Prediction of muscle fiber type from powermeter data, part 2

by Andrew R. Coggan, Ph.D.

In this prior blog entry:

http://www.trainingandracingwithapowermeter.com/2010/12/prediction-of-muscle-fiber-type-from.html

I briefly discussed some of the differences between the two major muscle fiber types found in human skeletal muscle (i.e., slow-twitch, or type I, and fast-twitch, or type II), and also indicated some ways in which knowledge of an athlete’s muscle fiber type distribution could, at least in theory, be helpful in optimizing their approach to training and racing. Continuing on from that introduction, in this entry I will describe one of two ways of estimating an individual’s fiber type based on data obtained using a powermeter.

Method #1: Estimation of muscle fiber type from muscle contractile properties

As first demonstrated by Gasser and Hill in 1924 (1), the force-velocity relationship of isolated muscle is non-linear – that is, as the speed of muscle shortening increases, force falls off quite rapidly at first, then more slowly thereafter. On a molecular basis, this is because it takes a finite amount of time for the head of the myosin protein to attach to the actin filament, generate force, and then detach before reattaching again at another binding site. Consequently, fewer and fewer such force-generating bonds can be formed as the myosin and actin filaments slide past each other at higher and higher speeds. However, because the myosin found in type II fibers can complete this cycle (and hydrolyze ATP) more rapidly than that found in type I fibers, force declines less rapidly as a function of contraction speed in type II than in type I fibers. The result is not only a higher maximal speed of shortening, but also a higher maximal power output. As well, the speed of shortening at which maximal power is developed is also higher in type II than in type I fibers. These functional differences are readily apparent in Figures 1 and 2 below, which are based on the data Gilliver et al. (2).

Figure 1. Force-velocity relationship of isolated type I and type II human muscle fibers.

Figure 2. Power-velocity relationship of isolated type I and type II human muscle fibers.

Unlike the force-velocity relationship found in isolated muscle, during cycling the relationship between force and velocity is essentially linear, as first shown by McCartney et al. (3) and as illustrated in Figure 3 below. This is apparently due to the complex interaction of multiple muscle groups, each having their own unique force-velocity relationship, acting over multiple joints. Consequently, rather than the positively-skewed curve found for isolated muscle, the power-velocity relationship is well-described by a parabolic function, as shown in Figure 4. (Note that the constants of the two equations differ slightly due to the way individual data points are weighted somewhat differently while calculating the linear and non-linear regressions.)

Figure 3. Force-velocity relationship during cycling.

Figure 4. Power-velocity relationship during cycling.

Despite these differences, the same general principles described above apply, i.e., the higher the percentage of type II fibers (especially when expressed as a fraction of total muscle area or volume) an individual has, the less of a decline in force they would be expected to exhibit as velocity (cadence) increases. Consequently, all else being equal they would be expected to have a higher maximal neuromuscular power and a higher optimal pedaling velocity. Indeed, this is precisely what both McCartney et al. (3) and Hautier et al. (4) found for n=2 and n=10 subjects of varying fiber type, respectively. Furthermore, Gardner et al. (5) have demonstrated that field-based tests performed using an SRM can provide force-velocity data comparable to that obtained under more controlled conditions, i.e., on an ergometer in a laboratory setting. Thus, at least in theory it should be possible predict someone's muscle fiber type distribution with reasonable accuracy from such field tests.

To derive an equation for doing so, I converted the cadence data of Hautier et al. (4) to circumferential pedal velocity (CPV in m/s; which is equal to (cadence * 2 * Pi * crank length in m)/60) and then calculated the regression of the % type I fiber area on the optimal pedal velocity (CPVopt, the pedal velocity at which maximal power is produced) (instead of the reverse as originally presented by Hautier et al. (4)). The result was:

% type I fiber area = 242.7 - 89.5 * CPVopt (m/s)

R^2 = 0.784

P = 0.001

S.E.E. = 2.8%

For the subject whose data are shown in Figures 3 and 4, this equation would predict a % type I area of just 26% (therefore 74% type II). This is consistent with their extremely high maximal power output as shown in Figure 4, as well as their extremely high maximal theoretical CPV (CPVmax), i.e., 4.84 m/s or 280 rpm on 175 mm cranks. (In fact, provided the relationship between force and velocity is truly linear, CPVopt will always be one-half of CPVmax.)

Cool! But how do I measure my own force-velocity or power-velocity relationship?

A detailed answer to this question is beyond the scope of this blog. In essence, however, doing so requires measuring power and cadence with high temporal resolution while performing a maximal effort against just the right amount of external resistance. If the resistance is too great, then significant fatigue may develop before a sufficient number of data points are obtained out/down the force-velocity line or up-and-over the peak of the power-velocity curve. Conversely, if the resistance is too small, then too few (or even no) data points will be obtained on the right-most half of the force-velocity line or on the ascending portion of the power-velocity curve. Perhaps the best suggestion I can make, then, is that people simply experiment, e.g., by performing maximal accelerations from a dead-stop using various gear ratios. Depending upon the conditions (e.g., outdoors vs. upon some form of trainer), this may entail the use of either very small gears (e.g., 39 x 23) or very large gears (e.g., 53 x 12). Regardless, the SRM should be set to record data as frequently as possible, to try to "capture" as many individual pedal strokes as possible. If an insufficient number of data points are obtained during a single effort to permit reliable determination of the force-velocity or power-velocity relationship, data from several short efforts can be combined. For example, the data shown in Figs. 3 and 4 were drawn from five different "gate starts" performed by an elite BMX cyclist, each one of which provided data for just 1-3 pedal strokes (recorded at 0.1 s intervals).

