Until recently, if asked - can the DFA a1 index be used to find the "anaerobic" threshold (VT2, LT2), I would have said ... no way. My reasoning would be that the dynamic range is becoming compressed at this data range and artifact related bias would obscure the detailed results needed. However, when we decided to take a more formal look at data from high quality ECG, near zero artifact tracings, it does appear that a relationship does exist. Moreover, it seems to coincide with the DFA a1 value of .5, which has mathematical significance as an indicator of a random correlation pattern.
Today our article was published and I would like to share some of my own tracings looking for the HRVT2.
The following was done with a Polar H10, BSX Insight muscle O2 sensor, smart trainer and Zwift to control power.
Based on previous gas exchange and lactate testing, my second threshold was about 270 watts. I decided to cycle 15w below and above this power to look at a1 and the muscle O2 curve of the Rectus femoris.
- I crammed everything in one graph.
- Muscle O2 (orange) is quite stable during the interval below LT2, but rapidly declines at 15w above it. This is typical for rectus femoris behavior below and above the LT2 and confirms my previous testing.
- DFA a1 average during each of the 6 minutes was .65 for the below, .51 for above the threshold power. Artifact was below 2%.
The next was a typical ramp, 10w/min until near failure.
- HRVT2 came in about at a HR of 155 and power of 275w. Artifact was 3 to 6% toward the end, so the a1 figures might be biased somewhat.
- Although this does agree with my lactate figures, a redo with fewer artifacts would be highly recommended.
- The presence of artifact is a major limitation to this technique.
- Either a traditional ramp or 5-6 minute sequential intervals could be done to look for the HRVT2, the point at which DFA a1 crosses the .5 value.
- However, it could be a "heavy lift" - artifacts, device bias and software bias (HRV logger vs Kubios) could be complicating factors to getting accurate values
- In addition, even our study ECG data had relatively wide "limits of agreement" and moderate-strong correlation coefficients (as opposed to very strong).
- Future research is needed to better understand, confirm and apply this information to athletic training and intensity distribution.