Thursday, August 19, 2010

"Successful aging" and changes in functional threshold power

by Andrew R. Coggan, Ph.D.

"Successful aging" has been defined by gerontologists as "multidimensional, encompassing the avoidance of disease and disability, the maintenance of high physical and cognitive function, and sustained engagement in social and productive activities" (1). Successful aging is therefore considered the antithesis of "usual aging", in which extrinsic factors such as habitual diet, level of physical activity, other personal health habits, and psychosocial factors magnify the effects of aging per se, contributing to the development of disability/disease and declines in physical, cognitide, and social functioning with the passage of time. In this context, Carl Groves' recent performances at master nationals as described in Hunter's post here:

http://www.trainingandracingwithapowermeter.com/2010/08/can-playing-saxophone-raise-your-vo2.html

strikes me as a superb example of successful aging, at least from the perspective of physical function. The fact that Carl was one of the first cyclists I met when I started riding and racing back in the mid-1970s makes his story especially inspirational to me.

In thinking about such issues, though, I began to wonder about the extent to which his high level of performance could be ascribed to minimizing the reduction in physiological characteristics (such as VO2max) that occur with aging, versus simply starting from a much higher-than-average level when he was younger. In other words, is he so fast (powerful) at age 82 because he hasn't slowed down as much as his peers, or has he always just been really, really fast (powerful)?

The question posed above obviously can't be answered with any certainty, since there is no way of knowing what Carl's functional threshold power actually was when he was younger. It is, however, possible to make a reasonable approximation, at least if you are willing to make the following assumptions with respect to the effects of aging:

1) regardless of training status, VO2max declines at a rate of 0.5 mL/min/kg/y starting at age 30;
2) lactate threshold does not change relative to VO2max;
3) gross efficiency remains constant at 23%; and
4) consumption of 1 mL of O2 yields 20.9 J of energy at the cellular level.

In the interests of space, I won't go into any detail regarding the basis for these assumptions, other than to emphasize that they are generally well-supported by the scientific literature. The one exception to this claim might be assumption #1 above, as some studies have suggested that training may slow the rate of decline in VO2max with age. Other studies, however, have not supported this conclusion, and if anything assumption #1 is conservative when extrapolating out beyond the 7th or 8th decade of life. Assumptions #2 and #3 are also conservative in the present context (since if anything both relative lactate threshold and efficiency seem to increase with age), thus tending to counter any error introduced by assumption #1.

Combining the above assumptions, the prediction would be that with aging (and assuming no significant changes in body mass over time), functional threshold power should decrease at a rate of 0.04 W/kg/y. If you then apply that rate-of-change to estimate Carl's functional threshold power at, say, age 40 the answer you derive is 3.83 W/kg + (42 y x 0.04 W/kg/y) = 5.51 W/kg. That is certainly a very high value, but clearly not unbelievable, and in fact is consistent with his performance (e.g., 2nd in the Indiana state championships, 13th at nationals) when he raced in the Amateur Bicycle League of America's (as USA Cycling used to be know) 40+ 'Veteran' class back in the 1970s.

As indicated above, probably the most questionable assumption I have made in performing this back-of-the-envelope analysis is that VO2max declines linearly at fixed rate throughout adulthood. In fact, based on both direct laboratory-based measurements as well as world record performances in most, if not all, endurance sports, it appears that the rate of decline in VO2max may actually acclerate as we go from being among the "young old" to being among the "older old" and "oldest old". This is why assuming a fixed rate of decline of 0.5 mL/min/kg/y as I have done can be considered a conservative assumption, as mentioned previously. Thus, my explanation for Carl's impressive performance ability at age 82, not only relative to others his age but even relative to many younger riders, is not that he was a physiological outlier when he was younger or even that he has managed to avoid the usual age-related decrement in VO2max and hence in sustainable power output. Rather, it appears to me that where he stands out is in the fact that he seems to still be on the linear portion of the curve relating VO2max to age, i.e., he appears to me to be biologically younger than suggested by his chronological years.

