Sistemas de Visualisation e OAuth

React Native Decisions

• React Native was chosen over native development for cross-platform development efficiency, a large component ecosystem, a fast development cycle, and strong community support offering native-like performance.
• Platform-specific notifications, SVG implementation complexities, state management across components, offline data synchronization, and cross-platform styling differences were challenges faced using React Native.

Firebase Integration

• Data security in Firebase is ensured through multiple layers such as authentication rules, data validation, user data isolation, secure API access, and encryption of sensitive data.

Offline Functionality

• The application caches data locally, queues changes when offline, and synchronizes when a connection is restored. It handles conflicts and maintains data integrity.

FatSecret API

• FatSecret was chosen because it offers a comprehensive food database, a reliable API service, good documentation, and reasonable rate limits, as well as regular database updates.

Data Flow (User Logs a Meal)

• The process involves user input of meal details, calculation of nutritional values by the app, data saved to Firebase, real-time updates to the UI, and historical stats recalculation.

Architecture

• Components are organized based on functionality (e.g., reminders, diet tracking) and reusability. State management is handled using React Context.

User Interface

• The progress visualization uses SVG circles to provide clear visual feedback, intuitive progress displays, responsive design, and customizable appearance.
• Accessibility considerations addressed clear text contrast, appropriate text sizing, touch target sizes, error messaging, and navigation clarity.

Data Handling

• The historical view handles large datasets through data pagination.

Efficient Queries

• Optimization techniques included component memoization, lazy loading, query optimization, and image optimization. State management efficiency and local caching are also noted.

Validation

• Nutritional calculations are validated through unit testing, comparisons with known values, edge-case testing, and user profile variations/real-world data validation.
• Notification reliability is tested under different device states and time zone changes, as well as by repeating patterns and rebooting devices.

Integration Questions

• Continuous glucose monitoring integration requires device API integration, real-time data handling, data visualization updates, and alert system integration (including historical data incorporation).
• Key challenges for adding AI features revolve around model selection, training data requirements, and privacy concerns.

Code Organization (Firebase Database)

• Firebase databases are organized by user data isolation, query optimization, real-time updates, security rules, and data relationships.

Error Handling

• API failures are handled by retry mechanisms, error messaging, and fallback options (including data caching and user feedback).

Development Process

• Research, design, implementation, testing, and refinement phases form the development timeline.

Complex Features

• Progress circle calculations, notification scheduling, data synchronization, and real-time updates were among the most challenging features to implement.

BMR Calculation

• The Mifflin-St Jeor equation with fallback defaults for missing data is used, averaging male and female calculations, and including validation to ensure height and weight inputs are within realistic ranges. Data accuracy is ensured by using a verified nutritional API (FatSecret).

Notification System

• The notification system uses a channel-based approach that allows for custom notification channels, scheduling exact alarms, and handling recurring notifications for alarm functionality. Android permission management is managed gracefully and notifications persist after app closure or device restart. Optimization strategies include OAuth 1.0a implementation and caching of frequently accessed food items.

Data Privacy and Security

• The application protects user data through Firebase Authentication, secure data storage, and OAuth for API access. User data is isolated using security rules, and sensitive operations require re-authentication (to assure privacy and security).

Performance Optimization

• The application uses strategies like memoization, caching, batch updates, and virtualized rendering to improve performance for large datasets and user interactions.

Data Aggregation

• Complex data aggregation for trend analysis uses techniques such as time range handling, data aggregation by time intervals, and trend calculations to provide visualizations (includes moving averages, pattern detection, and other analysis methods).

API Integration (FatSecret)

• The FatSecret API integration uses OAuth 1.0a for secure API authentication, including generation of unique timestamps/nonces. Request formatting and handling API responses are also noted.

Serving Size Calculations

• The system handles different serving types/units, validates input, converts units to grams, and performs calculations for accurate nutritional information.

Meal Breakdown Component

• This component provides safe number handling, net carb calculations, high-carb warnings, organized meal information, and conditional styling.

Real-time Updates

• Real-time updates/synchronization involve Firebase listeners, updating UI components/recalculating totals, and optimistic updates/optimistic UI updates to enhance responsiveness/prevent unnecessary rerenders.

Data Persistence/Synchronization

• Data persistence and synchronization are handled using Firebase persistence; it includes offline functionality and handling network conditions. The solution also addresses issues of concurrency and manages updates using Firebase transactions and optimistic updates.

Meal Modification Handling

• Optimistic updates, Firebase transactions, error handling (including rollback), and state update management are all part of this process.

Date Handling

• Handling date-based queries with timezone considerations, as well as date formatting, is handled by normalizing dates to midnight UTC and handling timezone considerations in the query with firestore.

Notification System (Handling Android Doze)

• Alarms set on Android use a high-priority channel, and the app requests exact alarm permissions on Android 12+.

User Experience (Error Handling)

• The application uses an error boundary component to gracefully handle errors, log errors to a service, show user-friendly messages, provide recovery options, and track/report error metrics.

Research Methodologies & Diabetes Handling

• Validation was achieved by user testing with diverse groups, quantitative analysis of usage patterns, qualitative feedback, comparisons with existing solutions, and performance metric monitoring. Diabetes-specific needs are addressed using a personalization approach (including calculations to adjust for diabetes-specific targets). Security is also handled securely, including data encryption at rest.

Este quiz explora le functiones del sistema de visualisation e le implementazione de OAuth. Examina le interrelation tra le caching de datos e le requisites de securitate pro requests. Asocia terminologias con lor descriptiones e comprenda aspectos essential del processar datos historic.