True but these examples illustrates how fitness watches' underlying value prop, their pitch, is to convince users to make it a daily lifestyle. There's a spectrum of practicality though: there's less reason to monitor blood pressure on a minute-by-minute basis, with graphs and trends and metrics, but an initial novelty wears off after a few weeks of monitoring sleep that way. It's cool and interesting at first, but hard to justify it as a constant lifestyle. Maybe insulin / glucose is interesting from a data-heavy perspective, but diabetics eventually gain an instinct for what meals spoke what and when, and start to lay off the data and metrics. The novelty wears off.

The heart-rate monitoring on my Garmin gives highly accurate tracking of calories burned, better than anything I could do by hand. Very valuable, I was able to lose 50 pounds and I was able to do minimal calorie counting. Basically I ate a very consistent weekly diet and used the watch to tell me if I had done enough exercise that I could eat something else. It's still very useful, I look at my calorie count regularly to guide how much extra to eat before and after activities.

I've also found some of the other ML-powered derived metrics surprisingly useful. There's a "training status" that has "productive/maintaining/strained/recovery/detraining." When I've got a bad cold/flu/covid type illness it often says "strained" which I can feel in my body but it's nice to have that objective external metric of "yes, your body is not working right and you should take it easy."

Similarly when I am working out it's nice to be able to look at my heart rate at a glance and know if I am over/under exerting myself.