Monday, September 30, 2024

Improving the HRVT1 agreement - IJSPP 10/2024

Much has been written and discussed regarding the agreement between the first HRV a1 threshold (a DFA a1 of 0.75) and established standards (VT1, GET, LT1). There are multiple studies and opinions both for and against whether this marker is a valid proxy of the first ventilatory or lactate threshold. Who is correct, are any correct?  

After the last a1 article that appeared in MSSE, I felt that a "back to the drawing board" approach was needed to better answer this question. After some pondering, I decided not to discard our original concept of the HRVT1, that of being midway between a "correlated" and an uncorrelated DFA a1 pattern. Instead, after looking at the participant ramp data done over multiple studies by different groups, it appeared that the "midpoint" was not always at an a1 of 0.75. so a mathematical correction was needed. With the help of my co-authors and a set of blinded RR data, an attempt was made at estimating the HRVT1 based on individual midpoints for each person's ramp. The results came out fairly well, and in this post I will provide some details from the published article. References are also included.

Introduction
The identification of physiological boundaries pertaining to endurance exercise is of fundamental importance for both fitness monitoring and training intensity distribution 1,2. Unfortunately, proven methods to obtain exercise intensity thresholds separating exercise intensity domains (i.e., moderate to heavy, heavy to severe domains) often require elaborate equipment for evaluation of gas exchange and ventilatory responses, or invasive monitoring such as lactate testing. Therefore, the search for “surrogate” markers of these exercise boundaries without these requirements has attracted much interest. One such concept revolves around the measurement of a nonlinear index of heart rate (HR) variability (HRV) termed Detrended Fluctuation Analysis alpha 1 (DFA a1) during ramp incremental testing 3. DFA a1 is related to the degree of fractal behavior of the cardiac beat sequence as well as “correlation properties” over short time measurements 4,5. Like coastlines, snowflakes or tree branching, fractals are regarded as structures that possess self-similarity at various degrees of magnification. No matter the scale or magnification, the fundamental pattern is similar. As opposed to these structural examples, fractal behavior of the cardiac beat sequence is characterized by degrees of self-similarity of the beat series over different time scales6. This fractal behavior can also be mathematically quantified as “correlation properties” of the cardiac beat repetition patterns over variable time spans. To better comprehend the concept of correlation properties, parallels to a random walk have been drawn5. For example, during a random walk, at each next step, the walker can choose to go either right or left. If the choice the walker makes is not random but based on the previous sequence (series of right or left decisions), the pattern is described as being well “correlated” (DFA a1 near or above 1.0), since the future pattern is based on the past history. Values above 1.0 denote progressively higher degrees of correlation. But, if each new step is taken with equal, random chances of right or left, an “uncorrelated” pattern exists (DFA a1 of 0.5).Unlike most conventional HRV indexes that change with exercise, DFA a1 has the advantage of a wider dynamic range encompassing the moderate, heavy, and severe exercise intensity domains 7–9.
For these surrogate thresholds to be of realistic use, good agreement to existing standards as well as day to day repeatability needs to be present. Studies by multiple groups using both cycling and treadmill models have shown encouraging results supporting the second HRV threshold (HRVT2, DFA a1 value of 0.5) as a viable marker of the respiratory compensation point (RCP) or second lactate threshold 10–15. Additionally, DFA a1 behavior at constant power 16, during replicate ramp incremental testing 14,15 and over differing ramp incremental slope protocols 17 has been shown to be reliable/repeatable. However, in the case of the first HRV threshold (DFA a1 value of 0.75) agreement with the gas exchange threshold (GET) or first lactate threshold, results are somewhat conflictive. Although HRVT1 has been demonstrated to have strong correlation and minimal bias to different markers of the first threshold during incremental tests on the treadmill 18,19 or during cycling exercise 11,20, other studies have not shown such agreement. For example, a report comparing different ramp incremental slopes 17, three studies involving cycling 12,14,15 and one composed of only female participants 21 have been consistent with an overestimation of the first HRV threshold in comparison to traditional markers of the first threshold. However, even though the first HRV threshold was overestimated in some of these these trials, the reliability/repeatability was still good 14,15. Another situation that could theoretically lead to HRV threshold divergence is that of participant fitness status. However, this does not seem relevant as studies with high athletic fitness show both good agreement11,19,20 and poor agreement14 with GET or first lactate thresholds. It should be noted that DFA a1 including the first and second HRV thresholds can be affected by HRV artifact correction, recording device, sensor lead placement and preprocessing software 3,22 which could potentially affect results. However, careful examination of studies evaluating the first HRV threshold to gas exchange or lactate-derived indicators generally employs similar methodology, artifact correction and are even authored by similar research groups.
To explore why these differences in the first HRV threshold to the GET or the lactate threshold agreement may occur, an examination of the reasoning behind the HRVT1 concept is in order. DFA a1 correlation patterns are thought to be due to changes in sinoatrial pacemaker function controlled by the balance between the branches of the autonomic nervous system (ANS) 23. During exercise there is both a withdrawal of parasympathetic and an increase of sympathetic activity resulting in a change of HRV including DFA a1 24. Past studies demonstrated that during a ramp incremental test, values were well above 1.0 at very low intensities (well correlated), moved through a “partially” correlated (±0.75) region at moderate intensities, passed the “uncorrelated” value of 0.5 near the heavy/severe intensity boundary, finally reaching values below 0.5, signifying an “anticorrelated” pattern at severe intensities 25. It was posited that at the first threshold, the cardiac beat pattern would be found in an intermediate zone between well correlated (DFA a1 ≥ 1) and uncorrelated (DFA a1 = 0.5) behavior, which was numerically set to 0.75 7. In addition, it was conjectured that the second threshold would correspond to a second DFA a1 threshold of 0.5 (HRVT2), which represents a random interbeat pattern 10. DFA a1 values below 0.5 correspond to “anticorrelated” patterns representing an autonomic response indicating organismic destabilization consistent with exceeding a metabolic steady state 5,26. More careful inspection of prior data indicates that some individuals have a DFA a1 well above 1.0 early in the ramp incremental, with these values still considered strongly correlated 27. Since the initial DFA a1 in some individuals may be higher, the midpoint between well correlated and uncorrelated values (defining the first HRV threshold) in these cases would be shifted to a higher DFA a1 as a mathematical correction. For example, if an individual had a DFA a1 of 1.5 early in the ramp incremental, the midpoint denoting the first HRV threshold would be 1.0 with the associated HR and V̇O2 calculated accordingly. This suggests that the lack of agreement that some studies observed between the physiological responses observed at the first threshold might be explained, at least to a given extent, by the methodological approach used for the first HRV threshold determination.
Therefore, the aim of this study was to compare the agreement between the V̇O2/HR at the GET with the standard first HRV threshold (HRVT1s, DFA a1 = 0.75), and a custom first HRV threshold (HRVT1c) based on the DFA a1 response observed early in the ramp. It is hypothesized that as in some prior studies, the conventional HRVT1s will overestimate the V̇O2/HR in relation to the GET but after correction of the individual midpoint, the HRVT1c would show good agreement with established standards. Additionally, since no change in HRVT2 methodology is occurring, there would still be comparable agreement to the RCP as seen in earlier studies.

