Friday, November 8, 2019

VT1 correlation to HRV indexes - revisited

In a previous post, the correlation of the first ventilatory threshold with various HRV indexes was discussed.  Since that post was based on my data only, was before I knew my true VT1 by gas exchange and was early on in my learning curve regarding HRV, I though it might be interesting to take another look at the situation addressing these issues.  To that end, let's revisit the possibility of getting an idea of VT1 power or VT1 heart rate based on HRV.  The data will be that of myself, and the cross country skier (on a treadmill, running).  We will focus on the following HRV indexes:
  • SDNN
  • SD1
  • HF power x HF freq 
  • DFA a1

SDNN:
The SDNN index is defined as:
The standard deviation of the IBI of normal sinus beats (SDNN) is measured in ms. "Normal" means that abnormal beats, like ectopic beats (heartbeats that originate outside the right atrium’s sinoatrial node), have been removed. While the conventional short-term recording standard is 5 min (11), researchers have proposed ultra-short-term recording periods from 60 s (30) to 240 s (31).
If the RR intervals are artifact and arrhythmia free, this equates to calling this SDRR - standard deviation of RR intervals over a time period.  
Note should be made that this is a time domain index compared the HF power which is frequency domain with SD1/DFA a1 both in the non linear category.

SDNN as a measure of VT1 detection was looked at by Karapetian et al in 2007

Methods:
The testing consisted of 3-min stages [17, 32], allowing more stability of RR
intervals, and began with the subject resting on the bike, with
no pedaling as a baseline rest. Pedaling began at the second
stage, at which exercise intensity started at 25 W. Every 3min,
intensity increased at 25-W increments. Subjects were instructed
to maintain a cycling speed of 50 revolutions per min.
Exercise test time ranged from 15 to 35min
The RR intervals from the last 2min of rest and each stage of exercise
were used for analysis of HRV.

To determine the HRVT, the MSD and SD of heart rate intervals
for each stage of exercise were graphically plotted against work
rate (l" Fig. 1 and 2, respectively). Then, in a manner similar to
the determination of LT, a visual interpretation was made to locate
the point at which therewas no further decline in HRV, thus
indicating vagal withdrawal. Thus, this HRV deflection point was
defined as the HRVT

This resulted in a plot of work rate vs SDNN, second figure on the right.  The flattening of the curve was the recorded deflection point:
Interestingly they found a difference between LT1 and VT1 that was statistically significant:
The mean difference between VO2 vt and VO2 lt was small (0.12 L/min) but
statistically significant (p < 0.05); however, a strong linear relationship
was observed (r = 0.89).
SDNN deflection vs LT1:
The results for the determination of HRVT during incremental
exercise testing, using RR interval data, showed similarities in
VO2 (L/min) values between LT detection and HRVT detected by
HRV deflection point. The mean difference between VO2 hrvt and
VO2 lt was nonsignificant (p > 0.05), and shows no bias between
mean values. A strong correlation betweenVO2 values for LT and
HRVT was observed (r = 0.82). The mean VO2 hrvt–V˙ O2 lt was
1.40 ± 0.46 (L/min).
SDNN deflection vs VT1:
The mean difference between VO2 hrvt and VO2 vt was also nonsignificant
(p > 0.05), and shows no bias between mean values.
A strong linear relationship betweenVO2 hrvt andV˙ O2 vt was also
detected (r = 0.89), where the mean VO2 hrvt–V˙ O2 vt was 1.46 ±
0.46 (L/min)
  • Therefore, from this study, SDNN seems a valid HRV index to explore for VT1/LT1 association.
  • They also found a disparity between VT1 and LT1 power.

More on what SDNN signifies:
How does SDNN look graphically at low and high intensities?
This is a distribution of RR values from Kubios of a 2 minute window while coasting before a 5 minute maximal interval:


  • Notice the wide area of distribution of the values of RR intervals (in red).

There is a more narrow RR value spread with moderate effort:


And finally, a very restricted set of RR values at high intensity (this is the 5 minute maximal interval):



SD1:
In a prior post, SD1 was covered and I would refer back to those studies for details.  An early study not reviewed previously was done by Tulppo et al showing correlation of the VT1 with the SD1 index:

With SD1 dropping from rest to VT1 with minimal drop thereafter:

HF power and HF peak frequency:
The HF power and HF frequency were used by Cottin et al to find VT1.  They also concluded that in some subjects whose VT1 could not be detected by HF power alone, the product of HF power x HF frequency peak uncovered the breakpoint:





Individual examples of two subjects data:

The cross country skier underwent a partial VO2 max test recently.  I say partial, in that the test was not done to total exhaustion and the peak lactate was 4.5 mmol.  Therefore, the metabolic cart software could not spit out it's usual results such as VT1 nor LT1.  Before looking at the HRV index correlations, we will need to get the VT1 and LT1 ourselves (putting into practice some of the concepts covered in other posts).  

Here are the raw values of the ramp.  The protocol was 5 minute per stage, increasing by 1 km/hr with both gas exchange and Hexoskin vest as monitoring units.


Here is my attempt to get the VT1 by the V slope technique.  

First a plot of VO2 vs heart rate was done to confirm that the relationship is linear:

It looks great, with a nice linear regression.  As discussed before, this relationship is the foundation of Garmin's attempt at the "performance condition" metric which should be valid but will be subject to error if the comparison is of a session with different ambient temperature, altitude, hydration status or cardiac drift.

Next - a plot of VO2 vs VCO2.  The v slope technique is where we pick two different linear set of values, draw the lines and see where the intersection is.  The underlying idea is that the linear relation shifts after the VT1:

As the legend says, the fit lines were ended/started at a VO2 of about 3 L/min.  The exact intersection was 3.08 L/min which corresponded to a heart rate of 155 bpm on the first graph (HR vs VO2).

LT1 determination as per the Newell formula:
The LT via log log corresponds to a heart rate of 147 bpm.  The accuracy may be affected by not having a lactate higher than 4.5 mmol on the ramp and instead, using another value of 5 mmol during a separate 5 minute constant 15 km/hr interval later during the testing.  Regardless, the LT1 and VT1 are seldom at the same work rate which is part of the problem of training intensity exercise prescriptions.

Now that the LT1, VT1 are known, how do they match up with the HRV indexes? 

SDNN:
Here is a tracing of VO2 vs SDNN, with a marker at the corresponding VT1:


And Heart rate vs SDNN with the range of VT1/LT1 in red:
  • The SDNN deflection point conveniently fits right between the VT1/LT1!



