The DFA alpha 1 app for threshold tracking (Garmin, Zwift)

DFA alpha 1 is a heart rate variability (HRV) metric that allows tracking of the aerobic and anaerobic threshold. We explain the breakthrough potential of this new metric and how you can track your DFA alpha 1 thresholds with the AI Endurance app, Garmin and Zwift.

What is DFA alpha 1?

TLDR: In short, DFA (detrended fluctuation analysis) alpha 1 measures the noise spectrum of your heart. As you ramp up the intensity of a workout and your heart has to work harder, the kind of noise - which the value of DFA alpha 1 represents - changes. Recent research has shown that DFA alpha 1 crosses below 0.75 at your aerobic threshold [1] and below 0.5 at your anaerobic threshold [2]. For a more detailed explanation, check our in-depth blog post about DFA alpha 1.

What is the benefit of using DFA alpha 1?

DFA alpha 1 might prove to be a groundbreaking advancement to correctly set our training zones and track fitness. It only requires you to wear a heart rate monitor that tracks HRV (Polar H10 recommended). This way, you will be able to

• Determine your aerobic and anaerobic thresholds. For example, your aerobic threshold sets the upper bound of your Endurance training zone. It is important this bound is set correctly, to properly polarize your training, and actually take your easy activities easy for constructive training progression.
• Avoid pitfalls of common test protocols. If you don't have access to a physiology lab, you can currently assess your aerobic threshold as the pace/power where you can comfortably hold a conversation. Since this method is obviously not very accurate, DFA alpha 1 presents an accurate but affordable and non-invasive alternative. For the anaerobic threshold, time trial fitness tests are hard to pace and lead to significant fatigue. On the other hand, ramp tests which take a percentage of your max power/pace as your anaerobic threshold depend on the ramp speed [3].
• Get high quality threshold data into AI Endurance - all the time. Both thresholds provide defining information about your current fitness state. Therefore, assessing them regularly is crucial for our AI to determine which training leads to successful outcomes. We are working to automate these assessments from your HRV data to the point that you don't even have to do a dedicated test.

Imagine being able to detect your fitness level during most activities that are hard enough to cross either threshold, without even thinking about fitness testing. This is what we're striving towards.

How can I get my heart rate variability data into AI Endurance?

• Use a heart rate monitor that records high quality HRV data. Currently, the Polar H10 is generally the most recommended strap for HRV measurements.
• Connect your heart rate monitor via Bluetooth not ANT+ to your device. Bluetooth transmission leads to less artifacts, see Bruce Roger's blog.
• Use a Garmin watch or bike computer. Right now we are only supporting Garmin for HRV data. Strava is not sharing HRV data (yet).
• Enable HRV logging on your Garmin device: Settings -> System -> Data Recording -> Log HRV or Settings -> Physiological Metrics -> Log HRV.
• Connect AI Endurance with "Garmin Connect - Import Data".

You can use real time logging of HRV data as well, see for example the HRV Logger by HRV4Training (Android and iOS) or FatMaxxer (Android). Your DFA alpha 1 data is available for each activity under "View Analysis" for your past activities in the AI Endurance Dashboard.

How can the AI Endurance app detect my DFA alpha 1 aerobic and anaerobic thresholds?

We have created dedicated ramp tests for cycling and running in the AI Endurance app. These make it as easy as possible for you to accurately test your DFA alpha 1 thresholds with Garmin and Zwift. Simply go to your AI Endurance Account page and either

• Push the DFA alpha 1 ramp test as a Garmin Workout to your Garmin Connect or
• Download our Zwift DFA alpha 1 ramp test. To record HRV data, you need to simultaneously record the activity with your Garmin device. Zwift doesn't record HRV data.

In both cases, you need 'Garmin Connect - Import Data' to be connected to AI Endurance.

We also attempt to automatically detect your thresholds for any activity with HRV data if the ramping is sufficiently slow. For the aerobic threshold this means at least 4 min of continuously decreasing DFA alpha 1 past 0.75. For the anaerobic threshold, we need at least 6 min of continuously decreasing DFA alpha 1 past 0.5. If these criteria are met, we will display the power/pace and heart rate of your thresholds on your activity page as well. While this feature is in beta and more research on the topic is progressing, our AI will not take thresholds from DFA alpha 1 into account automatically just yet. However, you always have the option to manually set the detected values for your thresholds and the upper bound of your Endurance zone in your Account page (see below).

How should I set my training zones in AI Endurance?

Once you have determined your aerobic and anaerobic thresholds with DFA alpha 1, you can set your training zones in the AI Endurance app as follows:

• Set the upper bound of your Endurance training zone to your aerobic threshold in the Account page.
• For cycling, set your FTP to your anaerobic threshold. For running, set your 10k pace to your detected anaerobic threshold.

References:

1. A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of Heart Rate Variability - Bruce Rogers, David Giles, Nick Draper, Olaf Hoos, Thomas Gronwald - Front. Physiol. 2021
2. Detection of the Anaerobic Threshold in Endurance Sports: Validation of a New Method Using Correlation Properties of Heart Rate Variability - Bruce Rogers, David Giles, Nick Draper, Laurent Mourot, Thomas Gronwald - J. Funct. Morphol. Kinesiol. 2021
3. Establishing the VO2 versus constant-work-rate relationship from ramp-incremental exercise: simple strategies for an unsolved problem - Danilo Iannetta, Rafael de Almeida Azevedo, Daniel A. Keir, Juan M. Murias - J Appl Physiol 2019