Saturday, May 30, 2020

Ultimate limiter - cardiac output, can we approach the pros?

Over the years there has been debate about what constitutes the "ultimate" limiter of endurance exercise.  Concepts such as VO2 max, cardiac output, stroke volume, muscle size and fiber type, mitochondrial number and quality, O2 diffusion, capillary density, ventilation, lactate production and disposal are all involved.  However, after reading a recent study on cardiac structure and function in elite cyclists, I think a step away from the woods and an better appreciation of the forest is in order.
A good review by Joyner and Dominelli on the central limits of aerobic capacity should be read.  In it are some important fundamentals such as
  • The VO2 max plays a central role in aerobic performance.
  • VO2 max is largely dependent on stroke volume and blood volume.
  • The MLSS also determines the ability to run or cycle fast and far. 
For example, an athlete able to run at 90% of his VO2 max (VO2 max=70 ml per kg per min) may do better than another with a higher VO2 max (lets say 80 ml per kg per min) but having a lower percent of the VO2 max "cruising speed" or MLSS.  
  
Athlete 1 - VO2 max = 70 with MLSS at 90% VO2 max = 63 ml/min/kg
Athlete 2 - VO2 max = 80 with MLSS at 70% VO2 max = 56 ml/min/kg

If both race at their MLSS the athlete with the lower VO2 max should be faster

Taking this a step further, if athlete number 2 with the higher VO2 max gets his act together and improves his MLSS, he ultimately will have a superior result.

Despite some occasional commentary that a high VO2 max is not that essential to top end performance, some further thoughts on this come from another recent study.  In this paper, in depth structure and functional comparative analysis was done on 3 populations - elite, sub elite and normal young men.  The elite were members of UCI cycling racing teams, the sub elite were class 1, 2, 3 riders and the normal were inactive men the same age:

Male elite-level road cyclists (EC, n = 69) actively competing
in UCI World Tour and UCI Pro Continental level events,
male sub-elite road cyclists (SEC, n = 30) actively racing
under a 1st, 2nd, or 3rd category British Cycling license, and
healthy, non-smoking male non-athlete university students/
staff (NA, n = 46) engaging in fewer than 3 hours recreational
activity per week were recruited into this cross-sectional
study.
Two different echocardiographic techniques were done for both structure and function.

Results:


With these conclusions:

A significantly greater LV mass was observed in Elite C compared
to SubElite C, who presented with greater LV mass compared
to NA. Differences in LV mass between Elite C and SubEliteC are
primarily driven by increased wall thickness (and therefore
concentricity), whereas chamber dilatation differentiates
SubEliteC and NonAthletes.

It seems that we have several "levels" of cardiac structural change that occurs with cycling ability level.  The striking observation (to me) are the yellowed portion above.  LV mass is 50% greater in the elite than non-athlete group, an impressive finding.  In addition, the EDV (end diastolic volume) was also about 50% greater then the non-athletes.  The EDV is the volume of blood in the left ventricle (the main pump chamber to the peripheral circulation) right before a contraction.  Therefore we have the potential for a massive amount of blood to be pumped given the high volume and the large LV mass.  The LV ESV (end systolic volume) was a bit higher as well.  This is the volume in the chamber after contraction.  If this was a patient with heart failure, they may have a large mass and large EDV but the ESV would be relatively high from poor contractility.
The sub elite group have values in between but do not approach the extreme LV mass as the elite group.

The question arises, is this part of a genetic trait or one that can be trained.  I have explored some of the controversy around VO2 nature vs nurture in an old post, but some new comments and study data review is in order.
An impressive study was done a few years back looking at this issue.  Their premise was:
It is unclear whether, and to what extent, the striking cardiac morphological manifestations of endurance athletes are a result of exercise training or a genetically determined characteristic of talented athletes. We hypothesized that prolonged and intensive endurance training in previously sedentary healthy young individuals could induce cardiac remodeling similar to that observed cross-sectionally in elite endurance athletes.
The training methods started with very easy base training progressing to:
By the end of the year-long training program, subjects were exercising for 7 to 9 hours per week, including long runs of up to 3 hours plus regular interval sessions on the track and races. The purpose of this template was to maximize training efficiency and to provide a periodization of the training program

The key point here is that both large amounts of volume as well as some interval work was being done by the end.  

What did the results look like?
Resting values:

There was a major increase in plasma volume, especially at the end.
Resting heart rate dropped substantially at the end perhaps related to the plasma volume
Resting cardiac output was stable throughout

Exercise values:
Interesting observations:
  • Although VO2 max did increase markedly at the end, it was already up at 3 mos and plateaued at 6 mos - which was due to mainly easy zone 1 training.
  • VO2 max rise was not related to a higher heart rate.
  • Stroke volume rose at 3 mos, and plateaued at 6 mos.  It seems that intensity was not needed for the stroke volume change.
  • Cardiac output followed stroke volume as it should (CO = SV x HR).

How about cardiac structure?

