# Research Plan: Health Data from Phone Frameworks

## Objective
Research what health data can be accessed from mobile health frameworks with proper permissions:
- **Apple HealthKit** (iOS)
- **Google Health Connect** (Android)
- **Samsung Health SDK** (optional - additional coverage)

## Research Areas

### 1. Apple HealthKit (iOS)
Research questions:
- What health data types are available?
- What permissions are required for each data type?
- What are the user privacy requirements?
- How to read historical vs real-time data?
- What metadata is available (timestamps, device info, units)?
- Any special requirements for medical data vs fitness data?

### 2. Google Health Connect (Android)
Research questions:
- What health data types are supported?
- What permissions model does Health Connect use?
- How does it differ from the old Google Fit API?
- What are the integration requirements?
- Can it read data from third-party apps (Fitbit, Strava, etc.)?
- What metadata is available?

### 3. Data Type Comparison
Compare both frameworks:
- Which data types are available on both platforms?
- Which are platform-specific?
- Unit differences and conversions needed
- Timestamp precision differences
- Data granularity differences

### 4. Technical Implementation Requirements
Research:
- SDK/library requirements for both platforms
- Background data sync capabilities
- Battery usage considerations
- Data storage strategies (local cache vs direct sync)
- Error handling for missing permissions

## Expected Deliverables
1. Complete list of available health data types for each platform
2. Permission requirements mapping
3. Comparison matrix (iOS vs Android)
4. Recommended data model additions for MVP
5. Integration complexity assessment
6. Privacy and security considerations

## Search Strategy
Will research:
- Official Apple HealthKit documentation
- Official Google Health Connect documentation
- Developer guides and best practices
- Data type reference documentation
- Permission and privacy requirements
- Code examples and integration patterns