Coospo HW9 for Heart Rate Variability (HRV) Analysis
Another affordable option.
On a recent run in Brisighella, I met a friend who mentioned using the Coospo HW9 with HRV4Training for morning measurements. I had tested only an older Coospo sensor years ago, which could not provide accurate RR intervals, despite claiming otherwise, and therefore had not looked into the newer version.
However, given that linking HRV4Training to an inaccurate sensor would prompt the app to report poor signal quality (due to the erratic nature of the RR intervals) and that this was not happening, I figured it was time to test the more recent models.
Well, long story short, the HW9 is a decent alternative as well for your morning measurements or biofeedback sessions, should you have issues with the phone camera.
Why morning measurements?
I often discuss morning protocols and their superior utility with respect to night data and wearables: we avoid issues such as saturation in athletes, we acquire data that is more sensitive to stressors, and we measure far enough from the previous days’ stressors that the data is truly representative of the response, not just the behavior.
As mentioned in other occasions, the options out there are not many if we want to use a sensor with a third-party app and not be locked within whatever ecosystem the sensor / wearable manufacturer has created. In this context, Kyto and Coospo sensors are a good option.
The data
Below is some data collected using the Coospo HW9 armband.
The protocol consisted of either normal breathing at rest (minutes 1-3 and 7-8), while seated, or slow breathing exercises (minutes 4-6).
For data collection, I used the HRV Logger app to collect RR intervals from the chest strap, and another HRV Logger app to collect RR intervals using the Coospo HW9 (my apps are available here).
RR and PP intervals:
We can see that the PP and RR intervals track very well the different phases of the protocol (minutes 1-3 with regular breathing, minutes 4-6 with slow breathing, and again regular breathing for the last 2 minutes), and are rather similar.
While below we have RMSSD for both sensors, which, as often reported, is slightly higher for PPG as opposed to ECG:
Still, we can see that the relative changes within sessions are captured very well. As my recommendation is never to do anything with absolute values of HRV, but to track changes over time, I consider this device good enough for the task, given the PP intervals above, and the ability to clearly capture relative changes even in a short segment of data.
In HRV4Training, the app will automatically re-learn your normal range as it is always kept current, to make sure data is properly contextualized. This adjustment will take a few weeks in case there is a large gap between your current data and what is collected with the Coospo sensor.
Endurance Coaching for Runners
If you are interested in working with me, please learn more here, and fill in the athlete intake form, here.
How to Show Your Support
No paywalls here. All my content is and will remain free.
As a HRV4Training user, the best way to help is to sign up for HRV4Training Pro.
Thank you for supporting my work.
Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering, and a M.Sc. cum laude in human movement sciences and high-performance coaching. He is a certified ultrarunning coach.
Marco has published more than 50 papers and patents at the intersection between physiology, health, technology, and human performance.
He is co-founder of HRV4Training, Endurance Coach at Destination Unknown Endurance Coaching, advisor at Oura, guest lecturer at VU Amsterdam, and editor for IEEE Pervasive Computing Magazine. He loves running.
Social:
Personal Substack
Instagram
