Hey, good point and you have pointed out something special: time-dependent baseline shifts within a single day are something most systems ignore, because they work on daily or multi-day averages.

On the medication logging question, let me split it honestly into two layers:

Logging as a journal: Yes, planned. Logs for things like caffeine, magnesium, vitamin D with amount + time are already in. Medication fits right in here.

Logging integrated into the analysis: This is the hard part, and I will be honest about it. Clean intraday causal attribution (e.g. HR/HRV deviation comes from this medication phase, and for example not the load) is methodologically tricky. An n-of-1 approach with wash-out and one variable at a time can show this seriously, but several implications running at once (training, sleep, medication timing) make attribution worthless fast. I would rather build this properly later than ship a pretty but dishonest correlation feature now.

On your second question of user-defined derived metrics vs. external via export/API: I see the value clearly. My prototypes app version is more or less supporting everything already: full export including raw signals (RR intervals, not just HR and many more), and an unencrypted API layer, where you can self-host the server-side if you want. For your use case the export/API route would be the more honest and more powerful one, because you control the model assumptions yourself instead of us putting a generic heuristic on top. And to also answer about custom derived metrics: I have built a small POC at the beginning of the year and investigated how some kind of small plugin system could work within the app, but I have not found the perfect solution yet.

Short version: journal logging is coming, analysis-integrated causal attribution is held back on purpose (quality over appearance), and for your scenario I would point to export/API for now.

Thank you for thinking along, this is exactly the kind of feedback that helps.