PCOS. Tracked, correlated, explained by AI.
A comprehensive Android digital therapeutic for women with PCOS — eight health modules, a 30-day Pearson correlation dashboard, AI health insights from Claude, and a separate web portal for doctors. All offline-first.
The problem
PCOS affects roughly 1 in 5 women in India — one of the highest prevalence rates globally. Yet most women manage it with fragmented tools: a period tracker here, a nutrition log there, a notes app for symptoms, and whatever their gynaecologist remembers from the last appointment.
The deeper problem is that PCOS is a complex, multi-system condition. Sleep quality affects symptom severity. Workout intensity interacts with cycle phase. Calorie intake correlates with lab markers. No single tracking app connected these dots — they just logged data without insight.
The brief was to build a medically-informed digital therapeutic that could surface these correlations automatically and explain them in plain language — bridging the gap between patient data and the gynaecologist's appointment.
What we built
Eight distinct health modules, each built with offline-first Room database storage and background Supabase sync: PCOS screening (4-section questionnaire with risk scoring), daily symptom logger (5-metric scoring with 30-day trend graphs), cycle tracker with Ogino ovulation prediction, lab investigation logging with auto-interpretation of 6 key markers (LH/FSH ratio, HOMA-IR, HbA1c, androgens, thyroid, lipid panel), nutrition logging with 80+ Indian foods with ICMR macros, medication adherence tracker, sleep tracker with Health Connect sync, and cycle-phase-aware workout recommendations.
The correlation dashboard is the centrepiece. It computes Pearson correlation coefficients across 30-day windows — sleep quality vs symptom severity, workout intensity vs mood scores, calorie intake vs cycle regularity, and more. Any |r| ≥ 0.3 surfaces as an actionable insight. Claude Haiku analyses six defined correlation rules and generates plain-language explanations contextualised to the user's specific data.
Smart alerts run via WorkManager daily jobs — spike/deficit/streak/overdue notifications that are delivered even when the app is backgrounded. PDF health reports export the full 30-day data set with AI-generated insights and a medical disclaimer, formatted for sharing with a gynaecologist.
The doctor portal is a separate React 18 web app with a consent-based patient data access workflow — doctors see the correlation charts and timeline, not raw personal details, until the patient explicitly grants access.
Architecture
Android app uses MVVM + Clean Architecture with Hilt for dependency injection. Room is the source of truth — all reads go to Room, a background SyncWorker replays queued changes to Supabase when connectivity returns. Supabase enforces row-level security so no user can access another's data even with a valid JWT.
The lab interpretation engine (InvestigationInterpreter) applies clinical reference ranges to flag abnormal values — it was built with the assumption that a gynaecologist reviews findings before any action is taken, and the UI makes this explicit with appropriate disclaimers.