My data (5 minute ramp stages, 2 minute Kubios window calculations):

  • The SDNN deflection point yet again falls between the VT1, LT1 as in the cross country skier example.



SD1 examples:
Cross country skier (5 minute ramp stages, 2 minute Kubios windows at the end of each stage):
  • The SD1 curve "breakpoint" occurs within the range of the Vt1 to LT1 zone.
  • No further change occurs after that heart rate.


My ramp (5 minute stages, 30w per stage, 2 minute Kubios windows):
  • Here things are not so clear.
  • There is no pattern, no breakpoint.
  • The dynamic range of SD1 is very restricted, 2.1 to 2.7.  In the XC skier example, he starts above 6 and the nadir is around 3.  For some reason I don't have high values even at low power.  On other tracings this pattern is the same:

Another look at SD1:
The following is from my VO2 max test (3 minute stages, 2 minute Kubios windows).  Perhaps SD1 will now show a pattern.


  • Same issues, no pattern and restricted range of values.

The possibility of having an SD1 rise at even trivial levels of effort (for whatever reason) was explored.  I recently traveled to North Carolina where the roads were either going up or down, with nothing flat.  Therefore, the efforts were either above VT1 power or coasting downhill with no pedaling.  

As you can see in the following SD1 tracing (2 minute windows), the SD1 is capable of increasing to "normal" levels (value=8) with a rise during each episode of coasting:

The bottom line is that SD1 may be quite valid in some individuals for VT1/LT1 demarcation, however in my case it was not.  In my example, SD1 drops very early with super low intensity efforts, making one wonder if it may signify another physiologic/biochemical threshold that in most subjects takes place near the VT1.


HF power x HF peak freq:
Using the metric derived by Cottin at al, this is the XC skier data:
Raw data



The plot:
  • Both the raw data (HF peak freq, HF power and product of the two) and plots appear to have no correlation to the work rate - and certainly not the VT1/LT1.

This is a plot of HF power in the time varying feature of Kubios, since there may be a bug in the software, giving inaccurate HF power results as "m2" (see below):
  • There still appears to be no relation of heart rate to HF power in normalized units.

My data:
Here is my HF peak freq x HF power vs cycling power:
 Based on the raw data:


Since this did not make any sense given the published study, I went back into Kubios and looked at the time varying curve for HF power in normalized units:
The grey colored area is the normalized HF power which clearly rises at the VT1 and plateaus at the VT1 +30 watts.  There may be a bug in the Kubios software, since the single display values do not show this.  I have reported it and will update this post as needed.

HF peak x power validity:
This is much murkier than the SDNN/SD1 and I don't know if it's related to a Kubios bug, my failure to calculate the index or simply from a less than universal index.  Remember why the authors derived an additional formula in the first place, it was due to some subjects not having a proper HF power relation.  
Furthermore, Blain et al. have demonstrated that it was possible to detect both VTs from fHF (TRSA) when TRSA1 was corresponding to VT1 and TRSA2 was corresponding to VT2. However, in 20% of their data it was not possible
to detect VTs 
Given these issues, it may not be the best choice for VT1 derivation.

DFA a1
I have already presented my data as well as the cross country skier in a previous post.  You may notice that the XC skier had a loss of RR complexity at a much lower heart rate (in that post) than during the VO2 max test below.  Part of that differential may be because the VO2 max test was done at altitude, whereas the previous ramps were not.  

How does the DFA a1 tracing look during the VO2 max test done at altitude?

  • The plot shows that the heart rate corresponding to a DFA a1 of .7 is about 145, just below LT1.
  • A DFA a1 of .5 (white noise) is reached at the stage above the VT1.  Avoiding exercise associated with this index value should provide sessions at or below the VT1.
  • The relationship of complexity loss with work rate is present with the heart rate shifted up about 10 bpm, presumably from altitude. 

Comparison between SDNN and DFA a1 during and after a 5 minute interval at VO2 max power:
Since SDNN and DFA a1 seem to be the most reliable and consistent of the markers tested for delineating low intensity exercise, here is a comparison of each side by side, before, during and after a 5 minute interval at VO2 peak power (watt level derived from my last and highest stage on the VO2 max test).  After a 5 minute interval at that high power, the following 5 minutes were done at about 20 watts above the VT1, then a brief active rest for 3 minutes then another 6 minutes at 173w (at VT1):


The RR intervals were extracted from the Hexoskin data, raw values and tracing below:

  • Although the scaling is different, the patterns are remarkably similar (including overlap of values).  Initial data values during coasting are high, typical of at rest readings.  At the end of the 5 minute max interval, DFA a1 and SDNN are at historic lows, then rise somewhat but are still in the area above VT1/LT1.  Both indexes still don't recover fully 15 minutes later but are getting closer to the usual values at VT1.
  • Both DFA a1 and SDNN appear to be reasonable markers for VT1/LT1 transition and behave in a parallel fashion over an undulating set of intervals.





Summary:
  • Changes in both time domain (SDNN) as well as nonlinear measures (SD1, DFA a1) of HRV seem related to the transition to work rates above VT1/LT1.
  • Although SD1 inflection did agree nicely with the cross country skier's VT1/LT1, mine did not.  For some reason my SD1 reduced at minimal levels of exertion, but was normal at rest.
  • The frequency domain index, HF power did not show much correlation.  That could have been partly due to Kubios software issues or between subject differences.  The authors of the published study freely admit this index is not for everyone.
  • HRV index analysis may have potential utility in exercise intensity prescription recommendations.  Further studies comparing indexes side by side as well as in diverse populations should be interesting.



Thursday, October 24, 2019

Training zone concepts, benefits of polarized, heat and low carb exposure

The past and present sports literature is replete with training recommendations as well as methods to optimize endurance performance.  Not only do we have multiple opinions and protocols but even the Garmin Firstbeat fitness trackers have something to say about it: 



Although I generally avoid reviewing training techniques in this blog, I feel strongly about the potential of the polarized technique as the most promising modality to improve ones results.  Polarized training is a very simple concept which revolves around the usage of many hours of boring, low intensity exercise (below VT1) with a small amount of very high intensity (above VT2) interval work (notice the "High aerobic shortage", is intentional).  Although some time can be spent at VT2 or between VT1 and VT2, it should be minimal.  Is there any logical rational behind this?  

