HRV-based aerobic threshold estimation for endurance athletes: a practical guide

A new tool in the box for polarized training

What‘s the aerobic threshold?

We can train at different intensities. Normally, we do so to trigger different physiological adaptations that should lead to improved performance. To define training intensities we use thresholds that separate different intensity domains. In particular, we normally refer to the aerobic threshold as the separation between easy and moderate intensity.

Why do we care?

Quite some research in the past 20 years showed how elite endurance athletes spend much of their time training at low intensities, or below the aerobic threshold (see Stephen Seiler’s work).

Where does exercise HRV play a role?

According to a few recent papers co-authored by Bruce Rogers and Thomas Gronwald, HRV data, and in particular a non-linear HRV feature derived from Detrended Fluctuation Analysis (DFA alpha 1) can be used to separate the easy and moderate-intensity domains, or in other words, to identify the aerobic threshold.

Figure from “A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of HRV” showing how the first ventilatory threshold matches the proposed method using exercise HRV. You can find the full text of the paper at this link

How can you try this method?

Currently, the only app that can compute DFA alpha 1 in real-time is the Heart Rate Variability Logger for iPhone and Android.

  • Set artifact correction to “Workout” and feature computation window to “2 minutes” in the app Settings
  • Link your chest strap, we highly recommend a Polar strap for this test
  • Run or cycle. Either a rather constant easy effort or an easy progression are ideal for this kind of analysis. Short intervals or frequent changes in intensity should not be used for this test as at least 2 minutes of stable data are required
  • Your aerobic threshold will be at alpha 1 = 0.75, as reported in the app’s HRV features and also in real-time.
Apple Watch extension for HRV Logger and app screenshot during a 6 hours run. The number of artifacts removed during the last computation window is also shown

What are the advantages of this method with respect to other techniques?

The main advantage of this approach with respect to checking your aerobic threshold with a lactate test or ventilatory test is that it is much easier, non-invasive, and does not require any fancy equipment.

  • Threshold value independent of your fitness level, with the threshold always at about 0.75 for both very fit and unfit individuals (see validation paper linked above for more details)

What are common issues with this method?

The main challenge of this method seems to be getting high-quality data. Even the best chest straps out there (Polar H7 or H10), generate quite a few artifacts when running, often causing the following computations to be impacted.

The two low alpha 1 values shown in the middle figure are correctly excluded when hiding noisy features. Using this visualization and the workout mode for artifact correction, you should be able to get meaningful estimates of your workout intensity

What’s next?

Using DFA alpha 1 for aerobic threshold estimation is a new method that needs to be validated on a larger population and different sports. Additionally, how this parameter changes over time in relation to fatigue or cardiac decoupling, has also not been thoroughly investigated.

Additional resources

The best resource to learn more about this method is Bruce Rogers’ FAQ, which includes links to many blog posts and other useful material (podcasts, videos).


Founder, Data Science @ouraring Lecturer @VUamsterdam. PhD in Machine Learning, 2x MSc: Sport Science, Computer Science Engineering. Runner

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