Saturday, June 22, 2019

HRV with dynamic exercise and Ventilatory thresholds

Heart rate variability has received much attention as a means of assessing readiness to train hard or need to back off and recover.  This measurement is usually done at rest, sitting or standing with a duration of 3 to 5 minutes.  However over the past few years some investigators have begun to look at HRV during dynamic exercise.  The hypothesis is that certain HRV parameters may change as exercise loads increase and potentially indicate thresholds such as the MLSS or the first ventilatory threshold.  In addition, if your aim is to recover after a particularly hard session, avoiding HRV values that are associated with cardiac-autonomic stress may be wise.  The field of HRV study is huge and from a single post standpoint, there is no way I could do it justice.  What I will do is explore some published studies using HRV during intense exercise and along the way intersperse the appropriate HRV definitions.  This will also serve as a "how to - do it yourself" if anyone is interested in the analysis of their own data.
For an excellent review of HRV during exercise and recovery see this paper.

Background:
The statistical analysis of beat to beat variability can be done in many different ways.  
Time-domain indices of HRV quantify the amount of variability in measurements of the interbeat interval (IBI), which is the time period between successive heartbeats (see Table ​Table1). These values may be expressed in original units or as the natural logarithm (Ln) of original units to achieve a more normal distribution.


Frequency-domain measurements estimate the distribution of absolute or relative power into four frequency bands. The Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996) divided heart rate (HR) oscillations into ultra-low-frequency (ULF), very-low-frequency (VLF), low-frequency (LF), and high-frequency (HF) bands.
Power is the signal energy found within a frequency band. Frequency-domain measurements can be expressed in absolute or relative power. Absolute power is calculated as ms squared divided by cycles per second (ms2/Hz). Relative power is estimated as the percentage of total HRV power or in normal units (nu), which divides the absolute power for a specific frequency band by the summed absolute power of the LF and HF bands. This allows us to directly compare the frequency-domain measurements of two clients despite wide variation in specific band power and total power among healthy, age-matched individuals
The LF band (0.04–0.15 Hz) is comprised of rhythms with periods between 7 and 25 s and is affected by breathing from ~3 to 9 bpm. Within a 5 min sample, there are 12–45 complete periods of oscillation (9). The HF or respiratory band (0.15–0.40 Hz) is influenced by breathing from 9 to 24 bpm (11). The ratio of LF to HF power (LF/HF ratio) may estimate the ratio between sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) activity under controlled conditions. Total power is the sum of the energy in the ULF, VLF, LF, and HF bands for 24 h and the VLF, LF, and HF bands for short-term recordings


The HF band is usually associated with the parasympathetic (PS) and the LH band with the sympathetic nervous system (SNS) .  At rest the PS predominates and as exercise intensity rises it is withdrawn and the SNS is activated.  Therefore we would expect the LF band to be useful in more intense exercise domains.  However, it turns out that the respiratory rate effects on HRV occur in the HF band which would be expected to play a larger role as exercise intensity rises.  In recent years it has also been noted that the parasympathetic system affects both the LF and HF bands making interpretation more complex.

Non-linear measurements (​Table3) allow us to quantify the unpredictability of a time series:

Nonlinear data significance (SD1):
Non-linearity means that a relationship between variables cannot be plotted as a straight line. Non-linear measurements index the unpredictability of a time series, which results from the complexity of the mechanisms that regulate HRV. Non-linear indices correlate with specific frequency- and time-domain measurements when they are generated by the same processes.
A Poincaré plot (return map) is graphed by plotting every R–R interval against the prior interval, creating a scatter plot. Poincaré plot analysis allows researchers to visually search for patterns buried within a time series (a sequence of values from successive measurements). Unlike frequency-domain measurements, Poincaré plot analysis is insensitive to changes in trends in the R–R intervals.
We can analyze a Poincaré plot by fitting an ellipse (curve which resembles a squashed circle) to the plotted points. After fitting the ellipse, we can derive three non-linear measurements, S, SD1, and SD2. The area of the ellipse which represents total HRV (S) correlates with baroreflex sensitivity (BRS), LF and HF power, and RMSSD.
The standard deviation (hence SD) of the distance of each point from the y = x axis (SD1), specifies the ellipse’s width. SD1 measures short-term HRV in ms and correlates with baroreflex sensitivity (BRS), which is the change in IBI duration per unit change in BP, and HF power. The RMSSD is identical to the non-linear metric SD1, which reflects short-term HRV (). SD1 predicts diastolic BP, HR Max − HR Min, RMSSD, pNN50, SDNN, and power in the LF and HF bands, and total power during 5 min recordings.

