Actionable Intelligence for Running Part 8 : Effect of Shoe Type and Footpod Mounting Position on LSS and Power

Update, April 6 2017 : Since writing this post, several comments have been triggered on Stryd's Facebook community. 

Stryd developers acknowledged the data sharing exercise and this led to a conversation about the "sweetspot" in mounting height where this device has been validated. The sweetspot is somewhere 10cm and below. Andrew Coggan PhD is also pretty active in these discussions :

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In Part 3 of this series, I had conducted an experiment with the Stryd to understand what had a bigger effect on LSS - running cadence or ground contact time (GCT). While high step frequency is related to high LSS, I found that lesser GCT's have a stronger correlation with high LSS than was higher cadence. I also found that at a fixed step rate, there existed a monotonic positive relationship between speed and LSS. 

Since then, I've analyzed much more data and plugged them into my own spreadsheet models. I'm fairly certain to have cracked the math behind LSS, i.e knowing what parameters affect LSS.

However, before I put my entire weight in the LSS train, it was important for me to answer one question. Does shoe type and where you mount the Stryd footpod affect the reported values? This a fundamental topic not only with the Stryd but any consumer device that measures athletic performance.

The reason it's fundamental is that when you track your own changes longitudinally over the course of a season or several seasons, you need to be sure that the % change in your own performance is greater than the noise introduced by the device you're using to measure with. If instead, the noise is greater than the % change, any change you say you made through training is unclear or baseless scientifically.

Discovering what is the "noise" in the Stryd LSS data is the subject of the post. Results are organized into two sections - 1) with the Stryd correctly oriented with the pointed edge facing down, and 2) an earlier test with the Stryd oriented in the upward direction. In each of these orientations, 2D position (X,Y) of the footpod was varied on the laces. I also tested with 3-4 different brands of shoes at two different running speeds.

Readers will find the results in section one with the Stryd in the correct orientation useful, as it is the recommended installation position. However, it will also be interesting to see what happens when the footpod is oriented in the wrong direction.

Actionable intelligence is highlighted in blue.


Motivation for Experiment

Attached below is a table of run data I have consolidated spanning 2 months. I've also included body weight and landing style as best as I recall.

Fig 1 : Collection of some run data from Jan 24-March 31,2017. Click to zoom.

Some runs didn't make sense in the context of a "few parameters".

A low GCT 800m track race run on clay track in Asics spikes yielded lower LSS (9.8kN/m) than during a slow jog on a tiled footpath in the Under Armor Bandit 2 (10.8 kN/m).

Meanwhile, a 2:53 interval run done in Nike Lunarlon Lunarglides on a purpose built concrete bike path showed high LSS (12 kN/m) but a similar GCT interval done in Mizuno's at faster pace was 0.5 kN/m lower.

More recently, a high speed VO2 max test done in a test lab with Mizuno's at somewhat lower power output than the track race also showed high LSS (10.6 kN/m). Puzzling that the slow jog in the Under Armor shoes displayed a slightly great amount of LSS for a GCT that was 50% higher than that in the VO2 test.

The complete list of LSS values are within -6% / +16% of the mean value of LSS in the table. One question to answer would be whether this much spread in the reported LSS is normal in the context of wearing different kinds of shoes. In that respect, it would also be interesting to study how different shoes and footpod mounting positions affect the reported LSS.


Design of Experiment

Fig 2 : Experimental equipment included 4 shoes, a Stryd and a Cybex 770 Treadmill which has it's own display of power.

Step 1. Round up 4 running shoes/sneakers from my collection (all I have). Label them A-D. Some basic characteristics about these shoes are shown below. Because I do not own a low drop shoe, the best I could find was a pair of Fox Motion sneakers from 2011/2012 which had a drop of 8mm. 

Fig 3 : Description of shoes used in the experiment.

Step 2. Choose 2 treadmill belt speeds to carry out test. Speed 1 = 10kph. Speed 2 = 12kph. Speed 1 would be run for 5 minutes and Speed 2 would be run for 3 minutes preserving a basic duration vs intensity relationship. 

Step 3. Choose two mounting positions for the Stryd footpod. Position in 2D space was defined as the X-Y distance of the footpod on the shoe. X distance was measured from medial malleolus of the tibia-foot interface to the center of footpod. Y distance was measured from the floor up to center of footpod. 

Fig 4 : Definition of X and Y footpod mounting positions.

The X-Y positions on all 4 shoes were as follows :

Fig 5 : Data table of X-Y positions of footpod. Click to zoom.

Step 4. Randomly assign the shoe A-D to run the test in. Within each shoe selected, randomly assign running speed with mounting position 1. The experiment table below will speak for itself. 

Note that in the interest of time, all shoes were tested twice to check repeatability except for shoe D. All shoes were tested atleast once with mounting position 2. All shoes were tested a total of 5 times each, except for shoe D. 18 tests were conducted in all, spanning more than 2.5 hours. 

Fig 6 : Protocol for randomized testing. Click to zoom.

Step 5. "As far as possible", all run tests for a specific speed would be carried out at similar cadence and the same running style. This was done to control step rate.  Note that I did not use a metronome which would have been a better way to control step rate. I accept that there would be some variation in step rate.

Step 6. All tests would be conducted using the Styrd App on treadmill mode. Treadmill would be set to 1% incline to mimic the stress of running outdoors.

Step 7. Collect data into post processors for analysis.