Both LV mass and EDV rose with training in similar fashion to the cardiac output/VO2max:
The largest increase in LV mass and mean wall thickness occurred in the first 6 months of endurance training. In contrast, LVEDV did not change significantly after 3 months, and it increased by only 8% after 6 months, reaching 39% of its maximum change within the testing period (Figure 3). The largest increase in LVEDV occurred during the last half of the training program, after the addition of interval sessions and long bouts lasting >60 minutes.
The last statement is of interest as it shows that some additional cardiac change occurred after 6 mos.

This is an extract from the discussion:
The primary new findings from this study are as follows: (1) Prolonged and intensive endurance training in previously sedentary individuals resulted in a large increase in LV mass, approaching a level similar to that reported cross-sectionally in elite endurance athletes.8,44,45 (2) Contrary to conventional thinking, the LV responded to the initiation of endurance training with an increase in mass without a change in volume (concentric hypertrophy); an increase in LVEDV occurred only after 6 to 9 months of progressive training, restoring the baseline mass-to-volume ratio (eccentric hypertrophy). (3) In contrast to the LV, the RV responded to endurance training with a balanced increase in mass and volume, thereby maintaining a constant mass-to-volume ratio (eccentric hypertrophy) at all levels of training. (4) Despite these morphological adaptations, and although maximal cardiac output and V̇o2 max increased substantially during the training period, they did not reach levels typically observed in trained endurance athletes.5,8 (5) One year of intensive endurance training led to a modest increase in LV distensibility and compliance but remained substantially below that observed in elite athletes
Therefore endurance training in sedentary individuals:
  • Lead to LV mass increase.
  • Later on LV EDV increased.
  • Despite these changes, VO2 max did not approach true endurance athletes.
  • Cardiac muscle compliance and distensibility did not reach those of elite athletes.

There are several possible interpretations of this data.  On one hand, the study appears to indicate that a wide array of subjects with no prior training have the ability to change cardiac function to that of high end athletes.  However, they don't reach the Elite levels seen in the first study discussed and their VO2 is substantially lower than usually seen in even sub elite groups.  Perhaps elite athletes just have genetic factors favoring faster and more profound changes in cardiac function/structure.  However, it possible that with continued training (years) many of these subjects would have continued to improve.  It is interesting that demonstrable changes occurred as soon as 3 mos but also some plateauing was seen with minimal change afterward.  
Going back to the first study comparing elite vs sub elite, it would be interesting to compare the training histories of the two groups.  My bet is that the elite group trained for more hours a week and possibly did more intensity as well.  Another factor seldom thought of in these studies is the ability of the athlete to recover from repeated daily volume and intensity.  I suspect elite riders recovered faster, making overall training more flexible and allowing more easy as well as HIT sessions.
The bottom line is that it is certainly possible to change cardiac structure and function with training.  The question is what type of training is going to accomplish this?  Since the majority of the change seems to have occurred at time 3 to 6 mos, it seems HIT is not necessarily needed.  However, according to some previous work, VO2 max non responders need to increase intensity.  Another potential clue to the "volume vs intensity" question comes from another way of looking at training outcome.
Here is a recent study that posed the question of what factors determined the VO2 max response to a course of polarized training.

The subjects consisted of fit mountain cyclists who did not do HIT the previous year.  
 Twenty well-trained male mountain bike cyclists (age range 16-31
years) volunteered to participate in the study (Table 1). All had been
training for a minimum of 2 years and had on average participated in
14 sanctioned cross-country Olympic or cross-country marathon races.
Training workload was at least 7 hours per week for 10 months per
year at an intensity above50%maximal aerobic power (Pmax). Before
study outset, none of the subjects had any experience with periodic
interval training that involved repeated 30-second to 5-minute cycling
bouts performed at an intensity greater than 90% Pmax.
They were given a polarized model of training as the experimental regime with the bulk of training in the zone 1 range:
Endurance Training. Subjects cycled for 2–3 hours at 70–80%
power at the ventilatory threshold. Training was terminated if the
subjects could not maintain the specified intensity
Both various SIT and HIT sessions were used as well. 
Sprint interval training involved 8–16 maximal cycling sprints.
The sprints were performed in sets, with each set consisting of four 30-
second all-out bouts interspersed with 90 seconds of rest. The sets
were separated by 25–40 minutes of moderate-intensity cycling
(70–75% maximal heart rate) as a form of active recovery.
Subjects alternated between 4-
minute high-intensity cycling (at 85–95% Pmax) and 12-minute
moderate-intensity cycling (60–70% maximal heart rate). This
cycle was repeated until the subjects was unable to maintain the
required intensity. The subjects completed on average four to 6
cycles per training session
The results:
  •  We can see that the prior yearly mileage was high but variable 7000 km plus or minus 4000 km

Physiologic improvements:
  •  VO2 max did improve as expected in the group as a whole.

What factors predicted the improvement?
  • The best correlation with improvement of VO2 max was the prior distance done the year before.  Prior study Ve and HRV did show some mild correlation as well.