Several physiologic factors come to mind about why this is logical.  Let's list them:
Junk miles are not worthless.  High volume, low load exercise benefits mitochondrial mass, plasma volume, blood volume, type 1 muscle conversion and local capillary growth.  More importantly, even if these effects were more pronounced with HIT training (with some evidence supporting), one simply can't do more than a given amount of very high intensity work without either over reaching or over training.  This becomes even more critical with aging athletes, they need more time to recover after a HIT session.  So instead of sitting around taking rest days, continuing your training with low load, higher volume makes sense.
Most studies show multiple benefits of HIT sessions and intervals.  These range from improved stroke volume, cardiac contractility, VO2 max, MLSS, lactate disposal as well as overlap with some of the other benefits mentioned above.  So HIT intervals are great to do, but will cause fatigue and overall body stress, so there is a limit to what can be done.
What about the middle zone between?  Are there benefits to spending time at or just below VT2 power levels?  Does introducing some middle zone training actually hurt your performance if you were otherwise just doing low and very high intensity training?  It may be so - perhaps adding the middle zone (just under VT2) would cause enough fatigue and stress to interfere with proper HIT intervals.  That is my concern and something that I have noted from my own training.

With that preamble, the following study is very enlightening.


Methods: The authors recruited a wide variety of young athletes:
Participants were members
of the Austrian cross-country skiing national team(n = 8), run-
ning (n = 21), triathlon(n = 4) or cycling(n = 15) teams during
or since the year before the current study.

The training intensity was con-
trolled by HR based on the baseline incremental test:(i)LOW
(HR at blood lactate value <2 mmol·L−1); (ii)LT(HR corresponding to a blood lactate of 3–5mmol·L−1); (iii)HIGH(>90% HRpeak)] 

It is a bit regrettable that the LOW group was defined as such since that may have been over LT1/VT1, but lets be optimistic and call it LT1 power.

Here is the protocol map
Each cycle was repeated x 3 for a total of 9 weeks:

The first group A (LOW) was essentially training at LT1 with 2 sessions of LT2 intervals over 2 weeks.
The "threshold" group B (THR) trained at both the LT1 and LT2 without any HIT.
The polarized group (POL) labeled as C trained primarily at LT1 with several HIT sessions.
Finally the HIT group D just did high intensity work and as expected, was of shorter duration.

How did this turn out with respect to the actual training intensity distribution?
Here is the breakdown:
A few observations:
The numbers don't add up.  For instance look at the HVT (high vol at low intensity).  About 49% of the training was at low intensity, 9% at LT2 but what about the other 40% or so?  The reason (thanks to Dr Stoggl for the clarification) is a typesetting error.  The number in the brackets is the true percent:
  • The POL group spent 68% at the "low" intensity, only 6% at LT2 and 26% at HIT values.
  • The THR group spent about 54% of their training at the LT2.  That surely must have been exhausting. 
  • The HVT group spent 16% at the LT2, the remainder at "low".  Despite this group being labeled as low intensity, they did train almost 16% at the LT2 which is a commonly used routine for endurance sports.

What were the outcomes?
Before discussing this, the results can be deceptive in some respects.  For an analogy, remember strength training.  If you train for strength (high load, few reps) you will have a better 1 rep max.  However if you train conventionally (moderate load x 10+ reps) you will get better muscle mass.  So depending on what you are looking at, it may be easy to get fooled.  It is possible that if they tested for Wingate 30 power, the HIT group would have done the best.  The authors did look at established parameters such as time to exhaustion and VO2 max which is helpful.  The other concern is the relative short nature of the study (9 weeks).  Endurance exercise related physiologic improvement may take many years to manifest fully.  However, given the practical reality of athlete schedules, study funding and commitments the 9 weeks is admirable and probably the best that can be practically done


Here are some breakdowns, with particular attention to VO2 max:


Comments:
  • The POL group had the largest increase in VO2 max.  This will be explored below.
  • Despite over twice the HIT sessions, the HIT group did not have as good a VO2 max response as POL.  Therefore fewer HIT sessions with a large amount of low intensity training was superior to just HIT.
  • Although I'm not quite certain about statistical significance, the THR group had a decline in VO2 max.  This was spoken of in the discussion:
THR improves VO2peak, lactate or ventilatory thresholds and
endurance performance in untrained persons(Denis etal.,1984;
Londeree,1997;Gaskilletal.,2001). These findings contrast those
of the current study, as we did not observe improvements in
VO2peak, V/P4, TTEorV/Ppeak in our elite athletes in response to
THR. Additionally, it is possible that in well-trained endurance
athletes,repeated training bouts at LT might generate unwar-
ranted sympathetic stress.
Time to exhaustion and Power/velocity at 2 or 4 mmol lactate:
In addition to the above VO2 parameters, a TTE test was done as well as a comparison of either power or velocity at a fixed lactate (2 or 4 mmol).  For example during exercise at a lactate of 2 mmol, the POL group improved their power by 9.3% but the HVR did not change.  

  • We see the biggest improvements in TTE with POL, and the least with THR (although that one is not significant).  
  • Power at a lactate of 2 or 4 mmol improved with POL or HIT.
  • Despite over twice the HIT sessions, the HIT group did not have as good a TTE response as POL

So what some final thoughts?
Obviously, polarized training seems advantageous in this population of young elite athletes.
This protocol lead to better VO2 max, TTE and power at fixed lactate than the other training distributions.  Does this extend to other populations, especially older, non elite subjects?  My suspicion is not only does it extend, it probably is even more critical.  The THR group did relatively poorly in VO2, TTE and power at fixed lactate.  I think this is a good example of what happens when one trains hard but takes very little time to have recovery sessions.  As discussed previously, runners did best when their easy volume was high, not from their HIT sessions.  The THR group probably was training too hard too often.  In contrast the POL group spent some time at even higher work rates but compensated by a large fraction of training time at "easy" levels.  The key issue here is what is the definition of easy.  For these young, creme of the crop, national team athletes, "easy" may be quite different than what a 50 year old masters cyclist or runner will consider easy.  As discussed in my posts on lactate and ventilatory thresholds, there is controversy on where these limits are.  My personal viewpoint is that the easy zone should be well away from uncorrelated DFA a1 values, which turn out to be near VT1.  Since a potential rational of easy training is to still get some benefit while recovering enough to perform HIT thereafter, the easy training must be of low cardiac stress.  In regards to the near 25% training time in the HIT zone for the POL group, that may not be possible for most.  Even if it was achievable, longer term maintenance at this intensity distribution (75:25 low:HIT) could be detrimental to non elite or older subjects.