RR Measurements, HRVT1s, HRVT1c, HRVT2
Each participant's RR time series was recorded by a Polar H10 strap (Polar Electro, Kempele, Finland). The Polar strap electrodes were covered with conductive gel and firmly fitted to the subpectoral area with the module centered over the sternum. Before testing, the Polar H10 ECG waveform was visually evaluated with the Android app ECG Logger (https://ecglogger.en.aptoide.com/app). To optimize DFA a1 measurements, the strap was shifted slightly to the left if the R peak amplitude was lower than the S wave3. H10 data was transmitted through Bluetooth to an Android smartphone using the ECG Logger app for recording both RR intervals, ECG and time alignment for further analysis.
Thresholds corresponding to HRVT1s, HRVT1c and HRVT2 were calculated by a third evaluator who was blinded to the gas exchange and ventilatory data results. RR data previously recorded for each participant were imported into Kubios Scientific HRV Software (Version 4.1, Biosignal Analysis and Medical Imaging Group, Department of Physics, University of Kuopio, Kuopio, Finland). Kubios preprocessing settings were set to a detrending method of “Smoothness priors”, with the smoothing parameter at 500, and the cutoff frequency at 0.035. DFA short-term fluctuation window width was set to 4 ≤ n ≤ 16 beats18. Visual inspection of the entire test recording was performed to determine sample quality, noise, arrhythmia, and missing beat artifact. The RR series of each participant was corrected by the Kubios “automatic method” and DFA a1 was calculated every 5 seconds with 2-minute measurement windows and results exported as text files for further analysis. Acceptable percent artifact occurring during threshold interpretation segments was set to below 5% 22.
Plotting of DFA a1 vs HR was performed for the determination of HRVT1s HR and HRVT2 HR as seen in Figure 1 and detailed previously18. HRVT1s HR defined as the HR where DFA a1 equaled 0.75 and HRVT2 HR defined as the HR where DFA a1 equaled 0.5 18.
The HRVT1s and HRVT2 V̇O2 was derived via the DFA a1 vs time plot. The times that DFA a1 reached 0.75 (HRVT1s) and 0.5 (HRVT2) were used to then derive the V̇O2 via the V̇O2 vs time relation based on gas exchange and ventilatory analysis 18.
The determination for HRVT1c HR and V̇O2 was identical to the above, except that instead of specifying DFA a1 = 0.75 as the first threshold, each participant was assigned a custom value. The HRVT1c custom value was defined as the midpoint between the maximal DFA a1 (Max DFA a1) value seen in the early portion of the ramp incremental and 0.5. This maximal value was always below 3 SD from the running 45 second local mean value. For example, if the maximal value during the early ramp incremental portion was 1.5, the corresponding V̇O2/HR HRVT1c was based on a DFA = 1.0 (midway between 1.5 and 0.5) as seen in Figure 1.

Results
Oxygen Uptake Agreement:
GET and HRVT1s: Group mean values were statistically different (p = 0.010, d = 0.80; Table 2, Figure 2). Bland Altman analysis showed lower GET than HRVT1s (bias = 4.4, LOA -6.3 to 15 mL·kg-1·min-1; Figure 3). Regression plots with Pearson’s r, SEE and ICC are presented in Figure 4. As seen in Figure 5, the GET vs HRVT1s were not equivalent.
GET and HRVT1c: Group mean values were not statistically different (p = 0.77, d = 0.08; Table 2, Figure 2). Bland Altman analysis showed similar GET and HRVT1c (bias = -0.4, LOA -9.0 to 8.3 mL·kg-1·min-1; Figure 3). Regression plots with Pearson’s r, SEE and ICC are presented in Figure 4. As seen in Figure 5, the GET vs HRVT1c were equivalent.
RCP and HRVT2: Group mean values were not statistically different (p = 0.26, d = 0.31; Table 2, Figure 2). Bland Altman analysis showed similar RCP and HRVT2 (bias = -1.4, LOA -10.2 to 7.4 mL·kg-1·min-1; Figure 3). Regression plots with Pearson’s r, SEE and ICC are presented in Figure 4.
Heart Rate Agreement:
GET and HRVT1s: Group mean values were statistically different (p = 0.005, d = 0.91; Table 2, Figure 2). Bland Altman analysis showed lower GET than HRVT1s (bias = 16, LOA -18 to 50 bpm; Figure 3). Regression plots with Pearson’s r, SEE and ICC are presented in Figure 4. As seen in Figure 5, the GET vs HRVT1s were not equivalent.
GET and HRVT1c: Group mean values were not statistically different (p = 0.65, d = 0.12; Table 2, Figure 2). Bland Altman analysis showed similar GET and HRVT1c (bias = 2, LOA -24 to 27 bpm; Figure 3. Regression plots with Pearson’s r, SEE and ICC are presented in Figure 4. As seen in Figure 5, the GET vs HRVT1c were equivalent.
RCP and HRVT2: Group mean values were not statistically different (p = 0.98, d = 0.007; Table 2, Figure 2). Bland Altman analysis showed similar RCP and HRVT2 (bias = 0, LOA -21 to 21 bpm; Figure 3). Regression plots with Pearson’s r, SEE and ICC are presented in Figure 4.
Mean Max DFA a1 during the early ramp incremental was 1.52 ± 0.22 with mean calculated HRVT1c at a DFA a1 of 1.01±0.11.





