In this previous blog entry:
http://www.trainingandracingwithapowermeter.com/2010/12/prediction-of-muscle-fiber-type-from_07.html
I described a way of predicting muscle fiber type distribution based on force-velocity (or power-velocity) data collected using an SRM. Because each individual's situation is different, I had not intended to go into detail regarding how to actually collect such data. My previous post generated more interest than I anticipated, however, so in this post I will attempt to at least provide some general guidelines. Nonetheless, I anticipate that anyone attempting to perform such testing themselves will likely have to go through a bit of trial-and-error to perfect their own approach.
Force-velocity testing using the inertial load method
As described previously, the force-velocity relationship during cycling is essentially linear (such that the power-velocity relationship is parabolic in nature). This has been demonstrated in various studies, either via use of a specially-constructed isokinetic ergometer (1) or by simply having subjects perform multiple, maximal efforts against varying resistances on a standard friction-braked (Monark) ergometer (2). However, the simplest and hence most elegant approach of all is the inertial load method devised by Dr. Jim Martin (3). Dr. Martin's method utilizes a standard Monark ergometer that has been modified such that 1) the only source of resistance is the inertia of the flywheel and 2) the position of the flywheel can be measured with high temporal (i.e., 1 microsecond) resolution (cf. Fig. 1).
Figure 1. Inertial load ergometer developed by Dr. Jim Martin.
With this ergometer, near-instantaneous power can be measured every 3 deg of crank revolution based on the rate of acceleration of the flywheel. Alternatively, power can be averaged over a complete pedal revolution, thus reflecting the combined extension/flexion of both legs. In either case, judicious choice of the inertial load (which depends upon the moment of inertia of the flywheel and the gearing) enables the subject to reach their optimal pedaling rate in just 2 s, and to complete 6.5 revolutions in just 4 s. This makes it possible to determine an individual's force-velocity (or power-velocity) relationship during a single, brief test in which significant fatigue does not occur.
Few readers of this blog are likely to have access to an ergometer like the one developed by Dr. Martin. However, it is possible to obtain similar data using an SRM by 1) recording data at a sufficiently high frequency, and 2) selecting an appropriate resistance. The "how" and "why" of this approach are described below.
Force-velocity testing using an SRM
Sampling frequency
Although a standard SRM system cannot match the very high temporal/spatial resolution of Dr. Martin's ergometer, it is possible to capture the average data for each individual pedal stroke, which is all that is needed to determine the force-velocity relationship. This can be achieved by selecting the highest possible recording frequency/shortest possible time interval (e.g., 10 Hz, or every 0.1 s, when using a PowerControl IV). This will cause the SRM to "stutter", i.e., to repeatedly report the same values for power and cadence for individual pedal strokes, as shown in Table 1 below:
In this particular example, the SRM began recording data upon completion of (presumably) the first crank revolution, which was performed at an average of 444 W/55 rpm. These same values were then repeated until completion of the second crank revolution, which occurred between 1.1 and 1.2 s after the first and was performed at an average of 668 W/88 rpm. The third crank revolution was then completed between 1.7 and 1.8 s at an average of 721 W/105 rpm, etc. In other words, as long as the time required for a single crank revolution is shorter than the sampling interval, the results obtained are actually event-based (i.e., are averages over individual pedal revolutions) rather than time-based. Indeed, even with a newer PowerContol (i.e., version V and above) that can only record data at a maximum of 2 Hz (i.e., every 0.5 s), an individual must be pedaling at >120 rpm before multiple pedal strokes will be averaged together. Consequently, essentially identical results are obtained regardless of whether data are recorded at 10 Hz (every 0.1 s), 5 Hz (every 0.2 s), or 2 Hz (every 0.1 s), as shown in Figure 2 below:
Figure 2. Effect of different sampling frequencies on data obtained during force-velocity testing.