Since it appears desirable to spend the majority of training at low intensity levels, what unique physiologic benefits occur in this zone?

Increased capillary density - A prime goal of endurance exercise training is improving net fuel delivery (O2) to the muscle cell.  Having a more extensive network of capillaries will certainly be great benefit.  An interesting study done several years ago looked at the changes in capillary density and vascular endothelial growth factor (VEGF) after either constant moderate vs high intensity cycling.  The HIT intervention consisted of multiple 1 minute intervals of 120% VO2 max power versus the constant cycling group of 60% VO2 max (probably near VT1).  Capillary density increased after a initial 4 week conditioning session that consisted of cycling at 64% of VO2 max power.  However, the performance of the multiple HIT sessions did not further enhance density:

VEGF levels derived from muscle biopsy also supported this with higher post moderate vs post HIT intervals:

VO2 max changes:
Maximal oxygen uptake. The intense intermittent
training period led to an increase (P <0.05) in maximal
oxygen uptake from 3.59±0.21 to 3.87±0.20 lmin−1 and
from 41.43±1.45 to 45.43±1.80 ml min−1 kg−1. 
  • Therefore despite the enhancement in capillary density in the constant group, only the HIT subjects improved VO2 max.

Comments:
  • Low intensity training stimulates VEGF secretion and capillary growth.  HIT does not appear to do so.
  • Undoubtedly both HIT and LOW are useful modalities for improving fitness parameters.
  • We should be reassured that low intensity exercise is not simply "junk miles".  


Optimizing low intensity training:
Now that we see that low intensity work should be a focus of one's training distribution, is there any "optimization" that can be done?   For instance, is it better to do low intensity exercise in heat vs cool conditions or fasting vs fed?  What I am more interested here is heat/low carb training as a way to boost the metabolic benefits of low intensity training.  As will be seen with low carb exercise, there may be actual detrimental effects if performing this in a low carb state.  In addition, HIT during severe heat can be extremely dangerous to multiple organ systems and from the medical perspective if done at all, should be brief.

Heat training:
A recent review discussed the effects of heat on endurance factors in detail.  There are numerous biologic changes that occur after either passive or during exercise heat exposure.  Unfortunately, the test tube studies and animal models may not be pertinent to humans.  The authors did state the following however:
Heat stress per se can stimulate HIF1-a and its downstream
target genes Vegf, heme oxygenase-1 in rat myocardium, and
epo and epo receptor in rat kidney (Maloyan et al., 2005).
Long-term passive heat acclimation increased HIF1-a protein
levels and thus induced larger upregulation of gene activation
for Vegf, HO1, epo, and epo receptor in response to a single
bout of heat stress (Maloyan et al., 2005). Heat stress increases
circulating epo concentration in humans, although this finding is
not universal despite substantive heat stress (Akerman et al.,
2017), perhaps reflecting the important potentiating effect
gained from long-term heat acclimation. The success of runners
from Kenya and Ethiopia in endurance-based events in the
context of thermal adaptation warrants consideration
. Athletes
who train in these countries are chronically exposed to lowgrade
heat stress that might be considered ‘‘at risk’’ for physically
active individuals (i.e., annual mean daytime dry bulb temperatures
>22 C). From an evolutionary perspective, warm and
relatively dry climates helped homo sapiens gain a major advantage
in terms of superior thermoregulation (outweighing a poorer
running economy) compared to other large mammals, conferring
superior endurance capabilities (Bramble and Lieberman, 2004)
An interesting study was done about 12 years ago subjecting athletes to a hot sauna immediately after exercise for about 30 minutes.  They showed increased blood and plasma volume as well as TTE in the sauna treated runners:

The question then arises whether there is an advantage to exercising in the heat in preparation for a race at normal temperatureThis is quite different than training in the heat for a race at high temps where acclimatization is essential. There has been controversy on this matter but two recent abstracts (full papers pending) are interesting.
The first one addresses this question exactly:
Heat acclimation involves physiological adaptations that directly promote exercise performance in hot environments. However, for endurance-athletes it is unclear if adaptations also improve aerobic capacity and performance in cool conditions, partly because previous randomized controlled trial (RCT) studies have been restricted to short intervention periods.
The intervention:
Participants were instructed to maintain total training volume and complete habitual high intensity intervals in normal settings; but HEAT substituted part of cool training with 28 ± 2 sessions in the heat (1 hour at 60% VO2max in 40°C; eliciting core temperatures above 39°C in all sessions), while CON completed all training in cool conditions.
That translates to 1 hour at probably zone 1 (VT1) at 104 degrees F.  So pretty extreme!
I have done this and believe me, it's not fun.

The results:
When tested in cool conditions, both peak power output and VO2max remained unchanged for HEAT (pre 60.0 ±1.5 vs. 59.8±1.3 mL O2/min/kg)

Conclusion:
Based on the present findings, we conclude that training in the heat was not superior compared to normal (control) training for improving aerobic power or TT performance in cool conditions.  

The second abstract seems like it used the same subjects (same authors, protocol) as above but looked at hemoglobin mass and plasma volume:
Heat acclimation is associated with plasma volume (PV) expansion that occurs within the first week of exposure. However, prolonged effects on hemoglobin mass (Hbmass) are unclear as intervention periods in previous studies have not allowed sufficient time for erythropoiesis to manifest. Therefore, Hbmass, intravascular volumes and blood volume (BV)-regulating hormones were assessed with 5½ weeks of exercise-heat acclimation (HEAT) or matched training in cold conditions (CON) in 21 male cyclists
Methods:
HEAT (n=12) consisted of 1h cycling at 60%VO2peak in 40°C for 5 days/week in addition to regular training, whereas CON (n=9) trained exclusively in cold conditions (<15°C)
Results:
PV increased (p=0.004) in both groups, by 303 ± 345ml in HEAT and 188 ± 286ml in CON. There was also a main effect of time (p=0.038) for Hbmass with +34 ± 36g in HEAT and +2 ± 33g in CON and a tendency towards a higher increase in Hbmass in HEAT compared to CON (time*group interaction: p=0.061). The Hbmass changes were weakly correlated to alterations in PV (r=0.493, p=0.023). Reticulocyte count and BV-regulating hormones remained unchanged for both groups
Conclusion:
Hbmass was slightly increased following prolonged training in the heat and although the mechanistic link remains to be revealed, the increase could represent a compensatory response in erythropoiesis secondary to PV expansion.