With the conclusion:
Our results show that training status, quantified as the distance
cycled in the previous year, was the strongest predicator of

V̇O2max. This is also in agreement
with previous research, in which the effects (measured by V̇O2max
and Pmax) of a similar training intervention involving SIT, HIIT,
and ET were maximized if this intervention was preceded by
a greater volume of training
Do the pros actually do the distance? 
Years ago I was watching the TDF and saw an interview with Lance.  He was describing his training - consisting of at least 7 hours a day most days of the week!  Despite whatever natural aptitude he possessed, the training volume was incredible.
Here is a link to what some of the pros were doing recently on Zwift and it is quite impressive!

If you are dissatisfied with your performance, consider what the pros are doing - basically a full time job on the bike, probably 5 to 7 hours a day.  The majority of what they must be doing has to be zone 1/easy training with various HIT/SIT regimes.  
This is not to say that one should be doing this type of program (who has the time).  Working a job, taking care of a house/family with a training schedule like this is a recipe for stress, exhaustion and illness.  I only bring it up as a reason why most of us will never approach the elite ranks, no matter what type of coach is employed or training method followed - we just don't have the time.


Some final thoughts:
  • Depending on the level of athletic fitness (amateur, moderate, subelite, elite), there is a range of cardiac muscle and  cardiac chamber structural enhancement.  This leads to higher stroke volume which is the main determinant of VO2 max.  The elite group possess the extreme limits of cardiac size, mass and function.
  • Although partially seen after even short term training of easy to moderate intensity, the impressive levels seen in the elite group probably take years of constant high volume training to develop (unless genetic factors are present).
  • It appears that very large amounts of time at low intensity both promote cardiac changes and enable the athlete to optimally benefit from future high intensity sessions.
  • Whether this is due to various combinations of cardiac remodeling, capillary density and other metabolic alteration is not critically important - the fact is, to perform at a high level most athletes need to put the time and distance into training.



Monday, May 11, 2020

DFA a1 decline with intensity, effect of elevated skin temperature

Sometimes when you have a spell of bad luck, unexpected positive outcomes can arise.  As an example, a couple of years ago, I had a chance to monitor some interesting physiologic changes after getting a flat tire.  Fast forward to a few weeks ago when I was all set to do some more DFA a1 ramps (as part of the last post).  I was all prepped (Hexoskin on, well rested, calibrated power meter, timed and regulated breakfast) when a typical Florida thunder storm knocked out the electricity to my end of town.  Fortunately, I have a backup generator but it does not power my cycling room and the house temperature/humidity rose a few degrees.  I decided to still do the ramps (by manual reckoning since Zwift was offline) and see what happened.  In the past I was always curious what DFA a1 behavior would look like with heat exposure but never got around to looking.  However, let me emphasize, the following tests were done just a few degrees above the usual 73F and slightly higher humidity.  The main difference from usual is the lack of a good fan.  With the standard set up I have, there is never any sweat on the floor due to the excellent fan air flow rate.  So this is not a test of high thermal strain, simply one of thermal discomfort and higher skin temp.  In addition, I was well hydrated pre test and drank 500 cc over the 60 minutes or so of the ramps.

Before going over the data, I'd like to briefly review what happens to heart rate, stroke volume and cardiac output in the face of high skin temperature.  In the past I reviewed a very clever study from the lab of Dr Coyle.  This was their introduction:

The progressive decline in stroke volume (SV) is one of
the most salient cardiovascular responses to prolonged
exercise, and the degree of decline in SV is exaggerated
when exercising in a hot environment
(1,2). There are

two theories of the mechanisms of how SV is lowered during
hyperthermic exercise. The traditional and possibly prevailing
theoretical mechanism is that the decline in SV is caused by an
increase in cutaneous blood flow (CBF)
, which leads to an increase

in cutaneous venous volume (1–3). Blood accumulated
in the cutaneous veins might reduce ventricular filling pressure,
end-diastolic volume, and subsequently SV, whereas
HR increases just to maintain cardiac output (CO) and blood
pressure (1–3). The alternative mechanism proposes that the
HR drift during prolonged exercise reduces the ventricular filling
time and possibly filling pressure and thus decreases SV
during exercise, especially with heat stress
(4–7).



Thus when looking at heat related heart rate drift we have the two possibilities - the prevailing one of a primary redistribution on blood flow and a secondary rise in heart rate to compensate for the lower stroke volume vs the possibility that the heart rate rise is primary and the lower stroke volume is a consequence of decreased ventricular filling pressure.
If the heart rate rise was primary, normalizing the rate with a beta blocker (BB) should prevent the change in stroke volume.  However if the heart rate rise was secondary, due to higher CBF, preventing heart rate rise would should not change the stroke volume.
They compared 3 groups doing moderate exercise control, hot and hot-BB:

Here is the result:
I put red arrows on each top (cardiac output), middle (heart rate) and bottom (stroke volume) panel at the end of exercise (the rest is the cooling phase).
The results clearly show that:
  • Stroke volume is impaired by hot, but normalized by BB
  • Heart rate is higher in hot but normalized by BB
  • Cardiac outputs are all about the same at the end of exercise.