References

1. Gasser HS, Hill AV. The dynamics of muscular contraction. Proc Royal Soc B 1924; 96:398-427.

2. Gilliver SF, Degens H, Rittweger J, Sargeant AJ, Jones DA. Variation n the determinants of power of chemically-skinned human muscle fibers. Exp Physiol 2009; 94:1070-1078.

3. McCartney N, Heigenhauser GJF, Jones NL. Power output and fatigue of human muscle in maximal cycling exercise. 1983; 55:218-224.

4. Hautier CA, Linossier MT, Belli A, Lacour JR, Arsac LM. Optimal velocity for maximal power production in non-isokinetic cycling is related to muscle fiber type composition. Eur J Appl Physiol 1996; 74:114-118.

5. Gardner AS, Martin JC, Martin DT, Barras M, Jenkins DG. Maximal torque- and power-pedaling rate relationships for elite sprint cyclists in laboratory and field tests. Eur J Appl Physiol 2007; 101:287-292.

Prediction of muscle fiber type from powermeter data, part 1

by Andrew R. Coggan, Ph.D.

As many readers of this blog are undoubtly aware, the skeletal muscles of humans and other animals can be classified into various "types". A number of such classification schemes exist, but the most common approach is to characterize muscle fibers based on their speed of contraction, which is primarily determined by the isoform of myosin protein they express. Thus, in simplest terms muscle fibers can described as slow-twitch, or type I, or fast-twitch, or type II. In addition to being slower to contract (and relax), type I muscle fibers tend to be smaller, but have more mitochondria and are surrounded by more capillaries, than type II muscle fibers located within the same muscle. As a result of these (and other) differences, "tonically-active" type I fibers tend to be less powerful but more resistant to fatigue, whereas "phasically-active" type II fibers are generally more powerful but also fatigue more rapidly. (Note that many, if not all, muscle fiber properties mentioned in this blog entry change in response to exercise training. However, the inherent differences between type I and type II fibers, even if markedly diminished, will generally tend to remain.)

Given the above, it is perhaps not surprising that, at least at the elite level, endurance athletes tend to have more type I fibers than average, whereas athletes in sprint sports tend to have more type II fibers. For example, in 1976 Costill and coworkers obtained biopsy samples from the gastrocnemius (calf) muscle of 40 male and female international-caliber track-and-field athletes (1). Although the fiber type distribution of those competing in field events was notably quite unexceptional, the gastrocnemius of the distance (5000 m to marathon) runners was composed of ~70% type I and ~30% type II fibers, whereas that of the sprint (100 m) runners was ~25% type I and ~75% type II. (The gastrocnemius of the average untrained individual usually contains 55-60% type I and 40-45% type II fibers(2).) As a result of the study by Costill et al., as well as numerous others, it is now well-established that muscle fiber type distribution can be an important determinant of athletic performance.*

Presented with the above information, it is natural for any athlete to wonder about their own personal fiber type distribution – in fact, it was partially because of such curiousity that I first volunteered for a research study involving muscle biopsies approximately 30 y ago. The muscle biopsy procedure, however, is somewhat invasive, and although it is generally quite safe, it is not entirely without risks. As well, the variability in determining the percentage of type I and type II fibers based on a single biopsy can be quite large (3), meaning that multiple samples may need to be obtained (ideally from multiple muscles) to really “nail down” someone’s true fiber type distribution. Thus, few, if any, exercise physiologists would argue that it is worth having a biopsy performed simply to satisfy an athlete’s curiousity, or even in hopes of improving their performance by altering their approach to training, the tactics they use in races, the events they choose to enter, etc. On the other hand, if information regarding an individual’s muscle fiber type were more easily obtained, at least in theory it could prove valuable in this regard, and if nothing else, might help satisfy their curiousity.

The purpose of this series of blog entries, then, is to describe two equations for predicting an individual's muscle fiber type distribution based on data easily collected using a powermeter. Specifically, in part 2 I will discuss how to do so based on force-velocity (really, power-velocity) measurements. This method is the more precise of the two, but requires use of an SRM powermeter, as none of the other devices currently on the market appear to provide data with sufficient fidelity and temporal resolution to utilize this approach. Thus, in part 3 I will describe how to estimate fiber type based on measurement of fatigue resistance. Being based on a secondary characteristic (i.e., fatigability vs. contractile properties) of the different muscle fiber types, this method is less precise, but has the advantage of being available to all powermeter users, not just those who own SRM cranks.

*Interestingly, however, this influence seems to be less evident in cycling than in running. For example, in a study of road cyclists Burke et al. (4) found no difference in fiber type distribution of the v. lateralis (thigh) muscle between those who had achieved national or international success and those who had not. Along the same lines, Mackova et al. (5) found that although international caliber match sprint cyclists had a greater percentage of type II fibers in the v. lateralis than non-athletes, the difference observed was less than previously reported for track-and-field sprinters by Costill et al. (1). The reason for this is not known. It may, however, be because in road racing the dynamics of pack cycling would tend to disfavor those who have an extremely high percentage of type I fibers, whereas in track racing access to different gears on a bicycle would tend negate some of the advantage provided by having an extremely high percentage of type II fibers.

References

1. Costill DL, Daniels J, Evans W, Fink W, Krahenbuhl G, Saltin B. Skeletal muscle enzymes and fiber composition in male and female track athletes. J Appl Physiol 1976; 40:149-154.

2. Coggan AR, Spina RJ, Rogers MA, King DS, Brown M, Nemeth PM, Holloszy JO. Histochemical and enzymatic comparison of the gastrocnemius muscle of young and elderly men and women. J Geront 1992; 47:B71-B76.

3. Nygaard E, Sanchez. Intramuscular variation of fiber types in the brachial biceps and the lateral vastus muscles of elderly men: how representative is a small biopsy sample? J Anat Rec 1982; 203:451-459.