Discussion
The purpose of this study was to explore the effects of modifying the value of DFA a1 used for determining the oxygen uptake and heart rate associated with the first HRV threshold, from a fixed value of 0.75 for all participants, to that of one based on individual DFA a1 ramp characteristics. Findings indicate that by using a custom first HRV threshold derived from the DFA a1 value midway between the maximal seen during the early portion of the ramp incremental and 0.5, the positive bias of HR and V̇O2 observed using the conventional HRVT1s was eliminated. Furthermore, since the line of regression used for determining HRVT1c was unchanged from standard methodology (Figure 1), the tight relationship between the HRVT2 and RCP was maintained.
HRVT1s vs HRVT1c agreement to the GET
As in some previous reports, the V̇O2/HR seen at the HRVT1s was substantially higher than the GET V̇O2/HR 12,14,15,17. This bias was virtually eliminated by individually defining each participant’s DFA a1 predicted to correspond to the GET, with no differences in group mean HRVT1c V̇O2/HR comparisons to GET V̇O2/HR. Additionally, there was a reduction in the LOA (for example LOA reduced from  a range of ±35 to ±26 bpm for HR), with trivial bias in the V̇O2/HR associated to HRVT1c compared to the V̇O2/HR responses associated to the HRVT1s (Figure 3). In terms of group correlation to the GET V̇O2/HR, both HRVT1s and HRVT1c showed similar strength in Pearson’s r and ICC to prior studies11,15,18,19,21 (Figure 4).  Equivalence between GET and HRVT1c V̇O2/HR was also verified (Figure 5).  Mean Max DFA a1 during the ramp incremental was 1.5, resulting in a calculated HRVT1c threshold near 1.0. These observations support the hypothesis that a given individual’s DFA a1 ramp incremental midpoint between well correlated and an uncorrelated pattern does not necessarily occur at a value of 0.75. In past studies, early ramp incremental DFA a1 values were not systematically provided. However, it is of interest to highlight a group of participants that showed both good agreement of the HRVT1s to the GET and also had DFA a1 recorded during a low intensity warmup pre-ramp 16,19. The mean DFA a1 during the pre-ramp warmup was 1.12±0.23 (corresponding HR = 141±13), making the calculated DFA a1 at HRVT1c of 0.81 and standard HRVT1s of 0.75 almost the same. Thus, as with the current report, matching the V̇O2/HR to the calculated DFA a1 ramp incremental midpoint resulted in good first HRV threshold to GET agreement via mathematical adjustment of first threshold targets. Furthermore, the high day-to-day reliability 16 of each participant’s warmup DFA a1 (ICC = 0.85) and the repeatability of the HRVT214,15,17 adds confidence to the notion that midpoint calculation might be a repeatable occurrence.


HRVT2 vs RCP agreement
There was good agreement between the RCP V̇O2/HR with that of the HRVT2 V̇O2/HR in terms of group mean values (Table 2), minimal bias, moderate LOA (Figure 3) and high degrees of correlation (Figure 4). This was not unexpected as the recording device, procedures, calculating software and plotting methodology were the same as in previous reports which indicated similar findings 11–14,21. However, it was important to present this data not only to corroborate previous results, but also to highlight that there is no alteration in the derivation of the HRVT2 as a significant advantage of the proposed HRVT1c modification. 


Contrasting ANS vs cardiorespiratory thresholds
While the use of a custom HRVT1c calculation did improve overall agreement with the GET, variation still exists on an individual basis. This may partially relate to the underlying conceptual differences between a marker of ANS response and measures derived from gas exchange indicators such as V̇O2, V̇CO2 and ventilatory responses which may diverge in some individuals. HRV in general arises from the competing influences of the stimulatory sympathetic and inhibitory parasympathetic branches of the ANS on the sinoatrial node resulting in fractal correlation behavior of the cardiac beat sequence 23,24. It is theorized that a potential rationale for this behavior is an attempt by the cardiovascular system to best cope with a rapidly changing environment 4,32,33. These environmental changes affect multiple neuromuscular, biochemical, peripheral and central nervous system inputs leading to alterations in “systems integration”34. Within this framework, various physiologic responses and cardiovascular advantages may exist behind the changes seen in correlation patterns due to increasing exercise intensity and overall organismic demands35. Therefore, at low to moderate exercise intensity where future physiologic requirements may be quite variable, cardiac ANS measures consistent with plasticity/flexibility are preferred (DFA a1 is correlated, 0.75 ≤ n ≤ 1.5) but these same measures becomes more rigid (DFA a1 is uncorrelated/anticorrelated, ≤ 0.5), in the heavy to severe domain as an ultimate protective response 32,36. Anticorrelated behavior refers to a pattern viewed as an immediate self-correction mechanism associated with the potential failure of homeostatic regulation and can only be tolerated for short time spans, in agreement with the severe intensity domain 26. Consistent with entering this metabolically unstable zone, it has also been shown that DFA a1 drops to below 0.5 in normal volunteers during high dose norepinephrine infusion 37. An analogy to these correlation behaviors could be optimization strategies employed by a soccer goalie. During most game play, the goalie would maintain a flexible approach, able to respond to highly variable threats (as with the HRVT1c). Under more critical circumstances, as in an imminent scoring opportunity, the choice of goalie position would be greatly constrained to prevent a catastrophic response (as with DFA a1 ≤ 0.5). The consequence of these conceptual differences in correlation properties (flexibility vs rigidity) is that the first HRV threshold may need to be mathematically realigned to account for individual variability in the DFA a1 ramp incremental range, as done in the present report. 


Experimental considerations
As previously discussed, many circumstances can alter DFA a1 determination and the resulting HRV thresholds including prior fatigue, software preprocessing (detrending), ECG/RR artifact correction, recording device bias and chest belt placement 3,22. Additionally, little data exists comparing ramp incremental derived HRVTs with that of DFA a1 during constant load exercise. It has been recognized that lengthy time spans at low intensity 38 or short time spans at high intensity 19 can suppress the DFA a1 that would otherwise be expected. As a consequence of the different underlying physiological concepts involving cardiorespiratory thresholds and markers of ANS status, individualistic differences may remain between the GET/RCP and ANS linked HRVTs even with further advances in HRV methodology. Continued research into combining other noninvasive surrogate markers with the HRVT such as muscle NIRS desaturation breakpoints 13, or ECG derived respiratory rate thresholds 12 appears reasonable despite the increased cost or complexity involved. Additionally, even established methods such as gas exchange based respiratory responses have both error, device bias and day to day variations to some degree39. With regards to equivalence testing, setting different boundaries for Cohen’s d could affect these results.