Appropriate resistance
The other key aspect when performing force-velocity testing using an SRM is selection of an appropriate resistance. If the resistance is too low, then the individual will be able to accelerate the cranks too rapidly, and only a few point(s) far to the right/down the force-velocity relationship will be obtained, and/or force will fall off excessivly due to the difficulty in coordinating muscular actions at very high pedaling rates. On the other hand, if the resistance is too great, the subject will not be able to accelerate the cranks rapidly enough, and only a few data points at the upper left end of the line will be obtained before fatigue begins to occur. If the resistance is just right, however, data will be obtained across a broad span of velocities (and hence forces) before fatigue develops.
These points are illustrated in Figure 3 below, which displays data from force-velocity tests performed with different inertial loads. With the high and medium inertial loads, I was not able to accelerate the cranks rapidly enough, and hence "fell off" my force-velocity line after 4 s (denoted by the arrows) at very low and moderate velocities, respectively. Conversely, with a lower inertial load, comparable to that of Dr. Martin's ergometer, data were obtained over a broader range of velocities before fatigue ensued.
Figure 3. Effect of inertial load on data obtained during force-velocity testing.
Note that the regression line was calculated by excluding all data collected after 4 s then pooling the results from all three tests. The SRM was set to record data at 2 Hz.
The data shown above were obtained by mounting my bicycle in a Velodyne trainer and then varying the inertial load provided by the Velodyne's flywheel by simply using different gear ratios. While it would be possible to provide guidelines for appropriate inertial loads to for others to try, the wide variety of conditions under which such testing may be performed as well as uncertainty regarding the exact mass/moment of inertia of particular trainers, rollers, etc., means that this would be much less helpful than it might at first appear. As a general rule, however, individuals attempting such testing using the typical low-inertia magnetic or fluid trainer are likely to find that they need to use moderate-to-large gears to obtain good data. On the other hand, those attempting such testing outdoors will need to use very low gears - lower, in fact, than usually found on a road racing bicycle. In any case, the key point is that the cyclist must be able to accelerate the pedals rapidly, but not too rapidly, something that is readily determined via preliminary tests.
Reproducibility
When adhering to the above guidelines, excellent reproducibility can be obtained, with both within-day and between-day coefficient of variations generally being 2% or less as shown in Table 2 below:
In particular, the circumferential pedal velocity at which maximal power is produced (i.e., CPVopt)which is the basis for prediction of muscle fiber type distribution using the data of Hautier et al. (2), is highly reproducible.
Standing vs. sitting
By recruiting additional upper-body musculature, standing out of the saddle increases the maximal power that an individual can produce by roughly 10%. With the exception of experienced BMX riders, however, few cyclists are well-practiced at pedaling both rapidly and powerfully while standing. As well, the need to support 100% of body mass means that fatigue may develop more rapidly. In any case, the usual effect of standing is to increase the Y interecept but also to steepen the slope of the force-velocity relationship, as shown in Figure 4:
Figure 4. Effect of standing on the force-velocity relationship.
As a consequence, the circumferential pedal velocity associated with maximal power output will generally be shifted to a lower value, which will lead to underestimation of the percentage of type II fibers than an individual possess when using the equation presented previously (which is based data collected while seated). This would therefore seemingly preclude use of data from, e.g., standing start efforts performed using typical gears as part of normal training or racing to predict an individual's muscle fiber type.
What about other powermeters?
Unfortunately, as indicated at the outset of this series of blog entries, at least in my hands powermeters other than the SRM do not appear to be able to provide data of sufficient quality to permit accurate (or at least easy) determination of an individual's force-velocity relationship while cycling. I will discuss these issues and provide some examples in the next entry.
References
1. McCartney N, Heigenhauser GJF, Jones NL. Power output and fatigue of human muscle in maximal cycling exercise. 1983; 55:218-224.
2. 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.
3. Martin JC, Wagner BM, Coyle EF. Inertial-load method determines maximal cycling power in a single exercise bout. Med Sci Sports Exerc 1997; 29:1505-1512.
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