In separate graphs they did show that CBF was higher in hot conditions but:

The present study demonstrated that the decline in SV was
unrelated to changes in CBF and CVC in several ways. First,
βB restored SV of HOT-βB to the same level as MOD despite
significantly higher CBF and CVC
The bottom line is that exercise in the heat leads to a higher heart rate that secondarily results in lower SV but maintained cardiac output.
This heart rate elevation is presumably due to changes in both hormonal and autonomic effects on the sino atrial node pacemaker.


Back to DFA a1 behavior in the heat:
My case report did look at beta blocker (BB) effects on DFA a1 in normal conditions and as expected, heart rate was much lower, but the DFA a1 to watt power relation was not altered.  Both Ve, lactate and muscle O2 were looked at as well with no change between control and BB, implying VO2 was similar between conditions.  My conclusion was that DFA a1 was mainly affected by cycling power/VO2 and not absolute heart rate.  In regards to the lack of a fan, I was honestly curious what would happen with skin temp elevation.  My thought (given the above study by Dr Coyle's group) was that although heart rate would be higher, net cardiac output and VO2 would be the same so there would be no major shift where DFA a1 hits the .5 or .7 range.  On the other hand, multiple metabolic, cardiac and neurologic inputs can lead to changes in the balance of the sympathetic and parasympathetic effects on the sino atrial node (the pacemaker cells) responsible for heart rate and HRV.  Therefore the possibility that DFA a1 would drop prematurely at lower power should be entertained.


The test protocol was exactly as in the last post, 5 minute intervals at 157, 177 and 197 watts (+-) with a 5 minute active rest between the 3 series of ramps.  In order to get an idea of skin temperature the following graphic is helpful.  The Garmin watch I wear does track wrist temp, which came in handy.  Here is a normal session of doing the "control" ramp:

  • The wrist temp with a fan blowing is about 72F.
  • Room temp was about 73F.
  • Temp is the gray line, power gray background

Here is the session with no fan (and slightly warmer house):


  • Power in light gray.
  • Gray line is wrist temp running between 85 and 90F over the last 2 sessions.
  • The red line is a wrist temp of 85F.
  • Room temp was about 76F

Now to the interesting part - DFA a1 behavior.  Each point on the graphs below represent the average of the 3 separate segments of the interval power levels.

Watts vs DFA a1 decline - both "Normal conditions (Ctrl)" and Heat are graphed:


  • At each power level the DFA a1 was lower in the heat.  At each power value below VT1+5 watts (177 watts), DFA a1 is markedly down from control.
  • Although DFA a1 drops with increasing power in both, it had already decreased below .7 at the first power level (with control at .95).
  • Regression values are .95 or better.
  • By the final power level tested (VT1+25 watts), both DFA a1 values were quite low, below .4 - if the ramp was continued at higher power levels, little further change would occur.  This is good for repeat testing in that higher intensities are not going to needed.
  • The slope is not as steep in the hot condition since the start value was down and the end was already at nadir.  This may represent a curve shift to the right.

Heart rate vs DFA a1 decline:

  • As expected, heart rate was higher in the hot condition at each measurement point, almost 10 bpm higher at the start and stayed that way throughout.
  • The values of DFA a1 at a heart rate near 134 were very different (.63 Ctrl vs .39 Heat).
  • But - If one were looking at heart rates to gauge zone 1 training limits, the DFA a1 of .71 at 128 bpm for Ctrl is not that different than the projected heart rate on the blue line of the heat interval:

What can we conclude from this so far?
  • Skin temp elevation causes heart rate drift - no surprise and well known.
  • From previous studies, the higher heart rate will drive down the SV but cardiac output is preserved (and as long as dehydration does not occur, VO2 should be similar).
  • DFA a1 decline related to intensity is accelerated in the heat and occurs at lower much watt levels.
  • DFA a1 decline related to heart rate is altered but values at mid-range (DFA a1 = .75) are similar (same bpm).  So if one were to keep their heart rate below the point of DFA a1 transition from correlated to uncorrelated (.75 or between 1 to .5) they would probably maintain a zone 1 intensity according to HRV.
  • This also raises the question whether the limits of zone 1 intensity change according to external factors such as heat, skin temp.


A popular theory of DFA a1 behavior is that of it being a final common pathway of multiple sensory inputs that alters autonomic/neural balance to the cardiac rhythm.
The next question is whether the change in DFA a1 decline with skin temp elevation is an effect from a particular branch of the autonomic system (parasympathetic withdrawal + sympathetic stimulation both cause heart rate elevation).  If the HRV index decline with skin temp is related to the sympathetic activation side, then blocking this with Atenolol should prevent both the heart rate elevation, stroke volume drop as well as the change in DFA a1.  On the other hand, if the DFA a1 index change is mostly caused by parasympathetic withdrawal, we would see no change in behavior between hot and hot-BB, although heart rate would be lower.