Practical Applications
The results of this study refine and support the original hypothesis that the V̇O2/HR found at the GET would correspond with that of the DFA a1 seen at a midpoint between well correlated and uncorrelated values. However, instead of a fixed midpoint value of 0.75 used for all circumstances, the novel finding presented here indicates that this target needs to be derived from the maximum DFA a1 seen early in an individual’s ramp incremental. Therefore, by simply altering a single facet of the first HRV threshold computation, we can resolve the differences between conflicting study outcomes such as the poor GET to HRVT1s agreement in this data set with that of the stronger agreement seen by Van Hooren et. al. 19. However, even with the HRVT1c adjustment, underlying differences between ANS vs cardiorespiratory responses may preclude exact agreement between HRV related thresholds and established standards.


Conclusion
An individualized first HRV threshold estimate based on the V̇O2/HR occurring at the DFA a1 midpoint between that observed early in the ramp incremental and an uncorrelated pattern showed greater agreement to the GET than the conventional methodology using a fixed DFA a1 of 0.75. Mean values for GET V̇O2/HR and that of HRVT1c derived values were in closer agreement than what was observed using the standard approach. Application of this refinement to existing methodology will hopefully resolve conflictive first HRV threshold study outcomes, while preserving the already established RCP to HRVT2 relationship.

Personal comments:

First off, many thanks to Juan and Pablo for making this happen. What an honor to work with one of the top exercise physiologists (and physiology labs) in the world. Additionally, these investigators are renown for their work on accurate gas exchange thresholds, making the collaboration even more appropriate. The HRV data was analyzed in a totally blind fashion by myself (with no knowledge of gas exchange data), making the study a strong one.

Is this the end of the HRVT1 controversy? I doubt it (see below). There will continue to be demographics ill-suited to ANS threshold surrogates (post covid comes to mind) and we are still in the early stages of understanding dynamic HRV pitfalls and usage. However, this mathematical correction seems to go a long way to reconciling the conflicts seen in the literature. The LOAs are still high, making a case for adding another surrogate marker (as in EDRT).

Although not focused on, the RCP to HRVT2 agreement continues to be consistent with prior work and appears to be a solid concept. 

A postscript...

You may or may not have noticed in our methods (in bold above) that we go to great lengths to optimize the H10 belt placement, to get the best R peak voltage possible. Why, it's simple - poor R peaks will skew the a1 and also lead to potential artifact (from poor signal-to-noise ratio). One can also get RR bias with alternate ECG leads, making placement an overlooked issue in a1 research.


This practice has been done with the Murias team related studies, with excellent results. As you can see in our article looking at SmO2 and HRVT2 combo thresholds, there were no cases of missed beat artifact (just two cases of arrhythmias that precluded analysis). The reason I mention this is a recent paper concluding a1 thresholds were not very aligned with gas exchange data. I looked over the methods, and on first perusal, found nothing wrong. Kubios - check, H10 - check, gas exchange - check. 

What did they find? That HRVT1 occurred at an average a1 of 0.67!!! I put the exclamation marks because no one has seen that to my knowledge. If anything, the a1 at the GET/VT1 has been above 0.75 (we have been seeing near 1.0).

The midpoint of a1 was not calculated, but it did not appear to be an issue based on a sample they showed:


 

So what are some factors that lead to early a1 decline?

  • Fatigue, recent HIT - no mention of abstaining from recent exercise was in the methods. It is possible there was an effect from fatigue/recent training/racing.
  • Poor R peaks - as noted above, poor signal to noise ECG signals can lead to both artifacts (which the authors were complaining about) and an effect on the RR precision, causing a1 to measured lower than it should. this is my best guess.
  • Older generation HRMs - the H7 had this issue

What do other groups see regarding average a1 at the VT/GET? A recent study, mirrored ours closely in that the HRVT1 HR was well above the GET HR, which is what we saw in the MSSE publication. 

Is this paper a case of garbage in, garbage out - I just don't know? The authors make no attempt to scientifically explain the discrepancy, just disparage the process. Next time they work with the H10, perhaps they can read the instructions...

 

 

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Monday, September 23, 2024

Garmin Resting HRV - my experience

Garmin Resting HRV - my experience

As the basis for many fitness related concepts, the measurement of resting HRV is of critical importance. These metrics include "readiness to train", sleep quality/stages and autonomic strain/recovery. Consequently, even if some of the newer "hyped" tracking devices have these features, we are still at the mercy of whether they can accurately measure resting HRV (yes I'm talking about the Fenix 8 and the newer "rings"). Over the past several years, I have used numerous Garmin devices, including the Fenix 5, 6, 7, Epix 2 and FR965. Despite generational advances in the optical sensors (according to the manufacturer), my experience has not been positive regarding resting HRV (or exercise HR). Time after time, my H10 or Movesense ECG overnight readings (using Kubios with an ECG) were 200-300% different from Garmin! This frustrated me to no end considering my interest in accurate HRV.....So, I sought a solution to the problem.

 

Let's go through some of my testing and a solution to this issue. 

Baseline - A typical night recording with my Garmin FR 965:

  • Garmin FR 965 (wrist) RMSSD = 47 ms:

 

Comparison to Gold standard 

And at the same time wearing a Movesense ECG, sample rate 250 Hz with Kubios HRV results: 

  • Movesense ECG with Kubios, full night average RMSSD = 18 ms:

  • The Garmin unit is about 2.5x too high!
  • I've done this many times before on all the Garmin devices mentioned above, and if anything, the error is getting worse with the newer units. You can see my Garmin range of 46 to 56 ms, which I never see on ECG overnight measurements.
  • Hence, the Garmin HRV related metrics/recommendations/sleep phases are assumed to be inaccurate. 

My solution:

My wife has an Apple Watch and from my testing (as above) the HRV and exercise HR have been spot on! But I'm an android user, so that's out for me. My thought was that perhaps the big "players" like Apple (who could attract the best developers through higher budgets and benefits) might provide the best accuracy, so I did the next best thing - I bought a Pixel 3 watch (Google/Fitbit). 

Here are the initial HRV results (I've only had it a week or so).

Gold standard -  

  • Movesense ECG with Kubios, full night average RMSSD = 14.3ms:


 

  • Pixel Watch 3 wrist (RMSSD = 14 ms):

  • As you can see with the Garmin (46-50ms) and Pixel (14-18ms) daily RMSSD numbers, they are internally consistent within device, with only small changes on a day-to-day basis. 