4. Burke ER, Cerny F, Costill D, Fink W. Characteristics of skeletal muscle in competitive cyclists. Med Sci Sports 1977; 9:109-112.

5. Mackova E, Melichna J, Havlickova L, Placheta Z, Blahova D, Semiginovsky B. Skeletal muscle characteristics of sprint cyclists and nonathletes. Int J Sports Med 1986; 7:295-297.

Analyzing interval workouts using power data

by Andrew R. Coggan, Ph.D.

Interval training has been used by athletes in practically all endurance sports for decades. In many cases, designing and interpreting such workouts with respect to their specific physiological demands is relatively straightforward. For example, constant-intensity efforts of, say, 3-8 min duration with a work:rest ratio of 1:0.5 to 1:2 would, if done at an adequate intensity, be expected to place significant strain upon the O2 transport system, and thus contribute to an improvement in VO2max. At the same time, however, it is unlikely that any individual could repeatedly perform efforts of this duration at a high enough intensity to result in truly marked improvement in anaerobic capacity (although there might be some increase).

While the physiological demands of workouts similar to the one described above are fairly easy to understand, cyclists often perform interval sessions that are far more complex in nature, and thus much more difficult to interpret. For example, the work:rest ratio is often manipulated to try to mimic the demands of competition, i.e., to prepare for the seemingly stochastic nature of racing. In addition, most cyclists perform their interval sessions on the open road, where the terrain may vary, versus on a flat track as, e.g., runners tend to do. Finally, the higher speeds achievable by cyclists vs. runners (or swimmers) means that they often accelerate “violently” at the start of intervals, which at least has the potential to result in significant neuromuscular demands not found with steadier efforts, or those that begin with more of a rolling or flying start. Examples of the sorts of training sessions that would fall into this 2nd category include the 30 s on, 30 s off (or 40 s on, 20 s) off intervals often used to simulate the changes in pace that occur in criteriums or when racing off-road, ladder sessions such as the 1 min – 2 min – 3 min – 4 min – 4 min – 3 min – 2 min – 1 min intervals described by Joe Friel or the 1500 m descending to 100 m session popularized by Eddie Borysewicz, etc.

In our book we provide guidelines (via the training levels) for appropriate power ranges to target when doing different types of intervals, and also discuss how to pace such intervals as well as when to stop doing them (i.e., the intervals-to-exhaustion concept). These guidelines and suggestions, however, are most useful when applied to “plain vanilla” intervals workouts such as described in the 1st paragraph above, and don’t always tell the fully story when it comes to the more complex sorts of training sessions described in the 2nd paragraph. What I would like to discuss here is how you can gain deeper insight into the specific demands of such interval workouts by analyzing them to answer three questions:

What was the power after smoothing the data using a 30 s rolling average?

The physiological responses to exercise follow a characteristic time course, many of which are directly or indirectly related to the kinetics of changes in the rate of mitochondrial respiration in muscle at the onset and offset of exercise. Thus, smoothing of the power data using a 30 s rolling average can be used to provide an approximation of the time course of changes in muscle, and hence whole-body, VO2, and hence aid in understanding the metabolic and cardiorespiratory demands of a given training session. (An exponentially-weighted moving average would theoretically provide an even better estimate of changes in VO2, but even that would be an oversimplification due to intensity- and duration/fatigue-related changes in O2 demand and kinetics.)

For example, if some particular combination of work and rest periods and power outputs results in a smoothed power output that approaches or exceeds perhaps 110% of the individual’s functional threshold power for several minutes at a time, then that workout likely represents a significant stimulus for adaptation in, or at least maintenance of, VO2max. On the other hand, if the work periods are of insufficient intensity or duration and/or the rest periods between them are so easy and/or so long that this is not true, then this will not be true – in that case, there is much less assurance that the individual’s maximal aerobic capacity has been significantly stressed, and some other aspect of physiological function, e.g., neuromuscular power or anaerobic capacity must have been primarily “targeted”. Indeed, if the smoothed power significantly exceeds approximately 120% of functional threshold power for any significant duration, then the athlete’s anaerobic capacity has almost certainly been stressed to at least some degree, since in most cyclists 120% of functional threshold power approximates power at VO2max, and VO2max sets the upper limit to aerobic energy production. Improvements in anaerobic capacity may also be stimulated by repeatedly incurring a large O2 deficit at the onset of each interval, which would be apparent from a significant difference in the areas under the curves of the actual and smoothed powers during the early portion of each effort.

What does a quadrant analysis show?

Examining the smoothed power from a series of intervals can provide insight into the metabolic and cardiovascular demands of the workout, as described above. To fully understand the neuromuscular demands, however, it is often useful to perform a quadrant analysis, i.e., to calculate the average effective pedal force (AEPF) and circumferential pedal velocity (CPV) used to generate the power and plot them against each other. Doing so provides an easy way of appreciating the effects of, e.g., starting each interval with a hard jump in a large gear from a slow speed versus just rolling into the start of each effort in a more moderate gear, or performing intervals on a hill versus on the flats. Quadrant analysis is especially helpful in understanding how use of very short work and rest periods (i.e., performing “microintervals”) can at least partially dissociate the metabolic/cardiorespiratory and neuromuscular demands, allowing greater emphasis on the latter than would otherwise occur.

Less obviously, quadrant analysis also provides an effective (albeit clearly not only) means of quickly understanding what the rider did during the rest periods. In particular, less experienced or less well-trained cyclists often experience the overwhelming urge to stop pedaling entirely after a strenuous effort. If so, there will be an absence of, or at least a relative paucity of, points in quadrants III and IV (since when cadence and hence CPV is zero, AEPF is undefined). Except when descending, though, it isn’t often that you get to coast for any extended period of time when racing. Continuing to “soft pedal” during each rest period may therefore not only better mimic the demands of actual competition, but could also enhance recovery from a physiological perspective, e.g., by increasing clearance of lactate, by activating the muscle pump, etc. Coaches therefore may (or may not) wish to encourage their athletes to pedal easily instead of coasting between intervals, with quadrant analysis providing a quick-and-easy way of visualizing whether this instruction was followed.

