Friday, July 28, 2017

How Valid is Polar V800 HR to Measure HRV Metrics?

According to a 2015 paper by Giles et.al, the Polar V800 "improves" over previous generation Polar devices with narrower intra-class correlation coefficients, limits of agreement and effect sizes when compared to R-R readings obtained from a 3 lead Biopac ECG.

One of the key aspects of the paper is that it highlights the importance of understanding the effect of different softwares and their error correction routine on the R-R time series.  The paper references some earlier studies which flagged the concern that each system's individual HRV processing capability can potentially alter the HRV metrics in their own way making cross-comparability a big issue.

The paper discusses chiefly 7 errors that can be associated with the Polar V800 HRM signal. Potential factors leading to those errors are also stated. I'd like to borrow the learning and place them here :

Type of ErrorDescription of ErrorPotential Cause
T1Single interval of discrepancyNot stated
T2Long interval and short intervalNot stated
T3Short interval and long intervalNot stated
T4Too few intervals detectedDecrease or loss in contact between electrode-skin and resulting decrease in amplitude of the R wave
T5Too many intervals detectedNot stated
T6-aInterval(s) missed entirely, undetectableSoftware error due to time asynchronicity in the HRM and/or Loss of, or decrease in contact between electrode-skin.
T6-bInterval(s) missed entirely, detectableSame as T6-a

Although the causes of T1-T5 errors are not addressed in the paper, the authors state that all of them may be recognized and corrected without the use of a simultaneous ECG recording. However, they highlight the fact that the most appropriate technique for correction of R-R time series is still pending proper research and agreement.

The combined error rate of Polar V800 in standing and supine positions was 0.086% which is a marked improvement over previous research findings from the Polar S810 (0.40%) and Polar S810 (6.93%). The paper concludes that the device is a "valid" tool for the detection of RR intervals "at rest" and improves on previous HRM models with regard to comparability against ECG. 

The meat of the paper is it's tabulated summary of means, bias (LoA), ICC (95% CI) and effect size, which I'd like to reproduce here. These numbers are the justification for stating the validity of the V800. An interesting area to note is the level of discrepancy in HF power between V800 and ECG in the supine position. It is an order of magnitude different, whereas much more agreeable in the standing position. Is this a typo or the actual finding?


Saturday, July 22, 2017

Heart Rate to Power Ratio Metric for Running

Attached below are some squigglies of HR:Pw profiles from my own runs. Data indicates slope is very dependant on what the ambient conditions are. For instance, the body's challenge to cool is one fundamental driver of high HR. The body's ability to process oxygen at high altitudes is another. 

Long, slow, flat beachside run, hot and humid : First image is a long, slow controlled jog outdoors at 38 deg C, 50% humidity and medium wind for an effective temp index of 38. The squiggly goes straight up which is interesting. What this means is that under such hot conditions, prolonged exercise in low power still leads to gradual HR elevation or "drift". 



Treadmill run : Second image is from a 30 min easy run on a treadmill in an airconditioned gym. Power is limited to a tight range, run is well controlled and slope of squiggly is gentler than that of previous scenario.



Interval training, beachside, hot and humid : An example of 2 x 6 min, interspersed with 3 min slow running. 35 deg C, 50% humidity and medium wind for an effective temp index of 33. Key information - recovery jogs involved dousing self with water, on the head, down the back etc. During work segments, convection helps a bit to dissipate heat.



High altitude run : The last example is from a 40min run at high altitude, cool environment. Maximum elevation was around 11,800 ft and much of the running terrain was uphill, therefore power numbers are higher than equivalent runs done on flatland. This was the first run in such an environment, hence the body was coping with acclimitizing and effort can be classified oxygen limited.



The histograms on the x-y axes are another key piece of data. Skewness of the bars indicate frequency distribution at high and low ends of the heart rate or power spectrum.  

Altitude running when non-acclimatized is a case of high HR dominant running, where those bars are skewed to the higher numbers. For example, a predominantly dual peaked histogram for running power could point to an interval session, as the third image shows.

Another way to show the HR:Pw plot would be to express the numbers on the axis in terms of a percentage. This gives better visual cues on intensity, both to runner and a coach.

Apart from looking at slope, the area distribution of running data on a HR;Pw map might also be useful. At a conceptual level, the way datapoints can be arranged on a %HR:%Pw plot is depicted below by 3 types of colored dots. High HR:Pw runs in black and low HR:Pr runs in green. Red dots falling on the diagonal line might be an idealized scenario, where the increase in HR follows organically from an increase in running power.



Concluding Remarks

Heart rate to power ratio with frequency distribution information is one quick and easy way to classify the intensity of run. The advantage of the HR:Pw plot is that it captures whole body state of affairs (HR) and activity specific intensity (power). Ofcourse, the Hr:Pw profiles maybe slightly different for everyone, since everyone reacts a bit differently to temperature, humidity, altitude and stress. Looking at runs in this way also helps in controlling for ambient factors and in trying to compare apples to apples when analyzing performance across several runs.

Read more of my articles at Running Science.

Thursday, July 13, 2017

Harmonising VDOT Pace with Power Based Running : A Case Review

Note : If you're an elite athlete, please take the guidance of your coach. 

The VDOT based system of paces is a tried and tested system for run training designed by Jack Daniels and a colleague of his who worked for NASA. The system, described in JD's book "The Running Bible" appears to be sound at first glance - it is established from metabolic testing and the foundational idea that equally performing runners can be assigned equal aerobic profiles. 

JD has ofcourse outdone himself in both the national and international running stage so we can't argue whether he had his wits about him when he sat down to make VDOT.

