Add comprehensive user profile and onboarding system with secure health data management

[Copilot]

Overview

Implements a complete multi-step onboarding system for a nutrition mobile app that collects comprehensive user health and preference data. This foundation component enables personalized nutrition plan generation throughout the app with production-ready security and validation.

What's New

Multi-Step Onboarding Flow (6 Steps)

A guided onboarding experience with visual progress indicators that collects:

1. Basic Demographics - Age, weight, height, and gender with validated input ranges
2. Activity Level - Four activity levels from sedentary to very active with descriptions
3. Nutrition Goals - Multiple selection including weight loss, muscle gain, diabetes management, maintenance, and general health
4. Medical Conditions - Comprehensive dropdown with diabetes, hypertension, heart disease, and more
5. Food Allergies - 11 allergy options including nuts, shellfish, lactose, eggs, and others
6. Dietary Preferences - 10 options including vegetarian, vegan, keto, mediterranean, gluten-free, dairy-free with conflict detection

Intelligent Validation System

Three-level validation ensures data quality and user safety:

• Field Validation: Age (13-120 years), weight (20-300 kg), height (100-250 cm)
• Logical Consistency: Detects conflicting dietary preferences (e.g., vegan + keto) and redundancies (vegan + vegetarian)
• Health Recommendations: BMI-based suggestions, medical condition alignment (celiac → gluten-free), and allergy-diet correlation (milk allergy → dairy-free)

Production-Ready Security

Health data protection using hardware-backed encryption:

• iOS: AES-256 encryption via Keychain with kSecAttrAccessibleWhenUnlockedThisDeviceOnly
• Android: AES-256 encryption via Android Keystore with EncryptedSharedPreferences
• Integrity Verification: SHA-256 hashing to detect data tampering
• Privacy First: All data stored locally, no network transmission, no sensitive data logging

Profile Management

Complete profile lifecycle management:

• View formatted profile with BMI calculation and health categorization
• Edit profile by re-running onboarding with pre-populated data
• Delete profile with confirmation dialog
• Last updated timestamp tracking

Privacy & Compliance

Clear disclosure and user consent:

• Comprehensive privacy policy screen explaining data collection and usage
• Security measures documentation
• User rights outlined (access, update, delete)
• Medical disclaimer for health-related features
• Required acceptance before data collection

Technical Implementation

Architecture

• React Native with Expo for cross-platform development (iOS, Android, Web)
• TypeScript with strict mode for type safety
• React Navigation for smooth screen transitions
• Expo SecureStore for encrypted storage
• Expo Crypto for SHA-256 hashing

Code Quality

• 2,691 lines of production code across 17 components
• 16 comprehensive tests covering validation logic
• ESLint configuration with rules preventing console.log
• TypeScript strict mode for compile-time safety
• Reusable components (ProgressIndicator, MultiSelectList, SingleSelectList)

Components Created

src/
├── screens/
│   ├── onboarding/ (6 step components)
│   ├── HomeScreen.tsx
│   ├── ProfileEditScreen.tsx
│   ├── PrivacyPolicyScreen.tsx
│   └── OnboardingContainer.tsx
├── components/
│   ├── ProgressIndicator.tsx
│   ├── MultiSelectList.tsx
│   └── SingleSelectList.tsx
├── utils/
│   ├── validation.ts (comprehensive validation logic)
│   └── secureStorage.ts (encrypted storage with integrity checks)
├── types/
│   └── UserProfile.ts (complete type system)
└── constants/
    └── options.ts (all dropdown options)

Documentation

Six comprehensive documentation files totaling 1,299 lines:

• PROJECT_OVERVIEW.md - Complete project summary with metrics and user journey
• README.md - Updated with full project information and setup instructions
• ARCHITECTURE.md - System architecture, data flow, and security implementation
• CONTRIBUTING.md - Contribution guidelines and code standards
• SETUP_GUIDE.md - Step-by-step setup instructions with troubleshooting
• IMPLEMENTATION_SUMMARY.md - Detailed technical implementation notes

Example Usage

// User completes onboarding
const profile: UserProfile = {
  age: 30,
  weight: 70,
  height: 175,
  gender: 'male',
  activityLevel: 'moderately_active',
  nutritionGoals: ['weight_loss', 'general_health'],
  medicalConditions: ['none'],
  foodAllergies: ['none'],
  dietaryPreferences: ['mediterranean', 'gluten_free'],
  createdAt: '2024-01-15T10:00:00Z',
  updatedAt: '2024-01-15T10:00:00Z'
};

// Securely saved with hardware-backed encryption
await SecureProfileStorage.saveProfile(profile);

// Validation catches conflicts
ProfileValidator.validateDietaryPreferences(['vegan', 'keto']);
// Returns: "Vegan and Keto diets are difficult to combine"

Testing

Comprehensive test coverage for validation logic:

npm test

16 tests covering:

• Demographics validation (age, weight, height ranges)
• Dietary preference conflict detection
• Health consistency checks (medical conditions → goals alignment)
• Complete profile validation

Security Considerations

This implementation prioritizes user privacy and data security:

• No custom encryption: Relies on OS-level hardware-backed encryption (AES-256)
• Data integrity: SHA-256 hashing detects unauthorized modifications
• Local only: No data transmission to servers
• User control: One-tap profile deletion
• No logging: Sensitive information never logged to console

Future Enhancements

The modular architecture supports easy extension:

• Meal plan generation based on collected preferences
• Progress tracking over time
• Integration with Apple Health / Google Fit
• Cloud backup (optional, with user consent)
• Multi-language support (i18n)

Platform Support

✅ iOS (iPhone & iPad)
✅ Android (phones & tablets)
✅ Web (responsive design)

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This implementation provides a solid, secure foundation for personalized nutrition planning while maintaining user privacy and data security as top priorities.

Original prompt

User Profile and Onboarding System

Project Context

Foundation component that collects comprehensive user health and preference data to enable personalized nutrition plan generation throughout the app.

Details

Create a multi-step onboarding flow that collects:

• Basic demographics (age, weight, height, gender)
• Activity level selection (sedentary, lightly active, moderately active, very active)
• Nutrition goals (weight loss, muscle gain, diabetes management)
• Medical conditions dropdown (diabetes, hypertension, heart disease, etc.)
• Food allergies and intolerances (nuts, shellfish, lactose, etc.)
• Dietary preferences (vegetarian, vegan, keto, Mediterranean, gluten-free, dairy-free)

Implement data validation for health metrics and logical consistency checks (e.g., conflicting dietary preferences). Store user profiles securely with proper data encryption for health information.

System Context

Cross-platform mobile app development using React Native or Flutter. Local secure storage for user profile data. Form validation and user experience design for health data collection.

Acceptance Criteria

• Multi-step onboarding form with progress indicator
• Comprehensive dropdown options for medical conditions and dietary preferences
• Data validation for all health metrics and logical consistency
• Secure local storage of user profile data
• Ability to edit profile after initial setup
• Clear privacy policy and health data handling disclosure

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