Part I

- With Stryd Footpod Mounted With Pointed End Facing Down (Recommended Position)
- Controlled Cadence

Results and Discussion 

In this test, I used only shoes A, B and C due to lack of time.

I controlled cadence using a metronome tone set to 90 cadence (180 steps per minute). Results table is shown in Fig.7.

Fig 7 & 8 : Results table for controlled cadence tests using 3 shoes and 2 footpod mounting heights with the Stryd installed in the recommended orientation. Cadence was controlled in this test using a metronome tone set to 90 cadence. 

Conclusion : LSS registered increased values at increased mounting height regardless of shoe or running speed.

Sample std deviation (10kph belt speed) = 0.93kN/m
Sample std deviation (12kph belt speed) = 0.65kN/m
%Variation from mean (10kph belt speed) = -9% / + 12.3%
%Variation from mean (12 kph belt speed) = -4.2% / + 8.9%

Variation appears to decrease with speed, although this test is limited to two speeds.

If the Stryd can show this much variation just from moving it's position higher on the shoe, I think it is best I control this position on a given shoe for my training going forward. However, when changing shoes, I understand that the absolute position from center of pod to the ground can still change due to the different exterior designs among shoes.  

This also means comparing my LSS longitudinally across a season to track improvements is a bit troublesome until I know that the pod had more or less the same ground height when installed on the shoe I chose to wear.

I'm happy to hear about the discoveries with your own device if variations are any different.

Part II (Optional Reading)

- With Stryd Footpod Mounted With Pointed End Facing Up (Not The Recommended Position)
- Uncontrolled Cadence

Results and Discussion 

II.A Effect of Mounting Position 

A 3 minute test conducted at 10kph belt before the 18 tests showed that freely chosen step rate = 178 spm. The step rate objective was to try as far as possible to stick with one cadence for a specific speed, without frying myself at cadences I'm not used to.

The full results table is shown below and sorted by shoe tested and then by ascending order of speed and ascending order of GCT. This makes repeatability and mounting position comparisons easy. Click to zoom in.

Fig 9 : Results table sorted by shoe type and ascending order of speed and GCT.  Click to zoom.

Eyeballing the data, within each subset of shoe tests, the highest LSS value reported by Stryd was for mounting position 2. It would then seem that the position of the device somehow influences LSS. However, if the data is organized in ascending order of GCT, the high values of LSS correspond to the lowest GCT values. In other words, lesser the time spent with feet on ground, higher LSS. It is possible that the combination of shoe type and the GCT is manifested in the difference in LSS values.

At the same time, the corresponding step rates unfortunately change as well. But the good news is that cadence variation is less than 3% so I did an almost good job. If mounting position is considered as the base position, then % variations in SPM, GCT and LSS with a given shoe and speed are as follows :

Fig 10 : % Change in LSS from footpod mounting positions 1 and 2 at a given running speed. Click to zoom.

With tests conducted on shoes A and B, variation in Stryd reported SPM was less than +/- 2.5% while in shoes C and D, SPM was limited to +/- 1%. Variation in reported GCT in mounting position 2 relative to mounting position 1 were less than 5% in all 4 shoes. Variation in corresponding LSS were all within +10%.

With shoes C and D, the change in LSS is the same as in shoes A and B even though cadence was extremely close. Therefore, whether it is the mounting position itself that is influencing GCT and LSS is not clear from the data above. It appears something else is influencing the modeled LSS. 


II.B Effect of Shoe Type

The results table is organized based on speed to help in shoe comparison.

Fig 11 : Results table sorted by speed. Click to zoom.

Eyeballing the data table, there doesn't seem to be any appreciable effect from shoe, except for line items where the footpod was mounted higher. This is my first hypothesis.

Digging further, I was interested to find a couple of things.

First, what is the standard deviation of Stryd reported speed, SPM, GCT and LSS sorted by treadmill belt speed with different mounting positions included in data? Results are tabulated below.

Fig 12 : Standard deviation of pace, SPM, GCT and LSS at 10 and 12kph belt speed between 4 different shoes. Click to zoom.

Second, what is the standard deviation of Stryd reported speed, SPM, GCT and LSS sorted by treadmill belt speed at a fixed mounting position? For this, I had to remove the data for mounting position 2 so that all shoes were compared on mounting position 1 (closer to ground).

Fig 13 : Standard deviation of pace, SPM, GCT and LSS at 10 and 12kph belt speed between 4 different shoes for a given mounting height. Click to zoom.

Comparing both the tables in Figs 10 and 11, the standard deviation in reported LSS gets small at a given mounting position. Infact, it is not entirely true that the X-Y distance at mounting position 1 for all shoes are the same, so it is possible the absolute mounting position has a finite influence here. The right way to conduct this experiment would have been to fix the absolute mounting position for all shoes, but I doubt this is really possible since different shoes have different exterior structural design.

Because the data indicates it is possible that shoes can have an influence on the reported LSS values, it is necessary to express the uncertainty in GCT and LSS.

Fig 14 : 95% confidence intervals calculated for GCT and LSS at 10 and 12 kph belt speeds. Click to zoom.

The influence of shoes could be due to the relative mounting position differences amongst them. I'm not entirely sure at this point. Whatever it might be, the findings here tell me that it is important to establish an uncertainty range when I talk about my own LSS to others : 

At 10kph and average 173spm : 

GCT falls between 256.1 +/- 3 ms (95% confidence).
LSS falls between 10.47 +/- 0.248 kN/m (95% confidence). 
Power falls between 196.7 +/- 1W (95% confidence).

