Health & fitness app PRD template

Health and fitness apps live or die on tracking — and most PRD templates treat that as a single vague 'log your workout' feature instead of the actual data model underneath it: what's an exercise vs. a set vs. a session, how does progress roll up over weeks, and which wearable or health platform feeds data in automatically? Skip that and you end up rebuilding your schema three times.

Draftlytic asks about the specifics that decide a health app's architecture — tracking granularity, device/wearable integrations, whether the app coaches or just logs — then turns your answers into a structured, editable spec: a typed data model, a prioritized feature list, and a navigation map you can hand straight to Cursor, Claude Code, or any AI coding tool.

The core entities matter more here than in most app categories: Exercise, Set, Session, and (if you're tracking nutrition) Meal and Food entries need distinct relationships, not one flat 'activity log' table. Draftlytic's typed data model captures these as separate entities with real fields — reps, weight, duration, calories — so your AI coding tool scaffolds a schema that can actually support progress charts later.

The spec also covers device and platform integrations explicitly: does the app read from Apple Health, Google Fit, or a specific wearable's API? Those land in your external-services list as named dependencies, not something the AI has to guess at when it starts wiring up data sync.

Describe your fitness app idea — the workout style, whether it's solo tracking or has a social/community layer, whether there's a coaching or program component. Draftlytic's follow-up questions probe monetisation (free, freemium, subscription), auth methods, and whether you need offline support for a gym with no signal.

The output is a prioritized feature list (must-have through future) with acceptance criteria at Detailed depth, a design-token block so the UI direction is set before your AI coding tool starts on screens, and an explicit non-goals list — useful for keeping a v1 to 'log workouts and show progress' instead of an AI coding tool bolting on a social feed you didn't ask for. Export as Markdown, PDF, or Word doc, or push straight to a connected GitHub repo, and run an AI Scan first to catch gaps like a tracked metric with no matching screen.