Background on VT1 and VT2:
HRV during dynamic exercise has been expored as a means of determining the first and second ventilatory thresholds.   Let's explore the basic science behind each one.  Some of the following figures were taken from a sports physiology textbook by Plowman.
As exercise load increases, aerobic glucose metabolism eventually is unable to meet all the energy requirements to continue.  There comes a point when lactate begins to be accumulated from incomplete glucose breakdown.  

This causes an increase in CO2 through the following:


Here is a series of graphs looking at heart rate, VO2, ventilation and lactate with increasing exercise intensity:




The VT1 and VT2:
The author makes it very clear (with multiple reasons why) that the VT1 and the LT1 as well as the VT2 and the LT2 are not caused by the same process:

 And:
 With the conclusions:

The reason I have gone to some length in getting proper definitions spelled out is that although one can use lactate associated breakpoints LT1, LT2 during a cycling ramp to get a rough idea about the VT1, VT2, they are not necessarily the same.  To actually get a true VT1 and VT2, you will need to do a gas exchange study in a lab.  Here is a figure from the study looking at equivalency of VT vs LT:
So although there is some correlation, it is not precise.  For instance the highest value for LT1 in the figure is near 275w with the true VT1 is about 230w.
In the rest of the post, for ballpark purposes, I will use the MLSS power as a VT2 surrogate, but it is a only a similarity.



Back in 2007, Cottin et al looked at a derived index (HF power x HF frequency peak) in relation to the VT1 and VT2.  Typical ramp testing was done in moderately trained soccer players with gas exchange and HRV parameters.  They were particularly interested in the HF band since respiratory HRV occurs there.  Apparently, the RSA (respiratory sinus arrhythmia) has 2 main components:

Furthermore, spectral analysis of beat-to-beat RR series allows the computation of the two main components of the -1.The first spectral component of the RSA is its magnitude. It is given by the spectral power (HF) contained within the high frequency band. HF is computed as the sum of the power spectral density ranging from 0.15 Hz to fmax during exercise. The fmax is the maximal frequency induced by the sampling scale of the RR signal.2.The second spectral component of the RSA is the frequency of the HF peak (fHF). It has been shown  that fHF closely corresponded to the breathing frequency (BF).
HF thresholds assessment: The present study performed two VTs assessment methods from RSA:1.From fHF: successive values of fHF were averaged to provide a data point for each 20-s period synchronous with the ventilatory data. HF thresholds were detected from the curve of fHF plotted vs. the work rate by an independent investigator. The first HF threshold (TRSA1) corresponded to the last point before a first increase in fHF. The second HF threshold (TRSA2) corre-sponded to a second nonlinear increase in fHF.2.From HF·fHF: successive values of HF·fHF were averaged to provide a data point for each 20-s period synchronous with the ventilatory data. Therefore, HF thresholds were detected from the curve of HF·fHF plotted vs. the work rate by an independent investigator. The first HF threshold (HFT1) corresponded to the first nonlinear increase in HF·fHF after it had reached a minimum. The second HF threshold (HFT2) corresponded to a second nonlinear increase in HF·fHF 

They showed a reasonable correlation of their index with both thresholds:
They also found a very steady linear relation of the HF peak frequency with running speed:
The study conclusion:
The main result of the present study is that it is possible to assess the ventilatory thresholds from HRV components during an incremental exhaustive running test. However, the VTs assessment was possible from the HF·fHF index, whereas it could not be determined from fHF only.
In addition they did comment that it may be possible to estimate breakpoints using the HF frequency only in cycle ramp tests due to the difference in breathing rate entrainment in running vs cycling: 
BF(breathing freq) data occurring at VTs presented in the present study (Table3) seemed to be higher than the corresponding examples of BF data obtained at VTs during cycle ergometer tests [3,5,42]. This observation confirms the different relationships between BF vs. running speed during running (linear) and cycling (nonlinear). While it is possible to assess VTs from fHF during a cycle ergometer test, it is not possible during a running test.
How does my ramp data look?
This is a portion of a ramp test I did some months ago using the Hexoskin shirt (with near perfect signal quality), RR intervals extracted, analysis with the Kubios HRV software.  This was on an indoor turbo trainer back in January.  I am using the premium package ($$) and therefore am able to plot "time varying" trends.  The basic version will not allow this, but you can manually create interval blocks (with the free version), look at the HRV metrics (all of them) and then make your own graph.