Taking both of the studies together it appears that there is a subtle hemoglobin mass improvement, yet no noticeable improvement in power outputs or VO2 max.  It is certainly possible that a few seconds could have been shaved off a 40 K TT after the heat intervention however, is it worth training under such miserable conditions?  In addition, unless you live in the tropics, you would need an indoor set up with a strong heater.  
Without question there may be a genetic component where some athletes will have a better heat effect than others.  However it seems like a fair amount of trouble for a minimal effect.

Training under low carbohydrate conditions:
Another area that has received much attention is that of training with either a low carb diet or at least low carbohydrate conditions (fasting).  As with heat training, both cellular, animal and human models are somewhat confusing.  On one hand, it makes sense to optimize fat utilization by withholding carbs, but at the same time, one may be handicapped by not being able to perform with enough intensity and duration to get the usual training benefits.  Here is a helpful diagram taken from an excellent review on the subject:

From the text:
Short-term (3–10 weeks) training
programs in which some workouts are commenced with either
low muscle glycogen and/or low exogenous glucose availability
increase the maximal activities of selected genes and proteins
involved in carbohydrate and/or lipid metabolism and promote
mitochondrial biogenesis to a greater extent than when all workouts
are undertaken with normal or elevated glycogen stores

(Hulston et al., 2010; Yeo et al., 2008). These adaptations accrue
despite 7%–8% lower self-selected training intensities (Hulston
et al., 2010; Yeo et al., 2008).
Importantly the authors state that a mixture of training intensities and carbohydrate usage is needed for optimal performance goals:
As noted, a loss of training quality or intensity has been associated
with ‘‘train-low’’ sessions
(Hulston et al., 2010; Yeo et al.,
2008) and must be balanced against aspects of ‘‘molecular upregulation’’
within the muscle cell. Overall, it appears important
to carefully periodize and integrate the number and type of sessions
completed with low carbohydrate availability within a
training program so that the overall goals of training are
achieved. This can be accomplished by a balance of
sessions undertaken with low carbohydrate availability to drive
molecular adaptations, while high-intensity workouts should be
commenced with high carbohydrate availability to allow an
athlete to mimic competition pace and habituate to competition
strategies
(Bartlett et al., 2015; Stellingwerff, 2013; Jeukendrup,
2017). This ‘‘balance’’ appears to be more easily achieved with
sub-elite athletes (Marquet et al., 2016) than their elite counterparts
(Burke et al., 2017; Gejl et al., 2017).
In the conclusion of the review some important points are raised:
Another caveat involves the possibility that a training strategy
that promotes one attribute may endanger or impair another.
An
impairment rather than improvement of metabolic flexibility
might result from the complex interactions between pathways
of substrate utilization; an upregulation of one pathway may
result in a simultaneous and reciprocal downregulation of others.
For example, in previous investigations of either short-term
(5 days) exposure to high-fat, low-carbohydrate diets or longer term (3 weeks) ketogenic diets (Burke et al.,2017), we found robust changes in muscle characteristics that promoted substantial increases in maximal rates of fat oxidation during exercise. However, this was associated with a reduction in the performance response to a training block (Burke et al.,
2017) and a failure to acutely enhance performance, even
when carbohydrate availability was restored before or maximized
during exercise
(Burke et al., 2002; Carey et al., 2001).
Likely mechanisms underpinning these findings include a loss
of exercise economy (i.e., an increased O2 cost of exercise)
associated with the reliance on fat-based fuels (Burke et al.,
2017) and an impairment in muscle glycogenolysis underpinned
by decreased activity of the rate-limiting enzyme in carbohydrate
metabolism, pyruvate dehydrogenase (Stellingwerff et al., 2006).
These effects are observed during models involving sustained
(Burke et al., 2017) or periodic (Havemann et al., 2006) higher-intensity
exercise that forms a critical component of high-level
endurance events.
Ergogenic potential of PPAR agonist therapy:
The above pathway caught my interest since a well known diabetes drug family leads to enhancement of of the PPAR pathway, namely pioglitazone (Actos).  Over the years I have used this drug extensively for patients with type 2 diabetes.  The other drugs in the family are off the market and since the class has "seen better days" in regards to clinical use, little new research has been done.  However, a paper came out looking at Actos exercise related effects in mice that we can examine.   
Rational for study:
Experimental evidence reveals that TZDs can induce the same
biological effects in glucose metabolism, mitochondrial biogenesis,
and skeletal muscle fibre type as the prohibited drugs
GW1516 and AICAR
. This study assessed the effects of
pioglitazone on performance and on skeletal muscle mitochondrial
biogenesis. For this purpose, blood glucose levels, the
protein expression of the intermediates involved in the mitochondrial
biogenesis pathway (cytochrome C, PGC-1α, NRF-1,
and TFAM), and citrate synthase activity in both soleus and gastrocnemius
muscles were measured. Maximal aerobic velocity
(MAV), endurance capacity, and grip strength were also determined
before and after a training period.
Results and conclusion:
Treatment of exercising mice with either drug or placebo did not show any notable enhancement in maximal aerobic velocity, endurance capacity, citrate synthase or other markers of mitochondrial biogenesis
In our model, exercise-induced mitochondrial biogenesis is not
due to PPARgamma agonist pioglitazone administration. Overall,
oral pioglitazone administration enhances neither training adaptations
nor performance:



I include this for two reasons:
  • Attempting to augment endurance exercise performance by PPAR manipulation probably is ineffective and could be detrimental (Actos causes fluid retention, weight gain and altered fat distribution).
  • Even though molecular pathways appear simplistic, the translation to actual performance effects may be very complex and even contradictory.