What surprises me the most is not the huge error in the Garmin metric (I already knew that for years), but how close the Pixel was aligned with Kubios/ECG results. This is very encouraging, and I'll be keeping track over the next few weeks to make sure the good agreement continues.

  • What does this mean for those of you using various rings and watches? It means that your device may or may not be in alignment with true resting HRV. If it is, then great, but if not, then the "recommendations" put forth by (let's say) Garmin may be fatally flawed.

The solution

There is a simple way of knowing where you and your device stands - use the Polar H10 chest belt, record a full night of data (with Fatmaxxer, Garmin units or other apps) and use Kubios (free version) to ascertain the RMSSD. You may want to temporarily disable the Garmin from its pairing with the H10 just to be sure that it's using optical input.

In this post I have a brief tutorial on how to use Kubios free version (you will need to use a measurement window size of the entire night, not 2 minutes as used for DFA a1). If this is too complex, there is a simpler way - use Fatmaxxer, which does log the RMSSD to the "features" file. Yet again, another great feature of Fatmaxxer! These would be 2-minute window measurements, so you will need to average the column:

This is the "features" file from Kubios (not mine, a random test case):

 

My night data test: Full night measurement window vs averaging all the 2 minute Fatmaxxer measurements:

  • I did a comparison between the RMSSD of an 8 hour nightly session as a single measurement window in Kubios (8 hrs) vs the same RMSSD as 2 minute windows averaged (over 6000 overlapping Kubios samples). The results were quite close - 17 vs 18 ms.

 

Exercise HR - Outdoor road cycling

The other item I was curious about was how well the Pixel 3 watch optical HRM agreed with the H10 in simple HR. In the past, the Garmin devices have been dismal for outdoor cycling and even unreliable indoors on a trainer (for me). Therefore, I did about 3 hours outdoors on bumpy roads with a few intervals. Here is a partial sample of the results:

  • You get the picture - and it's impressive for a wrist device during road riding at various intensities.

Why should we care about absolute values?

You may think that it's ok to have this systematic huge bias in HRV as long as the trend is helpful. Perhaps. But there is a great deal of value in knowing your true resting RMSSD (or SDNN). Here are a couple of interesting studies looking at mortality prediction and biologic aging


 


Summary and Thoughts

  • The above results are based on an N=1 sample, me. Perhaps your device/wrist combo will function differently and match to a gold standard. However, I do want to caution getting a false sense of confidence after a single good correlation between a Kubios or Fatmaxxer RMSSD sample and your device. What if "X" vendor's RMSSD (Garmin, Suunto, etc.) just coincidently matches your reading with Kubios? After all, my Garmin readings are certainly plausible for many individuals and could match gold standards just from statistical luck. How do we get around this circumstance? In that case, one could follow along for several nights and make sure the correlation holds up.
  • Further, this type of discrepancy in a basic wearable metric vs an established method could be present elsewhere in a company's product. If Garmin can't get optical HRV correct, what else are they messing up that we can't easily measure? This illustrates the need for us to "trust but verify". It's unfortunate we can't blindly trust what the various wearable vendors are saying, but these are not FDA approved medical devices (therefore, anything goes). 
  • The other clue to optical HRM quality could also be how well the HR tracks during exercise. If your wearable aligns well with a chest belt, especially during intervals, that could be a favorable sign. 
  • I have abandoned my Garmin FR965 after being a loyal user for many years. I still use the Edge 1050 for cycling (which is great for recording anything ANT+ or bluetooth and displaying alphaHRV a1). I'd rather have accurate resting HRV (and wrist exercise HR) than the nice watch faces.
  • Bottom line - Resting HRV is a promising metric for readiness, ANS stress, illness and recovery. Please make sure your readings are trustworthy....




Tuesday, February 20, 2024

HRVTs: Agreement with Established Approaches and Reproducibility, MSSE 2024

The field of HRV threshold research has progressed over the past several years.  We have learned quite a bit, but some questions still remain.  One issue that continues is just how well the first threshold, HRVT1 agrees with established measures such as lactate and gas exchange.  In addition, the repeatability of these thresholds is quite important for everyday use.  Today, our newest work was released in one of the most well known sports medicine journals, MSSE.  The manuscript stands on it's own merits and I encourage you to read through it (I'm including the intro and discussion below).

I want to thank Juan Murias, the team at Calgary and his grad student, Pablo Fleitas-Paniagua, whose hard work made this possible.  What started as some email correspondence with Juan many years ago has blossomed into some very cool research into a series of cutting edge topics.  This has been a fun project and a valuable learning experience on my end.


Intro:

An important consideration for endurance exercise training and fitness intervention is the proper delineation of the boundaries separating exercise intensity domains (i.e., moderate, heavy, and severe) (1–5), and the various exercise intensity zones that can be compartmentalized within them. To that end, efforts are continuously made to circumvent reliance on the more invasive, costly, and laboratory-based testing modalities such as gas exchange and lactate blood testing (6–9).
Detrended fluctuation analysis alpha 1 (DFA a1) is an index of heart rate variability (HRV) that has gathered interest as a means to demarcate the boundaries separating the moderate, heavy, and severe exercise intensity domains (9). Unlike other well-established practices for threshold determination that require gas-exchange, ventilatory, and lactate evaluations during incremental tests, or the performance of relatively long and/or exhaustive trials (i.e., maximal lactate steady state and critical power) (10–14), these boundaries can be estimated by analyzing DFA a1 using inexpensive wearable devices and software applications (15–18). This makes the use of HRV-derived thresholds (HRVT) suitable for exercise training prescription and monitoring for the large population unable to utilize “gold standard” methods. Although several publications have found good group agreement of these HRVTs with either gas exchange or lactate-based measures (7, 8, 16–19), others have not and have shown large degrees of bias in the moderate to heavy boundary (7, 20, 21). Even when group mean measures are in good agreement, individual levels of bias may remain high. To help reduce individual bias, recent attempts have been made to improve the accuracy of surrogate threshold determination by combining HRVT with other physiological variables such as breathing frequency (20) and near-infrared spectroscopy-derived thresholds (8).
Similar to other HRV indexes studied during an incremental test (22, 23), the DFA a1 index decreases as exercise intensity increases. A value of 0.75 (HRVT1) has been associated with responses corresponding with those observed at the gas exchange threshold (GET) or the first lactate threshold (LT1) (16, 17, 19). Although HRVT1 has been reported as an effective tool to demarcate the moderate-heavy exercise intensity boundary during incremental tests on the treadmill (17) as well as during cycling exercise in male participants (19), other studies have not been as clear (7, 20, 21). Careful evaluation of the results seen in a study comparing different ramps (7), another involving cycling (20) and one composed of only female participants (21), revealed an overestimation of the HRVT1 in comparison to the GET. As the intensity of the exercise increases above the GET, DFA a1 continues to decline with a value of 0.5 (HRVT2) being strongly associated with the respiratory compensation point (RCP) (7, 8, 18, 20, 21) or second lactate threshold (19), which can be used to identify the metabolic rate demarcating the boundary between the heavy and the severe exercise intensity domains (24, 25). Studies that have investigated HRVT2 during incremental exercise on a treadmill (18) and on a cycle-ergometer in females and males (8, 20, 21) have shown a strong agreement between HRVT2 and the boundary separating heavy from severe intensity exercise. Additionally, a recent study has shown during three different ramp incremental rates (15 W·min-1, 30 W·min-1, and 45 W·min-1) that the heart rate (HR) and the oxygen consumption (V̇O2) at which HRVT1 and HRVT2 occur were consistent across ramps in both female and male participants, independently of the intensity increase rate (7).
Despite the practical value of using HRVTs, the reasons for some of the inconsistent results indicated above remain unknown. Thus, to confidently use DFA a1 derived thresholds to demarcate exercise intensity domains, more research is needed to establish the validity and reproducibility of DFA a1 as a proxy to estimate physiological thresholds (9). Additionally, to date, no study has examined the reproducibility of identical incremental protocols of HRVT measures. This is a critical aspect because consistent responses are needed for HRVT estimations to be useful in practical settings. Therefore, given the conflicting results concerning HRVT1 and GET/LT1 agreement, a study with larger sample sizes of both male and female participants, and consideration of both lactate and gas exchange thresholds would be helpful.
The present study aimed to determine i) the agreement between GET, LT1 and HRVT1; ii) the agreement between RCP, LT2 and HRVT2 and; iii) the reproducibility of HRVT1 and HRVT2 determination during 2 min incremental step protocols in female and male trained individuals. We hypothesize that, based on prior studies (7, 20, 21), the HR and V̇O2 at the HRVT1 may be overestimated compared to those at the GET/LT1, but that the HR and V̇O2 at the HRVT2 would show excellent agreement to the RCP/LT2. Moreover, based on the previous observation that individual HRVTs performed using ramp slopes between 15 to 45 W·min-1 showed excellent correlation and agreement (7), we hypothesize that HRVT1/2 reproducibility during similar step-tests would be high as well.

DISCUSSION
The aim of this study was to explore the strength of agreement between the HRVT1 with the GET/LT1, the HRVT2 with the RCP/LT2, as well as to assess the reproducibility of HRVT1 and HRVT2 across two identical testing protocols. The main findings were that: i) there was a strong agreement between the V̇O2 and HR reached at the HRVT2 compared to the values observed at the RCP/LT2; ii) there was significant bias in the V̇O2 and HR seen at the HRVT1 compared to the values obtained at the GET/LT1; iii) regardless of whether HRVT1 or HRVT2 agreed with recognized “gold standard” measurements, the reproducibility of individual HRV related thresholds was good.
HRVT2 and RCP/LT2 agreement.
The strong HRVT2 agreement with established methods to determine the heavy-severe intensity boundary such as the RCP and the LT2, reinforces results from previous investigations using both cycling and running test modalities (8, 18, 20, 21). Group bias of the HRVT2 compared to the RCP was negligible (i.e., < ±1 bpm for HR and < ±1 mL∙kg-1∙min-1 for V̇O2), with LOAs of ~15 bpm and 9 mL∙kg-1∙min-1 and no meaningful differences when stratified by sex (Figure 3). The HRVT2 agreement to the LT2 was also high (i.e., < ±1 bpm for HR and < ±1 mL∙kg-1∙min-1 for V̇O21), with LOAs of ~17 bpm and 10 mL∙kg-1∙min-1, which also displayed no meaningful differences between females and males (Table 2). Furthermore, moderate to strong correlations of HR and V̇O2 at the HRVT2 to the HR and V̇O2 at the RCP (r > 0.65) and the LT2 (r > 0.50) were found. The confidence interval within the limits of agreements confirm equivalence for V̇O2-HRVT2 and HR-HRVT2 for both, females and males. Although the small biases and the high correlations between the HRVT2 and well-established outcomes of the threshold separating the heavy to severe intensity domains (i.e., the RCP and the LT2) existed, one should still consider the moderately wide LOA of either V̇O2 or HR, which could make exact individual participant threshold identification challenging. As previously proposed, using a combination of non-invasive, consumer grade device-based signals such as HRV, near-infrared spectroscopy deoxyhemoglobin, and/or breathing frequency (e.g., HRVT2, [HHb]BP, and EDRT2, respectively) for the estimation of the heavy-severe boundary of exercise appears to lead to more precise delineation of this threshold (8, 20). Further, it should be considered that exact identification of exercise intensity thresholds is not absolutely precise, even when using the commonly accepted gold-standard evaluations. For example, gas exchange and ventilatory parameters are known to have noise in the signal of ~100-150 mL∙kg-1∙min-1 (38, 39), and to rely on the interpretation of an experienced evaluator (with typically at least two evaluators contributing to the analysis) (3). Lactate derived thresholds also include limitations for the analysis that can be impacted by the approach used for their estimations (e.g., visual inspection, mathematical model, etc.) (31, 40), and by the amplitude of the steps selected for the evaluation. Then, given the uncertainties that even the so-called “gold-standard” approaches, and considering the negligible bias in HR and V̇O2 and the high correlations reported in this study, the HRVT2 can be considered a viable low-cost, non-invasive surrogate to approximate the RCP or LT2.
HRVT1 and GET/LT1 agreement.
The HRVT1 agreement to the GET/LT1 was considerably weaker than that of the HRVT2 to the RCP/LT2. The mean bias for the GET was significant and relatively high in females and males for both HR and V̇O2 (i.e., > ±11 bpm and > ±5 mL∙kg-1∙min-1) (Figure 2). This was similar for the HRVT1 to LT1 agreement, which showed a significant bias in both females and males that was greater than 14 bpm (Table 2). Similarly, the LOA for HRVT1 in relation to the GET for both HR and V̇O2 were large (>22 bpm and 9 mL∙kg-1∙min-1, respectively), and the correlations were also weaker than those seen for the HRVT2 metrics. Thus, unlike what was observed for the HRVT2 in relation to the RCP and LT2, the viability of the HRVT1 as a low-cost, non-invasive surrogate to the GET or LT1 is not evident based on the present data. The positive bias observed for the HRVT1 (i.e., greater HR and V̇O2) in relation to the GET has been noted in several other reports (7, 20, 21). In a study exploring HRVTs in female participants, the positive bias in the HRVT1 was speculated to be a consequence of sex hormone differences. However, in the current report, although the positive bias seen in the female cohort was similar to the previous study in women (21), the group bias in males was even larger, which is at odds with the idea postulated above. Nevertheless, contrary to the current study, Mateo March et al. (19), using a similar cycling model with LT1 determination criteria, the same HRV interpretation software (Kubios), and recording device (Polar H10), found strong agreement and negligible bias in the HRVT1 compared to the LT1. Strong agreement with minimal bias was also seen in smaller studies using both the Polar H10 (16, 41) and ECG recording devices (17, 42).


