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] and often poorly correlate with your threshold.
• 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 have automated these assessments from your HRV data to the point that you don't even have to do dedicated tests anymore.

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 AI Endurance does.

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, Suunto or Polar watch or bike computer. Right now Garmin, Suunto and Polar are supporting in-activity HRV data. Coros, Wahoo, Strava and Zwift are not sharing HRV data (yet).
• Enable HRV logging on your Garmin device: Settings -> System -> Data Recording -> Log HRV or Settings -> Physiological Metrics -> Log HRV. Confirm your Suunto or Polar watch tracks HRV.
• Connect AI Endurance with Garmin Download, Suunto or Polar.

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 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.

We also detect thresholds via clustering: a cluster threshold is the average of all pace/power values recorded during the first 30 minutes of an activity with DFA alpha 1 values close to 0.75 (0.5). Generally, cluster thresholds are more abundant than ramps.

Our AI automatically takes into account the detected thresholds from ramps and clustering for your performance predictions. No more fitness testing anxiety and fatigue - focus on your training plan and we’ll detect your thresholds automatically over time and keep your digital twin up to date.

We have also 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/or Zwift. Simply go to your AI Endurance Apps 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 Download to be connected to AI Endurance.

How should I set my training zones in AI Endurance?

The aerobic threshold sets the upper bound of your Endurance training zone. Conversely, the anaerobic threshold sets the upper bound of your Threshold zone [4].

We automatically keep track of your training zones over time. As your fitness evolves, we notify you if your DFA alpha 1 data indicates an update to your zones might be warranted.

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
4. Time to Exhaustion at the Respiratory Compensation Point in Recreational Cyclists - Susana Moral-González, Javier González-Sánchez, Pedro L. Valenzuela, Sonia García-Merino, Carlos Barbado, Alejandro Lucia, Carl Foster, David Barranco-Gil - Int J Environ Res Public Health. 2020