What was the normalized power for the entire session?

The normalized power algorithm is intended to be a predictor of physiological strain, not the adaptation(s) resulting from that strain. As such, the actual power really says more about the specific adaptations likely to result from a particular interval workout, especially when the data are smoothed in a physiologically-relevant manner as described above. Nonetheless, examining the normalized power for an interval training session can often be enlightening. Specifically, if the normalized power for a series of intervals (across all sets, and including the final rest period) lasting more than 20 min significantly exceeds the individual’s well-established mean maximal power curve for that duration, this implies that either:


1) their fitness has improved,
2) their power meter was miscalibrated, or
3) they have generated an “NP buster”.


While performing a series of interval-like efforts is one way of “breaking” the normalized power algorithm, this is actually quite difficult to do, especially when considering longer workouts. Calculating the normalized power for a series of intervals therefore provides a way of quickly ascertaining whether either of the first 1st possibilities listed above may hold true. In addition, the normalized power algorithm provides a good “sanity check” when designing new intervals sessions, i.e., if the normalized power over the entire series of work and rest periods exceeds a rider’s mean maximal power curve, it is highly unlikely that they will be able to complete the workout as planned. (Indeed, I have previously posted a list of theoretically-impossible intervals workouts on the wattage list here: http://groups.google.com/group/wattage/msg/22bd6beb194270eb and only one person reported that they were able to meet the challenge I put forth.) Conversely, if based on the normalized power (and the rider’s functional threshold power) the workout should be “doable” yet the rider still must cut the workout short, then this provides evidence that something else is amiss, e.g., they have not recovered adequately from prior training sessions, they are getting ill, their motivation was abnormally low due to outside stressors, etc.

Analyzing interval workouts using power data: a case study of 30 s on, 30 s off intervals

To help illustrate the ideas presented above, I analyzed an interval workout performed by another cyclist that consisted (after a ~30 min warm-up) of 20 x 30 s on, 30 s off, during which the rider targeted 370 W (~125% of their functional threshold power) during the work periods and 250 W (~85% of their functional threshold power) during the rest periods. Figure 1 below shows the actual power data during the intervals both without and with smoothing using a 30 s rolling average:

Figure 1. Power output during an interval training session consisting of 20 x 30 s on, 30 s off

As shown in Figure 1, the rider was reasonably successful in repeatedly hitting their goal powers, and completed the workout as planned. This is consistent with the fact that their normalized power for that 20 min block was 316 W, versus a functional threshold power of 300 W (i.e., the ratio of the functional threshold power to their normalized power during those 20 min was 300/316 = 0.95). The 30 s rolling average of power, on the other hand, oscillated between ~100 and ~120% of functional threshold power during the work periods. Based on this, the workout would be expected to place a sufficiently high demand upon the cardiovascular system to be considered a VO2max, or level 5, training session, at least from a maintenance perspective. On the other hand, the short rest periods and especially the relatively high intensity at which they were performed constrained the power the rider could produce during the work periods such that their anaerobic capacity does not appear to have been significantly challenged. They would have incurred a small O2 deficit at the onset of each intervals, but based on the “work deficit” determined by comparison of the unsmoothed and smoothed power data this would have represented only ~10% of their likely anaerobic capacity. Thus, to improve the latter they would need to increase the duration or reduce the intensity of the rest periods (or both), so that they could increase the intensity of work periods. They could also better target anaerobic capacity by increasing the duration (and intensity) of the work periods, e.g., by performing 10 x 60 s on, 60 s off, targeting 400 W, or 133% of their functional threshold power, during the work periods and 100 W, or 33% of functional threshold power, during the recovery periods (thus keeping the normalized power for the 20 min block to less than 316 W).

Although the workout in question is not likely to have contributed to any improvement in the rider’s anaerobic capacity, it did entail a moderate neuromuscular demand, as evidenced by the quadrant analysis plot shown in Figure 2 below:

Figure 2. Quadrant analysis of an interval training session consisting of 20 x 30 s on, 30 s off

As shown in the figure, the rider spent a significant portion of their time (56%, to be exact) in quadrants I and II during the intervals, implying that significant recruitment of type II, or fast-twitch, muscle fibers must have occurred. This was the result of not only the rider’s supra-functional threshold power during each work period, but also the fact that they initiated each 30 s effort from a cadence somewhat below their preferred cadence when time-trialing. Whether their AEPF represented a sufficient overload to improve their maximal neuromuscular power could only really be determined by comparing the data from this interval session to their maximal AEPF-CPV relationship. At a minimum, however, it is apparent that the intervals were effective in replicating the frequent, moderately high power accelerations that occur during mass start races, e.g., when exiting a sharp turn.

In summary, in this article I have described how to analyze power data from interval training sessions to gain deeper insight into their true physiological demands, and hence the nature of the adaptation(s) they are likely to produce. Applied to a typical 30 s, 30 s off series of intervals, the approach demonstrates why such intervals are so commonly used to prepare for the constant, “on-off” nature of criterium racing, which tend to place greater emphasis on neuromuscular power (e.g., jumping out of turns, launching attacks, sprinting at the finish) and VO2max (e.g., chasing down breakaways) than on anaerobic capacity or functional threshold power, both of which are called upon during more extended efforts of somewhat lower intensity than typically occur during criteriums. Indeed, this is reflected in the ‘\/\’ power profile found in many riders who tend to focus on/excel in such events (or who do a lot of unstructured endurance training combined with group rides and races). This is not to say, of course, that these are the perfect way of preparing for such races – rather, the point is that the approach I have described can often be profitably used to analyze interval workouts sessions of all types, so that training sessions are appropriately prescribed to meet the demands of any event.