At 12kph and average 177spm : 

GCT falls between 229.7 +/- 2 ms (95% confidence).
LSS falls between 10.46 +/- 0.317 kN/m (95% confidence).
Power falls between 233.5 +/- 1.8W (95% confidence).

The influence of shoe type on GCT, LSS and power is finite (but small) within the context of mounting positions and shoes tested by me. The other finding is that as speed increases, the influence of shoe type and mounting position increases, which widens the 95% confidence interval on GCT, LSS and Power. At both speeds, the effect of my shoes and mounting positions are extremely small on power. 

Since weight, footpod mounting position and shoes seem to have some influence on LSS, I would prefer to express LSS as :

 [ LSS +/- 3% (LSS) kN/m ] ÷ Weight

II.C Interaction Effects on LSS and Power

The above investigations ignore interaction effects of shoe and mounting positions with other parameters like step rate and GCT for example.

The two mounting positions I arbitrarily labelled 1 and 2 have different absolute X-Y values. Even though differences are small, there is a difference. Therefore, I thought it would be best to express the position of the footpod as a mounting height Y and ignore the X position which is assumed to have no bearing in what the footpod measures.

To get an understanding of relative effects of key inputs on LSS, I assumed there are 4 independant factors within the experiment that would affect LSS either by themselves or through interaction effects. I did not consider speed as independant as I figured speed would be a manifestation of step rate and GCT.

Key factors considered :
Shoe type 1-4
Mounting heights, Y
Device reported step rate
Device reported GCT

Interaction Effects Considered

Main Effects 
Shoe alone
Mounting height
SPM
GCT

2-Way
Shoe + Mounting pos
Shoe + SPM
Shoe + GCT
Mounting height + SPM
Mounting height + GCT
SPM + GCT

3-Way
Shoe + Mounting height + SPM
Shoe + Mounting height + GCT
Shoe + SPM + GCT
Mounting height + SPM + GCT (s)

4-Way
Shoe + Mounting height + SPM + GCT

A pareto chart (influence chart) of the interaction effects on LSS (kN/m) at a default and widely used significance level of alpha = 0.05 is as follows :

Fig 13 : Pareto chart of standalone + interaction effects on LSS organized by importance. Only GCT, Shoe+GCT and Shoe+GCT+Mounting Height (Y) play a significant role at α = 0.05. Click to zoom.

Fig 14 : Normal plot of standardized effects. Significant factors like GCT, Shoe+GCT and Shoe+GCT+Mounting Height (Y) are negatively correlated to LSS. Click to zoom.

The interaction study might be proof of the earlier assessment that as standalone factors, shoe and footpod mounting height haved played no significant role in the reported LSS in the experiment. However, the combination of shoe type, mounting height and GCT seem to be influencing LSS more significantly than other factors.  

One disadvantage of this study is that I do not include the leg attack angle as a parameter. Infact, from a model I have developed, I know that leg attack angles influences LSS a great deal! It could be that while mounted on different shoes, the landing angle the accelerometer thinks in is different. 

Shoe type and mounting height, in so far as this set of data is concerned, have negligible effect on reported power for the two speeds considered. 

The study also helps prove an earlier post in Part 3 that :

1) GCT is a predominant factor in the LSS model, even more so than step rate (pareto chart Fig 13). 
2) GCT and LSS are negatively correlated. Lower GCT equates to higher LSS. 

If building LSS is considered "free speed", then focusing on injury limiting biomechanics that yield lower GCT appears fruitful. 

I also understand that any exercise intervention to improve my own LSS should bring out positive LSS changes (post intervention) greater than the maximum uncertainty range I have indicated here, which is 3%. This is done so that I can safely consider the changes in LSS are not just from day to day running variations in GCT and higher order shoe and footpod influences.

My interpretations are open to discussion.


In the next post, I'll be taking Stryd to the mountains. Stay tuned!

NYUAD Ultimate Athletics Track Meet : 800m and 400m

On the 16th of March, I decided to drive over to NYUAD and test myself in the Ultimate Athletics 800m and 400m timed events. Medals were up for grabs for the 1st - 3rd places. 

Long story short, I managed a podium in the men's seniors category in both the 800m and 400m events, spaced about 45 minutes apart. I went limping back home and was happy to shampoo the sand off from my hair. 

To be fair, it was a heck of a windy day (Beufort scale 4) so there wasn't much of a turnout at the event. Which was good because I was wearing an ugly pair of Hypersprint 6 neon track shoes by Asics and was praying that no one would see me. 

But those who turned up were pretty solid runners. In the 800m, I ran with one of the top teenage female running stars in Dubai - Megan Dingle - and was hanging on for dear life. Definitely felt 100 years older. 

In the 400m, I was served some solid African competition but in hindsight, that turned out to be a great testosterone booster because I turned out my fastest 400m. In both events, I managed a PR from the last indoor meet at the same place.

A couple of preliminary photos from the action (check out the massive number of spectators!) :

The results were encouraging for someone who has had virtually no track running since last September 2016. I had great hopes to set up the Stryd powermeter and record data from the run. Unfortunately, the device didn't wake itself up during the second shorter run (the 400m), meaning I only captured data from the first 800m run. Bit of an annoyance.

