Daily tips and tricks from the experts at Adafruit!
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Heart Rate Variability (HRV) has been shown to be an excellent biometric indicator of stress. A high HRV value implies a lower stress level. Athletes have known this for years and often check their HRV first thing in the morning to get an idea how recovered their system is from the previous days events. A low HRV value might indicate that one is getting sick, over trained, suffering from anxiety or just did not get enough sleep. This process of testing each morning is time consuming and erratic at best. It can be greatly improved if it was a passive process.
The HRV4Training app┬áis available for iOS and Android devices. It makes the process of collecting and analyzing HRV data passive by pulling it from wearables platforms like the Oura Ring Cloud or sucking up exercise history from Strava. It supports many other apps including Apple Health and Training Peaks. The HRV4Training app costs $10 for the basic mode which we will review some of it’s functionality below. There is a Pro option which can be purchased for $49 annually and includes many features such as insights through a web platform and includes coaching abilities (so coaches can assess their athletes readiness and performance goals).

There is a history option which allows one to review the last week of data collected one screen at a time. I have been slowly increasing the environmental stressors around my training. More weights, faster swims, runs in hot summer temperatures and sleeping at elevation. I’m starting to see the gains with my recent rMSSD (aka HRV) peaking at 68 ms yesterday which is 13 ms over my baseline for this three week period.

The baseline view provides the same data in terms of “Recovery Points” (higher is better), “Heart rate” (lower is better) and rMSSD (aka HRV – higher is better). You want to see the dark blue line climbing on the Recovery Points and rMSSD and dropping for Heart rate to know that you are progressing in a positive direction. An hard training session will briefly skew the data, but the point of baseline is to see overall progress and not get hung up on a single event.

The population view is one of my favorites as it shows where I stand compared to others my age and gender. This is a helpful metric to set realistic goals around. It is also fascinating to see the outliers. An example of a goal to set is to boost rMSSD from 55.4 ms (my average for the last month) to This feature of comparing oneself to others based 70 ms next month. This might require not drinking alcohol, going to bed an hour earlier each night and bringing in an extra high intensity interval training session each week.

Finally, there are some really cool insights. Most of these require a minimum of 30 – 60 days of data collected. These tools provide just what an active person needs to understand how much they can push themselves and still be optimizing performance.