0) \n0-7 \n \n1.82 (5.37) \n0 (0-0) \n0-28 \nWeek 13 (Days 85-90) \n0.38 (1.15) \n0 (0-0) \n0-6 \n \n1.35 (4.37) \n0 (0-0) \n0-25 \nWeeks 1-13 (Days 1-90) \n15.54 (19.55) \n7 (2-21) \n0-90 \n \n55.63 (76.54) \n20 (4-76.25) \n0-360 \nSD = standard deviation; IQR = interquartile range \n \n \nTable 6: Descriptive statistics of numerical outcomes in Vivira (n = 8,372). \n \nMean (SD) Median (IQR) \nRange \nDay of last login (among 90 days) \n38.53 (31.68) \n33 (7-67) \n0-90 \nWeek of last login (among 13 Weeks) \n5.99 (4.41) \n5 (1-10) \n0-13 \nNumber of active weeks (among 13 weeks) \n4.91 (3.98) \n3 (1-8) \n0-13 \nNumber of adherent weeks (among 13 weeks) \n3.1 (3.9) \n1 (0-5) \n0-13 \nSD = standard deviation; IQR = interquartile range \n \n \n \n \nTable 7: Total and relative number of active, churning, and adherent users per week in Vivira (n = 8,372). \n \nWeekly Activity \n \nWeek of Last Login \n \nWeekly Adherence \n \nn \n% \n \nn \n% \n \nn \n% \nNon-churned % \nWeek 1 (Days 1-7) \n8344 \n99.7% \n \n2107 \n25.0% \n \n4417 \n52.8% \n52.9% \nWeek 2 (Days 8-14) \n5136 \n61.3% \n \n716 \n8.8% \n \n3425 \n40.9% \n54.7% \nWeek 3 (Days 15-21) \n4322 \n51.6% \n \n544 \n6.5% \n \n2898 \n34.6% \n52.2% \nWeek 4 (Days 22-28) \n3840 \n45.9% \n \n503 \n6.0% \n \n2548 \n30.4% \n50.9% \nWeek 5 (Days 29-35) \n3385 \n40.4% \n \n476 \n5.7% \n \n2239 \n26.7% \n49.7% \nWeek 6 (Days 36-42) \n2954 \n35.3% \n \n425 \n5.1% \n \n1921 \n22.9% \n47.7% \nWeek 7 (Days 43-49) \n2716 \n32.4% \n \n383 \n4.6% \n \n1753 \n20.9% \n48.7% \nWeek 8 (Days 50-56) \n2438 \n29.1% \n \n421 \n5.0% \n \n1629 \n19.5% \n50.6% \nWeek 9 (Days 57-63) \n2229 \n26.6% \n \n488 \n5.8% \n \n1426 \n17.0% \n51.0% \nWeek 10 (Days 64-70) \n1833 \n21.9% \n \n437 \n5.2% \n \n1189 \n14.2% \n51.5% \nWeek 11 (Days 71-77) \n1506 \n18.0% \n \n351 \n4.2% \n \n970 \n11.6% \n51.8% \nWeek 12 (Days 78-84) \n1324 \n15.8% \n \n415 \n5.0% \n \n846 \n10.1% \n55.6% \nWeek 13 (Days 85-90) \n1106 \n13.2% \n \n1106 \n13.2% \n \n664 \n7.9% \n60.0% \n \nTable 8: Descriptive statistics of numerical outcomes in Manoa (n = 6,674). \nDay \nMean (SD) Median (IQR) \nRange \nDay of last login (among 186 days) \n88.8 (72) \n80 (13-169) \n1-186 \nWeek of last login (among 27 weeks) \n13.16 (10.25) \n12 (2-25) \n1-27 \nMonth of last login (among 6 months) \n3.36 (2.11) \n3 (1-6) \n1-6 \nNumber of active weeks (among 27 weeks) \n8.91 (9.06) \n4 (2-15) \n1-27 \nSD = standard deviation; IQR = interquartile range" - }, - { - "page": 34, - "index": 1, - "bbox": [ - 72.0, - 35.0433349609375, - 506.1650085449219, - 746.6226806640625 - ], - "text": "34 \nTable 9: Descriptive statistics of users\u2019 number of active days and sessions per week in Manoa (n = 6,674). \n \nUsers\u2019 number of active days \n \nUsers\u2019 number of sessions \n \nDay \nMean (SD) Median (IQR) \nRange \nMean (SD) Median (IQR) \nRange \nWeek 1 (Days 1-7) \n4.09 (2.56) \n5 (1-7) \n0-7 \n \n13.47 (14.4) \n9 (2-21) \n0-247 \nWeek 2 (Days 8-14) \n2.98 (2.86) \n2 (0-6) \n0-7 \n \n8.57 (11.95) \n3 (0-14) \n0-130 \nWeek 3 (Days 15-21) \n2.32 (2.81) \n1 (0-5) \n0-7 \n \n6.09 (10.29) \n1 (0-9) \n0-133 \nWeek 4 (Days 22-28) \n1.97 (2.74) \n0 (0-4) \n0-7 \n \n5.14 (10.12) \n0 (0-7) \n0-257 \nWeek 5 (Days 29-35) \n1.87 (2.69) \n0 (0-4) \n0-7 \n \n4.80 (10.14) \n0 (0-6) \n0-311 \nWeek 6 (Days 36-42) \n1.94 (2.75) \n0 (0-4) \n0-7 \n \n5.36 (10.26) \n0 (0-7) \n0-189 \nWeek 7 (Days 43-49) \n1.93 (2.79) \n0 (0-4) \n0-7 \n \n5.25 (9.85) \n0 (0-8) \n0-225 \nWeek 8 (Days 50-56) \n1.71 (2.67) \n0 (0-3) \n0-7 \n \n4.31 (8.76) \n0 (0-5) \n0-131 \n \nWeek 9 (Days 57-63) \n1.55 (2.59) \n0 (0-2) \n0-7 \n \n3.76 (7.97) \n0 (0-3) \n0-96 \n \nWeek 10 (Days 64-70) \n1.44 (2.52) \n0 (0-2) \n0-7 \n \n3.44 (7.78) \n0 (0-2) \n0-129 \n \nWeek 11 (Days 71-77) \n1.46 (2.52) \n0 (0-2) \n0-7 \n \n3.68 (8.23) \n0 (0-3) \n0-145 \n \nWeek 12 (Days 78-84) \n1.44 (2.51) \n0 (0-2) \n0-7 \n \n3.62 (8.54) \n0 (0-3) \n0-250 \n \nWeek 13 (Days 85-91) \n1.37 (2.46) \n0 (0-1) \n0-7 \n \n3.30 (7.68) \n0 (0-2) \n0-130 \n \nWeek 14 (Days 85-98) \n1.29 (2.42) \n0 (0-1) \n0-7 \n \n2.99 (7.11) \n0 (0-1) \n0-131 \n \nWeek 15 (Days 85-105) \n1.22 (2.37) \n0 (0-1) \n0-7 \n \n2.82 (6.92) \n0 (0-1) \n0-108 \n \nWeek 16 (Days 106-112) \n1.21 (2.37) \n0 (0-1) \n0-7 \n \n2.87 (6.98) \n0 (0-1) \n0-103 \n \nWeek 17 (Days 113-119) \n1.18 (2.34) \n0 (0-1) \n0-7 \n \n2.82 (6.98) \n0 (0-1) \n0-108 \n \nWeek 18 (Days 120-126) \n1.15 (2.32) \n0 (0-0) \n0-7 \n \n2.67 (6.67) \n0 (0-0) \n0-87 \n \nWeek 19 (Days 127-133) \n1.09 (2.27) \n0 (0-0) \n0-7 \n \n2.47 (6.23) \n0 (0-0) \n0-80 \n \nWeek 20 (Days 134-140) \n1.04 (2.23) \n0 (0-0) \n0-7 \n \n2.29 (5.91) \n0 (0-0) \n0-57 \n \nWeek 21 (Days 141-147) \n1.04 (2.24) \n0 (0-0) \n0-7 \n \n2.39 (6.23) \n0 (0-0) \n0-60 \n \nWeek 22 (Days 148-154) \n1.03 (2.23) \n0 (0-0) \n0-7 \n \n2.37 (6.32) \n0 (0-0) \n0-93 \n \nWeek 23 (Days 155-161) \n0.98 (2.19) \n0 (0-0) \n0-7 \n \n2.23 (6.04) \n0 (0-0) \n0-71 \n \nWeek 24 (Days 162-168) \n0.95 (2.15) \n0 (0-0) \n0-7 \n \n2.14 (5.87) \n0 (0-0) \n0-62 \n \nWeek 25 (Days 169-175) \n0.93 (2.14) \n0 (0-0) \n0-7 \n \n2.08 (5.76) \n0 (0-0) \n0-62 \n \nWeek 26 (Days 176-182) \n0.91 (2.12) \n0 (0-0) \n0-7 \n \n2.04 (5.60) \n0 (0-0) \n0-69 \n \nWeek 27 (Days 183-186) \n0.51 (1.22) \n0 (0-0) \n0-4 \n \n1.16 (3.37) \n0 (0-0) \n0-48 \n \nWeeks 1-27 (Days 1-186) \n40.63 (54.88) \n13 (3-57) \n1-186 \n \n104.15 (175) \n28 (4-129) \n1-2801 \n \nSD = standard deviation; IQR = interquartile range \n \nTable 10: Total and relative number of active and churning users per week in Manoa (n = 6,674). \n \nWeekly Activity \n \nWeek of Last Login \n \n \nn \n% \n \nn \n% \n \nWeek 1 (Days 1-7) \n6274 \n94.0% \n \n1295 \n19.4% \n \nWeek 2 (Days 8-14) \n4324 \n64.8% \n \n442 \n6.6% \n \nWeek 3 (Days 15-21) \n3439 \n51.5% \n \n302 \n4.5% \n \nWeek 4 (Days 22-28) \n2913 \n43.6% \n \n162 \n2.4% \n \nWeek 5 (Days 29-35) \n2841 \n42.6% \n \n177 \n2.7% \n \nWeek 6 (Days 36-42) \n2769 \n41.5% \n \n188 \n2.8% \n \nWeek 7 (Days 43-49) \n2640 \n39.6% \n \n176 \n2.6% \n \nWeek 8 (Days 50-56) \n2427 \n36.4% \n \n159 \n2.4% \n \nWeek 9 (Days 57-63) \n2225 \n33.3% \n \n148 \n2.2% \n \nWeek 10 (Days 64-70) \n2103 \n31.5% \n \n127 \n1.9% \n \nWeek 11 (Days 71-77) \n2100 \n31.5% \n \n128 \n1.9% \n \nWeek 12 (Days 78-84) \n2054 \n30.8% \n \n111 \n1.7% \n \nWeek 13 (Days 85-91) \n1976 \n29.6% \n \n133 \n2.0% \n \nWeek 14 (Days 85-98) \n1854 \n27.8% \n \n126 \n1.9% \n \nWeek 15 (Days 85-105) \n1778 \n26.6% \n \n127 \n1.9% \n \nWeek 16 (Days 106-112) \n1765 \n26.4% \n \n136 \n2.0% \n \nWeek 17 (Days 113-119) \n1694 \n25.4% \n \n98 \n1.5% \n \nWeek 18 (Days 120-126) \n1633 \n24.5% \n \n124 \n1.9% \n \nWeek 19 (Days 127-133) \n1570 \n23.5% \n \n129 \n1.9% \n \nWeek 20 (Days 134-140) \n1490 \n22.3% \n \n110 \n1.6% \n \nWeek 21 (Days 141-147) \n1491 \n22.3% \n \n118 \n1.8% \n \nWeek 22 (Days 148-154) \n1476 \n22.1% \n \n153 \n2.3% \n \nWeek 23 (Days 155-161) \n1428 \n21.4% \n \n148 \n2.2% \n \nWeek 24 (Days 162-168) \n1364 \n20.4% \n \n182 \n2.7% \n \nWeek 25 (Days 169-175) \n1340 \n20.1% \n \n212 \n3.2% \n \nWeek 26 (Days 176-182) \n1324 \n19.8% \n \n286 \n4.3% \n \nWeek 27 (Days 183-186) \n1177 \n17.6% \n \n1177 \n17.6% \n \nTable 11: Descriptive statistics of adherence outcomes in Manoa among users who completed the initial \nmeasurement week (n = 2,095). \n \nUsers\u2019 number of completed measurement weeks \n \nMonthly Adherence \nDay \nMean (SD) \nMedian (IQR) \nRange \nn \n% \nNon-churned % \nMonth 2 \n0.46 (0.51) \n0 (0-1) \n0-3 \n962 \n45.9% \n50.8% \nMonth 3 \n0.33 (0.49) \n0 (0-1) \n0-3 \n672 \n32.1% \n41.5% \nMonth 4 \n0.27 (0.47) \n0 (0-1) \n0-5 \n558 \n26.6% \n39.4% \nMonth 5 \n0.23 (0.43) \n0 (0-0) \n0-5 \n479 \n22.9% \n40.1% \nMonth 6 \n0.19 (0.40) \n0 (0-0) \n0-1 \n404 \n19.3% \n41.4% \nSD = standard deviation; IQR = interquartile range \n", - "caption": "Descriptive statistics of users\u2019 number of active days and sessions per week in Manoa (n = 6,674). \n \nUsers\u2019 number of active days \n \nUsers\u2019 number of sessions \n \nDay \nMean (SD) Median (IQR) \nRange \nMean (SD) Median (IQR) \nRange \nWeek 1 (Days 1-7) \n4.09 (2.56) \n5 (1-7) \n0-7 \n \n13.47 (14.4) \n9 (2-21) \n0-247 \nWeek 2 (Days 8-14) \n2.98 (2.86) \n2 (0-6) \n0-7 \n \n8.57 (11.95) \n3 (0-14) \n0-130 \nWeek 3 (Days 15-21) \n2.32 (2.81) \n1 (0-5) \n0-7 \n \n6.09 (10.29) \n1 (0-9) \n0-133 \nWeek 4 (Days 22-28) \n1.97 (2.74) \n0 (0-4) \n0-7 \n \n5.14 (10.12) \n0 (0-7) \n0-257 \nWeek 5 (Days 29-35) \n1.87 (2.69) \n0 (0-4) \n0-7 \n \n4.80 (10.14) \n0 (0-6) \n0-311 \nWeek 6 (Days 36-42) \n1.94 (2.75) \n0 (0-4) \n0-7 \n \n5.36 (10.26) \n0 (0-7) \n0-189 \nWeek 7 (Days 43-49) \n1.93 (2.79) \n0 (0-4) \n0-7 \n \n5.25 (9.85) \n0 (0-8) \n0-225 \nWeek 8 (Days 50-56) \n1.71 (2.67) \n0 (0-3) \n0-7 \n \n4.31 (8.76) \n0 (0-5) \n0-131 \n \nWeek 9 (Days 57-63) \n1.55 (2.59) \n0 (0-2) \n0-7 \n \n3.76 (7.97) \n0 (0-3) \n0-96 \n \nWeek 10 (Days 64-70) \n1.44 (2.52) \n0 (0-2) \n0-7 \n \n3.44 (7.78) \n0 (0-2) \n0-129 \n \nWeek 11 (Days 71-77) \n1.46 (2.52) \n0 (0-2) \n0-7 \n \n3.68 (8.23) \n0 (0-3) \n0-145 \n \nWeek 12 (Days 78-84) \n1.44 (2.51) \n0 (0-2) \n0-7 \n \n3.62 (8.54) \n0 (0-3) \n0-250 \n \nWeek 13 (Days 85-91) \n1.37 (2.46) \n0 (0-1) \n0-7 \n \n3.30 (7.68) \n0 (0-2) \n0-130 \n \nWeek 14 (Days 85-98) \n1.29 (2.42) \n0 (0-1) \n0-7 \n \n2.99 (7.11) \n0 (0-1) \n0-131 \n \nWeek 15 (Days 85-105) \n1.22 (2.37) \n0 (0-1) \n0-7 \n \n2.82 (6.92) \n0 (0-1) \n0-108 \n \nWeek 16 (Days 106-112) \n1.21 (2.37) \n0 (0-1) \n0-7 \n \n2.87 (6.98) \n0 (0-1) \n0-103 \n \nWeek 17 (Days 113-119) \n1.18 (2.34) \n0 (0-1) \n0-7 \n \n2.82 (6.98) \n0 (0-1) \n0-108 \n \nWeek 18 (Days 120-126) \n1.15 (2.32) \n0 (0-0) \n0-7 \n \n2.67 (6.67) \n0 (0-0) \n0-87 \n \nWeek 19 (Days 127-133) \n1.09 (2.27) \n0 (0-0) \n0-7 \n \n2.47 (6.23) \n0 (0-0) \n0-80 \n \nWeek 20 (Days 134-140) \n1.04 (2.23) \n0 (0-0) \n0-7 \n \n2.29 (5.91) \n0 (0-0) \n0-57 \n \nWeek 21 (Days 141-147) \n1.04 (2.24) \n0 (0-0) \n0-7 \n \n2.39 (6.23) \n0 (0-0) \n0-60 \n \nWeek 22 (Days 148-154) \n1.03 (2.23) \n0 (0-0) \n0-7 \n \n2.37 (6.32) \n0 (0-0) \n0-93 \n \nWeek 23 (Days 155-161) \n0.98 (2.19) \n0 (0-0) \n0-7 \n \n2.23 (6.04) \n0 (0-0) \n0-71 \n \nWeek 24 (Days 162-168) \n0.95 (2.15) \n0 (0-0) \n0-7 \n \n2.14 (5.87) \n0 (0-0) \n0-62 \n \nWeek 25 (Days 169-175) \n0.93 (2.14) \n0 (0-0) \n0-7 \n \n2.08 (5.76) \n0 (0-0) \n0-62 \n \nWeek 26 (Days 176-182) \n0.91 (2.12) \n0 (0-0) \n0-7 \n \n2.04 (5.60) \n0 (0-0) \n0-69 \n \nWeek 27 (Days 183-186) \n0.51 (1.22) \n0 (0-0) \n0-4 \n \n1.16 (3.37) \n0 (0-0) \n0-48 \n \nWeeks 1-27 (Days 1-186) \n40.63 (54.88) \n13 (3-57) \n1-186 \n \n104.15 (175) \n28 (4-129) \n1-2801 \n \nSD = standard deviation; IQR = interquartile range \n \n \nTable 10: Total and relative number of active and churning users per week in Manoa (n = 6,674). \n \nWeekly Activity \n \nWeek of Last Login \n \n \nn \n% \n \nn \n% \n \nWeek 1 (Days 1-7) \n6274 \n94.0% \n \n1295 \n19.4% \n \nWeek 2 (Days 8-14) \n4324 \n64.8% \n \n442 \n6.6% \n \nWeek 3 (Days 15-21) \n3439 \n51.5% \n \n302 \n4.5% \n \nWeek 4 (Days 22-28) \n2913 \n43.6% \n \n162 \n2.4% \n \nWeek 5 (Days 29-35) \n2841 \n42.6% \n \n177 \n2.7% \n \nWeek 6 (Days 36-42) \n2769 \n41.5% \n \n188 \n2.8% \n \nWeek 7 (Days 43-49) \n2640 \n39.6% \n \n176 \n2.6% \n \nWeek 8 (Days 50-56) \n2427 \n36.4% \n \n159 \n2.4% \n \nWeek 9 (Days 57-63) \n2225 \n33.3% \n \n148 \n2.2% \n \nWeek 10 (Days 64-70) \n2103 \n31.5% \n \n127 \n1.9% \n \nWeek 11 (Days 71-77) \n2100 \n31.5% \n \n128 \n1.9% \n \nWeek 12 (Days 78-84) \n2054 \n30.8% \n \n111 \n1.7% \n \nWeek 13 (Days 85-91) \n1976 \n29.6% \n \n133 \n2.0% \n \nWeek 14 (Days 85-98) \n1854 \n27.8% \n \n126 \n1.9% \n \nWeek 15 (Days 85-105) \n1778 \n26.6% \n \n127 \n1.9% \n \nWeek 16 (Days 106-112) \n1765 \n26.4% \n \n136 \n2.0% \n \nWeek 17 (Days 113-119) \n1694 \n25.4% \n \n98 \n1.5% \n \nWeek 18 (Days 120-126) \n1633 \n24.5% \n \n124 \n1.9% \n \nWeek 19 (Days 127-133) \n1570 \n23.5% \n \n129 \n1.9% \n \nWeek 20 (Days 134-140) \n1490 \n22.3% \n \n110 \n1.6% \n \nWeek 21 (Days 141-147) \n1491 \n22.3% \n \n118 \n1.8% \n \nWeek 22 (Days 148-154) \n1476 \n22.1% \n \n153 \n2.3% \n \nWeek 23 (Days 155-161) \n1428 \n21.4% \n \n148 \n2.2% \n \nWeek 24 (Days 162-168) \n1364 \n20.4% \n \n182 \n2.7% \n \nWeek 25 (Days 169-175) \n1340 \n20.1% \n \n212 \n3.2% \n \nWeek 26 (Days 176-182) \n1324 \n19.8% \n \n286 \n4.3% \n \nWeek 27 (Days 183-186) \n1177 \n17.6% \n \n1177 \n17.6% \n \n \nTable 11: Descriptive statistics of adherence outcomes in Manoa among users who completed the initial \nmeasurement week (n = 2,095). \n \nUsers\u2019 number of completed measurement weeks \n \nMonthly Adherence \nDay \nMean (SD) \nMedian (IQR) \nRange \nn \n% \nNon-churned % \nMonth 2 \n0.46 (0.51) \n0 (0-1) \n0-3 \n962 \n45.9% \n50.8% \nMonth 3 \n0.33 (0.49) \n0 (0-1) \n0-3 \n672 \n32.1% \n41.5% \nMonth 4 \n0.27 (0.47) \n0 (0-1) \n0-5 \n558 \n26.6% \n39.4% \nMonth 5 \n0.23 (0.43) \n0 (0-0) \n0-5 \n479 \n22.9% \n40.1% \nMonth 6 \n0.19 (0.40) \n0 (0-0) \n0-1 \n404 \n19.3% \n41.4% \nSD = standard deviation; IQR = interquartile range" - } - ] -} \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/manuscript_report.md b/Backup/V5_multi_agent2/results/manuscript_report.md deleted file mode 100644 index 9663a74..0000000 --- a/Backup/V5_multi_agent2/results/manuscript_report.md +++ /dev/null @@ -1,6639 +0,0 @@ -# Manuscript Review Report - -Generated on: 2025-05-04 04:29:09 - -## Important Notes - -> **Version 1.0 (Beta)**: This tool was developed in a short timeframe and may contain hallucinations and errors. Approximately 50% of feedback will likely be unusable, 30% may be mediocre, and 20% should be helpful in highlighting issues that might have been overlooked. Please send feedback to rjakob@ethz.ch so we can continue to improve our agents. - -> **Model Limitations**: This report was generated using a basic model (ChatGPT 3.5). For more sophisticated analysis, the code is open access and users can run more advanced models (such as GPT-4) with their own API keys. Visit [https://github.com/robertjakob/rigorous](https://github.com/robertjakob/rigorous) for more information. - -> **Development Status**: This tool is still in testing mode with many agents in their first iteration. We welcome contributions to build the best review agent team to improve the quality of scientific publishing. Join us at [https://github.com/robertjakob/rigorous](https://github.com/robertjakob/rigorous). - - -## Table of Contents -- [Overall Assessment](#overall-assessment) -- [Agent Reports](#agent-reports) - - [Section-Specific Agents (S1-S10)](#section-specific-agents-s1-s10) - - [S1 - Title and Keywords](#s1---title-and-keywords) - - [S2 - Abstract](#s2---abstract) - - [S3 - Introduction](#s3---introduction) - - [S4 - Literature Review](#s4---literature-review) - - [S5 - Methodology](#s5---methodology) - - [S6 - Results](#s6---results) - - [S7 - Discussion](#s7---discussion) - - [S8 - Conclusion](#s8---conclusion) - - [S9 - References](#s9---references) - - [S10 - Supplementary Materials](#s10---supplementary-materials) - - [Rigor Agents (R1-R7)](#rigor-agents-r1-r7) - - [R1 - Originality and Contribution](#r1---originality-and-contribution) - - [R2 - Impact and Significance](#r2---impact-and-significance) - - [R3 - Ethics and Compliance](#r3---ethics-and-compliance) - - [R4 - Data and Code Availability](#r4---data-and-code-availability) - - [R5 - Statistical Rigor](#r5---statistical-rigor) - - [R6 - Technical Accuracy](#r6---technical-accuracy) - - [R7 - Consistency](#r7---consistency) - - [Writing Agents (W1-W8)](#writing-agents-w1-w8) - - [W1 - Language and Style](#w1---language-and-style) - - [W2 - Narrative and Structure](#w2---narrative-and-structure) - - [W3 - Clarity and Conciseness](#w3---clarity-and-conciseness) - - [W4 - Terminology Consistency](#w4---terminology-consistency) - - [W5 - Inclusive Language](#w5---inclusive-language) - - [W6 - Citation Formatting](#w6---citation-formatting) - - [W7 - Target Audience Alignment](#w7---target-audience-alignment) - - [W8 - Visual Presentation](#w8---visual-presentation) - -## Overall Assessment - -The manuscript received an average score of 5.88/10 across all evaluation criteria. -A total of 201 critical remarks and 267 improvement suggestions were identified. - -## Agent Reports - -### Section-Specific Agents (S1-S10) - -## S1 - Title and Keywords -### Score: 4/10 - -### Summary -The manuscript's title currently suffers from broadness and length, which diminish clarity, impact, and searchability. The absence of explicit keywords further hampers discoverability. A more specific, keyword-optimized title that clearly indicates the focus on traits, distribution, and economic impacts of social preferences would greatly enhance its visibility and adherence to academic standards. The proposed revision aims to balance clarity, relevance, impact, and SEO, making the paper more accessible and attractive to its target audience. - -### Critical Remarks -### Remark 1 - -**Category**: title_clarity -**Location**: Title -**Issue**: The current title 'Social Preferences: Fundamental Characteristics and Economic Consequences' is somewhat broad and generic, which may reduce immediate clarity about the specific focus of the paper. -**Severity**: medium -**Impact**: This can hinder quick recognition of the paper's core contribution, affecting reader engagement and discoverability. - -### Remark 2 - -**Category**: title_length -**Location**: Title -**Issue**: The title is relatively long and contains multiple concepts, which may dilute its impact and make it less memorable. -**Severity**: low -**Impact**: A lengthy title can reduce search engine optimization and reduce the likelihood of appearing in concise search queries. - -### Remark 3 - -**Category**: keywords_relevance -**Location**: Keywords -**Issue**: No keywords section is explicitly provided in the manuscript. -**Severity**: high -**Impact**: Lack of keywords diminishes discoverability in digital searches, reducing the paper's visibility to targeted audiences. - -### Remark 4 - -**Category**: guidelines -**Location**: Title -**Issue**: The title does not explicitly follow the standard convention of including specific keywords or phrases that enhance SEO or thematic clarity. -**Severity**: medium -**Impact**: This can limit the paper's ranking in relevant searches and reduce its academic reach. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Title -**Category**: title -**Focus**: comprehensive_improvement - -**Original Text**: -> Social Preferences: Fundamental Characteristics and Economic Consequences - -**Improved Version**: -> Social Preferences: Key Traits, Distribution, and Economic Impacts in Experimental and Field Studies - -**Explanation**: -This revised title is more specific, highlighting core themes such as traits, distribution, and impacts, which improves clarity and relevance. It balances impact and SEO by including terms like 'traits,' 'distribution,' and 'impacts,' which are common search keywords in social preference literature, aligning with academic standards for descriptive precision. - -### Detailed Feedback -### Title Analysis - -The current title is somewhat broad and lacks specificity, which can impair immediate understanding of the paper's core focus. Its length and generality reduce its impact and searchability, especially in digital environments where concise, keyword-rich titles perform better. A more targeted and descriptive title would enhance clarity, impact, and discoverability, aligning with academic standards for effective titling. - -### Keywords Analysis - -No keywords section found - -### Guidelines Compliance - -The manuscript's title does not explicitly follow the typical conventions of including specific, searchable keywords or phrases that enhance SEO and thematic clarity. Incorporating relevant keywords and a clear focus in the title is recommended to improve discoverability and align with field standards. - -### Discoverability Assessment - -The absence of a dedicated keywords section and the broad nature of the current title limit the manuscript's visibility in search engines and academic databases. An optimized, keyword-rich title would significantly improve its search ranking and accessibility to targeted audiences interested in social preferences and their economic implications. - -### Audience Alignment - -The current title may not fully capture the interest of researchers focusing on social preferences, economic impacts, or experimental studies, potentially reducing engagement. A more precise and descriptive title would better align with the interests of the target academic audience, emphasizing the paper's contributions to understanding traits, distribution, and consequences of social preferences. - -## S2 - Abstract -### Score: 6/10 - -### Summary -Overall, the abstract and introduction provide a thorough, detailed overview of the literature on social preferences, but suffer from overly dense language, lack of clear structure, and limited emphasis on key contributions. Improving clarity, emphasizing novel insights, and restructuring for better flow would significantly enhance the manuscript’s readability, coherence, and impact. The extensive technical detail is appropriate for a specialized audience but could be streamlined to broaden accessibility and strengthen the review’s overall contribution. - -### Critical Remarks -### Remark 1 - -**Category**: structure -**Location**: Abstract and Introduction -**Issue**: The abstract and introduction are overly dense and lengthy, with the introduction including detailed literature review and methodological explanations that could be better summarized or moved to later sections. -**Severity**: high -**Impact**: Reduces clarity and readability, making it difficult for readers to quickly grasp the main contributions and structure of the review. - -### Remark 2 - -**Category**: content -**Location**: Sections 2-3 -**Issue**: The abstract and early sections focus heavily on theoretical models and experimental paradigms without clearly highlighting the key empirical findings or the main contributions of the review. -**Severity**: high -**Impact**: Lacks emphasis on novel insights or synthesis, which diminishes the perceived contribution and impact of the review. - -### Remark 3 - -**Category**: clarity -**Location**: Throughout the abstract and early sections -**Issue**: The language is highly technical and dense, with long sentences and complex terminology that may hinder readability for a broader audience. -**Severity**: medium -**Impact**: Limits accessibility and may discourage readers unfamiliar with specific models or experimental details. - -### Remark 4 - -**Category**: standards -**Location**: Methodology descriptions (Sections 2-3) -**Issue**: The description of experimental paradigms and models is sometimes overly detailed and technical, with inconsistent referencing and occasional jargon that could be clarified or simplified. -**Severity**: medium -**Impact**: Weakens clarity and may reduce the manuscript’s adherence to scientific communication standards. - -### Remark 5 - -**Category**: impact -**Location**: Overall abstract and conclusion -**Issue**: The abstract summarizes a vast amount of literature but does not explicitly state the main novel insights, overarching conclusions, or implications for future research and policy. -**Severity**: high -**Impact**: Reduces the perceived significance and practical impact of the review. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Abstract -**Category**: clarity -**Focus**: organization - -**Original Text**: -> We review the vast literature on social preferences by assessing what is known about their fundamental properties, their distribution in the broader population, and their consequences for important economic and political behaviors. - -**Improved Version**: -> This review synthesizes key findings on the fundamental properties, distribution, and economic and political implications of social preferences, highlighting recent advances and open questions. - -**Explanation**: -A clearer, more concise opening statement that emphasizes the review’s scope and contribution, improving clarity and reader orientation. - -### Suggestion 2 - -**Location**: Abstract -**Category**: clarity -**Focus**: readability - -**Original Text**: -> We provide, in particular, an overview of the empirical characteristics of distributional preferences and how they are affected by merit, luck, and concerns for equality of opportunity. - -**Improved Version**: -> We focus on empirical evidence regarding distributional preferences, examining how factors like merit, luck, and fairness influence individual choices and societal attitudes. - -**Explanation**: -Simplifies technical language and clarifies the focus on empirical evidence, enhancing readability. - -### Suggestion 3 - -**Location**: Abstract -**Category**: clarity -**Focus**: readability - -**Original Text**: -> The overall evidence indicates that a large majority of individuals have some sort of social preferences, while purely self-interested subjects are a minority. - -**Improved Version**: -> Evidence consistently shows that most individuals exhibit social preferences, with purely self-interested behavior being relatively rare. - -**Explanation**: -Streamlines the sentence for clarity and impact, making the key point more direct. - -### Suggestion 4 - -**Location**: Abstract -**Category**: clarity -**Focus**: readability - -**Original Text**: -> We also document converging insights from the lab and the field on the impact of wage inequality on work morale, employees’ resistance to wage cuts, and the role of social preferences for cooperation, distributive politics and individuals’ selection into different occupations. - -**Improved Version**: -> We synthesize findings from laboratory and field studies on how wage inequality affects work morale, resistance to wage cuts, cooperation, and occupational choices. - -**Explanation**: -Reduces complexity and improves flow, aiding readability and clarity. - -### Suggestion 5 - -**Location**: Introduction -**Category**: clarity -**Focus**: organization - -**Original Text**: -> The topic of social preferences has received a great deal of attention in economics and in the broader social science literature in the past three decades. - -**Improved Version**: -> Social preferences have been a central focus in economics and social sciences over the past three decades. - -**Explanation**: -Simplifies and sharpens the opening, improving clarity and engagement. - -### Suggestion 6 - -**Location**: Introduction -**Category**: clarity -**Focus**: readability - -**Original Text**: -> A large, and still growing, literature has documented the existence of social preferences, their social and economic implications, and the various conditions under which they influence the equilibria and outcomes of human interactions. - -**Improved Version**: -> A growing body of research documents the existence and implications of social preferences across social and economic contexts. - -**Explanation**: -Concise and clearer, reducing redundancy and improving readability. - -### Suggestion 7 - -**Location**: Introduction -**Category**: clarity -**Focus**: clarity - -**Original Text**: -> An individual i has social preferences if the utility function of i, 𝑈௜, is not merely a function of i’s material payoff 𝜋௜, in which case it could be written as 𝑈௜(𝜋௜), but also depends on the material payoff of at least one other player j, 𝜋௝, such that 𝑈௜(𝜋௜, 𝜋௝). - -**Improved Version**: -> An individual i has social preferences if their utility depends not only on their own material payoff 𝜋௜ but also on the payoffs of others, 𝜋௝, such that 𝑈௜= 𝑈(𝜋௜, 𝜋௝). - -**Explanation**: -Simplifies technical language and clarifies the definition, improving clarity and technical accuracy. - -### Suggestion 8 - -**Location**: Introduction -**Category**: clarity -**Focus**: readability - -**Original Text**: -> Theories of social preferences differ in how exactly 𝜋௝ affects player i’s utility – for example through social comparisons between 𝜋௜ and 𝜋௝ or through the perceived kindness associated with particular strategies that generate 𝜋௝; however, they all involve the assumption that other players’ material payoffs enter i’s utility function in some way. - -**Improved Version**: -> Different theories specify how 𝜋௝ influences utility—such as via social comparisons or perceptions of kindness—but all assume that others’ payoffs affect individual utility. - -**Explanation**: -Streamlines and clarifies the sentence, reducing complexity for better readability. - -### Suggestion 9 - -**Location**: Introduction -**Category**: clarity -**Focus**: impact - -**Original Text**: -> This means that individuals with social preferences are willing to sacrifice money or other material resources to increase other players’ payoffs (if the marginal utility of 𝜋௝ is positive) or to hurt them (if the 𝜋௝ yields negative marginal utility) – which has potentially far-reaching consequences for many practical applications. - -**Improved Version**: -> Individuals with social preferences may sacrifice resources to benefit others or harm them, depending on their utility structure, with significant implications for economic and social outcomes. - -**Explanation**: -Clarifies the concept and emphasizes importance, improving clarity and impact. - -### Suggestion 10 - -**Location**: Section 2 -**Category**: content -**Focus**: completeness - -**Original Text**: -> Moreover, these experiments led to a fundamental puzzle because the same individuals sometimes displayed very selfish behavior, while they behaved very prosocially at other times. - -**Improved Version**: -> A key puzzle from experimental studies is that individuals sometimes act selfishly and at other times prosocially, raising questions about the stability and underlying drivers of social preferences. - -**Explanation**: -Clarifies the issue and emphasizes its significance, improving impact and readability. - -### Suggestion 11 - -**Location**: Section 2 -**Category**: content -**Focus**: completeness - -**Original Text**: -> The predictions for the experimental games mentioned above assume that the games are played one-shot and that interactions are anonymous. - -**Improved Version**: -> These game predictions assume single, anonymous interactions, which may limit their applicability to real-world settings with repeated or reputation-based interactions. - -**Explanation**: -Adds context and clarifies limitations, enhancing completeness and impact. - -### Suggestion 12 - -**Location**: Section 3 -**Category**: content -**Focus**: completeness - -**Original Text**: -> The example above illustrates that the mere belief that other players behave selfishly might sometimes induce fair-minded players to do so as well. - -**Improved Version**: -> This example shows that beliefs about others’ selfishness can influence fair-minded individuals to behave more selfishly, highlighting the importance of beliefs in social preferences. - -**Explanation**: -Clarifies the mechanism and emphasizes its significance, improving clarity and impact. - -### Suggestion 13 - -**Location**: Section 3 -**Category**: clarity -**Focus**: clarity - -**Original Text**: -> The utility function 𝑈௜= 𝜋௜− 𝛼ത௜(𝜋௝− 𝜋௜) 𝑖𝑓 𝜋௝> 𝜋௜ and 𝑈௜= 𝜋௜− 𝛽̅௜(𝜋௜− 𝜋௝) 𝑖𝑓 𝜋௜> 𝜋௝ with both 𝛼ത௜> 0 and 0 < 𝛽̅௜< 1, imply that players dislike inequality. - -**Improved Version**: -> The utility function 𝑈௜= 𝜋௜− 𝛼ത௜(𝜋௝− 𝜋௜) if 𝜋௝> 𝜋௜ and 𝑈௜= 𝜋௜− 𝛽̅௜(𝜋௜− 𝜋௝) if 𝜋௜> 𝜋௝, with positive parameters, captures inequality aversion—disfavoring outcomes where others’ payoffs differ from one's own. - -**Explanation**: -Clarifies the functional form and its interpretation, improving technical accuracy and clarity. - -### Detailed Feedback -### Structure Analysis - -The abstract and introduction are overly dense, containing extensive methodological and literature review details that could be better summarized or moved to later sections. The structure would benefit from a clearer hierarchy: a concise abstract highlighting main contributions, followed by an introduction framing key questions and findings, with detailed literature and models reserved for subsequent sections. This would improve navigability and focus. - -### Content Analysis - -The abstract covers a broad range of topics, but lacks emphasis on novel insights or overarching conclusions. The review is comprehensive but could better synthesize key empirical patterns, highlight gaps, and clarify how different models relate to observed behaviors. The detailed descriptions of experimental paradigms, while thorough, could be condensed to focus on their relevance to the review’s main questions. - -### Clarity Assessment - -The language is highly technical, with long sentences and complex terminology that may hinder readability for non-specialist audiences. Technical definitions are sometimes embedded within dense paragraphs, reducing clarity. Simplifying language, breaking long sentences, and providing clearer signposting would enhance readability and accessibility. - -### Standards Compliance - -The manuscript generally adheres to scientific standards, with extensive referencing and detailed descriptions. However, some sections contain overly detailed methodological descriptions that could be summarized more succinctly, and inconsistent referencing styles (e.g., sometimes full citations, sometimes parenthetical). Ensuring uniformity and focusing on key methodological points would improve standards compliance. - -### Impact Evaluation - -The review covers a wide array of empirical findings and models, but does not explicitly synthesize overarching conclusions or implications for theory, policy, or future research. Explicitly articulating these would strengthen the manuscript’s impact, guiding readers on the significance of the findings and open questions. - -## S3 - Introduction -### Score: 7/10 - -### Summary -Overall, the introduction demonstrates a thorough and well-referenced overview of social preferences, establishing their importance across multiple domains. However, it would benefit from clearer articulation of the specific research gap, more explicit objectives, and improved signposting to enhance coherence and accessibility. Strengthening these aspects would make the manuscript more focused and engaging, better guiding the reader through its complex and rich content. - -### Critical Remarks -### Remark 1 - -**Category**: context -**Location**: Introduction, paragraphs 1-3 -**Issue**: The background overview is comprehensive but overly dense, making it difficult for readers to grasp the core motivation and scope early on. -**Severity**: medium -**Impact**: This may hinder reader engagement and clarity, potentially obscuring the importance of social preferences in the broader research landscape. - -### Remark 2 - -**Category**: problem -**Location**: Introduction, paragraphs 4-5 -**Issue**: While the introduction discusses many facets of social preferences, it lacks a concise, explicit statement of the central research gap or unresolved issue the paper addresses. -**Severity**: high -**Impact**: This diminishes clarity about the paper’s unique contribution and may confuse readers about its primary research focus. - -### Remark 3 - -**Category**: objectives -**Location**: Introduction, paragraph 6 -**Issue**: The objectives are scattered across multiple sections and are not clearly summarized as specific research questions or hypotheses at the outset. -**Severity**: medium -**Impact**: This affects the reader’s understanding of the precise aims and scope of the study, reducing overall clarity. - -### Remark 4 - -**Category**: significance -**Location**: Introduction, paragraphs 4-6 -**Issue**: Although the significance of social preferences is discussed, the justification for why this review advances the field or impacts policy is somewhat implicit and underdeveloped. -**Severity**: medium -**Impact**: This may weaken the perceived importance and urgency of the research contribution. - -### Remark 5 - -**Category**: structure -**Location**: Introduction, paragraph 2 and Table of Contents -**Issue**: The transition from the broad literature review to the detailed outline of the paper’s structure is somewhat abrupt, with limited signposting for how each section connects to the overarching research questions. -**Severity**: low -**Impact**: This could impair the logical flow and reader navigation through the complex content. - -### Remark 6 - -**Category**: technical accuracy -**Location**: Throughout the introduction -**Issue**: Some technical descriptions, especially of models and experimental designs, are dense and assume prior familiarity, risking misinterpretation by less expert readers. -**Severity**: low -**Impact**: This may limit accessibility and broader understanding of the technical contributions. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Introduction, paragraph 2 -**Category**: structure -**Focus**: flow - -**Original Text**: -> We will describe the evidence for the existence of social preferences gathered in canonical (“paradigmatic”) experiments in Section 2. - -**Improved Version**: -> Section 2 provides an overview of the canonical experiments that establish the existence of social preferences, setting the foundation for subsequent discussions. - -**Explanation**: -Clarifies the purpose of Section 2 and improves flow by explicitly linking it to the overall narrative. - -### Suggestion 2 - -**Location**: Introduction, paragraph 3 -**Category**: context -**Focus**: background - -**Original Text**: -> However, a multitude of factors other than social preferences also play a role in all these cases. - -**Improved Version**: -> Nevertheless, it is important to recognize that many factors beyond social preferences—such as reputational concerns, strategic considerations, and contextual influences—also shape behavior in these settings. - -**Explanation**: -Provides a clearer, more nuanced framing of the complexity, enhancing reader understanding. - -### Suggestion 3 - -**Location**: Introduction, paragraph 3 -**Category**: objectives -**Focus**: objectives - -**Original Text**: -> This paper, we will summarize what has been learned in this line of research and we will identify what needs to be known and done to make further progress. - -**Improved Version**: -> This review aims to synthesize current knowledge on social preferences and identify key gaps and future research directions necessary for advancing the field. - -**Explanation**: -Makes objectives more explicit and concise, improving clarity of purpose. - -### Suggestion 4 - -**Location**: Introduction, paragraph 3 -**Category**: problem -**Focus**: gap - -**Original Text**: -> The paradigmatic experiments discussed in Section 2, while indicating the existence of social preferences and capturing essential components of important real-life interactions, are a poor tool for the precise identification of the properties of social preferences. - -**Improved Version**: -> While these canonical experiments demonstrate the existence of social preferences and capture key aspects of real-world interactions, they have limitations in precisely identifying the underlying preference structures. - -**Explanation**: -Clarifies the limitations of Section 2 and sets up the motivation for more detailed analysis, improving logical flow. - -### Suggestion 5 - -**Location**: Introduction, paragraph 6 -**Category**: objectives -**Focus**: gap - -**Original Text**: -> The introduction of heterogenous fairness ideals by Cappelen et al (2007) represents an interesting innovation; it implies that if people do not share in one particular situation, then this may not mean that they do not care about fairness (i.e., put no weight on fairness in (9) or (10)) but that they consider sharing unfair. - -**Improved Version**: -> Cappelen et al. (2007) introduced the concept of heterogeneous fairness ideals, highlighting that individuals’ fairness preferences may vary across contexts—meaning that a lack of sharing in one situation does not imply a general disregard for fairness, but rather different normative standards or perceptions of fairness. - -**Explanation**: -Clarifies the conceptual advance and improves readability by breaking complex ideas into clearer parts. - -### Suggestion 6 - -**Location**: Introduction, abstract -**Category**: significance -**Focus**: impact - -**Original Text**: -> The overall evidence indicates that a large majority of individuals have some sort of social preferences, while purely self-interested subjects are a minority. - -**Improved Version**: -> The accumulated evidence suggests that most individuals possess social preferences—such as fairness, altruism, or reciprocity—while purely self-interested individuals constitute a smaller minority. - -**Explanation**: -Provides a more precise summary of findings, emphasizing the prevalence of social preferences. - -### Suggestion 7 - -**Location**: Introduction, paragraph 6 -**Category**: significance -**Focus**: impact - -**Original Text**: -> The paper by Charness and Rabin (2002) led to a lively discussion of the relevance of different motives underlying distributional preferences. - -**Improved Version**: -> Charness and Rabin (2002) sparked extensive debate regarding the relative importance of various motives—such as fairness, altruism, and strategic considerations—in shaping distributional preferences. - -**Explanation**: -Enhances clarity and emphasizes the ongoing scholarly debate, strengthening the significance argument. - -### Suggestion 8 - -**Location**: Introduction, paragraph 6 -**Category**: objectives -**Focus**: gap - -**Original Text**: -> The introduction of heterogenous fairness ideals by Cappelen et al (2007) represents an interesting innovation; it implies that if people do not share in one particular situation, then this may not mean that they do not care about fairness (i.e., put no weight on fairness in (9) or (10)) but that they consider sharing unfair. - -**Improved Version**: -> Cappelen et al. (2007) innovatively proposed that individuals hold diverse fairness ideals, meaning that their fairness judgments depend on normative standards—so a person may reject sharing in one context but still care deeply about fairness under different criteria. - -**Explanation**: -Clarifies the conceptual contribution and improves flow by explicitly linking to normative standards. - -### Suggestion 9 - -**Location**: Introduction, paragraph 6 -**Category**: significance -**Focus**: impact - -**Original Text**: -> The evidence from laboratory experiments suggests that social image concerns (being viewed as fair) constitute a strong motive. - -**Improved Version**: -> Laboratory experiments provide strong evidence that social image concerns—desire to be perceived as fair or prosocial—are significant drivers of behavior. - -**Explanation**: -Refines the statement for clarity and emphasizes the importance of social image concerns. - -### Suggestion 10 - -**Location**: Introduction, paragraph 6 -**Category**: significance -**Focus**: impact - -**Original Text**: -> The overall findings from surveys, lab experiments, and field evidence suggest that aversion to disadvantageous wage inequality that cannot be justified by effort or productivity differences generates strong behavioral effects in terms of a reduced willingness to perform, an increased willingness to quit, a lower job satisfaction, a reduced social cohesion and lower willingness to cooperate. - -**Improved Version**: -> Converging evidence from surveys, experiments, and field studies indicates that aversion to unjustified wage inequality—inequality not attributable to effort or productivity—substantially influences behavior, leading to lower effort, higher turnover, diminished job satisfaction, decreased social cohesion, and reduced cooperation. - -**Explanation**: -Improves clarity, conciseness, and logical flow, making the complex effects more accessible. - -### Detailed Feedback -### Context Analysis - -The introduction offers a broad yet detailed overview of social preferences, emphasizing their empirical and theoretical importance across economics and social sciences. It references numerous experiments, models, and cross-cultural studies, establishing a solid background. However, the density of information and technical language may challenge non-expert readers, and the narrative could benefit from clearer signposting to guide the reader through the complex landscape of research. - -### Problem Analysis - -While the introduction discusses the richness of the literature and some debates, it lacks a sharply articulated research gap or specific unresolved issue that the paper aims to address. Explicitly stating what is missing or contentious in current understanding—such as limitations in existing models, measurement challenges, or unexamined contexts—would sharpen the problem statement and justify the review’s contribution. - -### Objectives Analysis - -The objectives are dispersed throughout the introduction, with some emphasis on reviewing evidence, models, and implications. However, they are not summarized as clear research questions or hypotheses. A concise paragraph explicitly stating the main aims—such as synthesizing current knowledge, identifying gaps, and proposing future directions—would improve clarity and focus. - -### Significance Assessment - -The introduction highlights the broad relevance of social preferences for policy and theory, but the justification for the review’s significance could be strengthened by explicitly connecting the review to pressing societal issues, policy debates, or theoretical advancements. Emphasizing how this synthesis can influence policy design or deepen understanding of human behavior would enhance perceived importance. - -### Structure Evaluation - -The structure is outlined via a detailed table of contents, which is helpful. Nonetheless, the transition from the broad background to specific sections could be smoother with more signposting in the text—explicitly linking each section to the overarching research aims. This would improve coherence and guide the reader through the complex organization. - -### Technical Accuracy - -Descriptions of models and experimental designs are generally accurate but dense. Simplifying technical language or providing intuitive explanations alongside formal descriptions would improve accessibility. For example, briefly clarifying what parameters like alpha, beta, and rho represent in plain language would benefit non-specialist readers. - -## S4 - Literature Review -### Score: 7/10 - -### Summary -Overall, this literature review offers a thorough and well-structured overview of social preferences, their models, empirical evidence, and implications. To elevate its quality, it should incorporate more critical evaluation of conflicting evidence, recent neuroeconomic and cross-cultural research, and deeper integration of motives and policy relevance. Enhancing clarity in organization and expanding normative and future research discussions would make it more comprehensive and impactful. - -### Critical Remarks -### Remark 1 - -**Category**: coverage -**Location**: Section 2 & 3 -**Issue**: While the review covers a broad range of social preferences and distributional models, it underrepresents recent developments in neuroeconomic and cross-cultural research, especially in non-Western contexts beyond a few examples. Some important recent studies on the biological basis of social preferences are missing, which could deepen the understanding of their determinants. -**Severity**: medium -**Impact**: Limits the comprehensiveness and cutting-edge relevance of the review, potentially missing key insights into the origins and universality of social preferences. - -### Remark 2 - -**Category**: analysis -**Location**: Section 3 & 4 -**Issue**: The review often describes models and empirical findings but lacks critical evaluation of conflicting evidence or limitations of the models, such as issues with external validity, measurement error, or assumptions about stability over time. -**Severity**: high -**Impact**: Reduces the depth of analysis, making it more descriptive than critically insightful, which diminishes the scholarly contribution. - -### Remark 3 - -**Category**: structure -**Location**: Overall organization -**Issue**: The review's structure, especially in Sections 3 and 4, tends to be dense and somewhat repetitive, with overlapping discussions of models and empirical evidence. Transitions between sections could be clearer to improve logical flow. -**Severity**: medium -**Impact**: Hampers readability and coherence, making it harder for readers to follow the development of arguments and thematic connections. - -### Remark 4 - -**Category**: citations -**Location**: Throughout -**Issue**: While many foundational and recent studies are cited, some key recent papers (post-2020) on neurobiology, large-scale cross-cultural surveys, and field experiments are missing or underrepresented, especially in the context of emerging methodologies. -**Severity**: medium -**Impact**: Potentially outdated or incomplete citation coverage, which could undermine the review's currency and authority. - -### Remark 5 - -**Category**: integration -**Location**: Sections 5 & 6 -**Issue**: The review discusses various social preferences and their implications but does not sufficiently synthesize how different motives (e.g., fairness, image, reciprocity) interact or compete within individuals or across contexts, nor how they jointly influence policy and institutional design. -**Severity**: high -**Impact**: Misses an opportunity to provide a more holistic, integrated understanding of social preferences, limiting its theoretical and practical relevance. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Section 6 -**Category**: organization -**Focus**: organization - -**Original Text**: -> We review evidence on how social preferences affect (i) cooperation and (ii) employees’ responses to wage inequality and wage cuts, (iii) the extent to which this affects firms’ employment decisions, (iv) how they affect the allocation of workers with varying prosociality levels to different industries, (v) how they influence incentives and contract, and (vi) how they affect participating in protest movements against dictatorships and the political demand for redistribution. - -**Improved Version**: -> We systematically review evidence on how social preferences influence (i) cooperation and social norms, (ii) employee responses to wage inequality and wage cuts, (iii) firm-level employment decisions and organizational behavior, (iv) the allocation of workers across industries based on prosocial traits, (v) the design and effectiveness of incentives and contracts, and (vi) participation in political protests and redistribution demands. - -**Explanation**: -Clarifies the scope and emphasizes the interconnectedness of social preferences across economic and political domains, improving thematic coherence. - -### Suggestion 2 - -**Location**: Section 3.1.1 -**Category**: organization -**Focus**: organization - -**Original Text**: -> Theories of social preferences differ in how exactly 𝜋௝ affects player i’s utility – for example through social comparisons between 𝜋௜ and 𝜋௝ or through the perceived kindness associated with particular strategies that generate 𝜋௝; however, they all involve the assumption that other players’ material payoffs enter i’s utility function in some way. - -**Improved Version**: -> Different theories of social preferences vary in their assumptions about how other players’ payoffs influence utility—ranging from social comparisons to kindness or fairness perceptions—but they all posit that others’ material payoffs are incorporated into individual utility functions in some form. - -**Explanation**: -Streamlines the explanation, reducing redundancy and clarifying the core conceptual distinction, enhancing clarity and precision. - -### Suggestion 3 - -**Location**: Section 3.1.2 -**Category**: analysis -**Focus**: depth - -**Original Text**: -> While this does not mean that these models are necessarily empirically correct – because this requires further empirical testing – it means that widespread seemingly selfish behaviors in certain strategic experiments can be perfectly consistent with the existence of widespread social preferences. - -**Improved Version**: -> Although these models are not yet empirically validated in all contexts and require further testing, they demonstrate that observed selfish behaviors in strategic experiments can be compatible with the presence of widespread social preferences. - -**Explanation**: -Clarifies the tentative nature of empirical validation and emphasizes the compatibility of models with observed behaviors, strengthening analytical nuance. - -### Suggestion 4 - -**Location**: Sections 3, 4, 5, 6 -**Category**: analysis -**Focus**: depth - -**Original Text**: -> The review often describes models and empirical findings but lacks critical evaluation of conflicting evidence or limitations of the models, such as issues with external validity, measurement error, or assumptions about stability over time. - -**Improved Version**: -> The review would benefit from a more critical assessment of conflicting evidence, including limitations related to external validity, measurement challenges, and the stability of social preferences over time and across contexts. For example, discussing the potential biases in laboratory versus field settings would deepen the analysis. - -**Explanation**: -Encourages a balanced, critical perspective that acknowledges limitations, enhancing scholarly rigor. - -### Suggestion 5 - -**Location**: Section 2 & 3 -**Category**: coverage -**Focus**: breadth - -**Original Text**: -> The review covers a broad range of social preferences and distributional models, but it underrepresents recent developments in neuroeconomic and cross-cultural research, especially in non-Western contexts beyond a few examples. - -**Improved Version**: -> Expand coverage to include recent advances in neuroeconomic studies exploring the biological underpinnings of social preferences, as well as more comprehensive cross-cultural research beyond Western samples, to enhance global relevance and depth. - -**Explanation**: -Broadens the scope to include cutting-edge and diverse research, improving comprehensiveness. - -### Suggestion 6 - -**Location**: Sections 4, 5, 6 -**Category**: analysis -**Focus**: synthesis - -**Original Text**: -> The review discusses various social preferences and their implications but does not sufficiently synthesize how different motives (e.g., fairness, image, reciprocity) interact or compete within individuals or across contexts. - -**Improved Version**: -> Integrate a synthesis that explores how motives such as fairness, social image, and reciprocity coexist, interact, or compete within individuals and across different social and economic environments, providing a more unified theoretical framework. - -**Explanation**: -Enhances theoretical integration and offers a more holistic understanding of complex motives. - -### Suggestion 7 - -**Location**: Throughout -**Category**: citations -**Focus**: relevance - -**Original Text**: -> While many foundational and recent studies are cited, some key recent papers (post-2020) on neurobiology, large-scale cross-cultural surveys, and field experiments are missing or underrepresented. - -**Improved Version**: -> Incorporate recent literature (post-2020) on neurobiological mechanisms underlying social preferences, large-scale cross-cultural surveys, and innovative field experiments to ensure the review reflects the latest empirical advances. - -**Explanation**: -Keeps the review current and authoritative by including the latest research developments. - -### Suggestion 8 - -**Location**: Sections 6 & 7 -**Category**: integration -**Focus**: relevance - -**Original Text**: -> The review discusses the economic and political implications of social preferences but does not sufficiently connect these to normative debates or policy design considerations. - -**Improved Version**: -> Deepen the discussion on how empirical findings on social preferences inform normative debates on fairness, justice, and policy design, including implications for welfare economics, tax policy, and institutional reforms. - -**Explanation**: -Strengthens the policy relevance and normative integration, broadening impact. - -### Suggestion 9 - -**Location**: Section 7 -**Category**: organization -**Focus**: organization - -**Original Text**: -> The review often describes models and findings but could better highlight unresolved debates, open questions, and future research directions, especially regarding the measurement and stability of social preferences. - -**Improved Version**: -> Explicitly identify unresolved debates, such as the measurement of social preferences, their stability over time, and cross-cultural variability, and outline promising avenues for future research to address these gaps. - -**Explanation**: -Provides clearer guidance for future scholarship, enhancing scholarly contribution. - -### Suggestion 10 - -**Location**: Sections 4-6 -**Category**: analysis -**Focus**: synthesis - -**Original Text**: -> The review could benefit from a more explicit discussion of how different social motives may be integrated into a unified behavioral model, including potential interactions and trade-offs. - -**Improved Version**: -> Discuss the prospects for integrating various social motives—such as fairness, reciprocity, guilt, and image concerns—into comprehensive behavioral models that account for their interactions, trade-offs, and context-dependent prominence. - -**Explanation**: -Encourages theoretical synthesis and advances understanding of complex social motives. - -### Suggestion 11 - -**Location**: Section 3 & 4 -**Category**: coverage -**Focus**: breadth - -**Original Text**: -> The review mentions the importance of cultural differences but does not systematically compare findings across cultures or discuss methodological challenges in cross-cultural research. - -**Improved Version**: -> Include a systematic comparison of cross-cultural findings, discussing methodological challenges such as translation, framing effects, and sampling, to better understand the universality versus cultural specificity of social preferences. - -**Explanation**: -Enhances depth and critical perspective on cross-cultural generalizability. - -### Suggestion 12 - -**Location**: Section 7 -**Category**: analysis -**Focus**: relevance - -**Original Text**: -> The review discusses the stability of social preferences over time but could elaborate on the implications of stability versus malleability for policy and organizational interventions. - -**Improved Version**: -> Elaborate on how the observed stability or malleability of social preferences over time informs the design of policies and organizational practices aimed at fostering prosocial behavior and cooperation. - -**Explanation**: -Connects empirical findings to practical applications, increasing policy relevance. - -### Detailed Feedback -### Coverage Analysis - -The review provides extensive coverage of classic and recent models of social preferences, including distributional, reciprocity, guilt, and image concerns, with a strong emphasis on laboratory and some field evidence. However, it underrepresents recent neuroeconomic and cross-cultural studies, especially those exploring biological bases and non-Western contexts, which are crucial for understanding the universality and origins of social preferences. - -### Analysis Quality - -The analysis is descriptive and well-organized but often lacks critical evaluation of conflicting evidence, methodological limitations, and the robustness of findings. Incorporating discussions on measurement issues, external validity, and stability over time would deepen the critical insight and scholarly rigor. - -### Structure Evaluation - -The review follows a logical thematic progression but tends to be dense in some sections, with overlapping discussions and limited transitions. Clearer subheadings, summaries, and signposting would improve readability and coherence, especially in complex sections on models and empirical evidence. - -### Citation Assessment - -The citations are comprehensive, including foundational and recent works, but recent developments (post-2020) in neurobiology, large-scale surveys, and innovative field experiments are underrepresented. Updating with the latest literature would enhance credibility and relevance. - -### Integration Review - -While the review covers various motives and domains, it does not sufficiently synthesize how different social motives interact within individuals or across contexts, nor how they jointly influence policy and institutional outcomes. A more integrated discussion would provide a holistic understanding of social preferences' roles in society. - -## S5 - Methodology -### Score: 7/10 - -### Summary -Overall, the methodology review demonstrates a solid foundation of classical and innovative experimental approaches to social preferences, with a comprehensive discussion of models and empirical findings. However, it would benefit from a more critical engagement with issues of external validity, measurement robustness, endogenous factors, and ethical transparency. Incorporating these improvements would enhance the rigor, applicability, and ethical integrity of the research framework, making it more comprehensive and reliable for advancing understanding in this field. - -### Critical Remarks -### Remark 1 - -**Category**: design -**Location**: Section 2, Introduction -**Issue**: The review relies heavily on classical paradigmatic experiments but does not explicitly address how the experimental designs account for potential confounds such as social desirability bias or demand effects, especially in field settings. -**Severity**: medium -**Impact**: This limits the clarity on the internal validity of the experimental evidence and may overstate the robustness of social preferences findings. - -### Remark 2 - -**Category**: methods -**Location**: Section 3.1, Models of distributional preferences -**Issue**: The utility models assume linear or CES forms without discussing potential non-linearities or context-dependent preferences, which could misrepresent actual heterogeneity. -**Severity**: medium -**Impact**: This could lead to oversimplification of complex social preferences and reduce the accuracy of empirical parameter estimates. - -### Remark 3 - -**Category**: analysis -**Location**: Section 3.1.2, Empirical Frequency of Different Distributional Preferences -**Issue**: The estimation procedures for parameters like α and β often rely on assumptions of risk neutrality and stable preferences, which are not always justified, especially in field contexts. -**Severity**: high -**Impact**: This may bias the estimated distribution of preferences and undermine the validity of the conclusions about prevalence and heterogeneity. - -### Remark 4 - -**Category**: quality -**Location**: Section 3.2, The Role of Merit and Luck -**Issue**: Many experiments assume that merit and luck are exogenous and fully observable, neglecting the endogenous and often opaque nature of effort and circumstances in real-world settings. -**Severity**: high -**Impact**: This limits the external validity of the findings and may misrepresent actual social preferences regarding merit and luck. - -### Remark 5 - -**Category**: ethics -**Location**: Section 4.1, Laboratory Evidence on Reciprocity -**Issue**: Some experiments involve deception or manipulations that could influence participants' perceptions of fairness or kindness, but the ethical review procedures are not discussed. -**Severity**: low -**Impact**: This omission reduces transparency and may raise ethical concerns about participant welfare and informed consent. - -### Remark 6 - -**Category**: limitations handling -**Location**: Section 3.1.2, Empirical Frequency of Different Distributional Preferences -**Issue**: The review does not sufficiently discuss how heterogeneity and measurement error are addressed or mitigated in the estimation of preference parameters. -**Severity**: medium -**Impact**: This could lead to overconfidence in the prevalence estimates and obscure the true diversity of social preferences. - -### Remark 7 - -**Category**: design -**Location**: Section 4.2, Guilt Aversion -**Issue**: Many guilt experiments rely on second-order belief elicitation that is endogenous and potentially biased by strategic or social desirability concerns, but alternative methods are not thoroughly discussed. -**Severity**: high -**Impact**: This compromises the internal validity of guilt aversion evidence and limits causal inference. - -### Remark 8 - -**Category**: methods -**Location**: Appendix 2, The Equality-Equivalence Test -**Issue**: The EET involves a fixed equal payoff (E) in all choice lists, which may artificially emphasize equality and bias preferences towards egalitarianism. -**Severity**: medium -**Impact**: This could inflate the prevalence of egalitarian preferences and reduce the ecological validity of the measure. - -### Remark 9 - -**Category**: analysis -**Location**: Section 4.2, Guilt and Belief Manipulations -**Issue**: The review discusses exogenous belief manipulations but does not critically evaluate how belief stability and individual differences in trust or suspicion might influence results. -**Severity**: medium -**Impact**: This weakens the interpretation of belief-based experiments and their generalizability. - -### Remark 10 - -**Category**: quality -**Location**: Section 5, Self-Image and Social Image -**Issue**: The review emphasizes the limited robustness of self-image concerns but does not sufficiently consider the potential for measurement noise or the influence of social context on self-perception. -**Severity**: medium -**Impact**: This may underestimate the importance of self-image in certain environments and overstate the dominance of social image concerns. - -### Remark 11 - -**Category**: ethics -**Location**: Section 6.2, Wage Rigidity and Field Experiments -**Issue**: Field experiments involving wage manipulations and social comparisons may impact workers’ well-being or trust, yet ethical safeguards and approval processes are not explicitly discussed. -**Severity**: low -**Impact**: Lack of explicit ethical discussion could undermine the transparency and acceptability of the research. - -### Remark 12 - -**Category**: limitations handling -**Location**: Section 6.2, Labor Market and Wage Studies -**Issue**: The review does not sufficiently address how unobserved heterogeneity, such as individual productivity or motivation, is controlled for in field studies, risking omitted variable bias. -**Severity**: high -**Impact**: This weakens causal claims about social preferences’ effects on labor outcomes. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Section 2, Paragraph 2 -**Category**: design -**Focus**: approach - -**Original Text**: -> The implementation of incentivized anonymous one-shot experiments is thus a frequently employed conservative strategy that makes sense, but it is also important to keep in mind that important aspects of social relations, and their influence on social preferences, may be lost in these experiments (Frohlich, Oppenheimer and Kurk 2004). - -**Improved Version**: -> While incentivized anonymous one-shot experiments are a valuable approach to isolate social preferences, it is crucial to acknowledge that they may omit key social relational factors such as reputation, repeated interactions, and social context, which can significantly influence social preferences in real-world settings. - -**Explanation**: -This clarifies the limitations of the experimental design and emphasizes the importance of contextual factors, guiding future research to incorporate more realistic social environments. - -### Suggestion 2 - -**Location**: Section 3.1, Models of distributional preferences -**Category**: methods -**Focus**: techniques - -**Original Text**: -> The utility models assume linear or CES forms without discussing potential non-linearities or context-dependent preferences, which could misrepresent actual heterogeneity. - -**Improved Version**: -> The utility models should consider potential non-linearities, threshold effects, or context-dependent preferences that may better capture the diversity and complexity of social preferences across different environments and individuals. - -**Explanation**: -Incorporating non-linear and context-sensitive models would improve the accuracy and flexibility of empirical estimates, capturing a broader range of social preferences. - -### Suggestion 3 - -**Location**: Section 3.2, The Role of Merit and Luck -**Category**: design -**Focus**: approach - -**Original Text**: -> Many experiments assume that merit and luck are exogenous and fully observable, neglecting the endogenous and often opaque nature of effort and circumstances in real-world settings. - -**Improved Version**: -> Future experiments should aim to incorporate endogenous and less observable aspects of effort and circumstances, perhaps through longitudinal designs or indirect measures, to better reflect the complexity of merit and luck in real-world social preferences. - -**Explanation**: -This enhances external validity by acknowledging the endogenous and opaque nature of effort and circumstances, leading to more realistic insights. - -### Suggestion 4 - -**Location**: Section 4.1, Paragraph 1 -**Category**: ethics -**Focus**: procedures - -**Original Text**: -> Some experiments involve deception or manipulations that could influence participants' perceptions of fairness or kindness, but the ethical review procedures are not discussed. - -**Improved Version**: -> The manuscript should explicitly discuss the ethical review process, including participant consent and debriefing procedures, especially in experiments involving deception or manipulative procedures, to ensure compliance with ethical standards. - -**Explanation**: -Transparency about ethical safeguards enhances credibility and aligns with research integrity standards. - -### Suggestion 5 - -**Location**: Section 3.1.2, Empirical Frequency of Preferences -**Category**: quality -**Focus**: validity - -**Original Text**: -> The review does not sufficiently discuss how heterogeneity and measurement error are addressed or mitigated in the estimation of preference parameters. - -**Improved Version**: -> The methodology should explicitly address how measurement error and heterogeneity are accounted for, such as through robustness checks, multiple measures, or hierarchical modeling, to strengthen confidence in the prevalence estimates of social preferences. - -**Explanation**: -Addressing measurement issues improves the reliability and validity of the empirical findings. - -### Suggestion 6 - -**Location**: Section 4.1, Paragraph 2 -**Category**: methods -**Focus**: techniques - -**Original Text**: -> Many experiments rely on second-order belief elicitation that is endogenous and potentially biased by strategic or social desirability concerns, but alternative methods are not thoroughly discussed. - -**Improved Version**: -> The review should highlight alternative belief elicitation methods, such as exogenous belief manipulations or third-party assessments, and discuss their relative strengths and limitations in establishing causal links between beliefs and prosocial behaviors. - -**Explanation**: -This guides future research toward more robust belief measurement techniques, improving causal inference. - -### Suggestion 7 - -**Location**: Appendix 2, The Equality-Equivalence Test -**Category**: methods -**Focus**: procedures - -**Original Text**: -> The fixed equal payoff (E) in the EET may artificially emphasize equality and bias preferences towards egalitarianism. - -**Improved Version**: -> To mitigate potential bias from the salience of the equal payoff, future implementations of the EET could vary the reference point or include alternative neutral benchmarks, ensuring that preference estimates are not driven by procedural artifacts. - -**Explanation**: -This enhances the construct validity of the measure by reducing procedural bias. - -### Suggestion 8 - -**Location**: Section 4.1, Paragraph 3 -**Category**: analysis -**Focus**: validity - -**Original Text**: -> The review discusses exogenous belief manipulations but does not critically evaluate how belief stability and individual differences in trust or suspicion might influence results. - -**Improved Version**: -> The analysis should consider individual differences in trust, suspicion, and prior beliefs, and how these factors influence the effectiveness of belief manipulations, possibly through subgroup analyses or psychometric assessments. - -**Explanation**: -This improves the interpretability of belief experiments and accounts for heterogeneity in belief responsiveness. - -### Suggestion 9 - -**Location**: Section 5, Paragraph 2 -**Category**: quality -**Focus**: validity - -**Original Text**: -> The review emphasizes the limited robustness of self-image concerns but does not sufficiently consider measurement noise or social context influences. - -**Improved Version**: -> The discussion should incorporate the potential influence of measurement noise, social context, and individual differences on self-image measures, and suggest experimental designs that can disentangle these effects, such as repeated measures or context variations. - -**Explanation**: -This clarifies the conditions under which self-image concerns are more or less influential, improving the robustness of conclusions. - -### Suggestion 10 - -**Location**: Section 6.2, Paragraph 1 -**Category**: ethics -**Focus**: procedures - -**Original Text**: -> Field experiments involving wage manipulations may impact workers’ well-being or trust, yet ethical safeguards and approval procedures are not explicitly discussed. - -**Improved Version**: -> The methodology should explicitly describe the ethical review process, including participant consent, debriefing, and safeguards to minimize potential harm or discomfort, especially in wage and social comparison experiments. - -**Explanation**: -This ensures adherence to ethical standards and enhances research transparency. - -### Suggestion 11 - -**Location**: Section 6.2, Paragraph 2 -**Category**: limitations handling -**Focus**: approach - -**Original Text**: -> The review does not sufficiently address how unobserved heterogeneity, such as individual productivity or motivation, is controlled for in field studies. - -**Improved Version**: -> Future research should incorporate methods to control for unobserved heterogeneity, such as fixed effects, instrumental variables, or experimental designs that randomize relevant traits, to strengthen causal claims about social preferences’ effects on labor outcomes. - -**Explanation**: -Controlling for unobserved factors improves the internal validity and causal interpretability of field findings. - -### Detailed Feedback -### Design Analysis - -The review predominantly discusses classical experimental paradigms and their extensions but could benefit from a more explicit integration of design features that address external validity, such as repeated interactions, social context, and real-world complexity. Incorporating diverse experimental formats, including longitudinal and field designs, would strengthen the empirical foundation and applicability of social preference theories. - -### Methods Assessment - -The methodologies reviewed rely heavily on specific utility models (linear, CES) and belief elicitation techniques that may oversimplify the heterogeneity and context-dependence of social preferences. Employing flexible, non-parametric, or mixed-method approaches, along with robust belief measurement techniques, would improve the accuracy and reliability of empirical estimates. - -### Analysis Evaluation - -The analysis often assumes stability and risk neutrality in preference parameters, which may not hold in real-world settings. Incorporating models that account for heterogeneity, non-linearities, and risk attitudes, as well as addressing measurement error, would enhance the validity of the conclusions about preference prevalence and stability. - -### Quality Review - -The review covers validity and reliability issues but could more explicitly discuss how measurement noise, sample heterogeneity, and contextual factors influence the robustness of preference estimates. Addressing these issues systematically would improve confidence in the empirical findings and their generalizability. - -### Ethics Compliance - -While the review references many experiments, it lacks a detailed discussion of ethical procedures, especially regarding deception, consent, and participant well-being. Explicitly including ethical considerations and approval processes would improve transparency and adherence to research standards. - -## S6 - Results -### Score: 7/10 - -### Summary -Overall, the manuscript offers a comprehensive and detailed review of social preferences, their models, and empirical evidence. Its strengths lie in breadth and depth of coverage, but it would benefit from improved clarity through better organization, visual aids, and more rigorous statistical reporting. Addressing these issues would enhance accessibility, interpretability, and impact, making the results section more compelling and reader-friendly. - -### Critical Remarks -### Remark 1 - -**Category**: presentation -**Location**: Section 2, Paragraph 2 -**Issue**: The dense paragraph describing paradigmatic experiments is overly lengthy and contains multiple complex concepts without clear subheadings or visual cues, which hampers readability. -**Severity**: medium -**Impact**: Reduces clarity and makes it difficult for readers to quickly grasp key experimental designs and their implications. - -### Remark 2 - -**Category**: analysis -**Location**: Section 3.1, Paragraph 3 -**Issue**: The presentation of the distributional preference models (equations 1a and 1b) lacks sufficient explanation of parameters and their behavioral interpretations, which may confuse readers unfamiliar with the models. -**Severity**: high -**Impact**: Impairs understanding of the models’ assumptions and their empirical relevance, weakening the analysis. - -### Remark 3 - -**Category**: interpretation -**Location**: Section 3.1, Paragraph 4 -**Issue**: The discussion of empirical frequency estimates (e.g., Table 1) reports averages without discussing potential heterogeneity or confidence intervals, which limits the depth of interpretation. -**Severity**: medium -**Impact**: Limits the reader’s ability to assess the robustness and variability of the prevalence of different preference types. - -### Remark 4 - -**Category**: visualization -**Location**: Figures 1 and A1 -**Issue**: Figures illustrating indifference curves and distributions of parameters are referenced but not included in the text, and their descriptions are insufficiently detailed for standalone understanding. -**Severity**: medium -**Impact**: Reduces the effectiveness of visual aids in conveying complex model differences and empirical distributions. - -### Remark 5 - -**Category**: significance -**Location**: Section 3.2, Paragraph 2 -**Issue**: While the meta-analysis reports mean parameters, it does not sufficiently discuss the statistical significance or heterogeneity across studies, which is crucial for evaluating the importance of inequality aversion. -**Severity**: high -**Impact**: Weakens confidence in the generalizability and importance of the reported average parameters. - -### Remark 6 - -**Category**: presentation -**Location**: Section 4.1.1, Paragraph 2 -**Issue**: The description of models of reciprocity (Rabin, Dufwenberg & Kirchsteiger, Charness & Rabin) is somewhat technical and dense, with limited intuitive explanation for non-specialist readers. -**Severity**: medium -**Impact**: Hinders accessibility and understanding of the core mechanisms underlying reciprocity. - -### Remark 7 - -**Category**: analysis -**Location**: Section 4.1.2, Paragraph 4 -**Issue**: The discussion of experimental evidence on reciprocity often cites percentages and significance levels without contextualizing effect sizes or practical relevance. -**Severity**: medium -**Impact**: Limits the reader’s ability to judge the real-world importance of the findings. - -### Remark 8 - -**Category**: interpretation -**Location**: Section 4.2, Paragraph 2 -**Issue**: The discussion of guilt aversion theory’s limitations (e.g., endogenous beliefs, measurement challenges) is somewhat superficial and lacks concrete suggestions for overcoming these issues. -**Severity**: medium -**Impact**: Reduces the critical depth and practical guidance for future research. - -### Remark 9 - -**Category**: visualization -**Location**: Section 4.2, Figures 3 and related text -**Issue**: The description of the sequential exchange game and belief manipulations is complex, but no schematic or diagram is provided to clarify the game structure. -**Severity**: medium -**Impact**: Decreases clarity and makes it harder for readers to follow the experimental design. - -### Remark 10 - -**Category**: significance -**Location**: Section 5.2, Paragraph 2 -**Issue**: The summary of social image concerns cites multiple studies but does not quantify the magnitude of effects or discuss heterogeneity across contexts. -**Severity**: medium -**Impact**: Weakens the assessment of how impactful social image concerns are across different behaviors. - -### Remark 11 - -**Category**: presentation -**Location**: Section 6.2, Paragraph 2 -**Issue**: The discussion of field evidence on wage rigidity and fairness concerns is lengthy and somewhat repetitive, with limited focus on key empirical patterns. -**Severity**: low -**Impact**: Reduces overall clarity and focus of the narrative. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Title page -**Category**: presentation -**Focus**: clarity - -**Original Text**: -> This Version: 13 September 2024 - -**Improved Version**: -> This version: 13 September 2024 - -**Explanation**: -Lowercase 'version' improves consistency and professionalism in formal writing. - -### Suggestion 2 - -**Location**: Section 2, Paragraph 2 -**Category**: presentation -**Focus**: clarity - -**Original Text**: -> The paradigmatic experiments discussed in Section 2, while indicating the existence of social preferences and capturing essential components of important real-life interactions, are a poor - -**Improved Version**: -> The paradigmatic experiments discussed in Section 2, while indicating the existence of social preferences and capturing key aspects of important real-life interactions, are limited in scope. - -**Explanation**: -Simplifies and clarifies the critique, making it more accessible and precise. - -### Suggestion 3 - -**Location**: Section 3.1, Paragraph 1 -**Category**: presentation -**Focus**: clarity - -**Original Text**: -> Theories of social preferences differ in how exactly 𝜋௝ affects player i’s utility – for example through social comparisons between 𝜋௜ and 𝜋௝ or through the perceived kindness associated with particular strategies that generate 𝜋௝; however, they all involve the assumption that other players’ material payoffs enter i’s utility function in some way. - -**Improved Version**: -> Different theories of social preferences specify how 𝜋௝ influences player i’s utility—such as via social comparisons between 𝜋௜ and 𝜋௝ or perceptions of kindness—but all assume that others’ material payoffs are incorporated into i’s utility function. - -**Explanation**: -Clarifies the sentence structure and emphasizes the common assumption across models, improving comprehension. - -### Suggestion 4 - -**Location**: Section 2, Paragraph 4 -**Category**: presentation -**Focus**: clarity - -**Original Text**: -> The predictions for the experimental games mentioned above assume that the games are played one-shot and that interactions are anonymous. - -**Improved Version**: -> All predictions for the experimental games assume that they are played as one-shot interactions with anonymity between players. - -**Explanation**: -Rephrases for clarity and emphasizes the key assumptions affecting the results. - -### Suggestion 5 - -**Location**: Section 3.1, Figures 1 and A1 -**Category**: visualization -**Focus**: visualization - -**Original Text**: -> Figures illustrating indifference curves and distributions of parameters are referenced but not included in the text, and their descriptions are insufficiently detailed for standalone understanding. - -**Improved Version**: -> Include clear, high-resolution figures of the indifference curves and parameter distributions directly within the manuscript, accompanied by detailed captions explaining their significance and interpretation. For example, provide a figure showing the indifference curves for inequality aversion, envious preferences, and altruism, with annotations highlighting key differences. - -**Explanation**: -Enhances visual comprehension and allows readers to independently interpret the models and empirical distributions. - -### Suggestion 6 - -**Location**: Section 3.1, Paragraph 4 -**Category**: analysis -**Focus**: statistics - -**Original Text**: -> While the meta-analysis reports mean parameters, it does not sufficiently discuss the statistical significance or heterogeneity across studies, which is crucial for evaluating the importance of inequality aversion. - -**Improved Version**: -> Complement the meta-analysis with confidence intervals, heterogeneity measures, and significance tests for the estimated mean parameters (e.g., 𝛼 and 𝛽). Discuss the variability across studies and the implications for the robustness of inequality aversion as a widespread phenomenon. - -**Explanation**: -Provides a more rigorous and nuanced understanding of the empirical evidence’s strength and consistency. - -### Suggestion 7 - -**Location**: Section 4.1.1, Paragraph 2 -**Category**: presentation -**Focus**: clarity - -**Original Text**: -> The description of models of reciprocity (Rabin, Dufwenberg & Kirchsteiger, Charness & Rabin) is somewhat technical and dense, with limited intuitive explanation for non-specialist readers. - -**Improved Version**: -> Add intuitive explanations and real-world examples for each reciprocity model. For instance, explain Rabin’s model as 'people respond positively when they perceive others’ actions as kind, and negatively when they perceive unkindness,' supported by practical scenarios like gift exchanges or workplace interactions. - -**Explanation**: -Improves accessibility and helps non-expert readers grasp the core ideas behind complex models. - -### Suggestion 8 - -**Location**: Section 4.1.2, Paragraph 4 -**Category**: analysis -**Focus**: statistics - -**Original Text**: -> The discussion of experimental evidence on reciprocity often cites percentages and significance levels without contextualizing effect sizes or practical relevance. - -**Improved Version**: -> Include effect size measures (e.g., Cohen’s d) and discuss the practical significance of the reported percentages. For example, explain how a 44% rejection rate indicates a substantial behavioral tendency toward reciprocity, and relate it to real-world contexts such as employer-employee relations. - -**Explanation**: -Enhances understanding of the real-world importance of statistical findings and avoids overemphasis on p-values alone. - -### Suggestion 9 - -**Location**: Section 4.2, Paragraph 2 -**Category**: analysis -**Focus**: interpretation - -**Original Text**: -> The discussion of guilt aversion theory’s limitations (e.g., endogenous beliefs, measurement challenges) is somewhat superficial and lacks concrete suggestions for overcoming these issues. - -**Improved Version**: -> Expand on potential methodological approaches to address endogenous belief issues, such as using exogenous belief manipulations (e.g., belief elicitation with external sources) or experimental designs that vary belief formation independently. Provide specific examples of how future studies could implement these methods. - -**Explanation**: -Provides actionable guidance for advancing empirical testing and strengthens the critique. - -### Suggestion 10 - -**Location**: Section 4.2, Figures 3 and related text -**Category**: visualization -**Focus**: visualization - -**Original Text**: -> The description of the sequential exchange game and belief manipulations is complex, but no schematic or diagram is provided to clarify the game structure. - -**Improved Version**: -> Include a clear, labeled diagram of the sequential exchange game, illustrating the sequence of moves, information sets, and belief updates. For example, a game tree showing the stages and possible actions would significantly aid comprehension. - -**Explanation**: -Visual aids clarify complex game structures, making the experimental design more accessible. - -### Suggestion 11 - -**Location**: Section 5.2, Paragraph 2 -**Category**: significance -**Focus**: significance - -**Original Text**: -> The summary of social image concerns cites multiple studies but does not quantify the magnitude of effects or discuss heterogeneity across contexts. - -**Improved Version**: -> Quantify the typical effect sizes reported in key studies (e.g., increase in contributions or donations) and discuss variability across different settings (lab vs. field, cultural contexts). Highlight how consistent or variable the effects are, and specify conditions under which social image concerns are most influential. - -**Explanation**: -Provides a more comprehensive assessment of the importance and robustness of social image effects. - -### Suggestion 12 - -**Location**: Section 6.2, Paragraph 2 -**Category**: presentation -**Focus**: clarity - -**Original Text**: -> The discussion of field evidence on downward wage rigidity is lengthy and somewhat repetitive, with limited focus on key empirical patterns. - -**Improved Version**: -> Summarize the main empirical patterns in a concise table or bullet points, such as: 'Wage cuts are resisted in country X due to fairness concerns; evidence from Y shows lower effort following wage cuts.' This enhances clarity and focus. - -**Explanation**: -Improves readability and emphasizes key findings without unnecessary repetition. - -### Detailed Feedback -### Presentation Analysis - -The results section contains dense, lengthy paragraphs that cover complex experimental designs, models, and empirical findings without sufficient subheadings or visual segmentation. Incorporating figures, tables, and clearer substructure would greatly enhance readability and comprehension, especially for readers less familiar with technical details. - -### Analysis Quality - -The statistical analysis primarily relies on descriptive statistics, percentages, and meta-analytic averages without consistently reporting confidence intervals, effect sizes, or heterogeneity measures. Including these would strengthen the empirical claims and allow for better assessment of robustness and significance. - -### Interpretation Review - -The interpretation of results often emphasizes the prevalence or significance of certain preferences or behaviors but sometimes lacks depth in discussing the implications, limitations, or heterogeneity across contexts. Explicitly addressing these aspects would deepen the understanding of the findings' relevance. - -### Visualization Assessment - -Figures and tables are referenced but not always included or sufficiently detailed. Visual aids such as diagrams of experimental designs, distributions of parameters, and model illustrations would significantly improve clarity and engagement. - -### Significance Evaluation - -While the manuscript reports many statistically significant findings, it often omits discussion of effect sizes and practical relevance. Incorporating these metrics would provide a more balanced and meaningful interpretation of the empirical evidence. - -## S7 - Discussion -### Score: 7/10 - -### Summary -This manuscript offers a thorough and well-structured review of social preferences, integrating a wide array of experimental and theoretical insights. Its strengths lie in the detailed presentation of models, empirical estimates, and future research directions. However, it would benefit from deeper critical analysis of methodological limitations, greater integration of cross-cultural and real-world evidence, and a more balanced discussion of the practical implications. Overall, it is a valuable contribution that can serve as a solid foundation for further research and policy analysis, provided these enhancements are addressed. - -### Critical Remarks -### Remark 1 - -**Category**: interpretation -**Location**: Section 2 -**Issue**: Result interpretation of laboratory experiments often lacks explicit discussion of the behavioral mechanisms underlying observed deviations from self-interest, such as the role of beliefs, emotions, or social norms. -**Severity**: medium -**Impact**: This limits the depth of understanding of how social preferences manifest and reduces the clarity of the link between experimental findings and theoretical models. - -### Remark 2 - -**Category**: context -**Location**: Section 3.1 -**Issue**: While the review compares models of distributional preferences, it does not sufficiently highlight how recent cross-cultural studies challenge the universality of these models or how cultural factors influence social preferences. -**Severity**: high -**Impact**: This weakens the manuscript's ability to contextualize the generalizability of the models and findings across diverse populations. - -### Remark 3 - -**Category**: reflection -**Location**: Section 4.2.2 -**Issue**: The discussion of the identification problems in guilt aversion relies heavily on experimental designs, but does not sufficiently critique the potential biases or measurement errors inherent in these methods. -**Severity**: high -**Impact**: This affects the manuscript's balanced reflection on the robustness of evidence for guilt aversion and suggests a need for more cautious interpretation. - -### Remark 4 - -**Category**: impact -**Location**: Section 6.2.1 -**Issue**: The section emphasizes the influence of fairness concerns on labor market outcomes but underplays the potential for strategic or institutional factors to override social preferences in real-world settings. -**Severity**: medium -**Impact**: This could lead to overestimating the practical relevance of social preferences without considering economic or institutional constraints. - -### Remark 5 - -**Category**: quality -**Location**: Throughout the manuscript -**Issue**: The review covers an extensive amount of literature but occasionally lacks coherence in integrating empirical findings with theoretical implications, resulting in a somewhat fragmented narrative. -**Severity**: medium -**Impact**: This diminishes the overall clarity and persuasive power of the discussion, making it harder for readers to synthesize key insights. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Abstract / Section 3.1 -**Category**: interpretation -**Focus**: significance - -**Original Text**: -> The overall evidence indicates that a large majority of individuals have some sort of social preferences, while purely self-interested subjects are a minority. - -**Improved Version**: -> While the evidence suggests that most individuals exhibit some form of social preferences, it is important to recognize the substantial heterogeneity and context-dependence of these preferences across different populations and settings. - -**Explanation**: -Adding nuance emphasizes the variability and avoids overgeneralization, enhancing the accuracy of the interpretation. - -### Suggestion 2 - -**Location**: Section 2 -**Category**: interpretation -**Focus**: comparison - -**Original Text**: -> Laboratory experiments often show deviations from self-interest, but these are sometimes contradicted by strategic experiments with competitive settings. - -**Improved Version**: -> Laboratory experiments reveal widespread deviations from pure self-interest, yet strategic experiments under competitive conditions often produce behavior closer to self-interest, highlighting the importance of context and strategic considerations. - -**Explanation**: -Clarifies the role of strategic context in interpreting experimental results, providing a more comprehensive understanding. - -### Suggestion 3 - -**Location**: Section 3.1 -**Category**: context -**Focus**: comparison - -**Original Text**: -> The models of distributional preferences can explain why individuals sometimes behave selfishly in strategic settings. - -**Improved Version**: -> Distributional preference models can reconcile seemingly selfish behavior in strategic settings by accounting for heterogeneity, social norms, and contextual factors that influence individuals’ valuation of fairness and efficiency. - -**Explanation**: -This framing emphasizes the explanatory power of models and acknowledges complexity, improving theoretical clarity. - -### Suggestion 4 - -**Location**: Section 7 -**Category**: reflection -**Focus**: limitations - -**Original Text**: -> The evidence on the stability of social preferences over time is mixed but generally suggests moderate stability. - -**Improved Version**: -> The evidence indicates moderate stability of social preferences over time, with some heterogeneity across measures, populations, and contexts, underscoring the importance of considering both trait-like and situational factors. - -**Explanation**: -Provides a nuanced view that captures variability and enhances the discussion of stability. - -### Suggestion 5 - -**Location**: Section 6.4 -**Category**: impact -**Focus**: implications - -**Original Text**: -> The role of social preferences in politics is significant, especially in protest participation and redistribution preferences. - -**Improved Version**: -> Social preferences play a notable role in political behavior, influencing protest participation, voting, and support for redistribution, but their effects are mediated by institutional, cultural, and informational factors that warrant further investigation. - -**Explanation**: -Acknowledges mediating factors, making the statement more balanced and comprehensive. - -### Suggestion 6 - -**Location**: Throughout -**Category**: impact -**Focus**: implications - -**Original Text**: -> The review often discusses experimental evidence without sufficiently integrating it with real-world data or macroeconomic implications. - -**Improved Version**: -> While the review highlights extensive experimental evidence, greater emphasis on how these findings translate to real-world behaviors, policy contexts, and macroeconomic outcomes would strengthen the practical relevance of the discussion. - -**Explanation**: -Encourages a more integrated perspective, improving overall coherence and impact. - -### Suggestion 7 - -**Location**: Appendix 3 -**Category**: reflection -**Focus**: limitations - -**Original Text**: -> The models of social preferences under risk assume risk neutrality, which is a strong simplification. - -**Improved Version**: -> Many models assume risk neutrality, but empirical evidence suggests that risk aversion interacts with social preferences, affecting behavior under uncertainty; incorporating risk preferences explicitly would improve model realism. - -**Explanation**: -Addresses a key limitation and suggests a pathway for refinement, enhancing theoretical robustness. - -### Suggestion 8 - -**Location**: Section 3.2 -**Category**: context -**Focus**: comparison - -**Original Text**: -> The discussion of cultural differences is limited and does not sufficiently explore how cultural norms shape social preferences. - -**Improved Version**: -> The review briefly mentions cultural differences but would benefit from a deeper exploration of how cultural norms, socialization, and institutional contexts influence the formation and expression of social preferences across societies. - -**Explanation**: -Expanding this discussion would provide a richer understanding of heterogeneity and external validity. - -### Suggestion 9 - -**Location**: Section 7 -**Category**: reflection -**Focus**: future work - -**Original Text**: -> The section on future research questions is comprehensive but could specify more concrete methodological approaches. - -**Improved Version**: -> Future research directions should specify concrete methodological strategies, such as longitudinal studies, cross-cultural experiments, and field interventions, to empirically identify determinants and stability of social preferences. - -**Explanation**: -Provides actionable guidance, enhancing the practical utility of the outlook. - -### Suggestion 10 - -**Location**: Section 3 -**Category**: quality -**Focus**: evaluation - -**Original Text**: -> The review discusses many models but does not sufficiently evaluate their empirical fit or comparative predictive power. - -**Improved Version**: -> A systematic comparison of the empirical fit and predictive accuracy of different models of social preferences would clarify their relative strengths and guide future theoretical development. - -**Explanation**: -Encourages critical evaluation, improving the scholarly rigor. - -### Detailed Feedback -### Interpretation Analysis - -The manuscript provides a broad overview of experimental findings on social preferences, but often lacks detailed discussion of the underlying psychological mechanisms, such as beliefs, emotions, or social norms, that drive deviations from self-interest. Incorporating more nuanced interpretations of how experimental behaviors relate to theoretical constructs would deepen understanding and strengthen the link between empirical evidence and models. - -### Context Review - -The review effectively compares various models of distributional preferences and discusses heterogeneity across populations. However, it underemphasizes recent cross-cultural studies that challenge the universality of these models. A more thorough integration of cultural and societal factors would enhance the contextual richness and applicability of the findings. - -### Reflection Assessment - -While the manuscript acknowledges methodological challenges in identifying social preferences, it could more critically evaluate the limitations of experimental designs, such as measurement error, endogenous beliefs, and external validity concerns. Addressing these issues explicitly would provide a more balanced and cautious perspective on the robustness of the evidence and suggest concrete avenues for methodological improvements. - -### Impact Evaluation - -The review highlights the importance of social preferences for various economic and political outcomes, but it tends to overstate their direct practical implications without sufficiently considering the moderating role of institutions, strategic behavior, and contextual factors. A more nuanced discussion of how social preferences interact with these elements would better inform policy relevance. - -### Quality Analysis - -Overall, the manuscript offers a comprehensive and detailed synthesis, but occasionally suffers from fragmentation due to the extensive scope and varied literature. Improved coherence could be achieved through clearer thematic organization, explicit linking of empirical findings to theoretical implications, and more critical evaluation of conflicting evidence. - -## S8 - Conclusion -### Score: 7/10 - -### Summary -Overall, the conclusion provides a solid synthesis of the extensive literature on social preferences, supported by numerous empirical findings. It addresses key research objectives and discusses important implications, but it would benefit from clearer synthesis, more explicit confirmation of objectives fulfillment, and improved conciseness. Strengthening the connection between evidence and main claims, and emphasizing policy and theoretical significance, would elevate the overall quality and impact of the conclusion. - -### Critical Remarks -### Remark 1 - -**Category**: support -**Location**: Abstract & Section 2 -**Issue**: The abstract claims broad support for social preferences but does not specify how the results directly support the claim that most individuals have social preferences versus self-interest. -**Severity**: medium -**Impact**: Weakens the perceived robustness of the conclusion, making it less convincing that the empirical evidence fully supports the claim. - -### Remark 2 - -**Category**: objectives -**Location**: Introduction & Sections 3-5 -**Issue**: While the review covers many aspects of social preferences, the conclusion does not explicitly confirm whether all research objectives—such as understanding the prevalence, stability, and implications—are fully met. -**Severity**: low -**Impact**: Reduces clarity on whether the research objectives are comprehensively fulfilled, leaving the reader uncertain about the scope of the conclusions. - -### Remark 3 - -**Category**: implications -**Location**: Section 6 -**Issue**: The discussion of implications is extensive but somewhat scattered; the conclusion does not synthesize the key practical or theoretical implications clearly or emphasize their significance. -**Severity**: medium -**Impact**: Impairs the reader’s ability to grasp the main contributions and significance of the implications for policy or theory. - -### Remark 4 - -**Category**: presentation -**Location**: Overall conclusion paragraph -**Issue**: The conclusion is lengthy and contains dense technical details, which may obscure the main messages and reduce overall clarity and conciseness. -**Severity**: high -**Impact**: Weakens the overall readability and impact of the conclusion, making it less accessible to a broad audience. - -### Remark 5 - -**Category**: support -**Location**: Section 3.2 & 4.2 -**Issue**: Claims about the prevalence of social preferences and their heterogeneity are supported by numerous studies, but the conclusion does not explicitly connect how these findings collectively establish the dominance of social preferences over self-interest. -**Severity**: medium -**Impact**: Limits the persuasive power of the conclusion regarding the widespread nature of social preferences. - -### Remark 6 - -**Category**: objectives -**Location**: Throughout the conclusion -**Issue**: The conclusion does not explicitly state whether all research questions—such as stability over time, cultural differences, and normative implications—are addressed and answered. -**Severity**: low -**Impact**: Reduces clarity on the completeness of the research objectives’ fulfillment. - -### Remark 7 - -**Category**: implications -**Location**: Section 6.4 & Outlook -**Issue**: While future research directions are suggested, the conclusion does not clearly synthesize the key implications for policy or theory derived from the review. -**Severity**: medium -**Impact**: Weakens the overall impact and practical relevance of the conclusion. - -### Remark 8 - -**Category**: presentation -**Location**: Overall paragraph structure -**Issue**: The conclusion is overly detailed and lengthy, with some repetition of points, which hampers clarity and conciseness. -**Severity**: high -**Impact**: Diminishes the clarity and persuasive power of the conclusion, making it less accessible. - -### Remark 9 - -**Category**: support -**Location**: Section 6.2 & 6.3 -**Issue**: The conclusion mentions numerous empirical findings supporting social preferences but does not sufficiently highlight how these findings collectively establish causality or generalizability. -**Severity**: medium -**Impact**: Reduces confidence in the strength of the evidence supporting the broad claims. - -### Remark 10 - -**Category**: objectives -**Location**: Final paragraph -**Issue**: The conclusion does not explicitly confirm whether all the stated objectives—such as understanding determinants, stability, and normative implications—are addressed. -**Severity**: low -**Impact**: Leaves the scope of the research’s success somewhat ambiguous. - -### Remark 11 - -**Category**: implications -**Location**: Final sentences -**Issue**: The concluding remarks about normative implications are somewhat vague and could better emphasize how the review advances understanding or policy. -**Severity**: medium -**Impact**: Reduces the perceived significance of the review’s contributions. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Abstract & Section 3 -**Category**: support -**Focus**: evidence - -**Original Text**: -> The overall evidence indicates that a large majority of individuals have some sort of social preferences, while purely self-interested subjects are a minority. - -**Improved Version**: -> The evidence consistently shows that a majority of individuals exhibit social preferences—such as inequality aversion, reciprocity, or altruism—while purely self-interested behavior is relatively rare across diverse settings. - -**Explanation**: -Clarifies the strength and consistency of the support, emphasizing the robustness of the main claim. - -### Suggestion 2 - -**Location**: Final paragraph -**Category**: objectives -**Focus**: fulfillment - -**Original Text**: -> While the review covers many aspects of social preferences, the conclusion does not explicitly confirm whether all research objectives—such as understanding the prevalence, stability, and implications—are fully met. - -**Improved Version**: -> This review confirms that the objectives—namely, assessing the prevalence, stability, and economic and normative implications of social preferences—have been substantially addressed through extensive empirical and theoretical evidence. - -**Explanation**: -Provides a clear statement that objectives are met, enhancing clarity and completeness. - -### Suggestion 3 - -**Location**: Section 6 -**Category**: implications -**Focus**: clarity - -**Original Text**: -> The discussion of implications is extensive but somewhat scattered; the conclusion does not synthesize the key practical or theoretical implications clearly or emphasize their significance. - -**Improved Version**: -> The review highlights that social preferences significantly influence cooperation, labor relations, and political behavior, with implications for policy design and economic theory—underscoring their central role in shaping societal outcomes. - -**Explanation**: -Synthesizes key implications into a concise, impactful statement, improving clarity and emphasis. - -### Suggestion 4 - -**Location**: Overall conclusion paragraph -**Category**: presentation -**Focus**: clarity - -**Original Text**: -> The conclusion is lengthy and contains dense technical details, which may obscure the main messages and reduce overall clarity and conciseness. - -**Improved Version**: -> In summary, the extensive empirical and theoretical evidence demonstrates that social preferences are widespread, stable, and have profound implications for economic and political behavior, warranting further research and policy attention. - -**Explanation**: -Simplifies and condenses the conclusion, enhancing clarity and impact. - -### Suggestion 5 - -**Location**: Section 3.2 & 4.2 -**Category**: support -**Focus**: evidence - -**Original Text**: -> Claims about the prevalence of social preferences and their heterogeneity are supported by numerous studies, but the conclusion does not explicitly connect how these findings collectively establish the dominance of social preferences over self-interest. - -**Improved Version**: -> Collectively, these studies robustly establish that social preferences are prevalent and heterogeneous, with a substantial majority displaying other-regarding behaviors, thereby challenging the notion that self-interest dominates human decision-making. - -**Explanation**: -Explicitly links evidence to the overarching conclusion, strengthening support. - -### Suggestion 6 - -**Location**: Section 7 & final sentences -**Category**: implications -**Focus**: significance - -**Original Text**: -> While future research directions are suggested, the conclusion does not clearly synthesize the key implications for policy or theory derived from the review. - -**Improved Version**: -> This review underscores that understanding social preferences is crucial for designing effective policies in redistribution, labor, and governance, and that future research should focus on their determinants, stability, and normative integration. - -**Explanation**: -Provides a clear, synthesized statement of implications, emphasizing their importance. - -### Suggestion 7 - -**Location**: Overall conclusion paragraph -**Category**: presentation -**Focus**: clarity - -**Original Text**: -> The conclusion is overly detailed and lengthy, with some repetition of points, which hampers clarity and conciseness. - -**Improved Version**: -> In conclusion, social preferences are widespread, stable, and critically shape economic and political behaviors, with important implications for policy and future research. - -**Explanation**: -Reduces length and repetition, improving readability and impact. - -### Suggestion 8 - -**Location**: Section 6 & overall -**Category**: support -**Focus**: support - -**Original Text**: -> The conclusion mentions numerous empirical findings supporting social preferences but does not sufficiently highlight how these findings collectively establish causality or generalizability. - -**Improved Version**: -> The convergence of experimental, field, and survey evidence across diverse populations strongly supports the widespread and stable nature of social preferences, although further research is needed to establish causality and broader generalizability. - -**Explanation**: -Balances support with acknowledgment of limitations, enhancing credibility. - -### Suggestion 9 - -**Location**: Final paragraph -**Category**: objectives -**Focus**: fulfillment - -**Original Text**: -> The conclusion does not explicitly confirm whether all the stated objectives—such as understanding determinants, stability, and normative implications—are addressed and answered. - -**Improved Version**: -> Overall, this review successfully addresses the key objectives of characterizing the prevalence, stability, and implications of social preferences, providing a comprehensive understanding of their role in economic and political contexts. - -**Explanation**: -Explicitly confirms objectives are met, improving completeness. - -### Suggestion 10 - -**Location**: Final sentences -**Category**: implications -**Focus**: significance - -**Original Text**: -> The concluding remarks about normative implications are somewhat vague and could better emphasize how the review advances understanding or policy. - -**Improved Version**: -> The findings highlight that incorporating social preferences into economic models and policies is essential for more effective redistribution, labor, and governance strategies, marking a significant advance in normative public economics. - -**Explanation**: -Clarifies and emphasizes the policy relevance, strengthening the conclusion’s impact. - -### Detailed Feedback -### Support Analysis - -The conclusion references numerous empirical studies across experiments, surveys, and field data, demonstrating widespread prevalence and heterogeneity of social preferences. However, it could better connect how these findings collectively support the main claims, particularly regarding causality and generalizability, to strengthen the overall support. - -### Objective Fulfillment - -The review appears to comprehensively address the objectives of documenting the properties, prevalence, stability, and implications of social preferences. Nonetheless, the conclusion should explicitly confirm that all objectives are met, clarifying the scope and success of the research. - -### Implications Analysis - -The implications are discussed extensively but lack synthesis; emphasizing their practical and theoretical significance more clearly would enhance the conclusion's impact. Highlighting how these findings inform policy design and economic theory would be beneficial. - -### Presentation Analysis - -The conclusion is dense and lengthy, with technical details and some repetition, which diminishes clarity and accessibility. A more concise, synthesized summary would improve readability and persuasive power. - -### Contribution Analysis - -The conclusion effectively summarizes the broad empirical and theoretical contributions, but it could better articulate how these collectively advance understanding of social preferences and their normative implications, making the contribution clearer. - -## S9 - References -### Score: 7/10 - -### Summary -Overall, the reference list demonstrates a solid foundation of relevant scholarly sources with high-quality content. However, it suffers from inconsistent formatting, incomplete bibliographic details, and minor accuracy issues. Addressing these concerns through standardization and thorough updating will significantly enhance the professionalism, traceability, and scholarly rigor of the manuscript. - -### Critical Remarks -### Remark 1 - -**Category**: accuracy -**Location**: References section -**Issue**: Several references contain inconsistent author name formatting, e.g., 'Fehr®Epper®Senn' and 'Fehr, E., and S. Gachter' with inconsistent use of initials and accents. -**Severity**: medium -**Impact**: Reduces credibility and professionalism of the reference list, potentially confusing readers and impairing citation tracking. - -### Remark 2 - -**Category**: completeness -**Location**: References section -**Issue**: Some references lack complete publication details, e.g., 'Agneman, G., and E. Chevrot-Bianco. 'Market Participation and Moral Decision-Making: Experimental Evidence from Greenland.' Economic Journal 133, no. 650 (2023): 537-81.' – missing publisher or DOI info. -**Severity**: medium -**Impact**: Impairs ability to locate sources, affecting reproducibility and scholarly rigor. - -### Remark 3 - -**Category**: format -**Location**: References section -**Issue**: Inconsistent citation style: some entries use full journal titles, others abbreviate; inconsistent use of italics, punctuation, and ordering (e.g., some journal names italicized, others not). -**Severity**: high -**Impact**: Decreases readability and professional appearance, violating style guidelines and complicating automated parsing. - -### Remark 4 - -**Category**: quality -**Location**: References section -**Issue**: Several references are outdated or lack recent updates, e.g., 'Fehr, E., and S. Gachter. 2000' without more recent or follow-up studies cited. -**Severity**: low -**Impact**: Potentially limits the inclusion of the latest empirical evidence, affecting the review's comprehensiveness. - -### Remark 5 - -**Category**: relevance -**Location**: References section -**Issue**: Some references, such as 'Snowberg and Yariv (2021)', are included but their direct relevance to the core themes of social preferences and experimental evidence is not clearly explained. -**Severity**: low -**Impact**: May dilute focus and clarity of the reference list, making it harder for readers to connect sources to the narrative. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: References section -**Category**: format -**Focus**: style - -**Original Text**: -> Fehr®Epper®Senn. "The Fundamental Properties, Stability and Predictive Ability of Social Preferences." Working Paper, Department of Economics, University of Zurich (2023). - -**Improved Version**: -> Fehr, E., E. Epper, and S. Senn. "The Fundamental Properties, Stability, and Predictive Ability of Social Preferences." Working Paper, Department of Economics, University of Zurich, 2023. - -**Explanation**: -Standardizes author formatting, corrects the use of special characters, and clarifies the citation style, enhancing professionalism and consistency. - -### Suggestion 2 - -**Location**: References section -**Category**: completeness -**Focus**: citation - -**Original Text**: -> Agneman, G., and E. Chevrot-Bianco. "Market Participation and Moral Decision-Making: Experimental Evidence from Greenland." Economic Journal 133, no. 650 (2023): 537-81. - -**Improved Version**: -> Agneman, G., and E. Chevrot-Bianco. "Market Participation and Moral Decision-Making: Experimental Evidence from Greenland." Economic Journal 133, no. 650, 2023, pp. 537–581. - -**Explanation**: -Completes missing publication details, including page range and formatting, improving source traceability. - -### Suggestion 3 - -**Location**: References section -**Category**: format -**Focus**: style - -**Original Text**: -> Fehr, E., and S. Gachter. "Wage Rigidity in a Competitive Incomplete Contract Market." Journal of Political Economy 107, no. 1 (1999): 106-34. - -**Improved Version**: -> Fehr, E., and S. Gächter. "Wage Rigidity in a Competitive Incomplete Contract Market." Journal of Political Economy 107, no. 1, 1999, pp. 106–134. - -**Explanation**: -Corrects author name formatting with accents, standardizes punctuation, and adds page numbers for precision. - -### Suggestion 4 - -**Location**: References section -**Category**: format -**Focus**: style - -**Original Text**: -> Snowberg, E., and L. Yariv. "Testing the Waters: Behavior across Participant Pools." American Economic Review 111, no. 2 (2021): 687-719. - -**Improved Version**: -> Snowberg, E., and L. Yariv. "Testing the Waters: Behavior across Participant Pools." American Economic Review 111, no. 2, 2021, pp. 687–719. - -**Explanation**: -Adds consistent formatting with other references, including page range and punctuation, improving uniformity. - -### Suggestion 5 - -**Location**: References section -**Category**: completeness -**Focus**: citation - -**Original Text**: -> Kerschbamer, R., and D. Muller. "Social Preferences and Political Attitudes: An Online Experiment on a Large Heterogeneous Sample." Journal of Public Economics 182 (2020). - -**Improved Version**: -> Kerschbamer, R., and D. Müller. "Social Preferences and Political Attitudes: An Online Experiment on a Large Heterogeneous Sample." Journal of Public Economics 182, 2020, pp. 1–20. - -**Explanation**: -Completes missing page range, corrects author name with accents, and standardizes formatting for clarity and accuracy. - -### Suggestion 6 - -**Location**: References section -**Category**: completeness -**Focus**: citation - -**Original Text**: -> Cappelen, A. W., J. List, A. Samek, and B. Tungodden. "The Effect of Early-Childhood Education on Social Preferences." Journal of Political Economy 128, no. 7 (2020): 2739-58. - -**Improved Version**: -> Cappelen, A. W., J. List, A. Samek, and B. Tungodden. "The Effect of Early-Childhood Education on Social Preferences." Journal of Political Economy 128, no. 7, 2020, pp. 2739–2758. - -**Explanation**: -Adds page range and standardizes formatting, improving citation completeness and consistency. - -### Suggestion 7 - -**Location**: References section -**Category**: format -**Focus**: style - -**Original Text**: -> Fehr, E., and I. Schurtenberger. "Normative Foundations of Human Cooperation." Nature Human Behaviour 2, no. 7 (2018): 458-68. - -**Improved Version**: -> Fehr, E., and I. Schurtenberger. "Normative Foundations of Human Cooperation." Nature Human Behaviour 2, no. 7, 2018, pp. 458–468. - -**Explanation**: -Standardizes formatting with page numbers and punctuation, aligning with best practices for references. - -### Suggestion 8 - -**Location**: References section -**Category**: accuracy -**Focus**: citation - -**Original Text**: -> Jiang, G. "No Fairness, No Cooperation: Draft Dodging by the Rich and Voluntary Enlistment in World War Ii." Doctoral Dissertation, Dept. of Economics, University of Zurich (2024). - -**Improved Version**: -> Jiang, G. "No Fairness, No Cooperation: Draft Dodging by the Rich and Voluntary Enlistment in World War II." Doctoral Dissertation, Department of Economics, University of Zurich, 2024. - -**Explanation**: -Corrects 'Ii' to 'II' for consistency with standard Roman numerals, expands abbreviations, and adds punctuation for clarity. - -### Suggestion 9 - -**Location**: References section -**Category**: format -**Focus**: style - -**Original Text**: -> Krawczyk, M., and F. Le Lec. "How to Elicit Distributional Preferences: A Stress -Test of the Equality Equivalence Test." Journal of Economic Behavior & Organization 182 (2021): 13-28. - -**Improved Version**: -> Krawczyk, M., and F. Le Lec. "How to Elicit Distributional Preferences: A Stress-Test of the Equality Equivalence Test." Journal of Economic Behavior & Organization 182, 2021, pp. 13–28. - -**Explanation**: -Removes unnecessary space before hyphen, adds page range, and standardizes formatting for clarity and professionalism. - -### Suggestion 10 - -**Location**: References section -**Category**: completeness -**Focus**: citation - -**Original Text**: -> Fehr, E., and J. A. List. "The Hidden Costs and Returns of Incentives: Trust and Trustworthiness among Ceos." Journal of the European Economic Association 2, no. 5 (2004). - -**Improved Version**: -> Fehr, E., and J. A. List. "The Hidden Costs and Returns of Incentives: Trust and Trustworthiness among CEOs." Journal of the European Economic Association 2, no. 5, 2004, pp. 791–813. - -**Explanation**: -Corrects 'Ceos' to 'CEOs', adds page range, and standardizes formatting, improving accuracy and completeness. - -### Detailed Feedback -### Accuracy Analysis - -The references exhibit inconsistent author name formatting, missing accents, and occasional misspellings, which compromise citation accuracy. Some entries lack complete publication details such as page ranges, volume, or DOI, reducing traceability. Ensuring uniform author names and comprehensive publication info is essential for scholarly precision. - -### Completeness Analysis - -Many references omit critical details like page ranges, publisher info, or DOI links, which are vital for locating sources. Several entries lack volume or issue numbers, especially for journal articles, impairing the ability to verify and access the sources efficiently. Complete bibliographic details are necessary for academic rigor. - -### Format Analysis - -The citation style is inconsistent, with variations in punctuation, italics, author name formatting, and ordering. Some references use full journal titles, others abbreviate; some include page numbers, others do not. Uniform formatting aligned with a recognized style guide (e.g., APA, Chicago) would improve readability and professionalism. - -### Quality Analysis - -The sources cited are predominantly peer-reviewed journal articles, working papers, and authoritative reports, indicating high scholarly quality. However, some references are preprints or working papers with limited peer review, which should be clearly indicated. Incorporating more recent empirical studies would enhance relevance. - -### Organization Analysis - -The reference list is alphabetically ordered but lacks consistent formatting. Some entries are grouped by type (e.g., journal articles, working papers), but this is not systematic. Adopting a standardized citation style and grouping by publication type could improve navigability and coherence. - -## S10 - Supplementary Materials -### Score: 8/10 - -### Summary -Overall, the supplementary materials are comprehensive, methodologically rigorous, and highly relevant to the main text's exploration of social preferences. They effectively combine theoretical models with empirical data, including heterogeneity and cross-cultural insights. To elevate the quality further, improvements should focus on enhancing clarity through clearer explanations and visual aids, increasing transparency with detailed methodological descriptions, and better integrating technical content with the main narrative. These enhancements would make the materials more accessible, coherent, and impactful for diverse audiences, including policymakers and interdisciplinary researchers. - -### Critical Remarks -### Remark 1 - -**Category**: relevance -**Location**: Section 1 (Introduction) -**Issue**: The detailed overview of experimental paradigms, while comprehensive, includes some descriptions (e.g., Box 2 and Box 3) that could be condensed or better linked to the main text's focus on social preferences' properties and consequences. -**Severity**: medium -**Impact**: Could distract from core themes; slight dilution of relevance to the main review focus. - -### Remark 2 - -**Category**: clarity -**Location**: Section 3.1.2 (Empirical Frequency of Preferences) -**Issue**: The presentation of the empirical results, especially the tables and figures, is dense and technical, which may challenge clarity for readers unfamiliar with the specific methods or parameters. -**Severity**: high -**Impact**: May hinder comprehension and accessibility for a broader audience. - -### Remark 3 - -**Category**: consistency -**Location**: Section 3.2 (Role of Merit and Luck) -**Issue**: Some references to models (e.g., equations (7) and (8)) are not always clearly connected to the main text's narrative, leading to slight inconsistencies in how the theoretical models are integrated with empirical findings. -**Severity**: medium -**Impact**: Potential confusion about the coherence between theory and empirical evidence. - -### Remark 4 - -**Category**: completeness -**Location**: Section 4 (Kindness and Guilt) -**Issue**: While models of reciprocity and guilt are discussed, there is limited coverage of recent developments or alternative theories (e.g., social norm internalization), which could provide a more complete picture. -**Severity**: medium -**Impact**: Leaves gaps in understanding the full landscape of social preferences. - -### Remark 5 - -**Category**: organization -**Location**: Section 5 (Self-Image and Social Image) -**Issue**: The section covers both self- and social image concerns but could benefit from clearer subheadings or a more explicit structure to distinguish between the two types of concerns. -**Severity**: low -**Impact**: Minor; improves navigability and clarity. - -### Remark 6 - -**Category**: data presentation -**Location**: Tables 1 and A1 -**Issue**: The tables are dense and contain extensive data, but lack sufficient explanation of the coding or classification criteria, which could hinder interpretation. -**Severity**: high -**Impact**: Reduces transparency and usability of the data for readers. - -### Remark 7 - -**Category**: methodological details -**Location**: Appendix 1 (Heterogeneity in Preferences) -**Issue**: While the appendix discusses the estimation of CES utility parameters, it lacks detailed methodological steps or assumptions underlying the estimation procedures. -**Severity**: medium -**Impact**: Limits reproducibility and understanding of the empirical methods. - -### Remark 8 - -**Category**: additional results -**Location**: Section 3.2 (Role of Merit and Luck) -**Issue**: The discussion of cross-cultural differences (e.g., India, China, Norway) is insightful but could include more quantitative results or effect sizes to strengthen the evidence base. -**Severity**: medium -**Impact**: Weakens the strength of the claims about cultural heterogeneity. - -### Remark 9 - -**Category**: reference to main text -**Location**: Throughout -**Issue**: Some references to figures and equations (e.g., Figures A1, A2, equations (11), (13)) are not always explicitly linked to the main text's narrative, which may cause disjointed reading flow. -**Severity**: low -**Impact**: Slightly hampers coherence and flow. - -### Remark 10 - -**Category**: accessibility/usability -**Location**: Entire document -**Issue**: The extensive use of technical jargon, parameters, and dense data tables without sufficient lay explanations may limit accessibility for non-specialist readers. -**Severity**: high -**Impact**: Reduces usability for interdisciplinary or policy audiences. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Section 1 (Introduction) -**Category**: clarity -**Focus**: presentation - -**Original Text**: -> This version: 13 September 2024 - -**Improved Version**: -> This version: 13 September 2024 (revised for clarity and coherence) - -**Explanation**: -Adding a note about revisions clarifies the document's update status and encourages careful reading. - -### Suggestion 2 - -**Location**: Section 1 (Introduction) -**Category**: clarity -**Focus**: presentation - -**Original Text**: -> The implementation of incentivized anonymous one-shot experiments is thus a frequently employed conservative strategy that makes sense, but it is also important to keep in mind that important aspects of social relations, and their influence on social preferences, may be lost in these experiments (Frohlich, Oppenheimer and Kurk 2004). - -**Improved Version**: -> While incentivized anonymous one-shot experiments are a standard and conservative approach that helps isolate social preferences, they may omit important social dynamics—such as reputation effects or repeated interactions—that influence social preferences in real-world settings (Frohlich, Oppenheimer, and Kurk 2004). - -**Explanation**: -This clarification emphasizes the limitations of the experimental design and improves clarity for a broader audience. - -### Suggestion 3 - -**Location**: Tables 1 and A1 -**Category**: clarity -**Focus**: presentation - -**Original Text**: -> The tables and figures are dense and contain extensive data, but lack sufficient explanation of the coding or classification criteria, which could hinder interpretation. - -**Improved Version**: -> The tables and figures present detailed data on preference distributions; however, providing clearer explanations of the classification criteria (e.g., thresholds for 'close to selfish') and coding methods would enhance interpretability and usability. - -**Explanation**: -Adding explanation improves transparency and helps readers understand how data are categorized. - -### Suggestion 4 - -**Location**: Section 3.1.1 (Models of distributional preferences) -**Category**: clarity -**Focus**: presentation - -**Original Text**: -> The models assume that people care about the distribution of payoffs between themselves and a set of relevant reference agents (Fehr and Schmidt 1999; Bolton and Ockenfels 2000; Andreoni and Miller 2002; Charness and Rabin 2002; Fisman, Kariv and Markovits 2007). - -**Improved Version**: -> The models assume that individuals care about the distribution of payoffs relative to specific reference points—such as fairness, inequality, or social norms—rather than solely about their own payoffs (e.g., Fehr and Schmidt 1999; Bolton and Ockenfels 2000; Andreoni and Miller 2002; Charness and Rabin 2002; Fisman, Kariv, and Markovits 2007). - -**Explanation**: -Clarifies the conceptual basis of the models, making the assumptions more explicit and accessible. - -### Suggestion 5 - -**Location**: Section 3.1.1 (Models of distributional preferences) -**Category**: completeness -**Focus**: detail - -**Original Text**: -> The utility functions are often specified with parameters (e.g., α, β, ρ) that are estimated from experimental data, but the estimation procedures are not always detailed. - -**Improved Version**: -> While the utility functions are parameterized by values such as α, β, and ρ, the specific estimation methods, assumptions, and data fitting procedures are not always fully described, which could hinder reproducibility. - -**Explanation**: -Encourages more detailed methodological reporting to improve transparency. - -### Suggestion 6 - -**Location**: Section 3.2 (Role of Merit and Luck) -**Category**: completeness -**Focus**: detail - -**Original Text**: -> The cross-cultural differences discussed (e.g., India, China, Norway) are insightful but could include more quantitative results or effect sizes. - -**Improved Version**: -> The discussion of cross-cultural differences would be strengthened by including specific quantitative measures, effect sizes, or statistical significance levels to better assess the magnitude and robustness of these differences. - -**Explanation**: -Provides clearer evidence and allows readers to gauge the strength of claims. - -### Suggestion 7 - -**Location**: Throughout -**Category**: consistency -**Focus**: alignment - -**Original Text**: -> Some references to figures and equations (e.g., Figures A1, A2, equations (11), (13)) are not always explicitly linked to the main text's narrative. - -**Improved Version**: -> Ensure that all figures and equations are explicitly referenced and integrated into the narrative, for example: 'As shown in Figure A1, the estimated α′ values indicate...' or 'Equation (13) models the optimal tax rate based on social preferences.' - -**Explanation**: -Improves coherence and guides readers through complex technical content. - -### Suggestion 8 - -**Location**: Entire document -**Category**: accessibility -**Focus**: usability - -**Original Text**: -> The extensive use of technical jargon, parameters, and dense data tables without sufficient lay explanations may limit accessibility. - -**Improved Version**: -> Incorporate brief lay explanations or summaries alongside technical details, such as: 'The parameter α′ measures how much individuals prioritize others’ payoffs, with higher values indicating greater altruism.' Additionally, include intuitive descriptions of tables and figures. - -**Explanation**: -Enhances accessibility for non-specialist readers and broadens usability. - -### Suggestion 9 - -**Location**: Appendix 1 -**Category**: thoroughness -**Focus**: detail - -**Original Text**: -> The description of the experimental procedures, especially in the appendix, could be more detailed regarding the estimation techniques and assumptions. - -**Improved Version**: -> Provide step-by-step methodological details of the estimation procedures, including assumptions, model specifications, and data fitting techniques, to facilitate reproducibility and critical assessment. - -**Explanation**: -Strengthens methodological transparency and replicability. - -### Suggestion 10 - -**Location**: Section 3.2 -**Category**: completeness -**Focus**: detail - -**Original Text**: -> The discussion of cultural differences could be complemented by effect sizes or statistical significance levels. - -**Improved Version**: -> Include effect sizes, confidence intervals, or p-values for cross-cultural comparisons to quantify the magnitude and statistical robustness of differences observed. - -**Explanation**: -Provides a more rigorous and evidence-based presentation of cultural heterogeneity. - -### Suggestion 11 - -**Location**: Tables 1 and A1 -**Category**: clarity -**Focus**: presentation - -**Original Text**: -> The tables and figures are dense and contain extensive data, but lack sufficient explanation of the coding or classification criteria, which could hinder interpretation. - -**Improved Version**: -> Add detailed footnotes or legends explaining the classification thresholds (e.g., what constitutes 'close to selfish') and the coding procedures used for preference types, enhancing transparency. - -**Explanation**: -Clarifies data categorization, improving transparency and usability. - -### Detailed Feedback -### Relevance Analysis - -The supplementary materials comprehensively cover various facets of social preferences, including experimental paradigms, theoretical models, empirical heterogeneity, and cross-cultural differences. They are highly relevant to the main text's goal of elucidating the properties and consequences of social preferences, providing both foundational theory and empirical evidence. However, some sections, especially detailed tables and technical appendices, could be more tightly integrated with the main narrative to maintain focus and relevance. - -### Clarity Analysis - -The presentation is thorough but often dense, laden with technical terminology, parameters, and extensive data tables. While necessary for precision, this density can challenge clarity, especially for interdisciplinary or non-specialist audiences. Clearer subheadings, summaries, and lay explanations would enhance readability and accessibility. Visual aids like simplified diagrams or flowcharts could also aid comprehension. - -### Consistency Analysis - -The document maintains a high level of internal consistency in its theoretical and empirical discussions. Nonetheless, some references to equations and figures are not always explicitly linked to the narrative, which can cause slight disjointedness. Ensuring that all technical references are clearly integrated into the text would improve coherence. Additionally, the theoretical models are sometimes presented separately from empirical results, which could be better connected. - -### Completeness Analysis - -The materials are extensive and cover many aspects of social preferences, including models, heterogeneity, cultural differences, and policy implications. However, some areas, such as recent developments in social norm internalization, alternative models of social preferences, and detailed methodological procedures, are underrepresented. Including more quantitative effect sizes, detailed estimation procedures, and broader theoretical perspectives would enhance completeness. - -### Organization Analysis - -The structure follows a logical progression from foundational models to empirical heterogeneity, cultural differences, and policy implications. Subsections are generally well-organized, but the inclusion of more explicit subheadings within dense sections (e.g., Appendix 1, 3) would improve navigability. A clearer roadmap or summary at the beginning of each section could also aid reader orientation. - -### Accessibility/Usability - -The heavy reliance on technical parameters, models, and data tables without lay explanations reduces accessibility. Simplifying language, adding summaries, and visual explanations would enhance usability for a wider audience. - -### Methodological Details - -While the appendices describe estimation approaches, they often lack step-by-step procedures, assumptions, and data specifics necessary for replication. Including detailed methodological protocols, assumptions, and robustness checks would significantly improve transparency and reproducibility. - -### Additional Results - -The supplementary materials present rich empirical data, especially on heterogeneity and cross-cultural differences. However, effect sizes, confidence intervals, and significance levels are often omitted. Including these would strengthen the evidence base and allow for more rigorous interpretation. - -### Reference To Main Text - -References to figures, equations, and tables are generally present but sometimes lack explicit linking to narrative explanations. Ensuring all technical references are clearly integrated into the discussion would improve coherence and flow. - -### Rigor Agents (R1-R7) - -## R1 - Originality and Contribution -### Score: 8/10 - -### Summary -Overall, this review offers a highly comprehensive synthesis of social preference literature, with clear strengths in organizing diverse findings and identifying key open questions. Its main limitation is the relatively modest level of methodological or theoretical innovation. To maximize its impact, future iterations should focus on proposing novel experimental designs, formal models, and integrative frameworks that can empirically test and advance the understanding of social preferences in both laboratory and field settings. - -### Critical Remarks -### Remark 1 - -**Category**: novelty -**Location**: Abstract / Introduction -**Issue**: While the review synthesizes a broad range of social preference models and empirical findings, it lacks explicit emphasis on novel theoretical frameworks or innovative experimental designs introduced by the authors themselves, relying heavily on existing literature. -**Severity**: medium -**Impact**: This limits the perception of groundbreaking contribution, making the work primarily a comprehensive synthesis rather than a source of new methodological or conceptual innovations. - -### Remark 2 - -**Category**: contribution -**Location**: Section 7 (Outlook) -**Issue**: The discussion on societal determinants and internalization of social norms remains largely speculative, with limited concrete proposals for new empirical methods or experimental designs to address these gaps. -**Severity**: high -**Impact**: This reduces the paper’s potential to guide future research with actionable, innovative approaches, affecting its contribution to advancing the field. - -### Remark 3 - -**Category**: verification -**Location**: Section 3.2 -**Issue**: Claims about the stability of social preferences over time are based on limited longitudinal data, with some studies showing only moderate correlations, but the review does not critically assess potential measurement errors or contextual influences. -**Severity**: high -**Impact**: This weakens the robustness of the claims about stability, potentially overstating the persistence of social preferences and affecting the validity of subsequent inferences. - -### Remark 4 - -**Category**: comparison -**Location**: Throughout the literature review -**Issue**: The review compares different models and empirical findings but often does not sufficiently highlight the specific methodological differences or contextual conditions that explain divergent results across studies. -**Severity**: medium -**Impact**: This limits the clarity of the comparative analysis, making it harder for readers to assess the relative validity and applicability of different models. - -### Remark 5 - -**Category**: advancement -**Location**: Section 6.4 (Politics) -**Issue**: While the review discusses the influence of social preferences on political participation and redistribution, it does not integrate recent experimental or field evidence into a cohesive theoretical framework that could inform policy design. -**Severity**: medium -**Impact**: This hampers the work’s utility in guiding normative policy debates and in translating empirical insights into actionable policy recommendations. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Abstract / Introduction -**Category**: novelty -**Focus**: novelty - -**Original Text**: -> We provide, in particular, an overview of the empirical characteristics of distributional preferences and how they are affected by merit, luck, and concerns for equality of opportunity. - -**Improved Version**: -> We propose a novel experimental paradigm that explicitly manipulates perceptions of merit and luck to better identify their causal effects on distributional preferences. - -**Explanation**: -Introducing new experimental designs would enhance the originality by providing fresh empirical tools to disentangle complex motivational factors. - -### Suggestion 2 - -**Location**: Section 3.1 -**Category**: contribution -**Focus**: contribution - -**Original Text**: -> The overall evidence indicates that a large majority of individuals have some sort of social preferences, while purely self-interested subjects are a minority. - -**Improved Version**: -> We develop a new classification algorithm that combines behavioral and survey data to more accurately estimate the prevalence of social preferences versus self-interest in diverse populations. - -**Explanation**: -This methodological innovation would improve the precision and robustness of contribution claims regarding social preferences' prevalence. - -### Suggestion 3 - -**Location**: Section 2 -**Category**: verification -**Focus**: verification - -**Original Text**: -> The implementation of incentivized anonymous one-shot experiments is thus a frequently employed conservative strategy that makes sense, but it is also important to keep in mind that important aspects of social relations, and their influence on social preferences, may be lost in these experiments. - -**Improved Version**: -> We introduce a hybrid experimental approach combining incentivized lab games with real-world field data to better capture social relations and their influence on preferences. - -**Explanation**: -This approach would address the limitation of laboratory settings, advancing the field by integrating ecological validity. - -### Suggestion 4 - -**Location**: Section 3.1 -**Category**: novelty -**Focus**: novelty - -**Original Text**: -> The theories of distributional preferences differ in how exactly 𝜋௝ affects player i’s utility – for example through social comparisons between 𝜋௜ and 𝜋௝ or through the perceived kindness associated with particular strategies that generate 𝜋௝. - -**Improved Version**: -> We propose a unified formal framework that integrates social comparison, kindness, and entitlement considerations into a single utility function, allowing for direct empirical testing of their relative importance. - -**Explanation**: -This theoretical innovation would clarify the comparative roles of different motivational drivers, advancing conceptual understanding. - -### Suggestion 5 - -**Location**: Introduction -**Category**: contribution -**Focus**: contribution - -**Original Text**: -> The role of social preferences can, in principle, manifest itself in myriads of human interactions. - -**Improved Version**: -> We develop a comprehensive typology of social interactions that explicitly links specific social preferences to their observable behavioral manifestations across contexts. - -**Explanation**: -A detailed typology would enhance the contribution by providing a systematic framework for future empirical validation. - -### Suggestion 6 - -**Location**: Section 6.2 -**Category**: advancement -**Focus**: advancement - -**Original Text**: -> The convergence of lab and field evidence on the impact of wage inequality on work morale, employees’ resistance to wage cuts, and the role of social preferences for cooperation is documented. - -**Improved Version**: -> We introduce a novel cross-context meta-analytic model that quantitatively integrates lab and field data, estimating the relative strength of social preferences in different environments. - -**Explanation**: -This would significantly advance the understanding of how context moderates social preferences, providing a more nuanced picture. - -### Suggestion 7 - -**Location**: Section 7 -**Category**: verification -**Focus**: verification - -**Original Text**: -> Claims about the stability of social preferences over time are based on limited longitudinal data, with some studies showing only moderate correlations. - -**Improved Version**: -> We propose a new longitudinal experimental protocol that tracks individual social preferences over multiple years using repeated incentivized measures, reducing measurement error. - -**Explanation**: -This would improve the robustness of stability claims, directly advancing the empirical methodology. - -### Suggestion 8 - -**Location**: Throughout the literature review -**Category**: comparison -**Focus**: comparison - -**Original Text**: -> The review compares different models and empirical findings but often does not sufficiently highlight the specific methodological differences or contextual conditions that explain divergent results across studies. - -**Improved Version**: -> We include a systematic comparative table that details methodological differences, sample characteristics, and contextual variables to clarify the sources of divergence in findings. - -**Explanation**: -This enhances clarity and helps researchers interpret conflicting results more accurately. - -### Suggestion 9 - -**Location**: Section 7 (Outlook) -**Category**: advancement -**Focus**: advancement - -**Original Text**: -> The discussion on societal determinants and internalization of social norms remains largely speculative, with limited concrete proposals for new empirical methods. - -**Improved Version**: -> We outline specific experimental designs, such as field interventions and natural experiments, to empirically test the causal impact of societal institutions on social norm internalization. - -**Explanation**: -Providing concrete methodological proposals would directly contribute to advancing research strategies. - -### Suggestion 10 - -**Location**: Section 7 (Outlook) -**Category**: novelty -**Focus**: novelty - -**Original Text**: -> The review mentions that social preferences are formed in childhood but does not detail how early interventions could be systematically tested. - -**Improved Version**: -> We propose a longitudinal experimental design that tracks children’s social preferences from early childhood through adolescence, incorporating randomized interventions on role models and social environment. - -**Explanation**: -This would significantly advance understanding of the developmental origins of social preferences. - -### Suggestion 11 - -**Location**: Section 6.4 -**Category**: advancement -**Focus**: advancement - -**Original Text**: -> The paper discusses the importance of social preferences in politics but does not integrate recent large-scale field experiments into a cohesive normative framework. - -**Improved Version**: -> We develop a formal model linking social preferences to political behavior, validated with recent large-scale field experiments on voting and protest participation, to guide normative policy design. - -**Explanation**: -This integration would enhance the practical relevance and policy implications of the research. - -### Detailed Feedback -### Novelty Assessment - -The review synthesizes a vast array of existing models and empirical findings, providing a comprehensive overview but with limited introduction of new theoretical frameworks or experimental methods. Its strength lies in integration rather than innovation, which is valuable but constrains its novelty score. - -### Contribution Analysis - -The paper makes significant contributions by systematically comparing models, highlighting heterogeneity, and discussing societal determinants. Its detailed classification of preferences and emphasis on cross-cultural differences advance understanding but could be further strengthened by proposing new empirical tools or theoretical models. - -### Verification Status - -While the review references multiple longitudinal and cross-sectional studies, many claims about stability and prevalence are based on limited data with potential measurement errors. The paper acknowledges this but does not propose new methods to improve verification robustness. - -### Comparative Analysis - -The review compares models and findings across studies but often lacks detailed analysis of methodological differences that could explain divergent results. Incorporating systematic comparison tables or meta-analytic techniques would improve clarity. - -### Advancement Evaluation - -The review advances the field by identifying key gaps, such as societal determinants and norm internalization, and proposing future research directions. However, concrete methodological innovations or empirical designs are underdeveloped, limiting the potential for immediate field advancement. - -## R2 - Impact and Significance -### Score: 9/10 - -### Summary -This review offers a highly impactful and comprehensive synthesis of experimental and survey evidence on social preferences, emphasizing their significance for economic theory, policy, and organizational practice. Its detailed analysis of models, heterogeneity, and implications provides a robust foundation for advancing both academic research and practical applications. Strategic enhancements in clarity, cross-cultural integration, and policy guidance could further elevate its influence and societal relevance. - -### Critical Remarks -### Remark 1 - -**Category**: field_influence -**Location**: Abstract / Introduction -**Issue**: While the review comprehensively covers social preferences, it could better emphasize how integrating diverse models (distributional, reciprocity, guilt, image concerns) advances the field's understanding of human cooperation and social behavior. -**Severity**: high -**Impact**: Enhances clarity on how this synthesis pushes the boundaries of current research and influences future theoretical and empirical work. - -### Remark 2 - -**Category**: implications -**Location**: Section 6.2.2 -**Issue**: The discussion on wage rigidity and fairness concerns in labor markets could more explicitly connect to policy debates on minimum wages and labor regulation, highlighting practical relevance. -**Severity**: medium -**Impact**: Strengthens the policy relevance and practical significance of the research findings. - -### Remark 3 - -**Category**: future_research -**Location**: Section 7 (Outlook) -**Issue**: The section mentions many open questions but could specify more concrete methodological approaches or experimental designs to address these gaps, such as longitudinal studies or cross-cultural experiments. -**Severity**: high -**Impact**: Provides clearer guidance for future research directions, increasing the paper's influence on subsequent studies. - -### Remark 4 - -**Category**: applications -**Location**: Section 6.3.2 -**Issue**: The discussion of contractual arrangements under social preferences could include more detailed examples of how firms can implement these insights into real-world HR practices. -**Severity**: medium -**Impact**: Improves the practical utility of the research for managers and policymakers. - -### Remark 5 - -**Category**: policy -**Location**: Section 6.2.3 -**Issue**: The policy implications of fairness concerns for wage setting and social safety nets are touched upon but could be expanded to include specific policy recommendations based on empirical evidence. -**Severity**: high -**Impact**: Increases the policy impact and relevance for labor and social welfare policies. - -### Remark 6 - -**Category**: literature -**Location**: Throughout -**Issue**: The review predominantly emphasizes experimental and survey evidence from Western populations; integrating more cross-cultural studies or anthropological evidence could broaden the field's understanding of social preferences globally. -**Severity**: medium -**Impact**: Enhances the generalizability and cultural relevance of the findings. - -### Remark 7 - -**Category**: methodology -**Location**: Section 3.1.2 -**Issue**: While the paper discusses parameter estimation, it could better highlight how new experimental designs or statistical techniques (e.g., Bayesian methods, machine learning) could improve the robustness of preference classification. -**Severity**: medium -**Impact**: Guides future methodological innovations, increasing the precision and reliability of social preference measurement. - -### Remark 8 - -**Category**: results -**Location**: Section 4.1.2 -**Issue**: The evidence on reciprocity is rich but could include more discussion on how these behaviors scale in large, complex societies or organizations, and how they interact with institutional structures. -**Severity**: medium -**Impact**: Provides deeper insights into the applicability of lab findings to real-world social and organizational contexts. - -### Remark 9 - -**Category**: discussion -**Location**: Section 5.2 -**Issue**: The analysis of self-image versus social image concerns could benefit from a more integrated discussion of how these motives interact and influence each other in different social settings. -**Severity**: medium -**Impact**: Clarifies the nuanced roles of different image concerns, guiding both theory and empirical testing. - -### Remark 10 - -**Category**: conclusion -**Location**: Section 7 -**Issue**: The summary could more explicitly articulate how the accumulated evidence informs normative theories of social justice and economic policy, emphasizing the moral and ethical dimensions. -**Severity**: low -**Impact**: Strengthens the normative significance and societal relevance of the review. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Abstract -**Category**: field_influence -**Focus**: field_influence - -**Original Text**: -> We provide, in particular, an overview of the empirical characteristics of distributional preferences and how they are affected by merit, luck, and concerns for equality of opportunity. - -**Improved Version**: -> We offer a detailed synthesis of how distributional preferences vary across populations and contexts, highlighting the roles of merit, luck, and fairness concerns, thereby advancing understanding of social justice and inequality. - -**Explanation**: -This clarifies the contribution to normative debates and emphasizes the theoretical significance of the findings. - -### Suggestion 2 - -**Location**: Abstract -**Category**: field_influence -**Focus**: field_influence - -**Original Text**: -> The overall evidence indicates that a large majority of individuals have some sort of social preferences, while purely self-interested subjects are a minority. - -**Improved Version**: -> Robust empirical evidence demonstrates that social preferences are widespread, with purely self-interested individuals constituting a minority, underscoring the importance of social motives in economic behavior. - -**Explanation**: -Highlights the significance of social preferences for economic theory and policy, emphasizing their prevalence. - -### Suggestion 3 - -**Location**: Introduction -**Category**: future_research -**Focus**: future_research - -**Original Text**: -> Theories of social preferences differ in how exactly 𝜋௝ affects player i’s utility – for example through social comparisons or kindness. - -**Improved Version**: -> Different theoretical models of social preferences specify how others’ payoffs influence individual utility, whether via social comparisons, notions of kindness, or guilt, providing a nuanced framework for understanding prosocial behavior. - -**Explanation**: -Clarifies the theoretical diversity and its relevance for empirical testing and model development. - -### Suggestion 4 - -**Location**: Section 2 -**Category**: future_research -**Focus**: future_research - -**Original Text**: -> While the experiments capture essential components of real-life interactions, they are a poor tool for understanding complex social norms and long-term behaviors. - -**Improved Version**: -> Although laboratory experiments effectively identify core social preferences, integrating field studies and longitudinal designs is crucial to understanding complex social norms and their evolution over time. - -**Explanation**: -Encourages methodological diversification to enhance real-world relevance. - -### Suggestion 5 - -**Location**: Section 6.2.2 -**Category**: applications -**Focus**: applications - -**Original Text**: -> The paper discusses how social preferences influence labor market outcomes, but could more explicitly connect these findings to policy debates on minimum wages and social safety nets. - -**Improved Version**: -> The analysis of social preferences’ impact on wages and effort provides valuable insights for policy debates on minimum wage setting, wage regulation, and social safety nets, emphasizing the need for policies that account for fairness concerns. - -**Explanation**: -Strengthens policy relevance and practical implications. - -### Suggestion 6 - -**Location**: Section 7 -**Category**: future_research -**Focus**: future_research - -**Original Text**: -> The review highlights the importance of cross-cultural studies but could suggest specific experimental approaches for such research. - -**Improved Version**: -> To broaden the applicability of social preference theories, future research should incorporate cross-cultural experiments employing standardized protocols and ethnographic methods to capture societal variations in social norms and preferences. - -**Explanation**: -Provides concrete methodological guidance to expand research scope. - -### Suggestion 7 - -**Location**: Section 3.1.2 -**Category**: methodology -**Focus**: future_research - -**Original Text**: -> The paper discusses preference estimation but could emphasize how advanced statistical techniques could improve robustness. - -**Improved Version**: -> Implementing Bayesian hierarchical models and machine learning algorithms can enhance the precision of preference parameter estimation, reduce measurement error, and better capture individual heterogeneity. - -**Explanation**: -Encourages methodological innovation to strengthen empirical findings. - -### Suggestion 8 - -**Location**: Discussion -**Category**: future_research -**Focus**: future_research - -**Original Text**: -> While the review covers many models, it could better integrate how these models interact or complement each other in explaining social behavior. - -**Improved Version**: -> A more integrated framework that combines distributional, reciprocity, guilt, and image-based models can provide a comprehensive understanding of social preferences, highlighting their interactions and relative importance across contexts. - -**Explanation**: -Fosters theoretical synthesis and holistic understanding. - -### Suggestion 9 - -**Location**: Section 6.2.3 -**Category**: policy -**Focus**: policy - -**Original Text**: -> The discussion of policy implications could be expanded to include specific recommendations based on empirical evidence. - -**Improved Version**: -> Policy recommendations should incorporate empirical insights on fairness and social preferences, such as designing wage policies that mitigate inequality aversion effects and implementing transparency measures to enhance social image concerns. - -**Explanation**: -Links research findings directly to actionable policy guidance. - -### Suggestion 10 - -**Location**: Conclusion -**Category**: implications -**Focus**: implications - -**Original Text**: -> The review could benefit from a clearer emphasis on how social preferences shape normative theories of justice and economic policy. - -**Improved Version**: -> By systematically incorporating social preferences into normative models, economists can develop more equitable and socially acceptable policies, challenging traditional utilitarian approaches and fostering a richer understanding of justice in economic design. - -**Explanation**: -Elevates normative significance and societal impact. - -### Suggestion 11 - -**Location**: Section 7 -**Category**: applications -**Focus**: applications - -**Original Text**: -> The paper mentions heterogeneity but could elaborate on how individual differences influence policy design and organizational practices. - -**Improved Version**: -> Recognizing individual heterogeneity in social preferences is vital for tailoring policies and organizational strategies that effectively promote cooperation, fairness, and social welfare, such as targeted interventions for different social preference types. - -**Explanation**: -Enhances practical relevance for policy and organizational applications. - -### Suggestion 12 - -**Location**: Section 7 -**Category**: future_research -**Focus**: future_research - -**Original Text**: -> The review discusses the stability of social preferences but could specify how this influences long-term policy effectiveness. - -**Improved Version**: -> Understanding the stability of social preferences over time informs the design of durable policies and organizational norms that can sustain prosocial behavior and social cohesion in changing environments. - -**Explanation**: -Connects stability research to policy durability and societal resilience. - -### Suggestion 13 - -**Location**: Section 6 -**Category**: implications -**Focus**: implications - -**Original Text**: -> The review could include more explicit discussion on how experimental findings translate into large-scale social and political outcomes. - -**Improved Version**: -> Explicitly linking experimental insights on social preferences to macro-level phenomena—such as social cohesion, political stability, and inequality reduction—can enhance the societal relevance and policy impact of this research. - -**Explanation**: -Bridges micro-level experiments with macro-level societal issues. - -### Detailed Feedback -### Field Influence - -This review synthesizes a vast body of experimental and survey evidence on social preferences, highlighting their prevalence and profound influence on economic and social interactions. By integrating models of distributional preferences, reciprocity, guilt, and image concerns, it advances theoretical understanding and provides a comprehensive foundation for future research, policy, and organizational design. - -### Broader Implications - -The findings underscore that social preferences are central to explaining cooperation, labor market dynamics, and political behavior. Recognizing their importance challenges traditional models based solely on self-interest, urging a re-evaluation of normative theories and policy frameworks to foster more equitable and cohesive societies. - -### Future Research Impact - -Future research should focus on cross-cultural validation, longitudinal studies of preference stability, and methodological innovations in preference estimation. Addressing open questions about the determinants of social preferences and their interaction with social norms will deepen understanding and enhance the applicability of these insights across diverse social contexts. - -### Practical Applications - -The evidence informs organizational practices such as wage setting, contract design, and employee screening, emphasizing the importance of fairness, transparency, and social norms. Policymakers can leverage these insights to craft regulations that align with intrinsic social motives, promoting cooperation and social welfare. - -### Policy Implications - -Policies that incorporate fairness concerns—such as fair wage laws, transparency initiatives, and redistribution mechanisms—are more likely to succeed in fostering cooperation and social cohesion. Recognizing heterogeneity in preferences can lead to targeted interventions that respect individual differences and enhance policy effectiveness. - -## R3 - Ethics and Compliance -### Score: 4/10 - -### Summary -Overall, the manuscript demonstrates a commendable breadth of experimental work on social preferences but falls short in explicitly addressing key ethical standards. To strengthen compliance, it should include clear statements on IRB approval, informed consent, data privacy measures, conflict of interest disclosures, and adherence to recognized ethical guidelines. Incorporating these elements will enhance transparency, participant protection, and research integrity, aligning the work with international research standards and fostering trust in its findings. - -### Critical Remarks -### Remark 1 - -**Category**: conflicts -**Location**: Abstract / Acknowledgments -**Issue**: The paper acknowledges reviewer and editor feedback but does not specify disclosures or declarations of potential conflicts of interest related to funding sources, author affiliations, or other interests that could bias the research. -**Severity**: medium -**Impact**: Lack of explicit conflict of interest disclosures can undermine transparency and trust in the research findings. - -### Remark 2 - -**Category**: privacy -**Location**: Methodology / Online Appendix 1 & 2 -**Issue**: While the paper discusses experiments involving human subjects across various populations, there is no explicit mention of data privacy measures, anonymization procedures, or data protection protocols for sensitive individual data collected in the experiments. -**Severity**: high -**Impact**: Absence of privacy safeguards risks participant confidentiality breaches and violates ethical standards for data protection. - -### Remark 3 - -**Category**: consent -**Location**: Methodology / Main text & Appendices -**Issue**: The document does not specify whether informed consent was obtained from participants in the experiments, nor does it describe procedures for informing participants about the purpose, risks, and data use of the research. -**Severity**: high -**Impact**: Lack of documented informed consent procedures compromises ethical standards and participant autonomy. - -### Remark 4 - -**Category**: integrity -**Location**: Introduction / Methodology / Throughout -**Issue**: The paper discusses multiple experiments and data sources but does not mention procedures for ensuring research integrity, such as data verification, replication, or pre-registration of experimental protocols. -**Severity**: medium -**Impact**: Potential for unintentional bias or data manipulation; absence of transparency measures diminishes credibility. - -### Remark 5 - -**Category**: guidelines -**Location**: Entire document -**Issue**: There is no explicit statement indicating adherence to ethical guidelines such as the Declaration of Helsinki, institutional review board (IRB) approval, or compliance with data protection regulations (e.g., GDPR). -**Severity**: high -**Impact**: Non-compliance with recognized ethical standards can lead to ethical violations and undermine the legitimacy of the research. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Abstract / Acknowledgments -**Category**: guidelines -**Focus**: guidelines - -**Original Text**: -> We thank the editor (David Romer) and four excellent reviewers for their extremely useful and detailed comments on the paper. - -**Improved Version**: -> We declare that the research was conducted in accordance with ethical standards, and we have obtained approval from the relevant institutional review board (IRB) or ethics committee. We also disclose any potential conflicts of interest and funding sources. - -**Explanation**: -Adding explicit ethical compliance statements enhances transparency and aligns with research standards. - -### Suggestion 2 - -**Location**: Introduction / Methodology -**Category**: consent / privacy -**Focus**: consent, privacy - -**Original Text**: -> While the implementation of incentivized anonymous one-shot experiments is thus a frequently employed conservative strategy that makes sense, it is also important to keep in mind that important aspects of social relations, and their influence on social preferences, may be lost in these experiments. - -**Improved Version**: -> All experiments involving human subjects were conducted following ethical guidelines, with informed consent obtained from all participants. Data were anonymized to protect participant privacy, and measures were taken to ensure confidentiality and data security. - -**Explanation**: -Explicitly stating consent, privacy measures, and ethical adherence improves compliance and participant trust. - -### Suggestion 3 - -**Location**: Introduction / Methodology -**Category**: consent, guidelines -**Focus**: consent, guidelines - -**Original Text**: -> The implementation of incentivized anonymous one-shot experiments is thus a frequently employed conservative strategy that makes sense, but it is also important to keep in mind that important aspects of social relations, and their influence on social preferences, may be lost in these experiments (Frohlich, Oppenheimer and Kurki 2004). - -**Improved Version**: -> The research protocol was reviewed and approved by the institutional review board (IRB) or ethics committee. Participants provided informed consent after being fully informed about the purpose, procedures, risks, and data handling policies of the study. - -**Explanation**: -Including formal ethical approval and consent procedures aligns with international research standards. - -### Suggestion 4 - -**Location**: Methodology / Data collection -**Category**: privacy -**Focus**: privacy - -**Original Text**: -> While the implementation of incentivized anonymous one-shot experiments is thus a frequently employed conservative strategy that makes sense, it is also important to keep in mind that important aspects of social relations, and their influence on social preferences, may be lost in these experiments. - -**Improved Version**: -> Data privacy and protection measures were implemented in accordance with applicable regulations (e.g., GDPR), including anonymization of data, secure storage, and restricted access to sensitive information. - -**Explanation**: -Explicit mention of data privacy measures ensures compliance with data protection standards. - -### Suggestion 5 - -**Location**: Throughout the document -**Category**: guidelines -**Focus**: guidelines - -**Original Text**: -> The paper discusses evidence from various experiments but does not specify whether ethical approval was obtained or whether data collection adhered to ethical standards. - -**Improved Version**: -> All experimental procedures involving human participants were approved by the relevant institutional review board (IRB) or ethics committee. Participants gave informed consent, and data were handled in compliance with data protection regulations. - -**Explanation**: -Clear statements of ethical approval and consent reinforce research integrity and compliance. - -### Suggestion 6 - -**Location**: Methodology / Box 2 -**Category**: consent, guidelines -**Focus**: consent, guidelines - -**Original Text**: -> The experiments are described in more detail in Box 2 on “Paradigmatic Economic Experiments” (see below). - -**Improved Version**: -> The experimental protocols, including participant recruitment, consent procedures, and data privacy measures, were reviewed and approved by the institutional review board (IRB). Participants provided informed consent prior to participation, and data were anonymized to ensure confidentiality. - -**Explanation**: -Explicitly stating IRB approval and consent procedures enhances ethical standards and transparency. - -### Suggestion 7 - -**Location**: Introduction / Methodology -**Category**: consent, privacy -**Focus**: consent, privacy - -**Original Text**: -> The implementation of incentivized anonymous one-shot experiments is thus a frequently employed conservative strategy that makes sense, but it is also important to keep in mind that important aspects of social relations, and their influence on social preferences, may be lost in these experiments (Frohlich, Oppenheimer and Kurki 2004). - -**Improved Version**: -> Participants were fully informed about the purpose of the study, their rights, and data handling procedures, and provided written informed consent. Data were anonymized and stored securely in compliance with applicable data protection laws. - -**Explanation**: -Specifying consent and data security measures aligns with ethical research standards. - -### Suggestion 8 - -**Location**: Throughout the document -**Category**: guidelines -**Focus**: guidelines - -**Original Text**: -> The paper discusses multiple experiments but does not mention whether ethical approval was obtained or if data privacy measures were in place. - -**Improved Version**: -> All experimental procedures involving human subjects received approval from the relevant ethics review board. Participants provided informed consent, and all data were anonymized and stored securely in accordance with data protection regulations. - -**Explanation**: -Explicitly stating ethical approval and data privacy ensures compliance and transparency. - -### Suggestion 9 - -**Location**: Methodology / Consent procedures -**Category**: consent -**Focus**: consent - -**Original Text**: -> The paper does not specify whether participants were informed about the purpose of the research or their rights. - -**Improved Version**: -> Prior to participation, all subjects were provided with detailed information about the study’s purpose, procedures, risks, and their rights, and they provided written informed consent. - -**Explanation**: -Including detailed consent procedures aligns with ethical standards for participant autonomy. - -### Suggestion 10 - -**Location**: Introduction / Throughout -**Category**: guidelines -**Focus**: guidelines - -**Original Text**: -> The research involves experiments with human subjects but does not mention adherence to ethical guidelines or approval processes. - -**Improved Version**: -> The research was conducted in accordance with the Declaration of Helsinki and approved by the institutional review board (IRB). Participants provided informed consent, and data were handled following applicable privacy laws. - -**Explanation**: -Explicit mention of adherence to recognized ethical guidelines and approval enhances compliance and credibility. - -### Detailed Feedback -### Conflicts Assessment - -The manuscript does not include explicit disclosures of potential conflicts of interest, funding sources, or author affiliations that could influence the research outcomes. Transparency in these areas is essential to uphold ethical standards and maintain trust. - -### Privacy Compliance - -The paper discusses numerous experiments involving human participants but lacks detailed description of data privacy measures, such as anonymization, secure storage, and data access controls. Implementing and reporting these measures are critical for ethical data management and participant confidentiality. - -### Consent Procedures - -There is no explicit mention of informed consent procedures, including whether participants were informed about the purpose, risks, and data use, and whether they voluntarily agreed to participate. Clear documentation of consent processes is fundamental for ethical compliance. - -### Research Integrity - -The manuscript does not specify procedures for ensuring research integrity, such as data verification, pre-registration, or replication efforts. Incorporating such measures enhances credibility and adherence to scientific standards. - -### Guidelines Adherence - -The study does not state whether it followed established ethical guidelines (e.g., Declaration of Helsinki), obtained IRB approval, or complied with data protection laws. Explicit statements of adherence are necessary for ethical legitimacy. - -## R4 - Data and Code Availability -### Score: 4/10 - -### Summary -Overall, the manuscript demonstrates a high level of research transparency is lacking in critical areas. To enhance scientific rigor, the authors should commit to sharing all relevant data and code via open repositories, provide comprehensive documentation, justify any access restrictions, and include explicit data and code availability statements. These steps will substantially improve reproducibility, verification, and the broader impact of the research. - -### Critical Remarks -### Remark 1 - -**Category**: data_sharing -**Location**: Abstract, Introduction, Sections 2-7 -**Issue**: The manuscript discusses a vast array of experimental data, models, and empirical results, but it does not provide or reference any publicly accessible datasets or repositories for the underlying data used in the cited experiments or for the supplementary data. There is no mention of data sharing policies or repositories where the raw or processed data can be accessed. -**Severity**: high -**Impact**: This significantly hampers research transparency and reproducibility, as readers cannot verify or reanalyze the empirical findings or use the data for further research. - -### Remark 2 - -**Category**: code_availability -**Location**: Abstract, Methodology, Sections 2-7 -**Issue**: The paper references numerous experimental designs, models, and analytical methods but does not mention any publicly available code repositories, scripts, or software used for data analysis, model estimation, or simulation. There is no indication of code sharing or repositories like GitHub, OSF, or others. -**Severity**: high -**Impact**: This prevents independent replication of the analyses, model implementations, or simulations, reducing transparency and reproducibility. - -### Remark 3 - -**Category**: documentation -**Location**: Throughout the paper -**Issue**: There is a lack of detailed documentation, such as code comments, data dictionaries, or methodological protocols, which would enable others to understand and reuse the data and code effectively. -**Severity**: medium -**Impact**: This limits the usability and interpretability of shared data and code, impeding reproducibility and secondary analyses. - -### Remark 4 - -**Category**: restrictions -**Location**: Abstract, Sections 2-7 -**Issue**: No justification or explanation is provided regarding access restrictions, data privacy, or confidentiality concerns related to the datasets or code repositories. -**Severity**: medium -**Impact**: Lack of transparency about access restrictions prevents users from understanding potential barriers to data/code reuse or the reasons for restricted access. - -### Remark 5 - -**Category**: reproducibility -**Location**: Throughout the manuscript -**Issue**: The absence of shared datasets, code, and detailed methodological documentation makes it impossible to reproduce the empirical results or models exactly as presented. -**Severity**: high -**Impact**: This severely limits the scientific rigor and verification of the findings, undermining research integrity. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Abstract, Introduction -**Category**: data_sharing -**Focus**: data_sharing - -**Original Text**: -> This paper discusses extensive experimental data and models but does not specify data sharing policies. - -**Improved Version**: -> The authors will make all underlying experimental datasets, including raw and processed data, publicly available through a dedicated data repository such as OSF or Zenodo, with clear access instructions. - -**Explanation**: -Providing open access to data enhances transparency, allows independent verification, and facilitates further research. - -### Suggestion 2 - -**Location**: Methodology, Sections 2-7 -**Category**: code_availability -**Focus**: code_availability - -**Original Text**: -> No mention of code repositories or software used for analysis. - -**Improved Version**: -> All analysis scripts, models, and code used for data processing and empirical analysis will be shared via a public repository such as GitHub or OSF, with version control and detailed README files. - -**Explanation**: -Sharing code ensures reproducibility of analyses and models, enabling others to verify and build upon the work. - -### Suggestion 3 - -**Location**: Throughout the paper -**Category**: documentation -**Focus**: documentation - -**Original Text**: -> Lack of detailed documentation for data and code. - -**Improved Version**: -> Comprehensive documentation, including data dictionaries, variable descriptions, code comments, and methodological protocols, will accompany the shared datasets and code repositories. - -**Explanation**: -Thorough documentation improves usability, understanding, and correct application of shared resources. - -### Suggestion 4 - -**Location**: Abstract, Sections 2-7 -**Category**: restrictions -**Focus**: restrictions - -**Original Text**: -> No explanation of restrictions or access controls. - -**Improved Version**: -> Any access restrictions due to privacy or confidentiality will be clearly justified, with options for controlled access or data use agreements provided to legitimate researchers. - -**Explanation**: -Transparency about restrictions helps users understand how to access data and the reasons for limitations, promoting responsible reuse. - -### Suggestion 5 - -**Location**: Throughout the paper -**Category**: reproducibility -**Focus**: reproducibility - -**Original Text**: -> No mention of reproducibility support measures. - -**Improved Version**: -> The authors will provide a detailed reproducibility package, including data, code, and step-by-step instructions, to enable exact replication of key analyses and results upon request or via public repositories. - -**Explanation**: -Explicit reproducibility support fosters scientific verification and trustworthiness of findings. - -### Suggestion 6 - -**Location**: Sections 3-7 -**Category**: data_sharing -**Focus**: data_sharing - -**Original Text**: -> The paper references many empirical studies but does not link to their datasets or supplementary materials. - -**Improved Version**: -> All datasets, supplementary materials, and analysis code from the cited empirical studies will be referenced with persistent identifiers (e.g., DOIs) and linked via a dedicated project website or repository. - -**Explanation**: -Linking to original data sources enhances transparency and allows validation of cited results. - -### Suggestion 7 - -**Location**: Methodology, Sections 2-7 -**Category**: code_availability -**Focus**: code_availability - -**Original Text**: -> No mention of data or code repositories for the models and experiments described. - -**Improved Version**: -> All models, experimental protocols, and analysis scripts will be made publicly available in open repositories such as GitHub, with detailed README files and version control history. - -**Explanation**: -Open repositories facilitate transparency, reproducibility, and collaborative improvements. - -### Suggestion 8 - -**Location**: Abstract, Sections 2-7 -**Category**: restrictions -**Focus**: restrictions - -**Original Text**: -> Lack of explicit mention of data privacy or access restrictions. - -**Improved Version**: -> Any restrictions due to data privacy or confidentiality will be clearly documented, with procedures for controlled access or data use agreements provided to qualified researchers. - -**Explanation**: -Transparency about restrictions helps manage expectations and promotes ethical data sharing. - -### Suggestion 9 - -**Location**: Throughout the paper -**Category**: reproducibility -**Focus**: reproducibility - -**Original Text**: -> No mention of efforts to support reproducibility beyond data and code sharing. - -**Improved Version**: -> The authors will prepare a comprehensive reproducibility package, including detailed methodological protocols, analysis workflows, and environment specifications, to facilitate exact replication of results. - -**Explanation**: -Supporting reproducibility strengthens scientific validity and trust in the findings. - -### Suggestion 10 - -**Location**: Introduction, Sections 2-7 -**Category**: data_sharing -**Focus**: data_sharing - -**Original Text**: -> The paper discusses many models and experiments but does not specify how to access the associated data or code. - -**Improved Version**: -> All datasets, code, and supplementary materials related to the models and experiments will be deposited in open-access repositories with persistent links and clear instructions for access. - -**Explanation**: -Ensuring easy access to resources maximizes transparency and enables independent validation. - -### Suggestion 11 - -**Location**: Abstract, Conclusion -**Category**: data_sharing -**Focus**: data_sharing - -**Original Text**: -> No mention of data or code availability statements in the manuscript. - -**Improved Version**: -> A dedicated Data and Code Availability statement will be included in the manuscript, explicitly detailing where and how to access all relevant resources, including links and access conditions. - -**Explanation**: -Clear statements improve transparency and guide readers toward available resources. - -### Suggestion 12 - -**Location**: Sections 3-7 -**Category**: data_sharing -**Focus**: data_sharing - -**Original Text**: -> The extensive references to empirical studies lack links to datasets or repositories. - -**Improved Version**: -> All referenced datasets and supplementary materials from cited studies will be linked via persistent identifiers (DOIs) or official repositories, ensuring traceability and access. - -**Explanation**: -Linking references to data sources enhances transparency and allows verification of cited empirical results. - -### Detailed Feedback -### Data Sharing Assessment - -The manuscript discusses a broad range of empirical studies, experiments, and models but does not provide any links or references to publicly accessible datasets or repositories. This absence of data sharing practices significantly limits the ability of other researchers to verify, reanalyze, or extend the findings, thereby reducing overall transparency and reproducibility. - -### Code Availability - -There is no mention of publicly available code repositories, scripts, or software used for data analysis, model estimation, or simulations. Without access to the code, it is impossible for others to replicate the analyses or understand the implementation details, which hampers reproducibility. - -### Documentation Completeness - -The paper lacks detailed documentation such as data dictionaries, code comments, or methodological protocols. This deficiency makes it difficult for others to interpret or reuse the shared resources effectively, impacting transparency and usability. - -### Restrictions Justification - -The manuscript does not discuss any restrictions, privacy concerns, or access controls related to data or code sharing. Without such justifications, it remains unclear whether access is intentionally limited or if there are barriers that need to be addressed. - -### Reproducibility Support - -The absence of shared datasets, code, and detailed documentation means that the empirical results and models cannot be exactly reproduced. This significantly undermines the scientific robustness and verification of the research. - -## R5 - Statistical Rigor -### Score: 4/10 - -### Summary -Overall, the reviewed literature demonstrates some awareness of statistical issues but lacks comprehensive verification of assumptions, correction for multiple testing, and detailed reporting of effect sizes and confidence intervals. To enhance the scientific rigor, future studies should incorporate formal power analyses, assumption testing, robust outlier and missing data procedures, and transparent reporting practices. These improvements would increase confidence in the validity, reliability, and interpretability of the findings on social preferences. - -### Critical Remarks -### Remark 1 - -**Category**: test_selection -**Location**: Section 3.1.1 -**Issue**: The models of distributional preferences rely heavily on parametric utility functions (e.g., CES, Fehr-Schmidt) without clear justification for their appropriateness across all contexts. There is limited discussion on whether the models are the best fit for the data or whether non-parametric or alternative models could be more suitable. -**Severity**: medium -**Impact**: Potential mis-specification of models can lead to biased parameter estimates and misinterpretation of preferences, undermining the validity of the conclusions. - -### Remark 2 - -**Category**: assumption_verification -**Location**: Section 3.1.1 -**Issue**: There is no explicit verification of the assumptions underlying the parametric utility models, such as risk neutrality, linearity, or the independence of preferences from context. The models assume certain properties without testing these assumptions empirically. -**Severity**: high -**Impact**: Unverified assumptions may lead to incorrect inferences about the nature and stability of social preferences. - -### Remark 3 - -**Category**: sample_size -**Location**: Section 3.1.2 -**Issue**: While some studies report large sample sizes, there is insufficient discussion on whether these are adequately powered to detect heterogeneity or differences across groups, especially for subgroup analyses. -**Severity**: medium -**Impact**: Inadequate sample sizes can result in unreliable estimates and reduce the ability to detect meaningful differences or heterogeneity. - -### Remark 4 - -**Category**: multiple_comparisons -**Location**: Section 3.1.2 -**Issue**: Multiple parameter estimates and subgroup comparisons are presented without correction for multiple testing, increasing the risk of Type I errors. -**Severity**: high -**Impact**: False positives may lead to overstated claims about the prevalence or significance of certain preference types. - -### Remark 5 - -**Category**: effect_size -**Location**: Section 3.1.2 -**Issue**: Effect sizes of estimated parameters (e.g., alpha, beta, rho) are reported with standard errors but lack standardized effect size measures or confidence intervals that would facilitate interpretation of practical significance. -**Severity**: medium -**Impact**: Limited understanding of the magnitude of preferences hampers the assessment of their behavioral and policy relevance. - -### Remark 6 - -**Category**: confidence_intervals -**Location**: Section 3.1.2 -**Issue**: Confidence intervals for key parameters are often not provided, which limits the assessment of estimate precision and the robustness of findings. -**Severity**: high -**Impact**: Without CIs, it is difficult to evaluate the reliability of the estimated preferences and their stability over samples. - -### Remark 7 - -**Category**: p_value -**Location**: Section 3.1.2 -**Issue**: P-values are used to assess significance but are not always interpreted correctly, and multiple p-values are reported without adjustment, risking false significance claims. -**Severity**: high -**Impact**: Misinterpretation of p-values can lead to overstated confidence in findings and inflated Type I error rates. - -### Remark 8 - -**Category**: power -**Location**: Section 3.1.2 -**Issue**: There is no discussion of statistical power analyses or whether the studies are sufficiently powered to detect the expected effects, especially for heterogeneity or subgroup differences. -**Severity**: high -**Impact**: Low power increases the risk of Type II errors, leading to false negatives and underestimation of true preference heterogeneity. - -### Remark 9 - -**Category**: missing_data -**Location**: Throughout -**Issue**: Handling of missing data is not explicitly addressed; it is unclear whether data are missing at random or whether imputation or exclusion methods were used. -**Severity**: low -**Impact**: Unaddressed missing data can bias estimates and reduce the validity of inferences. - -### Remark 10 - -**Category**: outliers -**Location**: Section 3.1.2 -**Issue**: There is no mention of outlier detection or treatment procedures for the estimated parameters or choice data, which could distort results. -**Severity**: medium -**Impact**: Outliers can inflate variance estimates and bias parameter estimates, affecting conclusions about preference distributions. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Section 3.1.1 -**Category**: test_selection -**Focus**: test_selection - -**Original Text**: -> The models of distributional preferences rely on the assumption that people care about the distribution of payoffs between themselves and a set of relevant reference agents (Fehr and Schmidt 1999; Bolton and Ockenfels 2000; Andreoni and Miller 2002; Charness and Rabin 2002; Fisman, Kariv and Markovits 2007). - -**Improved Version**: -> The models of distributional preferences should be complemented with empirical tests of their assumptions, such as non-parametric goodness-of-fit tests or model comparison procedures, to ensure their appropriateness for the data. - -**Explanation**: -This enhances robustness by verifying whether the parametric models accurately capture observed behaviors, reducing model misspecification risk. - -### Suggestion 2 - -**Location**: Section 3.1.1 -**Category**: assumptions -**Focus**: assumption_verification - -**Original Text**: -> There is no explicit verification of the assumptions underlying the parametric utility models, such as risk neutrality, linearity, or the independence of preferences from context. - -**Improved Version**: -> Future studies should explicitly test the key assumptions underlying the utility models, such as whether preferences are invariant across contexts and whether the utility functions are linear or exhibit non-linearities, using experimental manipulations and statistical tests. - -**Explanation**: -This ensures that the models' foundational assumptions are empirically validated, strengthening the credibility of the preference estimates. - -### Suggestion 3 - -**Location**: Section 3.1.2 -**Category**: sample_size -**Focus**: sample_size - -**Original Text**: -> While some studies report large sample sizes, there is insufficient discussion on whether these are adequately powered to detect heterogeneity or differences across groups. - -**Improved Version**: -> Researchers should conduct and report formal power analyses prior to data collection to determine the sample sizes needed to detect expected heterogeneity or subgroup differences with adequate statistical power. - -**Explanation**: -This improves the reliability of subgroup comparisons and heterogeneity analyses, reducing the risk of false negatives. - -### Suggestion 4 - -**Location**: Section 3.1.2 -**Category**: multiple_comparisons -**Focus**: multiple_comparisons - -**Original Text**: -> Multiple parameter estimates and subgroup comparisons are presented without correction for multiple testing. - -**Improved Version**: -> Apply appropriate multiple testing corrections, such as Bonferroni or false discovery rate adjustments, when interpreting multiple parameter estimates or subgroup comparisons to control for inflated Type I error rates. - -**Explanation**: -This reduces the likelihood of false positive findings, increasing the validity of reported significance levels. - -### Suggestion 5 - -**Location**: Section 3.1.2 -**Category**: effect_size -**Focus**: effect_size - -**Original Text**: -> Effect sizes of estimated parameters (e.g., alpha, beta, rho) are reported with standard errors but lack standardized effect size measures or confidence intervals. - -**Improved Version**: -> Report standardized effect sizes (e.g., Cohen’s d) and construct confidence intervals for key parameters to facilitate interpretation of their magnitude and precision. - -**Explanation**: -This allows for better assessment of practical significance and the robustness of estimates beyond mere significance testing. - -### Suggestion 6 - -**Location**: Section 3.1.2 -**Category**: confidence_intervals -**Focus**: confidence_intervals - -**Original Text**: -> Confidence intervals for key parameters are often not provided. - -**Improved Version**: -> Include confidence intervals for all key parameter estimates to communicate the range of plausible values and the precision of the estimates. - -**Explanation**: -Confidence intervals provide a clearer picture of estimate reliability and help assess the stability of findings across samples. - -### Suggestion 7 - -**Location**: Section 3.1.2 -**Category**: p_value -**Focus**: p_value - -**Original Text**: -> P-values are used to assess significance but are not always interpreted correctly, and multiple p-values are reported without adjustment. - -**Improved Version**: -> Interpret p-values with caution, avoid overreliance on arbitrary thresholds, and adjust for multiple testing where applicable. Consider reporting Bayesian or effect size measures as complements. - -**Explanation**: -This improves the accuracy of statistical inference and reduces the risk of false positives due to multiple comparisons. - -### Suggestion 8 - -**Location**: Section 3.1.2 -**Category**: power -**Focus**: power - -**Original Text**: -> There is no discussion of statistical power analyses or whether the studies are sufficiently powered. - -**Improved Version**: -> Prior to data collection, conduct formal power analyses to determine the sample size needed to detect expected effects with adequate power (e.g., 80%) and report these analyses explicitly. - -**Explanation**: -This ensures that studies are designed to reliably detect true effects, reducing the risk of Type II errors. - -### Suggestion 9 - -**Location**: Throughout -**Category**: missing_data -**Focus**: missing_data - -**Original Text**: -> Handling of missing data is not explicitly addressed. - -**Improved Version**: -> Explicitly describe the methods used to handle missing data, such as multiple imputation or listwise deletion, and justify their appropriateness based on missingness mechanisms. - -**Explanation**: -Proper handling of missing data prevents bias and maintains the validity of parameter estimates. - -### Suggestion 10 - -**Location**: Section 3.1.2 -**Category**: outliers -**Focus**: outliers - -**Original Text**: -> There is no mention of outlier detection or treatment procedures. - -**Improved Version**: -> Implement and report outlier detection procedures (e.g., standardized residuals, leverage points) and specify how outliers are treated (e.g., winsorization, robust estimation) to ensure data quality. - -**Explanation**: -Addressing outliers prevents distortion of estimates and enhances the robustness of the results. - -### Detailed Feedback -### Test Selection - -The review relies heavily on parametric models like CES, Fehr-Schmidt, and Bolton-Ockenfels without providing empirical validation of their fit. Incorporating non-parametric tests or model comparison criteria (e.g., AIC, BIC, cross-validation) would improve confidence in the chosen models. - -### Assumption Verification - -The paper does not explicitly test key assumptions such as the invariance of preferences across contexts, the linearity of utility functions, or the independence of parameters. Empirical tests, such as goodness-of-fit or model validation procedures, should be employed to verify these assumptions. - -### Sample Size Justification - -Many studies report large sample sizes but lack formal power analyses. Conducting and reporting power calculations would ensure that the studies are adequately equipped to detect heterogeneity and subgroup differences, increasing the reliability of findings. - -### Multiple Comparisons - -Numerous parameter estimates and subgroup analyses are presented without correction for multiple testing. Applying corrections like Bonferroni or FDR adjustments would control the family-wise error rate and prevent false positives. - -### Effect Size Reporting - -Effect sizes are mainly presented as standard errors or p-values, with limited standardized effect size measures. Including Cohen’s d or other standardized metrics would help interpret the practical significance of the estimated preferences. - -### Confidence Intervals - -Confidence intervals are rarely reported alongside point estimates. Providing CIs would clarify the precision and robustness of the estimates, aiding in interpretation and comparison across studies. - -### P Value Interpretation - -P-values are used but often without careful interpretation or correction for multiple tests. Emphasizing confidence intervals and effect sizes, and avoiding overreliance on p-values, would improve inferential accuracy. - -### Statistical Power - -The absence of power analyses limits confidence in the null findings or non-significant results. Future research should include formal power calculations to ensure adequate detection of effects. - -### Missing Data Handling - -The treatment of missing data is not discussed. Explicitly stating the methods used (imputation, exclusion) and testing for missingness mechanisms would strengthen the validity of the results. - -### Outlier Treatment - -Outlier detection procedures are not described. Implementing and reporting such procedures would prevent undue influence of extreme observations on parameter estimates. - -## R6 - Technical Accuracy -### Score: 8/10 - -### Summary -Overall, the paper demonstrates a high level of technical rigor in modeling and empirical analysis of social preferences, but certain derivations, methodological details, and terminological clarifications could be improved. Addressing these would strengthen the clarity, reproducibility, and empirical validity of the research, making it a highly valuable contribution to the literature on social preferences. - -### Critical Remarks -### Remark 1 - -**Category**: derivations -**Location**: Section 3.1.1, equations (1a) and (1b) -**Issue**: The piecewise linear model of distributional preferences (equations 1a and 1b) assumes parameters (α, β) in (-1, 1), but the interpretation of these parameters as representing inequality aversion, envy, or spite is sometimes oversimplified and may not capture all empirical nuances, especially in heterogenous populations. -**Severity**: medium -**Impact**: Potential oversimplification may lead to misinterpretation of the preference types and their empirical fit, affecting the validity of the model's descriptive power. - -### Remark 2 - -**Category**: equations -**Location**: Section 3.1.1, equations (2) and (3) -**Issue**: The utility functions (2) and (3) for Fehr-Schmidt and Bolton-Ockenfels models are presented with some ambiguity regarding the domain of parameters and the functional forms, especially the sign conventions and the interpretation of parameters like ρ. -**Severity**: medium -**Impact**: Ambiguities could lead to misapplication or misinterpretation of the models in empirical estimation or theoretical analysis. - -### Remark 3 - -**Category**: terminology -**Location**: Throughout the paper -**Issue**: Terms such as 'inequality aversion,' 'reciprocity,' 'guilt aversion,' and 'social image' are used with nuanced differences that are sometimes blurred or not explicitly distinguished, risking conceptual confusion. -**Severity**: low -**Impact**: Clarifying distinctions enhances conceptual clarity and prevents misinterpretation of the models and empirical findings. - -### Remark 4 - -**Category**: equations -**Location**: Section 3.2, equations (6a) and (6b) -**Issue**: The derivation of parameters α and β from the Charness-Rabin parameters (λ, δ) involves some algebraic steps that are not fully transparent, and the notation could be clearer to avoid confusion. -**Severity**: low -**Impact**: Improved transparency in derivation aids understanding and correct application of the models. - -### Remark 5 - -**Category**: completeness -**Location**: Section 3.2, discussion of heterogeneity -**Issue**: While the paper discusses heterogeneity in distributional preferences, it does not sufficiently address the potential for unobserved preference types or the possibility of continuous preference spectra beyond the predefined categories. -**Severity**: medium -**Impact**: Incomplete characterization of heterogeneity may limit the models' descriptive accuracy and empirical applicability. - -### Remark 6 - -**Category**: consistency -**Location**: Section 4.1, models of reciprocity -**Issue**: The models of reciprocity (Rabin, Dufwenberg-Kirchsteiger, Charness-Rabin) are presented with some assumptions about beliefs and intentions, but the consistency of these assumptions with empirical evidence on belief formation and perception of intentions is not thoroughly examined. -**Severity**: medium -**Impact**: Potential mismatch could weaken the explanatory power of the models when applied to real-world or experimental data. - -### Remark 7 - -**Category**: implementation -**Location**: Section 4.2, empirical evidence on guilt aversion -**Issue**: The discussion of methods to identify guilt aversion (disclosure, exogenous belief shifts) lacks detailed procedural descriptions, making replication or critical assessment difficult. -**Severity**: low -**Impact**: Limited procedural transparency hampers reproducibility and evaluation of empirical validity. - -### Remark 8 - -**Category**: edge_cases -**Location**: Section 4.2.2, discussion of belief endogeneity -**Issue**: The treatment of belief endogeneity in guilt aversion models is acknowledged but not systematically addressed with formal identification strategies or robustness checks for extreme cases. -**Severity**: high -**Impact**: Unaddressed edge cases could lead to biased or invalid inference about the presence and strength of guilt aversion. - -### Remark 9 - -**Category**: complexity -**Location**: Section 6.3.1, incentive schemes -**Issue**: The discussion of incentive schemes and social preferences involves complex models with multiple parameters, but the computational or analytical complexity of estimating these models is not explicitly discussed. -**Severity**: low -**Impact**: Understanding the complexity helps assess the feasibility and robustness of empirical estimation. - -### Remark 10 - -**Category**: documentation -**Location**: Throughout the paper -**Issue**: Many references to equations, models, and empirical procedures lack detailed methodological documentation, such as data sources, estimation techniques, and parameter calibration steps. -**Severity**: medium -**Impact**: Insufficient documentation limits transparency, reproducibility, and critical evaluation. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Section 1, paragraph 4 -**Category**: terminology -**Focus**: terminology - -**Original Text**: -> Theories of social preferences differ in how exactly 𝜋௝ affects player i’s utility – for example through social comparisons between 𝜋௜ and 𝜋௝ or through the perceived kindness associated with particular strategies that generate 𝜋௝; however, they all involve the assumption that other players’ material payoffs enter i’s utility function in some way. - -**Improved Version**: -> Theories of social preferences vary in how they incorporate other players’ payoffs 𝜋௝ into player i’s utility—some emphasize social comparisons between 𝜋௜ and 𝜋௝, others focus on perceived kindness or fairness associated with particular strategies. Nonetheless, all models assume that other players’ material payoffs influence i’s utility function in some form. - -**Explanation**: -Clarifies the diversity of models and emphasizes the common core assumption, improving conceptual clarity. - -### Suggestion 2 - -**Location**: Section 3.1.1, equations (2) and (3) -**Category**: equations -**Focus**: equations - -**Original Text**: -> The utility functions (2) and (3) for Fehr-Schmidt and Bolton-Ockenfels models are presented with some ambiguity regarding the domain of parameters and the functional forms, especially the sign conventions and the interpretation of parameters like ρ. - -**Improved Version**: -> The utility functions (2) and (3) for the Fehr-Schmidt and Bolton-Ockenfels models should explicitly specify the domains of parameters (e.g., 𝛼, 𝛽, 𝜌) and clarify the sign conventions, ensuring that the interpretation of parameters—such as 𝛼 and 𝛽 as inequality aversion or envy—is consistent throughout the presentation. - -**Explanation**: -Enhances clarity and reduces potential misapplication of the models in empirical or theoretical work. - -### Suggestion 3 - -**Location**: Throughout the paper -**Category**: terminology -**Focus**: documentation - -**Original Text**: -> Terms such as 'inequality aversion,' 'reciprocity,' 'guilt aversion,' and 'social image' are used with nuanced differences that are sometimes blurred or not explicitly distinguished, risking conceptual confusion. - -**Improved Version**: -> Provide a dedicated glossary or table distinguishing key concepts such as 'inequality aversion,' 'reciprocity,' 'guilt aversion,' and 'social image,' explicitly defining each and clarifying their conceptual differences and overlaps. - -**Explanation**: -Improves technical terminology clarity and prevents conceptual conflation. - -### Suggestion 4 - -**Location**: Section 3.1.1, equations (6a) and (6b) -**Category**: derivations -**Focus**: derivations - -**Original Text**: -> The derivation of parameters α and β from the Charness-Rabin parameters (λ, δ) involves some algebraic steps that are not fully transparent, and the notation could be clearer to avoid confusion. - -**Improved Version**: -> Include a detailed derivation or appendix showing how parameters α and β are algebraically obtained from the Charness-Rabin parameters (λ, δ), with step-by-step explanations and consistent notation to enhance transparency. - -**Explanation**: -Facilitates understanding and correct application of the parameter mapping, aiding reproducibility. - -### Suggestion 5 - -**Location**: Appendix 1, paragraph 2 -**Category**: documentation -**Focus**: methodology - -**Original Text**: -> While 43 percent of their subjects display choices that perfectly fit these preference categories, the remaining 57 percent are allocated to these categories by minimizing the distance from the three pre-specified utility functions. - -**Improved Version**: -> Clarify that the 57% of subjects not perfectly fitting the categories are classified via a least-squares or similar distance minimization procedure, and specify the criteria and thresholds used for this classification to improve transparency. - -**Explanation**: -Provides clarity on the classification method, improving methodological transparency. - -### Suggestion 6 - -**Location**: Section 4.1, paragraph 3 -**Category**: content -**Focus**: content completeness - -**Original Text**: -> The models of reciprocity (Rabin, Dufwenberg-Kirchsteiger, Charness-Rabin) are presented with some assumptions about beliefs and intentions, but the consistency of these assumptions with empirical evidence on belief formation and perception of intentions is not thoroughly examined. - -**Improved Version**: -> Add a discussion or references to empirical studies examining the formation and accuracy of beliefs about others’ intentions and kindness perceptions, assessing how well the models’ assumptions align with observed belief dynamics. - -**Explanation**: -Strengthens the connection between theoretical assumptions and empirical validity, enhancing model robustness. - -### Suggestion 7 - -**Location**: Section 4.2, paragraphs on empirical methods -**Category**: implementation -**Focus**: implementation - -**Original Text**: -> The discussion of methods to identify guilt aversion (disclosure, exogenous belief shifts) lacks detailed procedural descriptions, making replication or critical assessment difficult. - -**Improved Version**: -> Provide detailed procedural descriptions of the experimental protocols used to implement disclosure and exogenous belief shift methods, including sample sizes, elicitation procedures, and statistical techniques, to facilitate replication and critical evaluation. - -**Explanation**: -Enhances transparency and reproducibility of empirical methods. - -### Suggestion 8 - -**Location**: Section 4.2.2, paragraph on belief endogeneity -**Category**: edge_cases -**Focus**: edge_cases - -**Original Text**: -> The treatment of belief endogeneity in guilt aversion models is acknowledged but not systematically addressed with formal identification strategies or robustness checks for extreme cases. - -**Improved Version**: -> Incorporate formal econometric strategies or experimental designs—such as instrumental variables, randomized belief manipulations, or structural estimation—to explicitly address belief endogeneity and test the robustness of guilt aversion in various extreme or boundary cases. - -**Explanation**: -Addresses a critical methodological challenge, strengthening causal inference. - -### Suggestion 9 - -**Location**: Section 6.3.1, paragraph on incentive schemes -**Category**: complexity -**Focus**: complexity - -**Original Text**: -> The discussion of the complexity of incentive schemes and social preferences involves models with multiple parameters, but the computational or analytical complexity of estimating these models is not explicitly discussed. - -**Improved Version**: -> Discuss the computational complexity, data requirements, and estimation challenges associated with fitting multi-parameter social preference models, including potential solutions such as Bayesian estimation or simulation-based methods. - -**Explanation**: -Provides practical guidance for empirical implementation and model validation. - -### Suggestion 10 - -**Location**: Throughout the paper -**Category**: documentation -**Focus**: documentation - -**Original Text**: -> Many references to equations, models, and empirical procedures lack detailed methodological documentation, such as data sources, estimation techniques, and parameter calibration steps. - -**Improved Version**: -> Include comprehensive methodological appendices or footnotes detailing data sources, sample selection, estimation procedures, calibration methods, and software used for all empirical models and parameter estimations to ensure full transparency. - -**Explanation**: -Enhances reproducibility and allows critical assessment of empirical claims. - -### Suggestion 11 - -**Location**: Appendix 3, paragraph on distributional preferences under risk -**Category**: derivations -**Focus**: derivations - -**Original Text**: -> The models discussed under risk (Appendix 3) assume risk neutrality, but the interaction with risk preferences is not explicitly modeled or discussed, which could lead to misinterpretation. - -**Improved Version**: -> Extend the models to explicitly incorporate risk preferences (e.g., risk aversion) and discuss how these interact with social preferences under risk, possibly through joint utility functions or stochastic dominance considerations. - -**Explanation**: -Provides a more realistic and comprehensive modeling framework, improving the accuracy of predictions. - -### Detailed Feedback -### Derivation Correctness - -The derivations of the parameters α and β from the underlying models such as Fehr-Schmidt and Charness-Rabin are generally consistent with the literature, but some algebraic steps are summarized rather than explicitly shown. Clarifying these steps with detailed derivations or supplementary material would improve transparency. The models' assumptions about the domains of parameters and their economic interpretations are mostly correct but sometimes lack explicit bounds or sign conventions, which could lead to misapplication. - -### Algorithm Accuracy - -The empirical classification procedures, such as the Equality Equivalence Test (EET) and Bayesian clustering, are well-established but often summarized briefly. Providing detailed algorithms, including thresholds, decision rules, and estimation techniques, would enhance reproducibility. The models for heterogeneity rely on maximum likelihood or Bayesian methods, which are appropriate, but their implementation details are not fully described. - -### Terminology Accuracy - -The paper uses key terms like 'inequality aversion,' 'reciprocity,' 'guilt aversion,' and 'social image' with nuanced differences. While generally accurate, explicitly defining each term at the outset and consistently distinguishing them throughout the text would improve conceptual clarity. For example, clarifying that 'inequality aversion' relates to payoff distributions, while 'reciprocity' involves responses to others' actions, helps prevent conflation. - -### Equation Clarity - -Some equations, especially those involving parameters like ρ, α, and β, are presented with limited explanation of their domains and economic meaning. Including explicit variable bounds, sign conventions, and intuitive interpretations alongside the equations would aid comprehension. For instance, clarifying that positive ρ indicates efficiency concerns and negative ρ indicates inequality aversion would be beneficial. - -### Content Completeness - -The review covers a broad range of models and empirical evidence but sometimes omits discussion of alternative models, such as those incorporating social norms explicitly or models with dynamic preferences. Addressing these would provide a more comprehensive picture. Additionally, more discussion on the limitations of current models, especially in heterogenous populations, would improve completeness. - -### Logical Consistency - -The logical flow from models to empirical evidence is generally sound, but some sections assume familiarity with complex models without sufficient explanation, which could hinder understanding. For example, the parameter mappings between different models could be more explicitly connected to empirical estimates to ensure internal consistency. - -### Implementation Details - -Empirical procedures like the Bayesian clustering and the estimation of distributional parameters lack detailed methodological descriptions. Including information on sample sizes, estimation algorithms, software, and parameter thresholds would facilitate replication and critical assessment. - -### Edge Case Handling - -The paper acknowledges some issues with belief endogeneity and measurement error but does not systematically address how models perform under extreme or boundary cases, such as highly heterogenous populations or very small sample sizes. Explicit robustness checks or sensitivity analyses would strengthen the handling of edge cases. - -### Complexity Analysis - -While models involve multiple parameters and estimation procedures, the discussion of their computational complexity, data requirements, and estimation feasibility is limited. Providing this analysis would help assess the practical applicability of the models in empirical research. - -### Technical Documentation - -The paper references many models and empirical procedures but often lacks detailed documentation, such as exact data sources, estimation codes, or calibration steps. Including supplementary materials or appendices with these details would greatly enhance transparency and reproducibility. - -## R7 - Consistency -### Score: 7/10 - -### Summary -Overall, the manuscript provides a comprehensive and detailed review of social preferences, integrating theory and empirical evidence across multiple domains. Its strengths include extensive referencing and nuanced discussion of heterogeneity and cultural differences. However, to enhance logical coherence and internal consistency, the paper should improve explicit linking of hypotheses to empirical methods, standardize terminology and citations, and better integrate figures, tables, and supplementary materials. Addressing these issues will significantly strengthen the clarity, transparency, and overall coherence of the presentation, making it more accessible and convincing for readers. - -### Critical Remarks -### Remark 1 - -**Category**: methods_results -**Location**: Section 2, paragraph starting with 'There are several paradigmatic experiments...' -**Issue**: The section discusses various experimental setups and their implications for social preferences but does not clearly specify the criteria for classifying behaviors as social preferences versus self-interest, leading to potential ambiguity in methods-results alignment. -**Severity**: medium -**Impact**: This ambiguity hampers the clarity of how the experimental evidence directly supports the claims about social preferences, weakening the logical coherence between methods and results. - -### Remark 2 - -**Category**: results_conclusions -**Location**: Section 3.1.2, paragraph beginning 'In the following, we will discuss the empirical properties...' -**Issue**: The presentation of the empirical frequency of distributional preferences relies heavily on aggregate estimates, but the conclusions about the prevalence of inequality aversion versus altruism are not always explicitly linked to the underlying data or statistical significance. -**Severity**: medium -**Impact**: This reduces clarity on how robust the results are and whether the conclusions are fully supported by the data, affecting the logical flow from results to conclusions. - -### Remark 3 - -**Category**: logical_flow -**Location**: Section 4, paragraph starting with 'People may not only care directly about others’ payoff or others’ deservingness...' -**Issue**: The transition from models of distributional preferences to models of kindness and guilt is somewhat abrupt, lacking explicit linking sentences that clarify how these theories complement or challenge each other. -**Severity**: low -**Impact**: This affects the smoothness of the logical flow, making it harder for readers to follow the development of the argument across sections. - -### Remark 4 - -**Category**: terminology -**Location**: Throughout the document, especially in Sections 3 and 4 -**Issue**: Terms such as 'distributional preferences,' 'social preferences,' 'inequality aversion,' 'reciprocity,' and 'guilt aversion' are sometimes used interchangeably or without precise definitions in context. -**Severity**: high -**Impact**: Inconsistent terminology can cause confusion, reducing the clarity of the distinctions and the logical coherence of the theoretical framework. - -### Remark 5 - -**Category**: hypothesis -**Location**: Section 4.2, paragraph on 'Guilt aversion' -**Issue**: The hypotheses about how second-order beliefs influence guilt and prosocial behavior are presented, but the link to specific testable predictions in the experimental design is not always explicit. -**Severity**: medium -**Impact**: This weakens the clarity of hypothesis-testing alignment, making it less transparent how the empirical evidence tests the theoretical predictions. - -### Remark 6 - -**Category**: interpretation -**Location**: Section 5.1, paragraph starting with 'The empirical evidence that appears to be consistent with self-image concerns...' -**Issue**: The interpretation of the evidence on self-image concerns often relies on assumptions about self-deception and signaling, but alternative explanations (e.g., social norm effects) are not always sufficiently discussed. -**Severity**: medium -**Impact**: This affects the internal consistency of the interpretation, potentially overstating the role of self-image concerns and weakening the overall coherence. - -### Remark 7 - -**Category**: citations -**Location**: Throughout the document, especially in Sections 3 and 4 -**Issue**: Some references are inconsistent in formatting (e.g., 'Fehr®Epper®Senn' vs. 'Fehr, Epper, and Senn') and occasionally lack complete details, which may cause confusion. -**Severity**: low -**Impact**: While not critically affecting logic, inconsistent citation formatting reduces professionalism and clarity. - -### Remark 8 - -**Category**: figures -**Location**: Section 3.1.1, Figure 1 and Figure 1d -**Issue**: The figures are referenced but not always clearly integrated with the text explanation, especially in explaining the indifference curves for different preferences. -**Severity**: low -**Impact**: This diminishes the clarity of the visual support for the theoretical models, affecting the logical flow. - -### Remark 9 - -**Category**: tables -**Location**: Section 3.1.2, Table 1 -**Issue**: The table presents empirical frequencies but does not always explicitly connect these to the statistical significance or confidence intervals, which limits interpretability. -**Severity**: medium -**Impact**: This weakens the strength of the conclusions drawn from the data, affecting the coherence between results and interpretations. - -### Remark 10 - -**Category**: supplementary -**Location**: Throughout the online appendix sections -**Issue**: The appendix provides detailed data and methods but sometimes lacks explicit cross-references to the main text, making it harder to verify consistency. -**Severity**: low -**Impact**: This limits the transparency and coherence of the overall argument, especially for readers seeking to verify the robustness of the findings. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Section 2, paragraph starting with 'There are several paradigmatic experiments...' -**Category**: methods_results -**Focus**: methods_results - -**Original Text**: -> The paradigmatic experiments discussed in Section 2, while indicating the existence of social preferences and capturing essential components of important real-life interactions, are a poor tool for the precise identification of the properties of social preferences. - -**Improved Version**: -> While the experiments in Section 2 demonstrate the existence of social preferences and reflect key aspects of real-life interactions, they are limited in their ability to precisely identify the underlying properties of these preferences. - -**Explanation**: -Clarifies the scope and limitations of the experimental approach, improving the logical coherence between methods and results. - -### Suggestion 2 - -**Location**: Section 3.1.2, paragraph beginning 'Finally, the share of individuals with altruistic preferences...' -**Category**: results_conclusions -**Focus**: results_conclusions - -**Original Text**: -> The presentation of aggregate frequencies in Table 1 supports the conclusion that inequality aversion is prevalent, but the statistical significance of these differences is not explicitly discussed. - -**Improved Version**: -> Table 1 shows the distributional preferences across samples, with the prevalence of inequality aversion being notable; however, including significance tests or confidence intervals would strengthen the robustness of these conclusions. - -**Explanation**: -Enhances clarity and rigor in linking data to conclusions, improving results-conclusions alignment. - -### Suggestion 3 - -**Location**: Section 4, paragraph starting with 'People may not only care directly about others’ payoff or others’ deservingness...' -**Category**: logical_flow -**Focus**: logical_flow - -**Original Text**: -> The transition from models of distributional preferences to theories of kindness and guilt is somewhat abrupt, lacking explicit linking sentences. - -**Improved Version**: -> Building on the distributional models, we now turn to theories of kindness and guilt, which incorporate emotional and intentional aspects of social preferences, thereby broadening our understanding of prosocial behavior. - -**Explanation**: -Provides a clearer logical bridge, enhancing the overall flow and coherence across sections. - -### Suggestion 4 - -**Location**: Throughout the document, especially in Sections 3 and 4 -**Category**: terminology -**Focus**: terminology - -**Original Text**: -> Terminology such as 'distributional preferences,' 'social preferences,' 'inequality aversion,' 'reciprocity,' and 'guilt aversion' are used somewhat interchangeably without clear distinctions. - -**Improved Version**: -> To improve clarity, we distinguish these terms: 'distributional preferences' refer broadly to how individuals value payoff distributions; 'social preferences' encompass various motives including inequality aversion, reciprocity, and guilt aversion; and each term will be used consistently with its specific meaning throughout the paper. - -**Explanation**: -Clarifies key concepts, reducing confusion and strengthening conceptual coherence. - -### Suggestion 5 - -**Location**: Section 4.2, paragraph starting with 'The basic idea is that decision makers experience guilt if they believe they disappointed or “harmed” others...' -**Category**: hypothesis -**Focus**: hypothesis - -**Original Text**: -> The hypotheses about second-order beliefs in guilt aversion are not always explicitly linked to the experimental design. - -**Improved Version**: -> In Section 4.2, we explicitly state that the hypotheses predict that higher second-order beliefs about the other’s expectations will increase the likelihood of prosocial choices, and we describe how the experimental designs directly test this prediction. - -**Explanation**: -Enhances hypothesis-testing clarity and alignment with methods, improving logical coherence. - -### Suggestion 6 - -**Location**: Section 5.1, paragraph starting with 'The empirical evidence that appears to be consistent with self-image concerns...' -**Category**: interpretation -**Focus**: interpretation - -**Original Text**: -> The interpretation of self-image concerns often relies on assumptions about self-deception and signaling, but alternative explanations like social norms are not always discussed. - -**Improved Version**: -> While self-image concerns can explain behaviors via signaling and self-deception, it is important to consider alternative explanations such as social norm enforcement, which may also account for observed behaviors. A balanced discussion of these mechanisms would clarify their relative roles. - -**Explanation**: -Provides a more nuanced and internally consistent interpretation, strengthening the interpretation section. - -### Suggestion 7 - -**Location**: Throughout the document -**Category**: citations -**Focus**: citations - -**Original Text**: -> Citations are sometimes inconsistent in formatting, e.g., 'Fehr®Epper®Senn' vs. 'Fehr, Epper, and Senn'. - -**Improved Version**: -> Standardize all citations to a consistent format, e.g., 'Fehr, Epper, and Senn (2023)', throughout the manuscript to improve professionalism and clarity. - -**Explanation**: -Improves citation clarity and academic professionalism, aiding reader comprehension. - -### Suggestion 8 - -**Location**: Section 3.1.1, paragraph referencing Figure 1 -**Category**: figures -**Focus**: figures - -**Original Text**: -> Figures such as Figure 1 and Figure 1d are referenced but not always clearly integrated with the text explanation. - -**Improved Version**: -> Ensure that each figure is explicitly introduced with a descriptive sentence linking it to the text, e.g., 'Figure 1 illustrates the indifference curves for different distributional preferences, highlighting...'. - -**Explanation**: -Enhances figure-text coherence, aiding visual understanding and logical flow. - -### Suggestion 9 - -**Location**: Section 3.1.2, Table 1 -**Category**: tables -**Focus**: tables - -**Original Text**: -> Table 1 presents empirical frequencies but does not specify confidence intervals or significance levels. - -**Improved Version**: -> Add statistical significance indicators or confidence intervals to Table 1 to clarify the robustness of the frequency estimates and support stronger conclusions. - -**Explanation**: -Strengthens the empirical evidence and its interpretability, improving results-conclusions coherence. - -### Suggestion 10 - -**Location**: Throughout the main text referencing appendix sections -**Category**: supplementary -**Focus**: supplementary - -**Original Text**: -> The online appendix sections provide detailed data but lack explicit cross-references to the main text. - -**Improved Version**: -> Include clear cross-references in the main text, e.g., 'see Appendix 1 for detailed heterogeneity analyses', to improve transparency and coherence between main and supplementary material. - -**Explanation**: -Facilitates verification and enhances overall logical coherence of the document. - -### Suggestion 11 - -**Location**: N/A -**Category**: N/A -**Focus**: N/A - -**Original Text**: -> Some terminology, such as 'social preferences' and 'distributional preferences,' is used inconsistently. - -**Improved Version**: -> Define 'social preferences' as a broad category encompassing altruism, reciprocity, guilt aversion, etc., and consistently distinguish it from 'distributional preferences,' which focus specifically on payoff distributions, at first mention and throughout the text. - -**Explanation**: -Clarifies key concepts, reducing ambiguity and improving terminological consistency. - -### Suggestion 12 - -**Location**: N/A -**Category**: N/A -**Focus**: N/A - -**Original Text**: -> The development of hypotheses from models to empirical tests is not always explicitly articulated. - -**Improved Version**: -> Explicitly state the hypotheses derived from each model before presenting the empirical tests, e.g., 'Based on the guilt aversion model, we hypothesize that higher second-order beliefs increase prosocial behavior, which is tested via...'. - -**Explanation**: -Strengthens the hypothesis-testing link, improving logical flow and clarity. - -### Suggestion 13 - -**Location**: N/A -**Category**: N/A -**Focus**: N/A - -**Original Text**: -> The discussion of cultural differences in social preferences could be better integrated with the empirical results. - -**Improved Version**: -> Introduce a subsection explicitly comparing cultural differences, linking empirical findings (e.g., from Jakiela and Almas et al.) with theoretical implications, to enhance coherence. - -**Explanation**: -Provides a clearer narrative on how cultural context influences social preferences, improving logical flow. - -### Suggestion 14 - -**Location**: N/A -**Category**: N/A -**Focus**: N/A - -**Original Text**: -> The summary sections sometimes lack explicit connection back to the hypotheses or data presented. - -**Improved Version**: -> In the summary, explicitly restate how the evidence supports or challenges the initial hypotheses, e.g., 'The evidence on social image concerns confirms the hypothesis that public observability increases prosocial behavior, as shown in...'. - -**Explanation**: -Reinforces the logical coherence between data, hypotheses, and conclusions. - -### Detailed Feedback -### Methods Results Alignment - -The paper discusses a wide array of experimental designs and their findings, but the connection between the specific methods used (e.g., types of experiments, measurement techniques) and the properties of social preferences inferred (e.g., inequality aversion, reciprocity) could be more explicitly articulated. For example, when presenting the estimates of distributional parameters, it would help to clarify how the experimental choices directly map onto the theoretical constructs, ensuring that the methods are transparent and results are interpretable within the specified models. - -### Results Conclusions Alignment - -The conclusions about the prevalence and stability of social preferences are generally supported by the data, but some claims (e.g., about the dominance of inequality aversion in the population) would benefit from explicit mention of statistical significance or confidence levels. Additionally, the interpretation of heterogeneity findings should be more tightly linked to the empirical estimates, avoiding overgeneralization from aggregate data. - -### Logical Flow - -The manuscript transitions between theories, models, and empirical evidence, but some sections jump abruptly without clear linking sentences. For example, moving from the discussion of distributional preferences to kindness and guilt theories could be smoothed with explicit statements about how these theories complement or challenge each other. Similarly, the flow from laboratory findings to field evidence sometimes lacks explicit connective commentary, which would improve coherence. - -### Terminology Consistency - -Terms such as 'social preferences,' 'distributional preferences,' 'inequality aversion,' 'reciprocity,' and 'guilt aversion' are sometimes used interchangeably or without precise definitions. Establishing clear, consistent definitions at the outset and adhering to them throughout the text would greatly enhance clarity and prevent confusion. - -### Hypothesis Testing - -While the paper discusses various models and their predictions, the explicit articulation of hypotheses derived from each model and how the empirical tests confirm or refute these hypotheses is often implicit. Making these links explicit—stating hypotheses before each empirical section—would improve transparency and coherence. - -### Interpretation Consistency - -The interpretations of experimental results, especially regarding self-image and social image concerns, sometimes rely on assumptions about signaling and self-deception without considering alternative explanations such as social norms or strategic behavior. A more balanced discussion would strengthen internal consistency. - -### Citation Consistency - -Citations are inconsistently formatted, with some using special characters like '®' instead of standard parentheses or brackets. Standardizing citation style throughout would improve professionalism and clarity. - -### Figure Text Alignment - -Figures are referenced but not always well integrated with the text explanation. For example, the description of Figure 1d could be more explicitly linked to the discussion of the indifference curves, with clear references to axes and key features, to enhance understanding. - -### Table Text Alignment - -Table 1 presents empirical frequencies but lacks statistical significance indicators or confidence intervals. Including these would clarify the robustness of the findings and strengthen the link between data and conclusions. - -### Supplementary Consistency - -The appendix sections contain detailed data and methods but often lack explicit cross-references to the main text. Adding such references would improve transparency and help readers verify the consistency of the overall narrative. - -### Writing Agents (W1-W8) - -## W1 - Language and Style -### Score: 7/10 - -### Summary -Overall, the manuscript demonstrates strong academic writing with clear structure and comprehensive coverage of complex topics. Nonetheless, improvements in sentence clarity, punctuation, and consistency—especially in technical and reference sections—would significantly enhance readability and professionalism. Addressing minor grammatical and stylistic issues will make the document more polished and accessible for an academic audience. - -### Critical Remarks -### Remark 1 - -**Category**: grammar -**Location**: Introduction, paragraph 2 -**Issue**: Inconsistent use of singular/plural forms, e.g., 'behavior' vs. 'behaviors'. -**Severity**: medium -**Impact**: Reduces clarity and grammatical consistency. - -### Remark 2 - -**Category**: spelling -**Location**: Throughout the document -**Issue**: Typographical errors such as 'paradigmatic' misspelled as 'paradigmatic' in some instances, and 'behavior' as 'behaviour' inconsistently. -**Severity**: low -**Impact**: Minor; slightly affects professionalism. - -### Remark 3 - -**Category**: punctuation -**Location**: Section 4.1.1 -**Issue**: Overuse of commas in complex sentences, e.g., 'In a seminal paper, Rabin (1993) applied the tools of psychological game theory (Geanakoplos, Pierce and Stachetti 1989) to the study of intentions-based reciprocity.' -**Severity**: low -**Impact**: Can disrupt flow and readability. - -### Remark 4 - -**Category**: sentence_structure -**Location**: Section 3.1.1 -**Issue**: Long, complex sentences that could be split for clarity, e.g., 'A number of social preference models (see Box 3) are based on the assumption that people care about the distribution of payoffs between themselves and a set of relevant reference agents (Fehr and Schmidt 1999; Bolton and Ockenfels 2000; Andreoni and Miller 2002; Charness and Rabin 2002; Fisman, Kariv and Markovits 2007).' -**Severity**: medium -**Impact**: Impairs readability and comprehension. - -### Remark 5 - -**Category**: verb_tense -**Location**: Section 3.2 -**Issue**: Inconsistent tense usage, e.g., 'This section also indicates that considerable progress has been made, including interesting insights into the large cultural differences in how luck affects individuals’ fairness views and how cognitive biases shape individuals’ perception of merit.' -**Severity**: low -**Impact**: Slightly affects temporal clarity. - -### Remark 6 - -**Category**: subject_verb -**Location**: Section 4.2.1 -**Issue**: Incorrect subject-verb agreement: 'The authors measure both the finder’s expected reward y and the owner’s beliefs about the finder’s expected reward E(y) in an incentivized way.' -**Severity**: low -**Impact**: Minor grammatical inconsistency. - -### Remark 7 - -**Category**: articles -**Location**: Section 3.2 -**Issue**: Missing definite article: 'The potential importance of these factors is widely acknowledged...' (should be 'The potential importance of these factors is widely acknowledged...') -**Severity**: low -**Impact**: Minimal but affects grammatical correctness. - -### Remark 8 - -**Category**: prepositions -**Location**: Section 6.2.2 -**Issue**: Incorrect preposition: 'The firm placed a job advertisement stipulating an hourly wage of about €10 on an online search platform.' (preferably 'in an online search platform') -**Severity**: low -**Impact**: Minor; improves prepositional accuracy. - -### Remark 9 - -**Category**: conjunctions -**Location**: Section 6.2.3 -**Issue**: Overuse of 'and' in lists, e.g., 'they also show that workers do not consider real wage cuts that arise from avoiding nominal pay rises in response to inflation to be unfair – a finding that is consistent with data reported in Kahneman, Knetsch and Thaler (1986).' -**Severity**: low -**Impact**: Can be improved for clarity and style. - -### Remark 10 - -**Category**: academic_conventions -**Location**: References section -**Issue**: Inconsistent citation formatting, e.g., some references lack journal volume and page numbers, some have inconsistent punctuation. -**Severity**: medium -**Impact**: Reduces professionalism and hampers proper referencing. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Header -**Category**: academic_conventions -**Focus**: style - -**Original Text**: -> This Version: 13 September 2024 - -**Improved Version**: -> This version: 13 September 2024 - -**Explanation**: -Lowercase 'version' aligns with standard formatting for document titles or version labels, enhancing consistency. - -### Suggestion 2 - -**Location**: Abstract, paragraph 1 -**Category**: abstract -**Focus**: sentence_structure - -**Original Text**: -> We review the vast literature on social preferences by assessing what is known about their fundamental properties, their distribution in the broader population, and their consequences for important economic and political behaviors. - -**Improved Version**: -> We review the extensive literature on social preferences, focusing on their fundamental properties, distribution within the broader population, and implications for key economic and political behaviors. - -**Explanation**: -Enhances conciseness and clarity by reducing redundancy and improving flow. - -### Suggestion 3 - -**Location**: Abstract, paragraph 1 -**Category**: abstract -**Focus**: sentence_structure - -**Original Text**: -> In addition, we discuss the evidence for reciprocity and guilt aversion and assess the empirical relevance of self-image and social image concerns in prosocial behaviors. - -**Improved Version**: -> Additionally, we discuss evidence for reciprocity and guilt aversion, assessing the empirical relevance of self-image and social image concerns in prosocial behaviors. - -**Explanation**: -Streamlines sentence structure and improves readability by avoiding conjunction overuse. - -### Suggestion 4 - -**Location**: Introduction, paragraph 2 -**Category**: sentence_structure -**Focus**: sentence_structure - -**Original Text**: -> Theories of social preferences differ in how exactly 𝜋௝ affects player i’s utility – for example through social comparisons between 𝜋௜ and 𝜋௝ or through the perceived kindness associated with particular strategies that generate 𝜋௝; however, they all involve the assumption that other players’ material payoffs enter i’s utility function in some way. - -**Improved Version**: -> Theories of social preferences vary in how 𝜋௝ influences player i’s utility—for example, through social comparisons between 𝜋௜ and 𝜋௝ or via perceptions of kindness associated with strategies that generate 𝜋௝. Nonetheless, they all assume that other players’ material payoffs affect i’s utility in some manner. - -**Explanation**: -Splits complex sentence for clarity, corrects punctuation, and improves flow. - -### Suggestion 5 - -**Location**: Introduction, paragraph 3 -**Category**: sentence_structure -**Focus**: sentence_structure - -**Original Text**: -> Behavior associated with social preferences range from helping friends and neighbors to support for co-workers, from charitable donations to participation in collective actions against oppressive dictatorships, from individuals’ support for sustainable investments to workers’ responses to wage inequality in companies, from affluent individuals’ support for taxing the rich to workers’ collective stance against wage cuts. - -**Improved Version**: -> Behaviors associated with social preferences include helping friends and neighbors, supporting co-workers, donating charitably, participating in collective actions against oppressive regimes, endorsing sustainable investments, responding to wage inequality, supporting higher taxes on the wealthy, and opposing wage cuts. - -**Explanation**: -Improves parallelism, clarity, and conciseness by listing behaviors more systematically. - -### Suggestion 6 - -**Location**: Introduction, paragraph 4 -**Category**: sentence_structure -**Focus**: sentence_structure - -**Original Text**: -> In this paper, we will summarize what has been learned in this line of research and we will identify what needs to be known and done to make further progress. - -**Improved Version**: -> This paper summarizes current findings in this research area and identifies key gaps for future investigation. - -**Explanation**: -Uses more formal, concise phrasing typical of academic writing, eliminating future tense 'will' for immediacy. - -### Suggestion 7 - -**Location**: Introduction, paragraph 4 -**Category**: sentence_structure -**Focus**: sentence_structure - -**Original Text**: -> The implementation of incentivized anonymous one-shot experiments is thus a frequently employed conservative strategy that makes sense, but it is also important to keep in mind that important aspects of social relations, and their influence on social preferences, may be lost in these experiments (Frohlich, Oppenheimer and Kurki 2004). - -**Improved Version**: -> Implementing incentivized, anonymous, one-shot experiments is a common conservative strategy that is methodologically sound; however, it is important to recognize that such experiments may omit significant aspects of social relations and their influence on social preferences (Frohlich, Oppenheimer, and Kurki 2004). - -**Explanation**: -Clarifies sentence structure, improves formality, and enhances readability by splitting complex ideas. - -### Suggestion 8 - -**Location**: Section 2, paragraph 2 -**Category**: sentence_structure -**Focus**: sentence_structure - -**Original Text**: -> The predictions for the experimental games mentioned above assume that the games are played one-shot and that interactions are anonymous. - -**Improved Version**: -> These predictions assume that the games are played as one-shot interactions with complete anonymity among participants. - -**Explanation**: -Refines phrasing for clarity and academic tone, emphasizing assumptions clearly. - -### Suggestion 9 - -**Location**: Section 3.1.1, paragraph 3 -**Category**: sentence_structure -**Focus**: sentence_structure - -**Original Text**: -> The utility function 𝑈௜= 𝜋௜− 𝛼ത௜(𝜋௝− 𝜋௜) 𝑖𝑓 𝜋௝> 𝜋௜ and 𝑈௜= 𝜋௜− 𝛽̅௜(𝜋௜− 𝜋௝) 𝑖𝑓 𝜋௜> 𝜋௝ with both 𝛼ത௜> 0 and 0 < 𝛽̅௜< 1, imply that players dislike inequality. - -**Improved Version**: -> The utility function 𝑈௜= 𝜋௜− 𝛼ത௜(𝜋௝− 𝜋௜) when 𝜋௝> 𝜋௜, and 𝑈௜= 𝜋௜− 𝛽̅௜(𝜋௜− 𝜋௝) when 𝜋௜> 𝜋௝, with both 𝛼ത௜> 0 and 0 < 𝛽̅௜< 1, indicates that players dislike inequality in payoffs. - -**Explanation**: -Clarifies the conditional structure and improves grammatical correctness for clarity. - -### Suggestion 10 - -**Location**: Section 3.1.1, paragraph 4 -**Category**: sentence_structure -**Focus**: sentence_structure - -**Original Text**: -> The utility function 𝑈௜= 𝜋௜+ 𝑓(𝜎) where 𝜎= 𝜋௜/(𝜋௜+ 𝜋௝) measures A’s relative payoff. - -**Improved Version**: -> The utility function 𝑈௜= 𝜋௜ + 𝑓(𝜎), where 𝜎= 𝜋௜/(𝜋௜ + 𝜋௝), captures individual i’s relative payoff in the game. - -**Explanation**: -Adds clarity by explicitly stating what the function measures and improves sentence flow. - -### Detailed Feedback -### Grammar Correctness - -The text generally maintains good grammatical structure, but some sentences are overly long or complex, leading to potential confusion. Occasional inconsistent use of singular/plural forms and minor tense shifts can be improved for consistency and clarity. - -### Spelling Accuracy - -Spelling is largely correct throughout; however, minor typographical errors such as inconsistent hyphenation or misspellings (e.g., 'paradigmatic' sometimes misspelled) should be corrected to uphold academic professionalism. - -### Punctuation Usage - -Punctuation is mostly appropriate, but complex sentences often contain excessive commas or missing punctuation marks, which can hinder readability. Proper use of hyphens, dashes, and semicolons can improve clarity. - -### Sentence Structure - -Many sentences are lengthy and contain multiple ideas, which can be difficult to follow. Breaking complex sentences into shorter, clearer statements would enhance readability and comprehension. - -### Verb Tense Consistency - -The document mostly maintains consistent tense, primarily present and past, but some sections switch tenses unnecessarily. Ensuring uniform tense usage aligned with the context (e.g., describing current knowledge vs. past research) would improve coherence. - -### Subject Verb Agreement - -Subject-verb agreement is generally correct, but some sentences with complex subjects or multiple clauses could benefit from careful review to prevent subtle errors. - -### Article Usage - -Articles are generally used correctly; however, occasional omissions or unnecessary insertions (e.g., 'the' before general nouns) can be refined for grammatical accuracy. - -### Preposition Usage - -Prepositions are mostly appropriate, but some phrases could be improved for precision, such as 'in an online search platform' instead of 'on an online search platform'. - -### Conjunction Usage - -Conjunctions are used adequately, but overuse of 'and' in lists can be replaced with clearer punctuation or varied conjunctions to improve flow. - -### Academic Conventions - -Citations and references mostly follow academic standards, but inconsistent formatting (e.g., missing volume/page numbers, inconsistent punctuation) should be standardized according to a specific style guide (e.g., APA, Chicago). - -## W2 - Narrative and Structure -### Score: 7/10 - -### Summary -The paper offers a comprehensive and detailed review of social preferences, but its narrative could benefit from clearer signposting, better paragraph structuring, and more accessible language. Enhancing transitions, explicitly tracking hypotheses, and integrating visual elements more effectively will significantly improve coherence and reader engagement. Overall, with targeted revisions, the narrative flow can be made more cohesive, accessible, and compelling, facilitating better understanding of complex models and empirical findings. - -### Critical Remarks -### Remark 1 - -**Category**: narrative_coherence -**Location**: Introduction - Paragraph 2 -**Issue**: The paragraph introduces the utility function of social preferences with technical notation, which may be dense and disrupt overall coherence for non-technical readers. -**Severity**: medium -**Impact**: This hampers the accessibility and smooth flow of the narrative, potentially alienating readers unfamiliar with formal models. - -### Remark 2 - -**Category**: logical_progression -**Location**: Section 3.1.1 Models of distributional preferences -**Issue**: The section jumps between different models (Fehr-Schmidt, Bolton-Ockenfels, Charness-Rabin) without clear comparative framing or transition sentences. -**Severity**: high -**Impact**: This weakens the logical flow, making it harder for readers to follow the development and distinctions among models. - -### Remark 3 - -**Category**: transitions -**Location**: Section 3.2 - The Role of Merit and Luck -**Issue**: Transitions between discussing theoretical concepts and empirical findings are abrupt, lacking connecting sentences. -**Severity**: medium -**Impact**: This interrupts the narrative flow, causing reader disorientation and reducing coherence. - -### Remark 4 - -**Category**: paragraph_organization -**Location**: Section 4.2 - The Role of Guilt Aversion -**Issue**: The section contains dense blocks of text with multiple ideas (models, evidence, criticisms) without clear paragraph breaks for subtopics. -**Severity**: high -**Impact**: This impairs readability and makes it difficult for readers to digest complex information systematically. - -### Remark 5 - -**Category**: topic_sentences -**Location**: Section 6.2.2 - Fairness Concerns and Resistance to Wage Cuts -**Issue**: Some paragraphs lack clear topic sentences that preview the content, leading to less focused paragraphs. -**Severity**: medium -**Impact**: Reduces the clarity of paragraph purpose, weakening overall narrative guidance. - -### Remark 6 - -**Category**: evidence_integration -**Location**: Section 6.1 - The Role of Social Preferences in Cooperation -**Issue**: Evidence from field and lab studies is presented in a list-like manner without explicit synthesis or comparison. -**Severity**: medium -**Impact**: This diminishes the narrative coherence and makes it harder to see overarching patterns or conclusions. - -### Remark 7 - -**Category**: conclusion_alignment -**Location**: Section 7 - Outlook -**Issue**: The outlook lists open questions but does not explicitly connect them back to the main findings or hypotheses discussed earlier. -**Severity**: low -**Impact**: Weakens the narrative closure, making the conclusion feel somewhat disconnected from the main text. - -### Remark 8 - -**Category**: hypothesis_tracking -**Location**: Throughout the paper -**Issue**: The research questions or hypotheses are not explicitly restated or tracked across sections, especially in complex model discussions. -**Severity**: high -**Impact**: This hampers the reader’s ability to follow the research narrative and assess how evidence supports specific hypotheses. - -### Remark 9 - -**Category**: visual_integration -**Location**: Figures and Tables references -**Issue**: References to figures and tables (e.g., Figures 1, 2, A1, A2) are frequent but lack integration into the narrative flow or explanatory captions within the main text. -**Severity**: medium -**Impact**: This reduces visual support effectiveness and may cause readers to overlook or misinterpret visual data. - -### Remark 10 - -**Category**: reader_engagement -**Location**: Entire document -**Issue**: The dense technical language and extensive referencing may overwhelm or disengage non-specialist readers. -**Severity**: high -**Impact**: This limits accessibility and reduces overall engagement, especially for interdisciplinary or less technical audiences. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Section 3.1.1 -**Category**: literature -**Focus**: narrative_coherence - -**Original Text**: -> A number of social preference models (see Box 3) are based on the assumption that people care about the distribution of payoffs between themselves and a set of relevant reference agents (Fehr and Schmidt 1999; Bolton and Ockenfels 2000; Andreoni and Miller 2002; Charness and Rabin 2002; Fisman, Kariv and Markovits 2007). - -**Improved Version**: -> Many social preference models (see Box 3) assume that individuals care about how payoffs are distributed between themselves and relevant reference agents (e.g., Fehr and Schmidt 1999; Bolton and Ockenfels 2000; Andreoni and Miller 2002; Charness and Rabin 2002; Fisman, Kariv and Markovits 2007). - -**Explanation**: -Rephrasing clarifies the models' core assumption upfront, improving clarity and flow for readers unfamiliar with the models. - -### Suggestion 2 - -**Location**: Section 3.1.1 -**Category**: transitions -**Focus**: logical_progression - -**Original Text**: -> The section jumps between different models (Fehr-Schmidt, Bolton-Ockenfels, Charness-Rabin) without clear comparative framing or transition sentences. - -**Improved Version**: -> After introducing the general idea of distributional preferences, we now compare key models—starting with Fehr-Schmidt (1999), followed by Bolton-Ockenfels (2000), and then Charness-Rabin (2002)—highlighting their differences and similarities. - -**Explanation**: -Adding explicit transitions improves logical progression and helps readers follow the comparative narrative. - -### Suggestion 3 - -**Location**: Section 4.2 - The Role of Guilt Aversion -**Category**: paragraph_organization -**Focus**: paragraph_organization - -**Original Text**: -> The paragraph contains dense blocks of text with multiple ideas (models, evidence, criticisms) without clear paragraph breaks for subtopics. - -**Improved Version**: -> Divide this dense paragraph into smaller sections with subheadings: e.g., 'Models of Distributional Preferences,' 'Empirical Evidence,' and 'Criticisms and Limitations.' - -**Explanation**: -Structured subheadings and paragraph breaks enhance readability and guide the reader through complex content. - -### Suggestion 4 - -**Location**: Section 6.2.1 -**Category**: topic_sentences -**Focus**: topic_sentence_effectiveness - -**Original Text**: -> Some paragraphs lack clear topic sentences that preview the content, leading to less focused paragraphs. - -**Improved Version**: -> Add topic sentences at the beginning of each paragraph, e.g., 'This paragraph discusses the empirical evidence supporting guilt aversion in laboratory settings.' - -**Explanation**: -Clear topic sentences improve focus and help readers anticipate the paragraph’s purpose, enhancing coherence. - -### Suggestion 5 - -**Location**: Section 6.1 -**Category**: evidence_integration -**Focus**: evidence_integration - -**Original Text**: -> Evidence from field and lab studies is presented in a list-like manner without explicit synthesis or comparison. - -**Improved Version**: -> Introduce synthesis sentences that compare findings, e.g., 'Both lab and field studies consistently show that social preferences influence effort and cooperation, though the magnitude varies across contexts.' - -**Explanation**: -Synthesizing evidence creates a cohesive narrative, emphasizing overarching themes and patterns. - -### Suggestion 6 - -**Location**: Section 7 -**Category**: conclusion -**Focus**: conclusion_alignment - -**Original Text**: -> The outlook lists open questions but does not explicitly connect them back to the main findings or hypotheses discussed earlier. - -**Improved Version**: -> Conclude the outlook by explicitly linking open questions to the main findings, e.g., 'Building on our review, future research should explore how these open questions—such as the determinants of social preferences—can inform policy design.' - -**Explanation**: -Explicit connections reinforce the narrative arc and clarify how future directions relate to the current review. - -### Suggestion 7 - -**Location**: Throughout the document -**Category**: hypothesis_tracking -**Focus**: hypothesis_tracking - -**Original Text**: -> The research questions or hypotheses are not explicitly restated or tracked across sections, especially in complex model discussions. - -**Improved Version**: -> Begin each major section with a brief restatement of the key research question or hypothesis it addresses, e.g., 'This section examines whether social preferences are stable over time, testing the hypothesis that they are relatively persistent.' - -**Explanation**: -Explicit tracking of hypotheses maintains narrative clarity and helps readers evaluate how evidence supports or refutes them. - -### Suggestion 8 - -**Location**: Section 3.1.2 -**Category**: visual_integration -**Focus**: visual_integration - -**Original Text**: -> References to figures and tables are frequent but lack integration into the narrative or explanatory captions within the main text. - -**Improved Version**: -> Embed references to figures/tables within explanatory sentences, e.g., 'As shown in Figure A1a, the estimated weight on self-payoff (α′) is higher among broad population samples, indicating greater concern for others.' - -**Explanation**: -Integrating visuals into the narrative enhances comprehension and guides the reader’s attention effectively. - -### Suggestion 9 - -**Location**: Throughout the document -**Category**: reader_engagement -**Focus**: reader_engagement - -**Original Text**: -> The dense technical language and extensive referencing may overwhelm or disengage non-specialist readers. - -**Improved Version**: -> Include brief summaries or intuitive explanations of complex models and results, e.g., 'In simple terms, this model captures how individuals balance their own payoffs against their concern for others, with parameters indicating the strength of these preferences.' - -**Explanation**: -Simplifying language and providing intuitive summaries broadens accessibility and enhances reader engagement. - -### Suggestion 10 - -**Location**: N/A -**Category**: N/A -**Focus**: N/A - -**Original Text**: -> The conclusion section lists open questions but does not revisit the main findings or their implications. - -**Improved Version**: -> Add a closing paragraph that summarizes key insights from the review and explicitly states how the open questions build on these findings, e.g., 'In sum, our review highlights the robustness of social preferences and their implications, while identifying key gaps that future research should address.' - -**Explanation**: -This reinforces the narrative arc, providing closure and emphasizing the significance of the review. - -### Suggestion 11 - -**Location**: Section 3.1.1 -**Category**: paragraph_organization -**Focus**: paragraph_organization - -**Original Text**: -> The paper introduces complex models with formal notation early on without sufficient contextual explanation. - -**Improved Version**: -> Introduce informal descriptions or real-world examples before presenting formal models, e.g., 'To illustrate, individuals often care about fairness in wage distributions, which can be modeled as preferences over payoff differences.' - -**Explanation**: -Preliminary explanations improve accessibility and help readers grasp technical content more easily. - -### Suggestion 12 - -**Location**: N/A -**Category**: N/A -**Focus**: N/A - -**Original Text**: -> The extensive references and technical details may distract from the main narrative flow. - -**Improved Version**: -> Streamline references by integrating key citations into the main text and relegating detailed references to footnotes or online appendices, e.g., 'Several models (see Fehr and Schmidt 1999; Bolton and Ockenfels 2000) have been proposed to explain social preferences.' - -**Explanation**: -This reduces cognitive load, maintaining focus on the narrative while preserving scholarly rigor. - -### Suggestion 13 - -**Location**: N/A -**Category**: N/A -**Focus**: N/A - -**Original Text**: -> The discussion of empirical findings often lacks explicit linkage to the hypotheses or theoretical predictions they test. - -**Improved Version**: -> Explicitly state the hypotheses or predictions before presenting empirical results, e.g., 'We hypothesize that social preferences are stable over time; the following data tests this prediction.' - -**Explanation**: -Clear hypothesis linkage guides the reader through the evidence and its implications, strengthening narrative coherence. - -### Detailed Feedback -### Narrative Coherence - -The overall narrative is logically structured but occasionally hampered by technical density and abrupt transitions. Simplifying complex sections and adding summary sentences would improve flow. - -### Logical Progression - -While each section develops specific themes, the progression between models, evidence, and implications can be made clearer with explicit signposting and comparative framing. - -### Section Transitions - -Transitions are often implicit; incorporating linking sentences and signposting phrases will help connect sections smoothly, especially when shifting from theory to empirical evidence. - -### Paragraph Organization - -Many paragraphs are densely packed with ideas. Introducing subheadings and breaking long paragraphs into smaller units will enhance readability and focus. - -### Topic Sentence Effectiveness - -Some paragraphs lack clear topic sentences, which diminishes clarity. Adding explicit topic sentences at the start of each paragraph will clarify purpose and improve coherence. - -### Supporting Evidence Integration - -Evidence is presented in lists or separate blocks without synthesis. Integrating findings through comparative summaries will strengthen the narrative and highlight overarching themes. - -### Conclusion Alignment - -The conclusion section lists open questions but does not revisit key findings. Summarizing main insights and explicitly linking them to future questions will improve closure. - -### Hypothesis Tracking - -The paper does not explicitly restate or track hypotheses across sections. Restating research questions at the beginning of each section will aid comprehension. - -### Visual Element Integration - -References to figures/tables are frequent but lack integration into the text. Embedding visual references within explanatory sentences will improve their utility. - -### Reader Engagement - -Technical language and dense references may reduce engagement. Simplifying explanations and providing intuitive summaries will broaden accessibility. - -## W3 - Clarity and Conciseness -### Score: 4/10 - -### Summary -This document presents a comprehensive and detailed review of social preferences, but its clarity and conciseness are hampered by overly complex language, long sentences, dense data, and frequent jargon. Strategic simplification, segmentation, and explanation of technical content would greatly improve readability and accessibility, allowing a broader audience to grasp its key insights effectively. - -### Critical Remarks -### Remark 1 - -**Category**: language_simplicity -**Location**: Abstract -**Issue**: The abstract uses complex sentences and abstract terminology that may be difficult for non-specialist readers to follow. -**Severity**: medium -**Impact**: Reduces accessibility and immediate understanding of the key message. - -### Remark 2 - -**Category**: jargon -**Location**: Introduction -**Issue**: Heavy use of technical terms (e.g., 'distributional preferences', 'inequality aversion', 'utility function') without immediate explanation. -**Severity**: high -**Impact**: Creates barriers for readers unfamiliar with specific terminology, impairing clarity. - -### Remark 3 - -**Category**: wordiness -**Location**: Introduction -**Issue**: Many sentences are overly long and contain multiple ideas, making them hard to parse. -**Severity**: high -**Impact**: Obscures key points and hampers quick comprehension. - -### Remark 4 - -**Category**: sentence_length -**Location**: Section 2 -**Issue**: Sentences such as 'All of these experiments have been replicated dozens of times (or more) and show robust, replicable behavioral patterns...' are excessively long. -**Severity**: high -**Impact**: Difficult to follow and reduces readability. - -### Remark 5 - -**Category**: paragraph_length -**Location**: Section 3 -**Issue**: Many paragraphs, especially in the literature review, are very dense, covering multiple concepts and empirical findings. -**Severity**: high -**Impact**: Overwhelms the reader and hampers focus on main ideas. - -### Remark 6 - -**Category**: active_passive_voice -**Location**: Methodology sections -**Issue**: Frequent use of passive voice (e.g., 'The utility function is given by...') which can obscure agency. -**Severity**: medium -**Impact**: Reduces immediacy and clarity of who performs actions or makes decisions. - -### Remark 7 - -**Category**: redundancy -**Location**: Throughout -**Issue**: Repeated phrases and concepts (e.g., multiple mentions of 'distributional preferences' and 'empirical evidence') without adding new information. -**Severity**: medium -**Impact**: Wastes space and distracts from core messages. - -### Remark 8 - -**Category**: ambiguity -**Location**: Section 3 -**Issue**: Complex models (e.g., equations (1a), (1b)) are introduced with minimal explanation of their intuition. -**Severity**: high -**Impact**: Confuses readers unfamiliar with the models, reducing clarity. - -### Remark 9 - -**Category**: readability -**Location**: Overall -**Issue**: The dense technical language and long sentences make the text difficult to read smoothly. -**Severity**: high -**Impact**: Limits engagement and quick understanding. - -### Remark 10 - -**Category**: information_density -**Location**: Sections 3 and 4 -**Issue**: High density of empirical results, models, and references in a single paragraph without clear structuring. -**Severity**: high -**Impact**: Makes it hard to extract main points and follow logical flow. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Abstract -**Category**: language_simplicity -**Focus**: language_simplicity - -**Original Text**: -> We review the vast literature on social preferences by assessing what is known about their fundamental properties, their distribution in the broader population, and their consequences for important economic and political behaviors. - -**Improved Version**: -> This paper reviews the key properties, how social preferences are distributed in the population, and their impact on economic and political behaviors. - -**Explanation**: -Simplifies language and reduces wordiness for clearer, quicker understanding. - -### Suggestion 2 - -**Location**: Introduction -**Category**: wordiness -**Focus**: wordiness - -**Original Text**: -> A large, and still growing, literature has documented the existence of social preferences, their social and economic implications, and the various conditions under which they influence the equilibria and outcomes of human interactions. - -**Improved Version**: -> A growing body of research shows that social preferences influence economic outcomes and human interactions. - -**Explanation**: -Reduces complexity and length, making the statement more direct and accessible. - -### Suggestion 3 - -**Location**: Introduction -**Category**: sentence_length -**Focus**: sentence_length - -**Original Text**: -> Theories of social preferences differ in how exactly 𝜋௝ affects player i’s utility – for example through social comparisons between 𝜋௜ and 𝜋௝ or through the perceived kindness associated with particular strategies that generate 𝜋௝; however, they all involve the assumption that other players’ material payoffs enter i’s utility function in some way. - -**Improved Version**: -> Different theories explain how 𝜋௝ affects player i’s utility—such as through social comparisons or perceptions of kindness—but all assume that others’ payoffs influence utility. - -**Explanation**: -Breaks down complex sentence into clearer parts, improving readability and clarity. - -### Suggestion 4 - -**Location**: Introduction -**Category**: wordiness -**Focus**: wordiness - -**Original Text**: -> Behaviors associated with social preferences range from helping friends and neighbors to support for co-workers, from charitable donations to participation in collective actions against oppressive dictatorships, from individuals’ support for sustainable investments to workers’ responses to wage inequality in companies, from affluent individuals’ support for taxing the rich to workers’ collective stance against wage cuts. - -**Improved Version**: -> Social preferences influence behaviors like helping friends, donating to charity, supporting sustainable investments, and resisting wage cuts. - -**Explanation**: -Condenses list into a more manageable, clearer enumeration, reducing redundancy. - -### Suggestion 5 - -**Location**: Introduction -**Category**: language_simplicity -**Focus**: language_simplicity - -**Original Text**: -> The implementation of incentivized anonymous one-shot experiments is thus a frequently employed conservative strategy that makes sense, but it is also important to keep in mind that important aspects of social relations, and their influence on social preferences, may be lost in these experiments (Frohlich, Oppenheimer and Kurki 2004). - -**Improved Version**: -> One-shot anonymous experiments are common and useful, but they may miss important social factors that influence preferences. - -**Explanation**: -Simplifies and clarifies the trade-off, improving readability and focus. - -### Suggestion 6 - -**Location**: Section 3 -**Category**: sentence_length -**Focus**: sentence_length - -**Original Text**: -> Section 3 discusses the theories of distributional preferences that helped resolve this puzzle by providing a unifying explanation of these seemingly disparate facts, which then also led to a rich empirical literature on the distribution of distributional preferences in the broader population. - -**Improved Version**: -> Section 3 reviews theories that unify different findings about distributional preferences and summarize their distribution in the population. - -**Explanation**: -Reduces sentence length and complexity for clearer comprehension. - -### Suggestion 7 - -**Location**: Section 2 -**Category**: language_simplicity -**Focus**: language_simplicity - -**Original Text**: -> The predictions for the experimental games mentioned above assume that the games are played one-shot and that interactions are anonymous. - -**Improved Version**: -> These game predictions assume they are played once and anonymously. - -**Explanation**: -Simplifies language and shortens sentence for clarity. - -### Suggestion 8 - -**Location**: Appendix 3 -**Category**: language_simplicity -**Focus**: language_simplicity - -**Original Text**: -> The utility function is given by 𝑈(𝜋௔, 𝜋௕) that obeys the plausible restriction 𝑈(𝑅, 0) > 𝑈(0, 𝑅) will always choose x = 0. - -**Improved Version**: -> The utility function 𝑈(𝜋௔, 𝜋௕) reflects the idea that players prefer fairness, so they will choose x=0 if they value equality more than inequality. - -**Explanation**: -Clarifies the model assumption and simplifies language for better understanding. - -### Suggestion 9 - -**Location**: General comments -**Category**: readability -**Focus**: readability - -**Original Text**: -> Many experiments have been conducted with large, dense paragraphs of empirical results, which can overwhelm readers and obscure key insights. - -**Improved Version**: -> Many sections contain dense data, which can overwhelm and obscure main points. - -**Explanation**: -Condenses and clarifies the issue of density and readability. - -### Suggestion 10 - -**Location**: Section 3 -**Category**: ambiguity -**Focus**: ambiguity - -**Original Text**: -> Equations such as (1a) and (1b) are introduced with minimal explanation, risking confusion about their intuition and application. - -**Improved Version**: -> Equations (1a) and (1b) are complex and need clearer explanation of their meaning and use. - -**Explanation**: -Highlights the need for better clarity on technical content. - -### Suggestion 11 - -**Location**: Overall -**Category**: sentence_length -**Focus**: sentence_length - -**Original Text**: -> Long, complex sentences throughout the document reduce readability and make it harder for readers to grasp main ideas quickly. - -**Improved Version**: -> Many sentences are lengthy and complex, which hampers quick understanding and engagement. - -**Explanation**: -Summarizes the issue and emphasizes the importance of sentence brevity. - -### Suggestion 12 - -**Location**: Overall -**Category**: readability -**Focus**: readability - -**Original Text**: -> The dense presentation of empirical data, theoretical models, and references in single paragraphs makes it difficult to identify core messages. - -**Improved Version**: -> Heavy data and reference density in paragraphs make key points hard to identify. - -**Explanation**: -Simplifies and clarifies the impact of dense information on comprehension. - -### Detailed Feedback -### Language Simplicity - -The text often employs complex sentences and technical language, which can be challenging for non-experts. Simplifying sentence structures and using plain language where possible would improve accessibility and comprehension. - -### Jargon Usage - -While necessary in academic contexts, excessive jargon without immediate explanation can hinder understanding. Introducing technical terms with brief definitions or context can help broaden clarity. - -### Wordiness - -Many sentences contain redundant phrases or multiple ideas, which could be condensed. Eliminating unnecessary words and splitting long sentences into shorter, focused ones would enhance clarity. - -### Sentence Length - -The frequent use of long, compound sentences reduces readability. Shortening sentences and focusing on one idea per sentence will make the text easier to follow. - -### Paragraph Length - -Paragraphs are often very dense, covering multiple concepts. Breaking them into smaller, thematic paragraphs would improve readability and allow readers to better grasp individual points. - -### Active Passive Voice - -Passive constructions are common and can obscure agency. Rephrasing sentences to active voice will increase immediacy and clarity. - -### Redundancy - -Repeated mentions of concepts like 'social preferences' and 'empirical evidence' add little new information and clutter the text. Removing or consolidating repetitions will streamline the narrative. - -### Ambiguity - -Some models and equations are introduced with minimal explanation, risking confusion. Providing intuitive descriptions alongside formal definitions will clarify their purpose and interpretation. - -### Readability - -Overall, the dense technical language, long sentences, and complex structure hinder smooth reading. Simplification and better organization will significantly improve flow. - -### Information Density - -The text packs a large amount of empirical and theoretical detail into few paragraphs, making it difficult to extract main messages. Using clearer subheadings, summaries, and focused paragraphs will enhance comprehension. - -## W4 - Terminology Consistency -### Score: 8/10 - -### Summary -Overall, the document demonstrates strong consistency in core terminology but would benefit from clarifying and standardizing the use of technical terms, notation, acronyms, and variable references. Implementing explicit definitions, consistent notation, and uniform terminology throughout will significantly enhance clarity and reduce potential ambiguities, making the complex material more accessible to a broad readership. - -### Critical Remarks -### Remark 1 - -**Category**: term_usage -**Location**: Section 3.1.1, paragraph starting with 'A number of social preference models' -**Issue**: The term 'distributional preferences' is used interchangeably with 'distributional social preferences' without clear distinction, which may cause confusion. -**Severity**: medium -**Impact**: This inconsistency can lead to ambiguity about whether the focus is on distributional outcomes or social preferences more broadly. - -### Remark 2 - -**Category**: notation -**Location**: Equations (1a) and (1b), Section 3.1.1 -**Issue**: The parameters 𝛼 and 𝛽 are introduced as weights but are sometimes described as 'weights' and other times as 'parameters' without clarification. -**Severity**: low -**Impact**: Potential confusion about the role of these parameters; clearer notation explanation would improve clarity. - -### Remark 3 - -**Category**: acronyms -**Location**: Throughout, e.g., 'SVO', 'EET', 'ALP' -**Issue**: Some acronyms are introduced without initial definition, e.g., 'SVO' (Social Value Orientation) is used before its first full form in Appendix 1. -**Severity**: high -**Impact**: This hampers understanding for readers unfamiliar with the acronyms, reducing clarity and consistency. - -### Remark 4 - -**Category**: variable_naming -**Location**: Section 3.1.1, equations (1a) and (1b) -**Issue**: The variables 𝜋௜ and 𝜋௝ are used for material payoffs, but in some parts, 'payoff' is used without subscript, which can cause ambiguity. -**Severity**: low -**Impact**: Inconsistent variable notation may lead to misinterpretation of which payoff is being referenced. - -### Remark 5 - -**Category**: unit_notation -**Location**: Section 3.2, paragraph discussing 'wages' and 'payoffs' -**Issue**: Wage amounts are sometimes given in euros (€), sometimes in dollars ($), but the notation is not consistently formatted or clarified. -**Severity**: medium -**Impact**: Inconsistent currency notation can cause confusion about the monetary units used across different studies. - -### Remark 6 - -**Category**: abbreviations -**Location**: Throughout, e.g., 'EET', 'SVO', 'ALP' -**Issue**: Some abbreviations are used inconsistently, e.g., 'EET' (Equality Equivalence Test) is sometimes spelled out fully and other times only as 'EET'. -**Severity**: low -**Impact**: Inconsistent abbreviation usage reduces readability and may cause confusion. - -### Remark 7 - -**Category**: technical_terms -**Location**: Section 3.1.1, paragraph on 'inequality aversion' -**Issue**: Terms like 'disutility', 'inequality aversion', and 'inequity' are used variably without clear definitions or distinctions. -**Severity**: medium -**Impact**: This can lead to misunderstandings about the specific technical concepts being discussed. - -### Remark 8 - -**Category**: field_terminology -**Location**: Throughout, e.g., 'distributional preferences', 'reciprocity', 'guilt aversion' -**Issue**: Some terms like 'distributional preferences' are used broadly, sometimes including inequality aversion, sometimes excluding it, without clarification. -**Severity**: medium -**Impact**: Inconsistent use of core terminology affects the precision of the discussion. - -### Remark 9 - -**Category**: cross_reference -**Location**: Section 3.1.1, references to equations (1a) and (1b) -**Issue**: Cross-references to equations and figures are sometimes inconsistent, e.g., 'Figure 1d' is used without always ensuring the figure is correctly labeled or referenced. -**Severity**: low -**Impact**: This can cause navigational confusion for readers trying to follow the arguments. - -### Remark 10 - -**Category**: definition -**Location**: Section 3.1.1, paragraph defining 𝛼 and 𝛽 -**Issue**: The definitions of 𝛼 and 𝛽 as weights are sometimes given in equations and sometimes described in text without explicit linkage. -**Severity**: low -**Impact**: Inconsistent presentation of definitions can hinder understanding of the parameters. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Section 3.1.1, paragraph on equations (1a) and (1b) -**Category**: notation -**Focus**: notation - -**Original Text**: -> The variables 𝜋௜ and 𝜋௝ represent the material payoffs of players i and j, respectively; 𝛼 is the weight on player j’s payoff when i is behind (𝜋௜< 𝜋௝), while 𝛽 is the weight when i is ahead (𝜋௜> 𝜋௝). - -**Improved Version**: -> The variables 𝜋ᵢ and 𝜋ⱼ denote the material payoffs of players i and j, respectively. The parameters 𝛼 and 𝛽 represent the weights assigned to the other player's payoff when player i is behind (𝜋ᵢ < 𝜋ⱼ) and when player i is ahead (𝜋ᵢ > 𝜋ⱼ), respectively. - -**Explanation**: -Clarifies the notation and explicitly links parameters to their roles, improving notation clarity and understanding. - -### Suggestion 2 - -**Location**: Section 3.2, first mention of 'EET' -**Category**: acronyms -**Focus**: acronym usage - -**Original Text**: -> The acronym 'EET' is used throughout the text. - -**Improved Version**: -> The acronym 'EET' (Equality Equivalence Test) is introduced at first mention as 'Equality Equivalence Test (EET)' and used consistently thereafter. - -**Explanation**: -Ensures clear initial definition and consistent usage of the acronym, aiding reader comprehension. - -### Suggestion 3 - -**Location**: Section 6.2, paragraph discussing wage experiments -**Category**: unit_notation -**Focus**: notation - -**Original Text**: -> Wage amounts are sometimes given in euros (€), sometimes in dollars ($), but the notation is not consistently formatted or clarified. - -**Improved Version**: -> All monetary amounts should include the currency symbol with a space, e.g., '€12' or '$12', and a note should specify the currency used in each context for clarity. - -**Explanation**: -Standardizes currency notation and clarifies units, reducing potential confusion about monetary units. - -### Suggestion 4 - -**Location**: Section 3.1.1, paragraph on 'inequality aversion' -**Category**: technical_terms -**Focus**: field_terminology - -**Original Text**: -> Terms like 'disutility', 'inequality aversion', and 'inequity' are used variably without clear definitions or distinctions. - -**Improved Version**: -> Include explicit definitions for 'disutility' (e.g., negative utility from inequality), 'inequality aversion' (preference to reduce payoff disparities), and 'inequity' (unfair or unequal outcomes), preferably in a dedicated glossary or at first mention. - -**Explanation**: -Provides clarity and consistency in technical terminology, preventing misunderstandings. - -### Suggestion 5 - -**Location**: Section 3.2, first mention of 'EET' -**Category**: abbreviations -**Focus**: abbreviation - -**Original Text**: -> Some abbreviations are used inconsistently, e.g., 'EET' (Equality Equivalence Test) is sometimes spelled out fully and other times only as 'EET'. - -**Improved Version**: -> Define the abbreviation 'EET' at first occurrence as 'Equality Equivalence Test (EET)' and then use 'EET' exclusively throughout the document. - -**Explanation**: -Ensures consistent abbreviation use, improving readability. - -### Suggestion 6 - -**Location**: Section 3.1.1, paragraph on 'Models of distributional preferences' -**Category**: field_terminology -**Focus**: field_terminology - -**Original Text**: -> The term 'distributional preferences' is used broadly, sometimes including inequality aversion, sometimes excluding it, without clarification. - -**Improved Version**: -> Clarify whether 'distributional preferences' encompass all social preferences related to payoff distribution (including inequality aversion, altruism, envy) or refer to a specific subset. Use precise definitions or categories when discussing different types. - -**Explanation**: -Reduces ambiguity about the scope of 'distributional preferences,' enhancing conceptual clarity. - -### Suggestion 7 - -**Location**: Throughout, e.g., in the discussion of equations and figures -**Category**: cross_reference -**Focus**: cross_reference - -**Original Text**: -> Cross-references to figures and equations are sometimes inconsistent, e.g., 'Figure 1d' is used without always ensuring the figure is correctly labeled or referenced. - -**Improved Version**: -> Ensure all figure and equation references are accurate and formatted uniformly, e.g., 'see Figure 1d' or 'see Equation (1a) and (1b)', and verify labels match the actual figures/equations. - -**Explanation**: -Improves navigability and reduces confusion for readers following the technical content. - -### Suggestion 8 - -**Location**: Section 3.1.1, paragraph on equations (1a) and (1b) -**Category**: definition -**Focus**: definitions - -**Original Text**: -> The definitions of 𝛼 and 𝛽 as weights are sometimes given in equations and sometimes described in text without explicit linkage. - -**Improved Version**: -> At first mention, explicitly define 𝛼 and 𝛽 as the parameters representing the weights on the other’s payoff when behind and ahead, respectively, and consistently refer back to these definitions when using the parameters. - -**Explanation**: -Provides consistent and clear understanding of key parameters, reinforcing their roles across the text. - -### Suggestion 9 - -**Location**: Throughout, especially in the discussion sections -**Category**: field_terminology -**Focus**: field-specific terminology - -**Original Text**: -> The term 'social preferences' is used broadly without always specifying the particular type (e.g., altruism, reciprocity, guilt aversion). - -**Improved Version**: -> When discussing 'social preferences,' specify the particular type (e.g., 'inequality aversion,' 'reciprocity,' 'guilt aversion') and clarify whether the term is used as a broad category or a specific concept in each context. - -**Explanation**: -Enhances terminological precision and clarity in the discussion of different social preference types. - -### Suggestion 10 - -**Location**: Section 6.2, paragraph discussing wage experiments -**Category**: unit_notation -**Focus**: notation - -**Original Text**: -> The notation for monetary amounts varies, e.g., '€12', '$12', or '12 euros' without standardization. - -**Improved Version**: -> Adopt a consistent notation for monetary amounts, e.g., always use the currency symbol immediately before the number without space, such as '€12' or '$12', and specify the currency in a footnote or in the first mention. - -**Explanation**: -Standardizes monetary notation, reducing ambiguity and improving professionalism. - -### Suggestion 11 - -**Location**: Section 3.1.1, paragraph on 'inequality aversion' -**Category**: field_terminology -**Focus**: field-specific terminology - -**Original Text**: -> The term 'inequality aversion' is sometimes used interchangeably with 'inequity aversion' without clarification. - -**Improved Version**: -> Use 'inequality aversion' consistently throughout the document, and define it explicitly at first mention as 'a preference to reduce payoff disparities,' to maintain terminological consistency. - -**Explanation**: -Prevents confusion between similar terms and ensures consistent technical language. - -### Detailed Feedback -### Term Usage Consistency - -The document generally maintains consistent use of core terms like 'social preferences,' 'distributional preferences,' and 'inequality aversion,' but occasional broad or overlapping usage without explicit clarification can cause ambiguity. For example, 'distributional preferences' sometimes broadly includes altruism, envy, and inequality aversion, but at other times seems to exclude some types. Clarifying these distinctions at first mention and maintaining consistent terminology throughout would improve clarity. - -### Notation Consistency - -Mathematical parameters such as 𝛼 and 𝛽 are introduced with clear roles but are sometimes described as 'weights' and other times as 'parameters' without explicit clarification. Consistent use of notation, with explicit definitions and references, would enhance understanding. Also, variables like 𝜋ᵢ and 𝜋ⱼ should be consistently subscripted and explained to avoid ambiguity. - -### Acronym Usage - -Acronyms like 'EET,' 'SVO,' and 'ALP' are sometimes introduced without initial definitions or are used inconsistently. Ensuring all acronyms are spelled out at first mention and used uniformly afterward would improve readability. - -### Variable Naming - -Variables such as 𝜋ᵢ, 𝜋ⱼ, and others are used with subscripts but sometimes without clear linkage to their specific roles in equations. Consistent variable naming and explicit explanations of their meaning at first use would help prevent misinterpretation. - -### Unit Notation - -Monetary amounts are given in different currencies and formats (€, $, 'dollars') without standardization. Adopting a uniform notation, e.g., always using the currency symbol immediately before the number, and clarifying the currency in the text, would improve clarity. - -### Abbreviations - -Some abbreviations are used inconsistently, e.g., 'EET' or 'SVO,' sometimes spelled out fully, sometimes abbreviated. Consistent initial definitions and uniform usage would enhance clarity. - -### Technical Terms - -Terms like 'disutility,' 'inequality aversion,' and 'inequity' are sometimes used interchangeably or broadly without precise definitions. Providing explicit definitions and distinctions at first mention would improve technical clarity. - -### Field Terminology - -Core concepts such as 'distributional preferences,' 'reciprocity,' and 'guilt aversion' are sometimes used broadly, which can obscure specific meanings. Clear, consistent definitions and distinctions are recommended. - -### Cross Reference Consistency - -References to figures and equations (e.g., 'Figure 1d,' 'Equation (1a)') are sometimes inconsistent or lack verification. Ensuring all references are accurate and uniformly formatted would aid navigation. - -### Definition Consistency - -Definitions of key parameters like 𝛼 and 𝛽 are sometimes scattered or implicit. Consolidating definitions in a dedicated section or at first mention, and referencing them consistently, would improve comprehension. - -## W5 - Inclusive Language -### Score: 4/10 - -### Summary -Overall, the document exhibits a solid foundation of scientific rigor but falls short in explicitly incorporating inclusive and unbiased language. By adopting gender-neutral terms, broadening cultural and geographic references, explicitly recognizing diversity in age, ability, and socioeconomic status, and avoiding stereotypes, the manuscript can significantly improve its inclusivity. These enhancements will make the research more respectful, applicable, and sensitive to the diversity of human experiences, thereby enriching its scholarly and societal impact. - -### Critical Remarks -### Remark 1 - -**Category**: gender_neutrality -**Location**: Introduction, paragraph 2 -**Issue**: The phrase 'an individual i has social preferences' uses gendered language ('individual i') without gender-neutral alternatives, potentially implying gender bias. -**Severity**: medium -**Impact**: Limits inclusivity by not explicitly acknowledging gender diversity, which could alienate or overlook non-binary or gender-diverse populations. - -### Remark 2 - -**Category**: cultural_sensitivity -**Location**: Section 3.2, paragraph 2 -**Issue**: The discussion of merit and luck references 'modern societies' and 'Western populations' without acknowledging cultural diversity or different societal norms. -**Severity**: high -**Impact**: Risks implying cultural superiority or universality of concepts, which can perpetuate ethnocentric biases and reduce cultural inclusivity. - -### Remark 3 - -**Category**: age_terminology -**Location**: Section 3.1.2, paragraph 2 -**Issue**: Use of 'students' and 'broad population samples' without specifying age ranges or demographic diversity may obscure age-related differences. -**Severity**: low -**Impact**: May limit understanding of how age influences preferences, reducing nuance and inclusivity regarding age groups. - -### Remark 4 - -**Category**: disability_inclusion -**Location**: Throughout the document -**Issue**: No explicit mention or consideration of individuals with disabilities or differing abilities in experimental design or interpretation. -**Severity**: high -**Impact**: Excludes or overlooks a significant portion of the population, reducing overall inclusivity and applicability of findings. - -### Remark 5 - -**Category**: socioeconomic_sensitivity -**Location**: Section 3.2, paragraph 4 -**Issue**: The discussion of 'broad population samples' and 'student samples' does not explicitly address socioeconomic status or access differences. -**Severity**: medium -**Impact**: Potentially masks socioeconomic biases and limits understanding of how social preferences vary across economic strata. - -### Remark 6 - -**Category**: geographic_inclusivity -**Location**: Section 3.1.2, paragraph 2 -**Issue**: References to 'Western populations' and 'European' or 'North American' samples dominate, with limited acknowledgment of non-Western or developing country contexts. -**Severity**: high -**Impact**: Risks ethnocentric bias, limiting the global relevance and cultural inclusivity of the research. - -### Remark 7 - -**Category**: professional_titles -**Location**: Throughout the document -**Issue**: Use of academic titles (e.g., 'Professor', 'Doctor') is absent; the text relies on author names and affiliations without titles, which is appropriate for neutrality but may overlook formal respect in some contexts. -**Severity**: low -**Impact**: Minimal, but explicit use of titles could enhance respectfulness in certain cultural contexts. - -### Remark 8 - -**Category**: stereotypes -**Location**: Section 3.2, paragraph 4 -**Issue**: The phrase 'people with high education and income in the ALP sample' could inadvertently reinforce stereotypes about socioeconomic status correlating with social preferences. -**Severity**: medium -**Impact**: May perpetuate stereotypes linking education and income with prosocial behavior, reducing nuanced understanding. - -### Remark 9 - -**Category**: identity_language -**Location**: Throughout the document -**Issue**: Predominant use of 'individuals' and 'subjects' rather than person-first language (e.g., 'people with social preferences') or identity-first language (e.g., 'socially motivated individuals'). -**Severity**: medium -**Impact**: Lacks sensitivity to personal identity and may reduce respect for individual agency. - -### Remark 10 - -**Category**: historical_context -**Location**: Section 7, paragraph 1 -**Issue**: Discussion of 'democratic rights' and 'mass movements' does not explicitly acknowledge historical struggles or diverse cultural histories. -**Severity**: low -**Impact**: Potentially oversimplifies complex historical processes and marginalizes non-Western or marginalized groups. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Introduction, paragraph 2 -**Category**: gender_neutrality -**Focus**: gender_neutrality - -**Original Text**: -> an individual i has social preferences - -**Improved Version**: -> a person or individual with social preferences - -**Explanation**: -Using 'person' or 'individual with social preferences' promotes gender-neutral and inclusive language, acknowledging diversity. - -### Suggestion 2 - -**Location**: Introduction, paragraph 2 -**Category**: cultural_sensitivity -**Focus**: cultural_sensitivity - -**Original Text**: -> The topic of social preferences has received a great deal of attention in economics and in the broader social science literature - -**Improved Version**: -> The topic of social preferences has received considerable attention across diverse cultures and societies in economics and social sciences - -**Explanation**: -Explicitly acknowledging cultural diversity enhances cultural sensitivity and global relevance. - -### Suggestion 3 - -**Location**: Throughout the document -**Category**: identity_language -**Focus**: person_first_language - -**Original Text**: -> participants in the experiments - -**Improved Version**: -> individuals participating in the experiments - -**Explanation**: -Using 'individuals' emphasizes person-first language, respecting personal identity and agency. - -### Suggestion 4 - -**Location**: Section 3.1.2 -**Category**: socioeconomic_sensitivity -**Focus**: socioeconomic_sensitivity - -**Original Text**: -> the broad population samples - -**Improved Version**: -> diverse and socioeconomically varied population samples - -**Explanation**: -Specifying diversity and socioeconomic variation promotes socioeconomic sensitivity and inclusivity. - -### Suggestion 5 - -**Location**: Section 3.1.2 -**Category**: geographic_inclusivity -**Focus**: geographic_inclusivity - -**Original Text**: -> Western populations - -**Improved Version**: -> populations from diverse cultural and geographic backgrounds, including non-Western societies - -**Explanation**: -Broadening the scope avoids ethnocentric bias and enhances geographic inclusivity. - -### Suggestion 6 - -**Location**: Introduction, paragraph 2 -**Category**: stereotypes -**Focus**: stereotype_avoidance - -**Original Text**: -> Theories of social preferences differ in how exactly 𝜋௝ affects player i’s utility - -**Improved Version**: -> Different theories of social preferences conceptualize how others’ payoffs influence a person’s utility - -**Explanation**: -Using neutral, inclusive language avoids implying stereotypes or biases about individuals’ motivations. - -### Suggestion 7 - -**Location**: Section 4 -**Category**: disability_inclusion -**Focus**: disability_inclusion - -**Original Text**: -> People may not only care directly about others’ payoff or others’ deservingness - -**Improved Version**: -> Individuals may care about others’ payoffs, deservingness, and social identities - -**Explanation**: -Including social identities recognizes diversity and avoids reducing individuals to a single trait. - -### Suggestion 8 - -**Location**: Throughout the document -**Category**: gender_neutrality -**Focus**: gender_neutrality - -**Original Text**: -> The phrase 'an individual i has social preferences' uses gendered language ('individual i') without gender-neutral alternatives - -**Improved Version**: -> A person with social preferences - -**Explanation**: -This change promotes gender neutrality and inclusivity, avoiding implicit gender bias. - -### Suggestion 9 - -**Location**: Section 3.2 -**Category**: cultural_sensitivity -**Focus**: cultural_sensitivity - -**Original Text**: -> The discussion of merit and luck references 'modern societies' and 'Western populations' without acknowledging cultural diversity - -**Improved Version**: -> The discussion of merit and luck considers diverse societal norms across different cultures and regions - -**Explanation**: -Acknowledging cultural diversity enhances cultural sensitivity and reduces ethnocentric bias. - -### Suggestion 10 - -**Location**: Throughout the document -**Category**: identity_language -**Focus**: person_first_language - -**Original Text**: -> The research often uses 'subjects' rather than 'people' or 'participants' - -**Improved Version**: -> individuals or participants in the research - -**Explanation**: -Using 'participants' or 'individuals' respects person-first language and emphasizes agency. - -### Suggestion 11 - -**Location**: Section 3.1.2 -**Category**: geographic_inclusivity -**Focus**: geographic_inclusivity - -**Original Text**: -> The references to 'Western populations' and 'European' or 'North American' samples dominate - -**Improved Version**: -> Research includes diverse populations from multiple regions, including Asia, Africa, and Latin America - -**Explanation**: -Explicitly broadening the scope promotes geographic inclusivity and cultural sensitivity. - -### Suggestion 12 - -**Location**: Section 3.2, paragraph 4 -**Category**: stereotypes -**Focus**: stereotype_avoidance - -**Original Text**: -> The phrase 'people with high education and income' could inadvertently reinforce stereotypes - -**Improved Version**: -> individuals across various educational and income levels - -**Explanation**: -Using inclusive language avoids stereotypes and emphasizes diversity. - -### Suggestion 13 - -**Location**: Throughout the document -**Category**: identity_language -**Focus**: person_first_language - -**Original Text**: -> The paper discusses 'individuals' and 'subjects' rather than persons with identities or lived experiences - -**Improved Version**: -> people with diverse identities and lived experiences - -**Explanation**: -This promotes recognition of personal identity and diversity, enhancing inclusivity. - -### Suggestion 14 - -**Location**: Section 3.1.2 -**Category**: cultural_sensitivity -**Focus**: cultural_sensitivity - -**Original Text**: -> The discussion of 'Western populations' and 'European' or 'North American' samples dominates - -**Improved Version**: -> populations from diverse cultural, geographic, and socioeconomic backgrounds worldwide - -**Explanation**: -This broadens the scope, reduces ethnocentric bias, and promotes global inclusivity. - -### Detailed Feedback -### Gender Neutral Language - -The manuscript predominantly uses terms like 'individual i' and 'subjects' which can be perceived as impersonal or gendered. Replacing these with 'person', 'participant', or 'individual' promotes gender neutrality and respects personhood. For example, instead of 'an individual i has social preferences', use 'a person with social preferences'. This enhances inclusivity by acknowledging diverse gender identities and avoiding implicit biases. - -### Cultural Sensitivity - -The references to 'Western populations' and 'European' or 'North American' samples risk ethnocentrism, implying that findings are universally applicable. To improve cultural sensitivity, explicitly acknowledge the diversity of populations studied, including non-Western societies, and discuss how cultural norms influence social preferences. For example, 'research includes diverse populations from multiple regions, including Asia, Africa, and Latin America, highlighting cultural differences in social preferences.' - -### Age Appropriate Terminology - -The use of 'students' and 'broad population samples' without specifying age ranges can obscure age-related differences in social preferences. Clarify the demographic scope by specifying age ranges or generational cohorts, e.g., 'young adults aged 18-25' or 'adults aged 18-65'. This promotes clarity and inclusivity across age groups. - -### Disability Inclusive Language - -The current text does not explicitly mention or consider individuals with disabilities or differing abilities. Incorporate language that recognizes diverse abilities, such as 'people with disabilities' or 'individuals with varying physical and cognitive abilities'. When discussing experimental participation, specify that accessibility considerations are included to ensure broad inclusivity. - -### Socioeconomic Sensitivity - -Discussions of 'broad population samples' and 'students' often overlook socioeconomic diversity. Explicitly acknowledge socioeconomic variation and its influence on social preferences, e.g., 'samples include individuals from diverse socioeconomic backgrounds, which can influence social preferences and behaviors.' This promotes sensitivity to economic disparities. - -### Geographic Inclusivity - -The focus on 'Western populations' limits the global relevance of findings. Broaden the scope by referencing studies from multiple regions, including Asia, Africa, and Latin America, and discuss how geographic and cultural contexts shape social preferences, e.g., 'including data from non-Western societies enhances understanding of cultural variability in social preferences.' - -### Professional Titles - -The manuscript does not use professional titles, which is appropriate for neutrality. However, in contexts where authors or practitioners are referenced, consider using respectful titles if relevant, e.g., 'Professor', 'Dr.', especially in formal or cross-cultural settings, to show respect. - -### Stereotypes - -Certain phrases, such as 'people with high education and income', may inadvertently reinforce stereotypes linking socioeconomic status with prosociality. Use neutral language like 'individuals across various educational and income levels' to avoid stereotypes and acknowledge diversity. - -### Identity Language - -The predominant use of 'subjects' and 'individuals' rather than person-first language diminishes recognition of personal identity. Rephrasing to 'people with diverse identities' or 'participants' emphasizes personhood and respects individual differences. - -### Historical Context - -Discussions of 'democratic rights' and 'mass movements' lack acknowledgment of historical struggles and diverse cultural narratives. Incorporate historical context that recognizes the varied pathways and cultural histories of democratic development worldwide, e.g., 'acknowledging the diverse historical struggles for democratic rights across different societies enhances cultural sensitivity.' - -### Overall - -While the manuscript demonstrates a strong scientific approach, integrating more inclusive language, acknowledging cultural and demographic diversity, and avoiding stereotypes will significantly enhance its accessibility and relevance across diverse populations. Explicitly addressing these areas will foster a more respectful and globally aware scholarly discourse. - -## W6 - Citation Formatting -### Score: 4/10 - -### Summary -Overall, the document demonstrates a basic level of citation consistency but suffers from significant formatting inconsistencies and incomplete references. Standardizing citation style, ensuring complete bibliographic details, and applying formatting uniformly will substantially improve the professionalism, readability, and scholarly credibility of the work. - -### Critical Remarks -### Remark 1 - -**Category**: in_text_format -**Location**: Section 2, Paragraph 2 -**Issue**: Inconsistent use of parentheses and brackets in in-text citations (e.g., Guth, Schmittberger and Schwarze 1982; Henrich et al. 2001). -**Severity**: medium -**Impact**: Reduces clarity and uniformity, making citations harder to read and follow. - -### Remark 2 - -**Category**: reference_format -**Location**: References list, multiple entries -**Issue**: Inconsistent formatting of author names, with some entries using initials first (e.g., -**Severity**: N/A -**Impact**: N/A - -### Improvement Suggestions -### Suggestion 1 - -**Location**: References list, first entry -**Category**: reference_format -**Focus**: reference_completeness - -**Original Text**: -> Guth, W., R. Schmittberger, and B. Schwarze. "An Experimental-Analysis of Ultimatum Bargaining." Journal of Economic Behavior & Organization 3, no. 4 (1982): 367-88. - -**Improved Version**: -> Guth, W., R. Schmittberger, and B. Schwarze. "An Experimental Analysis of Ultimatum Bargaining." Journal of Economic Behavior & Organization 3, no. 4 (1982): 367–388. - -**Explanation**: -Standardizes journal volume and page formatting, using en-dashes for page ranges, which improves consistency and readability. - -### Suggestion 2 - -**Location**: References list, relevant entry -**Category**: reference_format -**Focus**: volume_format - -**Original Text**: -> Fehr, E., and U. Fischbacher. "Third-Party Punishment and Social Norms." Evolution and Human Behavior 25, no. 2 (2004): 63-87. - -**Improved Version**: -> Fehr, E., and U. Fischbacher. "Third-Party Punishment and Social Norms." Evolution and Human Behavior 25, no. 2 (2004): 63–87. - -**Explanation**: -Uses en-dash for page range, aligning with standard formatting conventions for journal articles. - -### Suggestion 3 - -**Location**: References list, relevant entry -**Category**: reference_format -**Focus**: volume_format - -**Original Text**: -> Kerschbamer, R., and D. Muller. "Social Preferences and Political Attitudes: An Online Experiment on a Large Heterogeneous Sample." Journal of Public Economics 182 (2020). - -**Improved Version**: -> Kerschbamer, R., and D. Muller. "Social Preferences and Political Attitudes: An Online Experiment on a Large Heterogeneous Sample." Journal of Public Economics 182 (2020): 1–15. - -**Explanation**: -Adds volume and page range, completing the citation and ensuring consistency with journal formatting standards. - -### Suggestion 4 - -**Location**: Title and authors, appendix header -**Category**: reference_format -**Focus**: reference_completeness - -**Original Text**: -> Online Appendix for Social Preferences Fundamental Characteristics and Economic Consequences Ernst Fehr & Gary Charness ONLINE APPENDIX - -**Improved Version**: -> Online Appendix for *Social Preferences: Fundamental Characteristics and Economic Consequences*. Ernst Fehr & Gary Charness. ONLINE APPENDIX. - -**Explanation**: -Formats the appendix title in italics, clarifies authorship, and standardizes the presentation for clarity and professionalism. - -### Suggestion 5 - -**Location**: References list, relevant entry -**Category**: reference_format -**Focus**: author_format - -**Original Text**: -> Fehr, E., and J. List. "The Hidden Costs and Returns of Incentives: Trust and Trustworthiness among Ceos." Journal of the European Economic Association 2, no. 5 (2004). - -**Improved Version**: -> Fehr, E., and J. List. "The Hidden Costs and Returns of Incentives: Trust and Trustworthiness among CEOs." Journal of the European Economic Association 2, no. 5 (2004): 791–817. - -**Explanation**: -Corrects the spelling of 'CEOs', adds volume and page range for completeness, and ensures consistent formatting. - -### Suggestion 6 - -**Location**: References list, relevant entry -**Category**: reference_format -**Focus**: volume_format - -**Original Text**: -> Benabou, R., and J. Tirole. "Identity, Morals, and Taboos: Beliefs as Assets." Quarterly Journal of Economics 126, no. 2 (2011): 805-55. - -**Improved Version**: -> Benabou, R., and J. Tirole. "Identity, Morals, and Taboos: Beliefs as Assets." Quarterly Journal of Economics 126, no. 2 (2011): 805–855. - -**Explanation**: -Uses en-dash for page range, aligning with standard conventions for journal articles, enhancing consistency. - -### Suggestion 7 - -**Location**: References list, relevant entry -**Category**: reference_format -**Focus**: volume_format - -**Original Text**: -> Cappelen, A. W., J. List, A. Samek, and B. Tungodden. "The Effect of Early-Childhood Education on Social Preferences." Journal of Political Economy 128, no. 7 (2020): 2739-58. - -**Improved Version**: -> Cappelen, A. W., J. List, A. Samek, and B. Tungodden. "The Effect of Early-Childhood Education on Social Preferences." Journal of Political Economy 128, no. 7 (2020): 2739–2758. - -**Explanation**: -Adds en-dash for page range, ensuring consistency with journal citation standards. - -### Suggestion 8 - -**Location**: References list, relevant entry -**Category**: reference_format -**Focus**: page_range - -**Original Text**: -> Fisman, R., and S. Kariv. "Individual Preferences for Giving." American Economic Review 97, no. 5 (2007): 1858-76. - -**Improved Version**: -> Fisman, R., and S. Kariv. "Individual Preferences for Giving." American Economic Review 97, no. 5 (2007): 1858–1876. - -**Explanation**: -Uses en-dash for page range for consistency and adherence to formatting standards. - -### Suggestion 9 - -**Location**: References list, relevant entry -**Category**: reference_format -**Focus**: page_range - -**Original Text**: -> Snowberg, E., and L. Yariv. "Testing the Waters: Behavior across Participant Pools." American Economic Review 111, no. 2 (2021): 687-719. - -**Improved Version**: -> Snowberg, E., and L. Yariv. "Testing the Waters: Behavior across Participant Pools." American Economic Review 111, no. 2 (2021): 687–719. - -**Explanation**: -Standardizes page range with en-dash, improving uniformity across references. - -### Suggestion 10 - -**Location**: Appendix header -**Category**: reference_format -**Focus**: reference_completeness - -**Original Text**: -> Online Appendix for Social Preferences Fundamental Characteristics and Economic Consequences Ernst Fehr & Gary Charness ONLINE APPENDIX - -**Improved Version**: -> Online Appendix for *Social Preferences: Fundamental Characteristics and Economic Consequences*. Fehr, Ernst, and Gary Charness. ONLINE APPENDIX. - -**Explanation**: -Formats the title in italics, clarifies authorship, and standardizes the presentation for clarity and professionalism. - -### Suggestion 11 - -**Location**: References list, relevant entry -**Category**: reference_format -**Focus**: page_range - -**Original Text**: -> Fehr, E., and U. Fischbacher. "Third-Party Punishment and Social Norms." Evolution and Human Behavior 25, no. 2 (2004): 63-87. - -**Improved Version**: -> Fehr, E., and U. Fischbacher. "Third-Party Punishment and Social Norms." Evolution and Human Behavior 25, no. 2 (2004): 63–87. - -**Explanation**: -Uses en-dash for page range, aligning with standard citation conventions, improving consistency. - -### Detailed Feedback -### In Text Citation Format - -The in-text citations are inconsistent in style; some use parentheses with author-year, others list authors directly, and some omit parentheses altogether. Consistency is crucial for clarity; choose a standard style (e.g., author-year in parentheses) and apply it uniformly throughout the document. - -### Reference List Format - -The reference list exhibits inconsistent formatting: author names are sometimes initials first, sometimes full names; page ranges are often hyphenated rather than en-dashed; journal titles are inconsistently italicized or not; volume, issue, and page details are not uniformly formatted. Adopting a style guide (e.g., APA, Chicago) and applying it consistently will greatly improve professionalism. - -### Citation Style Consistency - -The document mixes different citation styles, which can confuse readers. Establish a single style (e.g., APA or Chicago) and ensure all references and in-text citations conform to it. This includes punctuation, author name formatting, and ordering. - -### Reference Completeness - -Many references lack complete details such as volume, issue, and page ranges. Some entries are missing these elements entirely. Complete references enable readers to locate sources easily; ensure all entries include full bibliographic information. - -### Doi Url Formatting - -DOI links are absent; URLs are not provided where available. Including DOI links (preferably as URLs, e.g., https://doi.org/xxxxx) enhances accessibility and aligns with current standards for digital references. - -### Author Name Formatting - -Author names are inconsistently formatted: some use initials first, others full names; some include middle initials, others do not. Standardize to surname followed by initials or full names, depending on the chosen style guide, to ensure uniformity. - -### Publication Date Formatting - -Dates are inconsistently formatted; some include only year, others include month or issue number. Use a consistent format (e.g., year only for journal articles, or year and month if necessary) across all references. - -### Journal Name Formatting - -Journal titles are inconsistently italicized or not; some abbreviate journal names, others spell them out fully. Decide on a standard (e.g., full journal names italicized) and apply uniformly. - -### Volume Issue Page Formatting - -Volume, issue, and page numbers are inconsistently formatted; some use parentheses, others omit them. Adopt a uniform format, e.g., Volume(issue): pages, with consistent punctuation and spacing. - -### Cross Reference Accuracy - -Cross-references within the text (e.g., section references, figure references) are absent or inconsistent. Ensure all internal references are accurate, correctly numbered, and linked to the corresponding sections or figures for ease of navigation. - -## W7 - Target Audience Alignment -### Score: 0/10 - -### Summary -Overall, the paper is highly suitable for an expert academic audience, with detailed models, empirical data, and comprehensive references. To broaden its impact and accessibility, especially for interdisciplinary or policy audiences, it should incorporate clearer signposting, visual summaries, simplified language in complex sections, and more explicit connections to real-world applications. These enhancements would improve engagement without compromising scholarly rigor. - -### Critical Remarks -### Remark 1 - -**Category**: methodology -**Location**: Section 3.1.2 -**Issue**: The description of the parameter estimation procedures for distributional preferences is highly technical and dense, potentially overwhelming non-specialist readers. -**Severity**: medium -**Impact**: May limit accessibility for readers outside the core experimental economics community, reducing broader engagement. - -### Remark 2 - -**Category**: references -**Location**: Throughout the document -**Issue**: The reference style is inconsistent, with some references in parentheses and others in footnotes or inline citations, and some lacking complete bibliographic details. -**Severity**: high -**Impact**: Could impair clarity and professionalism, making it harder for readers to verify sources and diminishing credibility. - -### Remark 3 - -**Category**: visuals -**Location**: Figures A1a, A1b, and Table A1 -**Issue**: The figures and tables are described textually but lack clear, standalone captions and are not integrated into the main narrative with interpretive commentary. -**Severity**: low -**Impact**: Reduces visual clarity and limits the ability of readers to quickly grasp key data insights. - -### Remark 4 - -**Category**: section organization -**Location**: Overall structure -**Issue**: The appendix sections, while comprehensive, sometimes repeat methodological details from the main text, leading to redundancy and potential confusion about core versus supplementary content. -**Severity**: medium -**Impact**: May hinder smooth navigation and dilute focus on primary findings. - -### Remark 5 - -**Category**: writing style -**Location**: Throughout the document -**Issue**: The language is highly formal and technical, with complex sentences and jargon that could alienate non-specialist readers or early-career researchers. -**Severity**: medium -**Impact**: Limits accessibility and broader dissemination of insights. - -### Remark 6 - -**Category**: conclusion -**Location**: Section 7 -**Issue**: The outlook section is somewhat abstract and broad, lacking specific, actionable research questions or policy implications that could guide readers’ future engagement. -**Severity**: low -**Impact**: Reduces clarity on practical or research-oriented next steps for a wider audience. - -### Remark 7 - -**Category**: results -**Location**: Section 3.1.2 -**Issue**: Results are presented with extensive statistical detail and technical parameters, which may be inaccessible to non-expert readers. -**Severity**: medium -**Impact**: Could hinder comprehension and engagement among interdisciplinary audiences. - -### Remark 8 - -**Category**: discussion -**Location**: Section 6.4 -**Issue**: The discussion of political implications and normative considerations is somewhat cursory and lacks concrete examples or case studies to illustrate real-world relevance. -**Severity**: low -**Impact**: May limit the appeal to policymakers or practitioners seeking applied insights. - -### Remark 9 - -**Category**: visuals -**Location**: Throughout the main text -**Issue**: The main body contains no visual summaries or infographics to synthesize complex theoretical models or empirical findings. -**Severity**: low -**Impact**: Misses opportunity to enhance comprehension and retention for diverse audiences. - -### Remark 10 - -**Category**: references -**Location**: Throughout -**Issue**: The reference list is extensive but sometimes lacks contextual annotation or grouping by themes, which could aid readers in navigating the literature landscape. -**Severity**: low -**Impact**: Reduces navigability for readers interested in specific subfields or themes. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Section 1, paragraph 3 -**Category**: abstract -**Focus**: clarity - -**Original Text**: -> The implementation of incentivized anonymous one-shot experiments is thus a frequently employed conservative strategy that makes sense, but it is also important to keep in mind that important aspects of social relations, and their influence on social preferences, may be lost in these experiments (Frohlich, Oppenheimer and Kurk 2004). - -**Improved Version**: -> While incentivized anonymous one-shot experiments are a common and valuable approach, it is important to recognize that they may overlook some aspects of real social interactions, such as repeated exchanges and reputation effects (Frohlich, Oppenheimer, and Kurk 2004). - -**Explanation**: -Simplifies the language for clarity and emphasizes the practical implications, making the point accessible to a broader audience. - -### Suggestion 2 - -**Location**: Section 3.1.1 -**Category**: methodology -**Focus**: clarity - -**Original Text**: -> Theories of social preferences differ in how exactly 𝜋௝ affects player i’s utility – for example through social comparisons between 𝜋௜ and 𝜋௝ or through the perceived kindness associated with particular strategies that generate 𝜋௝; however, they all involve the assumption that other players’ material payoffs enter i’s utility function in some way. - -**Improved Version**: -> Different theories of social preferences specify how others’ payoffs influence individual utility—for instance, via social comparisons or perceptions of kindness—but all share the core assumption that others’ material payoffs impact utility. - -**Explanation**: -Streamlines the sentence for better readability and emphasizes the commonality across theories, aiding comprehension for a diverse audience. - -### Suggestion 3 - -**Location**: Appendix 1, figures -**Category**: visuals -**Focus**: clarity - -**Original Text**: -> Figures A1a and A1b show the estimated weights on self-payoff and efficiency concerns, respectively, but lack detailed captions or interpretive guidance. - -**Improved Version**: -> Figures A1a and A1b illustrate the estimated distribution of individuals’ preferences regarding self-payoff weight (α’) and efficiency concerns (ρ). These figures highlight the variation across populations, with higher α’ indicating more self-centeredness and higher ρ reflecting less concern for equality. - -**Explanation**: -Provides context and interpretive guidance, making the visuals more meaningful and accessible to non-expert readers. - -### Suggestion 4 - -**Location**: Overall structure -**Category**: organization -**Focus**: organization - -**Original Text**: -> The appendix sections, while comprehensive, sometimes repeat methodological details from the main text, leading to redundancy and potential confusion. - -**Improved Version**: -> To enhance clarity, the appendix should focus on supplementary details and avoid repeating core methodological descriptions already presented in the main text, instead directing readers to key differences or additional data. - -**Explanation**: -Encourages better structural organization, reducing redundancy and improving navigability for readers seeking specific information. - -### Suggestion 5 - -**Location**: Throughout the document -**Category**: writing style -**Focus**: clarity - -**Original Text**: -> The language is highly formal and technical, with complex sentences and jargon that could alienate non-specialist readers or early-career researchers. - -**Improved Version**: -> The writing style could be made more accessible by simplifying complex sentences, reducing jargon where possible, and including brief explanations of technical terms for readers new to the field. - -**Explanation**: -Improves readability and broadens audience engagement without sacrificing technical accuracy. - -### Suggestion 6 - -**Location**: Section 7 -**Category**: conclusion -**Focus**: organization - -**Original Text**: -> The conclusion section is somewhat abstract and broad, lacking specific, actionable research questions or policy implications. - -**Improved Version**: -> The conclusion should explicitly outline concrete research questions and policy implications, such as how future studies can better measure social preferences or how policymakers can incorporate these insights into social programs. - -**Explanation**: -Provides clear guidance and relevance, increasing practical engagement for researchers and policymakers. - -### Suggestion 7 - -**Location**: Section 3.1.2 -**Category**: results -**Focus**: visuals - -**Original Text**: -> Results are presented with extensive statistical detail and technical parameters, which may be inaccessible to non-expert readers. - -**Improved Version**: -> Results could be summarized with key findings highlighted in plain language, supplemented by simplified tables or visual summaries that distill complex data into accessible insights. - -**Explanation**: -Enhances comprehension and appeal to interdisciplinary audiences, facilitating broader dissemination. - -### Suggestion 8 - -**Location**: Section 6.4 -**Category**: discussion -**Focus**: depth - -**Original Text**: -> The discussion of political implications and normative considerations is somewhat cursory and lacks concrete examples or case studies. - -**Improved Version**: -> The discussion could be enriched by including specific case studies or recent policy examples illustrating how social preferences influence redistribution policies or protest movements. - -**Explanation**: -Provides tangible relevance, engaging policymakers and practitioners more effectively. - -### Suggestion 9 - -**Location**: Throughout the main text -**Category**: visuals -**Focus**: organization - -**Original Text**: -> The main body contains no visual summaries or infographics to synthesize complex models or empirical findings. - -**Improved Version**: -> Incorporating visual summaries, such as flowcharts of models or infographics of empirical results, would aid in synthesizing complex information and improving reader retention. - -**Explanation**: -Visual aids enhance understanding, especially for complex theoretical or empirical content. - -### Suggestion 10 - -**Location**: N/A -**Category**: N/A -**Focus**: N/A - -**Original Text**: -> The reference list is extensive but sometimes lacks contextual annotation or thematic grouping. - -**Improved Version**: -> Adding thematic annotations or grouping references by themes (e.g., experimental methods, social preferences, policy implications) would improve navigability and help readers locate relevant literature efficiently. - -**Explanation**: -Facilitates targeted literature review and enhances scholarly utility. - -### Detailed Feedback -### Technical Depth - -The paper demonstrates a high level of technical depth appropriate for an expert audience, with detailed models, parameter estimates, and empirical methods. However, some sections, especially those describing estimation procedures and statistical results, could be simplified or summarized for broader accessibility without losing core insights. - -### Terminology Usage - -The use of field-specific terminology (e.g., 'distributional preferences,' 'inequality aversion,' 'CES utility functions') is consistent and precise, but at times dense. Including brief definitions or intuitive explanations for complex terms would help non-specialists and early-career researchers better grasp the concepts. - -### Writing Formality - -The writing style is formal and academic, suitable for a scholarly journal. To expand audience reach, occasional simplification of language and clearer signposting of key points would be beneficial, especially in sections dense with technical detail. - -### Section Organization - -The structure, with main sections and appendices, is logical and comprehensive. Nonetheless, some redundancy between main and appendix sections could be minimized. Clearer signposting of how appendix content complements the main text would improve navigability. - -### Visual Integration - -The document relies heavily on textual descriptions of data and models, with minimal visual summaries. Adding well-designed figures, flowcharts, or infographics would significantly enhance comprehension, especially for complex models and empirical results. - -### Reference Style - -References are numerous and detailed but inconsistent in formatting and presentation. Standardizing citation style and adding brief annotations or thematic groupings would improve clarity and scholarly professionalism. - -### Methodology Detail - -The methodology descriptions are thorough and appropriate for an expert audience. For broader accessibility, summarizing key methodological steps and emphasizing their significance would help non-specialists understand the core approaches. - -### Results Presentation - -Results are presented with extensive statistical detail, which may overwhelm non-expert readers. Summarizing key findings in accessible language, supported by visual summaries, would improve engagement. - -### Discussion Depth - -The discussion sections are comprehensive but sometimes lack concrete examples or policy relevance. Incorporating case studies or real-world applications would deepen engagement and practical understanding. - -### Conclusion Format - -The outlook provides a broad overview of future research directions but could benefit from more specific, actionable questions or policy implications to guide readers’ future work. - -## W8 - Visual Presentation -### Score: 2/10 - -### Summary -The manuscript currently lacks any visual presentation, severely limiting its clarity, engagement, and data communication effectiveness. Incorporating high-quality, well-placed figures and tables with complete captions, consistent style, and accessible design would greatly enhance its readability and impact. Visual elements are essential for illustrating complex models, empirical distributions, and data patterns, making the research more accessible and compelling for readers. - -### Critical Remarks -### Remark 1 - -**Category**: Figures -**Location**: Figures Information: [] -**Issue**: No figures are provided, resulting in a complete absence of visual data representations. -**Severity**: high -**Impact**: Significantly limits visual engagement and hampers quick comprehension of complex data, reducing overall clarity. - -### Remark 2 - -**Category**: Tables -**Location**: Tables Information: [] -**Issue**: No tables are included, missing opportunities for structured data presentation. -**Severity**: high -**Impact**: Prevents effective summarization and comparison of empirical data, making detailed information harder to access visually. - -### Remark 3 - -**Category**: Visual Element Placement -**Location**: Entire manuscript -**Issue**: Lack of visual elements means no placement or integration issues. -**Severity**: high -**Impact**: Reduces visual hierarchy and flow, making the text dense and less engaging. - -### Remark 4 - -**Category**: Caption Completeness -**Location**: None -**Issue**: No visual elements are captioned, which diminishes interpretability. -**Severity**: high -**Impact**: Readers lack contextual explanations, reducing clarity and understanding of data or diagrams. - -### Remark 5 - -**Category**: Color Scheme -**Location**: None -**Issue**: No visual elements mean no color scheme to evaluate. -**Severity**: low -**Impact**: Not applicable due to absence of visuals. - -### Remark 6 - -**Category**: Data Visualization Effectiveness -**Location**: None -**Issue**: Absence of charts or graphs limits data comprehension and quick insights. -**Severity**: high -**Impact**: Readers cannot easily grasp complex relationships or distributions, impairing overall communication. - -### Remark 7 - -**Category**: Visual Hierarchy -**Location**: Entire manuscript -**Issue**: No visual cues or elements to establish hierarchy or guide reader focus. -**Severity**: high -**Impact**: Text appears uniform and dense, making it difficult to prioritize key information. - -### Remark 8 - -**Category**: Accessibility -**Location**: None -**Issue**: No visual elements to evaluate for contrast or readability. -**Severity**: low -**Impact**: Not applicable; however, the lack of visuals misses an opportunity for inclusive design. - -### Remark 9 - -**Category**: Visual Consistency -**Location**: Entire manuscript -**Issue**: No visual style or formatting elements are present to ensure consistency. -**Severity**: high -**Impact**: The manuscript relies solely on text, which may lead to a monotonous presentation and reduced engagement. - -### Remark 10 - -**Category**: Text Integration -**Location**: Entire manuscript -**Issue**: No visual elements support or complement the textual content. -**Severity**: high -**Impact**: Limits the ability to reinforce key points visually, reducing overall clarity and retention. - -### Improvement Suggestions -### Suggestion 1 - -**Location**: Throughout the manuscript, especially in sections discussing empirical data and models -**Category**: Figures -**Focus**: Data visualization and clarity - -**Original Text**: -> Figures Information: [] - -**Improved Version**: -> Include at least 3-5 well-designed figures illustrating key empirical patterns, distributions, or theoretical models. - -**Explanation**: -Visuals can enhance understanding, provide quick insights, and break up dense text, improving engagement. - -### Suggestion 2 - -**Location**: Sections presenting empirical results and data summaries -**Category**: Tables -**Focus**: Data clarity and structure - -**Original Text**: -> Tables Information: [] - -**Improved Version**: -> Add comprehensive tables summarizing key empirical results, parameter estimates, and preference distributions with clear labels and hierarchical structuring. - -**Explanation**: -Structured tables facilitate comparison, highlight key data points, and improve readability. - -### Suggestion 3 - -**Location**: All visual elements once added -**Category**: Visual style -**Focus**: Consistency and aesthetics - -**Original Text**: -> None - -**Improved Version**: -> Use color coding and consistent formatting for all visual elements to establish a cohesive visual style. - -**Explanation**: -Consistent style and color schemes improve visual harmony and aid navigation. - -### Suggestion 4 - -**Location**: All figures and tables -**Category**: Captions -**Focus**: Clarity and completeness - -**Original Text**: -> None - -**Improved Version**: -> Add detailed, descriptive captions for each figure and table explaining their content, purpose, and key takeaways. - -**Explanation**: -Complete captions improve interpretability, especially for complex visuals or data summaries. - -### Suggestion 5 - -**Location**: All visual elements -**Category**: Figure quality -**Focus**: Resolution and clarity - -**Original Text**: -> None - -**Improved Version**: -> Design all visuals with high resolution and clarity, ensuring they are legible when printed or viewed on screens. - -**Explanation**: -High-quality visuals prevent pixelation and ensure data details are accessible. - -### Detailed Feedback -### Figure Quality - -Not applicable currently; future figures should be high-resolution, clear, and directly relevant. - -### Table Formatting - -Tables should use consistent font sizes, clear headers, and logical grouping to enhance readability. - -### Visual Placement - -Visuals should be integrated close to relevant textual discussions to support comprehension. - -### Caption Completeness - -Captions need to fully describe the visual content, purpose, and key messages. - -### Color Scheme - -Use contrasting colors for different data series or categories, ensuring accessibility for color-blind readers. - -### Data Visualization - -Effective use of bar charts, scatter plots, or distribution curves can significantly improve data interpretation. - -### Visual Hierarchy - -Implement size, bolding, and positioning to guide the reader’s focus toward key findings. - -### Accessibility - -Ensure all visuals meet contrast standards and include alternative text descriptions for screen readers. - -### Visual Consistency - -Adopt a uniform style, font, and color palette across all visuals for professional coherence. - -### Text Integration - -Visuals should complement and reinforce the text, not replace or distract from it. \ No newline at end of file diff --git a/Backup/V5_multi_agent2/results/manuscript_report.pdf b/Backup/V5_multi_agent2/results/manuscript_report.pdf deleted file mode 100644 index 69a1ea9a8536b493bbc9db5ed8897069027a7c99..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 240000 zcmbTcV{k7~xAhy_wrwZ>*tTukPIheDwv!#(Hg{}$$G-b{>sGzz)IA?g-EXUAcg@w+ 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'langchain-community>=0.0.10', - 'typing-extensions>=4.0.0', - 'requests>=2.31.0', - 'python-json-logger>=2.0.0', - 'nougat-ocr>=0.1.0', - 'pdf2image>=1.16.3', - 'pydantic>=2.0.0', - 'pytest>=7.0.0', - 'tqdm>=4.65.0', - 'numpy>=1.24.0', - 'pandas>=2.0.0' - ], -) \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/__init__.py b/Backup/V5_multi_agent2/src/__init__.py deleted file mode 100644 index 21bdfe4..0000000 --- a/Backup/V5_multi_agent2/src/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -This module contains the manuscript reviewer system. -""" \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/core/base_agent.py b/Backup/V5_multi_agent2/src/core/base_agent.py deleted file mode 100644 index c857055..0000000 --- a/Backup/V5_multi_agent2/src/core/base_agent.py +++ /dev/null @@ -1,151 +0,0 @@ -from typing import Dict, Any, List -import json -import os -from datetime import datetime -from openai import OpenAI -from dotenv import load_dotenv -from .config import REPORT_TEMPLATE, DEFAULT_MODEL -import sys -sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) - -class BaseReviewerAgent: - """Base class for all reviewer agents.""" - - def __init__(self, model: str = "gpt-4.1-nano"): - """ - Initialize the base reviewer agent. - - Args: - model (str): The language model to use - name (str): Name of the agent - category (str): Category of the agent (scientific_rigor) - """ - self.model = model - self.name = "Base_Reviewer_Agent" - self.category = "Scientific Rigor" - - # Initialize OpenAI client with API key from environment - api_key = os.getenv("OPENAI_API_KEY") - if not api_key: - raise ValueError(f"OPENAI_API_KEY environment variable not set. Please check {env_path}") - - # Print debug info - print(f"Using model: {model}") - print(f"API key found: {'Yes' if api_key else 'No'}") - - self.client = OpenAI(api_key=api_key) - - def llm(self, prompt: str) -> str: - """Call OpenAI API with the given prompt.""" - try: - response = self.client.chat.completions.create( - model=self.model, - messages=[ - {"role": "system", "content": "You are an expert academic reviewer. Provide detailed analysis in JSON format."}, - {"role": "user", "content": prompt} - ], - temperature=0.3, - response_format={"type": "json_object"} - ) - return response.choices[0].message.content - except Exception as e: - raise Exception(f"Error calling language model: {str(e)}") - - def create_report_template(self) -> Dict[str, Any]: - """Create a new report template.""" - template = REPORT_TEMPLATE.copy() - template["metadata"].update({ - "agent_name": self.name, - "category": self.category, - "model_used": self.model, - "timestamp": datetime.now().isoformat() - }) - return template - - def analyze_section(self, text: str, section_name: str) -> Dict[str, Any]: - """Analyze a specific section of the manuscript. - - Args: - text (str): Text content to analyze - section_name (str): Name of the section being analyzed - - Returns: - Dict[str, Any]: Analysis results - """ - prompt = f"""As a {self.name}, analyze the following {section_name} section: - -{text} - -Provide your analysis in the following JSON format: -{{ - "score": <1-10>, - "remarks": [ - "List of specific issues, questions, or observations" - ], - "concrete_suggestions": [ - "List of actionable steps for improvement" - ], - "automated_improvements": [ - "List of AI-generated improvements" - ] -}} - -Ensure your response is valid JSON and includes all required fields.""" - - try: - response = self.client.chat.completions.create( - model=self.model, - messages=[ - {"role": "system", "content": f"You are a {self.name} reviewer."}, - {"role": "user", "content": prompt} - ], - temperature=0.7 - ) - - # Extract JSON from response - content = response.choices[0].message.content - start_idx = content.find('{') - end_idx = content.rfind('}') + 1 - if start_idx >= 0 and end_idx > start_idx: - analysis = json.loads(content[start_idx:end_idx]) - else: - raise ValueError("No JSON found in response") - - # Create report with analysis - report = self.create_report_template() - report.update(analysis) - - return report - - except Exception as e: - print(f"Error analyzing section: {e}") - return self.create_report_template() - - def save_report(self, report: Dict[str, Any], output_path: str) -> None: - """Save the report to a file. - - Args: - report (Dict[str, Any]): Report to save - output_path (str): Path to save the report - """ - try: - with open(output_path, 'w') as f: - json.dump(report, f, indent=2) - except Exception as e: - print(f"Error saving report: {e}") - - def load_report(self, input_path: str) -> Dict[str, Any]: - """Load a report from a file. - - Args: - input_path (str): Path to load the report from - - Returns: - Dict[str, Any]: Loaded report - """ - try: - with open(input_path, 'r') as f: - return json.load(f) - except Exception as e: - print(f"Error loading report: {e}") - return self.create_report_template() \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/core/config.py b/Backup/V5_multi_agent2/src/core/config.py deleted file mode 100644 index d66f8b7..0000000 --- a/Backup/V5_multi_agent2/src/core/config.py +++ /dev/null @@ -1,318 +0,0 @@ -from typing import Dict, Any -import os -from dotenv import load_dotenv - -# Load environment variables -load_dotenv() - -# OpenAI API Configuration -OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") -if not OPENAI_API_KEY: - raise ValueError("OPENAI_API_KEY environment variable is not set") -DEFAULT_MODEL = os.getenv("DEFAULT_MODEL", "gpt-4.1-nano") - -# Agent configurations -AGENT_CONFIGS = { - "scientific_rigor": [ - { - "name": "R1_Methodology_Agent", - "category": "Scientific Rigor", - "expertise": ["Research Design", "Methodology", "Experimental Setup"], - "focus_areas": [ - "Methodology robustness", - "Experimental design", - "Control conditions", - "Sample size justification", - "Data collection procedures" - ] - }, - { - "name": "R2_Impact_Significance_Agent", - "category": "Scientific Rigor", - "expertise": ["Research Impact", "Scientific Significance", "Field Contribution"], - "focus_areas": [ - "Scientific impact", - "Field contribution", - "Practical implications", - "Future research directions" - ] - }, - { - "name": "R3_Ethics_Compliance_Agent", - "category": "Scientific Rigor", - "expertise": ["Research Ethics", "Compliance", "Data Protection"], - "focus_areas": [ - "Ethical considerations", - "Conflict of interest", - "Data privacy", - "Informed consent", - "Research integrity" - ] - }, - { - "name": "R4_Data_Code_Availability_Agent", - "category": "Scientific Rigor", - "expertise": ["Data Management", "Code Availability", "Reproducibility"], - "focus_areas": [ - "Data availability", - "Code sharing", - "Documentation", - "Reproducibility", - "Access restrictions" - ] - }, - { - "name": "R5_Statistical_Rigor_Agent", - "category": "Scientific Rigor", - "expertise": ["Statistical Analysis", "Data Validation", "Statistical Methods"], - "focus_areas": [ - "Statistical methods", - "Data validation", - "Statistical significance", - "Error analysis", - "Statistical reporting" - ] - }, - { - "name": "R6_Technical_Accuracy_Agent", - "category": "Scientific Rigor", - "expertise": ["Technical Content", "Mathematical Rigor", "Algorithm Analysis"], - "focus_areas": [ - "Technical accuracy", - "Mathematical correctness", - "Algorithm validation", - "Technical clarity", - "Implementation details" - ] - }, - { - "name": "R7_Consistency_Agent", - "category": "Scientific Rigor", - "expertise": ["Logical Coherence", "Cross-section Analysis", "Consistency Checking"], - "focus_areas": [ - "Logical coherence", - "Cross-section consistency", - "Terminology consistency", - "Results alignment", - "Conclusion support" - ] - } - ], - "writing_presentation": [ - { - "name": "W1_Language_Style_Agent", - "category": "Writing and Presentation", - "expertise": ["Grammar", "Spelling", "Punctuation"], - "focus_areas": [ - "Grammar correctness", - "Spelling accuracy", - "Punctuation usage", - "Sentence structure", - "Academic writing conventions" - ] - }, - { - "name": "W2_Narrative_Structure_Agent", - "category": "Writing and Presentation", - "expertise": ["Narrative Flow", "Structural Organization", "Logical Progression"], - "focus_areas": [ - "Narrative coherence", - "Logical progression", - "Section transitions", - "Paragraph organization", - "Reader engagement" - ] - }, - { - "name": "W3_Clarity_Conciseness_Agent", - "category": "Writing and Presentation", - "expertise": ["Language Simplicity", "Jargon Reduction", "Conciseness"], - "focus_areas": [ - "Language simplicity", - "Jargon usage", - "Wordiness", - "Readability", - "Information density" - ] - }, - { - "name": "W4_Terminology_Consistency_Agent", - "category": "Writing and Presentation", - "expertise": ["Terminology Consistency", "Notation Standards", "Acronym Usage"], - "focus_areas": [ - "Term usage consistency", - "Notation consistency", - "Acronym usage", - "Variable naming", - "Definition consistency" - ] - } - ] -} - -# Review criteria -REVIEW_CRITERIA = { - "scientific_rigor": { - "methodology": { - "weight": 0.15, - "criteria": [ - "Research design appropriateness", - "Methodology robustness", - "Experimental setup completeness", - "Control conditions adequacy", - "Sample size justification" - ] - }, - "impact": { - "weight": 0.15, - "criteria": [ - "Scientific significance", - "Field contribution", - "Practical implications", - "Future research potential" - ] - }, - "ethics": { - "weight": 0.15, - "criteria": [ - "Ethical considerations", - "Conflict of interest disclosure", - "Data privacy protection", - "Informed consent procedures", - "Research integrity" - ] - }, - "data_code": { - "weight": 0.15, - "criteria": [ - "Data availability", - "Code sharing", - "Documentation completeness", - "Reproducibility", - "Access restrictions justification" - ] - }, - "statistics": { - "weight": 0.15, - "criteria": [ - "Statistical methods appropriateness", - "Data validation", - "Statistical significance", - "Error analysis", - "Statistical reporting" - ] - }, - "technical": { - "weight": 0.15, - "criteria": [ - "Technical accuracy", - "Mathematical correctness", - "Algorithm validation", - "Technical clarity", - "Implementation details" - ] - }, - "consistency": { - "weight": 0.10, - "criteria": [ - "Logical coherence", - "Cross-section consistency", - "Terminology consistency", - "Results alignment", - "Conclusion support" - ] - } - }, - "writing_presentation": { - "language_style": { - "weight": 0.25, - "criteria": [ - "Grammar correctness", - "Spelling accuracy", - "Punctuation usage", - "Sentence structure", - "Academic writing conventions" - ] - }, - "narrative_structure": { - "weight": 0.25, - "criteria": [ - "Narrative coherence", - "Logical progression", - "Section transitions", - "Paragraph organization", - "Reader engagement" - ] - }, - "clarity_conciseness": { - "weight": 0.25, - "criteria": [ - "Language simplicity", - "Jargon usage", - "Wordiness", - "Readability", - "Information density" - ] - }, - "terminology_consistency": { - "weight": 0.25, - "criteria": [ - "Term usage consistency", - "Notation consistency", - "Acronym usage", - "Variable naming", - "Definition consistency" - ] - } - } -} - -# Report template -REPORT_TEMPLATE = { - "metadata": { - "timestamp": "", - "agent_name": "", - "category": "", - "model_used": "", - "version": "1.0" - }, - "analysis": { - "overall_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": {}, - "summary": "" - } -} - -# Controller Agent Configuration -CONTROLLER_CONFIG = { - "review_steps": [ - "initial_analysis", - "agent_review_comparison", - "remark_ranking", - "report_generation" - ], - "output_formats": ["json", "text"], - "score_range": (1, 10) -} - -# PDF Processing Configuration -PDF_PROCESSOR_CONFIG = { - "supported_formats": ["pdf"], - "max_pages": 100, - "image_quality": "high", - "ocr_enabled": True -} - -# File Paths -PATHS = { - "manuscripts": "manuscripts/", - "results": "results/", - "tests": "tests/", - "logs": "logs/" -} - -# Create directories if they don't exist -for path in PATHS.values(): - os.makedirs(path, exist_ok=True) \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/core/report_template.py b/Backup/V5_multi_agent2/src/core/report_template.py deleted file mode 100644 index 0ef68e6..0000000 --- a/Backup/V5_multi_agent2/src/core/report_template.py +++ /dev/null @@ -1,124 +0,0 @@ -from typing import Dict, Any, List -import json -from datetime import datetime - -class ReportTemplate: - """Template for generating standardized review reports.""" - - def __init__(self): - """Initialize the report template.""" - self.template = { - "metadata": { - "timestamp": datetime.now().isoformat(), - "agent_name": "", - "category": "", - "model_used": "", - "version": "1.0" - }, - "analysis": { - "overall_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": {}, - "summary": "" - } - } - - def generate_report(self, agent_name: str, category: str, model: str, - analysis_results: Dict[str, Any], output_path: str) -> None: - """ - Generate a standardized review report. - - Args: - agent_name (str): Name of the agent - category (str): Category of the agent - model (str): Model used for analysis - analysis_results (Dict[str, Any]): Results of the analysis - output_path (str): Path to save the report - """ - report = self.template.copy() - report["metadata"].update({ - "agent_name": agent_name, - "category": category, - "model_used": model - }) - report["analysis"].update(analysis_results) - - with open(output_path, 'w') as f: - f.write(f"Review Report - {agent_name}\n") - f.write("=" * (len(agent_name) + 13) + "\n\n") - - f.write("Metadata\n") - f.write("-" * 8 + "\n") - f.write(json.dumps(report["metadata"], indent=2)) - f.write("\n\n") - - f.write("Analysis Results\n") - f.write("-" * 16 + "\n") - f.write(json.dumps(report["analysis"], indent=2)) - f.write("\n") - - @staticmethod - def create_template(agent_name: str, section_name: str) -> Dict[str, Any]: - """Create a standardized report template. - - Args: - agent_name (str): Name of the reviewer agent - section_name (str): Name of the section being reviewed - - Returns: - Dict[str, Any]: Standardized report template - """ - return { - "agent_name": agent_name, - "section_name": section_name, - "timestamp": datetime.now().isoformat(), - "score": None, # 1-10 score - "remarks": [], # List of issues, questions, or observations - "concrete_suggestions": [], # Actionable steps for each remark - "automated_suggestions": [], # AI-generated improvements - "section_specific_analysis": {} # Additional section-specific analysis - } - - @staticmethod - def save_report(report: Dict[str, Any], output_dir: str) -> str: - """Save the report to a text file. - - Args: - report (Dict[str, Any]): The report to save - output_dir (str): Directory to save the report in - - Returns: - str: Path to the saved report file - """ - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - filename = f"{report['agent_name'].lower().replace(' ', '_')}_{timestamp}.txt" - filepath = f"{output_dir}/{filename}" - - with open(filepath, 'w') as f: - f.write(f"Review Report - {report['agent_name']}\n") - f.write(f"Section: {report['section_name']}\n") - f.write(f"Timestamp: {report['timestamp']}\n\n") - - f.write(f"Score: {report['score']}/10\n\n") - - f.write("Remarks:\n") - for remark in report['remarks']: - f.write(f"- {remark}\n") - f.write("\n") - - f.write("Concrete Suggestions:\n") - for suggestion in report['concrete_suggestions']: - f.write(f"- {suggestion}\n") - f.write("\n") - - f.write("Automated Suggestions:\n") - for suggestion in report['automated_suggestions']: - f.write(f"- {suggestion}\n") - f.write("\n") - - if report['section_specific_analysis']: - f.write("Section-Specific Analysis:\n") - f.write(json.dumps(report['section_specific_analysis'], indent=2)) - - return filepath \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/__init__.py b/Backup/V5_multi_agent2/src/reviewer_agents/__init__.py deleted file mode 100644 index ffe8f22..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -This package contains the reviewer agents for manuscript analysis. -""" \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/controller_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/controller_agent.py deleted file mode 100644 index 991f6ea..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/controller_agent.py +++ /dev/null @@ -1,148 +0,0 @@ -from typing import Dict, Any, List -import json -import os -from datetime import datetime -import sys -sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) - -from ..core.base_agent import BaseReviewerAgent -from ..core.report_template import ReportTemplate - -# Section agents -from .section.S1_title_keywords_agent import TitleKeywordsAgentS1 -from .section.S2_abstract_agent import AbstractAgentS2 -from .section.S3_introduction_agent import IntroductionAgentS3 -from .section.S4_literature_review_agent import LiteratureReviewAgentS4 -from .section.S5_methodology_agent import MethodologyAgentS5 -from .section.S6_results_agent import ResultsAgentS6 -from .section.S7_discussion_agent import DiscussionAgentS7 -from .section.S8_conclusion_agent import ConclusionAgentS8 -from .section.S9_references_agent import ReferencesAgentS9 -from .section.S10_supplementary_materials_agent import SupplementaryMaterialsAgentS10 - -# Rigor agents -from .rigor.R1_originality_contribution_agent import OriginalityContributionAgent -from .rigor.R2_impact_significance_agent import ImpactSignificanceAgent -from .rigor.R3_ethics_compliance_agent import EthicsComplianceAgent -from .rigor.R4_data_code_availability_agent import DataCodeAvailabilityAgent -from .rigor.R5_statistical_rigor_agent import StatisticalRigorAgent -from .rigor.R6_technical_accuracy_agent import TechnicalAccuracyAgent -from .rigor.R7_consistency_agent import ConsistencyAgent - -# Writing agents -from .writing.W1_language_style_agent import LanguageStyleAgent -from .writing.W2_narrative_structure_agent import NarrativeStructureAgent -from .writing.W3_clarity_conciseness_agent import ClarityConcisenessAgent -from .writing.W4_terminology_consistency_agent import TerminologyConsistencyAgent -from .writing.W5_inclusive_language_agent import InclusiveLanguageAgent -from .writing.W6_citation_formatting_agent import CitationFormattingAgent -from .writing.W7_target_audience_agent import TargetAudienceAlignmentAgent -from .writing.W8_visual_presentation_agent import VisualPresentationAgentW8 - -class ControllerAgent: - """Controller agent that coordinates all reviewer agents.""" - - def __init__(self, model="gpt-4.1-nano"): - self.model = model - self.agents = { - # Section agents - 'S1': TitleKeywordsAgentS1(model), - 'S2': AbstractAgentS2(model), - 'S3': IntroductionAgentS3(model), - 'S4': LiteratureReviewAgentS4(model), - 'S5': MethodologyAgentS5(model), - 'S6': ResultsAgentS6(model), - 'S7': DiscussionAgentS7(model), - 'S8': ConclusionAgentS8(model), - 'S9': ReferencesAgentS9(model), - 'S10': SupplementaryMaterialsAgentS10(model), - - # Rigor agents - 'R1': OriginalityContributionAgent(model), - 'R2': ImpactSignificanceAgent(model), - 'R3': EthicsComplianceAgent(model), - 'R4': DataCodeAvailabilityAgent(model), - 'R5': StatisticalRigorAgent(model), - 'R6': TechnicalAccuracyAgent(model), - 'R7': ConsistencyAgent(model), - - # Writing agents - 'W1': LanguageStyleAgent(model), - 'W2': NarrativeStructureAgent(model), - 'W3': ClarityConcisenessAgent(model), - 'W4': TerminologyConsistencyAgent(model), - 'W5': InclusiveLanguageAgent(model), - 'W6': CitationFormattingAgent(model), - 'W7': TargetAudienceAlignmentAgent(model), - 'W8': VisualPresentationAgentW8(model) - } - - def run_analysis(self, text: str) -> Dict[str, Any]: - """Runs analyses using all agents.""" - try: - # Determine research type - research_type = self._determine_research_type(text) - - # Run analyses for each agent - results = {} - - # Run section agent analyses - results["S1"] = self.agents["S1"].analyze_title_keywords(text, research_type) - results["S2"] = self.agents["S2"].analyze_abstract(text, research_type) - results["S3"] = self.agents["S3"].analyze_introduction(text, research_type) - results["S4"] = self.agents["S4"].analyze_literature_review(text, research_type) - results["S5"] = self.agents["S5"].analyze_methodology(text, research_type) - results["S6"] = self.agents["S6"].analyze_results(text, research_type) - results["S7"] = self.agents["S7"].analyze_discussion(text, research_type) - results["S8"] = self.agents["S8"].analyze_conclusion(text, research_type) - results["S9"] = self.agents["S9"].analyze_references(text, research_type) - results["S10"] = self.agents["S10"].analyze_supplementary_materials(text, research_type) - - # Run rigor agent analyses - results["R1"] = self.agents["R1"].analyze_originality_contribution(text, research_type) - results["R2"] = self.agents["R2"].analyze_impact_significance(text, research_type) - results["R3"] = self.agents["R3"].analyze_ethics_compliance(text, research_type) - results["R4"] = self.agents["R4"].analyze_data_code_availability(text, research_type) - results["R5"] = self.agents["R5"].analyze_statistical_rigor(text, research_type) - results["R6"] = self.agents["R6"].analyze_technical_accuracy(text, research_type) - results["R7"] = self.agents["R7"].analyze_consistency(text, research_type) - - # Run writing agent analyses - results["W1"] = self.agents["W1"].analyze_language_style(text, research_type) - results["W2"] = self.agents["W2"].analyze_narrative_structure(text, research_type) - results["W3"] = self.agents["W3"].analyze_clarity_conciseness(text, research_type) - results["W4"] = self.agents["W4"].analyze_terminology_consistency(text, research_type) - results["W5"] = self.agents["W5"].analyze_inclusive_language(text, research_type) - results["W6"] = self.agents["W6"].analyze_citation_formatting(text, research_type) - results["W7"] = self.agents["W7"].analyze_target_audience_alignment(text, research_type) - results["W8"] = self.agents["W8"].analyze_visual_presentation(text, research_type) - - return results - except Exception as e: - return self._generate_error_report(f"Error in analysis: {str(e)}") - - def _determine_research_type(self, text: str) -> str: - """Determine the type of research paper.""" - # Simple heuristic based on keywords - text_lower = text.lower() - - if any(word in text_lower for word in ['experiment', 'methodology', 'data collection']): - return 'experimental' - elif any(word in text_lower for word in ['review', 'literature', 'meta-analysis']): - return 'review' - elif any(word in text_lower for word in ['theory', 'framework', 'model']): - return 'theoretical' - else: - return 'general' - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "error": True, - "message": f"Error in analysis: {error_message}", - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": {}, - "summary": f"Analysis failed due to error: {error_message}" - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/report/R1_comprehensive_report_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/report/R1_comprehensive_report_agent.py deleted file mode 100644 index b92e76c..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/report/R1_comprehensive_report_agent.py +++ /dev/null @@ -1,343 +0,0 @@ -from typing import Dict, Any, List -import json -import os -from datetime import datetime -from ...core.base_agent import BaseReviewerAgent -from openai import OpenAI -from docx import Document -from docx.shared import Pt, RGBColor -from docx.enum.text import WD_ALIGN_PARAGRAPH - -class ComprehensiveReportAgent(BaseReviewerAgent): - """Agent responsible for generating a comprehensive report using GPT-4.1.""" - - def __init__(self, model="gpt-4.1"): - super().__init__(model) - self.name = "R1_Comprehensive_Report_Agent" - self.category = "Report Generation" - # Initialize OpenAI - self.client = OpenAI(api_key=os.getenv('OPENAI_API_KEY')) - self.model = model - - def generate_comprehensive_report(self, manuscript_text: str, markdown_report: str) -> Dict[str, Any]: - """Generates a comprehensive report using GPT-4.1.""" - try: - # Split the markdown report into sections - sections = self._split_markdown_into_sections(markdown_report) - - # Process each section separately - analysis = {} - for section_name, section_content in sections.items(): - # Generate prompt for this section - prompt = f"""You are a scientific manuscript reviewer. Your task is to analyze this section of the manuscript and markdown feedback, then provide a structured JSON response with detailed feedback. - -Section: {section_name} - -Manuscript Text: -{manuscript_text[:1000]}... # First 1000 chars for context - -Section Feedback: -{section_content} - -Structure your response as a JSON object with the following format: -{{ - "{section_name}": {{ - "score": 0-10, // Numerical score for this section - "summary": "A concise summary of the key points and recommendations for this section", - "remarks": [ - {{ - "remark": "What could be an issue?", - "original_text": "Citation of the text passage related to the issue", - "improved_version": "New improved text", - "explanation": "How does this new version improve the paper relative to the issue" - }} - ], - "not_applicable": false // Set to true if section doesn't apply - }} -}} - -Important: -1. Respond ONLY with the JSON object -2. Do not include any additional text or explanations -3. Ensure all text fields are properly escaped for JSON -4. Make sure the response is valid JSON that can be parsed -5. Only include genuinely helpful feedback -6. For sections that don't apply, set not_applicable to true -7. Focus on providing clear, actionable feedback without mentioning sources -8. Include numerical scores for each section -9. Extract specific feedback from the markdown report and incorporate it into your response -10. If a section has no feedback in the markdown report, provide your own assessment based on the manuscript -11. Ensure each section has at least one remark if applicable -12. Make sure the feedback is specific and actionable -13. Use the exact scores and feedback from the markdown report when available -14. Maintain the same level of detail and specificity as the markdown report -15. Include all critical remarks and improvement suggestions from the markdown report -""" - - # Generate content with GPT-4.1 - response = self.client.chat.completions.create( - model=self.model, - messages=[ - {"role": "system", "content": "You are a scientific manuscript reviewer. Your task is to analyze the manuscript and markdown feedback, then provide a structured JSON response with detailed feedback for each section."}, - {"role": "user", "content": prompt} - ], - temperature=0.1, # Lower temperature for more focused output - max_tokens=4000, # Increased token limit - top_p=0.8, - frequency_penalty=0.0, - presence_penalty=0.0 - ) - - response_text = response.choices[0].message.content.strip() - - # Try to parse the JSON response - try: - section_analysis = json.loads(response_text) - analysis.update(section_analysis) - except json.JSONDecodeError as e: - # If JSON parsing fails, try to extract JSON from the response - import re - json_match = re.search(r'\{.*\}', response_text, re.DOTALL) - if json_match: - try: - section_analysis = json.loads(json_match.group()) - analysis.update(section_analysis) - except json.JSONDecodeError: - # If still fails, try to clean the response - cleaned_text = re.sub(r'[\x00-\x1f\x7f-\x9f]', '', json_match.group()) - section_analysis = json.loads(cleaned_text) - analysis.update(section_analysis) - else: - print(f"Could not parse JSON for section {section_name}: {response_text}") - continue - - # Validate the analysis structure - required_sections = { - "Section-Specific Agents": [f"S{i}" for i in range(1, 11)], - "Rigor Agents": [f"R{i}" for i in range(1, 8)], - "Writing Agents": [f"W{i}" for i in range(1, 9)] - } - - for section_name, subsections in required_sections.items(): - for subsection in subsections: - if subsection not in analysis: - analysis[subsection] = { - "score": 0, - "summary": "No feedback available for this section.", - "remarks": [], - "not_applicable": True - } - - # Generate Word document report - doc_path = self._generate_word_report(analysis, manuscript_text, markdown_report) - - return { - "report_generation_score": 10, # Assuming successful generation - "doc_path": doc_path, - "analysis": analysis, - "summary": "Successfully generated comprehensive report", - "error": False - } - - except Exception as e: - return self._generate_error_report(f"Error generating comprehensive report: {str(e)}") - - def _split_markdown_into_sections(self, markdown_report: str) -> Dict[str, str]: - """Split the markdown report into sections.""" - sections = {} - - # Define section patterns - section_patterns = { - "Section-Specific Agents": { - "S1": "## S1 - Title and Keywords", - "S2": "## S2 - Abstract", - "S3": "## S3 - Introduction", - "S4": "## S4 - Literature Review", - "S5": "## S5 - Methodology", - "S6": "## S6 - Results", - "S7": "## S7 - Discussion", - "S8": "## S8 - Conclusion", - "S9": "## S9 - References", - "S10": "## S10 - Supplementary Materials" - }, - "Rigor Agents": { - "R1": "## R1 - Originality and Contribution", - "R2": "## R2 - Impact and Significance", - "R3": "## R3 - Ethics and Compliance", - "R4": "## R4 - Data and Code Availability", - "R5": "## R5 - Statistical Rigor", - "R6": "## R6 - Technical Accuracy", - "R7": "## R7 - Consistency" - }, - "Writing Agents": { - "W1": "## W1 - Language and Style", - "W2": "## W2 - Narrative and Structure", - "W3": "## W3 - Clarity and Conciseness", - "W4": "## W4 - Terminology Consistency", - "W5": "## W5 - Inclusive Language", - "W6": "## W6 - Citation Formatting", - "W7": "## W7 - Target Audience Alignment", - "W8": "## W8 - Visual Presentation" - } - } - - # Split the report into sections - lines = markdown_report.split('\n') - current_section = None - current_content = [] - - for line in lines: - # Check if this line starts a new section - found_section = False - for category, patterns in section_patterns.items(): - for section_id, pattern in patterns.items(): - if line.startswith(pattern): - # Save the previous section if it exists - if current_section: - sections[current_section] = '\n'.join(current_content) - # Start a new section - current_section = section_id - current_content = [line] - found_section = True - break - if found_section: - break - - # If not a new section, add to current content - if not found_section and current_section: - current_content.append(line) - - # Save the last section - if current_section: - sections[current_section] = '\n'.join(current_content) - - return sections - - def _generate_word_report(self, analysis: Dict[str, Any], manuscript_text: str, markdown_report: str) -> str: - """Generates a Word document report.""" - # Create output directory if it doesn't exist - output_dir = "results/reports" - os.makedirs(output_dir, exist_ok=True) - - # Generate filename with timestamp - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - doc_path = os.path.join(output_dir, f"comprehensive_report_{timestamp}.docx") - - # Create Word document - doc = Document() - - # Add title - title = doc.add_heading("Comprehensive Manuscript Review Report", 0) - title.alignment = WD_ALIGN_PARAGRAPH.CENTER - - # Add timestamp - timestamp_para = doc.add_paragraph() - timestamp_para.add_run(f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") - doc.add_paragraph() - - # Add overall summary - doc.add_heading("Overall Assessment", 1) - summary_para = doc.add_paragraph() - summary_para.add_run("This report provides a comprehensive review of the manuscript, analyzing its content, structure, and scientific rigor. The assessment is organized into three main categories: Section-Specific Analysis, Rigor Assessment, and Writing Quality Evaluation.") - doc.add_paragraph() - - # Process each section - sections = { - "Section-Specific Analysis": {f"S{i}": analysis.get(f"S{i}", {}) for i in range(1, 11)}, - "Rigor Assessment": {f"R{i}": analysis.get(f"R{i}", {}) for i in range(1, 8)}, - "Writing Quality Evaluation": {f"W{i}": analysis.get(f"W{i}", {}) for i in range(1, 9)} - } - - for section_name, subsections in sections.items(): - # Add section heading - doc.add_heading(section_name, 1) - - # Add section introduction - intro_text = { - "Section-Specific Analysis": "This section provides a detailed analysis of each component of the manuscript, from title to supplementary materials.", - "Rigor Assessment": "This section evaluates the scientific rigor, methodology, and technical aspects of the research.", - "Writing Quality Evaluation": "This section assesses the manuscript's writing quality, clarity, and adherence to academic standards." - } - doc.add_paragraph(intro_text[section_name]) - doc.add_paragraph() - - for subsection_name, subsection_data in subsections.items(): - if subsection_data.get("not_applicable", False): - para = doc.add_paragraph() - para.add_run(f"{subsection_name}: Not applicable").bold = True - continue - - # Add subsection heading - doc.add_heading(subsection_name, 2) - - # Add score with color-coded indicator - if "score" in subsection_data: - score = subsection_data["score"] - score_para = doc.add_paragraph() - score_run = score_para.add_run(f"Score: {score}/10") - score_run.bold = True - # Color code: Red (<5), Yellow (5-7), Green (>7) - if score < 5: - score_run.font.color.rgb = RGBColor(192, 0, 0) # Dark red - elif score < 7: - score_run.font.color.rgb = RGBColor(255, 192, 0) # Orange - else: - score_run.font.color.rgb = RGBColor(0, 176, 80) # Green - - # Add section summary - if "summary" in subsection_data: - summary_para = doc.add_paragraph() - summary_para.add_run("Summary:").bold = True - summary_para.add_run(f"\n{subsection_data['summary']}") - - # Add remarks - remarks = subsection_data.get("remarks", []) - if remarks: - doc.add_heading("Detailed Feedback", 3) - for i, remark in enumerate(remarks, 1): - # Add remark number - remark_para = doc.add_paragraph() - remark_para.add_run(f"Remark {i}").bold = True - - # Add issue - issue_para = doc.add_paragraph() - issue_para.add_run("Issue: ").bold = True - issue_para.add_run(remark["remark"]) - - # Add original text - original_para = doc.add_paragraph() - original_para.add_run("Original Text: ").bold = True - original_para.add_run(remark["original_text"]) - - # Add improved version - improved_para = doc.add_paragraph() - improved_para.add_run("Improved Version: ").bold = True - improved_para.add_run(remark["improved_version"]) - - # Add explanation - explanation_para = doc.add_paragraph() - explanation_para.add_run("Explanation: ").bold = True - explanation_para.add_run(remark["explanation"]) - - doc.add_paragraph() # Add space between remarks - else: - doc.add_paragraph("No specific remarks for this section.") - - doc.add_paragraph() # Add space between subsections - - doc.add_page_break() # Add page break between major sections - - # Save document - doc.save(doc_path) - - return doc_path - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "report_generation_score": 0, - "doc_path": None, - "analysis": None, - "summary": f"Error in report generation: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R1_originality_contribution_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R1_originality_contribution_agent.py deleted file mode 100644 index 9a8c55b..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R1_originality_contribution_agent.py +++ /dev/null @@ -1,93 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class OriginalityContributionAgent(BaseReviewerAgent): - """Agent responsible for assessing research novelty and unique contributions.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "R1_Originality_Contribution_Agent" - self.category = "Scientific Rigor" - - def analyze_originality_contribution(self, text: str, field_context: Dict[str, Any]) -> Dict[str, Any]: - """Analyzes the originality and contribution of the research.""" - prompt = f"""Analyze the following text for originality and contribution to the field. Focus on: - 1. Novelty of the research approach - 2. Unique contributions to the field - 3. Verification of stated novelty claims - 4. Comparison with existing literature - 5. Advancement of knowledge - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Clarity of novelty statement, contribution highlights - - Introduction: Research gap identification, novelty claims - - Literature Review: Comparison with existing work, gap analysis - - Methodology: Novel approach description, innovation details - - Results: Contribution presentation, advancement demonstration - - Discussion: Impact assessment, future implications - - Conclusion: Contribution summary, field advancement - - Text to analyze: {text} - Field context: {json.dumps(field_context, indent=2)} - - Provide a detailed analysis in the following JSON format: - {{ - "originality_contribution_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "novelty", "contribution", "verification", "comparison", "advancement" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects the research validity - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion" - "focus": str # "novelty", "contribution", "verification", "comparison", "advancement" - }}], - - "detailed_feedback": {{ - "novelty_assessment": str, # Detailed paragraph about research novelty - "contribution_analysis": str, # Detailed paragraph about contributions - "verification_status": str, # Detailed paragraph about novelty claims - "comparative_analysis": str, # Detailed paragraph about literature comparison - "advancement_evaluation": str # Detailed paragraph about knowledge advancement - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the research. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing originality and contribution: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "originality_contribution_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "novelty_assessment": "", - "contribution_analysis": "", - "verification_status": "", - "comparative_analysis": "", - "advancement_evaluation": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R2_impact_significance_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R2_impact_significance_agent.py deleted file mode 100644 index 9602d25..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R2_impact_significance_agent.py +++ /dev/null @@ -1,93 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class ImpactSignificanceAgent(BaseReviewerAgent): - """Agent responsible for evaluating research impact and significance.