Here is the HF power (black dots) along with the heart rate(blue dots) in 30 second blocks:

  • The HF power is relatively stable throughout the early ramp and increases once it is over.  To simplify analysis, I will just show the HF power and HF peak on separate graphs

The HF frequency peak is interesting:

If we ignore the possible early artifact, there is a notable breakpoint at a heart rate of about 120, and at 143 watts (artifact removed below):

A closer look from the Kubios software package using 30 second windows before and after the breakpoint:
Pre:

Post:

  • The pre HF frequency peak is .156 vs .656 for the post (in yellow).  The FFT tracing does not have a visually dominant band easily discernible.
  • Note should be taken of the HF band upper limit being 2 Hz (green).

For comparison here is a 30 sec window later on the ramp with a heart rate of 160:
  • The red arrow shows the dominant frequency on the FFT graph.  The magnitude and frequency are much different than the lower heart rate values.
  • I would be have been more comfortable with the data analysis if the lower heart rate frequency band was as visually dominant.

Hexoskin ventilation comparison:
A comparison of the power, heart rate and ventilation is here:
  • I circled the 135 watt/120 heart rate area as the point where ventilation starts to pick up, similar to the 143 watt noted above.
  • Whether or not these are actual breakpoints is arguable.  The tracing looks more curvilinear to me than segmented.  This is a problem with threshold detection in general, many studies use multiple "examiners" to decide where the breakpoints are.
As far as repeatability, an identical ramp was done several weeks before.  Since the heart rate, power values were virtually identical, I plotted the Ve values on the same curve as the ramp above:

Although the absolute Ve is a bit different (related to how the Hexoskin shirt is worn), the pattern is nearly the same.  There are still no "textbook" breakpoints.

 
Threshold assessment by HRV:
An issue with many of the studies done is that they generally all use different parameters to look for breakpoints and thresholds.  I am not even sure if the above study has been reproduced by other groups.  There are several other papers on HRV and VT/MLSS but they all use different heart rate derived metrics.
In fact a comment from the review mentioned above caught my eye:
During exercise, HRV measures demonstrate a curvilinear decay
as a function of exercise intensity, that is closely related
to exercising HR. HRV measures associated with cardiac
parasympathetic activity (e.g., RMSSD and HF) usually reach
a near-zero minimum at moderate intensity (possibly being
associated with the first ventilation/lactate threshold). These
measures are sometimes observed to increase slightly as exercise
intensity increases toward maximum, although this is likely
mediated by non-neural mechanisms such as direct mechanical
effects of respiration on the SA node. The data also leads
to further questioning of the use of frequency domain ratio
and normalized measures as indicators of sympathetic activity
or “sympatho-vagal balance.” In addition to demonstrating
inconsistent responses to exercise, the response of these measures
is rarely consistent with our current understanding of autonomic
control during exercise, namely progressive parasympathetic
withdrawal and sympathetic activation
.

A study in basketball players was done looking at the relationship of HRV to both ventilatory threshold 1 (VT1) and the VT2 (near the MLSS).  They chose a parameter called the SD1 (see above) to use as the indicator for VT1 and the HF power band to correlate with the MLSS.

Methods: 
The basketball players completed an incremental test to exhaustion on a treadmill (Run MedTechnogym, Cessena, Italy) in standard environmental conditions,with the grade set at 1%.
HF power (HFp) trend, as a function of time and frequency over the entire exercise period, was calculated from R-R interval series using a
time-varying short-term Fourier transform with 64 s moving
window. VT2 was determined from HFp at the final abrupt
increase in the HF band (HRVT2). HFp range was extended
from resting recordings (>0.15–0.5 Hz to >0.15–2 Hz).
Results:

VT1 correlation:
Although the results did have statistical significance, the correlation coefficient was not very high:

VT2/MLSS:

 The correlation was much better here:

 Conclusion:
We concluded that the ability of the HRV time
varying spectral analysis to estimate VT2 during incremental
running test, in professional basketball players, was demonstrated
to provide sufficient reliability and validity and thus may
be implemented into a training session without the use of a gas
analyzer to determine HR, speed, or VO2.
(the HF power band is useful to estimate the VT2/MLSS)
A very bold statement indeed!