I was interested to see how VDOT based paces, when converted to power, compares to the instruction in critical power based running zones. For this I pick an example runner, Tony. 


Converting VDOT Paces to Power

Let's assume that Tony recently raced 3km in 12 minutes for his season's best. Ambient conditions at the race were :

Temp = 95F
Altitude = Sea Level

This equates to a pace of 6:26/mile = 250 meters/min.VDOT is approximately 45 per the VDOT tables. 

The goal is to find an equivalent power for the VDOT pace so that he could implement power based training zones. Jim Vance has defined a simple factor tying speed and watts called Efficiency Index, EI, which is defined as the ratio of speed in meter per minute over watts, both continously captured over a 30 second rolling duration.

If Tony has knowledge that his short races in similar conditions in the past were done with an EI of 0.95 m/min/W, then knowing speed, corresponding power is : 

Equivalent Power for 3K = 250/0.95 = 263W

(According to the metric, as EI decreases, power to produce a given speed increases).

Given that 263W is an equivalent power estimated from race pace and an estimated EI, it must be checked whether this power value can be sustained when the powermeter is applied. 


VDOT Power Validation

1) As a first check, Tony can peer into his power duration (PD) curve in his favorite software of choice. If the PD curve looked something like the one below, which is a multiparameter exponental model fitted to all of Tony's runs 12 minutes and below, the chart seems to suggest a theoretical maximum of 220W for the 12 minute duration. 


Would it be correct to assume that Tony simply cannot sustain this wattage for this time? It might be fair to consider that the software generated PD curve in question just may not have race equivalent data. The model tries to make a trend around what is known, not what is unknown. If most of Tony's runs were from social runs in the park on weekends, the PD curve will underestimate his potential and would probably not be helpful. 

2)  The other bit of useful data from the VDOT system for this runner is the equivalency is terms of 5K, 10K, half and full marathon distances. For example, the equivalent 5K running time for the VDOT of 45 is 21:49 for the same ambient conditions. If you then used the Stryd's critical power model on their website, it suggests a critical power of 236W

By the old definition, critical power is the steady state rate of work output someone can theoretically sustain "indefinitely" although the notion of an indefinite time period is a bit corrupted - no one can exercise indefinitely! As now understood by the scientific community, it is a threshold below which VO2 and heart rate will not not see a rise and is also seen as the greatest point at which energy provision is still wholly oxidative (Poole, Burnley et.al).

Knowing that Tony's critical power is 236W, it seems "feasible" that he can pull off a short excursion into his anaerobic energy systems for 12 minutes at 263 Watts, which equates to 111% of his critical power. In this unsteady state, Tony will accumulate fatigue and have a rising heart rate and VO2.

3) Tony can validate this power (i.e know whether he can sustain it for 12 minutes or not) by performing a 3k at race pace in well rested conditions on similar terrain. Perhaps he chooses the same race the next time it's organized. If the average wattage generated by his powermeter is within 3% of 236W, he might be able to conclude that the conversion is roughly equivalent.  His pace to power conversion can also be adjusted by assuming a more generous Efficiency Index if prior data suggests that this is the case. It can also be adjusted by tuning the equivalent 10K pace resulting from the VDOT tables. 

While the VDOT-power conversion hasn't been perfect, it allows Tony to be able to go out and do a trial run or two and make necessary adjustments. Therefore, in so far as VDOTs are generated from race performances and the effect of ambient conditions on race paces are taken into account, I find no flaw with this procedure. 


Comparing VDOT Power Based Zones to Critical Power Based Zones

Under the VDOT guidance, Tony's training zones in terms of paces at a temperature of 95 F would have been  :


Based on a conservative EI estimate of 0.90, these training speeds can be converted to power values by the procedure noted in the earlier section. For example, the easy pace zone for the mile in terms of power would be 188-200Watts. The marathon pace for the mile would be 225W. And so on. 

The table below shows VDOT paces converted to power (watts) at two different EI estimates for the 1 mile distance. An EI = 0.9 for Tony would be conservative estimate for sluggish runs and sub-optimally executed fast races. An EI = 0.95 could be a theoretical maximum for optimal executed races. If evidence suggests the use of a more generous EI, I suppose that would be ok. 


The key question is how these VDOT pace based power zones compare to critical power based running zones prescribed by Stryd. 

Stryd's algorithm for power zones for a critical power of 236 is as follows :


Based on the comparison of the two zone tables, it seems that the choice of EI makes a key difference in interpretation. For example, for EI of 0.90, the VDOT marathon power of 225W would be considered to be threshold power under Stryd's model. For an EI of 0.95, the VDOT marathon power of 213W falls within the "moderate" power zone. But at the upper end, the VDOT repetition power of 261W comes conservative, whereas Stryd's power is a bit more aggressive at 10 extra watts. 


Conclusing Remarks

1) Equivalent running paces from VDOT tables, critical power and Efficiency Index (EI) appear to be key parameters affecting the conversion of VDOT paces to power. In the particular example, using Tony's equivalent 10K pace and an  EI estimate that corresponded to Tony's race data, the VDOT based powers bridge upto Stryd's power zones with minor differences. One might also make the assumption that training at two power values that are within 3-5% of each other elicits similar physiological responses.

2) A unique advantage of VDOT is that it accomodates pace derates for high temperature and altitude. Powermeter based running guidance has it's advantages, but it still has some catching up to do in terms of interpreting application for real world conditions - wind, temperature, humidity and altitude. The practicality of new, cookie cutter running programs based on power must be evaluated critically, particularly by runners living in hot, arid climates where the danger of heat stroke is real.

