I've been working on a similar concept (aggregate health data from multiple sources) but on a wider scale:
1) annual bloodwork as part of my annual preventive care;
2) InBody measurements, including grip strength;
3) quality of air in my region;
4) Apple Watch but mainly for steps, sleep data and resting heart rate;
5) allergy panel or minerals/vitamins screen plus something nutrition-related along those lines (TBD).
The idea is to see trends and try to apply AI for correlating, at the first glance, completely unrelated data layers. Example how I'm thinking about this one: there's somewhat clear correlation that I sleep better when I do above average steps per day. How is my sleep quality affected if, let's say, I did above avg steps with a bad air quality at that time? (i.e. wild fires / pollen season / etc.)
I've built a Go application to ingest those data sources and currently finishing my first import use case - Apple Watch data.
Would be happy to connect and chat about this.
The idea is to see trends and try to apply AI for correlating, at the first glance, completely unrelated data layers. Example how I'm thinking about this one: there's somewhat clear correlation that I sleep better when I do above average steps per day. How is my sleep quality affected if, let's say, I did above avg steps with a bad air quality at that time? (i.e. wild fires / pollen season / etc.)
I've built a Go application to ingest those data sources and currently finishing my first import use case - Apple Watch data.
Would be happy to connect and chat about this.