I'm building EdgeFit, an open-source, privacy-first fitness companion using React Native, Expo, TypeScript, Apple HealthKit, and SQLite.
My goal is to build meaningful activity insights while keeping personal health data on the device.
Recently, while testing the app on my iPhone, I discovered an interesting problem.
The app was producing mathematically correct insights that weren't particularly useful.
It also avoids generating discouraging comparisons early in the morning when there isn't enough activity data.
This update reinforced something important for me as a developer:
A mathematically correct result isn't always a meaningful result.
The context behind a calculation matters just as much as the calculation itself.
By changing the comparison baseline, I made EdgeFit's insights more useful without increasing the amount of personal health data stored on the device.
EdgeFit is open source, and I'd welcome feedback or contributions from React Native and iOS developers.
GitHub: https://github.com/raviseta/edgefit
Tags: #reactnative #ios #healthkit #opensource
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