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "R2_Impact_Significance_Agent" - self.category = "Scientific Rigor" - - def analyze_impact_significance(self, text: str, field_context: Dict[str, Any]) -> Dict[str, Any]: - """Analyzes the impact and significance of the research.""" - prompt = f"""Analyze the following text for impact and significance. Focus on: - 1. Potential influence on the field - 2. Broader implications of findings - 3. Influence on future research - 4. Practical applications - 5. Policy implications - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Impact statement, significance highlights - - Introduction: Research importance, field relevance - - Literature Review: Gap impact, field advancement - - Methodology: Innovation potential, scalability - - Results: Key findings impact, practical value - - Discussion: Broader implications, future directions - - Conclusion: Impact summary, application potential - - Text to analyze: {text} - Field context: {json.dumps(field_context, indent=2)} - - Provide a detailed analysis in the following JSON format: - {{ - "impact_significance_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "field_influence", "implications", "future_research", "applications", "policy" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects the research significance - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion" - "focus": str # "field_influence", "implications", "future_research", "applications", "policy" - }}], - - "detailed_feedback": {{ - "field_influence": str, # Detailed paragraph about field influence - "broader_implications": str, # Detailed paragraph about implications - "future_research_impact": str, # Detailed paragraph about future research - "practical_applications": str, # Detailed paragraph about applications - "policy_implications": str # Detailed paragraph about policy - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the research impact and significance. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing impact and significance: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "impact_significance_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "field_influence": "", - "broader_implications": "", - "future_research_impact": "", - "practical_applications": "", - "policy_implications": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R3_ethics_compliance_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R3_ethics_compliance_agent.py deleted file mode 100644 index dab8f7e..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R3_ethics_compliance_agent.py +++ /dev/null @@ -1,94 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class EthicsComplianceAgent(BaseReviewerAgent): - """Agent responsible for reviewing ethical considerations and research standards.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "R3_Ethics_Compliance_Agent" - self.category = "Scientific Rigor" - - def analyze_ethics_compliance(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes ethical considerations and compliance with research standards.""" - prompt = f"""Analyze the following text for ethical considerations and research standards compliance. Focus on: - 1. Conflicts of interest - 2. Data privacy and protection - 3. Informed consent procedures - 4. Research integrity - 5. Adherence to ethical guidelines - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Ethical statement, compliance summary - - Introduction: Research ethics framework, compliance approach - - Methodology: Ethical procedures, consent process - - Data Collection: Privacy measures, protection protocols - - Analysis: Integrity measures, bias prevention - - Results: Ethical presentation, privacy maintenance - - Discussion: Ethical implications, compliance reflection - - Conclusion: Ethical summary, compliance assurance - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "ethics_compliance_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "conflicts", "privacy", "consent", "integrity", "guidelines" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects ethical compliance - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "methodology", "data_collection", "analysis", "results", "discussion", "conclusion" - "focus": str # "conflicts", "privacy", "consent", "integrity", "guidelines" - }}], - - "detailed_feedback": {{ - "conflicts_assessment": str, # Detailed paragraph about conflicts of interest - "privacy_compliance": str, # Detailed paragraph about data privacy - "consent_procedures": str, # Detailed paragraph about informed consent - "research_integrity": str, # Detailed paragraph about research integrity - "guidelines_adherence": str # Detailed paragraph about ethical guidelines - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances ethical compliance and research standards. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing ethics and compliance: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "ethics_compliance_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "conflicts_assessment": "", - "privacy_compliance": "", - "consent_procedures": "", - "research_integrity": "", - "guidelines_adherence": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R4_data_code_availability_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R4_data_code_availability_agent.py deleted file mode 100644 index 74e38fb..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R4_data_code_availability_agent.py +++ /dev/null @@ -1,94 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class DataCodeAvailabilityAgent(BaseReviewerAgent): - """Agent responsible for evaluating data and code availability.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "R4_Data_Code_Availability_Agent" - self.category = "Scientific Rigor" - - def analyze_data_code_availability(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes data and code availability.""" - prompt = f"""Analyze the following text for data and code availability. Focus on: - 1. Data sharing practices - 2. Code repository availability - 3. Documentation completeness - 4. Access restrictions justification - 5. Reproducibility support - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Data/code availability statement - - Introduction: Research transparency approach - - Methodology: Data collection details, code implementation - - Data Description: Dataset structure, access methods - - Code Documentation: Implementation details, usage instructions - - Results: Data presentation, code results - - Discussion: Reproducibility considerations - - Conclusion: Availability summary, access information - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "data_code_availability_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "data_sharing", "code_availability", "documentation", "restrictions", "reproducibility" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects research transparency - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "methodology", "data_description", "code_documentation", "results", "discussion", "conclusion" - "focus": str # "data_sharing", "code_availability", "documentation", "restrictions", "reproducibility" - }}], - - "detailed_feedback": {{ - "data_sharing_assessment": str, # Detailed paragraph about data sharing - "code_availability": str, # Detailed paragraph about code availability - "documentation_completeness": str, # Detailed paragraph about documentation - "restrictions_justification": str, # Detailed paragraph about access restrictions - "reproducibility_support": str # Detailed paragraph about reproducibility - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances data and code availability. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing data and code availability: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "data_code_availability_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "data_sharing_assessment": "", - "code_availability": "", - "documentation_completeness": "", - "restrictions_justification": "", - "reproducibility_support": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R5_statistical_rigor_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R5_statistical_rigor_agent.py deleted file mode 100644 index f458014..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R5_statistical_rigor_agent.py +++ /dev/null @@ -1,109 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class StatisticalRigorAgent(BaseReviewerAgent): - """Agent responsible for evaluating statistical methods appropriateness and correctness.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "R5_Statistical_Rigor_Agent" - self.category = "Scientific Rigor" - - def analyze_statistical_rigor(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes statistical methods appropriateness and correctness.""" - prompt = f"""Analyze the following text for statistical methods appropriateness and correctness. Focus on: - 1. Statistical test selection - 2. Assumption verification - 3. Sample size justification - 4. Multiple comparison handling - 5. Effect size reporting - 6. Confidence intervals - 7. P-value interpretation - 8. Statistical power - 9. Missing data handling - 10. Outlier treatment - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Statistical approach summary - - Introduction: Statistical framework overview - - Methodology: Statistical methods description - - Data Preparation: Assumption checks, data cleaning - - Analysis: Statistical test implementation - - Results: Statistical findings presentation - - Discussion: Statistical interpretation - - Conclusion: Statistical significance summary - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "statistical_rigor_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "test_selection", "assumptions", "sample_size", "multiple_comparisons", "effect_size", "confidence_intervals", "p_value", "power", "missing_data", "outliers" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects statistical validity - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "methodology", "data_preparation", "analysis", "results", "discussion", "conclusion" - "focus": str # "test_selection", "assumptions", "sample_size", "multiple_comparisons", "effect_size", "confidence_intervals", "p_value", "power", "missing_data", "outliers" - }}], - - "detailed_feedback": {{ - "test_selection": str, # Detailed paragraph about statistical test selection - "assumption_verification": str, # Detailed paragraph about assumption verification - "sample_size_justification": str, # Detailed paragraph about sample size - "multiple_comparison_handling": str, # Detailed paragraph about multiple comparisons - "effect_size_reporting": str, # Detailed paragraph about effect size - "confidence_intervals": str, # Detailed paragraph about confidence intervals - "p_value_interpretation": str, # Detailed paragraph about p-value interpretation - "statistical_power": str, # Detailed paragraph about statistical power - "missing_data_handling": str, # Detailed paragraph about missing data - "outlier_treatment": str # Detailed paragraph about outlier treatment - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances statistical rigor. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing statistical rigor: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "statistical_rigor_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "test_selection": "", - "assumption_verification": "", - "sample_size_justification": "", - "multiple_comparison_handling": "", - "effect_size_reporting": "", - "confidence_intervals": "", - "p_value_interpretation": "", - "statistical_power": "", - "missing_data_handling": "", - "outlier_treatment": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R6_technical_accuracy_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R6_technical_accuracy_agent.py deleted file mode 100644 index 58b5746..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R6_technical_accuracy_agent.py +++ /dev/null @@ -1,110 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class TechnicalAccuracyAgent(BaseReviewerAgent): - """Agent responsible for reviewing mathematical derivations, algorithms, and technical content.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "R6_Technical_Accuracy_Agent" - self.category = "Scientific Rigor" - - def analyze_technical_accuracy(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes mathematical derivations, algorithms, and technical content.""" - prompt = f"""Analyze the following text for technical accuracy. Focus on: - 1. Mathematical derivation correctness - 2. Algorithm correctness and efficiency - 3. Technical terminology accuracy - 4. Equation clarity and presentation - 5. Technical content completeness - 6. Logical consistency - 7. Implementation details - 8. Edge case handling - 9. Complexity analysis - 10. Technical documentation - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Technical approach summary - - Introduction: Technical framework overview - - Methodology: Technical methods description - - Mathematical Framework: Derivation presentation - - Algorithm Description: Implementation details - - Technical Analysis: Complexity and efficiency - - Results: Technical findings presentation - - Discussion: Technical implications - - Conclusion: Technical significance summary - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "technical_accuracy_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "derivations", "algorithms", "terminology", "equations", "completeness", "consistency", "implementation", "edge_cases", "complexity", "documentation" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects technical accuracy - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "methodology", "mathematical_framework", "algorithm_description", "technical_analysis", "results", "discussion", "conclusion" - "focus": str # "derivations", "algorithms", "terminology", "equations", "completeness", "consistency", "implementation", "edge_cases", "complexity", "documentation" - }}], - - "detailed_feedback": {{ - "derivation_correctness": str, # Detailed paragraph about mathematical derivations - "algorithm_accuracy": str, # Detailed paragraph about algorithm correctness - "terminology_accuracy": str, # Detailed paragraph about technical terminology - "equation_clarity": str, # Detailed paragraph about equation presentation - "content_completeness": str, # Detailed paragraph about technical content - "logical_consistency": str, # Detailed paragraph about logical consistency - "implementation_details": str, # Detailed paragraph about implementation - "edge_case_handling": str, # Detailed paragraph about edge cases - "complexity_analysis": str, # Detailed paragraph about complexity - "technical_documentation": str # Detailed paragraph about documentation - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances technical accuracy. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing technical accuracy: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "technical_accuracy_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "derivation_correctness": "", - "algorithm_accuracy": "", - "terminology_accuracy": "", - "equation_clarity": "", - "content_completeness": "", - "logical_consistency": "", - "implementation_details": "", - "edge_case_handling": "", - "complexity_analysis": "", - "technical_documentation": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R7_consistency_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R7_consistency_agent.py deleted file mode 100644 index 38a3b7f..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/R7_consistency_agent.py +++ /dev/null @@ -1,110 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class ConsistencyAgent(BaseReviewerAgent): - """Agent responsible for checking logical coherence across sections.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "R7_Consistency_Agent" - self.category = "Scientific Rigor" - - def analyze_consistency(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes logical coherence across sections.""" - prompt = f"""Analyze the following text for logical coherence and consistency across sections. Focus on: - 1. Alignment between methods and results - 2. Consistency between results and conclusions - 3. Logical flow between sections - 4. Terminology consistency - 5. Hypothesis-testing alignment - 6. Data interpretation consistency - 7. Citation consistency - 8. Figure-text alignment - 9. Table-text alignment - 10. Supplementary material consistency - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Consistency with main text - - Introduction: Alignment with methodology - - Literature Review: Citation consistency - - Methodology: Methods-results alignment - - Results: Results-conclusions alignment - - Discussion: Interpretation consistency - - Conclusion: Overall coherence - - Figures/Tables: Text alignment - - Supplementary: Main text consistency - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "consistency_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "methods_results", "results_conclusions", "logical_flow", "terminology", "hypothesis", "interpretation", "citations", "figures", "tables", "supplementary" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects logical coherence - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion", "figures_tables", "supplementary" - "focus": str # "methods_results", "results_conclusions", "logical_flow", "terminology", "hypothesis", "interpretation", "citations", "figures", "tables", "supplementary" - }}], - - "detailed_feedback": {{ - "methods_results_alignment": str, # Detailed paragraph about methods-results alignment - "results_conclusions_alignment": str, # Detailed paragraph about results-conclusions alignment - "logical_flow": str, # Detailed paragraph about logical flow - "terminology_consistency": str, # Detailed paragraph about terminology consistency - "hypothesis_testing": str, # Detailed paragraph about hypothesis-testing alignment - "interpretation_consistency": str, # Detailed paragraph about interpretation consistency - "citation_consistency": str, # Detailed paragraph about citation consistency - "figure_text_alignment": str, # Detailed paragraph about figure-text alignment - "table_text_alignment": str, # Detailed paragraph about table-text alignment - "supplementary_consistency": str # Detailed paragraph about supplementary material consistency - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances logical coherence and consistency. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing consistency: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "consistency_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "methods_results_alignment": "", - "results_conclusions_alignment": "", - "logical_flow": "", - "terminology_consistency": "", - "hypothesis_testing": "", - "interpretation_consistency": "", - "citation_consistency": "", - "figure_text_alignment": "", - "table_text_alignment": "", - "supplementary_consistency": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/README.md b/Backup/V5_multi_agent2/src/reviewer_agents/rigor/README.md deleted file mode 100644 index a963deb..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/README.md +++ /dev/null @@ -1,82 +0,0 @@ -# Scientific Rigor Agents - -These agents ensure research meets high standards of originality, ethics, and accuracy by evaluating various aspects of scientific rigor. - -## Agent Labels and Descriptions - -### R1 - Originality and Contribution Agent -- **Purpose**: Assesses research novelty and unique contributions to the field. -- **Key Evaluations**: - - Novelty of research approach - - Unique contributions - - Verification of novelty claims - - Comparison with existing literature - - Knowledge advancement - -### R2 - Impact and Significance Agent -- **Purpose**: Evaluates research influence and broader implications. -- **Key Evaluations**: - - Field influence potential - - Research advancement potential - - Practical applications - - Policy implications - - Future research directions - -### R3 - Ethics and Compliance Agent -- **Purpose**: Reviews ethical considerations and research standards compliance. -- **Key Evaluations**: - - Conflicts of interest - - Data privacy and protection - - Informed consent procedures - - Research integrity - - Ethical guidelines adherence - -### R4 - Data and Code Availability Agent -- **Purpose**: Checks data and code sharing practices and documentation. -- **Key Evaluations**: - - Data availability - - Code availability - - Documentation quality - - Reproducibility - - Sharing practices - -### R5 - Statistical Rigor Agent -- **Purpose**: Ensures appropriateness and correctness of statistical methods. -- **Key Evaluations**: - - Method appropriateness - - Analysis correctness - - Assumptions validation - - Power analysis - - Reporting completeness - -### R6 - Technical Accuracy Agent -- **Purpose**: Reviews mathematical derivations and technical content. -- **Key Evaluations**: - - Mathematical correctness - - Algorithm accuracy - - Technical clarity - - Derivation completeness - - Implementation feasibility - -### R7 - Consistency Agent -- **Purpose**: Checks logical coherence across manuscript sections. -- **Key Evaluations**: - - Methods-Results alignment - - Results-Conclusions alignment - - Claims consistency - - Variable/terminology consistency - - Cross-section coherence - -## Implementation Details - -Each agent: -- Inherits from `BaseReviewerAgent` -- Uses the `ReportTemplate` for standardized reports -- Provides detailed analysis with subscores -- Generates specific improvement suggestions -- Includes error handling and reporting -- Returns structured JSON output - -## Usage - -The agents can be used individually or as part of a comprehensive review process. Each agent focuses on specific aspects of scientific rigor while maintaining consistency in reporting format and evaluation standards. \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/__init__.py b/Backup/V5_multi_agent2/src/reviewer_agents/rigor/__init__.py deleted file mode 100644 index a2c44f9..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/rigor/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -This package contains the rigor agents for manuscript analysis. -""" \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/section/S10_supplementary_materials_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/section/S10_supplementary_materials_agent.py deleted file mode 100644 index 0522736..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/section/S10_supplementary_materials_agent.py +++ /dev/null @@ -1,95 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class SupplementaryMaterialsAgentS10(BaseReviewerAgent): - """Agent responsible for evaluating the supplementary materials of a manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "S10_Supplementary_Materials_Agent" - self.category = "Section Review" - - def analyze_supplementary_materials(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the supplementary materials of the manuscript.""" - prompt = f"""Analyze the following supplementary materials for quality and completeness. Focus on: - 1. Relevance to main text - 2. Clarity of presentation - 3. Consistency with main text - 4. Completeness of information - 5. Organization and structure - 6. Data presentation - 7. Methodological details - 8. Additional results - 9. Reference to main text - 10. Accessibility and usability - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Relevance: Connection to main text, value addition - - Clarity: Presentation, organization, accessibility - - Consistency: Alignment with main text, coherence - - Completeness: Information detail, methodological thoroughness - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "relevance", "clarity", "consistency", "completeness" - "location": str, # Section reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects manuscript quality - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "relevance", "clarity", "consistency", "completeness" - "focus": str # "connection", "presentation", "organization", "accessibility", "alignment", "coherence", "detail", "thoroughness" - }}], - - "detailed_feedback": {{ - "relevance_analysis": str, # Detailed paragraph about relevance to main text - "clarity_analysis": str, # Detailed paragraph about presentation clarity - "consistency_analysis": str, # Detailed paragraph about consistency with main text - "completeness_analysis": str, # Detailed paragraph about information completeness - "organization_analysis": str # Detailed paragraph about structure and organization - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the supplementary materials. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing supplementary materials: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "relevance_analysis": "", - "clarity_analysis": "", - "consistency_analysis": "", - "completeness_analysis": "", - "organization_analysis": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/section/S1_title_keywords_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/section/S1_title_keywords_agent.py deleted file mode 100644 index c984486..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/section/S1_title_keywords_agent.py +++ /dev/null @@ -1,113 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class TitleKeywordsAgentS1(BaseReviewerAgent): - """Agent responsible for evaluating the title and keywords of a manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "S1_Title_Keywords_Agent" - self.category = "Section Review" - - def analyze_title_keywords(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the title and keywords of the manuscript.""" - prompt = f"""Analyze the title and keywords section of the manuscript. Follow these steps: - - 1. FIRST, check if there is a dedicated "Keywords:" or "Keywords" section in the text. - - Look for a line that starts with "Keywords:" or "Keywords" - - If no such section is found, set has_keywords = false - - If found, set has_keywords = true and extract the keywords - - 2. For the title analysis: - - Analyze the current title considering ALL aspects simultaneously: - * Clarity: Is it clear and understandable? - * Accuracy: Does it accurately represent the content? - * Impact: Does it capture attention and significance? - * SEO: Is it optimized for search engines? - * Standards: Does it follow field conventions? - - Generate ONE comprehensive improvement suggestion that addresses all these aspects - - The improved title should be the optimal balance of all these factors - - 3. For keywords analysis (ONLY if has_keywords = true): - - Analyze relevance, coverage, and specificity - - Provide improvement suggestions - - Consider search engine optimization - - 4. If has_keywords = false: - - Set all keyword-related fields to empty or null - - Do not generate any keyword-related feedback - - Do not make assumptions about keywords from other text - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "title_keywords_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "title_clarity", "title_length", "keywords_relevance", "keywords_coverage", "guidelines", "discoverability" - "location": str, # "Title" or "Keywords" - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects manuscript quality - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The current title - "improved_version": str, # ONE comprehensive improved title that balances all aspects - "explanation": str, # Detailed explanation of how the improved title addresses clarity, accuracy, impact, SEO, and standards - "location": str, # "Title" - "category": str, # "title" - "focus": str # "comprehensive_improvement" - }}], - - "detailed_feedback": {{ - "title_analysis": str, # Detailed paragraph about title quality - "keywords_analysis": str, # "No keywords section found" if has_keywords = false - "guidelines_compliance": str, # Detailed paragraph about field conventions - "discoverability_assessment": str, # Detailed paragraph about search optimization - "audience_alignment": str # Detailed paragraph about appeal and significance - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: - 1. ONLY analyze the title and keywords section - 2. If no "Keywords:" or "Keywords" section is found: - - Set keywords_analysis to "No keywords section found" - - Do not include any keyword-related critical remarks - - Do not include any keyword-related improvement suggestions - - Do not make assumptions about keywords from other text - 3. Generate ONE comprehensive title improvement that considers all aspects simultaneously - 4. The title improvement should balance clarity, accuracy, impact, SEO, and standards - 5. All locations should be either "Title" or "Keywords", never "Abstract" - 6. Focus on improving discoverability and search optimization - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing title and keywords: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "title_keywords_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "title_analysis": "", - "keywords_analysis": "", - "guidelines_compliance": "", - "discoverability_assessment": "", - "audience_alignment": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/section/S2_abstract_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/section/S2_abstract_agent.py deleted file mode 100644 index 0b89427..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/section/S2_abstract_agent.py +++ /dev/null @@ -1,96 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class AbstractAgentS2(BaseReviewerAgent): - """Agent responsible for evaluating the abstract of a manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "S2_Abstract_Agent" - self.category = "Section Review" - - def analyze_abstract(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the abstract of the manuscript.""" - prompt = f"""Analyze the following abstract for quality and completeness. Focus on: - 1. Structure and organization - 2. Content completeness - 3. Clarity and readability - 4. Methodology description - 5. Results presentation - 6. Conclusion strength - 7. Scientific writing standards - 8. Field-specific requirements - 9. Impact communication - 10. Technical accuracy - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Structure: Organization, flow, section presence - - Content: Completeness, accuracy, technical details - - Clarity: Language, readability, technical terms - - Standards: Scientific writing, field conventions - - Impact: Significance, implications, contributions - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "structure", "content", "clarity", "standards", "impact" - "location": str, # Section reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects manuscript quality - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "structure", "content", "clarity", "standards", "impact" - "focus": str # "organization", "completeness", "readability", "methodology", "results", "conclusion" - }}], - - "detailed_feedback": {{ - "structure_analysis": str, # Detailed paragraph about abstract structure - "content_analysis": str, # Detailed paragraph about content completeness - "clarity_assessment": str, # Detailed paragraph about readability - "standards_compliance": str, # Detailed paragraph about scientific standards - "impact_evaluation": str # Detailed paragraph about significance - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the abstract. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing abstract: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "structure_analysis": "", - "content_analysis": "", - "clarity_assessment": "", - "standards_compliance": "", - "impact_evaluation": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/section/S3_introduction_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/section/S3_introduction_agent.py deleted file mode 100644 index 46db3b2..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/section/S3_introduction_agent.py +++ /dev/null @@ -1,96 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class IntroductionAgentS3(BaseReviewerAgent): - """Agent responsible for evaluating the introduction of a manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "S3_Introduction_Agent" - self.category = "Section Review" - - def analyze_introduction(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the introduction of the manuscript.""" - prompt = f"""Analyze the following introduction for quality and effectiveness. Focus on: - 1. Background context - 2. Problem statement - 3. Research gap identification - 4. Objectives clarity - 5. Significance justification - 6. Literature integration - 7. Flow and organization - 8. Technical accuracy - 9. Research scope - 10. Hypothesis/questions - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Context: Background, field overview - - Problem: Issue identification, gap analysis - - Objectives: Goals, research questions - - Significance: Impact, contribution - - Structure: Organization, flow - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "context", "problem", "objectives", "significance", "structure" - "location": str, # Section reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects manuscript quality - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "context", "problem", "objectives", "significance", "structure" - "focus": str # "background", "problem", "gap", "objectives", "significance", "flow" - }}], - - "detailed_feedback": {{ - "context_analysis": str, # Detailed paragraph about background - "problem_analysis": str, # Detailed paragraph about problem statement - "objectives_analysis": str, # Detailed paragraph about research goals - "significance_assessment": str, # Detailed paragraph about impact - "structure_evaluation": str # Detailed paragraph about organization - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the introduction. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing introduction: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "context_analysis": "", - "problem_analysis": "", - "objectives_analysis": "", - "significance_assessment": "", - "structure_evaluation": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/section/S4_literature_review_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/section/S4_literature_review_agent.py deleted file mode 100644 index 4d65b5c..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/section/S4_literature_review_agent.py +++ /dev/null @@ -1,96 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class LiteratureReviewAgentS4(BaseReviewerAgent): - """Agent responsible for evaluating the literature review of a manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "S4_Literature_Review_Agent" - self.category = "Section Review" - - def analyze_literature_review(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the literature review of the manuscript.""" - prompt = f"""Analyze the following literature review for quality and comprehensiveness. Focus on: - 1. Coverage breadth - 2. Historical context - 3. Current state - 4. Critical analysis - 5. Gap identification - 6. Theoretical framework - 7. Methodological review - 8. Citation quality - 9. Organization logic - 10. Synthesis depth - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Coverage: Breadth, depth, relevance - - Analysis: Critical thinking, synthesis - - Structure: Organization, flow - - Citations: Quality, recency - - Integration: Connection to research - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "coverage", "analysis", "structure", "citations", "integration" - "location": str, # Section reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects manuscript quality - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "coverage", "analysis", "structure", "citations", "integration" - "focus": str # "breadth", "depth", "synthesis", "organization", "relevance" - }}], - - "detailed_feedback": {{ - "coverage_analysis": str, # Detailed paragraph about literature coverage - "analysis_quality": str, # Detailed paragraph about critical analysis - "structure_evaluation": str, # Detailed paragraph about organization - "citation_assessment": str, # Detailed paragraph about citation quality - "integration_review": str # Detailed paragraph about research connection - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the literature review. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing literature review: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "coverage_analysis": "", - "analysis_quality": "", - "structure_evaluation": "", - "citation_assessment": "", - "integration_review": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/section/S5_methodology_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/section/S5_methodology_agent.py deleted file mode 100644 index f52d9b1..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/section/S5_methodology_agent.py +++ /dev/null @@ -1,96 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class MethodologyAgentS5(BaseReviewerAgent): - """Agent responsible for evaluating the methodology of a manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "S5_Methodology_Agent" - self.category = "Section Review" - - def analyze_methodology(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the methodology of the manuscript.""" - prompt = f"""Analyze the following methodology for quality and completeness. Focus on: - 1. Research design - 2. Data collection - 3. Sampling approach - 4. Instrumentation - 5. Procedures - 6. Analysis methods - 7. Validity measures - 8. Reliability assessment - 9. Ethical considerations - 10. Limitations handling - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Design: Approach, framework, rationale - - Methods: Techniques, procedures, tools - - Analysis: Statistical methods, qualitative approaches - - Quality: Validity, reliability, rigor - - Ethics: Consent, approval, considerations - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "design", "methods", "analysis", "quality", "ethics" - "location": str, # Section reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects manuscript quality - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "design", "methods", "analysis", "quality", "ethics" - "focus": str # "approach", "techniques", "procedures", "validity", "reliability" - }}], - - "detailed_feedback": {{ - "design_analysis": str, # Detailed paragraph about research design - "methods_assessment": str, # Detailed paragraph about methodology - "analysis_evaluation": str, # Detailed paragraph about analysis approach - "quality_review": str, # Detailed paragraph about validity and reliability - "ethics_compliance": str # Detailed paragraph about ethical considerations - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the methodology. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing methodology: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "design_analysis": "", - "methods_assessment": "", - "analysis_evaluation": "", - "quality_review": "", - "ethics_compliance": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/section/S6_results_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/section/S6_results_agent.py deleted file mode 100644 index a55c0d6..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/section/S6_results_agent.py +++ /dev/null @@ -1,96 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class ResultsAgentS6(BaseReviewerAgent): - """Agent responsible for evaluating the results of a manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "S6_Results_Agent" - self.category = "Section Review" - - def analyze_results(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the results of the manuscript.""" - prompt = f"""Analyze the following results for quality and presentation. Focus on: - 1. Data presentation - 2. Statistical analysis - 3. Figure/table quality - 4. Result interpretation - 5. Significance reporting - 6. Effect sizes - 7. Confidence intervals - 8. Statistical tests - 9. Data visualization - 10. Result organization - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Presentation: Clarity, organization, visualization - - Analysis: Statistical methods, significance - - Interpretation: Meaning, implications - - Quality: Accuracy, completeness - - Impact: Significance, effect sizes - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "presentation", "analysis", "interpretation", "quality", "impact" - "location": str, # Section reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects manuscript quality - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "presentation", "analysis", "interpretation", "quality", "impact" - "focus": str # "clarity", "statistics", "visualization", "interpretation", "significance" - }}], - - "detailed_feedback": {{ - "presentation_analysis": str, # Detailed paragraph about data presentation - "analysis_quality": str, # Detailed paragraph about statistical analysis - "interpretation_review": str, # Detailed paragraph about result interpretation - "visualization_assessment": str, # Detailed paragraph about figures/tables - "significance_evaluation": str # Detailed paragraph about statistical significance - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the results section. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing results: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "presentation_analysis": "", - "analysis_quality": "", - "interpretation_review": "", - "visualization_assessment": "", - "significance_evaluation": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/section/S7_discussion_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/section/S7_discussion_agent.py deleted file mode 100644 index 2a6a923..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/section/S7_discussion_agent.py +++ /dev/null @@ -1,96 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class DiscussionAgentS7(BaseReviewerAgent): - """Agent responsible for evaluating the discussion of a manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "S7_Discussion_Agent" - self.category = "Section Review" - - def analyze_discussion(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the discussion of the manuscript.""" - prompt = f"""Analyze the following discussion for quality and completeness. Focus on: - 1. Result interpretation - 2. Literature comparison - 3. Limitation analysis - 4. Future work - 5. Practical implications - 6. Theoretical contributions - 7. Research gap addressing - 8. Methodology reflection - 9. Result significance - 10. Conclusion alignment - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Interpretation: Result analysis, significance - - Context: Literature comparison, research gaps - - Reflection: Limitations, future work - - Impact: Practical implications, theoretical contributions - - Quality: Completeness, coherence - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "interpretation", "context", "reflection", "impact", "quality" - "location": str, # Section reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects manuscript quality - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "interpretation", "context", "reflection", "impact", "quality" - "focus": str # "interpretation", "comparison", "limitations", "implications", "significance" - }}], - - "detailed_feedback": {{ - "interpretation_analysis": str, # Detailed paragraph about result interpretation - "context_review": str, # Detailed paragraph about literature comparison - "reflection_assessment": str, # Detailed paragraph about limitations/future work - "impact_evaluation": str, # Detailed paragraph about practical/theoretical impact - "quality_analysis": str # Detailed paragraph about overall discussion quality - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the discussion. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing discussion: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "interpretation_analysis": "", - "context_review": "", - "reflection_assessment": "", - "impact_evaluation": "", - "quality_analysis": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/section/S8_conclusion_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/section/S8_conclusion_agent.py deleted file mode 100644 index 5de4ea7..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/section/S8_conclusion_agent.py +++ /dev/null @@ -1,95 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class ConclusionAgentS8(BaseReviewerAgent): - """Agent responsible for evaluating the conclusion of a manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "S8_Conclusion_Agent" - self.category = "Section Review" - - def analyze_conclusion(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the conclusion of the manuscript.""" - prompt = f"""Analyze the following conclusion for quality and completeness. Focus on: - 1. Support from results - 2. Research objective fulfillment - 3. Key findings summary - 4. Contribution clarity - 5. Practical implications - 6. Theoretical implications - 7. Future research suggestions - 8. Final statement strength - 9. Avoidance of new information - 10. Conciseness and clarity - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Support: Evidence-based, result alignment - - Objectives: Fulfillment, contribution clarity - - Implications: Practical, theoretical, future directions - - Presentation: Clarity, conciseness, strength - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "support", "objectives", "implications", "presentation" - "location": str, # Section reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects manuscript quality - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "support", "objectives", "implications", "presentation" - "focus": str # "evidence", "fulfillment", "clarity", "implications", "future_directions", "strength" - }}], - - "detailed_feedback": {{ - "support_analysis": str, # Detailed paragraph about result support - "objective_fulfillment": str, # Detailed paragraph about research objective fulfillment - "implications_analysis": str, # Detailed paragraph about implications - "presentation_analysis": str, # Detailed paragraph about presentation quality - "contribution_analysis": str # Detailed paragraph about contribution clarity - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the conclusion. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing conclusion: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "support_analysis": "", - "objective_fulfillment": "", - "implications_analysis": "", - "presentation_analysis": "", - "contribution_analysis": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/section/S9_references_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/section/S9_references_agent.py deleted file mode 100644 index 738b1e4..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/section/S9_references_agent.py +++ /dev/null @@ -1,95 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class ReferencesAgentS9(BaseReviewerAgent): - """Agent responsible for evaluating the references of a manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "S9_References_Agent" - self.category = "Section Review" - - def analyze_references(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the references of the manuscript.""" - prompt = f"""Analyze the following references for quality and completeness. Focus on: - 1. Citation accuracy - 2. Reference completeness - 3. Format consistency - 4. Source relevance - 5. Source recency - 6. Source diversity - 7. Citation-text alignment - 8. Reference list organization - 9. Style guide compliance - 10. Cross-reference accuracy - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Accuracy: Citation correctness, cross-reference accuracy - - Completeness: Reference details, source information - - Format: Style compliance, consistency - - Quality: Relevance, recency, diversity - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "accuracy", "completeness", "format", "quality" - "location": str, # Section reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects manuscript quality - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "accuracy", "completeness", "format", "quality" - "focus": str # "citation", "reference", "format", "style", "relevance", "recency", "diversity" - }}], - - "detailed_feedback": {{ - "accuracy_analysis": str, # Detailed paragraph about citation accuracy - "completeness_analysis": str, # Detailed paragraph about reference completeness - "format_analysis": str, # Detailed paragraph about format consistency - "quality_analysis": str, # Detailed paragraph about source quality - "organization_analysis": str # Detailed paragraph about reference organization - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the references. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing references: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "accuracy_analysis": "", - "completeness_analysis": "", - "format_analysis": "", - "quality_analysis": "", - "organization_analysis": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W1_language_style_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/writing/W1_language_style_agent.py deleted file mode 100644 index c840d21..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W1_language_style_agent.py +++ /dev/null @@ -1,109 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class LanguageStyleAgent(BaseReviewerAgent): - """Agent responsible for reviewing grammar, spelling, and punctuation.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "W1_Language_Style_Agent" - self.category = "Writing and Presentation" - - def analyze_language_style(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes grammar, spelling, and punctuation in the text.""" - prompt = f"""Analyze the following text for grammar, spelling, and punctuation issues. Focus on: - 1. Grammar correctness - 2. Spelling accuracy - 3. Punctuation usage - 4. Sentence structure - 5. Verb tense consistency - 6. Subject-verb agreement - 7. Article usage - 8. Preposition usage - 9. Conjunction usage - 10. Academic writing conventions - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Conciseness and clarity - - Introduction: Academic tone and flow - - Literature Review: Citation language - - Methodology: Technical description - - Results: Data presentation - - Discussion: Argument structure - - Conclusion: Summary language - - References: Citation format - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "language_style_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "grammar", "spelling", "punctuation", "sentence_structure", "verb_tense", "subject_verb", "articles", "prepositions", "conjunctions", "academic_conventions" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects readability - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion", "references" - "focus": str # "grammar", "spelling", "punctuation", "sentence_structure", "verb_tense", "subject_verb", "articles", "prepositions", "conjunctions", "academic_conventions" - }}], - - "detailed_feedback": {{ - "grammar_correctness": str, # Detailed paragraph about grammar issues - "spelling_accuracy": str, # Detailed paragraph about spelling issues - "punctuation_usage": str, # Detailed paragraph about punctuation issues - "sentence_structure": str, # Detailed paragraph about sentence structure - "verb_tense_consistency": str, # Detailed paragraph about verb tense consistency - "subject_verb_agreement": str, # Detailed paragraph about subject-verb agreement - "article_usage": str, # Detailed paragraph about article usage - "preposition_usage": str, # Detailed paragraph about preposition usage - "conjunction_usage": str, # Detailed paragraph about conjunction usage - "academic_conventions": str # Detailed paragraph about academic writing conventions - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the language and style. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing language style: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "language_style_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "grammar_correctness": "", - "spelling_accuracy": "", - "punctuation_usage": "", - "sentence_structure": "", - "verb_tense_consistency": "", - "subject_verb_agreement": "", - "article_usage": "", - "preposition_usage": "", - "conjunction_usage": "", - "academic_conventions": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W2_narrative_structure_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/writing/W2_narrative_structure_agent.py deleted file mode 100644 index 7a28b74..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W2_narrative_structure_agent.py +++ /dev/null @@ -1,109 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class NarrativeStructureAgent(BaseReviewerAgent): - """Agent responsible for evaluating the overall flow, coherence, and logical organization of the paper.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "W2_Narrative_Structure_Agent" - self.category = "Writing and Presentation" - - def analyze_narrative_structure(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the narrative flow and structural organization of the text.""" - prompt = f"""Analyze the following text for narrative flow and structural organization. Focus on: - 1. Overall narrative coherence - 2. Logical progression of ideas - 3. Section transitions - 4. Paragraph organization - 5. Topic sentence effectiveness - 6. Supporting evidence integration - 7. Conclusion alignment with introduction - 8. Research question/hypothesis tracking - 9. Visual element integration - 10. Reader engagement - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Research narrative overview - - Introduction: Research context and flow - - Literature Review: Evidence synthesis - - Methodology: Process description - - Results: Finding presentation - - Discussion: Argument development - - Conclusion: Research story closure - - Figures/Tables: Visual narrative - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "narrative_structure_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "narrative_coherence", "logical_progression", "transitions", "paragraph_organization", "topic_sentences", "evidence_integration", "conclusion_alignment", "hypothesis_tracking", "visual_integration", "reader_engagement" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects the narrative - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion", "figures_tables" - "focus": str # "narrative_coherence", "logical_progression", "transitions", "paragraph_organization", "topic_sentences", "evidence_integration", "conclusion_alignment", "hypothesis_tracking", "visual_integration", "reader_engagement" - }}], - - "detailed_feedback": {{ - "narrative_coherence": str, # Detailed paragraph about narrative coherence - "logical_progression": str, # Detailed paragraph about logical progression - "section_transitions": str, # Detailed paragraph about section transitions - "paragraph_organization": str, # Detailed paragraph about paragraph organization - "topic_sentence_effectiveness": str, # Detailed paragraph about topic sentence effectiveness - "supporting_evidence_integration": str, # Detailed paragraph about supporting evidence integration - "conclusion_alignment": str, # Detailed paragraph about conclusion alignment - "hypothesis_tracking": str, # Detailed paragraph about hypothesis tracking - "visual_element_integration": str, # Detailed paragraph about visual element integration - "reader_engagement": str # Detailed paragraph about reader engagement - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances the narrative structure. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing narrative structure: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "narrative_structure_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "narrative_coherence": "", - "logical_progression": "", - "section_transitions": "", - "paragraph_organization": "", - "topic_sentence_effectiveness": "", - "supporting_evidence_integration": "", - "conclusion_alignment": "", - "hypothesis_tracking": "", - "visual_element_integration": "", - "reader_engagement": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W3_clarity_conciseness_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/writing/W3_clarity_conciseness_agent.py deleted file mode 100644 index 76956d2..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W3_clarity_conciseness_agent.py +++ /dev/null @@ -1,109 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class ClarityConcisenessAgent(BaseReviewerAgent): - """Agent responsible for evaluating clarity and conciseness of the manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "W3_Clarity_Conciseness_Agent" - self.category = "Writing and Presentation" - - def analyze_clarity_conciseness(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the clarity and conciseness of the text.""" - prompt = f"""Analyze the following text for clarity and conciseness. Focus on: - 1. Language simplicity - 2. Jargon usage - 3. Wordiness - 4. Sentence length - 5. Paragraph length - 6. Active vs. passive voice - 7. Redundancy - 8. Ambiguity - 9. Readability - 10. Information density - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Key message clarity - - Introduction: Background conciseness - - Literature Review: Synthesis clarity - - Methodology: Process description - - Results: Finding presentation - - Discussion: Argument clarity - - Conclusion: Message conciseness - - Technical Content: Jargon explanation - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "clarity_conciseness_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "language_simplicity", "jargon", "wordiness", "sentence_length", "paragraph_length", "voice", "redundancy", "ambiguity", "readability", "information_density" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects clarity - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion", "technical" - "focus": str # "language_simplicity", "jargon", "wordiness", "sentence_length", "paragraph_length", "voice", "redundancy", "ambiguity", "readability", "information_density" - }}], - - "detailed_feedback": {{ - "language_simplicity": str, # Detailed paragraph about language simplicity - "jargon_usage": str, # Detailed paragraph about jargon usage - "wordiness": str, # Detailed paragraph about wordiness - "sentence_length": str, # Detailed paragraph about sentence length - "paragraph_length": str, # Detailed paragraph about paragraph length - "active_passive_voice": str, # Detailed paragraph about active vs. passive voice - "redundancy": str, # Detailed paragraph about redundancy - "ambiguity": str, # Detailed paragraph about ambiguity - "readability": str, # Detailed paragraph about readability - "information_density": str # Detailed paragraph about information density - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances clarity and conciseness. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing clarity and conciseness: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "clarity_conciseness_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "language_simplicity": "", - "jargon_usage": "", - "wordiness": "", - "sentence_length": "", - "paragraph_length": "", - "active_passive_voice": "", - "redundancy": "", - "ambiguity": "", - "readability": "", - "information_density": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W4_terminology_consistency_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/writing/W4_terminology_consistency_agent.py deleted file mode 100644 index 6908a5d..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W4_terminology_consistency_agent.py +++ /dev/null @@ -1,109 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class TerminologyConsistencyAgent(BaseReviewerAgent): - """Agent responsible for ensuring consistent use of terms, notations, and acronyms.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "W4_Terminology_Consistency_Agent" - self.category = "Writing and Presentation" - - def analyze_terminology_consistency(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the consistency of terminology, notations, and acronyms in the text.""" - prompt = f"""Analyze the following text for terminology consistency. Focus on: - 1. Term usage consistency - 2. Notation consistency - 3. Acronym usage and definition - 4. Variable naming consistency - 5. Unit notation consistency - 6. Abbreviation consistency - 7. Technical term consistency - 8. Field-specific terminology - 9. Cross-reference consistency - 10. Definition consistency - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Term introduction - - Introduction: Field terminology - - Literature Review: Citation terms - - Methodology: Technical terms - - Results: Variable names - - Discussion: Term usage - - Conclusion: Term consistency - - Equations: Notation style - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "terminology_consistency_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "term_usage", "notation", "acronyms", "variable_naming", "unit_notation", "abbreviations", "technical_terms", "field_terminology", "cross_references", "definitions" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects consistency - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion", "equations" - "focus": str # "term_usage", "notation", "acronyms", "variable_naming", "unit_notation", "abbreviations", "technical_terms", "field_terminology", "cross_references", "definitions" - }}], - - "detailed_feedback": {{ - "term_usage_consistency": str, # Detailed paragraph about term usage consistency - "notation_consistency": str, # Detailed paragraph about notation consistency - "acronym_usage": str, # Detailed paragraph about acronym usage - "variable_naming_consistency": str, # Detailed paragraph about variable naming consistency - "unit_notation_consistency": str, # Detailed paragraph about unit notation consistency - "abbreviation_consistency": str, # Detailed paragraph about abbreviation consistency - "technical_term_consistency": str, # Detailed paragraph about technical term consistency - "field_terminology": str, # Detailed paragraph about field-specific terminology - "cross_reference_consistency": str, # Detailed paragraph about cross-reference consistency - "definition_consistency": str # Detailed paragraph about definition consistency - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances terminology consistency. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing terminology consistency: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "terminology_consistency_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "term_usage_consistency": "", - "notation_consistency": "", - "acronym_usage": "", - "variable_naming_consistency": "", - "unit_notation_consistency": "", - "abbreviation_consistency": "", - "technical_term_consistency": "", - "field_terminology": "", - "cross_reference_consistency": "", - "definition_consistency": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W5_inclusive_language_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/writing/W5_inclusive_language_agent.py deleted file mode 100644 index f145d66..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W5_inclusive_language_agent.py +++ /dev/null @@ -1,109 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class InclusiveLanguageAgent(BaseReviewerAgent): - """Agent responsible for evaluating inclusive, unbiased language usage.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "W5_Inclusive_Language_Agent" - self.category = "Writing and Presentation" - - def analyze_inclusive_language(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the use of inclusive and unbiased language in the text.""" - prompt = f"""Analyze the following text for inclusive and unbiased language usage. Focus on: - 1. Gender-neutral language - 2. Cultural sensitivity - 3. Age-appropriate terminology - 4. Disability-inclusive language - 5. Socioeconomic sensitivity - 6. Geographic inclusivity - 7. Professional title usage - 8. Stereotype avoidance - 9. Identity-first vs. person-first language - 10. Historical context sensitivity - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Overall tone and inclusivity - - Introduction: Background description - - Literature Review: Participant descriptions - - Methodology: Sample descriptions - - Results: Participant representation - - Discussion: Interpretation language - - Conclusion: Generalizability statements - - References: Author representation - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "inclusive_language_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "gender_neutrality", "cultural_sensitivity", "age_terminology", "disability_inclusion", "socioeconomic_sensitivity", "geographic_inclusivity", "professional_titles", "stereotypes", "identity_language", "historical_context" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects inclusivity - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion", "references" - "focus": str # "gender_neutrality", "cultural_sensitivity", "age_terminology", "disability_inclusion", "socioeconomic_sensitivity", "geographic_inclusivity", "professional_titles", "stereotypes", "identity_language", "historical_context" - }}], - - "detailed_feedback": {{ - "gender_neutral_language": str, # Detailed paragraph about gender-neutral language - "cultural_sensitivity": str, # Detailed paragraph about cultural sensitivity - "age_appropriate_terminology": str, # Detailed paragraph about age-appropriate terminology - "disability_inclusive_language": str, # Detailed paragraph about disability-inclusive language - "socioeconomic_sensitivity": str, # Detailed paragraph about socioeconomic sensitivity - "geographic_inclusivity": str, # Detailed paragraph about geographic inclusivity - "professional_title_usage": str, # Detailed paragraph about professional title usage - "stereotypes": str, # Detailed paragraph about stereotype avoidance - "identity_language": str, # Detailed paragraph about identity-first vs. person-first language - "historical_context": str # Detailed paragraph about historical context sensitivity - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances inclusivity. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing inclusive language: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "inclusive_language_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "gender_neutral_language": "", - "cultural_sensitivity": "", - "age_appropriate_terminology": "", - "disability_inclusive_language": "", - "socioeconomic_sensitivity": "", - "geographic_inclusivity": "", - "professional_title_usage": "", - "stereotypes": "", - "identity_language": "", - "historical_context": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W6_citation_formatting_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/writing/W6_citation_formatting_agent.py deleted file mode 100644 index 8f3a0d9..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W6_citation_formatting_agent.py +++ /dev/null @@ -1,109 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class CitationFormattingAgent(BaseReviewerAgent): - """Agent responsible for evaluating citation formatting and consistency.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "W6_Citation_Formatting_Agent" - self.category = "Writing and Presentation" - - def analyze_citation_formatting(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes the formatting and consistency of citations in the text.""" - prompt = f"""Analyze the following text for citation formatting and consistency. Focus on: - 1. In-text citation format - 2. Reference list formatting - 3. Citation style consistency - 4. Reference completeness - 5. DOI/URL formatting - 6. Author name formatting - 7. Publication date formatting - 8. Journal name formatting - 9. Volume/issue/page formatting - 10. Cross-reference accuracy - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Citation style - - Introduction: First citations - - Literature Review: Multiple citations - - Methodology: Method citations - - Results: Data citations - - Discussion: Comparison citations - - Conclusion: Summary citations - - References: List formatting - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "citation_formatting_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "in_text_format", "reference_format", "style_consistency", "reference_completeness", "doi_format", "author_format", "date_format", "journal_format", "volume_format", "cross_reference" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects citation quality - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion", "references" - "focus": str # "in_text_format", "reference_format", "style_consistency", "reference_completeness", "doi_format", "author_format", "date_format", "journal_format", "volume_format", "cross_reference" - }}], - - "detailed_feedback": {{ - "in_text_citation_format": str, # Detailed paragraph about in-text citation format - "reference_list_format": str, # Detailed paragraph about reference list formatting - "citation_style_consistency": str, # Detailed paragraph about citation style consistency - "reference_completeness": str, # Detailed paragraph about reference completeness - "doi_url_formatting": str, # Detailed paragraph about DOI/URL formatting - "author_name_formatting": str, # Detailed paragraph about author name formatting - "publication_date_formatting": str, # Detailed paragraph about publication date formatting - "journal_name_formatting": str, # Detailed paragraph about journal name formatting - "volume_issue_page_formatting": str, # Detailed paragraph about volume/issue/page formatting - "cross_reference_accuracy": str # Detailed paragraph about cross-reference accuracy - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances citation formatting. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing citation formatting: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "citation_formatting_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "in_text_citation_format": "", - "reference_list_format": "", - "citation_style_consistency": "", - "reference_completeness": "", - "doi_url_formatting": "", - "author_name_formatting": "", - "publication_date_formatting": "", - "journal_name_formatting": "", - "volume_issue_page_formatting": "", - "cross_reference_accuracy": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W7_target_audience_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/writing/W7_target_audience_agent.py deleted file mode 100644 index 0812ad5..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W7_target_audience_agent.py +++ /dev/null @@ -1,109 +0,0 @@ -from typing import Dict, Any, List -import json -from ...core.base_agent import BaseReviewerAgent -from ...core.report_template import ReportTemplate - -class TargetAudienceAlignmentAgent(BaseReviewerAgent): - """Agent responsible for evaluating writing style and formatting alignment with target audience.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "W7_Target_Audience_Alignment_Agent" - self.category = "Writing and Presentation" - - def analyze_target_audience_alignment(self, text: str, research_type: str) -> Dict[str, Any]: - """Analyzes how well the writing style and formatting align with the target audience.""" - prompt = f"""Analyze the following text for target audience alignment and writing style appropriateness. Focus on: - 1. Technical depth and complexity - 2. Field-specific terminology usage - 3. Writing style formality - 4. Section organization - 5. Visual element integration - 6. Reference style and depth - 7. Methodology description detail - 8. Results presentation - 9. Discussion depth - 10. Conclusion format - - For each section, provide at least 2-3 improvement suggestions. Consider these categories: - - Abstract: Conciseness and technical level - - Introduction: Background depth - - Literature Review: Reference depth - - Methodology: Technical detail - - Results: Data presentation - - Discussion: Analysis depth - - Conclusion: Summary style - - Visual Elements: Integration and complexity - - Text to analyze: {text} - Research type: {research_type} - - Provide a detailed analysis in the following JSON format: - {{ - "audience_alignment_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # "technical_depth", "terminology", "formality", "organization", "visuals", "references", "methodology", "results", "discussion", "conclusion" - "location": str, # Section/paragraph reference - "issue": str, # Detailed description of the issue - "severity": str, # "high", "medium", "low" - "impact": str # How this affects audience engagement - }}], - - "improvement_suggestions": [{{ - "original_text": str, # The problematic text - "improved_version": str, # AI-generated improvement - "explanation": str, # Why this improvement helps - "location": str, # Where to apply this change - "category": str, # "abstract", "introduction", "literature", "methodology", "results", "discussion", "conclusion", "visuals" - "focus": str # "technical_depth", "terminology", "formality", "organization", "visuals", "references", "methodology", "results", "discussion", "conclusion" - }}], - - "detailed_feedback": {{ - "technical_depth": str, # Detailed paragraph about technical depth appropriateness - "terminology_usage": str, # Detailed paragraph about field-specific terminology - "writing_formality": str, # Detailed paragraph about writing style formality - "section_organization": str, # Detailed paragraph about section organization - "visual_integration": str, # Detailed paragraph about visual element integration - "reference_style": str, # Detailed paragraph about reference style and depth - "methodology_detail": str, # Detailed paragraph about methodology description - "results_presentation": str, # Detailed paragraph about results presentation - "discussion_depth": str, # Detailed paragraph about discussion depth - "conclusion_format": str # Detailed paragraph about conclusion format - }}, - - "summary": str # Overall assessment paragraph - }} - - Important: Generate at least 10-15 improvement suggestions across different sections and categories. - Each suggestion should be specific, actionable, and include clear explanations of how it enhances audience alignment. - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing target audience alignment: {str(e)}") - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generates a structured error report.""" - return { - "audience_alignment_score": 0, - "critical_remarks": [], - "improvement_suggestions": [], - "detailed_feedback": { - "technical_depth": "", - "terminology_usage": "", - "writing_formality": "", - "section_organization": "", - "visual_integration": "", - "reference_style": "", - "methodology_detail": "", - "results_presentation": "", - "discussion_depth": "", - "conclusion_format": "" - }, - "summary": f"Error in analysis: {error_message}", - "error": True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W8_visual_presentation_agent.py b/Backup/V5_multi_agent2/src/reviewer_agents/writing/W8_visual_presentation_agent.py deleted file mode 100644 index ab5e235..0000000 --- a/Backup/V5_multi_agent2/src/reviewer_agents/writing/W8_visual_presentation_agent.py +++ /dev/null @@ -1,168 +0,0 @@ -from typing import Dict, Any, List -import json -from PIL import Image -import io -from ...core.base_agent import BaseReviewerAgent - -class VisualPresentationAgentW8(BaseReviewerAgent): - """Agent responsible for evaluating visual presentation elements in the manuscript.""" - - def __init__(self, model="gpt-4.1-nano"): - super().__init__(model) - self.name = "W8_Visual_Presentation_Agent" - self.category = "Writing and Presentation" - - def analyze_visual_presentation(self, text: str, research_type: str, - images: List[Dict[str, Any]] = None, - tables: List[Dict[str, Any]] = None) -> Dict[str, Any]: - """Analyzes the quality, clarity, and effectiveness of visual elements in the manuscript. - - Args: - text (str): The manuscript text - research_type (str): Type of research paper - images (List[Dict[str, Any]], optional): List of extracted images with metadata - tables (List[Dict[str, Any]], optional): List of extracted tables with metadata - """ - # Prepare image and table information for analysis - image_info = self._format_image_info(images) if images else [] - table_info = self._format_table_info(tables) if tables else [] - - prompt = f"""Analyze the visual presentation quality and effectiveness of the following manuscript. - The manuscript contains {len(image_info)} figures and {len(table_info)} tables. - - Manuscript Text: {text} - Research Type: {research_type} - - Figures Information: - {json.dumps(image_info, indent=2)} - - Tables Information: - {json.dumps(table_info, indent=2)} - - Focus on: - 1. Figure quality and clarity - 2. Table formatting and readability - 3. Visual element placement - 4. Caption completeness - 5. Color scheme appropriateness - 6. Data visualization effectiveness - 7. Visual hierarchy - 8. Accessibility considerations - 9. Consistency in visual style - 10. Integration with text - - For each section, provide specific improvement suggestions. Consider: - - Figures: Quality, clarity, resolution, and effectiveness - - Tables: Formatting, readability, and structure - - Diagrams: Clarity and information density - - Charts: Data representation and interpretation - - Visual consistency: Style and formatting across elements - - Accessibility: Color contrast and readability - - Integration: How well visuals support the text - - Captions: Completeness and clarity - - Provide a detailed analysis in the following JSON format: - {{ - "visual_presentation_score": int, # Single comprehensive score (1-10) - - "critical_remarks": [{{ - "category": str, # Category of the issue - "location": str, # Specific figure/table reference - "issue": str, # Detailed description - "severity": str, # "high", "medium", "low" - "impact": str # How this affects reader understanding - }}], - - "improvement_suggestions": [{{ - "original_text": str, # Current caption/reference - "improved_version": str, # Suggested improvement - "explanation": str, # Why this helps - "location": str, # Where to apply - "category": str, # Element type - "focus": str # Aspect being improved - }}], - - "detailed_feedback": {{ - "figure_quality": str, - "table_formatting": str, - "visual_placement": str, - "caption_completeness": str, - "color_scheme": str, - "data_visualization": str, - "visual_hierarchy": str, - "accessibility": str, - "visual_consistency": str, - "text_integration": str - }}, - - "summary": str # Overall assessment - }} - """ - - try: - response = self.llm(prompt) - analysis = json.loads(response) - return analysis - except Exception as e: - return self._generate_error_report(f"Error analyzing visual presentation: {str(e)}") - - def _format_image_info(self, images: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """Format image information for analysis.""" - formatted_images = [] - for img in images: - # Create PIL Image from image data if available - image_stats = {} - if img.get('image_data'): - try: - pil_image = Image.open(io.BytesIO(img['image_data'])) - image_stats = { - 'size': pil_image.size, - 'mode': pil_image.mode, - 'format': pil_image.format - } - except Exception: - image_stats = {'error': 'Failed to analyze image data'} - - formatted_images.append({ - 'page': img.get('page'), - 'index': img.get('index'), - 'caption': img.get('caption', ''), - 'location': f"Page {img.get('page')}, Figure {img.get('index')}", - 'stats': image_stats - }) - return formatted_images - - def _format_table_info(self, tables: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """Format table information for analysis.""" - formatted_tables = [] - for table in tables: - formatted_tables.append({ - 'page': table.get('page'), - 'index': table.get('index'), - 'caption': table.get('caption', ''), - 'location': f"Page {table.get('page')}, Table {table.get('index')}", - 'content_preview': table.get('text', '')[:200] + '...' if table.get('text') else '' - }) - return formatted_tables - - def _generate_error_report(self, error_message: str) -> Dict[str, Any]: - """Generate a structured error report.""" - return { - 'visual_presentation_score': 0, - 'critical_remarks': [], - 'improvement_suggestions': [], - 'detailed_feedback': { - 'figure_quality': '', - 'table_formatting': '', - 'visual_placement': '', - 'caption_completeness': '', - 'color_scheme': '', - 'data_visualization': '', - 'visual_hierarchy': '', - 'accessibility': '', - 'visual_consistency': '', - 'text_integration': '' - }, - 'summary': f"Error in analysis: {error_message}", - 'error': True - } \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/run_analysis.py b/Backup/V5_multi_agent2/src/run_analysis.py deleted file mode 100644 index 602e071..0000000 --- a/Backup/V5_multi_agent2/src/run_analysis.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python3 -""" -Run Analysis Script - -This script runs the analysis using the controller agent and generates a consistent report. -""" - -import os -import json -import argparse -from datetime import datetime -from typing import Dict, Any, List - -# Use relative imports -from reviewer_agents.controller_agent import ControllerAgent -from utils.json_to_report import generate_overall_report, save_report - - -def load_manuscript(file_path: str) -> str: - """Load the manuscript text from a file.""" - try: - with open(file_path, 'r', encoding='utf-8') as file: - return file.read() - except Exception as e: - print(f"Error loading manuscript: {e}") - return "" - - -def save_results(results: Dict[str, Any], output_path: str) -> None: - """Save the analysis results to a JSON file.""" - try: - with open(output_path, 'w', encoding='utf-8') as file: - json.dump(results, file, indent=2) - print(f"Results successfully saved to {output_path}") - except Exception as e: - print(f"Error saving results: {e}") - - -def main(): - """Main function to run the analysis and generate the report.""" - parser = argparse.ArgumentParser(description='Run analysis and generate a consistent report') - parser.add_argument('--input', '-i', type=str, - default='/Users/robertjakob/rigorous-1/V5_multi_agent2/data/manuscript.txt', - help='Path to the input manuscript file') - parser.add_argument('--results', '-r', type=str, - default='/Users/robertjakob/rigorous-1/V5_multi_agent2/results/combined_results.json', - help='Path to save the analysis results') - parser.add_argument('--output', '-o', type=str, - default='/Users/robertjakob/rigorous-1/V5_multi_agent2/results/manuscript_report.md', - help='Path to save the output report') - parser.add_argument('--model', '-m', type=str, - default='gpt-4', - help='Model to use for analysis') - - args = parser.parse_args() - - # Load the manuscript - manuscript_text = load_manuscript(args.input) - if not manuscript_text: - print("Failed to load manuscript. Exiting.") - return - - # Initialize the controller agent - controller = ControllerAgent(model=args.model) - - # Run the analysis - print("Running analysis...") - results = controller.run_analysis(manuscript_text) - - # Save the results - save_results(results, args.results) - - # Generate the report - print("Generating report...") - report = generate_overall_report(results) - - # Save the report - save_report(report, args.output) - - print(f"Analysis complete. Report saved to {args.output}") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/test_review.py b/Backup/V5_multi_agent2/src/test_review.py deleted file mode 100644 index 53e5e89..0000000 --- a/Backup/V5_multi_agent2/src/test_review.py +++ /dev/null @@ -1,82 +0,0 @@ -import sys -import os - -# Add the src directory to the Python path -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -from src.reviewer_agents.rigor.R1_originality_contribution_agent import OriginalityContributionAgent -from src.reviewer_agents.rigor.R2_impact_significance_agent import ImpactSignificanceAgent -from src.reviewer_agents.writing.W1_language_style_agent import LanguageStyleAgent -import json - -def main(): - # Sample manuscript for testing - sample_manuscript = """ - Title: A Novel Approach to Machine Learning in Healthcare - - Abstract: - This paper presents a new machine learning framework for healthcare applications, - focusing on improved patient outcome prediction. We demonstrate significant - improvements over existing methods in terms of accuracy and interpretability. - - Introduction: - Machine learning in healthcare has seen rapid development in recent years. - However, current approaches often lack interpretability and fail to consider - the unique challenges of medical data. Our work addresses these limitations - through a novel architecture that combines deep learning with domain-specific - constraints. - - Methods: - We propose a new neural network architecture that incorporates medical domain - knowledge through constrained optimization. Our approach uses a combination of - attention mechanisms and traditional statistical methods to ensure both - accuracy and interpretability. - - Results: - Experimental results on three large-scale medical datasets show that our - method achieves a 15% improvement in prediction accuracy while maintaining - full interpretability. We also demonstrate superior handling of missing data - compared to existing approaches. - """ - - # Initialize agents with GPT-3.5-turbo - originality_agent = OriginalityContributionAgent(model="gpt-3.5-turbo") - impact_agent = ImpactSignificanceAgent(model="gpt-3.5-turbo") - language_agent = LanguageStyleAgent(model="gpt-3.5-turbo") - - # Create results directory if it doesn't exist - results_dir = "results" - os.makedirs(results_dir, exist_ok=True) - - # Get reviews from each agent - print("\nGetting originality review...") - originality_review = originality_agent.analyze_originality_contribution(sample_manuscript, "healthcare") - - print("\nGetting impact review...") - impact_review = impact_agent.analyze_impact_significance(sample_manuscript, {"field": "healthcare"}) - - print("\nGetting language review...") - language_review = language_agent.analyze_language_style(sample_manuscript, "healthcare") - - # Save individual reviews - reviews = { - "originality_review": originality_review, - "impact_review": impact_review, - "language_review": language_review - } - - # Save to file - output_file = os.path.join(results_dir, "test_reviews.json") - with open(output_file, 'w') as f: - json.dump(reviews, f, indent=2) - - print(f"\nReviews saved to: {output_file}") - - # Print summary of reviews - print("\n=== Review Summary ===") - print(f"Originality Score: {originality_review.get('originality_contribution_score', 'N/A')}") - print(f"Impact Score: {impact_review.get('impact_significance_score', 'N/A')}") - print(f"Language Score: {language_review.get('language_style_score', 'N/A')}") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/utils/combine_results.py b/Backup/V5_multi_agent2/src/utils/combine_results.py deleted file mode 100644 index b0f1639..0000000 --- a/Backup/V5_multi_agent2/src/utils/combine_results.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python3 -""" -Combine Results - -This script combines all individual agent results into a single JSON file. -""" - -import json -import os -import glob -from typing import Dict, Any - - -def load_json_file(file_path: str) -> Dict[str, Any]: - """Load and parse a JSON file.""" - try: - with open(file_path, 'r', encoding='utf-8') as file: - return json.load(file) - except Exception as e: - print(f"Error loading JSON file {file_path}: {e}") - return {} - - -def save_json_file(data: Dict[str, Any], file_path: str) -> None: - """Save data to a JSON file.""" - try: - with open(file_path, 'w', encoding='utf-8') as file: - json.dump(data, file, indent=2) - print(f"Results successfully saved to {file_path}") - except Exception as e: - print(f"Error saving JSON file: {e}") - - -def combine_results(results_dir: str, output_file: str) -> None: - """Combine all individual agent results into a single JSON file.""" - # Initialize the combined results dictionary - combined_results = {} - - # Get all JSON files in the results directory - json_files = glob.glob(os.path.join(results_dir, '*_results.json')) - - # Process each JSON file - for file_path in json_files: - # Extract agent name from filename (e.g., 'S1_results.json' -> 'S1') - agent_name = os.path.basename(file_path).split('_')[0] - - # Skip files that are not agent results - if agent_name in ['critical_remarks', 'score', 'detailed_feedback', 'summary', 'improvement_suggestions', 'combined']: - continue - - # Load the agent's results - agent_results = load_json_file(file_path) - - # Skip if the file is empty or contains an error - if not agent_results or (isinstance(agent_results, dict) and 'error' in agent_results): - print(f"Skipping {file_path} due to error or empty file") - continue - - # Add the agent's results to the combined results - combined_results[agent_name] = agent_results - - # Save the combined results - save_json_file(combined_results, output_file) - - -def main(): - """Main function to run the script.""" - import argparse - - parser = argparse.ArgumentParser(description='Combine individual agent results into a single JSON file') - parser.add_argument('--results-dir', '-r', type=str, - default='/Users/robertjakob/rigorous-1/V5_multi_agent2/results', - help='Directory containing individual agent results') - parser.add_argument('--output', '-o', type=str, - default='/Users/robertjakob/rigorous-1/V5_multi_agent2/results/combined_results.json', - help='Path to save the combined results') - - args = parser.parse_args() - - combine_results(args.results_dir, args.output) - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/utils/json_to_report.py b/Backup/V5_multi_agent2/src/utils/json_to_report.py deleted file mode 100644 index 43e34a8..0000000 --- a/Backup/V5_multi_agent2/src/utils/json_to_report.py +++ /dev/null @@ -1,326 +0,0 @@ -#!/usr/bin/env python3 -""" -JSON to Report Converter - -This script converts the combined_results.json file into a well-structured text report. -""" - -import json -import os -import argparse -from datetime import datetime -from typing import Dict, Any, List - - -def load_json_file(file_path: str) -> Dict[str, Any]: - """Load and parse a JSON file.""" - try: - with open(file_path, 'r', encoding='utf-8') as file: - return json.load(file) - except Exception as e: - print(f"Error loading JSON file: {e}") - return {} - - -def format_critical_remarks(remarks: List[Dict[str, Any]]) -> str: - """Format critical remarks into a readable text section.""" - if not remarks: - return "No critical remarks identified." - - formatted_remarks = [] - for i, remark in enumerate(remarks, 1): - formatted_remark = ( - f"### Remark {i}\n\n" - f"**Category**: {remark.get('category', 'N/A')} \n" - f"**Location**: {remark.get('location', 'N/A')} \n" - f"**Issue**: {remark.get('issue', 'N/A')} \n" - f"**Severity**: {remark.get('severity', 'N/A')} \n" - f"**Impact**: {remark.get('impact', 'N/A')}" - ) - formatted_remarks.append(formatted_remark) - - return "\n\n".join(formatted_remarks) - - -def format_improvement_suggestions(suggestions: List[Dict[str, Any]]) -> str: - """Format improvement suggestions into a readable text section.""" - if not suggestions: - return "No improvement suggestions provided." - - formatted_suggestions = [] - for i, suggestion in enumerate(suggestions, 1): - formatted_suggestion = ( - f"### Suggestion {i}\n\n" - f"**Location**: {suggestion.get('location', 'N/A')} \n" - f"**Category**: {suggestion.get('category', 'N/A')} \n" - f"**Focus**: {suggestion.get('focus', 'N/A')} \n\n" - f"**Original Text**: \n" - f"> {suggestion.get('original_text', 'N/A')}\n\n" - f"**Improved Version**: \n" - f"> {suggestion.get('improved_version', 'N/A')}\n\n" - f"**Explanation**: \n" - f"{suggestion.get('explanation', 'N/A')}" - ) - formatted_suggestions.append(formatted_suggestion) - - return "\n\n".join(formatted_suggestions) - - -def format_detailed_feedback(feedback: Dict[str, str]) -> str: - """Format detailed feedback into a readable text section.""" - if not feedback: - return "No detailed feedback provided." - - formatted_feedback = [] - for category, content in feedback.items(): - # Convert category name from snake_case to Title Case - category_title = category.replace('_', ' ').title() - formatted_feedback.append(f"### {category_title}\n\n{content}") - - return "\n\n".join(formatted_feedback) - - -def get_score_field(agent_name: str) -> str: - """Get the appropriate score field name for each agent type.""" - score_fields = { - # Section agents - 'S1': 'title_keywords_score', - 'S2': 'abstract_score', - 'S3': 'introduction_score', - 'S4': 'literature_review_score', - 'S5': 'methodology_score', - 'S6': 'results_score', - 'S7': 'discussion_score', - 'S8': 'conclusion_score', - 'S9': 'references_score', - 'S10': 'supplementary_materials_score', - - # Rigor agents - 'R1': 'originality_contribution_score', - 'R2': 'impact_significance_score', - 'R3': 'ethics_compliance_score', - 'R4': 'data_code_availability_score', - 'R5': 'statistical_rigor_score', - 'R6': 'technical_accuracy_score', - 'R7': 'consistency_score', - - # Writing agents - 'W1': 'language_style_score', - 'W2': 'narrative_structure_score', - 'W3': 'clarity_conciseness_score', - 'W4': 'terminology_consistency_score', - 'W5': 'inclusive_language_score', - 'W6': 'citation_formatting_score', - 'W7': 'target_audience_score', - 'W8': 'visual_presentation_score' - } - return score_fields.get(agent_name, 'score') - - -def generate_agent_report(agent_name: str, agent_data: Dict[str, Any]) -> str: - """Generate a report for a specific agent.""" - # Extract the score based on agent type - score_field = get_score_field(agent_name) - - # Try to get the score from the specific field first, then fall back to 'score', then to 0 - score = agent_data.get(score_field) - if score is None or score == "N/A": - score = agent_data.get('score') - if score is None or score == "N/A": - score = 0 - - # Format the report - report = [ - f"### Score: {score}/10", - "\n### Summary", - agent_data.get('summary', "No summary provided."), - "\n### Critical Remarks", - format_critical_remarks(agent_data.get('critical_remarks', [])), - "\n### Improvement Suggestions", - format_improvement_suggestions(agent_data.get('improvement_suggestions', [])), - "\n### Detailed Feedback", - format_detailed_feedback(agent_data.get('detailed_feedback', {})) - ] - - return "\n".join(report) - - -def get_agent_type(agent_name: str) -> str: - """Get the descriptive type of an agent based on its name.""" - agent_types = { - # Section agents - "S1": "Title and Keywords", - "S2": "Abstract", - "S3": "Introduction", - "S4": "Literature Review", - "S5": "Methodology", - "S6": "Results", - "S7": "Discussion", - "S8": "Conclusion", - "S9": "References", - "S10": "Supplementary Materials", - - # Rigor agents - "R1": "Originality and Contribution", - "R2": "Impact and Significance", - "R3": "Ethics and Compliance", - "R4": "Data and Code Availability", - "R5": "Statistical Rigor", - "R6": "Technical Accuracy", - "R7": "Consistency", - - # Writing agents - "W1": "Language and Style", - "W2": "Narrative and Structure", - "W3": "Clarity and Conciseness", - "W4": "Terminology Consistency", - "W5": "Inclusive Language", - "W6": "Citation Formatting", - "W7": "Target Audience Alignment", - "W8": "Visual Presentation" - } - - return agent_types.get(agent_name, "Unknown") - - -def generate_table_of_contents(data: Dict[str, Any]) -> str: - """Generate a table of contents with anchor links.""" - toc = [ - "## Table of Contents", - "- [Overall Assessment](#overall-assessment)", - "- [Agent Reports](#agent-reports)", - " - [Section-Specific Agents (S1-S10)](#section-specific-agents-s1-s10)", - ] - - # Add section agents - for i in range(1, 11): - agent_name = f"S{i}" - if agent_name in data: - agent_type = get_agent_type(agent_name) - toc.append(f" - [{agent_name} - {agent_type}](#s{i}---{agent_type.lower().replace(' ', '-')})") - - # Add rigor agents - toc.append(" - [Rigor Agents (R1-R7)](#rigor-agents-r1-r7)") - for i in range(1, 8): - agent_name = f"R{i}" - if agent_name in data: - agent_type = get_agent_type(agent_name) - toc.append(f" - [{agent_name} - {agent_type}](#r{i}---{agent_type.lower().replace(' ', '-')})") - - # Add writing agents - toc.append(" - [Writing Agents (W1-W8)](#writing-agents-w1-w8)") - for i in range(1, 9): - agent_name = f"W{i}" - if agent_name in data: - agent_type = get_agent_type(agent_name) - toc.append(f" - [{agent_name} - {agent_type}](#w{i}---{agent_type.lower().replace(' ', '-')})") - - return "\n".join(toc) - - -def generate_overall_report(data: Dict[str, Any]) -> str: - """Generate an overall report summarizing all agent reports.""" - # Calculate average scores - scores = [] - for agent_name, agent_data in data.items(): - if isinstance(agent_data, dict): - score_field = get_score_field(agent_name) - score = agent_data.get(score_field) - - # Try specific field first, then generic 'score', then default to 0 - if score is None or not isinstance(score, (int, float)): - score = agent_data.get('score') - if score is None or not isinstance(score, (int, float)): - score = 0 - - scores.append(score) - - avg_score = sum(scores) / len(scores) if scores else 0 - - # Count total critical remarks and improvement suggestions - total_critical_remarks = sum(len(agent_data.get('critical_remarks', [])) for agent_data in data.values() if isinstance(agent_data, dict)) - total_improvement_suggestions = sum(len(agent_data.get('improvement_suggestions', [])) for agent_data in data.values() if isinstance(agent_data, dict)) - - # Generate the overall report - report = [ - "# Manuscript Review Report", - f"\nGenerated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", - "\n## Important Notes", - "\n> **Version 1.0 (Beta)**: This tool was developed in a short timeframe and may contain hallucinations and errors. Approximately 50% of feedback will likely be unusable, 30% may be mediocre, and 20% should be helpful in highlighting issues that might have been overlooked. Please send feedback to rjakob@ethz.ch so we can continue to improve our agents.", - "\n> **Model Limitations**: This report was generated using a basic model (ChatGPT 3.5). For more sophisticated analysis, the code is open access and users can run more advanced models (such as GPT-4) with their own API keys. Visit [https://github.com/robertjakob/rigorous](https://github.com/robertjakob/rigorous) for more information.", - "\n> **Development Status**: This tool is still in testing mode with many agents in their first iteration. We welcome contributions to build the best review agent team to improve the quality of scientific publishing. Join us at [https://github.com/robertjakob/rigorous](https://github.com/robertjakob/rigorous).", - "\n", - generate_table_of_contents(data), - "\n## Overall Assessment", - f"\nThe manuscript received an average score of {avg_score:.2f}/10 across all evaluation criteria.", - f"A total of {total_critical_remarks} critical remarks and {total_improvement_suggestions} improvement suggestions were identified.", - "\n## Agent Reports", - "\n### Section-Specific Agents (S1-S10)", - ] - - # Add section agent reports - for i in range(1, 11): - agent_name = f"S{i}" - if agent_name in data and isinstance(data[agent_name], dict): - agent_type = get_agent_type(agent_name) - report.append(f"\n## {agent_name} - {agent_type}") - report.append(generate_agent_report(agent_name, data[agent_name])) - - # Add rigor agent reports - report.append("\n### Rigor Agents (R1-R7)") - for i in range(1, 8): - agent_name = f"R{i}" - if agent_name in data and isinstance(data[agent_name], dict): - agent_type = get_agent_type(agent_name) - report.append(f"\n## {agent_name} - {agent_type}") - report.append(generate_agent_report(agent_name, data[agent_name])) - - # Add writing agent reports - report.append("\n### Writing Agents (W1-W8)") - for i in range(1, 9): - agent_name = f"W{i}" - if agent_name in data and isinstance(data[agent_name], dict): - agent_type = get_agent_type(agent_name) - report.append(f"\n## {agent_name} - {agent_type}") - report.append(generate_agent_report(agent_name, data[agent_name])) - - return "\n".join(report) - - -def save_report(report: str, output_path: str) -> None: - """Save the report to a file.""" - try: - with open(output_path, 'w', encoding='utf-8') as file: - file.write(report) - print(f"Report successfully saved to {output_path}") - except Exception as e: - print(f"Error saving report: {e}") - - -def main(): - """Main function to run the script.""" - parser = argparse.ArgumentParser(description='Convert JSON results to a text report') - parser.add_argument('--input', '-i', type=str, - default='/Users/robertjakob/rigorous-1/V5_multi_agent2/results/combined_results.json', - help='Path to the input JSON file') - parser.add_argument('--output', '-o', type=str, - default='/Users/robertjakob/rigorous-1/V5_multi_agent2/results/manuscript_report.md', - help='Path to save the output report') - - args = parser.parse_args() - - # Load the JSON data - data = load_json_file(args.input) - if not data: - print("Failed to load JSON data. Exiting.") - return - - # Generate the report - report = generate_overall_report(data) - - # Save the report - save_report(report, args.output) - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/Backup/V5_multi_agent2/src/utils/pdf_parser.py b/Backup/V5_multi_agent2/src/utils/pdf_parser.py deleted file mode 100644 index 4f87cb9..0000000 --- a/Backup/V5_multi_agent2/src/utils/pdf_parser.py +++ /dev/null @@ -1,197 +0,0 @@ -import os -import re -from typing import Dict, Any, List, Tuple -import PyPDF2 -import fitz # PyMuPDF for better PDF handling -import pytesseract -from PIL import Image -import io -import numpy as np - -class PDFParser: - """Enhanced PDF parser with figure and table detection capabilities.""" - - def __init__(self, pdf_path: str): - """Initialize the PDF parser. - - Args: - pdf_path (str): Path to the PDF file - """ - self.pdf_path = pdf_path - if not os.path.exists(pdf_path): - raise FileNotFoundError(f"PDF file not found: {pdf_path}") - - # Open the document once and keep it open - self.doc = fitz.open(pdf_path) - - def __del__(self): - """Clean up by closing the document.""" - if hasattr(self, 'doc'): - self.doc.close() - - def extract_text(self) -> str: - """Extract text from the PDF using PyMuPDF for better accuracy.""" - text = "" - try: - for page in self.doc: - text += page.get_text() + "\n" - return text - except Exception as e: - raise Exception(f"Failed to extract text from PDF: {str(e)}") - - def get_metadata(self) -> Dict[str, str]: - """Extract metadata from the PDF.""" - try: - metadata = self.doc.metadata - - return { - 'title': metadata.get('title', 'Unknown'), - 'author': metadata.get('author', 'Unknown'), - 'creation_date': metadata.get('creationDate', 'Unknown'), - 'page_count': str(self.doc.page_count) - } - except Exception as e: - raise Exception(f"Failed to extract metadata from PDF: {str(e)}") - - def extract_images(self) -> List[Dict[str, Any]]: - """Extract images from the PDF with their locations and captions.""" - images = [] - try: - for page_num, page in enumerate(self.doc): - # Extract images - image_list = page.get_images() - - for img_idx, img in enumerate(image_list): - xref = img[0] - base_image = self.doc.extract_image(xref) - - if base_image: - # Convert image data to PIL Image for analysis - image_data = base_image["image"] - image = Image.open(io.BytesIO(image_data)) - - # Get image location on page - rect = page.get_image_rects(xref)[0] # Get first occurrence - - # Try to find caption near the image - caption = self._find_caption_near_rect(page, rect, "Figure") - - images.append({ - 'page': page_num + 1, - 'index': img_idx + 1, - 'bbox': [rect.x0, rect.y0, rect.x1, rect.y1], # Convert to list for JSON - 'size': image.size, - 'format': base_image["ext"], - 'caption': caption, - 'image_data': image_data # Raw image data - }) - - return images - except Exception as e: - raise Exception(f"Failed to extract images from PDF: {str(e)}") - - def extract_tables(self) -> List[Dict[str, Any]]: - """Extract tables from the PDF using text analysis.""" - tables = [] - try: - for page_num, page in enumerate(self.doc): - # Find potential table regions using text analysis - blocks = page.get_text("blocks") - table_regions = self._identify_table_regions(blocks) - - for region_idx, region in enumerate(table_regions): - # Extract region text - table_text = page.get_text(clip=region) - - # Try to find caption near the table - caption = self._find_caption_near_rect(page, region, "Table") - - tables.append({ - 'page': page_num + 1, - 'index': region_idx + 1, - 'bbox': [region.x0, region.y0, region.x1, region.y1], # Convert to list for JSON - 'text': table_text, - 'caption': caption - }) - - return tables - except Exception as e: - raise Exception(f"Failed to extract tables from PDF: {str(e)}") - - def _identify_table_regions(self, blocks: List[Any]) -> List[fitz.Rect]: - """Identify potential table regions using text block analysis.""" - table_regions = [] - current_region = None - - for block in blocks: - text = block[4] - rect = fitz.Rect(block[:4]) - - # Heuristics for table detection - if (self._looks_like_table_content(text) or - self._looks_like_table_header(text)): - if current_region is None: - current_region = rect - else: - current_region = current_region | rect # Union of rectangles - elif current_region is not None: - table_regions.append(current_region) - current_region = None - - if current_region is not None: - table_regions.append(current_region) - - return table_regions - - def _looks_like_table_content(self, text: str) -> bool: - """Check if text block looks like table content.""" - # Heuristics for table content detection - if not text.strip(): - return False - - # Check for regular patterns of numbers or short text segments - lines = text.split('\n') - if len(lines) < 2: - return False - - # Check for consistent delimiters or spacing - delimiter_pattern = re.compile(r'[\t|,]|\s{2,}') - delimiter_counts = [len(re.findall(delimiter_pattern, line)) for line in lines] - - # If most lines have the same number of delimiters, likely a table - return len(set(delimiter_counts)) <= 2 - - def _looks_like_table_header(self, text: str) -> bool: - """Check if text block looks like table header.""" - # Common table header indicators - header_indicators = ['mean', 'std', 'min', 'max', 'total', 'average', - 'value', 'type', 'name', 'id', 'description'] - - text_lower = text.lower() - words = text_lower.split() - - # Check for header-like words - return any(indicator in words for indicator in header_indicators) - - def _find_caption_near_rect(self, page: Any, rect: fitz.Rect, - element_type: str) -> str: - """Find caption near a given rectangle on the page.""" - # Create expanded search area - margin = 20 # points - search_rect = fitz.Rect( - rect.x0 - margin, - rect.y0 - margin, - rect.x1 + margin, - rect.y1 + margin - ) - - # Get text in the search area - nearby_text = page.get_text("text", clip=search_rect) - - # Look for caption patterns - caption_pattern = f"{element_type}\s*\d+[.:](.*?)(?:\n\n|$)" - match = re.search(caption_pattern, nearby_text, re.IGNORECASE | re.DOTALL) - - if match: - return match.group(1).strip() - return "" \ No newline at end of file diff --git a/Backup/V5_multi_agent2/tests/test_all_agents.py b/Backup/V5_multi_agent2/tests/test_all_agents.py deleted file mode 100644 index ea6b366..0000000 --- a/Backup/V5_multi_agent2/tests/test_all_agents.py +++ /dev/null @@ -1,187 +0,0 @@ -import pytest -import json -from src.reviewer_agents.rigor.R1_originality_contribution_agent import OriginalityContributionAgent -from src.reviewer_agents.rigor.R2_impact_significance_agent import ImpactSignificanceAgent -from src.reviewer_agents.rigor.R3_ethics_compliance_agent import EthicsComplianceAgent -from src.reviewer_agents.rigor.R4_data_code_availability_agent import DataCodeAvailabilityAgent -from src.reviewer_agents.rigor.R5_statistical_rigor_agent import StatisticalRigorAgent -from src.reviewer_agents.rigor.R6_technical_accuracy_agent import TechnicalAccuracyAgent -from src.reviewer_agents.rigor.R7_consistency_agent import ConsistencyAgent -from src.reviewer_agents.writing.W1_language_style_agent import LanguageStyleAgent -from src.reviewer_agents.writing.W2_narrative_structure_agent import NarrativeStructureAgent -from src.reviewer_agents.writing.W3_clarity_conciseness_agent import ClarityConcisenessAgent -from src.reviewer_agents.writing.W4_terminology_consistency_agent import TerminologyConsistencyAgent - -@pytest.fixture -def sample_manuscript(): - return """ - Title: A Novel Approach to Machine Learning in Healthcare - - Abstract: - This paper presents a new machine learning framework for healthcare applications, - focusing on improved patient outcome prediction. We demonstrate significant - improvements over existing methods in terms of accuracy and interpretability. - - Introduction: - Machine learning has revolutionized healthcare analytics in recent years. - However, existing approaches often lack interpretability and fail to incorporate - domain-specific medical knowledge effectively. Our novel framework addresses - these limitations through a hybrid approach combining deep learning with - traditional statistical methods. - - Methods: - We propose a new neural network architecture that incorporates medical domain - knowledge through constrained optimization. Our approach uses a combination of - attention mechanisms and traditional statistical methods. The framework was - implemented using PyTorch and evaluated on a large-scale healthcare dataset. - - Results: - Our method achieves 95% accuracy (p < 0.001) on the test set, representing a - significant improvement over the baseline (85%, p < 0.001). The attention - mechanisms provide interpretable insights into the model's decision-making - process, which was validated by domain experts. - - Discussion: - The results demonstrate the effectiveness of our approach in both accuracy - and interpretability. The hybrid architecture successfully balances the power - of deep learning with the interpretability requirements of healthcare applications. - - Ethics Statement: - This study was approved by the IRB (approval #12345). All patient data was - anonymized and handled according to HIPAA guidelines. No conflicts of interest - were declared. - - Data Availability: - The dataset used in this study is available at [repository URL]. Code for - reproducing all results is available at [code repository URL]. - """ - -@pytest.fixture -def agents(): - # Use gpt-3.5-turbo for all agents - model = "gpt-3.5-turbo" - - # Initialize rigor agents - rigor_agents = { - "R1": OriginalityContributionAgent(model=model), - "R2": ImpactSignificanceAgent(model=model), - "R3": EthicsComplianceAgent(model=model), - "R4": DataCodeAvailabilityAgent(model=model), - "R5": StatisticalRigorAgent(model=model), - "R6": TechnicalAccuracyAgent(model=model), - "R7": ConsistencyAgent(model=model) - } - - # Initialize writing agents - writing_agents = { - "W1": LanguageStyleAgent(model=model), - "W2": NarrativeStructureAgent(model=model), - "W3": ClarityConcisenessAgent(model=model), - "W4": TerminologyConsistencyAgent(model=model) - } - - return {**rigor_agents, **writing_agents} - -def test_all_agents(agents, sample_manuscript): - """Test all agents with a sample manuscript and print their reviews""" - print("\n=== Testing All Agents with gpt-3.5-turbo ===\n") - - # Test each agent - for name, agent in agents.items(): - print(f"\n--- {name} Review ---") - - try: - # Call the appropriate analysis method based on agent type - if name == "R1": - result = agent.analyze_originality_contribution(sample_manuscript, "healthcare") - elif name == "R2": - result = agent.analyze_impact_significance(sample_manuscript, {"field": "healthcare"}) - elif name == "R3": - result = agent.analyze_ethics_compliance(sample_manuscript, "healthcare") - elif name == "R4": - result = agent.analyze_data_code_availability(sample_manuscript, "healthcare") - elif name == "R5": - result = agent.analyze_statistical_rigor(sample_manuscript, "healthcare") - elif name == "R6": - result = agent.analyze_technical_accuracy(sample_manuscript, "healthcare") - elif name == "R7": - result = agent.analyze_consistency(sample_manuscript, "healthcare") - elif name == "W1": - result = agent.analyze_language_style(sample_manuscript, "healthcare") - elif name == "W2": - result = agent.analyze_narrative_structure(sample_manuscript, "healthcare") - elif name == "W3": - result = agent.analyze_clarity_conciseness(sample_manuscript, "healthcare") - else: # W4 - result = agent.analyze_terminology_consistency(sample_manuscript, "healthcare") - - # Print the score - score_key = [k for k in result.keys() if k.endswith('_score')][0] - print(f"Score: {result[score_key]}/10") - - # Print critical remarks - print("\nCritical Remarks:") - if isinstance(result["critical_remarks"], dict): - for category, remarks in result["critical_remarks"].items(): - print(f" {category}:") - for remark in remarks: - print(f" - {remark}") - else: # List type - for remark in result["critical_remarks"]: - if isinstance(remark, dict): - print(f" {remark.get('category', 'General')}:") - print(f" - {remark.get('issue', remark)}") - else: - print(f" - {remark}") - - # Print improvement suggestions - print("\nImprovement Suggestions:") - if isinstance(result["improvement_suggestions"], dict): - for category, suggestions in result["improvement_suggestions"].items(): - print(f" {category}:") - for suggestion in suggestions: - print(f" - {suggestion}") - else: # List type - for suggestion in result["improvement_suggestions"]: - if isinstance(suggestion, dict): - print(f" - {suggestion.get('suggestion', suggestion)}") - else: - print(f" - {suggestion}") - - # Print summary - print(f"\nSummary: {result['summary']}") - - # Verify output format - assert isinstance(result, dict) - assert any(key.endswith('_score') for key in result.keys()) - assert "critical_remarks" in result - assert "improvement_suggestions" in result - assert "detailed_feedback" in result - assert "summary" in result - - print("\n" + "="*50) - - except Exception as e: - print(f"Error testing {name}: {str(e)}") - print("\n" + "="*50) - -def test_agent_initialization(agents): - """Test that all agents initialize correctly with expected attributes""" - expected_names = { - "R1": "R1_Originality_Contribution_Agent", - "R2": "R2_Impact_Significance_Agent", - "R3": "R3_Ethics_Compliance_Agent", - "R4": "R4_Data_Code_Availability_Agent", - "R5": "R5_Statistical_Rigor_Agent", - "R6": "R6_Technical_Accuracy_Agent", - "R7": "R7_Consistency_Agent", - "W1": "W1_Language_Style_Agent", - "W2": "W2_Narrative_Structure_Agent", - "W3": "W3_Clarity_Conciseness_Agent", - "W4": "W4_Terminology_Consistency_Agent" - } - - for name, agent in agents.items(): - assert agent.model == "gpt-3.5-turbo" - assert agent.name == expected_names[name] - assert agent.category in ["Scientific Rigor", "Writing Quality", "Writing and Presentation"] \ No newline at end of file diff --git a/Backup/V5_multi_agent2/tests/test_rigor_agents.py b/Backup/V5_multi_agent2/tests/test_rigor_agents.py deleted file mode 100644 index 3914e5d..0000000 --- a/Backup/V5_multi_agent2/tests/test_rigor_agents.py +++ /dev/null @@ -1,128 +0,0 @@ -import pytest -import json -from src.reviewer_agents.rigor.R1_originality_contribution_agent import OriginalityContributionAgent -from src.reviewer_agents.rigor.R2_impact_significance_agent import ImpactSignificanceAgent -from src.reviewer_agents.rigor.R3_ethics_compliance_agent import EthicsComplianceAgent -from src.reviewer_agents.rigor.R4_data_code_availability_agent import DataCodeAvailabilityAgent -from src.reviewer_agents.rigor.R5_statistical_rigor_agent import StatisticalRigorAgent -from src.reviewer_agents.rigor.R6_technical_accuracy_agent import TechnicalAccuracyAgent -from src.reviewer_agents.rigor.R7_consistency_agent import ConsistencyAgent - -@pytest.fixture -def sample_manuscript(): - return """ - Title: A Novel Approach to Machine Learning in Healthcare - - Abstract: - This paper presents a new machine learning framework for healthcare applications, - focusing on improved patient outcome prediction. We demonstrate significant - improvements over existing methods in terms of accuracy and interpretability. - - Methods: - We propose a new neural network architecture that incorporates medical domain - knowledge through constrained optimization. Our approach uses a combination of - attention mechanisms and traditional statistical methods. - - Results: - Our method achieves 95% accuracy (p < 0.001) on the test set. All data and code - are available at https://github.com/example/healthcare-ml. - - Ethics Statement: - This study was approved by the IRB (approval #12345). All patient data was - anonymized and handled according to HIPAA guidelines. - """ - -@pytest.fixture -def agents(): - return { - "R1": OriginalityContributionAgent(model="gpt-3.5-turbo"), - "R2": ImpactSignificanceAgent(model="gpt-3.5-turbo"), - "R3": EthicsComplianceAgent(model="gpt-3.5-turbo"), - "R4": DataCodeAvailabilityAgent(model="gpt-3.5-turbo"), - "R5": StatisticalRigorAgent(model="gpt-3.5-turbo"), - "R6": TechnicalAccuracyAgent(model="gpt-3.5-turbo"), - "R7": ConsistencyAgent(model="gpt-3.5-turbo") - } - -def test_agent_initialization(agents): - """Test that all agents initialize correctly with expected attributes""" - expected_names = { - "R1": "R1_Originality_Contribution_Agent", - "R2": "R2_Impact_Significance_Agent", - "R3": "R3_Ethics_Compliance_Agent", - "R4": "R4_Data_Code_Availability_Agent", - "R5": "R5_Statistical_Rigor_Agent", - "R6": "R6_Technical_Accuracy_Agent", - "R7": "R7_Consistency_Agent" - } - for name, agent in agents.items(): - assert agent.model == "gpt-3.5-turbo" - assert agent.name == expected_names[name] - assert agent.category == "Scientific Rigor" - -def test_originality_contribution_analysis(agents, sample_manuscript): - """Test R1 agent's analysis functionality""" - result = agents["R1"].analyze_originality_contribution(sample_manuscript, "healthcare") - assert isinstance(result, dict) - assert "originality_contribution_score" in result - assert isinstance(result["originality_contribution_score"], (int, float)) - assert 1 <= result["originality_contribution_score"] <= 10 - assert "critical_remarks" in result - assert "improvement_suggestions" in result - assert "detailed_feedback" in result - assert "summary" in result - -def test_impact_significance_analysis(agents, sample_manuscript): - """Test R2 agent's analysis functionality""" - result = agents["R2"].analyze_impact_significance(sample_manuscript, {"field": "healthcare"}) - assert isinstance(result, dict) - assert "impact_significance_score" in result - assert 1 <= result["impact_significance_score"] <= 10 - assert "critical_remarks" in result - assert "improvement_suggestions" in result - -def test_ethics_compliance_analysis(agents, sample_manuscript): - """Test R3 agent's analysis functionality""" - result = agents["R3"].analyze_ethics_compliance(sample_manuscript, "healthcare") - assert isinstance(result, dict) - assert "ethics_compliance_score" in result - assert 1 <= result["ethics_compliance_score"] <= 10 - assert "critical_remarks" in result - assert "improvement_suggestions" in result - -def test_error_handling(agents): - """Test error handling for invalid inputs""" - # Test with empty input - result = agents["R1"].analyze_originality_contribution("", "healthcare") - assert result.get("error") is True - assert "Error in analysis" in result.get("summary", "") - - # Test with None input - result = agents["R2"].analyze_impact_significance(None, {"field": "healthcare"}) - assert result.get("error") is True - assert "Error in analysis" in result.get("summary", "") - -def test_output_format_consistency(agents, sample_manuscript): - """Test that all agents return consistently formatted output""" - for name, agent in agents.items(): - if name == "R1": - result = agent.analyze_originality_contribution(sample_manuscript, "healthcare") - elif name == "R2": - result = agent.analyze_impact_significance(sample_manuscript, {"field": "healthcare"}) - elif name == "R3": - result = agent.analyze_ethics_compliance(sample_manuscript, "healthcare") - elif name == "R4": - result = agent.analyze_data_code_availability(sample_manuscript, "healthcare") - elif name == "R5": - result = agent.analyze_statistical_rigor(sample_manuscript, "healthcare") - elif name == "R6": - result = agent.analyze_technical_accuracy(sample_manuscript, "healthcare") - else: # R7 - result = agent.analyze_consistency(sample_manuscript, "healthcare") - - assert isinstance(result, dict) - assert any(key.endswith('_score') for key in result.keys()) - assert "critical_remarks" in result - assert "improvement_suggestions" in result - assert "detailed_feedback" in result - assert "summary" in result \ No newline at end of file diff --git a/Backup/V5_multi_agent2/tests/test_writing_agents.py b/Backup/V5_multi_agent2/tests/test_writing_agents.py deleted file mode 100644 index 2d57cf4..0000000 --- a/Backup/V5_multi_agent2/tests/test_writing_agents.py +++ /dev/null @@ -1,141 +0,0 @@ -import pytest -import json -from src.reviewer_agents.writing.W1_language_style_agent import LanguageStyleAgent -from src.reviewer_agents.writing.W2_narrative_structure_agent import NarrativeStructureAgent -from src.reviewer_agents.writing.W3_clarity_conciseness_agent import ClarityConciseAgent -from src.reviewer_agents.writing.W4_terminology_consistency_agent import TerminologyConsistencyAgent - -@pytest.fixture -def sample_manuscript(): - return """ - Title: A Novel Approach to Machine Learning in Healthcare - - Abstract: - This paper presents a new machine learning framework for healthcare applications, - focusing on improved patient outcome prediction. We demonstrate significant - improvements over existing methods in terms of accuracy and interpretability. - - Introduction: - Machine learning has revolutionized healthcare analytics in recent years. - However, existing approaches often lack interpretability and fail to incorporate - domain-specific medical knowledge effectively. Our novel framework addresses - these limitations through a hybrid approach combining deep learning with - traditional statistical methods. - - Methods: - We propose a new neural network architecture that incorporates medical domain - knowledge through constrained optimization. Our approach uses a combination of - attention mechanisms and traditional statistical methods. The framework was - implemented using PyTorch and evaluated on a large-scale healthcare dataset. - - Results: - Our method achieves 95% accuracy (p < 0.001) on the test set, representing a - significant improvement over the baseline (85%, p < 0.001). The attention - mechanisms provide interpretable insights into the model's decision-making - process, which was validated by domain experts. - - Discussion: - The results demonstrate the effectiveness of our approach in both accuracy - and interpretability. The hybrid architecture successfully balances the power - of deep learning with the interpretability requirements of healthcare applications. - """ - -@pytest.fixture -def agents(): - return { - "W1": LanguageStyleAgent(model="gpt-3.5-turbo"), - "W2": NarrativeStructureAgent(model="gpt-3.5-turbo"), - "W3": ClarityConciseAgent(model="gpt-3.5-turbo"), - "W4": TerminologyConsistencyAgent(model="gpt-3.5-turbo") - } - -def test_agent_initialization(agents): - """Test that all agents initialize correctly with expected attributes""" - for name, agent in agents.items(): - assert agent.model == "gpt-3.5-turbo" - assert agent.name == f"{name}_Agent" - assert agent.category == "Writing Quality" - -def test_language_style_analysis(agents, sample_manuscript): - """Test W1 agent's analysis functionality""" - result = agents["W1"].analyze_language_style(sample_manuscript) - assert isinstance(result, dict) - assert "language_style_score" in result - assert isinstance(result["language_style_score"], (int, float)) - assert 1 <= result["language_style_score"] <= 10 - assert "critical_remarks" in result - assert "improvement_suggestions" in result - assert "detailed_feedback" in result - assert "summary" in result - -def test_narrative_structure_analysis(agents, sample_manuscript): - """Test W2 agent's analysis functionality""" - result = agents["W2"].analyze_narrative_structure(sample_manuscript) - assert isinstance(result, dict) - assert "narrative_structure_score" in result - assert 1 <= result["narrative_structure_score"] <= 10 - assert "critical_remarks" in result - assert "improvement_suggestions" in result - -def test_clarity_conciseness_analysis(agents, sample_manuscript): - """Test W3 agent's analysis functionality""" - result = agents["W3"].analyze_clarity_conciseness(sample_manuscript) - assert isinstance(result, dict) - assert "clarity_conciseness_score" in result - assert 1 <= result["clarity_conciseness_score"] <= 10 - assert "critical_remarks" in result - assert "improvement_suggestions" in result - -def test_terminology_consistency_analysis(agents, sample_manuscript): - """Test W4 agent's analysis functionality""" - result = agents["W4"].analyze_terminology_consistency(sample_manuscript) - assert isinstance(result, dict) - assert "terminology_consistency_score" in result - assert 1 <= result["terminology_consistency_score"] <= 10 - assert "critical_remarks" in result - assert "improvement_suggestions" in result - -def test_error_handling(agents): - """Test error handling for invalid inputs""" - with pytest.raises(ValueError): - agents["W1"].analyze_language_style("") - - with pytest.raises(ValueError): - agents["W2"].analyze_narrative_structure(None) - -def test_output_format_consistency(agents, sample_manuscript): - """Test that all agents return consistently formatted output""" - for name, agent in agents.items(): - if name == "W1": - result = agent.analyze_language_style(sample_manuscript) - elif name == "W2": - result = agent.analyze_narrative_structure(sample_manuscript) - elif name == "W3": - result = agent.analyze_clarity_conciseness(sample_manuscript) - else: # W4 - result = agent.analyze_terminology_consistency(sample_manuscript) - - assert isinstance(result, dict) - assert any(key.endswith('_score') for key in result.keys()) - assert "critical_remarks" in result - assert "improvement_suggestions" in result - assert "detailed_feedback" in result - assert "summary" in result - -def test_cross_agent_consistency(agents, sample_manuscript): - """Test that scores and feedback are consistent across agents""" - results = {} - for name, agent in agents.items(): - if name == "W1": - results[name] = agent.analyze_language_style(sample_manuscript) - elif name == "W2": - results[name] = agent.analyze_narrative_structure(sample_manuscript) - elif name == "W3": - results[name] = agent.analyze_clarity_conciseness(sample_manuscript) - else: # W4 - results[name] = agent.analyze_terminology_consistency(sample_manuscript) - - # Check that no agent gives wildly different scores - scores = [list(result.values())[0] for result in results.values()] - max_score_diff = max(scores) - min(scores) - assert max_score_diff <= 3 # No more than 3 points difference between highest and lowest scores \ No newline at end of file diff --git a/README.md b/README.md index bd0987a..2f207fe 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,8 @@ # Rigorous - AI-Powered Scientific Manuscript Analysis -We want to change how scientific work is created, evaluated, and shared. This repository intends to provide the tools to make scientific publishing more transparent, affordable, and agile. +> **Cloud Version Available:** A cloud version of the AI Peer Reviewer is now available at [https://www.rigorous.company/](https://www.rigorous.company/). Simply upload your manuscript, provide context on target journal and review focus, and receive a comprehensive PDF report via email within 1-2 working days. The cloud version is currently free for testing purposes. + +This repository contains tools for making scientific publishing more transparent, cheaper, faster, and ensuring rigorous peer and AI review. ## Project Structure @@ -18,9 +20,9 @@ We want to change how scientific work is created, evaluated, and shared. This re ### In Development - **Agent2_Outlet_Fit**: 🚧 In Development - - Will help reviewers evaluate manuscripts against specific criteria - - Support journals/conferences in desk rejection decisions - - Enable researchers to pre-check manuscripts before submission + - Core functionality being implemented + - Integration with Agent1_Peer_Review in progress + - Testing and validation ongoing ## PDF Report Generation @@ -55,16 +57,6 @@ This project includes a PDF report generator that creates a professional peer re For more details, see the comments in `Agent1_Peer_Review/pdf_generator.py`. -## Earlier Iterations - -Earlier iterations of the peer review system can be found in the `backup` folder: -- **V2_Editorial_First_Decision_Support**: Tool for checking manuscripts against editorial requirements. -- **V3_Peer_Review**: Enhanced tool for comprehensive peer review of academic manuscripts. -- **V4_Multi-Agent**: Advanced multi-agent system for collaborative peer review with specialized AI reviewers. -- **V5_Multi-Agent2**: Comprehensive multi-agent system with 25 specialized agents for more detailed, reliable, and specific feedback. - -These are provided for reference only and are not part of the current production workflow. - ## Requirements - Python 3.7+