About one year ago, following a few interesting exchanges via email with Sander Berk at Dutch Triathlon, as well as with Raúl Celdrán, Alan Couzens and James Cobb on Twitter, I got more interested in HRV analysis during exercise.
Early research from Thomas Gronwald and co-authors had shown that you could potentially identify the aerobic threshold using this method. This looked like a potentially useful way to assess exercise intensity without the need for indirect calorimetry, lactate meters or even knowing our maximal heart rate.
A few months later I added DFA alpha 1 to our general purpose research app, the Heart Rate Variability Logger, which became the first tool able to offer this analysis in real-time. I had also released code for others to use, which was indeed picked up by many, developing the various options currently available.
The proliferation of these tools, led to many more people trying out the method, more research groups collecting and analyzing data, and more insights on the strengths and limitations of DFA alpha 1. This is exactly how research should work.
As scientists, we need to be able to look at the data and the new evidence, and update our view accordingly. Otherwise, we are doing a really poor job.
Given the data that I have seen in the past months, both anecdotally from users and in published literature, it is undeniable now that we cannot promote the use of a universal threshold (0.75) to detect the aerobic threshold at the individual level.
In this post, I cover in more detail current issues and potential applications of this type of analysis, which certainly remains of interest, even though not for the reasons originally thought.
Founder of HRV4Training, Advisor @Oura , Guest Lecturer @VUamsterdam , Editor @ieeepervasive. PhD Data Science, 2x MSc: Sport Science, Computer Science Engineering. Runner