Summary:
  • Polarized training appears to improve VO2max, power at fixed lactate and TTE in comparison to a regime of near 50:50 LT1:LT2 intensities.  Perhaps a take home message here is to spend plenty of time at VT1 to be fresh for high quality sessions of HIT.  There seems little benefit to adding zone 2 work loads or spending significant time at the MLSS. In other words if your Garmin watch says you need to train in the "High aerobic zone", ignore it or take it as a compliment that you are polarized.
Here is my "power zone" distribution over the year:
Based on these zones:
Zone 1 up to VT1
Zone 2 up to LT2
Zone 3 > LT2
  • I may be spending too much time in zone 2 but much of that consisted of blog post testing and multiple ramps for lactate curves.  On the other hand, at my age, I may not be able to handle more time in zone 3.  When planning out your training distribution, multiple factors need to be taken into account.
  • Changes in capillary density and vascular endothelial growth factor are more pronounced after constant moderate exercise than HIT.
  • Training in the heat (40C or 104F) can increase plasma and blood volume but does not appear to translate to better TT performance or VO2 max values.  This subject should continue to be monitored for other study results.  There may be subsets of individuals who can benefit from heat exposure in regards to performance in thermo neutral conditions.
  • Training under low carb conditions does improve multiple metabolic parameters that should lead to better endurance performance.  However, it can be detrimental if not mixed with moderate carb intake during HIT sessions.  From my perspective, low carb training is an ideal way to boost the benefits of a pure low intensity session - 1 to 2 hours at VT1 on awakening, while fasting (water only).  If HIT is to be done, carbs should be present.
  • If using a Garmin fitness tracker, take their training recommendation with a critical eye.  
  • Many thanks to Drs Stoggl and Sperlich for not only doing the polarized training study but their other contributions to training intensity distribution.

See also:
How to get your training zones from HRV and muscle O2 data

VT1 correlation to HRV indexes - revisited



Thursday, September 26, 2019

Training intensity guidelines from HRV and NIRS data

Training intensity distribution continues to be a hot topic for both athletic and basic fitness exercise.  In order to train within these guidelines, one needs to have a handle on two basic concepts, the maximum lactate steady state and the first ventilatory threshold.  Both have been explored in detail in prior posts.  Over the past I have been asked by several athletes to help estimate their upper zone 1 limits (of a 3 zone model) and MLSS (beginning of zone 3).  This post will be a review and step by step guide as to how I do it and will show examples of cycling, running and skiing.

Many believe the first ventilatory threshold is the upper boundary of the zone of low intensity exercise, which should make up the bulk of ones endurance training.  As noted in my VO2 max study, the VT1 may not correspond exactly to the LT1, making reliance on the LT1 problematic. Whether or not VT1 and LT1 are different physiologic concepts, or just the same process, but presenting as different values due to methodology, is still unclear to me.  There are proponents to both theories.  Regardless, I would like to formally propose two "simple" procedures to obtain the upper limit of zone 1 (easy) exercise as well as an approximation of the MLSS, as the beginning of zone 3 (high intensity) training.  Exercise intensity in between (zone 2) should probably be limited, but can be defined as above VT1 and below MLSS.

For simplicity, I'm going to assume for this post that you have only a chest belt HRM and an O2 sensor, so no formal lactate measurements are needed.  

We will start at low intensity training and work our way up.....

Zone 1 upper limit estimation using DFA a1:
The DFA a1 is a measure of short term cardiac interbeat fractal complexity.  While at rest or during very light exertion, the DFA a1 will have values between .8 and 1.2.  According to the excellent review by Gronwald et al, as intensity rises, the DFA a1 will drop, passing the .5 area (uncorrelated white noise) during substantial efforts.  With very high intensity, values reach even lower levels.  However, for our purposes, we want to see at what power or heart rate DFA a1 begins to approach the white noise value of .5.  In my VO2 max study, VT1 occurred just before DFA a1 white noise values were reached.  In my experience, a DFA a1 value of .7 would represent a good limiting value for easy sessions.  This represents a correlated RR interval series, low physiologic stress and probably an intensity state below VT1.

Requirements: 
Polar H10 or similar, Hexoskin even better (less artifact).  Make sure the belt is snug and use conductive cream.

How to: 
In order to get an idea of the work rate associated with uncorrelated DFA a1, a progressive ramp of some type is needed.  This can be running, cycling or even skiing.  Having a measure of power is helpful in keeping the intensity as a constant.  If your power is fluctuating markedly, that would make interpretation difficult.  It is not necessary to reach exhaustion, going up to LT2 is more than enough.  Here is an example of a ramp that I did on an indoor trainer:
Each interval was 5 minutes long, the heart rate and DFA a1 was taken from the last 2 minutes of each section.  Watts were the average of the entire 5 minute block.  There was about a 30 watt rise per segment.  For a guide to using Kubios software in this regard see this, about half way down the page.

DFA a1 and heart rate:



DFA A1 and power:

The heart rate and power associated with a DFA a1 of .7 are 128 bpm and 175 watts.  According to the gas exchange test, my VT1 was at about 172 watts, close enough to the above.  
Why did I pick a DFA a1 of .7?  This seems to represent the point just prior to poorly correlated values and as an analogy, is the edge of the intensity cliff.  A small increase in effort above this leads to a white noise value of .5. Given cardiac drift, fatigue and hydration changes, a .7 value provides a safe buffer zone of stability.
As an example, notice that at just 25 watts or 7 bpm more, the DFA a1 becomes an uncorrelated .5, which is seen with cardiovascular stress.
Knowing this, the upper limit of zone 1 (easy) training would be at about 128 bpm/175 watts.

Another cyclist example:
A fellow cyclist asked me to do a similar calculation based on his data.  This was derived from an outdoor session where the ramps were not perfectly even (Kubios sample time was last 2 minutes of each interval).  Regardless, the data seemed to make sense so lets take a look:
  • The DFA a1 of .7 is reached at about 170 watts.  The value becomes uncorrelated (.5), about 35 watts higher.

Here is the DFA a1 vs heart rate:
  • The points don't line up as well but there is still reasonable fit with a heart rate of 143 as the upper zone of easy riding.  Causes of mediocre fit include RR artifacts which artificially raise complexity as well as uneven pacing during the interval.
  • Unfortunately, the ramp did not go above MLSS, so we don't have the same shape as my tracing (or the one below)

Skiing:
This was done by a friend who is a competitive cross country skier.  Ramp intensity was controlled by pace, heart rate and RPE.
  
Here is the DFA a1 vs heart rate relation:
Since he was using the Hexoskin and had virtually no artifacts, I used the last 60 seconds of data rather than 120 seconds.  It's been my observation that as long as artifacts are near zero, 1 minute time samples can be used.



 From the raw Kubios data below:
  • There is an excellent fit of DFA a1 to heart rate with the .7 limit at 130 bpm.
  • The DFA a1 becomes uncorrelated just 5 beats above this at 135 bpm. 