Fatigability and BMX performance at the Olympic level

by Hunter Allen and Andrew R. Coggan, Ph.D.

HA: I have been involved in BMX since I was 11 years old, when I competed in my first race, and BMX has been dear to my heart ever since. It gave me the skills to become an elite MTB racer and later a pro on the road. Luckily for me, though, I realized that I don’t have enough fast twitch muscles to really succeed in BMX, and endurance was more my forté. However, I have continued to follow BMX over the years and when BMX became an Olympic sport in 2006 (for the 2008 Games), I knew that I wanted to contribute to the cause (besides, I had no data on elite BMXers!). As Technical Coach to the 2008 BMX Olympic Team, it was my job to outfit the team with SRM power meters, conduct a series of on-track and off -track tests, and begin to define the demands of BMX racing along with the abilities of the best riders in the world. Some of these data are described in the section on BMX in the new 2nd edition of our book, but we thought that it might be interesting to go into a bit more detail here.

For starters, it is important to realize that the Olympic BMX Super Cross track is not your regular backyard local track. This thing is practically a motocross track! It has a 30 foot tall starting ramp, the jumps are over 40 feet apart, the first turn berm is 25 feet tall, and the race lasted about 36 seconds, which is quite long for a BMX race. It is EXTREME. The demands of the track are different than a regular track and therefore some of the best BMXers on the national BMX circuit did not excel on this track. Check out the picture below (taken at the replica track at the Olympic Training Center in Chula Vista, CA)or view this quick video (http://feelbmx.com/videos/olympic-bmx-video-men-final) of the men’s Olympic final to see just how big and gnarly this track truly is!

One of the first things we tested in Chula Vista was the effort of the riders down the first straight away. I wanted to see how much time was spent pedaling vs. not pedaling (in the air mainly) and also how many watts they were able to put out coming down the start ramp and then coming out of the first turn. These were critical areas in the track and probably held the keys to success in BMX. The next thing we tested were full race laps with each athlete by themselves. This way we could see their fatigue resistance throughout the entire course without interference from other riders. Lastly, we did a few mock races to compare an actual race to the previous tests.

When I observed the riders during the tests themselves, it was really clear to me that they have quite a few “micro-rest” periods in each straightaway. So much time was spent in the air between the jumps that I knew this could be a critical component to an athlete’s ability to prevent fatigue near the end of the race. “Floating” over the jumps and relaxing in the air is definitely not a skill that all the Olympic hopefuls had and the riders that made the Olympic team clearly had this wired. Each race in fact was a series of “micro-bursts” and micro-rests. Another thing that I noticed was that the riders that were able to “corner-start” (that is, had the ability to replicate their effort leaving the starting gate while exiting the first turn) really had an advantage down the second straightway. If the rider could produce nearly the same watts they did off the starting gate, but now do it while rolling and exiting the first berm, it made a big difference.

When I looked at the data, I was blown away by some of the wattages that these athletes were putting out at the start and then down the first straight and exiting each turn. Another thing that blew me away was that not only were the best athletes putting out over 1800 W exiting the first turn, but they were pedaling at over 160 rpm and the best were over 180 rpm! Right away, it was obvious from the technical demands of the track and their physical performance that these BMXers were highly skilled and elite athletes. There was not a slacker in the bunch, and this was clearly not a sport for sissies.

I asked Andy to take a look at this data to see if he could see the same things that I was observing in analyzing the BMX power data and my observations at the track. I knew that if he put some thought and math into the data, that we might even learn more…..

ARC: When Hunter asked me to see what I could make of the power meter data mentioned above, the first question that came to mind was just how much of a decrease in power actually occurred during the race. In particular, I was curious as to how the fatigability of these athletes compared to other data that are available, e.g., to published standards for the fatigue index measured as part of the original Wingate test, and/or to the large amount of data we have collected on road and track cyclists since the power profiling was developed in 2003. This question, however, could not completely answered by simply looking at the “raw” power data. This is because single-speed BMX bicycles are typically fitted with low gears, e.g., 50-55 gear-inches, to help the rider get the “hole shot”, i.e., to maximize the rider’s ability to rapidly accelerate away from the starting gate and thus beat their competitors to the first jump or turn. As a consequence, for most of the race a BMX cyclist’s cadence is much higher than is optimal in terms of power output – for the men at the training camp, for example, peak cadence during the time trials was typically over 170 rpm. When combined with the long crank arms such riders often use, this means that their circumferential pedal velocity, and hence muscle shortening velocity, was extremely high, which in and of itself would tend to limit their power production later in the race. In other words, power might be lower at the end than at the start of the race not due to muscle fatigue per se, but simply due to the difference in cadence.

Fortunately, the “gate start” and “first straight” efforts that Hunter had the riders perform provided me with a way of correcting the data for the effect described above. I did so by using these multiple, but very brief (i.e., 4-6 pedal stroke), efforts to reconstruct each rider’s average effective pedal force (AEPF) – circumferential pedal velocity (CPV) relationship, as shown in Figure 1 below:

Figure 1: Relationship between average effective pedal force and circumferential pedal velocity from a representative athlete.

Based on this relationship, it was possible to calculate each rider’s:

1) maximal AEPF (AEPFmax), which is the Y intercept of the fitted line shown above;

2) maximal CPV (CPVmax), which is the X intercept of the fitted line shown above;

3) maximal power (Pmax), which is equal to 0.25 x AEPFmax x CPVmax; and

4) the CPV at which Pmax is produced (CPVopt), which is equal to 0.5 x CPVmax.

More importantly, these data allowed me to express the AEPF at any time during a full-lap effort as a percentage of the maximal AEPF (and hence power) that the rider could produce at their cadence, and hence CPV, at that time. By doing so, it was possible to determine the rider’s fatigability independent of changes in their pedaling rate. (Note that although all of the SRM handlebar computers were set to record data at 0.5 s intervals, in practice this actually means that all data are based on individual pedal cycles. This is because the SRM averages data over a full pedal revolution before calculating power, cadence, etc., and none of the riders ever pedaled fast enough to complete two full pedal revolutions in 0.5 s, i.e., in no case did the measured cadence exceed 240 rpm.)

Having determined that the above approach was feasible, I chose to analyze the data from the one or two solo full-lap efforts that each rider performed, on the assumption that these data would be more reflective of their “pure” physical abilities (i.e., skeletal muscle characteristics, fitness, motor control) than the data from the mock races, where interactions with other riders might occur. An example of the results of these analyses is shown below:

Figure 2: Average effective pedal force (AEPF) expressed as a percentage of circumferential pedal velocity (CPV)-specific maxima as a function of time during two TTs by a representative athlete.

As can be seen in the figure, the rider in question spent much of their time not actually pedaling, and even when they did pedal they did not and/or could not always do so very forcefully/powerfully. However, their pattern of force (and hence power, since the data are expressed relative to CPV) application was very consistent during the two time trial efforts, something that was true of the other riders as well. This indicated to me that the consistent variation in force/power must have to do with the placement of turns, jumps, etc., on the course, but since I was not present during the data collection I had to ask Hunter to fill me in on such details.

HA: The Super Cross course starts with a mammoth, 30 foot tall starting ramp that had up to a 53 percent drop at the steepest section, and then into a short flat section leading to the first double jump which was 40 feet from peak to peak. The riders coming off the starting ramp could only pedal so much before reaching a critical velocity and having to prepare for this first double jump. This first double jump was so intimidating that even World Champions had to ride it quite a few times before getting their nerve up to jump it. Next came another double jump into a long “tabletop” jump in which the riders couldn’t pedal over, and then lead into the first turn. Coming out of the first turn at over 35 miles per hour, the riders had to “corner-start” and pedal hard for a few pedal strokes in order to hit the next “step-up” jump, which peaked at 25 feet tall. This was followed by a smaller double jump, after which they had to jump over the women’s course berm, leaping a massive chasm onto the men’s course and second berm. If the rider did not have a solid “corner-start”, then that could play out badly at the end of the straight when they had to leap the chasm. The third straight was characterized by almost continual jumps, so much so that it was only possible to get in one or two pedal strokes and the final straight had two more jumps in it with a flat sprint to the finish.

ARC: Once I understood why these cyclists were or were not pedaling at certain times, I decided to focus on their force/power during the last two or three pedal strokes, as an indicator of how much they fatigued during each TT. These data, along with data derived from the force-velocity relationship previously described, are shown for four athletes in the table below. These individuals were chosen for comparison because although they were all very similar in terms of performance in the unfatigued state (i.e., data shown in the first four columns), the first two failed to make the U.S. Olympic team, whereas the last two won the Silver and Bronze medals, respectively.

Table 1. Force-velocity relationship during cycling, maximal power, and power at end of TT for four athletes.

Upon examining these data, what became evident is that the more successful athletes (i.e., th last two) were able to maintain a higher relative (to CPV) power output during the latter portion of the race, i.e., they exhibited less fatigue.

The question then arises as to what might account for the greater fatigue resistance (lesser fatigability) of the cyclists C and D compared to riders A and B. One possibility, of course, is a difference in fitness/conditioning, and indeed other men at the camp seemed to be somewhat lacking in this respect. This did not, though, appear to be true for cyclists A and B. Another possibility is an inherent difference in muscle fiber type, i.e., it is possible that cyclists A and B fatigued more rapidly because they had a higher percentage of type II, or fast-twitch, muscle fibers. As shown in Table 1, however, the slope of the AEPF-CPV relationship was similar in all four men (and indeed across all of those tested, including a rider who recorded what to my knowledge is the highest-ever 5 s human power output of 25.2 W/kg), suggesting that they were all also similar with respect to muscle fiber type (a higher percentage of type II fibers would be associated with a shallower slope of the AEPF-CPV line, i.e., force and hence power would fall off less rapidly with increases in CPV and hence muscle shortening velocity).

If not fitness or fiber type, what does explain the difference in fatigability between these otherwise very closely-matched athletes? As it turns out, it appears that cyclists A and B fatigued more than cyclists C and D simply because they pedaled more. Specifically, based on the SRM data cyclists A and B completed 27 and 34 pedal revolutions during their TTs, whereas cyclists C and D pedaled only 20 and 18 times, respectively. This is not because cyclists A and B used markedly lower gearing, as they did not – rather, the higher number of pedal revolutions was apparently the result of their attempting to generate power at times when it was difficult, or even impossible (i.e., when air-borne), to do so. In contrast, cyclists C and D pedaled less often, but when they did they did so with maximum effectiveness, e.g., the three pedal revolutions at ~100% of velocity-specific force and hence power performed 7-8 s into the race shown in Figure 2. The remainder of the time, they apparently “rested” their legs as much as possible by “floating” over the jumps, etc.

HA: To summarize, then, here are some important lessons to be learned from these data:

1. Pedal less, win more. While this is well known in road racing circles, it is not so well known in BMX. As described by Andy above, however, we found that the guys who made the Olympic team pedaled less than those that did not make the team.
2. 100% "fast twitchers" may not make the best BMXers, at least for the SX. In particular, the guy that cracked out the biggest numbers in terms of Pmax (which is measured over a single revolution) and maximal 5 s power also did not make the team.
3. 
   Fatigue resistance matters. Again as described above, the Olympians fatigued the least over the duration of the race. They were able to make the most of their "micro-rest" periods, along with superior fatigue resistance gained via conditioning. On the hand, riders who did not make the team fatigued more, either simply as a result of pedaling too much (see point #1 above) or (in the case of other riders whose data are not shown) due to lack of fitness. 

We sincerely thank Dr. Steve Johnson at USA Cycling for permission to share these data and Mike Day (Silver Medalist, 2008 Olympic Games) and Donny Robinson (Bronze Medalist, 2008 Olympic Games) for allowing themselves to be identified in this article.

The "other" quad...

(Reprinted with permission from the March 2010 issue of ROAD magazine: http://bluetoad.com/publication/?i=31609)

by Hunter Allen - Let’s think about those leg muscles and all that they do, especially the quadriceps muscles (the big ones on top of your upper leg). The ‘quads’ tend to be the leg muscles that get the most work and also the most sore, and the quads contribute a significant amount of work toward propelling you forward on a bicycle as they are the muscles that help to push the pedal downward on each stroke. As you know, sometimes you have to contract your quads more forcefully when your cadence is slower and when you are going up a steep hill for example and other times you don’t have to push down very forcefully at all, but your cadence is very quick. The quadriceps, along with the rest of the lower body muscles, need to be able to contract forcefully and slowly and also contract lightly and quickly in order for you to become a successful cyclist. To me, it’s another one of the great things that makes cycling so challenging: You need to have the ability to pedal both hard and slow, along with easy and fast. The best cyclists can do a bit of each and while even the best cyclists have strength in one or the other, they also train these skills as well to improve the weaker of the two skills. You see, the rider that feels more comfortable mashing a bigger gear most likely has more ‘fast’ twitch muscle fibers (type II), whereas the rider that likes to ‘spin’ typically uses more slow twitch fibers (type I) and this is important because if you event is going to require you to pedal hard and slow, but in training you always pedal easy and quick, then you might not be ready for your event.

This is where the ‘other’ quad comes into play. That other quad is called Quadrant Analysis. Quadrant Analysis is a tool that allows you to understand whether or not you are indeed pedaling correctly for your given event. What does ‘correct’ pedaling mean? Well, thinking back to this idea that “training to the specific demands of the sport is paramount to succeeding in that sport” and of course you would agree that practicing basketball all day is not going to help you win a 100 mile road race, and the same applies within the sport as well. Riding at a cadence of 100 rpm for 3 hours is not going to prepare you well for a race that is going demand that you ride at 80 rpm for 2 hours and then 100rpm for the last hour. You just simply are not training specifically for the demands of the event. This is where Quadrant Analysis comes into play.

Scientific studies using a variety of techniques have found that threshold power (FTP) represents not only a threshold in terms of the power that an athlete can sustain, but also somewhat of a threshold in terms of fast-twitch fiber recruitment. To state it another way: When pedaling at a typical self-selected cadence, functional threshold power appears to occur at the power (and thus force) at which significant fast-twitch fiber recruitment first begins. Thus, not only does cardiovascular fitness play a role in your success, but so does your neuromuscular function. Neuromuscular function sounds complicated, but it simply means how fast you can contract a muscle, how strongly you can contract it, and how long you can keep it contracted before relaxing it again. Even though no commercial power meter has the ability to directly measure the forces applied to the pedals, it’s possible to derive the average (over 360 degrees) effective (tangential to the cranks) pedal force (both legs combined) or AEPF from the power and cadence data. (Refer to page 132- Training and Racing with a Power Meter for more info). One must also understand the relationship to velocity in order to really get a better understanding of this and circumferential pedal velocity (how fast the pedal moves around the circle it makes) or CPV can also be derived from cadence and crank length.

What does this mean to you as a cyclist? Well, it means that with your power meter and Quadrant Analysis, you can make sure that you are indeed training properly for the cardiovascular AND neuromuscular demands of your event. Enough of this physiology speak; let’s examine some different Quadrant Analysis plots so you can understand how to apply this in your own training. The first plot is a plot showing you what a typical criterium would look like. Quadrant Analysis is incorporated into the newest version of the WKO+ software-Version 3.0 and is available online at http://www.trainingpeaks.com/. Figure 1 (below) shows how most of this race was spent in quadrant II (QII; high force, slow pedaling) and quadrant I (QI; high force, fast pedaling) and this is characteristic of a criterium in which the rider has to keep a high cadence to respond quickly to changes in speed, along with hard sprints which come relatively often either out of turns, for premes or for hard attacks.

Figure 1. Quadrant analysis of a typical criterium.

Quadrant Analysis is useful to first gain an understanding of just what the plots represent and then compare them to each other and to training. Let’s examine another race, this time the Tour De France stage win by Markus Burghardt in 2008 (Figure 2 below).

Figure 2. Quadrant Analysis of Markus Burghardt's Tour de France stage win in 2008.

In this Quadrant Analysis plot, we see an incredible amount of time, over 72%, in quadrant IV (QIV), which is very surprising since Markus is quite a large rider and one would assume he has a higher percentage of type II muscle fibers and he might self-select his cadence at a lower cadence with higher force. However, one explanation might be that this demonstrates how incredibly fatigued his muscles are (stage 18 after all!) and that he had to pedal with a higher cadence in order to produce the same wattage. The more fatigued we are, the less strength we have in the type II muscle fibers and therefore we have to shift our recruitment to more type I muscle fibers in order to achieve the same FTP. Another explanation might be that he simply is very good at conserving muscular strength and has learned over the years of training and racing that if he keeps his cadence over 95rpm, then he’ll have more energy in the finale since type II (fast twitch) muscles require more glycogen and the more he conserves the better he’ll be towards the end. Lastly, the red colored points in this plot are the points from the three climbs on this stage and shift to QII, as he has to produce a higher force at a slower cadence in order to get over the climbs near his FTP. Points in QII require a tremendous amount of glycogen, so time spent in QII is very ‘expensive’ so to speak from an energy standpoint, but at the same time a necessary ‘expense’ if he Markus was to stay with his breakaway companion.