From the data I did capture for the 800m, things went as follows :

Time : 2:30"

Pace : 5.3537 m/s

Cadence : 102 spm

Estimated VO2 : 55 ml/kg/min

Power to weight ratio : 5.30 W/kg 

Form Power : 64W

Vertical Oscillation : 6 cm

Leg Spring Stiffness : 10 kN/m

Ground Contact Time : 176ms

Run Effectiveness (RE) = 1.01 m/s / W/kg

Energy Cost of Running = 0.98 kJ/kg/km

A couple of things from the data :

1) You notice that even though power picks up in the first 20seconds of the race, the other variables such as GCT, LSS, VO etc take time to activate. This is bizarre and I label it a lag from the Powercenter screenshot. I grow discouraged from the behavior of Stryd on short, fast track runs. 

2) In the final lap, about 200m from the finish was a nice burst of 20kph headwind smack against the face. This was where I slowed down a bit but consciously picked up my legs in order not to fall behind (or fall off!). The delta in wattage from initial part of the race to this point was about 110W.

3) Knowing from a previous RAK half marathon race that my FTP maybe between 200-205W, the intensity factor of the 800m was approximately 330W ave / 200W = 1.65. 

Race results, in old school paper form. 

Highlight of the night was to get introduced to some solid fast twitchers from Zimbabwe (if I recall correctly). Somehow this 63kg slow twitcher was keeping up but it was a hard effort. I'll look forward to more, NYUAD!

Actionable Intelligence for Running Part 7 : Running Power Characteristics During a Duathlon

In Part 6 of this series, I inspected data from a VO2 lab test data and graphed it's relationship to my corresponding power to weight ratio for 6 different running speeds. What I discovered what the non-linear nature of VO2 (rising and settling dynamics) and the inability of a linear equation to predict instantaneous oxygen cost from a power to weight ratio. 

In general, what was encouraging to see the was the proportional rise in both VO2 and power to weight ratio as speed increased and this credits the Stryd footpod as a steady state "running cost" predictor even though it is an accelerometer / bouce meter, i.e it does not directly measure mechanical power but algorithmically outputs power based on components of velocity extracted from acceleration data.   

I also ended the post by stating that the Stryd does not account for outdoor wind resistance nor for the effect of temperature and humidity, so using an indoor treadmill based correlational equation is likely to underpredict the true cost of running outdoors, especially against winds pushing past Beufort scale #6.

Readers familiar with my previous posts on the GIANT Duathlon Series will know that this race frequently brings some top athletes from the region to the start line. The race format is 3K run, 25K bike, 3K run. This is my third season as a duathlete.

In this post, I'd like to inspect running power during the two running splits of Race#4 held on March 10, 2017. 

Equipment and Personal Data

Running Shoes : Mizuno Wave Ronin 2 (Pre-2010, yes I hold onto old stuff)

Shoes Weight (pair) : 7.5 oz.

Heel to Toe Drop : 9mm
Footpod : Stryd 

Body Weight (unclad) : 63.5 kg
Training (conditioning) : 8-10 hours weekly

Fig 1 : Mizuno Wave Ronin 2

The Course 

ADULTS
Run - 3.0km (https://ridewithgps.com/routes/19399829) - 2 laps
Bike - 26.9km (https://ridewithgps.com/routes/19399694)
Run - 3.0km (https://ridewithgps.com/routes/19399829) - 2 laps

The Data

Elsewhere, I will describe in a short race report the feelings and effort going through this race. In a nutshell, it's been one of my best performances to date, having placed 8th in my age category. However, it keeps getting more difficult race by race to move up and 1:17:36 is nothing to boast about.

Fig 2 : GIANT Duathlon 2017 Race 4 results (30-39 Age Category)

The Stryd and Stages powermeter will not automatically pair to the Polar V800 as sport mode changes in a duathlon. Further, the V800 does not have a power feature within running. Therefore, I relied on offline data saved on the footpod for post-processing.

Below is a composite plot showing running power and biomechanical characteristics of the race. Note that cycling has been ignored except to trace an average cycling power for that duration.

Fig 3 : Composite plot showing running power, form power, ground contact time, vertical oscillation and leg spring stiffness from the two running splits of the Giant duathlon Race#4 on March 10, 2017.

Focusing in on the two run splits, the performance variables are tabulated below :

Fig 4 : Performance tabulation of key power and biomechanical variables during the two running splits of GIANT Duathlon Race# 4.

Please note that speed was calculated from the distance vs time relationship from the clocked results of the race and not from the footpod.

Insights

All highlighted items - speed, avg. power, power to weight ratio, form power to avg power ratio, cadence and LSS suffered in the second run leg. Considering these facts, the effect of fatigue during short high intensity sprint duathlon is clear to see.

The delta between these variables (those in Run 2 minus those in Run 1) expressed in percentage are as follows :

Fig 5 : Calculated percentage differences in running performance variables in Run 2 compared to Run 1.

1. A 10.5% decrease in run power resulted in a 10.7% decrease in pace in run # 2. Knowing the proportional relationship between VO2 and running power from Part 6, I conclude that the internal running engine ran a bit out of steam. 

Fatigue is multifactorial, not just cardiovascular. There was a short duration decrease in power about 1/4th of the way into run # 2 which in reality coincided with a slight tightening of the right leg muscle where I had to throttle down power to 180W for a few seconds.

However, there were no cramps and no stopping to loosen the legs. The salt instake for the day was good considering the ingestion of both a GU gel worth of 180mg of sodium and an aerodynamic water bottle filled with water + 380mg serving of sodium in 50g of electrolyte mix. 

