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.