DIGITAL HEALTH vs LIFESTYLE CHOICES

This week I challenged my hypothesis that you can identify sickness automatically by looking at the resting heart rate (RHR) of the study population. Fortunately, I have a group of great clients who willingly share their digital health data with me.

In a "big brother" way I rang them up this week if I spotted a sudden increase of more than 10 beats per minute. Usually, I would expect the rise of the RHR in combination with bad sleep quality and lowered HRV. So these were the combinations I jumped at. My initial concerns were that this would trigger a kind of "creepy" feeling in my clients. But the three people I rang up were actually super grateful and appreciated the effort I have put into this analysis.

I was right with my hypothesis 1 out 3 times only- because there is one other scenario that comes with the same trias: bad sleep, elevated heart rate, and low HRV: partying with drinking too much alcohol or eating food that you do not digest well:) We have seen this phenomenon already at nambaya, with our medical-grade data- but we were missing the context with the baseline data. Having a 28 days / 90 days baseline of your vital parameters makes now more sense than ever.

So ideally digital health providers integrate a feedback loop on days where we find this combination of biomarker changes.

START COLLECTING, START SHARING & KNOW/UNDERSTAND YOUR NUMBERS