Health & Wellness Tracker

Part of the worlds-biggest-software-project initiative.

A unified, AI-native, open-source tracker that reasons across nutrition, exercise, sleep, and symptoms to surface personalised health insights.

People managing their health typically juggle separate apps for nutrition, exercise, sleep, and symptoms, leaving the connections between those dimensions invisible. Health & Wellness Tracker aggregates data across all of these domains and applies AI reasoning to deliver actionable, evidence-grounded insights for individuals taking ownership of their health between clinical appointments.

Why Health & Wellness Tracker?

Incumbents are siloed: MyFitnessPal, Cronometer, and Lose It! are nutrition-first with no symptom or mood journalling and no cross-domain correlation engine.
Wearable-first platforms (WHOOP, Oura, Fitbit) deliver strong biometrics and AI coaching but require proprietary hardware and lack nutrition or symptom tracking.
Behavioural-science apps (Noom) couple coaching with tracking but omit sleep, wearable biometrics, and symptom journals.
The closest open-source option, Open Wearables (MIT, v0.4), aggregates wearable data but has no nutrition, symptom logging, or production-ready UI; Wger (AGPL-3.0) covers fitness and nutrition but offers no AI insights, sleep, or symptom tracking.
No mainstream app generates a clinician-ready health summary spanning months of cross-dimensional data, and none reasons in natural language across nutrition, sleep, exercise, and symptoms together.

Key Features

Logging Across Health Dimensions

Nutrition logging via text search, barcode scan, and AI photo recognition against an openly licensed food database (USDA FoodData Central or Open Food Facts)
Exercise logging for workout type, duration, and intensity with wearable sync via Apple Health and Google Health Connect
Sleep logging with manual entry and optional wearable import from Oura, Garmin, or WHOOP via their public APIs
Symptom and mood journal with free-text and structured tagging for energy, mood, pain, and digestive symptoms with timestamps

AI Insight Engine

LLM-powered pattern detection across nutrition, exercise, sleep, and symptom data with natural-language explanations
Cross-dimension correlation alerts (e.g. recurring low energy on days following short sleep and high-carb dinners)
Natural-language health querying over the user's longitudinal data
Adaptive goal coaching that tunes calorie and macro targets based on recent activity and recovery

Dashboard, Trends, and Sharing

Unified daily dashboard across all logged dimensions
Interactive 30/90/365-day longitudinal trend charts
Medical appointment export: formatted PDF summary covering a user-defined period
Optional clinician sharing mode for secure, read-only diary access

Goals and Engagement

Configurable daily goals for calories, macros, and activity
Adaptive milestone tracking
Optional community and accountability features without exposing detailed health data

AI-Native Advantage

Existing trackers either reason inside a single domain (calories, sleep, recovery) or rely on generic chatbots disconnected from the user's data. This project applies an LLM to a normalised, longitudinal, cross-domain health record so users can ask "what has changed in my health this month?" and receive grounded, personalised answers. Pattern detection, proactive nudges, and adaptive goals run continuously over nutrition, exercise, sleep, and symptom data together — the correlation context no incumbent currently provides.

Tech Stack & Deployment

The tracker targets self-hosted and offline-first deployment, with local-first storage and background sync to support meal and workout logging in low-connectivity environments. Wearable and HealthKit / Google Health Connect integrations ingest steps, heart rate, sleep stages, and workouts; data normalisation harmonises units and timestamps across heterogeneous sources. Longitudinal data uses time-series storage patterns for fast trend queries, and on-device processing is preferred for sensitive health data, with HIPAA-aligned practices and explicit consent for any cloud storage.

Market Context

The Wellness Management Apps Market was estimated at $25.26 billion in 2025 and is projected to reach $61.27 billion by 2033 (CAGR 11.74%), with the global sleep aids market alone valued at $49.1 billion in 2025 and projected to reach $95.2 billion by 2033 (Mobisoft Infotech). Incumbent pricing ranges from MyFitnessPal Premium ($79.99/yr) and Premium+ ($99.99/yr), Cronometer Gold ($39.99/yr), and Oura ($5.99/mo on top of hardware) to subscription-bundled hardware platforms like WHOOP. Primary buyers are individuals tracking their own health between clinical visits, with adjacent demand from corporate wellness programmes and longevity-focused users.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.