A comprehensive Python toolkit for analyzing Apple Health export data, providing insights into various health metrics including heart rate, sleep patterns, activity levels, and more.

• Resting Heart Rate Analysis - Track trends and patterns in your resting heart rate
• Heart Rate Variability (HRV) - Monitor stress and recovery through HRV metrics
• Sleep Pattern Analysis - Understand your sleep duration and quality over time
• Oxygen Saturation Tracking - Monitor SpO2 levels and identify potential issues
• Activity Metrics - Analyze daily steps, calories burned, and exercise patterns
• Blood Pressure Monitoring - Track systolic/diastolic trends and identify hypertension risks
• Comprehensive Health Dashboard - Generate combined visualizations of all health metrics

• Python 3.7+
• Apple Health data export (XML format)

1. Clone this repository:

git clone https://github.com/yourusername/apple-health-data-analysis.git
cd apple-health-data-analysis

2. Install required dependencies:

pip install -r requirements.txt

1. Open the Health app on your iPhone
2. Tap your profile picture in the top right
3. Scroll down and tap "Export All Health Data"
4. Choose "Export" and save the ZIP file
5. Extract the ZIP file - you'll need the export.xml file
6. Rename it to apple_health_export.xml (or update the filename in scripts)

Analyze all health metrics at once:

python example_usage.py --input apple_health_export.xml --analysis all

Run specific analyses:

# Sleep analysis
python analyze_sleep_patterns.py

# Heart rate variability
python analyze_hrv.py

# Oxygen saturation
python analyze_oxygen_saturation.py

# Activity and energy
python analyze_activity_energy.py

# Blood pressure
python analyze_blood_pressure.py

For a quick overview of all your health data:

python extract_key_statistics.py

Create a combined visualization of all metrics:

python create_combined_dashboard.py

Edit config.py to customize:

• Default file paths
• Health metric thresholds
• Chart appearance settings
• Date ranges for analysis

All scripts generate:

• PNG charts - High-resolution visualizations saved in the current directory
• Console output - Statistical summaries and insights
• CSV exports - Some scripts export processed data for further analysis

The toolkit generates various charts including:

• Time series plots with moving averages
• Distribution histograms
• Correlation matrices
• Yearly/monthly aggregations
• Trend analysis with regression lines

• No data leaves your computer - All analysis is performed locally
• Add .gitignore - The included .gitignore file prevents accidental upload of personal health data
• Review before sharing - Always check generated images before sharing to ensure no sensitive information is visible

apple-health-data-analysis/
├── README.md                          # This file
├── requirements.txt                   # Python dependencies
├── config.py                         # Configuration settings
├── example_usage.py                  # Example script with CLI interface
├── .gitignore                        # Prevents uploading personal data
│
├── analyze_health_data_types.py      # Discover available data types
├── extract_key_statistics.py         # Quick statistical overview
│
├── analyze_sleep_patterns.py         # Sleep analysis
├── analyze_oxygen_saturation.py      # Blood oxygen analysis
├── analyze_hrv.py                    # Heart rate variability
├── analyze_blood_pressure.py         # Blood pressure tracking
├── analyze_activity_energy.py        # Activity and calorie analysis
├── analyze_resting_heart_rate.py     # Resting heart rate trends
│
└── create_combined_dashboard.py      # Generate comprehensive dashboard

• Normal resting HR: 60-100 BPM (lower is generally better)
• Athletes: May have resting HR as low as 40-60 BPM

• Higher HRV: Generally indicates better stress resilience
• Normal range: 20-100ms (varies by age and fitness)

• Normal: 95-100%
• Concerning: Below 95% (consult healthcare provider)

• Recommended: 7-9 hours per night for adults
• Quality matters: Consistency is as important as duration

• Steps target: 8,000-10,000 steps per day
• Active calories: Aim for 500+ kcal from activity daily

If processing times out due to large files:

1. Use extract_key_statistics.py for a sampled analysis
2. Modify scripts to process specific date ranges
3. Increase the sampling rate in configuration

Not all analyses will work if you haven't been tracking certain metrics. The scripts will indicate if data is missing.

For very large exports (>1GB), consider:

• Running analyses one at a time
• Using a computer with more RAM
• Modifying scripts to process data in chunks

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

This project is licensed under the MIT License - see the LICENSE file for details.

This tool is for informational purposes only and should not be used as a substitute for professional medical advice. Always consult with healthcare providers for medical decisions.

• Built with Python, pandas, and matplotlib
• Inspired by the quantified self movement
• Thanks to Apple for making health data exportable