Explaining the discrepancy.
The reasons for the ambiguities noted in the literature investigating HRVT1 are not easily explained. The strong agreement observed when assessing HRVT1, HRVT2, GET, and RCP in three different ramps and using the same methodology for thresholds detection might indicate that there is a tight physiological mechanism behind. Studies where GET was assessed based on the concept of a change in ventilation at the beginning of the isocapnic buffering (Figure 1A) related to an increase in [La-]b (Figure 1B) found an overestimation of HRVT1. The discrepancy in the results shown by different research groups might be due to the different incremental protocols and analysis models used to estimate thresholds. The rate of increase in workload can affect the dynamic profiles of some physiological responses such as V̇O2 and [La-]b, (40, 43). Further, the criteria or mathematical model used to evaluate exercise thresholds can also affects the V̇O2 or HR assigned to each of them (31). For instance, methods like the excess carbon dioxide detect a later breakpoint related to the change in the V̇E/V̇CO2 relationship (Figure 1A) (44). Several potential factors have been noted to affect DFA a1 measurement including artifact correction methodology (33), recording device bias (33), ECG vs chest strap sensor pad placement and HRV preprocessing/detrending (9, 45). Although recording devices have varied per published report, it is apparent that studies using the identical device (Polar H10), HRV software (Kubios HRV), and working off low artifact data have shown markedly different HRVT1 agreements to what is reported here (19). To explore other considerations, an examination of the logic behind the HRVT1 concept is in order. The HRVT1 was originally founded on the notion that cardiac beat to beat correlation patterns decline as exercise intensity rises, reaching certain benchmark values that denote the GET, then eventually, the RCP.  DFA a1 is a dimensionless number representative of the fractal complexity, correlation of the cardiac beat series and is primarily determined by autonomic nervous system balance. After reviewing past studies concerning DFA a1 decline with increasing work rates, it was observed that index values were well above 1.0 at very low intensities, moved through a “partially” correlated zone at moderate intensities, passed the “uncorrelated” value of 0.5 near the heavy/severe intensity boundary, and finally reaching values below 0.5, signifying an “anticorrelated” pattern at severe intensities (15).  It was originally theorized that at the GET/LT1, the cardiac beat pattern would be found in an intermediate zone between well correlated (DFA a1 > 1) and uncorrelated (DFA a1 = 0.5) behavior, which was set to 0.75 (17). Since the HRVT1 was based on a hypothetical midpoint, it can be argued that some individuals may have higher baselines and hence altered midpoints from the fixed 0.75. Although not done in the current data set, recalculation based on either adjusted midpoints or back calculation from the GET to derive “personal” DFA a1 HRVT1 values should be further explored. As seen in figure 2, utilizing the HRVT1 boundaries for training purposes would have led to inappropriate training targets in the majority of individuals, illustrating the need to better define the HRVT1.
In contrast, virtually all studies to date show strong agreement of the RCP to the HRVT2 (8, 18, 20, 21). The weaker agreement of the HRVT2 to the LT2 seen in one report could be attributed to a variation in LT2 definition (19). As opposed to the HRVT1, the reasoning behind the HRVT2 (a DFA a1 value of 0.5 corresponding to the RCP), is more straightforward. The value of 0.5 represents a random, uncorrelated beat pattern with loss of fractal properties (46). A potential functional objective of these correlation rhythm patterns may be a physiologic optimization and/or stabilization strategy to best suit internal load requirements (47). As DFA a1 values drop below the uncorrelated boundary (below 0.5), they enter an anticorrelated range. This behavior may denote a condition of maximum organismic energy flow at the cost of cardiovascular self-regulation and can only be maintained for short time spans (48). In view of these considerations, the HRVT2 association to a discrete mathematical concept of cardiac regulation differs from the observational “midpoint” nature of the HRVT1 and therefore may display less individual variability.


HRVT reproducibility.

When depending upon a given surrogate value for intensity threshold purposes, not only it is important to have good group and individual agreement, but it is imperative that the test is reproducible when done on a repeated basis. Thus far, no studies evaluating reproducibility of DFA a1 related HRVTs have been published. Even with a four month of gap between sessions, the results seen here indicate good to excellent degree of reliability based on two repeat tests with an ICC > 0.86, Pearson’s r > 0.79 for HRVT1, an ICC > 0.81, Pearson’s r > 0.68 for HRVT2, and no difference between mean values according to paired t testing (p > 0.05).  These results somewhat mimic those seen in the comparison of HRVTs derived from cycling ramps of different slopes (7). That analysis showed very strong correlation with negligible bias of both the HRVT1 and HRVT2 over ramp slopes of 15, 30 and 45 W·min-1. Based on the present results and those of the cycling ramp slope report, it appears that HRVT1 and HRVT2 are highly reproducible and repeatable even when a significant bias is present as in the case of the HRVT1 in relation to the GET/LT1. Therefore, if an athlete was able to calculate a personal HRVT1 and HRVT2 against an established method (GET, RCP, LT1, LT2, or others), they potentially could use HRVT boundaries for training intensity distribution and longitudinal physiologic benchmarks. Additionally, since deriving the HRVTs may not require exercise testing to exhaustion, incorporating brief testing sessions for the purpose of monitoring fitness status or assessing autonomic fatigue may be practical.
Experimental considerations
Discussions concerning HRVT and DFA a1 methodological limitations has been reviewed previously (9). Considerations include proper software preprocessing (detrending), low artifact containing data, optimal chest belt placement and mixed female/male participants. Further, in this study in which short steps were used, the dissociation between V̇O2 and PO from incremental compared to constant load exercise could not be fully resolved. Thus, the HR and V̇O2 rather than the PO associated with the HRVTs were calculated. Alternatively, when identifying the PO associated with the HRVTs is relevant, longer steps that allow for steady state response (49), or short steps/ramp incremental test that consider the mean response time and the slow component of V̇O2 (50, 51) and/or shallow ramp slopes (43) would be recommended. Additionally, since an autonomic index such as DFA a1 may be affected by fatigue, testing after prior intense exercise, stress or infection may affect the results. Although the 2-minute measurement window with a short-term fluctuation (N1) setting of 4 ≤ n ≤ 16 beats was used in previous HRVT studies, modification of these settings may or may not improve agreements with gold standards. The modifications could be further evaluated thought a sensitivity and/or exploratory factor analysis. Finally, the relationship of DFA a1 related behavior with constant work exercises below, at, and above threshold estimations needs further exploration.
Conclusion
The agreement of the DFA a1 based HRVT1 HR and V̇O2 to the GET/LT1 HR and V̇O2 exhibited a substantial bias with moderate correlation in both females and males. Despite this, there was strong agreement/correlation between the HRVT2 HR and V̇O2 with those values derived from the RCP/LT2. Test to test reproducibility of HRVT1/2 was excellent with similar ICC when comparing to established methods during test 1 and test 2. The current study provides evidence that the HRVT2 is a reproducible estimate of the RCP/LT2 but further investigation into better defining and improving HRVT1 methodology is needed.  