My data - I plotted the HF power in normalized units and came up with this.
The zone that would correspond to my approximate MLSS is circled:

And a look at the 30 sec windows pre and post at that section:
Pre:
Post:
  • The HF power rises from 2.5 to 4.4.  The FFT window shows more HF activity (green) in the post breakpoint section.
  • Whether this is real or just imagination is of course the question.

Another study was done to try to confirm the above observation, but in moderately trained subjects. 
Methods:
The incremental test was conducted on a cycle ergometer
(Lode; Corival, Groningen, The Netherlands). The protocol
involved a two-minute warm-up at 50 watts with increments
of 20 watts occurring every minute with subjects instructed to
maintain 80 revolutions per minute (rpm). The test was terminated
in the case of volitional fatigue, a failure to maintain
80 rpm or a plateau in VO2 despite an increase in workload.
Upon completion of the incremental test, ventilatory data were
averaged to every 20 s and exported to a personal computer for
further analysis. The simplified V-slope method was used to
detect the VT (Schneider et al., 1993). Here, VCO2 is plotted on
the y-axis and VO2 on the x-axis (Fig. 1). The first breakpoint
observed was the VT and is considered the point where glycolytic
activity increases so that the lactic acid starts accumulating.

Here, ventilation and VCO2 begin to increase as a result of
hydrogen ion buffering whilst VO2 remains constant (Beaver
et al., 1986).

 HRV analysis:
Nonlinear analysis of the R-R intervals was performed
adopting the Poincare Plot. This technique allows for a graphical
representation of the systems evolution in phase space
(Lombardi, 2000), where each interval is plotted as a function
of the previous one (R-Rn, R-Rn+1) to create a scatter plot
(Tulppo et al., 1998). Two axes are fitted 45 degree to one
another passing through an ellipse fitted to the centre of the
data points. As a marker of nonlinear short-term variability,
we adopted the SD1 parameter, which is shown to reflect
vagal modulation
(Tulppo et al., 1996).
For the spectral analysis, a short-term Fourier transform was
applied with a moving window of 64 s and time shift of 10 s
(Cassirame et al., 2015). The high-frequency band was
extended (>0 15–0 4 Hz to >0 15–2 Hz) to consider spectral
energy content resulting from increases in respiratory frequency
during high-intensity exercise
(Cottin et al., 2006a,b).
HFp was captured for further analysis. In addition, to validate
further observed results, frequency peak of HF (fHF) (Hz) in
the power spectral density was accounted for, which allows for
correlation analysis between respiratory frequency and HRV.
Notes - 
There is an extension of the HF band up to 2 Hz to encompass the respiratory frequency effects.  When looking at my data later on we will make an adjustment in the software to override the default values.  Some investigators also use a value of 1 Hz as the upper limit and as a compromise, I have set the software to this value.
The VT is defined as the VT1, not the MLSS/VT2.

Results:
The main findings of the present study were the following:
(i) TSD1 shows no relationship with the VT in moderately
trained healthy males. When expressed as both power output
and heart rate, a significant difference was observed, which
was confirmed by the Bland and Altman plots. (ii) Similarly,
THFp did not exhibit a significant correlation with the VT
when expressed as power output but in contrast exhibited a
strong correlation with the VT when expressed as heart rate.




It appears that the correlation of SD1 with the VT1 was poor but there was a reasonably good relation between HF power and VT1 associated heart rate.  Why the watt power did not correlate well with HRV derived indexes was not really discussed.


Conclusion:
Results of this study revealed that TSD1 derived through the
Poincare plot shows no relationship with the VT in moderately
trained healthy males. However, THFp showed a strong
relationship with the VT when expressed as heart rate
. Given
the relationship between HRV and respiration, this may be a
more robust indirect measure of the VT in trained individuals
allowing for both assessment of cardiovascular fitness and prescription
of training.
Lastly, in untrained individuals (VO2 max about 30 ml/kg/min) another study found good correlation using the SD1 and RMSSD metrics with VT1 and VT2 (as opposed to poor agreement to the SD1).
The first criteria, HRVT1, was determined
in the first physical effort intensity in which the SD1 and
RMSSD index were < 3 ms
[13]; that point occurs at the same intensity
of lactate and ventilatory threshold. The second additional
criteria, HRVT2, was determined in the first effort intensity
in which the difference between 2 consecutive intensities was < 1 ms
in the SD1 and RMSSD index, which occurs at same intensity
of ventilatory threshold.