Autonomic balance:
What about the balance between parasympathetic and sympathetic influences in regards to heart rate (and HRV)?
A good review on ANS balance during dynamic exercise showed the following figure:
  • Their main point was that there is not a simple on and off occurring in the opposing systems.
  • At each level of intensity there is some sort of balance.  However, there does come a time when parasympathetic activity drops and plays a minor role at higher intensities.
With their summary - 
In conclusion: (i) increases in exercise workload-related HR are not caused by a total withdrawal of the PSNS followed by an increase in sympathetic tone; (ii) reciprocal antagonism is key to the transition from vagal to sympathetic dominance, and (iii) resetting of the arterial baroreflex causes immediate exercise-onset reflexive increases in HR, which are parasympathetically mediated, followed by slower increases in sympathetic tone as workloads are increased.


The next step - Atenolol trial
As an aside, the RPE doing the ramps without a fan were much higher and it was not a pleasant experience.  In the name of science I recreated the power outage scenario (without turning off the main breakers) by turning off the AC in the cycling room, not using a fan and intentionally making the room a bit stuffy by having my friendly Great Dane next to me (pictured below - hard at work - he slept the whole time).  

The conditions were the same except for taking Atenolol 25 mg, 1 hour pre test.

Here is the wrist temp profile for the Atenolol trial:

  • The red line is 85F, making the wrist temp very similar to the initial set up.
  • Room temp was about 76F.
  • Background light gray are the 3 series of intervals

DFA a1 vs Power:

Orange - Control
Blue - Heat
Red - Heat Atenolol 
  • The Aten+Heat (red) vs Heat (blue) DFA a1 curves are virtually identical.  Therefore, the same accelerated DFA a1 decline with heat is present, with no effect from blocking sympathetic input.
  • Both Heat tracings still deviate substantially from the Control (orange).
  • R values are high, above .95, with .99 for the Aten+Heat.
  • Nadir values at VT1+25w (197 watts) are all comparable.

    DFA a1 vs Heart rate:

    • Heart rate is markedly reduce by Aten and is now about the same as in the control group (BB has done it's job).
    • Despite a reduction in heart rate, the DFA a1 values are about the same as in the original Heat only group (blue arrows to compare each power level). 
    • R is .99 for both the Aten Heat ramp as well as the Heat only ramp.
    • Even though the regression equations look very similar they yield diverse values - at a heart rate of 130 for Heat only, the DFA a1 is .41 but at the same heart rate in Heat Aten the DFA a1 is .71.
    Before discussing what this may mean, a look at Ve may be of some value.  The Hexoskin is capable of measuring minute ventilation with some accuracy (within 10%).  Since Ve is generally related to VO2, similar Ve between all three ramps can put the issue of variable VO2 to rest.
    Ve vs Power:

    Control - Green
    Heat - Orange
    Heat Atenolol - Blue 

    • The Ve at each power level is within 10% of each separate condition.
    • The center green curve is control with the blue Heat Aten, above and the orange Heat only, below.  Therefore, no trend in Ve is present for a shift in the heat.
    • R values are all above .97.
    • Although perhaps not rigorous enough for publication it seems that Ve is not different during the 3 conditions at each watt level.
    • If the Ve to VO2 relation is valid, no major change in VO2 is present between Heat, Heat Aten and Ctrl.
    • I did put in error bars that represent 1 standard deviation however paired t tests with unequal variance show a p=.1 for difference between the widest differential of the values at each power level.



    Summary so far:
    Some possible consequences of "mild skin temp elevation" (stuffy room with no fan):
    • Does not seem to affect the Ve to Intensity relation.  By inference, the VO2 to intensity (watts) relation is unchanged.  However at higher thermal loads it is recognized that VO2 max is reduced.
    • Causes heart rate elevation at each power level.
    • Results in an accelerated decline in DFA a1 (or curve shift) with cycling power.
    • Beta blockade will prevent the heart rate elevation/drift (and by inference the SV drop).
    • Beta blockade does not alter the accelerated decline in DFA a1 with heat.
    • Since DFA a1 decline is a result of sympathetic/parasympathetic balance, the lack of any effect of beta blockade on it's heat related decline (reducing the sympathetic side) seems to indicate that this HRV index is more dependent on parasympathetic withdrawal than sympathetic stimulation when responding to exercise load.
    A graphical representation of what may be happening:
    Multiple inputs from various body centers/systems (exercise power, emotions, skin temperature, recent food intake, altitude, hydration, glycogen availability, etc) are fed into the CNS/ANS resulting in a change in Sympathetic and Parasympathetic outflow to the SA node.  In the above scenario, skin temp elevation causes a direct stimulation of the sympathetic side and a withdrawal of the parasympathetic.
    This leads to a heart rate rise, a primary change in HRV with a secondary lower stroke volume (a la the Coyle data).
    With Atenolol blocking the sympathetic side, we still see the effects of parasympathetic withdrawal on HRV (since that is a primary effect).  Since we have blocked the sympathetic side, heart rate and SV do not change.

