u/InfamousBuddy7293

Hey, sharing my monthly tracking report - figured this sub would appreciate the visuals :)

I'm not a fan of tracking things manually so this is mostly done via automatically collected data.

Each Report-Summary covers one calendar month, via Apple Watch (HRV, resting HR, sleep stages, steps, active energy, walking HR) plus daily manual logging of sick/not-sick as a binary. I'm adding alcohol consumption in the future and hope to see some correlation with my vitals.

The Stress chart is a derived metric - deviation from my personal 21-day rolling baseline on HR and HRV, not a population-normed wellness score, which I find mostly useless at the individual level. Core hypothesis I'm testing is that personal-baseline deviation is more informative than any normalised score, especially once you have a few months of data.

Hey, sharing my monthly tracking report - figured this sub would appreciate the methodology more than my friends do.

Each Report-Summary covers one calendar month, mostly automatic via Apple Watch (HRV, resting HR, sleep stages, steps, active energy, walking HR) plus daily manual logging of sick/not-sick as a binary. Starting next month I'm adding alcohol consumption.

The Stress chart is a derived metric - deviation from my personal 21-day rolling baseline on HR and HRV, not a population-normed wellness score, which I find mostly useless at the individual level. Core hypothesis I'm testing is that personal-baseline deviation is more informative than any normalized score, especially once you have a few months of data.

Limitations to be honest about: Apple Watch HRV is spot-sampled and noisier than chest-strap or ring-based, sick-day logging is binary and there are obvious impacts visible from travel, work intensity, etc.

I hope to see some correlation between health and alcohol in the coming months - will probably share back here when I see something. And I have a good idea of why my steps dropped so significantly between March and April - any guesses? :)

One of my key learnings: If your numbers look great, suspect your evaluation before you celebrate.

Early on, our model hit a metric we were really proud of. Strong enough that I was already mentally drafting the investor slide. Then we rebuilt the pipeline more carefully and the same model - same architecture, same features - performed dramatically worse on the new evaluation. Same panic you're imagining.

It turned out the original setup had a subtle leak: the way we split training and test data let the model "see the future" in a way it never would in the real world. The lower number was the honest one. The first version wasn't a better model, it was a better-looking benchmark.

Most "wins" early in a startup are measurement artifacts. Conversion rates that look amazing because the denominator is wrong. Retention curves that look healthy because you're cherry-picking a cohort. ML metrics that look strong because the test setup leaked. The instinct when a number looks great is to celebrate and scale. The better instinct is to ask: what would make this number look good even if the underlying thing weren't?

I've been burned more by good numbers than bad ones - usually in the beginning everything goes wrong before it goes right. Stay vigilant my friends!

Context: Solo founder, 12+ months in, building a health-tech product out of Germany. We've built a self-improvement App “Sam” that also uses ML to spot when someone's health is drifting from their personal baseline across a bunch of wearable signals.