DFA (detrended fluctuation analysis) alpha 1 is an HRV (heart rate variability) based aerobic and anaerobic threshold estimation method. It only requires a heart rate monitor that tracks HRV data. It has the potential to track your thresholds automatically without dedicated test workouts.
DFA alpha 1 is based on DFA of your HRV data. Your HRV data is a list of the exact times between each successive heart beat. It contains more information than your simple, averaged, heart rate data. For example, take a heart rate of 60 bpm. This is your heart rate if your heart beats exactly in 1 second intervals. But it would also be 60 bpm if the time between beats is 0.9, 1.1, 0.8, 1.2, ... seconds. We usually refer to the time between beats as RR intervals.
Both recovery and DFA alpha 1 threshold detection are very sensitive to HRV artifacts. Artifacts are missed, short, extra, and ectopic beats. Artifact correction is crucial for a meaningful analysis of HRV data. We at AI Endurance have implemented our own artifact correction system and cross checked it against Kubios, the current gold standard in HRV analysis. To work with as little artifacts as possible, the Polar H10 is the recommended heart rate monitor at this point. Generally, we consider data with less than 5% artifact contamination to be acceptably clean.
This paragraph is about to get a little technical but we will provide a more intuitive explanation right after. DFA measures the self-similarity of a data set after removing the long term (>25 seconds) trends. In practice, it boils down to:
Ultimately what this is saying is that DFA alpha 1 can be interpreted as the noise index of the underlying data set. Larger values of alpha 1 indicate more correlation over different scales, while lower values indicate less correlation, and in a sense more chaotic and random data.
 hypothesized that the beating of the heart becomes more chaotic as it is exposed to increased acute exercise induced stress. The studies of  and  further established this connection by comparing oxygen intake (to detect the ventilatory aerobic and anaerobic thresholds) vs ECG (and later wearable heart rate monitor ) data.
The main result: DFA alpha 1 drops below 0.75 at the aerobic threshold and below 0.5 at the anaerobic threshold.
AI Endurance calculates DFA alpha 1 at high, Kubios-like accuracy, see for example this review by Bruce Rogers.
For more information check out the original publications on DFA alpha 1 threshold determination [1-4]. You can also find great content on this topic on
DFA alpha 1 has the potential to be a breakthrough for HRV based aerobic and anaerobic threshold detection for the following reasons:
The following two figures show DFA alpha 1 vs power/heart rate for an activity with a ramp test from 16:40 to 33:20.
To find out how to track your DFA alpha 1 thresholds with AI Endurance, check out this blog post.
And there is more. With DFA alpha 1 HRV based aerobic and anaerobic threshold detection, we may be able to detect thresholds even without dedicated test protocols/workouts. This is a realistic hope for the following reason: DFA alpha 1 will cross 0.75 and later 0.5 at some point during an activity if it is hard enough.
However, there is a catch: to detect your aerobic and anaerobic pace/power threshold the ramping up of intensity should not be too fast as your cardiovascular system has to adapt steadily to the intensity. If the ramping up is too fast, there is a lag in your cardiovascular response relative to your pace/power output . This is why at the moment we can only accurately estimate thresholds from dedicated DFA alpha 1 ramp protocols with rather slow ramp speeds. Alternatively, we are already able to detect thresholds if these slow ramp speeds naturally occur in an athlete's workout. There is ongoing research and development in this direction.
The ultimate goal is to track our fitness state consistently in an automated way. More often and without the dread of fitness tests. This will also mean more accurate predictions for AI Endurance with more data to correlate your training variables with your fitness state.
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