The following tracing is the above ramp plotted against a different session consisting of mainly zone 1 intensity, but provides a good example of what happens if a certain "threshold" is crossed:

  • The red markers are just different 2-4 minute sections of heart rate vs DFA a1.  This was recorded with the Hexoskin and artifacts were in the 0-3% range.   
  • This is a good example of using the DFA a1 as a guide to stay in zone one as well as showing how easy it is to drift out of that zone with just a minimal change in effort.
This is raw Kubios data:
  • Notice that if heart rate rises above 130 bpm, DFA a1 becomes uncorrelated.

Take home points:
  • Doing a simple incremental ramp with 5 minute segments allows easy determination of the heart rate or power associated with a loss of RR interval interbeat fractal complexity.
  • Although there are limited recommendations in the literature, it should be logical to avoid lengthy training time at uncorrelated values.  Finding where your own heart rate or power reaches a value of .7 should provide enough of a buffer to avoid undo stress during a session dedicated to high volume but low intensity.
  • According to my gas exchange data, the .7 value was reached at the VT1.  At 30 watts above this it was already .5 which was the location of the LT1 by lactate testing.
  • Whether or not VT1 is fundamentally equivilant to LT1 or not, it is clear that they may not test out to the same power or heart rate figure.  To avoid the confusion about the VT1 vs LT1 significance, use of a very objective cardiac index seems a more reliable metric for low intensity zone demarcation.


MLSS/LT2 estimation using muscle O2 desaturation pattern:
The maximal lactate steady state can be estimated in many ways. Various methods such VT2, RCP, LT2, OBLA, critical power and even FTP are present in the literature.  Unfortunately, we are again faced with arguments and disputes as to which is "best" and if they are even representing the same metabolic process or not.  I am not going into that rabbit hole, but lets just say that they correspond to grossly similar work rates.  Performing constant rate exercise over this output power will result in a non steady state Ve, rising lactate levels and limitation of exercise time.
Muscle O2 saturation kinetics have been used to explore this and appear to have a desaturation breakpoint corresponding to the rapid rise in lactate values.  Criticism to using this approach revolves around many legitimate reasons (skin thickness, depth of sensor penetration, etc), but the bottom line is that it appears to work.  Despite my dislike of the marketing hype (including false claims) from the manufacturers of these devices, I believe they have a role in providing information in high intensity zone demarcation.  After all, the original generation one BSX was only able to calculate the MLSS (based on a ramp) and did not provide muscle O2 data as an independent output.  So, in the spirit of the original BSX Insight, this post and the work of Murias et al. lets explore using muscle O2 sat for the MLSS.

This procedure requires you to either use a Moxy or Humon Hex sensor (BSX is fine if you still have one).  If you do not have one already, get the Hex, it's much less costly and will provide good data.
Placement of the sensor should be the same across sessions, so don't use the rectus femoris on one day and the vastus on another.  Stick to the same leg as well.  I favor the rectus femoris since it has a sharper desaturation at the MLSS than the VL.  However, the deltoid and costal locations are going to behave in a similar fashion (although for different reasons). 

What we are going to look for is an O2 desaturation pattern that continuously downslopes over a 5 minute constant power interval.  Fluctuating power may make it difficult to decide on a saturation trend so keep the power or run speed steady.  Here is an example of a series of 5 minute intervals I did as part of a lactate test:
Pay particular attention to the last 2 segments of 260 and 285 watts.  Notice that the rectus femoris saturation is relatively stable at 260w but continuously decreases at 285w.  Somewhere in between is the MLSS.  According to the LT2 calculations of that day it was about 270 watts.  
Once you have an idea of the power zone where the downslope occurs, you can monitor it on a regular basis looking for fitness improvement or detraining.  In this case, you really only need to do two intervals, one about 10-15w below and another 10-15w above the last MLSS.

Here is an example.  As part of a regular training ride outdoors, I did a five minute interval below and above the 270 watt area.  Notice that costal, deltoid and rectus femoris placement of sensors did not matter, the tracings are nearly the same.



The final result here is that my MLSS is somewhere between those 2 power figures.

To confirm this, a 5 1/2 minute interval at 275 watts does show relative stability of O2 sat past 3 minutes (270 watts would probably have been better):



Other athlete examples.
This was part of the outdoor cycling ramp done by the individual we were looking at above.
Although this is less than ideal, it does provide a learning experience in the methods recommended. 
Notice the saturation of the rectus femoris at 245 watts, it's steady or coming up.  The next increment at 268 watts does have a downslope pattern but it stabilizes at about 4 minutes into the section.  This would not be considered a valid marker of exceeding MLSS.  There should have been one more interval after this at 280 to 290w which probably would have exhibited the continuous downslope we are looking for.



Skiing ramp:
This was part of the test done by my friend the cross country skier.  The intensity modulation was a combination of heart rate, pace and "feel".  We will look at the heart rate where the rectus femoris downslope occurs.  We also have the treat of comparing the Moxy vs Hex in the same muscle group and person (but different legs).
  • Both the Moxy and Hex show very little desaturation at heart rates below 155 bpm.  According to some of the marketing claims of these devices they should help one train a low intensities for recovery.  That certainly does not seen to be the case here.
  • However, we see a definite continuous downslope of O2 saturation at a corresponding heart rate of 165 in both sensors.  This would indicate MLSS has been exceeded.  
  • Therefore the MLSS resides somewhere between a heart rate of 155 and 165. 
  • The tracings are not as smooth as constant bike power via meter, but are impressive given the limitations of ski monitoring.
Time trials:
Theoretically a self paced time trial should be associated with high, but steady state lactate and power levels at the MLSS/LT2/VT2.
In a study looking at 30 minute cycling time trials, 13 triathletes had extreme variability in lactate levels, but kept output power remarkably constant.  Even though the lactate levels varied, they were all well above 4 mmol indicating near or above MLSS power.
The conclusion of the study was 
In summary, the cycling ITT30 was characterised by a fairly self selected
constant energy expenditure (reflected by VO2 and
blood lactate responses) equivalent to the energy demand associated
to the second ventilatory threshold. In ITT events lasting
30 min, self-selected pace could be a valuable metabolic index
to determinate appropriate training and competition race. The
primary advantage of the ITT30 test at self-selected intensity is
the ease and objectivity with which endurance capacity can be
determined
In addition, observation of both Ve and heart rate showed continued rise throughout the 30 minutes:

As part of the pack of info sent to me, the cross country skier also included a running time trial.  His results are very similar to the above with a steady rise in HR and Ve (via Hexoskin) to near max levels.