Some others who have seen my data comment that this is an example of predominantly "metabolic fatigue".

2. Form power, i.e the cost of "perpendicular bouncing" as a percentage of total external running power, was 8% higher in run # 2 than run # 1 (external running power does not account for the swing in upper and lower body limbs).

3. Ground contact time (GCT) was 9.76% greater in run # 2.

4. Leg spring stiffness (LSS), extensively discussed in Part 1, Part 2 and Part 3 of this series, was 2.9% higher in run # 2. Cadence decreased and GCT increased between the two runs, but as previously discovered by experiment, the effect of change of GCT on LSS is greater than is the effect of cadence on LSS (Part 2). 

5. Run effectiveness (m/s over W/kg), a surrogate for running economy, decreased by a tiny fraction of a % in run # 2 compared to run # 1. 

Within each of the splits, the behavior of the RE trend (fraction of instantaneous RE over average RE) was a mildy increasing one for run # 1 and flat for run # 2. Please note that the calculated value of RE is sensitive to the data and abnormal spikes in speed or drop in power will result in higher than usual RE values.

My conclusions from the above data study for sprint duathlon are as follows :

In an ideal scenario :

a) Leg turnover would be similar in the two run legs. Longer swing times during the start of the second run affect GCT which consequently has bigger impact upon LSS than the lowering of step rate alone.

b) Vertical "bouncing" as a fraction of total power would be reduced in the second run so that more of the horizontal component makes up the total power. However, I  must confess my understanding on components of power is not on par with the coaching community. I believe one has to bounce to an extent to generate the potential energy needed to activate the storage potential of the leg spring, so an optimum must be struck between too little bouncing and too much bouncing. Too much bouncing is understandable since energy is being used to elevate and lower the center of mass and perhaps some of that could be acively focused instead on moving forward. Form power is something to continue experimenting with.

c) Nutrition points would be more optimally placed during the race to allow proper absorption by body before the demands of the second run. Ideally, this would allow a more even power to weight ratio between both the run splits. Race strategy is knowing exactly at what points to ingest and that can have a pronounced effect on performance during the second run split.

d) Based on 400m and 800m track results, I have the potential for RE > 1.00. What that will mean for performance in the second run split is something to be tried out. Racing is always learning by trial and error. In these short high intensity events, you always have to push your body past the limit to place well but you also have to throttle things down a notch to first finish ! 

In part 8 of this series, I assess if power readings and other Stryd metrics are affected by where you mount the device on the shoe. Testing was done with the shoes available to me. 

Actionable Intelligence for Running Part 6 : Relationship Between Oxygen Uptake (VO2) and Running Power

In Part 5, I mapped out the power displayed by the Cybex 770T "powermeter" treadmill against changes in speed, grade and weight. The displayed power was based on calibration with the AC motor frequency. It was an interesting exercise, showing how treadmill measured power is linearly related to the 3 parameters. A similar experiment can be done using the Stryd App by changing grade and speed however one would also have to change the weight and I didnt choose to mess with that. Mapping the information from the treadmill display reading gives me reasonably good information for indoor workouts. 

Today's post examines something much more fundamental : the relationship between volume of oxygen uptake against external mechanical power measured by Stryd (as opposed to total mechanical power, see Appendix Fig.2). The objective of this test was to examine how closely related are measured VO2 and footpod based running power. The insights I got from this test are given at the end of this post in blue italics.

Procedure & Preliminary Result

A week ago, I participated in a 20 minute graded VO2max test at a well established professional sports medicine laboratory in Dubai called Up and Running. The test was conducted on a lab worthy treadmill and using all standard equipment as employed in standardized VO2max tests at other facilities.  

Treadmill belt speed was increased from 8km/hr to 16km/hr in 3 min increments to capture not only the "linearly" rising component of VO2 but also the "settling" value at each speed. The machine was set to 1% incline as is standard practice.

The test ended at what point the sports consultant Dr.Ramzy Ross thought was a reasonable place to stop based on maximal VO2 stabilization, HR and samples of my blood lactate taken at regular intervals by prick method. Two lactate turnpoints were discovered.

During the test, I was wearing the Stryd footpod on my right shoe. Because operating the Stryd phone app was impossible during the fatiguing test, I relied on saved offline data to form the following conclusions.

VO2 curve can be approximated as linear but it really isn't. Below is the result from the preliminary graded VO2max test. I've hidden absolute values of VO2, the point being to show here the "rising" component and the "settling" component of the VO2 curve (blue color). 

Fig 1 ; Preliminary result showing HR and specific VO2 during course of a graded VO2 max test performed on the author. Incline grade = 1%.

Since instantaneous power data was from a foodpod not related to the VO2 equipment, careful stitching of the data had to be done. In other words, if the raw data from the VO2 test has a different sampling time than the Stryd data (every second), you will not be able to piece together the two information correctly.

Relationship of VO2 to Running Power

After some post processing, the best way I have found to present the data with respect to speed is through individual box plots of VO2 and power. In the following figure, both power and VO2 are normalized to weight.

Fig 2 ; Box plot of VO2 values with corresponding power to weight ratios at specific speeds during a graded VO2max test performed by the author. Median values of VO2 range are connected only for sake of representation.

