In the past few months, we’ve talked a lot about HRV during exercise. In this post, I’ll try to keep it simple and address some of the main motivations behind this approach, as well as provide practical tips and tools for the ones interested in trying it out.
Let’s start with the basics.
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.
Training below your aerobic threshold means training easy.
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).
Following early research, interventions have been carried out to randomize runners in groups including a greater amount of low-intensity training, and groups including more moderate-intensity training. Results showed consistent improvements in running performance for groups training at lower intensities for most of the time, typically 80% of the overall training load. Personally, I’ve experienced large performance improvements when using this approach, despite being a pretty average runner.
Training mostly at easy intensities, or in other words below the aerobic threshold, leads to better performance.
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.
In particular, when DFA alpha 1 is above 0.75, you are in the easy intensity domain
While the HRV metrics we normally use for morning or night measurements aim at quantifying parasympathetic activity, these metrics are of little use during exercise as the parasympathetic system is highly suppressed.
However, the beat to beat data changes properties as we exercise at different intensities. In particular, the self-similarity of the signal carries information that is not found in other metrics. These changes in self-similarly are what is captured by DFA alpha 1. For a more in-depth explanation, please check Thomas Gronwald’s paper, here.
How can you try this method?
This is a research app mostly used for collecting data from external sensors in the context of different studies. However, I have recently updated it to implement this method and add a few extra functionalities that can make it easier to use it to check training intensity during exercise, as well as if your strap is working well (more on this later).
To try this method:
- Get the HRV Logger app, for iOS or 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.
If you are following a polarized training approach, you want to keep your alpha 1 above 0.75 during your easy workouts.
You can also use this method to try to determine at what heart rate you crossed alpha 1 = 0.75 if your workout involved an easy progression for example. In this case, we recommend each step of the progression to be 4–6 minutes. This should always be feasible because only easy or moderate (at most) intensities are required in order to identify the aerobic threshold.
Your threshold will depend also on environmental conditions, so if it is hot and humid or you are riding indoors, the value might be reached at a lower external load, which is the whole point of monitoring internal load.
The HRV Logger app for iPhone has also an Apple Watch extension that makes it easier to check your alpha 1 while running or cycling outdoors. However, the Watch acts only as a display, as accurate beat to beat data from a chest strap is required for this analysis. You will still need the strap and phone with you.
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.
Similarly to using heart rate, you can also do this every workout, which makes it easier to adapt to environmental conditions or fatigue, which is not the case for the methods mentioned above. The main advantages with respect to other heart rate-based methods should be the following:
- No need for calibration or for knowing your maximal heart rate
- 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.
For these reasons, the HRV Logger allows you to employ an aggressive artifact removal strategy (“workout” mode under Settings). Additionally, the HRV Logger displays the number of artifacts removed during the recording, so that you can identify periods in which the strap did not work well (and generated many artifacts).
Finally, in order to get a better overview of your workout intensity, you can hide windows with many artifacts, so that you can focus on high-quality data, as shown below:
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.
More studies are starting as we speak, and we hope to be able to learn more during the year. In the meantime, if you end up experimenting with the HRV Logger, let me know how it goes!
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).
You can also find a FAQ page for the HRV Logger, here.
Marco holds a PhD cum laude in applied machine learning, an M.Sc. cum laude in computer science engineering, and an M.Sc. cum laude in human movement sciences and high-performance coaching.
He has published more than 50 papers and patents at the intersection between physiology, health, technology, and human performance.