Ramp study, 30 w/min rise:
Now that we have some idea what to look for how does this shape up in reality?  I went back to the old ramp I did using the Hexoskin and graphed out the SD1 with both power and heart rate.  The RR intervals were of high quality with artifacts below 1% and the Kubios premium software was used for analysis. 

Here is the SD1/HR graph from Kubios:

I took the liberty of drawing in a breakpoint (guess) with the corresponding HR near 140 bpm.

Here is the raw data with power averages per interval (30w/min):
Since I really do not know my true VT1/2 (yet) and ramp tests can lead to different values depending on the protocol, I'm not claiming any conclusion.

HF power:
Since the study above indicated that HF power seemed to track well with VT1, I was curious as to how this looked.

Here I am plotting the HF power in nu (normalized units).

Kubios first:


  • There does seem to be a defined step up in HF power at about a HR of 120 bpm.  The curve continues to rise to a max near the end of the ramp test.

Kubios data with power in watts:
  • The tracing here seems even better defined (same data but different scaling) with the jump in HF power occurring when the ramp power went from 143 to 175 watt average.  The HR zone corresponding to this is is about 120 at 143 watts to 127 at 175 watts.  If my VO2 max power is about 350w, this represents about 50% of the MAP which is at the low end for estimated VT1. 


Longer ramp intervals near VT1
HRV parameters are conventionally done over longer windows than 30 or 60 seconds.  To get a better handle on whether HRV is useful in looking at VT1 thresholds, longer ramp zones with finer power increments should be used.  To this end, I did a limited ramp from 152 up to 218 watts with 4 minute intervals and about 10 watt increments.  Since there are arguably better, more established ways of looking at VT2/MLSS power (lactate levels, SmO2), I decided to confine the ramp to the VT1 estimation.

Power profile + heart rate (polar H10)
The zone power, heart rates, cadence are listed in the table (laps 2-7):

HF power in normalized units:
  • HR is in blue, HF power in black.
  • The initial part of the ramp shows an undulating but relatively stable HF power.
  • There may be a shift in the HF power curve (black) that corresponds to the 172 watt interval at an average heart rate of 125.

HF frequency peak:

  • HF freq peak in black.
  • There does not appear to be much in the way of a pattern here in the power range that was tested.
  • However, a closer look at the FFT spectrum shows little in the way of a dominant band, making interpretation problematic:




SD1:
  • SD1 is in black, HR in blue.
  • Similar to the HF power, there is some undulation early in the ramp, but it appears to shift at 172 watts, 125 avg HR to a lower undulating pattern.


Summary:
  • HRV analysis is possible during dynamic exercise but is a challenge to implement. 
  • Several studies have concluded that both the VT1 and VT2 are able to be obtained from appropriate analysis of the HRV data of exercise ramp protocols.
  • The exact HRV parameters best suited to looking for these thresholds are somewhat confusing.  Almost each study uses either a different HRV variable or a relatively convoluted derivation of multiple variables.
  • Some studies have felt that the SD1 does correlate with VT1 and perhaps my longer, fine increment ramp seems to show a threshold in the area of interest.
  • The peak HF frequency may also be useful for VT1 as well as VT2 in cycling but not running.
  • The HF power has been shown to be associated with VT1 and VT2 in several studies.

  • However - Even in some of the more "successful" studies, threshold correlation may only be moderate.  Interpretation of thresholds is made difficult by undulating tracings, natural variation, artifact and the limitations of software analysis.  Many published works use the popular Kubios software but overlay the analysis with additional layers of derived values.
  • There does seem to be a potential place for using this technique in threshold monitoring as well as demarcation of intensity zones.  
  • Further, using HRV during dynamic exercise could be an "insurance" policy for a true recovery session. The goal being to keep out of the more stressful zones for optimal cardiac recovery.
Next - Detrended fluctuation analysis (DFA) as a modality in monitoring intensity domains and recovery sessions.

Saturday, June 8, 2019

Firstbeat VO2 estimates - exercise in the heat

Does the derived VO2 remain stable in diverse conditions of temperature?

I recently visited my son in cool clear San Francisco, leaving the warm Florida weather behind.  Before leaving, my VO2 max was a calculated 50 by 4 minute maximum power and the Garmin derived VO2 max was set at 49 ml/kg/min.  On the day of travel I did 30 minutes on an indoor trainer at home and the VO2 rose to 50.  Two days later I did 60 minutes on trainer outside (72F) with 1 MLSS interval and the VO2 max went to 51.  Two days after that was another trainer ride (but at 52F) with 2 MLSS intervals and VO2 max was displayed as 52.   When I returned back to Florida the heat and humidity kicked in and my heart rate skyrocketed.  The VO2 numbers were also dropping down somewhat.  This lead me to look both at the heart rate/power trends as well to review the literature in regards to VO2, heat, heart rate and stroke volume. 


The last post on VO2 estimation utilizing both maximal and submaximal tests introduced several concepts.  The VO2 (oxygen usage in ml/kg/min) is related to both the cardiac output (stroke vol x heart rate) and the oxygen drop across the active muscle (arterial- venous O2 difference).  Therefore, at a maximal VO2, the cardiac output and the muscular oxygen extraction should both be at their peak potential.  Predictive equations based on maximal running speed or bike power for 4-5 minutes equate oxygen usage to the amount of power used.  This makes sense since the faster one goes, more power is needed and more fuel will be burned.  The validity is also dependent on the particular exercise efficiency factor being similar across populations. In sports such as cross country skiing where optimal technique is essential, it may be more difficult to derive a VO2 value based on performance over a maximal 5 minute course.

Predicting the VO2 max based on submaximal efforts such as Firstbeat or Astrand algorithms make several different assumptions. The main assumption of a submaximal calculation of VO2 max is that there is a linear relation of VO2 to heart rate between certain loads.  This has been shown to be true up to the MLSS power.  The basis of the Astrand single stage test was founded by his research that 50% of the VO2 max was reached at a heart rate of 128 bpm for men (138 for women).  He then extrapolated to find the max or 100% VO2 value using this measure.  Given that there is a linear relation of heart rate to VO2 (in the low to high-moderate power range), factors that can effect heart rate like age,  temperature, humidity, hydration, nervousness, caffeine, will all modify the predicted VO2.  

In an effort to factor out heart rate related variables, Ekblom-Bak suggested looking at the HR at 2 different power levels based on the evidence that subjects with higher VO2 fitness had a blunted HR to Power slope:

Depending on both the absolute and relative increase in heart rate as power is increased, a better idea of VO2 peak can be obtained.  

The above technique is presumably also being used by Firstbeat in their VO2 estimation.  Under most circumstances it will usually be sound from a physiologic standpoint.  



Let's look at the sequence of the training sessions.  Even though an outdoor Florida ride may be at a similar temperature as an indoor, the humidity outside makes the "feels like" temperature about 10 degrees higher.

Pre trip Trainer:

Outdoor trainer in SF, warm outside:
  • An extra few watts leads to a 3 BPM increase in heart rate.
  • Rectus Femoris SMO2 slight down-slope signifying MLSS exceeded.

Outdoor trainer in SF, cool outside about 52F:

  • Similar heart rate and power from day prior, but higher VO2 value.
  • The watch temp was 63 vs 80F.
  • RF SMO2 pattern the same.

Back to Florida:
Outside temp 78F with high humidity:
  • Much higher heart rate despite lower power.
  • Stable RF SMO2 (at MLSS).
  • VO2 still same at 50.

Inside trainer two days later:


  • Some heart rate elevation at similar power.
  • Minimal SMO2 drift down at about MLSS plus.

Here is a summary:

                      Temp (F)         Watts     Last min HR    VO2 max                           Day
Pretrip              80                  255           145                 50                                    1
SF #1               72                  257           148                 50                                    3
SR cool           52                  257           147                 52                                    5
FL hot             90                   253           156                 50                                   9
FL indoor         78                   253           150                 50                                  14
FL hot              86                   252           156                 50  (graph not shown)   16




Comments:
  • The overall stability of the Firstbeat estimation is pretty amazing considering the variation of heart rate at the same power as I've shown. 
  • The bottom line here is despite very different riding conditions, the calculated VO2 was remarkably stable.  This is a huge accomplishment and my compliments to Firstbeat for the hard work that went into this.  

Some question remains though:
  • Does relative heart rate elevation from exercise in the heat effect the true VO2 max (as the actual gas exchange gold standard)?
  • Will the VO2 max derivation from power/heart rate kinetics still be valid, since it is based on certain assumptions such as "predictable" stroke volume change?

To start with some "normal" physiology, the stroke volume does not always stay the same as heart rate rises.  This is important to factor into the VO2 estimate since the VO2 relation to heart rate assumes the stroke volume not to change with high loads.  From the paper by Rowland, several studies show that endurance athletes have a progressive rise in SV with heart rate/load:

However, not all studies show this and the conclusion of his review was:
Whether or not endurance athletes demonstrate
a progressive rise in stroke volume during a
progressive exercise test, and the mechanisms
surrounding their generation of superior cardiac
output differ from those of untrained individuals
cannot be satisfactorily answered based on the
current body of research data. From this review,
it is apparent that equivalent amounts of evidence
can be mustered to support both sides of the
argument. The available information suggests the
possibility that testing methodology and protocol
might be responsible for the marked variation
in stroke volume observed in these reports. If a
non-plateau in stroke volume is typical of elite level
endurance athletes, enhanced diastolic
filling would appear to be the most likely mechanism
,
with a reliance on a Frank-Starling
mechanism to augment stroke volume as work
intensity rises
I have corresponded with the Firstbeat representative and he did indicate that a stroke volume correction is present in their formula.  This is certainly good news, but there will probably be individual variation in the amount that the SV alters.


Interaction of heat and heart rate
This where the issue of cardiac output, VO2 and stroke volume can become troublesome.  A very well done study was just released -
"Low Stroke Volume during Exercise with Hot Skin Is Due to Elevated Heart Rate"
The purpose of the study was to investigate the mechanism of the progressive decline in stroke volume seen during hyperthermic exercise.  The prevailing theory for this is that high cutaneous blood flow and cutaneous venous pooling (redistribution of blood volume for skin cooling) leads to decreasing central venous return and a lowering of cardiac filling pressure.  The reduced stroke volume (from the low filling pressure) in turn is compensated for by a higher heart rate.  The alternate possibility is that heat stress itself causes excessive cardiac rate drift upwards (as a primary process), leading to a shortening of the time that a chamber can fill with blood.  
To test which effect is actually happening the authors gave low dose beta blockers to prevent the heart rate rise in hot conditions (so same net heart rate in control vs hot subjects).  The results are fascinating:

  • You can see that the cardiac output (top graph) is lowest in the hot group (despite the highest heart rates).
  • The best stroke volume is in the beta blocker group but the overall cardiac output is the same as control since heart rate is a bit lower. 

The cutaneous flow and volume were increased as well:


There was no relation between venous forearm volume with stroke volume: 
The conclusion of the paper was:
Increased Tsk and Tes raised CO and CBF through a disproportionate increase in HR (almost to maximal) which in turn directly lowered SV. Increased Tsk and Tes also lower TPR and MAP, assumedly due to increased CBF. No one variable appears to be primarily regulated, yet there appears to be systematic compensation. For example, the increase in CBF is met by increased CO which is accomplished by increased HR. The increased CO also minimized the drop in TPR and MAP. The present findings also point out the robust nature by which CO can be achieved; through a disproportionately high HR in HOT or a disproportionately high SV in HOT-βB.

In conclusion, the relatively low SV observed during moderately intense exercise when Tsk is hot (38°C) can be explained largely by tachycardia and shortened diastolic filling time. Lowering HR with βB restored SV to the same level as when skin temperature was 33°C and occurred independent of measurable effects on CBF, CVC, or cutaneous venous compliance
Stroke volume vs heart rate interaction expanded:
The comment above - "The present findings also point out the robust nature by which CO can be achieved; through a disproportionately high HR in HOT or a disproportionately high SV in HOT-βB." is observable in a more general fashion as well.  Namely that the product of stroke volume x heart rate seems rather predetermined for a given individual at a given point in time.  In other words, total cardiac output can be manipulated by an atypical rise in HR or SV but not both.  How did I come to this conclusion?  Another great study done some years back looked at MLSS, VO2 max in normal subjects on and off beta blocker therapy.  The initial expectation was that using the beta blocker would cause a decrease in submax and max heart rate, leading to a reduction in cardiac output and the above performance metrics.  In fact, I was always taught that B blockers should be avoided in patients who desire maximal aerobic performance.
Methods:
Subjects randomly received oral bisoprolol (5 mg/day) or placebo
according to a randomized, double-blind crossover protocol. Each
treatment period lasted at least 2 weeks, with a washout phase of
1–3 weeks between the two treatment periods. Tests were performed
in the 2nd week of each period
Baseline characteristics of test subjects at rest:
  • The lower resting HR is most obvious, other parameters are the same.