Insights

1. Because of the fluctuation of oxygen demand and the nature of the stabilization curve, VO2 data exhibits a range of values at specific speeds (represented by the boxes in Fig.2). The range of Stryd measured power to weight values is, on the other hand, tighter (which is good). Connecting median values of VO2 box plots, it can be seen that an increase in speed accompanies both an increase in VO2 and power to weight ratio.

2. A linear regression relationship between range of VO2 values and the range of power to weight values suggests the following math, based on a R squared value of 52%. In other words, looking at the entire range of non-linear VO2 values, only a little more than half of that data is explained by corresponding power to weight values through a linear equation. This is specifically because of the nature of the VO2 curve - it is not linear.

3.  It is possible to look at the mean (or perhaps median) values of VO2 and mean values of power to weight ratio for each speed and correlate both of them. Mean values can signify the "steady state" value of VO2. This way, the relationship becomes more linear and the linear trend line shows an R-sq value of 99%. This helps to "approximate" a steady state metabolic cost from just power data alone.

Fig 3 ; Linear trend between mean value of VO2 max and mean power to weight ratio for specific speeds during a graded VO2max test performed by the author. 

4. When the VO2max test incorporates a blood lactate test, it is possible to know what values of VO2, HR and also running power correspond to the lactate turnpoints (lactate thresholds). It is believed in the latest exercise science literature that lactate is not a waste, rather it is a source of energy and also a prevention mechanism against fatigue. However, a host of processess associated with fatigue occur around the lactate turnpoints. By having knowledge of the power values associated with these turnpoints, it is now directly possible to target "high power" training zones around those markers, rather than using someone else's interpretation of what your thresholds should be. 

Please note that in making the above statement, indoor running power and outdoor running power are assumed to be close if you do not account for wind resistance. Please see appendix plots showing a narrow band of power values (185-190W) for jogging at 6mph. 

6. If you assume 20.1 Joules of energy per ml of O2, the steady state VO2 values can be converted to a steady state metabolic energy cost. Subtracting the resting metabolic rate requirements from this value then gives you a relationship between net metabolic rate per kg and mechanical power to weight ratio for steady state running. Dividing the mechanical power to weight ratio by the net metabolic rate per kg gives you a predicted efficiency. 

My resting metabolic rate from past data (Weight = 65kg) = 68 kcal/h = 1.216 W/kg

For a running speed of 12kph, this translates to :

VO2, ml/kg/min = 8.8766(3.41W/kg)+ 8.2972 = 38.56 ml/kg/min = 775.056 J/kg/min = 12.92 W/kg

Net Metabolic Rate = 12.92 - 1.216 = 11.70 W/kg

Predicted efficiency = (Power/Weight )/ (Net Metabolic Rate / Weight) = 3.41/11.70 = 29.14%

7. VO2 and power values are very individual and vary with training (or lack of). I do not believe it is correct to use someone else's VO2 data and use their VO2-power correlations to derive one's own physiological "state". The Dutch researchers Hans van Dijk and Ron van Megen have written on their blog that test data from 14 runners show differences in the VO2-power relationship because some happen to be more economical than others. Caution should be exercised while estimating VO2 from power, especially at submaximal running speeds.

In other words, there is no one absolute formula to estimate VO2 using power values and such approximations do not substitute for actual lab testing. That said, it is encouraging to see the proportional relationship of oxygen demand and measured running power for use in one's own study of training and racing performance. 

Stay tuned for more. In the next post, I will show how I utilized running power metrics during a local duathlon in Dubai.

APPENDIX 

Stryd does not account for wind resistance so on level ground, I'd be suprised if the power readings are too different between treadmill and outdoor running. See the two plots below, where at a slow jog at 6mph (10min/mile), I'm seeing a 180-190 wattage band for both scenarios. Such observations, I believe, are device dependant. Please experiment extensively with your footpod for establishing indoor and outdoor power values.

Appendix Fig 1: Two plots showing running power at a slow jog of 6mph while running indoor and outdoor (both circled). 

Appendix Fig 2: Components of external power measured by Stryd which flows into the metabolic cost of running. Slide adapted by Andrew Coggan, where he took a graphic developed by Hoogkamer, Taboga, and Kram, which depicts the various metabolic costs of running and their power components. 

Actionable Intelligence for Running Part 5 : Mapping Treadmill Power Against Speed, Weight and Incline

In part 4 of this series, I made baby steps with a commercially available footpod called Stryd and ran with it in in what is billed the 'fastest half marathon in the world'. I measured only power and made some indirect assessments of what would be a safe threshold power level to run that distance with current fitness level. I also looked at running effectiveness.

Today, I kept the Stryd aside and turned to a neat powermeter treadmill at the home gym called the Cybex 770T with Intelligent Suspension. In Part 2, I explained how this treadmill uses an AC motor with a variable frequency inverter drive. Treadmill belt speed is based on output frequency to the motor and according to the OEM, it would need no calibration. Infact, I measured the belt speed and even measured the time it would take for 10 revolutions. Comparing this to the treadmill speed readout gave a 1:1 match. 

The objective of today's short test was to study how the treadmill maps power output to speed, mass and grade. 

Objective : Curiosity, mostly. How does machine power vary with pace and mass? Perhaps mapping this would help in comparisons when doing the same with Stryd powermeter (to be tried later).

Test Protocol : Enter my correct weight of 64.5kg into the machine. Keep speed constant at 8kph (7:30 min/km) pace and get power readouts at 0%, 1%, 2%, 3%, 4%, 5% and 10% incline settings. Repeat this sequence with 10 kmph (6:00 min/km) and 12 kmph (4:36 min/km) speeds. I repeated the same experiment by entering weights of 70kg (+5 kg mass increase) and 80 kg (+10 kg). The total number of readings taken were 63.

