I am always looking for interesting wearable devices but since I don't run, have neglected that particular sport. I recently reviewed some impressive Stryd data metrics and wanted to share them. First, what is Stryd? It is essentially a power meter for runners. Using various motion sensor data fields and rules of physics it is able to compute effective running power. The hardware includes both 3-axis gyroscope, 3-axis accelerometer and now wind sensing in the newest version.
Is this tech new? No, in fact many years ago I used something called an "Ibike", now rebranded as the "Powerpod". This device used similar principles of physics to estimate cycling power:
Unfortunately, it was a pain to calibrate and was not very accurate compared to conventional power meters.
Given my prior poor experience with the "Powerpod" and my personal avoidance of running (bad knees), I never paid much attention to Stryd. However, recently my friend the cross country skier did his VO2 (near) max test and was wearing the Stryd. This gives us an ideal opportunity to see how well the Stryd tracks with VO2. Why is this important? With cycling power, we have the simple ability to test an established strain gauge related power meter (like the Powertap, Assomia) against something like the Powerpod that uses environmental sensors (speed, wind, acceleration). The Stryd has no easy direct comparison device. But, since work rate (power) is linearly related to VO2 (oxygen consumption), especially up to the second lactate threshold, we can benchmark the Stryd to an alternate reliable benchmark.
Are there any validation studies?
I searched through Pubmed and found very little published work on the Stryd. The company claims to have VO2 vs Stryd power testing results on their website, but of course that is open to skepticism.
We do have this comparison study:
Which does not look good at all. A very weak relation of Stryd power to VO2 exits, hence their conclusion:
The main issue here is that the test was done with an early device, the Stryd Pioneer which is a chest mounted triaxial accelerometer:
I almost wonder if they would have been better served not even producing this device.
In response to the comment from the Stryd developers (see below), here is the reply from the authors of the above paper:
A different study looked at the newer Stryd from a different point of view. They simply wanted to see how consistent the measure of power was on a treadmill at a constant velocity. Theoretically, with no change in wind, incline or running dynamics, there should be a steady power output as in cycling on an indoor trainer at constant cadence.
From the study:
To the best of the authors’ knowledge,
just one study has examined its validity and reliability (in this case,
to measure spatiotemporal gait characteristics [9]), with no data
to demonstrate the validity and reliability of this device for measuring
power and related variables. For this study, only two out of
twelve metrics were used (running velocity and power output).
The agreement between measured intervals (watts) at the same treadmill speed was good.
For example the last graph showed near identical power over 0-120 vs 0-180 seconds:
With the conclusion:
The results show that
power data during running, as measured through the Stryd™ system,
is a stable metric with negligible differences, in practical
terms, between shorter (i. e., 10, 20, 30, 60 or 120 s) and longer
recording intervals (i. e., 180 s).
In summary, these results show that power output during running,
measured through the Stryd™ system, is stable over time
when velocity is constant and under controlled conditions, with no
differences between different time intervals recorded during a
3-min run. Nevertheless, it is worth noting that the analysis conducted
shows that longer recording intervals yield smaller systematic
bias and narrower limits of agreement. Therefore, if maximum
accuracy is required (e. g., scientific approach), longer recording
periods must be used (i. e., 2–3 min).
- So one still needs to be careful with the shorter time recordings, but they are still reasonable.
We still don't have a published VO2 vs power study of the newer, foot mounted unit. Thanks to my friend the cross country skier we at least have data in one indivisual.
To start with, let's look at what VO2 vs cycling power looks like from my data. This was using the Assomia Duo pedals (1% accuracy) and VO2 done at the University of Florida Sports Performance Center.
Here is an example from my VO2 max testing - cycling power vs VO2 (oxygen usage):
(VO2 average from last 60 sec of interval, cycling power average from each 3 minute stage)
Note the last value falls off (it's an error in the gas exchange measurement), we won't count that. Otherwise, the curve is quite linear, with only a few watts deviation off the fit.
How did the cross country skier do in regards to VO2 vs Stryd power?
Here is his treadmill test, plotting power over time with each stage encompassing 5 minutes, followed by a brief time between stages to do lactate testing, then resumption of running. The trend of higher power per stage is very evident.
A close up of the last 2 stages shows no major lag from zero power to full treadmill speed.
Therefore, "heart rate like lag" is avoided (from Kubios):
Finally, the all important VO2 vs power:
(VO2 average from last 60 sec, Stryd power from the entire 5 minute interval)
- This looks every bit as close as my cycling ramp shown above!
Why do we need power when the heart rate to VO2 curve is just as good if not better?
After all the heart rate to VO2 relation is very tight and predicable.
- In this case, if he knew his heart rate, the VO2 would be easily extrapolated.
Why do we need power?
- One problem with simple heart rate is measuring high intensity work rates during HIT. For instance, if you do a 30 second burst interval at high power, the heart rate does not fully stabilize until the interval is over.
- In addition, if you train by pace/speed, knowing the speed is problematic given the state of GPS tracking accuracy.
- Many studies point to the importance of intensities beyond LT2/MLSS as critical for optimal training. Those higher power zones can't be properly monitored by heart rate, especially in the early part of the stage.
- If one is an advocate of the "fast start" strategy, power is an essential way of achieving that type of session. Careful power modulation is the only way to perform a fast start interval.
Where we don't necessarily need power monitoring is during the mind numbing, long, slow runs that should be kept below VT1. In that case, heart rate stability kinetics are very acceptable to keeping effort down. However, in situations like hill and trail running, having running power may be of additional benefit to staying in the proper zone 1 location.
Summary:
- The current Stryd device appears to measure power at stable levels at constant treadmill speeds. The older Pioneer device is not recommended.
- According to a plot of VO2 vs power in one individual, the Stryd relative power is linear. In other words, with a given percent boost in power, the percent oxygen consumption rises the same relative amount. This indicates there is relative accuracy of the device. Absolute accuracy is not easily testable with this method.
- Usage with HIT and running intensities above MLSS would make sense since heart rate will lag well behind the effort. Studies have shown the downsides to intervals at "threshold" intensity (MLSS). Stryd usage may be a way to avoid excessive zone 2 activity.
- Usage in recovery and zone 1 training is potentially useful especially with less than stable running conditions. As discussed in previous posts, high volumes of zone 1 are essential to proper performance development.
Many thanks to my friend for sharing his data!
See also:
How to get your training zones from HRV and muscle O2 data
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:
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 temperature. This 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:
