Examination of the Link Between Oxygen Uptake (VO2) and Stryd Run Power

Footpods utilizing 3D inertial measurement units to calculate external running power have been discussed previously on my blog several times. 

One of the purported advantages touted by product developers is the ability of the running "power meter" to track and inform about instantaneous metabolic rate (VO2). With the Stryd power pod, the existing support for this position has been that running power and VO2 are linearly proportional. 

Infact, a linear relationship has been shown on my blog earlier from a single VO2max test when we look at steady state values. But since the time I wrote it, I have gathered more data in order to re-examine the nature of this relationship in light of fitness changes in the body. 

Experiment 

I completed two VO2max tests in a running laboratory a year apart in 2017 and 2018. Both tests were conducted by an experienced consultant who is also a PhD in Physiology & Exercise Sciences. Name withheld. 

On both tests, I wore a Stryd footpod on my shoes and ran with a self-selected cadence. Key information : I also wore different shoes but the position of the pods themselves were standardized by mounting on the second criss-cross lacing from bottom. In 2017, I wore a Mizuno Ronin 5 and in 2018, I wore a Mizuno Sonic.

Treadmill grade was set to 1% and speed was increased by 2kph every 2 minutes until complete exhaustion. In 2017, I exhausted at 16kph. In 2018, I was fitter and exhausted at 18kph. 

There was no change in equipment - treadmill, masks or gas analyzers, heart rate chest strap and metabolic carts - used between the two tests. Physiological variables that changed were my body weight and running fitness between the two periods. I was 64kg in 2017 and just shy of 61kg in 2018.

I ran my personal best 10K of 41 minutes in January 2018 and posted several track PR's in the later months. Compared to 2017, actual performance data indicated increased running fitness. 

By special request, I gained all the raw data from both tests corresponding to several variables measured during the test for my own record.

Summary of Results

A 30s rolling average of weight normalized metabolic rate and the corresponding instantaneous heart rate against time are shown in separate plots below (Figs. 1, 2). Tabulated data shows that in 2018, I had significantly lower heart rates to achieve similar running speeds on the treadmill. I was fit enough to run into the 18kph territory and extended my time to exhaustion by a whopping 3 minutes. 

The VO2 trace on the other hand shows an increase in oxygen consumption in 2018 with a corresponding increase in power to weight ratio. The differences are significant. For example, at 16kph, the difference in oxygen consumption between both years are significant (p less than 0.05, f=68.96).

The strength of the correlation between oxygen demand and Stryd power weakened between 2017 and 2018, going from 99% in the former to being able to explain 96% of the variance in the latter. The particular relationship between 2018 oxygen consumption and power seems not exactly linear (Fig. 3).  

Fig 1 : Tabulated summary showing VO2, Stryd power and corresponding heart rate for 6 different speed regimes.

Fig 2 : VO2 and heart rate - time traces compared between two years.

Fig 3 : Strength of correlation between VO2 and Stryd power to weight ratio in two tests.

The specific percentage changes at each speed is shown for VO2 and power:weight ratio (Figs. 4, 5). Instantaneous VO2 measured by a metabolic cart is a scatter of points before achieving steady state so a boxplot of distribution is shown with the median value being used to calculate % changes. The same has been done for Stryd power. Outliers are also shown but median values are not affected by outliers.

Fig 4 : Comparison of VO2 distribution

Fig 5 : Comparison of Stryd power:weight ratio

Discussion

Shown above is two VO2max tests done within a year and a few days. On both tests, I wore a Stryd footpod on two different shoes. 

Specific discussion points are as follows. Note :

1) The correlation between Stryd power to weight and lab tested VO2 is strong, however the degree of the correlation weakens from 2017 to 2018. The reported requirement for higher power to weight ratios and decreased economy for the same speeds conflicts with the lowered heart rate data and the increased time to exhaustion and higher speed attained on the second test.  In other words, one set of data indicating worsened power-speed efficiency appears to conflict with the actual performance on the test. Interpretations are open.