3) Because training zones are outputs from an algorithm that takes user input, they are estimations at best and it is a given that adjustments will need to be made to tally up RPE and overall whole body feeling to the prescribed guidance. Crucially, it is important that the zones are judged well before jumping straight into applying them in training. No one wants to waste time finding out they can't sustain a prescription. 

4) As far as my experience goes, both the VDOT system and the power based training systems are equally time consuming, although the fruit of added insight for advanced training is a plus. Both systems require frequent data diving, critical evaluation and adjustments. More so, the zones have to be re-evaluated at regular intervals to see how training and racing have modified them, for the better (or for worse!).

5) The interesting and larger implication of my writeup is that a traditional VDOT based system might be nearly equivalent to the power based running prescription, "if used correctly", which means a) it doesn't create a strong case for conversion from one system to the other and b) means that fundamentally both are sound in that they are calibrated to metabolic test data.

6) Additional research is needed to give empirical guidance on how truly different is training by VDOT vs powermeter for different classes of athletes. For example, in cycling research, it has already been established that there is "no empirical evidence" to suggest the superiority of either power based training or HR based training in the implementation of interval training. Both were roughly equivalent in terms of responses seen in recreational cyclists after a multi-week training cycle. 

Friday, July 7, 2017

Perspective : Why is There No Indian in the Tour de France?

Such was a recent question on Quora.

There’s little reason to pick the Tour de France if you want to inspect the absence of Indians in international professional cycling. It’s what goes on at a more fundamental level of international racing that counts.

A quick example to analyze the parity (or lack of) in Indian performance with respect to other competitors in order to understand why it’s challenging to move up :-

At the 2017 Asian Road Racing Championships held in Bahrain, India did seem to have “decent” amount of pedal power in terms of two national time trial winners representing at the ITT and a total of six talented roadies at the TTT. Even though numbers weren’t on their side, certainly it was commendable that these six got an invitation to represent the country.

However, it panned out that the Kazakhs, South Koreans and Japanese would go on to cream the competition. The best placed Indian at the ITT was a distant 5th place from last in a total field of 18.

For a flat TT course, it is instructive to look at absolute power outputs as they tend to be an objective marker of sustainable intensity. The following power duration chart is from a Kazakh rider, who was among the winners of the men’s TTT that week.


You can see that 30 min power is pushing 500W. I’ll leave digging up the Indian power duration chart as an exercise for you.

These Indian pros could better describe the intensity of racing at this level to you. But personally, I see a gap of over 100 Watts for this duration between a Kazakh and an Indian, which is a,,,, little PACIFIC OCEAN to bridge in cycling parlance.

Performance in any sport is multi-factorial. Sympathisers will tend to say that national poverty plays a big factor in setting back the sport. On the other hand, if national poverty were the only deciding factor, you shouldn’t even be seeing a war torn country like Iraq or even for that matter, Mongolia or Uzbekistan showing up to the races, let alone place well in contention for podium spot at these races.

I’ll explore some other points that I think are important.

Facts of Life :

1) Physiological : As long as there is no raw talent, you will always be trying to fit a square peg in a round hole. At a fundamental level, performance in cycling is determined by a high VO2max and the maximum velocity and/or power output you can sustain AT steady state lactate level in your bloodstream. You take two cyclists - A and B. If B has a higher VO2 engine and can process work at a higher fraction of that for a longer period of time without fatigue, B is atleast on paper the faster cyclist at the end of the day. Unfortunately, sports science research and papers in Indian cycling are lacking in the public domain so we can’t make comparisons of national level Indian cyclists against similar international competitors. I do suspect there is a big gap in prime physiological indicators that define success in cycling.

2) Winning : One of ways to get into the TdF is through wild card teams. To be noticed for selection, you have to simply perform. You can have all the talent and train like a madman all year, but if you’re not winning and arguably by big margins, it’s going to be difficult. Essentially, the meat of a pro’s career is done and dusted by age 35 and the rest will be sobering to watch. It’s in the younger years that you can do some crazy things in life. For example, when ex US pro George Hincapie was still in his tender years, he was entering crits and lapping the entire field. Young Alberto Contador would show up at races with a heavy iron bike and still fry the field. The infamous Lance Armstrong competed in traithlons as a teenager and gave older experienced competitors a serious run for their money. Answering when can you see an Indian at the Tour de France is similar to how Africans entered the Tour de France in 2005, or for that matter, how Columbians entered the Tour de France in the 1980’s. Fundamentally, you need talent yes, but you need to win some BIG races on the cycling calender and you need the likes of Mr. Eddy Merckx and Mr. Bernard Hinault to notice you and bless your move forward. The Columbians had over 3 decades to mature as a cycling nation, today they have several top cyclists posted in the Tour de France.

3) Cultural : Cycling is a relatively new professional sport in India. In western countries, cycling talent is identified at a tender age and nurtured through the teens. People give a damn about it, they appreciate it. In India, families like to see their girls and boys get a good job, marry before 30 and get settled in life. Loitering the streets on a bicycle comes with a stigma. Furthermore, the collective culture of bike racing, when compared to other national pasttimes such as cricket, is dismal. On this point alone, we can write a big essay. On the other hand, one can argue that India is talented in sports like cricket, badminton, hockey and wrestling. TV air time and press coverage going to something India is good at, cannot be really argued against. There can be some balance, however.

4) Financial : There aren’t many bike races and professional development programs in the country. A few are springing up in parts of India, but nothing at the level to show normal average joe Indians that there is a professional future in cycling. If you can’t put food on the table and feed your family, I don’t care what it is - lorry driving, or gold merchandising, you won’t be doing it. In this respect, Indians are like any others from any other nation - they have their priorities.

5) Environmental : What we describe as the ‘quality’ of cycling, whether recreational or sport, depends in large part on the quality of the surroundings. Prime among them : clean air and good, safe roads. I observe that several Indians in inner cities defy the odds to enjoy cycling during weekends. However, the amount of particulate pollution (PM10, PM2.5) in cities like Delhi and Bangalore are ridiculous and exercising in these conditions would arguably shorten life span. Those who ride motored 2 wheelers on these roads are found to wear face masks to block pollution. Cyclists, on the other hand, are completely exposed and elevated breathing rates mean a lot of crap is going in. Something of a sea shift needs to happen in the traffic and emissions scene in India to provide an environment conducive to performance. This is a long term change that I don’t see happening any time soon. Even legislating that drivers are not allowed to use certain roads at certain times of the day comes with massive uproar. Until these changes come about, cyclists have to ride long miles to get out into the country from places of inhabitation. (The nice thing about India is that it is a democracy and if you make enough noise, people responsible for change will listen….or something like that. So use that vote!)


Positive Signs of Change :

The sport does seem to be exploding in India which is a positive sign. Bike shops and cycling themed cafes are springing up. Several cycling clubs attract people to buy bicycles and take up recreational riding and racing.

Another positive sign is a strong Indian presence in the management circles of the Asian Cycling Federation since Mr. Onkar Singh took office as it’s Secretary General.

A third is, as I mentioned, the growth of junior cycling development programs which is identifying talent and taking them abroad to countries like Belgium. One example is the Indian Pro Cycling Project.

One hopes these developments bring in :

1) A fresh pool of genetically talented Indians into the sport, whether that is nationally or from overseas residing Indians.

2) Support for Indian cycling at the international platform. Such is happening currently at Asian level as I already mentioned.

3) Provides Indians an exposure to international racing and a glimpse of the true demands of a professional career.

4) Encourages businesses to notice cycling and support talents with sponsorships.

5) Encourages talented sports scientists to study indian cyclists and publish findings in international journals. What are the physiological gaps and what training methodologies can best bridge them?

As a summation, I would argue that a string in essential supply chain needs to pop up to support the upward movement of Indian cycling professionals. Good bicycle manufacturers, mechanics, scientists, coaches, sport directors, sport management consultants, aerodynamicists, nutritionists, business people, sponsorships and importantly, partnerships with international facilities and people. The list goes on and on. In a challenging sport like cycling, you really have to sit on the shoulders of giants.

Sunday, May 7, 2017

The Sub-2Hr Marathon Attempt : Chasing Smallest Meaningful Changes Through Aerodynamics


We tried. Sub-2 is 20 odd seconds closer, thank you.

Such might have been Nike's drumbeat yesterday, as a months long planning and execution process bordering on OCD brought Eluid Kipchoge to run the fastest recorded marathon in human history. At 2:00:24, the attempt to break the sub-2 mark may have failed, but the feat of running 2 hours at an average speed of 5.84 m/s is astonishing to me as a runner.

The climate made for perfect running conditions. Temperatures were in the cool teens. A windrose chart shows winds were mostly ESE during the day. With the orientation of the track in the NNE-SSW direction and a counter clockwise running direction, a headwind would be felt if at all only at the southern short end of the track. Rest everywhere, you'd see either a tailwind or a sidewind. With near perfect pacelining and draft coverage even from a moving vehicle however, the task at hand looked daunting from get-go.

A whole 2 minutes.




So how did we get here?

Until now, the fall in the marathon world records have been very non-linear. Perhaps this is reflective of the course specific nature of marathon times as well as influence from a host of externalities such as training, hydration and climate. But some simple stats help understand the level of difficulty involved in every incremental improvement towards sub-2.


Fall in Marathon World Records

IAAF standardized marathons began in 1921. The marathon record in 1925 was 2:18:40. It took 28 years for the world record to fall 10.35 minutes, i.e 621 seconds. Put in a somewhat corrupted way, if you were to spread that among 28 years, that's not more than an average of 22 seconds per year.

Source : IAAF

From 1953, it took just 14 years to shave off another 538 seconds. Or, an average of 38 seconds time decrease per year which is an interesting rate of decrease, a subject for another day. But from thereon, it took almost 47 years to knock off another 399 seconds. That's a tiny decrease of 8 seconds average per year.

A better visual shows how the time knocked off between each marathon asymptotes to a window about 43 seconds wide. See below. I've included Kipchoge's time only for perspective of the advantage he had from a controlled strategy. However, the time is not recognized by IAAF.



Given this simple history, it would seem plausible that the remaining 178 seconds would be knocked off. But the question is how long that would take.

If we consider a 'generous' average of 30 second decrease per year, breaking 2:00:00 would take just 6 years. But if the task is considered exponentially difficult and we accept that only a 1 second decrease per year is possible at this level of racing, it would take a whopping 178 years to achieve. So is there an opportunity between these two extremes? Will it be 15 years or 20 years?

That's where the Nike's experiment fits in. Kipchoge and gang showed that even under the most controlled of conditions (some in clear violation of international marathon rules) and with the best athletes and level of technology available today, we humans are still shy of the barrier by 26 seconds. Close enough to warrant another try? Well, that's the debate.


The Smallest Meaningful Change

The best distance runners from Africa are nearly equal in abilities these days. Their training is similar. They eat similar kinds of foods. They live at the same altitudes. So what must be the differentiator among them?

Sports practitioners talk about the "smallest meaningful change" defined as the minimum change in performance worthy enough to determine differences between top competitors. For example, if a group of elite runners trained for months and showed a between-athlete standard deviation of 10 seconds in a 20K running test, the smallest meaninful change would be 0.2 x 20 = 4 seconds. In other words, anything above 4 seconds is an appreciable change. Anything less and it's just day-to-day noise.

Take another perhaps more representative example. Berlin 2014 was where the current world record shattered. In the last 5 years, the average standard deviation between the times of the 3 podium placers was just a minute and 25 seconds. The smallest meaningful change in an elite runner's performance for positive differentiation would be 0.2 x 85 = 17 seconds.

For today's elite marathoners showing such a tight cluster in finishing times, 17 seconds means everything in the world.

Nike publicly put their money in the aerodynamics basket as they pitched the marketing effort of the Sub-2Hr. Not much in the way of technical data has emerged from Monza. Therefore, I got curious enough to evaluate running energy savings on the table from just aerodynamics alone.


The Effect of Drafting off Other Runners

In human powered land transport, oxygen cost increases as a square of wind velocity. Experienced middle distance track runners can attest to the energy savings experienced even with a small amount of drafting. 

Pugh (1971) showed that at 4.5 m/s, his runner saved 0.250 l/min in oxygen cost by running behind another runner within a separation distance of 1 m. He extrapolated this figure to 0.332 l/min for a speed of 6 m/s and stated that for outdoor conditions, a runner can expect to overcome 80% of the energy cost of overcoming air resistance through shielded running.

Source : Pugh (1971)

In any mass event, it is not reasonable to be within touching distance of a runner in front of you. Pugh measured dynamic air pressure with a Pitostatic tube and showed that even at a separation distance of 1 m, air pressure was still only 7% of the value upstream of the lead runner. This might mean there are potential benefits to be had even at larger separation distances.

Source : Pugh (1971)

I also assume that like in cycling team time trial efforts, the leading runner can experience benefits as well by virtue of reduction in airflow separation in the wake due to the presence of a runner close behind. I'm not aware of any studies which have looked at the cost savings on the leading runner.

In an ideal marathon situation, it maybe possible for the favorites, all excellent in fitness and form, to getaway and stick with a tightly knit pacing formation until the very last kilometers of the course. The one competitor who will make most of this formation and slow down the least can be expected to win.

An analogy can be borrowed from long distance thoroughbred horse racing. The best finishing horses almost always spend the most time drafting. The horse that slows down the least emerges the winner.

Furthermore, thoroughbred horse racing is an interesting example because it exhibits a similar cluster of best times as seen in human marathons (although this has been the case for a longer period of years). If this means both horse racing and marathon running have 'plateaued' in terms of race times, looking to the next frontier to improve timing is justified.

Source : Five Thirty Eight




The Effect of Drafting off A Pace Car

At Monza, Nike used laser lights to guide the runners within an arrow formation which effectively shielded main-man Kipchoge from three directions. But we saw a pace car driving 10-15 metres before the lead runners which could have also potentially changed the flow field. The Sports Scientists joked that the unsung award for this sub2 attempt ought to go to the Tesla.




But must we exaggerate the effect of a vehicle 10-20 metres ahead of you? Certainly in the image above, it doesn't seem the runners would have a "massive" advantage at the distance they are positioned behind the car.

Infact, the difference in air pressure and x direction air speed is dependant on where you are behind the car. I did a simple 2D CFD simulation to show that the effect isn't is great as some may think it is. Since the simulation takes a long time to run, I ran it for a few milliseconds of real-world time. The road boundary is assumed to be moving at 5.84m/s and at the left end of the domain, the boundary condition for air velocity = 2.77m/s and air pressure of 100000 Pascal.

In the first image, I have sliced a cross-section in the X-direction at roughly the height of the car behind the car. The bold black line shows the profile of the difference in P - Po, where P = instantaneous air pressure and Po is the initial air pressure of 100000 Pascal. In the second, I've shown the distribution of horizontal air velocity in the same plane as above.





Summary : Yes, there is a reduction in both parameters which gets bigger the closer you are to the car. For example, in the P-Po simulation, the air pressure behind the car at where I think the runners were is only some 14.8 Pascals less than pressure upstream of the car, therefore total pressure is still intact. Therefore, at the distance the runners were shown in the live telecast, the effect is not "as great" as people are making it out to be.

However, the question is also whether it was justified to use such a prop in the first place. That's the debate in the running community.


The Effect of Reynold's Number (Re) on Running Drag 

Mechanical power for a runner to overcome air resistance is proportional to drag co-efficient CD, the relative velocity between wind and runner vr, horizontal running speed vf, air density ρ and the frontal area of the runner A, projected perpendicular to flow of air. 


In general, the dependance of power to overcome drag on speed is cubed and the dependance on density, area and CD are all to the power of 1. A step change in running speed on a zero wind day will triple the power requirements, all else kept the same.

Pugh (1971) estimated for a subject runner at a treadmill speed of 4.47 m/s and wind velocity of 14.14 m/s, wind pressure was 6.24 kgf which required a horizontal power of 27.89 kgf.m/s.

CD is dependant on Reynold's number, a useful dimensionless co-efficient in fluid dynamics. 



A running human can be approximated as a cylinder. While at low Reynold's number, the regime is one of laminar flow, rarely is this the case for a running human. Let's look at a simple example calculation using a bluff body approximation :



At this Reynold's number (Re) of 63,000, flow is all turbulent.

There is a 'critical' Reynold's number where a precipitous drop in drag co-efficient is exhibited, atleast in theory. For a cylinder, this is qualitatively shown below.

Source : Pugh (1971)


A table from Pugh (1971) also quantifies the curve shape of drag co-effcient as a function of Reynold's number.  Notice this effect first occuring at Reynold's number somewhere between 20,000 - 25,000. 

Source : Pugh (1971)

If I consider Kipchoge's smaller stature, a chest circumference of 90 cm instead of 100 cm and a speed of 5.84 m/s, I estimate an Re = 110,800. Comparing this value to the plot above, it already "seems" his drag co-efficient was in a low place, if not the optimal place for his speed. Again, this is an estimation. It would be quite nice to see whether Nike researchers found this in practice.


The Effect of Frontal Area on Running Drag

Ideally, a talented runner will have an optimized frontal area and will neither be too short or too tall. For example, a runner with a height of 179.9 cm, weighing 65 kg and having a surface area of 1.78 sq.m has a projected frontal area of 0.478 sq.m.

Perhaps Kipchoge was perfectly suited for the task. At a height of 168 cm with a race weight of 57 kg, Kipchoge's frontal area would have been 168/179.9 x 0.478  = 0.446 sq.m (notice I employed a simple ratio from the previous example).

Compared to the example runner, this reduction in Kipchoge's frontal area equates to a reduction in drag force of nearly 7%, all else kept the same.

Projected area is directly dependant on height and weight and to an extent, the type of running motion and running lean angle exhibited during the gait. Optimizing the latter two aspects should not come at the expense of running economy.


The Effect of Shoe on Running Drag

Nike's PR mainly revolved around a pair of shoes designed for Kipchoge called Zoom Vaporfly 4%. The shoe has an interesting shape and is streamlined at the back end. The purported benefits are 4% in energy savings through a carbon sole plate, although there is no published data from Nike to back this.

Some data from the research arena is starting to come out. A study supported by Nike and the laboratory of Rodger Kram (one of the few experts on the planet to know a thing or two about running energetics) found it took 4% lesser energy to run in the prototype Nike's compared to two other shoes.

Fascinatingly, one of the two control shoes was the Adidas Adios Boost 2, the same shoe worn by Kimetto while toppling the current world record. There's only an abstract available from the study and some key points are underlined below.



I certainly have my doubts over the 4% number, but if we assumed this were correct, then I have projected time savings that an ordinary runner could potentially experience. The calculated savings seem huge on account of economy improvement through the shoes itself, which is why I doubt we can apply the 4% number from the study to any running situation as is.



The running feet exhibits both translational speed of the runner and the rotational speed through the leg swing and leg pitch. An elite marathoner runs at step rates of around 3 Hz. The legs will swing a total of 14,400 times in an ideal scenario. That's 7,200 times each leg is thrown about. 

Cycling is another area which approximates the rotation and forward translation of the feet, although in a more constrained way.

Gibertini et. al tested three cycling shoe configurations in a wind tunnel and noted that a well fitting laced shoe exhibited the least power demand, around 15W in total. Given that this was a laced shoe, perhaps we could compare the numbers to that of a running shoe for perspective.



Source : Gibertini (2010)

The air speed in this cycling study was more than 2 times that of marathon running speed so it might not be entirely representative. Moreover, even during controlled running motion, African runners tend to show great degrees of knee lift, leg turnover and swing angle which differ from that of cycling.

Qualitatively, the crank angle based power diagram highlights that shoe drag power is a function of where the feet is during it's rotational motion. Drag power was greatest at top dead center  (Θ = 0) and least when the shoe was at 90 degrees before bottom dead center (Θ = 90).

Perhaps this idea can be extended to the running scenario by assuming that shoe drag might be the most when the lifted foot is at it's highest point and least when both feet are off the ground on the sample plane during mid-stance. I'm unaware of an actual study done in this fashion with running shoes.

Other reseachers have pointed to the benefits of a "dimpled" frontal shoe surface.

Finally, notice that none of the runners that attempted this event wore socks. One study by Ashford et. al compared a dozen socks on a form tested under the wind tunnel and found little variation in drag co-efficient among them. A couple of socks exhibited a drop in CD at low Reynold's numbers which maybe just a reflection of optimized behavior during lower running speeds.  In hunting for mere seconds to win a race, this maybe a potential avenue to look into. Any benefits though have to be traded-off with potential discomfort and blisters during a fast running attempt.

Source : Ashford (2011)

Another fairly obvious aspect was tight fitting clothes. Notice that none of the runners tucked their shirts into their shorts as many amateur runners do in marathons. Also, just Tedese, Desisa and Kipchoge wore arm warmers among all runners. I'm assuming it was more than just for warming the skin.




Conclusions

In the grand scheme of things, most marathon world records these days are pre-dominantly mental. Who can suffer the most for the longest? 

Having said that, my observations in this post are limited to aerodynamics alone. Summarizing key ideas :

1) In the best case scenario, I estimate that the 2 hour marathon record will be broken within the next 6 years. In the most conservative scenario, we'll be long dead before that happens. I'm an optimist however. 

2) Top podium worthy marathon timings are often spread by less than a minute and a half. The degree of improvements necessary are small and to be placed within context of the smallest meaningful change. The degree of enhancements from aerodynamics may help push past this threshold of beneficial change.  

Side point : This may also limit the scope of commercially sold running instruments for tracking performance if they do not have the required fidelity/sensitivity to capture small improvements. 

3) Running in a scientifically optimal ambient climate sets the density and viscosity of surrounding air for the optimal Reynold's number. Temperatures below 50 deg C may not be ideal.  The conditions in Monza have been noted to be picture perfect for sub 2 hour attempt. It was not too cold to for non-optimal Reynold's numbers and not too hot to demand increased cooling. 

4) Wind creates drag force which requires externally supplied power to overcome. It general, it increases as a cube of speed. As a rough rule of thumb, a +5 mph head wind relative to calm conditions increases oxygen demand by approximately 5% relative to calm conditions. Conversely, a tailwind decreases the horizontal power demand of running. Note that wind speed shall be considered at the center of gravity of the runner, not at the 10m wind station.

5) Optimal drag co-efficient is a function of Reynold's number and further on running speed and anthropomorphic aspects. Running posture may affect the frontal area presented to the wind. 

6) Optimizing clothing to improve aerodynamics is justified for small incremental performance benefits. Even shoes and socks may contribute to reduced power demand of fighting drag. A lack of adequate published literature in this area presents a good opportunity for engineers, biomechanists and sports scientists to get together.


Reference :

LG, Pugh, The influence of wind resistance in running and walking and the mechanical efficiency of work against horizontal or vertical forces. J Physiol. 1971 Mar;213(2):255-76. 

Sunday, April 23, 2017

Commentary : The Environmental Impact of Pre-Ride Food Choice and Driving to a Bike Ride



This is an Earth Day special commentary that I wrote on my Facebook page. The post was inspired by a similar opinion post along the same lines written by Thorpe & Keith from the Keith Group. They provide a host of reasonable references from where the numbers were picked.


The environmental impact of "getting to a ride" by car is analyzed below. What is also interesting to think about is the environmental burden due to choice of diet, which makes big marginal differences depending on what you put in your mouth.

Attached below are some graphs from a simple estimation of CO2equivalent emissions for a cyclist commuting for a typical 100km weekend ride. 

In one set of graphs, the cyclist is assumed to have eaten a 'typical American' diet and vehicle emissions were derived from test data for assumed 90kph driving speed. 

In scenario 1, driving distance = 20km in a conventional 4 door automatic petrol engined 2L Honda Civic. 

In scenario 2, driving distance = 20km in an automatic petrol engined 3.6L Porsche Panamera 4 PDK (Euro 5). 

In scenario 3, I look purely at impact of driving a fuel efficient, ULS diesel powered 2L Skoda Octavia Hatchback (Euro 5) as support vehicle for the cyclist (commute to ride is neglected). However, due to support function, the Skoda will be driven the 100km at a slow speed matching the cyclist (let's say 30-33 kph). Only the diet for the cyclist is taken into account, while that of the driver's is neglected.

Please note that life cycle environental impact of production of the vehicles, the bicycles and the construction of a public road system that the cyclist and driver utilize are neglected in the analysis.






The comparison is interesting if you account for the slow speed fuel efficiency degradation of any conventional vehicle. If you assume a 15% fuel efficiency derate due to low speed vehicular losses in the Skoda, then on a per km basis, I believe it makes little difference to be driving a Civic fast to the ride or a Skoda slow as a support car - they are more or less equally polluting regardless of what the sticker fuel efficiency is.  Meanwhile, it is approx 30% more polluting to carry bikes on a Porche when driven at similar highway speeds as a Civic, no surprise there.

The takeway? Reduce driving, carpool if possible and keep the luxury car at home. When driving, drive at the sweet spot fuel consumption speed. In urban environments, it helps to live close to a cycle track.

And finally, reduce or eliminate the use of conventional fuel support cars on long rides as pollution is spread over a larger area. If that's not possible, choose lighter cars with smaller displacement engines and reduce the number of start-stops. Start-stops cause relatively more pollution than constant speed driving regime due to the repeated accelerations and decelerations the vehicle must go through.

What is also interesting is to look at the effect of the cyclist's diet for this ride. In the first set of graphs above, I have assumed the cyclist to eat a typical diet with a life cycle burden of 2.6 gCO2eq/kcal. The carbon intensity of that ride on that diet is an average of 30-40 times less than driving the respective cars on a per km basis. 

However, if the same cyclist chose to eat a high protein meat rich Paleo diet with a pollution burden of 5.4g CO2eq/kcal, the difference is halved. For example, in the case of driving the Civic to the Paleo fueled ride, driving is only 15 times more polluting than cycling relative to a normal diet. Stunning if you think about it.

What I'm pointing to (as others have pointed out) is that on a gram of CO2eq per km basis, the high meat rich diet can be worser off than the transportation fuel in terms of the embedded carbon intensity (what goes into the production of the fuels). The reason can be attributed to the carbon intensive nature of raising cattle to produce beef. 





So the takeaway from this second analysis is that when possible, substitute lesser energy intensive forms of protein in your diet for fueling the ride. And while it, keep the beef away from the support car driver!


Post script : 

Just because cyclists reduce or avoid consuming a meat rich diet does not necessarily mean that the product will not be produced. Supermarkets will still carry the meat and someone else will buy and consume it. So on a global level, perhaps we'll see the same environmental burden of meat consumption.

However, if you account for the fact that products are sold because of relative consumer demand, if one segment of the market shifts outlook and reduces meat consumption, we can assume that somewhere else in the chain, production maybe cut. I'm not sure if its as simple an analysis as that and what timescales it takes to shift mindset overall. Something to think about though.


Saturday, April 22, 2017

GIANT Duathlon 16/17 Season Finale : Results and Analysis

Image courtesy : Paul Venn / Race.ME

The final duathlon in the Giant Duathlon series was held on April 14, 2017 at the District One cycling track in Dubai. The race format was 3k-25k-3k for a total distance of 31k. 

In a previous post, I described the runing dynamics data from race 4. In this post, I will use a similar level of analysis of race performance and will be comparing to the data in race 4.

The full results are hosted on the Race.ME results page here and my splits are shown there. The top 30 in men's overall times are posted at the end of this post.

The strategy going into this race was simple - throttle down the first run a notch and put everything you have for the day into the cycling aspect. Because this was a fast cycling track, I knew that the most of the field would be thinking along those lines.

For comparison purposes, I made a table of my timings for all 5 duathlon races this season along with a key piece of information - my training stress balance as a function of TRIMP. This is shown in Figure 1.

Figure 1 : Comparison of race performances over 5 x GIANT duathlons during the 2016/17 season.

TSB is an algorithmic surrogate for "freshness" or "readiness to perform". It is understood that the more negative it is immediately before an event, the more fatigue you bring into the race.

However, surrogates are just that - surrogates. Trimp or TSS based training stress balances are helpful, but I think they do not capture the stress in an adult working man's life who needs to commit 40-50 hours on a day job per week. In future posts, I'll be examining some interesting areas of physiology which might capture that aspect.

Graphing all my race timings from race 1 through race 5 helps form a visual story of what went on this season. This is shown in Figure 2.

Figure 2 : Plot of splits in 5 x Giant Duathlons during the 2016/17 season.

As shown above, I improved overall race timing in race 5 by 6.3% compared to race 1 and it would be my best overall timing this season.

Furthermore, duathlons are influenced by course. In race 3, there was a +4.9% degradation in overall timing compared to race 1 which was held on the same track. 

The reason is attributed to the poor visibility and foggy conditions in race 3 which pretty much hampered the average cycling speeds due to the technical nature of the course.  I also described in a past post about an inadvertent cramp during the last run segment which lost me atleast a minute in that race. 

Between race 5 and race 2 (same course), I improved overall timing by 4.9% mainly due to high motivation and better conditioning. All those brick sessions and gym workouts have helped.

Looking at transition times, the trends is one that shows decreasing t1 and t2 times. It appears feasible that minor improvements can be made here considering that I pulled out of transition in race 4 an average of 45 seconds. 

But I should think the transitions are also influenced by architecture of the transition zones. Race organisers optimizing the length and entrance/exit of the transition during race 4 helped me shave 10-15 seconds compared to other courses.


Running Dynamics 

Shown in the plot below is a composite of run dynamics for race 5. It shows run power (W), form power (W), ground contact times (ms) and leg spring stiffness (KN/m) vs duration. I also show an estimated average power for the biking duration. Unfortunately, I did not measure biking power due to a pairing handicap on the Polar V800s.

Figure 3 : Composite plot showing power and running dynamics in each split during the final duathlon race of 2016/17 season.

As the running started out, there was an overreaching in power due to the initial excitement and surge. As things settled down, I moved into a rhythm of 256W average. GCT was 210ms for a cadence of 93.

Form power is a surrogate for the metabolic cost of perpendicular bouncing. The form power data in the middle of run 1 looks rubbish due to data loss but overall, I displayed a mean form power : total average power ratio of 0.24, i.e about 24% of power was devoted to vertical motion. How much of that can be improved upon is debatable. What I do want to emphasize is that in all cases, vertical oscillation data says I limited it to < 3 inches, which is good rough guideline.

The story of the second run is that all metrics, by virtue of accumulated fatigue, worsened relative to the first run. This is shown in Figures 4 and 5.

Running power fell by 11.7% and pace fell by 10.6%. There was in increase in ground contact time of 9.5%, possibly the body's response to limit metabolic cost.

The surrogate of energy cost of running, ECOR, increased by 0.73% which may not be statistically significant to corroborate the increase in ground contact time (GCT).

Running economy (RE), a ratio of speed generated to power to weight ratio saw a fraction of a decrease.

I also kept cadence nearly the same as the first run. This may have been a way to compensate for the fall in stride length and gait push-off power.

Also note in Figure 3 the overreach in power towards the end of run 2. That is me pushing myself up a short hill just before the finish line. Power then dropped on the downhill segment and climbed back up again slightly for the home stretch on grass. By then I was flattened.

Figure 4 : Tabulated running dynamics from race 5 of the Giant Duathlon series, season 2016/17.

Figure 5 : Tabulated % change in running dynamics metrics in run split 2 compared to run split 1 during race 5 of the Giant Duathlon. 

What I thought would be interesting is to compare the fatiguing aspects of the second run in race 5 against the numbers in race 4.

Figure 6 : Running dynamics compared, race 4 vs race 5.

As expected, I ran a faster race in race 4. A major reason for the slower running metrics in race 5 was from the strategy to go slower in the running to perform in cycling.

Which brings me to state that duathlon is a fascinating exercise in energy management.  The mental and physical exertion of the short format was pretty taxing on the body all season and the excellent competition from my peers in the 30-39 age group kept me on my toes. Kudos to all those guys.

Training, self-coaching and making improvements have been fun. Tracking training volume and fitness changes through good data collection and record keeping has helped quite a bit. Keeping a tab on data comes natural to me from my engineering background and a major effort going forward would be to cut down on the sheer number of metrics and focus on actionable aspects. Keeping it simple stupid works.

The top 30 times from race 5 is shown below. Most of the fastest times in the race were from those in my age category, shown highlighted in yellow. The deficit I have to make up to be among the top 5 is a matter of 8-10 minutes. Thinking about that gives me some chills, it's a big gulf to cover.

If some optimization is carefully distributed among the various segments of the duathlon, I believe it is possible to narrow down the deficit but the question is by how much. Moreover, duathlon is a changing landscape with old competitors leaving, new ones coming along etc. This makes for pleasant surprises in each race. Sometimes you're the hammer, but often times you're the nail.

I also understand that the organisers may be considering cutting down the number of GIANT duathlon races for the next season, which means the room for error gets smaller. It's a shame because folks like me want to do nothing with swimming and duathlon is a way to show my A game. A lower number of races will be challenging, but hopefully a motivating aspect as well.

Thank you for following.

Figure 7 : Top 30 in the overall men's standings in race 5 of the Giant Duathlon series. Total competitors = 165. 

Image of motor


Image of mundane object to apply motor