    Is there precedence for DFA a1 change with parasympathetic withdrawal in the literature?
    Yes and No.
    Like everything else it turns out to be complex and depends on what fractal scaling is used, how much atropine or beta blockade is done as well as activity level of the subjects. 
    From a recent study:
    Sympathetic control seems to be capable of maintaining short-term fractal properties. When only 1-adrenoceptors are blocked, RR series become rougher and the dynamics of RR intervals tend to the random regime ( 0.5). In contrast, the vagal control is important to maintain the mid-term fractal properties of HRV, especially in the presence of sympathetic control, as midis not altered during double blockade. When only muscarinic receptors are blocked, RR series are smoother and their correlation properties cease to be a power law.
    While short is not affected by parasympathetic blockade, cardiac sympathetic blockade with atenolol decreases short. On the other hand, mid is not affected by sympathetic blockade with atenolol  but increases with parasympathetic blockade with methylatropine . For long window sizes( long), neither of the cardiac receptor blockades affected the scaling exponents for atenolol and for methylatropine
    The above animal study looked at the effects of both atenolol and/or atropine on various fractal scaling exponents (short, medium, long) and found varying effects.
    Depending on the window time scales, different effects on DFA were seen in both beta blocker and atropine use:

    B is baseline
    Ate is Atenolol
    Atr is atropine 
    Short is close to a1
     Here is a look at atropine only depending on window length:
    It does appear that with a short window length (a1), atropine lowers the scaling exponent.


    Also other studies have shown that atropine causes DFA a1 to increase at rest.
     


    Finally - could age of the subject (me) play a role?
    A recent article brings that point up.  They found that older subjects exercising in the heat had lower DFA a1 values than younger ones:
    The green arrows show the effect of age and moderate intensity on DFA a1.

    However, if we look at their methods, the exercise intensity was higher in the older subjects - potentially skewing the DFA a1 results:
    Therefore the results of this study may not be totally valid.


    Quick look at a published study of thermal comfort and HRV:
    Further insight into DFA a1 trends during thermal change come from this fascinating study.  Although done at rest, the investigation looked at various HRV parameters at neutral, hot and cold ambient conditions.  

    Cutting to the results:
    The study found that HRV evidently varies from one
    environment to another (p < 1 × 10−4). The short-term DFA
    coefficient is consistently highest in the hot environment and
    lowest in the cold environment
    Before we get too confused here, this actually may make sense - at least with rspect to heat.  Although we found DFA a1 lower with heat, my data was under significant load, where the above study was at total rest.
    If we hypothesize that a "shift" in the "DFA a1 vs intensity curve" takes place under higher skin temp, then that should apply at rest as well.  Although I generally don't emphasize this, the DFA a1 trend rises from rest to low levels of exercise - below is from Gronwald's excellent cycling study:

    • The red arrow indicates that from rest to low intensity there is a rise in DFA a1.
    • Higher skin temps causing a higher DFA a1 may simply be from moving the values further right on the curve.  At higher work rates (VO2) this would of course turn downward as noted in my testing. 
    Heat and Intensity Prescription and training distribution:
    A review by Wingo brings up the conundrum of how to incorporate heat stress into training limitation guidelines.  Although beyond the scope of this post, several points are made including which metric to follow - power vs heart rate:
    • As expected, maintaining a given power causes higher HR but keeping a stable heart rate is associated with a lower VO2/power.  Which do we choose as our zone demarcating indicator? 
    • In any event, these are questions that will be sure to arise in the future.  If one truly believes non linear HRV indexes such as DFA a1 reflect net internal stress, spending large amounts of time with suppressed values seems inappropriate.  
    • If you do read this review, the references to a lowering of VO2 max with heat exposure are related to much higher temps than seen with my data.  In addition the author seems to be a proponent of the conventional HR drift theory - that of a lowering of blood volume/SV from flow to the cutaneous beds with a secondary rise in HR.




      Final thoughts:
        Some important potential consequences may arise from the above observations.
        • Since the typical VO2 max test is done without a fan (to prevent gas measurement error from blowing air), ramps done without a fan may not correspond to ones with good air cooling with respect to DFA a1 behavior.
        • Interpretation of DFA a1 "curve" results between different published studies may be affected by skin temp and air flow. 
        • Exercise prescription recommendations based on DFA a1 suppression performed at one temperature condition may not correspond to another.  Therefore if you derive numbers at home in a cool room based on power, they may not work for a road ride at high temp at that power (or visa versa).
        • However, it seems heart rate still functions as a sufficient integrated metric combining intensity as well as thermal effects such as skin temperature.
        • From the academic standpoint, the use of beta blockade seems to have the potential to help safely elucidate the basic physiology surrounding HRV with heat and dynamic exercise.
        • Numerous factors appear to feed into (and maybe feed forward) the non linear HRV DFA a1, an index of self similarity/fractal complexity.  Although this does make result consistency difficult across studies, it does provide what appears to be an excellent index indicating overall body demands.