2) The boxplot distribution of VO2 at specific speeds are wide ranging and show the organic nature of oxygen rate according to the interval timing, run mechanics and the usage of elastic structures in the body. The boxplot distribution of algorthmic watts on the other hand is tight, which might potentially mislead when interpreting which value of run power corresponds to what oxygen demand. Therefore, caution must be exercised when comparing athlete(s) on the basis of run power to make value judgments of economical running. What is certain here is that Stryd power should be stated to be proportional only to steady state values of VO2, not transient data. If for example, a runner would run outdoors in heat conditions with a slowly rising component of VO2 which is a completely organic way the body functions, the meaning of the correlation of  VO2 and Stryd power measured in one set of controlled conditions is lost in another. 

3) The substantial decrease in heart rates to run the same speeds during the test show increased cardiovascular fitness. This correlates very well with the Polar Run Index recorded with Polar V800 for a period of 365 days between March 2017 and March 2018 (Fig. 6). In fact, around the January 2018 time frame, I'd been posting Run Indices in the 58-59 range which predicts my 5K/10K times within a margin of 1-2 minutes compared to actual performance. 

Fig 6 : Author's Polar Run Index time series scatter obtained from Polar Flow for a period of 365 days from March 2017 - March 2018

4) An inspection of preferred cadences on the two tests indicates non-signficant differences. The changes in cadence could not possibly explain the increased metabolic rate.

Fig 7 : Chosen stride rates between two VO2 tests conducted in 2017 and 2018.

5) An inspection of the speed error (device speed minus target belt speed) between the two years show increased error in the second year but within 2%. The reason for the increased error is not known, as calibration factors were not changed within the footpod. 

Fig 8 : Computed % error in run speed = 100 x (Device measured speed - Belt Speed)/(Belt Speed)  

6) The main variables that changed between the two tests were fitness, weight and the shoes worn. There is a possibility that simply wearing the meter on different shoes gave different readings but logically there is no reason why this should be so. However, on the Stryd forums, a variability in power measurements due to variations in mounting has been reported by users. 

7) Interpretations should be kept in context of sample size (n=1), the period of time between the two tests in which many things not accounted for may have changed (systematic changes in sensors, stiffness between shoe and treadmill interface, motivation, hydration status, calibration error).

Other Studies

1) In an outdoor setting, Aubrey et. al found statistically strong differences in oxygen consumption between different running surfaces that were not reflected in the strength of the differences in Stryd power to weight ratio (Aubrey, 2018). The device used was the first gen Stryd power meter worn on the chest. 

2) In an indoor study studying the influence of a change in cadence on running economy and Stryd power in competitive collegiate runners, investigators found that only 31% of the variability in running economy coudd be explained by power (Austin, 2018). They cautioned that the Stryd's power measures may not be sufficiently accurate to estimate differences in running economy of competitive runners. The device used was the second gen Stryd power meter worn on the shoe as a footpod. 

Conclusion

A positive correlation exists between Stryd power and metabolic demand IN STEADY STATE. However, in light of the reported case here and the two other peer reviewed and published studies, caution must be exercised when applying Stryd power for metabolic profiling specifically due to points explored above. The value of a footpod powermeter to inform about "real time" metabolic demand in situations where minute but critical transient VO2 changes might be prevelant  is suspect.  

The true accuracy of this relationship is unknown in a large sample of runners in different environmental conditions as found in real world running. Interventions in running , such as change in shoes, change of mechanics, circadian rythms, travel fatigue etc may reflect in VO2 but not in run power. This is a hypothesis, some of which is just starting to be shown in the research community. We hope the research community can come forward with more topic ideas and explorations.

As reported here, a worsened power-speed efficiency did not correlate with the increased time to exhaustion, higher speeds and better heart rate fitness achieved in the second VO2 test. This study shows there is both teneble and actionable value in longitudinal heart rate monitoring over long periods of time. Conventional measures such as heart rate is not superceded or replaced by running power meters but should be considered an essential ingredient of a holistic performance monitoring approach. 

References 

Austin, C., Hokanson, J., McGinnis, P., & Patrick, S. (2018). The Relationship between Running Power and Running Economy in Well-Trained Distance Runners. Sports, 6(4), 142. http://doi.org/10.3390/sports6040142

Rachel Aubry, Geoff Power, J. B. (2018). An Assessment of Running Power as a Training Metric for Elite and Recreational Runners. Journal of Strength and Conditioning Research, 32(8), 2258–2264. http://doi.org/10.1002/mrdd.20080

Polar Run Index Table