Results :

Power output readings to corresponding mass and grouped by pace are shown below.

Fig.1 : Power table for 3 speeds and 3 weights for 7 different grades as measured on Cybex 770T.

For a given running pace, the power reading was linearly related to grade. Below, I show example of linear power lines for my weight 64.5kg and 70kg and 80kg for running pace 13kph (4:36 min/km). Also, both the y intercept (power at 0 grade) and slope of the line increase with mass. 

Fig.2 : Power vs Treadmill incline for weight input of 64.5kg (yellow line), 70kg (orange line) and 80kg (green line).

To understand how power is mapped to speed, pace and mass, I performed a linear regression on the response Power with predictors speed, pace and weight input to the machine.

The regression equation I obtained was :

TREADMILL POWER (W) = - 225 + (7.46 x GRADE) + (3.00 x MASS) 
+ (18.8 x Speed)   [tested for running speeds only > 8kph]

where grade is expressed in % , Mass in kg, and Speed in kph.

An equation is derived to map treadmill belt power to running speed, grade setting and pace for the Cybex 770T treadmill. The key takeaway is that maintaining the same power on an incline as on level ground running is impossible unless running pace is slowed down. For perspective, if a 64kg person ran on this treadmill at 8kph with a 10% incline setting, he'd have to slow speed by 1 min/km or more to match the same power reading obtained with a 0% incline.

My finding is that for a given weight, the power reading increases by 1.4 times between 0% and 10% grade. My finding is also that for a given grade, power increases by 1.24 times between 64.5 kg and 80 kg. 

The equation tends to slightly overpredict at lower speeds and underpredict at higher speeds for a given weight. It also tends to slightly overpredict at lower weights and underpredict at higher weights for a given speed. The sweetspot of the prediction seems to be at a pace of around 10 kph where errors are less than 2%.

In the next post, I'll compare running uphill and power numbers using the Stryd to see if these same general takeaways are maintained. Ciao!

Actionable Intelligence for Running Part 4 : Pacing by Power & Running Effectiveness

In Part 3 of this series, I continued looking into Leg Spring Stiffness (LSS) relationship with speed and time. I found that LSS is a function of speed and while it has been considered by some in the running community to be linearly increasing with speed, I found no evidence of strict linearity. 

Some limited data from long runs suggested that  LSS may also drop with duration, hinting that running form was being affected. That information is subject for further confirmation from new runs.

This post focuses mainly on running power. LSS, ground contact time, VO, ground reaction force, swing angle etc are all biomechanical ingrediants of what makes power. Power is an input to create propulsive speed. 

Many weeks ago, one of the questions I had on my mind was whether pacing by a powermeter is a valid approach. In cycling time trials, it is common for athletes to pace by a known power as a percentage of critical power (CP). Could running adopt that strategy?

To test this, I ran what is billed as the "World's Fastest Half Marathon", the RAK Half in Ras al Khaimah, U.A.E on February 2017. Being a flat out race with little undulating terrain, it was a perfect ground to inspect running power. I completed the half in a net time of 1:50:49, the reasons for which are below. 

Prior to this run, I had no idea of the correct value of my CP since I hadn't bothered to do any of the track based tests that Stryd advised. However, I had an idea of estimated CP from a multicomponent exponential model fitted to all the past runs with power since January 1, 2017. 

CP from the model in the immediate week before the RAK half was approximately 207 Watts.

To test whether pacing by power was a valid approach, I had to run at what I call "suicide pace", which is an unsustainable power output greater than the estimated CP. This had to be mostly by feel or RPE (rate of perceived exertion). 

The test protocol is below and deceptively simple. 

Test Protocol

1. I chose my "suicide pace" as around 230 Watts average.

230 ÷ 207 = 1.11 Intensity Factor.

2. I switched the watch to power mode and ran by knowledge of power alone. No speed, no heart rate, no duration,

3. I ran till certain 'death', i.e complete fatigue. I then limped to finish line, plugged the computer to post process the data.

The intelligence I gained from this exercise is, as in previous posts, summarized in blue italics.


Pacing By Power Insights

The following plot shows power and speed as a function of duration.

Fig.1 : Time dependant running power (black) and speed (yellow) for the RAK half.

The following are the insights I gain from the data :

1. Power tracked running speed remarkably well. Ups and downs in power followed ups and downs in speed, except for data regions showing spikes in speed.

2. A regression analysis on speed vs power from my RAK run suggests that an increase of 1 Watt translates to an increase in running speed of 0.0134 m/s = 0.03 mph. In other words, a 1 mph increase in speed demands a 33 Watt increase in power. 

3. About 57 minutes into the race, another tracker of effort - heart rate - crossed into my anaerobic zone, inviting fatigue due to lactate metabolites accumulating in the body. Plot overlayed with HR data is below, showing the exact point at which HR crossed into my red zone.

Fig.2 : Power and speed overlayed with heart rate for the duration of the race.

4. For the next 27 minutes, I was in pure purgatory. Pace kept decreasing steadily. At precisely 1:23:00 which I call Point of Exhaustion, I had to drastically decrease my pace to gear down HR. Essentially my test was over and from hereon it just an effort to get across the finish line. 