         






        Saturday, May 2, 2020

        DFA a1 vs intensity metrics via ramp vs constant power intervals

        A popular theme of endurance training is how to measure the various physiologic thresholds.  Not only is this helpful in monitoring current performance level, but perhaps as useful is it's role in delineating training zone markers. Zone 3 (of a 3 zone model) is heralded by various concept ideas such as MLSS, RCP, VT2 and LT2.  These have been discussed before.  More importantly (in my opinion) is the transition from zone 1 to 2.  If inappropriate time is spent in zone 2 many negative consequences could occur.  As we age and recovery strategies become important, improper time in zone 2 becomes even more problematic.
        The non linear HRV index, DFA a1 may have great promise as a means of measuring exercise intensity and training markers.  Whether this will translate to a practical, non invasive clue for zone 1 to 2 transition remains to be seen.  In order to answer this question, further studies are needed.  As shown previously, some particular hurdles need to be jumped over to obtain accurate data, including avoidance of artifacts in the RR series.  Perhaps of equal importance is the method of ascertaining the change of the index as intensity rises.  A time honored approach involves doing an incremental exercise ramp (to failure).  As an example, the "gold standard" incremental ramp for VO2 max, also includes measurement of VT1 or LT1, both accepted markers of zone 1 to 2 transition.  LT1 can also be measured by longer steady state intervals of 4 to 8 minutes in length and they are usually equivalent (depending on ramp slope). 
        With a non linear HRV index that's measured over a span of 2 minutes (hopefully representing a state of equilibrium), we can't be assured that a calculation is proper or accurate since the ramp is not steady state.  In the past I have obtained DFA a1 values from 5 minute constant power cycling intervals, with values taken over the last 2 minutes.  However, most (?all) currently published research on the DFA a1 relation to intensity is based on the incremental ramp.  For example here is a figure from studies of Hautala (first) and Gronwald (second):



        Gronwald et al:


        The question arises, would a ramp derived value for DFA a1 correspond to one taken from the end of a constant 5 minute segment?  Based on the article last year from the Murias group, a slow ramp would largely negate the "mean response time" lag of VO2 parameters.  The following post aims to provide some preliminary data comparing slow ramps vs 5 minute constant power intervals in regards to DFA a1 behavior and value range.  

        Methods - all studies were done after identical light meals, about 8am, using the Hexoskin for RR recording, on an Elite Suito smart trainer, Assomia Duo pedals and with Zwift controlling both ramp progression as well as constant power.  

        As an aside, the Hexoskin provides an ideal platform for obtaining artifact free single lead ECG data for HRV.  Here is a screen recording from my android phone during one of the sessions:
        The waveform is downloadable and important for both arrhythmia detection and artifact confirmation.

        Kubios software (latest version 3.3.1) was used to determine both average heart rates and DFA a1 for the 2 minute measurement windows.  There were no artifacts except in one tracing where a single APC (atrial premature contraction) was noted.

        5 minute constant power intervals:
        After a 20 minute warmup, 5 minute intervals were done at about VT1-15W (152 or 157 watts), VT1+5W (172 or 177 watts) and VT1+25W (192 or 197 watts), then a 5 minute active recovery at VT1-20W (152W) for 5 minutes - then repeat x 2.  The constant power intervals were done on two different days with slightly different wattage on each day (5 watts higher on the second day).

        A total of 3 segments for each of the 3 intensities was plotted by averaging the DFA a1 at the last 2 minutes of each active segment. 
        For example here is the series of data points from the 4/22 session:

        The average DFA a1 for the 3 intervals done at 177 watts (in yellow) was about .71 (red arrow).


        DFA a1 vs Watts
        Blue arrows represent the point where DFA a1 reaches .7 and .5 values.  For more detail on what these numbers mean please read this or this.



        • Both ramps have very good linear plots with R>.95
        • As expected, DFA a1 declines with intensity from correlated, complex values near 1, pass through an intermediate stage until reaching uncorrelated, white noise levels of .5.
        • DFA a1 at .7 (between correlated and uncorrelated) was 173, 176 watts - essentially the same.
        • DFA a1 at .5 (white noise) occurred at 191 and 190 watts.  
        • Both ramps showed quite similar results despite using slightly different cycling power intervals.

        How does average heart rate vs DFA a1 look?


        • The curve R values were .89 and .99.
        • DFA a1 reached .7 at about 132 or 128 bpm.
        • DFA a1 reached .5 (white noise) at 135 or 132 bpm.
        • Not as tight as the power figures, but useful if we are trying to avoid the .5 value - just stay below 130 bpm.

        To put this information in some sort of personal historic perspective, here is the data presented in my case report several months ago (also at 5 minute constant power):


        The overall figures are in line with the above results.

        Now that we have an idea what 5 minute constant power interval data looks like, the next step is to compare this to a continuous ramp. 

        The first ramp protocol was 10 watts per minute which is a commonly used protocol (otherwise known as 30 watts per 3 minute), starting at 100 watts (after a 20 min warm up).
        Zwift ramps are a bit different though, they are truly continuous - there is no abrupt jump between stages.  So if you were to do 10 watts per minute incremental rise, the trainer would steadily increase power every second as an equal fraction to maintain that pattern.