Let’s examine one more power file from yet a different discipline and then we’ll take a look at how important it is to do Multi-File Quadrant Analyses as well. In this example, let’s take a look at a flat time trial.

Figure 3. Quadrant Analysis of a flat time trial.

A flat time trial produces another interesting and unique plot. In Figure 3 above, the yellow points that are following the ‘threshold curved lines’ demonstrate that instead of wildly fluctuating watts and cadence, his wattage stayed nearly constant (just below, at or just above FTP), while only his force and cadence changed. He focused his effort and maintained within a narrow range his wattage, so narrow that when you examine the percentage of time spent in QII and QIV, we find that he spent almost identical time in each. This does make sense as he was trying to maintain his FTP for the entire time and was limited by his cardiovascular system, but could still change the AEPF and CPV while maintaining his threshold power. This Quadrant Analysis plot also helps us to see that a time trial demands hardly any time in QI (maybe only the start and turn-around) and a flat time trial is characterized by an enormous amount of solid, steady hard effort, which is very obvious to anyone that has done a flat time trial, but on the other hand if you are only doing group rides and mass-start races, you might not be ready for the constant neuromuscular demand that a time trial takes.

Now that you have a good understanding about the different quadrants, the different shapes of plots, let’s compare two different workouts together. The purpose of this is very similar to how you might want to use this yourself. Let’s compare a training ride to a race and see if the neuromuscular demands are similar. If they are, then great! That means the athlete is training in the correct quadrant and should be well suited for the racing demands. If not, then the athlete needs to figure out a way to train in the same quadrant(s) as the races in order to better handle the neuromuscular demands of racing. In this example, let’s compare a group ride in which the athlete did, including a series of hills in preparation for an upcoming hilly road race against the actual race which had a steep hill in it.

Figure 4. Multi-file Quadrant Analysis of a hilly training session vs. a hilly race.

In Figure 4 above, we see the race in red and the training ride in yellow and right away we can see that they do not match each other. The training ride had a lot more time in QII and Quadrant III (QIII) then the race did. The yellow points in QII represent the hard hills in the group ride and it appears that the hills in the group ride demand a lot higher forces than the hills in the race, which are represented by the red points in QI. So, this means that the hills in the race were more like sprints (high force and fast cadence), whereas the hills in the group ride were more like…well…hard steep hills (high force and slower cadence). Note another significant difference as well, which is that the amount of time in QIV for the race (35%) versus for the group ride (20%), and this 15% difference is certainly significant as pedaling fast and not too hard is a critical skill to have in mass start races as you have to be able to match speed changes in order to stay on the wheels in front of you. In this example, the group training ride did not match up well with the actual race the athlete was training for, so my recommendation would be to skip his group training ride and do a ride more closely aligned with the upcoming race.

In our last example, let’s compare a criterium to a micro-burst workout. A micro-burst workout is one in which you ride at 150% of FTP for 15 seconds “ON” and then at 50% of FTP for 15 seconds “OFF” and you continue to repeat this for a period of ten minutes, often 3-6 sets of them. The micro-burst workout is a great one to do in preparation for a criterium or any type of ‘bursty’ cycling event, including cyclo-cross. The workout can be done on either an indoor trainer or outdoors on the road. When we create a multi-file Quadrant Analysis using the WKO+ version 3.0 software, we see right away that the micro-burst workout (yellow points) has a significant portion of time in QI and so does the criterium (red points).

Figure 5. Multi-file Quadrant Analysis of a micro-burst workout vs. a criterium.

This is where the two workouts are similar and the “ON” portion of the micro-burst workout matches up well with the criterium, so that would be a nice example of training specifically for the demands of the event. However, most of the criterium is actually in the QIV, which is low force, and fast pedaling and most of the points in QIV would be criteriums, but the rest period (OFF) in the micro-burst workout is in QIII, which is low force but slow pedaling. So, the athlete in the off period needed to maintain a higher cadence in the “OFF” period than he actually did in order to even better simulate the criterium. I would suggest that both the “ON” and “OFF” periods, the cadence should be in the 90rpm or higher range. Besides this, I would say that this workout was a good example of trying to match up the demands of a criterium with a workout based on power.

In conclusion, it’s not just your cardiovascular output (FTP) that determines your success as a cyclist. It’s also your neuromuscular output or how you create the watts that also determine your ability to succeed. Each of us has strengths and weaknesses related to how we prefer to create the watts. Some like to pedal at a faster cadence and some of us prefer to use a slower cadence but push harder on the pedals and while neither is necessarily better or worse than the other, certain races and terrain demand more of one than another. The key for you to understand is that when you train, you must train specifically for that event which has the unique demands so that you will be ready for those demands. If you need to be able to go up a 15% hill and do it in your 23 tooth cog, then you had better make sure you train in QII enough to be ready for that much muscular strength. If you are going to do a time trial, then it’s important that you are ready for a sustained hard effort in QII and QIV, without any ‘recovery breaks’ in QIII. These are just some of the examples of how important understanding the neuromuscular demands are in racing and training and as you go into your 2010 season, it makes good sense to get a clear picture of the exact demands you’ll need to meet in order to win!