The MLSS:

  • The heart rate at MLSS was 23 bpm down in the beta blocker group despite similar power, ventilation and lactate.  
  • The oxygen pulse (an index of stroke volume) was higher in the beta blocker group which presumably lead to a similar net cardiac output in both groups.

The VO2 max:
  • We see a similar situation as the MLSS.
  • Heart rate is much lower in the beta blocker group by 19 bpm but power output, ventilation, and VO2 max was the same.
  • Oxygen pulse (stroke vol) was higher with beta blockers.

Therefore an individuals net cardiac output potential is relatively fixed (at a given point in their training) and is achieved by the interplay of heart rate elevation and stroke volume compensation.  

Does maximal heart rate improve with training and fitness?
Is the max heart rate a trainable trait like stroke volume, mitochondrial number or strength?
It does not appear to be.  The estimate of 220 - age also appears to be incorrect.
According to the HUNT study:
The interaction between age and tertiles of VO2peak was not significant among men (P = 0.19) or women (P = 0.19).
Age was the best predictor with the resultant equation:
HRmax was univariately explained by the
formula 211 - 0.64·age (SEE, 10.8), and we found no evidence
of interaction with gender, physical activity, VO2max
level, or BMI groups
.
However, even though the fit subjects will not have a higher max heart rate, they do have the blunted heart rate rise with increasing load so can achieve much higher maximal power outputs.  Long term endurance training does enhance stroke volume so even if the max heart rate is no different, the overall cardiac output is certainly better.
I have extrapolated the Ekblom Bak drawing to show this:





The reason I present these studies is that the Firstbeat formula is heavily based on heart rate monitoring but also on an assumption of stroke volume. The interaction is obviously complex so a one size fits all approach may lead to errors.  

In addition, it appears that there area complex group of compensatory changes that occur with heat and dehydration to reduce time to exhaustion as well as VO2 max.  Here is a figure from an excellent review:
`

Final thoughts on getting an accurate Firstbeat VO2 max calculation:
  • Measuring equipment must be of high quality and relatively free from artifact.  The heart rate monitor should not be optical.  Drop outs from your power meter or poor GPS accuracy for runners can invalidate the results.
  • From trial and error as well as personal communication from Firstbeat, I believe the following types of intervals are optimal to VO2 formula accuracy - 5 to 10 minute continuous intervals at the MLSS, maximum constant power 3 to 5 minute intervals.  Doing low level constant pacing may also be helpful to provide further data points to the heart rate power relationship.
  • The formula seems to handle heat induced heart rate elevation fairly well.  Although it may not compensate fully, the true VO2 max may really be down depending on the severity of the hyperthermia.

 
Summary:
  • Estimation of VO2 max at less than maximal intensity is based on the relative linear relationship of heart rate to VO2 through a range of power intensities.
  • This extrapolation relies on the assumption of a similar stroke volume as intensity increases.  A rise in stroke volume can be seen in endurance athletes to a variable degree, but not every study agrees with this change.  There may be intra individual variation in the stroke volume, work rate relationship.
  • Firstbeat equations, take the athlete's stroke volume change into account in their calculation (personal communication).
  • Exercise in the heat leads to heart rate elevation at similar power intensities.  Although there is an increase in cutaneous blood volume, it's not primarily related to stroke volume reduction.
  • The heart rate elevation that occurs while exercising in the heat will lead to shorter times of cardiac filling and a reduction in stroke volume.  Cardiac output is preserved as a consequence of the higher heart rate.
  • Beta blocker therapy in a normal subject will not result in a change in VO2 max or MLSS.  Although heart rate is lower in this instance, the longer cardiac filling time leads to greater stroke volume and preserved net cardiac output.
  • Beta blocker therapy will prevent the heat induced rise in heart rate during exertion, prevent the fall in stroke volume and normalize cardiac output to that of a normal thermic state.
  • The maximal heart rate of an individual is not related to VO2 max and is not a trainable feature.  
  • The Firstbeat VO2 max estimation relies upon accurate heart rate and power measurements.  Failure to use "gold standard" heart rate devices will result in erroneous results.
  • Despite the complexities of temperature related heart rate interaction and stroke volume , the Firstbeat calculation was remarkably stable for me.  
 
 
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