  • Heart rates were generally in the 155 to 165 range, with a steady rise throughout.
  • The rise in Ve was steady throughout and did parallel the heart rate.  
  • The values make sense if the time trail was performed near the MLSS. 


Do these patterns make sense from the cycling TT observations?
From the discussion:
The continuous increase in VE measured over the ITT30
(+ 23 l N min–1) is somewhat higher with other observations reported
during cycling bouts at “maximal lactate steady state”
but with lower percentage of sustainedVO2max compared with
the present study. MacLellan and Cheung [23] measured a
20 l N min–1 increase in V˙ E over 30 min while exercising from 71
to 76% V˙ O2max and recently, Lajoie et al. [20] reported an increase
of 15 l N min–1 over 60 min (from 71 to 79% V˙ O2max). In
our study the continuous increase in VE was due mainly to a significant
increase in Bf
(r = 0.717, p < 0.01).
And:
In contrast with most laboratory conditions, ITT30 in
the field shows a steady profile responses in HR
[24]. The latter
observation and our present findings allow us to hypothesise
that thermoregulation could be probably the crucial factor leading
to a cardiovascular drift in laboratory setting. During road ITT
events £30 min, HR could be a more valuable metabolic index to
determine appropriate training and competition pace compared
to laboratory conditions. Considering % HRmax as a valuable index
of exercise intensity [12], the present results indicated that the
ITT30was performed at quite high exercise intensity levels (range
of 87–96% HRmax).
Therefore with a similar pattern, both heart rate and Ve rose in the running time trial consistant with the published study results.

Conclusion:
Power levels derived from both self seleted individual time trails as well as MLSS determined by gas exchange (and presumably equivilant measures) are very close.
But from the standpoint of proper handling of training loads and stress, performing 30 minute time trials on a regular basis does not make sense.
Getting surrogate information through O2 saturation kinetics would perferable.


VO2 max power
Although it is not possible to measure VO2 max directly from power or heart rate,one can can a general idea of the rise or fall of this metric.  It's been shown by Keir and others that a maximum 5 minute constant power interval will usually allow an individual to reach their VO2 max.  Obviously, the interval needs to be maximal, but if you were a few percent below max heart rate, that's probably good enough.  Augmenting this approach, David Poole has proposed a validity stage for VO2 testing - a constant power interval of 5 minutes duration to be done 20 minutes after the incremental ramp of the VO2 max session.


A reasonable approach for an athlete interested in following VO2 max power would be doing a regular 4 to 5 minute constant power interval with longitudinal tracking of the heart rate.  For example, if I were to do intervals of 330 watts for 5 minutes on a regular basis and noted a gradual reduction in average heart rate, that would imply that VO2 max is increasing (at the same ambient temperature, humidity, time of day, hydration, altitude etc).  This is what the Garmin Firstbeat formula attempts to do but their implementation just does not work.

Here is an example of what to look for.
I recently took about 10 days off from riding due to travel and a respiratory illness.  After about 2 weeks I felt fine and decided to do a 5 minute interval.  I then compared this to a 5 minute interval I did a year before.  The 2018 session was after a good 3 months of sustained training with no travel or illness.  Ambient temperature as about the same at 75 F.
This is the plot:



  • During the post detraining interval (date 2019) heart rate was faster to rise and reached higher levels than the session in 2018 which was at peak fitness.
  • Comparisons such as this should be done with outside conditions matched (heat, humidity) as well as duration of exercise session.  This comparison was of identical cycling routes.  For example, do not compare segments occurring at 30 minutes vs 90 minutes into an exercise session. 
  • This is good evidence that my VO2 max dropped a bit from lack of training.  Repeat testing in 6-8 weeks will be interesting to see.

A comment on using muscle O2 for low intensity guidance.
There is only minimal locomotor muscle O2 desaturation at low intensities in either my tracings, the cyclist or the ski athlete.  Given the day to day and site to site fluctuation in sensor values, using them for low intensity insight is misleading at best.  However both Humon and Moxy persist in claiming these devices can help athletes in their recovery efforts.
Here are some web screenshots:

Humon:



Moxy:


  • Not only are there no studies supporting this, but as we have seen above, they simply don't manifest enough dynamic range at low intensity zones. 
  • I understand these companies want to be profitable but putting out misleading statements like the above is not the way to do it.  I'm looking forward to the day that we see an honest approach.  


Summary:
  • Two key physiologic metrics are able to be obtained non invasively with simple equipment, namely VT1/LT1 and MLSS.  
  • Ramp type exercise efforts of at least 5 minutes in length are needed.
  • Whether or not VT1 is metabolically equivilant to LT1 is arguable, but corresponding power levels of each may vary significantly.  If you do have official testing data of each fogure, use the lower value as the upper limit of zone 1 training.  As a surrogate marker for this breakpoint, DFA a1 complexity loss may be helpful.
  • DFA a1 HRV values below .7 indicate the approach of uncorrelated cardiac interbeat fractal complexity.  By the time a value of .5 (white noise) is reached, the low intensity exercise threshold has already been passed. 
  • Muscle O2 desaturation patterns can provide information as to the location of the MLSS in relation to interval power or heart rate.  Direct locomotor muscle placement as well as respiratory muscle (costal) or even non involved sites (deltoid) seem to all have similar behaviors.  Care should be taken to maintaining interval length of at least 5 minutes to demonstrate the transition from stable to continued downsloping of the saturation tracing.  It is possible that during very long intervals there is a plateau in desaturation to a personal nadir.  
  • Once these zone markers are know it is recommended to spend the vast bulk of endurance exercise below the VT1/LT1/DFA a1 cutoffs.  The exact amount of relative training time here is unclear but many would say 85% or more should be in zone 1.
  • MLSS knowledge can be used as an intensity training guide for zone 3 intervals as well as a regular benchmark of fitness, race pace.
  • A constant maximal power interval of 5 minutes should achieve VO2 max in most subjects.  Although measurement of VO2 can't be measured directly, comparison of the heart rates associated with these intervals can be used a relative indicator of VO2 max fitness.  The exercise interval conditions must be matched for temperature, humidity, time of day, time into entire session, caffeine use and hydration.
  • Muscle O2 sensors do not have sufficient dynamic range to be useful for low intensity zone demarcation.


See also:
VT1 correlation to HRV indexes - revisited