Reviewer comments.

As a change of pace, I wanted to share a couple of interesting questions posed by the reviewers.  The first has to do with the sample rate of the Polar H10 and the second with altering the "window width" of the DFA a1 calculation (4 minutes instead of 2 minutes).

Comment #1 - As another example, although a sampling rate of 1,000 Hz is mentioned, as far as I know the ECG waveform with that sampling rate is not available from the H10 sensor, only the RR interval time series, which does not have a sampling rate of 1,000 Hz unless it was somehow resampled as such. A few additional details on the processing steps would help the reader understand potential limitations.

Thank you for your comment. Polar does publish an API that allows for ECG recording with a fixed sample rate of 130 Hz (as seen in the Polar API https://github.com/polarofficial/polar-ble-sdk). We used the Android app “ECG logger” which uses the Polar API and records the Bluetooth output from the H10 as continuous ECG data stream for the purpose of waveform optimization and belt placement. The actual
sample rate for RR recording for the H10 is conventionally noted as being 1000 Hz
(https://www.nature.com/articles/s41598-023-38329-w#Sec8 and
https://pubmed.ncbi.nlm.nih.gov/31004219/), however, there is no “official” documentation currently on the Polar site showing this (to our surprise). It has also been discussed on the Polar API GitHub site
(https://github.com/polarofficial/polar-ble-sdk/issues/343) and it seems that there is some relation to a
1/1024 time fractionation. The Polar H10 related DFA a1 agreement to a reference ECG (lead 2, 500 Hz
sample rate) during incremental exercise has been evaluated previously (Schaffarczyk et al., Validity of the Polar H10 Sensor for Heart Rate Variability Analysis during Resting State and Incremental Exercise in
Recreational Men and Women. Sensors 2022, 22, 6536. https://doi.org/10.3390/s22176536) and the
correlation /agreement was strong.

Take home lesson - there is no published "official" sample rate for the H10 RR series!

Comment #2 - The authors claim that 2 min recordings were chosen based on the minimum number of RR intervals recommended for DFA and cite Chen et al. as evidence. I'm not certain Chen et al. advocated for such short windows to obtain a consistent/robust DFA metric.
Although short time series (256-400 RR intervals) can produce accurate DFA values, it is by no means
guaranteed.


Thank you for highlighting this as it is a very interesting topic for discussion. This has been a concern from the start, with some theoretical exploration as well (clinical eval here, https://www.mdpi.com/1424-
8220/23/6/3325 and theoretical here, https://www.mdpi.com/1099-4300/24/1/61). Originally, based on
a previous study from Chen’s research group
(https://journals.aps.org/pre/pdf/10.1103/PhysRevE.64.011114), they recommended a ratio of N/10
between the total number of points included in the window and the short term fluctuation window width.
The reference was updated including their previous paper (line 166). Assuming HRVT1 above 80 bpm (or
160 bpm in the 2 minutes time window) this time window has been often chosen in the literature as a
compromise between accurate physiologic evaluation and DFA a1 validity. If we used longer time spans,
we potentially would not capture the dynamic changes to the index as exercise intensity rises. For
instance, with a 45 watt/minute ramp, a 4-minute DFA a1 measurement will encompass 180 watts of
intensity change. Therefore, even at peak intensity, a good portion of the HRV window will contain
substantially lower intensity effects. The example below may shed some light on this issue. The following
plot of DFA a1 over time is from a participant who performed a 45 watt/min ramp (data from
https://physoc.onlinelibrary.wiley.com/doi/10.14814/phy2.15782 and the same participant in Figure 1B).
The plot below includes warmup, a step up for MRT calculation (the dip in a1 at 1000 seconds) the full
ramp and cool down. We plotted the DFA a1 using the “conventional” 2-minute sample window as well
as a 4-minute sample window. One can observe that during steady state exercise (warmup/cooldown),
the a1 is quite similar. However, there is a difference in both curve shape and curve nadir during the ramp:

Take home lesson - a1 measurement windows longer than 2 min are not suitable for rapidly changing situations such as incremental ramps, but could be useful for more steady state conditions (pre/post ramp) or constant intensity testing.

Overall study comments:
  • This reports adds to the existing evidence that the HRVT2 is a valid proxy for the RCP/LT2 at least on a group basis.  Additionally, adding EDRT and/or NIRS O2 desaturation breakpoints should get you even closer to individual gold standards.
  • There needs to be further thought on the HRVT1 concept since this report along with what we cited above (and recently this) indicate over estimation compared to the GET/LT1/VT1.  We are currently working on that topic.....😁
  • Day to day repeatability of HRVT1/2 is quite good as noted in the current study and the new report in Frontiers. Therefore, even is the HRV threshold is biased, at least it will be repeatable, making it useful for following fitness, illness, stress and fatigue. This repeatability is also independent of ramp slope (15 to 45 w/min).
Heart rate variability during dynamic exercise