I've been climbing for a decade, but over the past 3 years I've put on a bunch of weight due to work and certain life events. But I want to change that.

I know what motivates me: seeing progress. The feedback loop of "do X, see Y gain" is what keeps me going.

So I started building an integrated dashboard that can aggregate data from multiple systems:

- My digital scale

- Apple Watch (sleep + running performance)

- Beastmaker Motherboard, which is an electronic board that you attach a hangboard to and it shows you various stats like how much force you're applying

The idea is that every morning I'll open the dashboard and be able to see exactly how much progress I've made the previous day: weight loss, strength gain, cardio performance.

It's an interesting problem. There's essentially two parts to it: Apple Health, which aggregates data from the scale and the Apple Watch and can POST-export it hourly, and the electronic board, which sends data via BLE in real time. The destination for both of these will probably be an always-on Raspberry Pi 5, but I haven't decided yet. Then I'll have a small server app that can pull the data from the Pi and draw some fancy charts.

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.