Stats for the first 57 minutes were as follows :

Duration = 57:00
Distance = 7.6 miles, 12.23 km
Pace = 07:28  min/mile, 4:39 min/km
Work = 804 kJ
Average Power = 235 Watts
Average Heart Rate = 195 bpm (Spot on at Threshold)
Average IF = 1.121
GOVSS = 66
Gradient = 0.1%
Average Step Rate = 185 SPM.


5. After feeding the data into the post processor, the time distribution of watts pins down the power target I should have honed into for the Half.  This is shown in Fig. 3. Had I geared down wattage to a range between 205-210 Watts, I'm led to believe that with current fitness level, I could have run the distance without issues. 

Fig. 3 : Time distribution of power readings. Zones established from Jim Vance methodology.

6. I believe running power helps quantify the demands of training and racing. It makes it possible to analyze and score training data based on Intensity Factor and Normalized Power, a 30 second rolling average algorithm introduced by Andrew Coggan and supported by Annan (2006) . 

7. Since power is Joules over second, it also accurately pinpoints the energy expenditure of a running race, giving clues as to why a runner 'bonked' for example. Power can be overlayed onto a course map by GIS techniques and used to pinpoint energy expenditute and power intensity on a specific stretch of the course. An example shown below in Fig.4, giving me some great data for one of the defining stretches of the RAK Half Marathon. 

Fig.4 : Segment of RAK Half course highlighted in red shows the power and energy requirements of running the 9 mile stretch of road, which happens to be a deciding point in the race.  

8. Running with power may also allow the study of how biomechanical adjustments translate to improvements in running. For example, it is possible to inspect if one style of running leads to better running effectiveness than another. I would want to look into these in future posts. Running effectivess is discussed below.

Anaerobic Work Capacity Insights (W prime)

By plugging the last one month's worth of running power data into a 3 parameter model, my critical power is estimated to be 235W.  Being 5W conservative, I took it to be 230W.  The figure below shows a composite plot with power, pace, heart rate and anaerobic work capacity (AWC or W prime, kilojoules). 

Fig.5 : Expenditure and reconstitution of work capacity above critical power (AWC). Such a plot is also called W' balance analysis. (Further reading : Skiba, 2012)

1. Comparing time constant of depletion of AWC against exertion through power and heart rate, a clearer story emerges of how much anaerobic work I did. I find that AWC crosses 0 at where my heart rate went into the red zone. From 17 minutes to 1:10:00, I'm largely anaerobic which again speaks to how wrong this pacing was with respect to speed and power. 

2. Again, this is only a preliminary look at AWC and W' balance. I would not trust it 100% just yet. I would like to give it much further thought as I get more 'good data', i.e data that correctly captures my maximum running power, critical power and W' through running to failure tests. 

3. This sort of analysis has the potential to help design power based workouts for this target race duration. For example, the analysis above tells me I need to do further tempo work but at a lower power level wrt to CP (Perhaps 80-90% would be a good start. 90% of 230W = 207W. Trial and error will decide what the actual number is). 

Running Effectiveness (RE) Insights

With running power data, one can analyze the ratio of speed (m/s) to power/weight ratio (watts/kg), effectively defining an achievable amount of speed for a given unit of power to weight ratio. RE is a surrogate for "efficiency" or "economy". Folks are also finding from track runs that finishing times among young runners correlate very strongly with RE. 

In the old days, you could take a ratio of speed to HR and come up with a running index. RE is a similar index idea, except what's at the numerator is speed and denominator is wattage, therefore if you could increase speed with the same or perhaps even lesser amount of watts, your HR goes up. 

I calculated RE for the RAK half and the following are insights I gain from the RE data. (Please note I assumed that my weight was a constant 65kg for the duration of the race).

1. My average running effectiveness for the entire duration of the RAK Half was 1.00194  m/s/W/kg. The plot of RE and speed vs duration is shown below, displaying that even though speed dropped off, the trend of RE was more or less constant. 

Fig.6 : Power and speed data overlaid with calculated RE for the duration of the race.

2. The Spearman ranked correlation co-efficient between speed and RE is +0.334. A linear regression between just the raw values of speed vs RE from the run suggests that an increase of 1 kph in speed demands an increase in RE of 1.25 m/s/W/kg. 

3. One interesting technique of analysis is to linearize power, speed and running effectiveness. This is done by expressing each as a ratio of their respective averages for the duration of the race and multplying by 100 for a percentage. The linear trend lines of these 3 curves are shown in the plot below. 

Fig. 6 : Linear trend lines for relative speed, relative power and relative RE vs time. 

The plot shows linear trends for relative power and relative speed dropping off from start to end of race, perhaps suggesting the effect of fatigue.  However, linear trend for relative RE stays constant. From this, I gather that for the duration of a half marathon, the amount of speed you can produce for a set amount of watt/kg stays more or less constant. It is possible, however, that a longer duration race, such as a marathon or an ultramarathon, shows a more clearer drop in RE with time. In other words, fatigue may be more pronouned during long races that it also shows up in the RE data. This is something I will continue to look at in future.

Your comments welcome!

In part 5, I fool around with a Cybex treadmill at the home gym that can read power and try to do a regression for power with treadmill speed, incline and body mass. Part 6 is where I do VO2 max testing with the Stryd on my feet and I was one of the first consumer of the device to publish VO2 vs power/weight data.

NOTE: My half marathon PB when I was younger is 1:40:51, done on hilly terrain. Link for results.