        The following is a 100 to 310 watt ramp to almost failure, done over 21 minutes, therefore 10 watts per minute:


        This is a standard data extract from Kubios with measurement windows every 120 seconds:
        Although one can draw a line through the points to get reasonable regression formulas, I decided to try something different.  The following is also an extract from Kubios, but with recalculation of 2 minute data every 20 seconds:

        DFA a1 vs Avg HR:


        • Three different regression curves were placed.  I decided to use the one in gray - a linear fit but just through the mid portion.  The R was .95 and the arrows in blue again point to heart rates at DFA a1 .7 and .5 values.
        • DFA a1 at .7 occurred at a heart rate of 132 bpm
        • DFA a1 at .5 occurred at a heart rate of 137 bpm


        How are we going to compare DFA a1 to power?

        Step one - plot power over time:

        Step two - Then use the regression equation to calculate what each watt figure is at each time interval on the DFA a1 curve: 




        Giving us the following:
        We have many more points and several different choices for plotting regression.

        • There are many ways to model the regression lines.  In light blue is a second order polynomial with an R of .91.  However, as in the heart rate curve above, I prefer to just select the mid portion of data (in orange) and do a simple linear regression through those points (R was .95).
        • DFA a1 at .7 occurred at 173 watts.
        • DFA a1 at .5 (white noise) occurred at 190 watts.

        Issue of Reproducibility?
        Advantages of not continuing past moderate power:

        In academic testing situations as well as for athletic guidance, this process may need to be repeated on a regular basis.  Doing a long ramp to failure is not going to fit into a polarized training model nor is something most athletes have time for.  However, based on curve shape there are alternatives.
        As noted above, at intensities past 200 watts, the curve is relatively flat.  Therefore, to minimize physical stress from repeatedly taking the test and avoiding unwanted time in zone 2, perhaps a restricted range ramp can work as well.  In addition, using an even lower ramp incremental rise may be helpful (5 watt per min instead of 10 watt per min).  The 2 minute window of measurement will still be used but recalculated every 20 seconds as a running data point.  


        In view of these thoughts, I decided to try an even slower rise through the mid portion - 5 watts per minute which translates to - 130 watts to 230 watts over 20 minutes.
        There were two sessions, separated by a few days.  Conditions were identical as the constant power tests above. Blue arrows refer to the point on the regression line of reaching a DFA a1 of .7 and .5:

        Heart rate vs DFA a1:

         DFA a1 vs Watts:
        • Linear regression was plotted as in the longer ramps with R values in the .83 to .89 range.
        • DFA a1 of .7 occurred at 179 watts or 131 bpm.
        • DFA a1 of .5 occurred at 201 watts or 138 bpm.

        The same protocol was repeated several days later:


        • The outliers (circled) represent the effect of just one APC (atrial premature contraction).  Unfortunately, Kubios does not handle this well. Here is a screenshot directly from Kubios, with heart rate and RR (red arrow) on the top, DFA a1 recalculated every 20 sec with 2 minute windows on the bottom.  The area in yellow is the result of just one APC (red arrow on top).  Correction method was "automatic".
        • However, with medium threshold correction, values drop to near .2 around the APC.




        • I tried to adjust the regression accordingly which yielded:
        • DFA a1 at .7 of 180 watts or 134 bpm.
        • DFA a1 at .5 of 193 watts or 142 bpm.
        Summary of both constant power and both types of ramps:



        • "Constant 1" and "Constant 2" were composed of three x 5 minute intervals, repeated three times.  Each "Ramp" value was derived from one incremental ramp test.
        • The DFA a1 values at .7 are all very near my officially measured VT1 (172w).
        • At just 20 watts above the VT1, DFA a1 reaches .5, uncorrelated white noise.


        Some thoughts:
        • It seems that a variety of incremental ramps will show the same ballpark "power vs DFA a1" profile with respect to values at .7 and .5 as the constant power intervals A 10 watt per minute to near failure as well as 5 watt per minute to only moderate intensity seem equivilant.  However, this does not mean it is reasonable to use a fast ramp such as 30 watts per minute to evaluate the index.
        • Based on this very preliminary look at DFA a1 behavior comparing incremental ramps with constant power intervals in just one subject, I am encouraged that multiple valid paths exist to observe the index change with exercise intensity. 
        • Potential use case senario - If subject "X" was to be tested, they probably should perform over the full range to near failure to find the intensity associated with DFA a1 curve flattening, but thereafter only restricted range testing would be required to minimize stress.
        • Hopefully this topic can be evaluated more thoroughly in the future with a careful look at multiple subjects doing both ramps and constant power sessions.
        • Even 1 APC can throw off the DFA a1 values in Kubios.  With auto correction, values of DFA a1 were higher than usual.  Threshold correction was much worse with DFA a1 below .2.  Be careful with trend interpretation if any premature beat is present.
        • Based on my previous experience, Kubios artifact correction will have significant effects on DFA a1 values.  Artifact free recordings are essential until such time as it's proven otherwise.
        • Finally, on a personal level, DFA a1 values near .7 seem to correspond to my VT1 as measured with gas exchange.  
        • At an intensity only 20 watts higher than VT1 (about 190 watts), the DFA a1 pattern becomes uncorrelated (white noise values of .5).