Advanced Technology

Cutting-edge algorithms, service-oriented architecture, and privacy-first design powering clinical-grade mental health support.

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Service-Oriented Architecture

SteadyMind uses a modern, scalable architecture designed for maintainability, clinical accuracy, and user privacy.

System Architecture Overview

🎨 Presentation Layer

React Native screens and components with TypeScript for type safety and enhanced developer experience. Navigation handled by React Navigation v7 with native stack navigators.

⚙️ Service Layer

Dedicated service classes handle all business logic, data transformations, and algorithm implementations. No global state management - services maintain stateless operations.

💾 Data Layer

AsyncStorage for local data persistence with JSON serialization. Firebase Analytics for anonymous usage tracking (optional). AdMob for ethical monetization.

Core Services

🧠 MoodService

Manages mood entry CRUD operations, data serialization, and integration with risk assessment systems. Handles date normalization and sorting.

  • Mood entry persistence
  • Trend calculation
  • Risk assessment integration
  • Data export capabilities

🚨 ManiaDetectionService

Advanced algorithmic detection of manic and depressive episodes using multi-factor analysis and statistical baselines.

  • DSM-5 compliant algorithms
  • Dual-pole crisis detection
  • Baseline establishment
  • Confidence scoring

⚡ TaskStorageService

Energy-based task management with cognitive load assessment and priority weighting systems.

  • Energy level categorization
  • Smart prioritization
  • Progress tracking
  • Recurring task patterns

📊 AnalyticsService

Privacy-preserving event tracking with local storage backup and batch uploading capabilities.

  • Anonymous event tracking
  • Local event storage
  • Batch upload management
  • Privacy controls

📝 JournalStorageService

Secure journaling with encryption, therapeutic prompt management, and mood integration capabilities.

  • End-to-end encryption
  • Mood correlation
  • Theme detection
  • Export functionality

🎯 PersonalizationService

Condition-specific customization with adaptive interface complexity and contextual tip generation.

  • Condition-based setup
  • Feature prioritization
  • Experience level adaptation
  • Dashboard customization

🧮 Pattern Recognition Algorithms

SteadyMind employs sophisticated statistical analysis to identify meaningful patterns in user data while maintaining privacy and accuracy.

Correlation Analysis

Statistical correlation between mood, energy, sleep, and anxiety using Pearson correlation coefficient: r = Σ((xi - x̄)(yi - ȳ)) / √(Σ(xi - x̄)² × Σ(yi - ȳ)²) Where: - r = correlation coefficient (-1 to 1) - xi, yi = individual data points - x̄, ȳ = means of datasets

Trend Detection

Linear regression analysis for mood trend identification: y = mx + b Where: - m = slope (trend direction and magnitude) - b = y-intercept (baseline) - Statistical significance tested using confidence intervals

Baseline Establishment

User baselines calculated over 14-21 days using statistical measures:

  • Mean and standard deviation calculation
  • Outlier detection and removal
  • Confidence interval establishment
  • Rolling baseline updates

🤖 Machine Learning Implementation

Privacy-preserving machine learning algorithms that operate entirely on-device to provide personalized insights without compromising user data.

Energy-Based Task Matching

Analyzes historical task completion rates at different energy levels to improve recommendations:

Completion Rate Analysis: - Track completion success by energy level - Weight recent completions more heavily - Account for task difficulty and duration - Generate personalized energy-task mapping

Adaptive Notification Timing

Learns optimal engagement windows based on user behavior patterns:

  • Hourly mood analysis for receptivity patterns
  • Engagement scoring based on session duration
  • Adaptive timing with user preference learning
  • Crisis sensitivity with notification suppression

Privacy-First Architecture

🔒 Local-First Data Processing

All sensitive mental health data remains on your device. No cloud synchronization for mood entries, journal content, or personal assessments.

🔐 End-to-End Encryption

Journal entries and sensitive content encrypted using AES-256 encryption before storage. Encryption keys never leave your device.

👤 Anonymous Analytics

Optional usage analytics contain no personally identifiable information. All analytics data aggregated and anonymized.

🛡️ HIPAA-Compliant Design

Healthcare data handling follows HIPAA principles with user-controlled sharing and professional integration options.

📱 Technology Stack

Frontend

  • React Native (Expo SDK 53)
  • TypeScript with strict mode
  • React Navigation v7
  • React Hooks pattern

Data & Storage

  • AsyncStorage with JSON serialization
  • Local encryption (AES-256)
  • Error recovery mechanisms
  • Data integrity validation

Analytics & Ads

  • Firebase Analytics (optional)
  • Google Mobile Ads (AdMob)
  • Custom analytics service
  • Privacy-preserving tracking

Development & Deployment

  • Expo Application Services (EAS)
  • Cross-platform compilation
  • Automated testing pipeline
  • Feature flag management

⚡ Performance Optimization

Memory Management

  • Efficient data loading with pagination for large datasets
  • Component lazy loading for improved startup times
  • Memoization of expensive calculations
  • Automatic garbage collection optimization

Data Processing

  • Background processing for statistical analysis
  • Incremental pattern recognition updates
  • Optimized chart rendering with data sampling
  • Efficient date handling and serialization

User Experience

  • Smooth transitions with native animations
  • Offline-first functionality
  • Progressive loading for better perceived performance
  • Adaptive UI complexity based on device capabilities