While Carl ascribes his succesful aging in terms of his cycling ability as being due to his lifelong saxophone playing, I believe it probably has more to due with his genes, as evidenced by his centarian mother. In any case, however, I do know this: I can only hope to be able to ride like Carl when I am his age!

References

1. Rowe JW, Kahn RL. Successful aging. Gerontologist. 1997; 37:433-440.

Friday, August 13, 2010

Can playing a saxophone raise your Vo2 Power? 82 year old says it's true!

Have to brag a little bit again.   Carl is an inspiration to us all. 

Carl has made some huge gains and won (2) national championship jerseys at Masters Nationals this year!    He's also on the way to World championships in Austria next week.... Hopefully he'll bring back a rainbow or two.

Carl Grove raised his FTP from 220 to 240 this year.


He's 82.......

weighs 138lbs.

He uses a Quarq, downloads each day

How many 82 year olds do you know that even use a computer much less train with a power meter?
Won both TT at nats- by over 7 minutes and Road race by 15 minutes


Training with power isn't just for the young guys...

Great article and pictures of Carl are here. Check it out!





Hunter



P.S. - Andy C. Carl said he remembers you from 1970's racing and thought you were "a talented young lad" back then......

Tuesday, August 10, 2010

Estimation of functional threshold pace based on blood lactate data: the Dmax method

by Andrew R. Coggan, Ph.D.

In response to my previous post:

http://www.trainingandracingwithapowermeter.com/2010/08/estimation-of-functional-threshold.html

a reader of this blog wrote to ask if using the Dmax method was also a good way to estimate Dr. Steve McGregor's functional threshold pace (http://home.trainingpeaks.com/articles/running/determining-functional-threshold-pace.aspx). Since I am not a runner and this blog is primarily about the use of bicycle-mounted powermeters, this question had not occurred to me. A quick PubMed search, though, turned up at least two studies of running performance in which the Dmax method has been compared with other approaches (there may be others as well):

1. Nicholson RM, Sleivert GG. Indices of lactate threshold and their relationship with 10-km running velocity. Med Sci Sports Exerc 33:339-342, 2001.

2. Papadopoulos C, Doyle JA, LaBudde BD. Relationship of running velocity of 2 distances and various lactate parameters. Int J Sports Physiol Perform 1:270-283, 2006.

As with the cycling research I cited in my previous post, in both of these studies LT as defined using the Dmax method was found to be more closely associated with actual performance, i.e., 10 km running pace, than other definitions of LT (although again some other definitions of LT were nearly as good). However, unlike with cycling pace at LT as determined using the Dmax method appears to be somewhat higher than functional threshold pace. The latter is defined a bit more fluidly than functional threshold power, but in performance terms it is defined as:

"Actual performance from a recent race or hard training run of 10-15 km.

a. If 10 km time was greater than 45 min, use 10 km

b. If 10 km time was less than 45 min, use 15 km or half marathon"

Since all of the athletes in these two studies were capable of running 10 km in under 45 min, their functional threshold pace would be determined using definition b, i.e., their pace during a 15-21.1 km race or hard training run. However, Nicholson and Sleivert (1) found that the Dmax point coincided almost exactly with 10 km race pace, whereas Papadopoulos, Doyle, and LaBudde (2) found that it was, on average, slightly (i.e., 0.2 km/h) albeit not significantly faster still. More specifically, the results of the latter study were:

Pace at Dmax: 16.8 ± 1.1 km/h

10 km pace: 16.6 ± 1.1 km/h

21.1 km pace: 15.5 ± 1.2 km/h

The reason for this apparent difference between cycling and running is not clear, but those wishing to use the Dmax approach to analyze blood lactate data from runners (or triathletes) should not lose sight of this offset or bias, especially when attempting to use the Dmax point as a surrogate for functional threshold power/pace.

The paper by Papadopoulos, Doyle, and LaBudde (2) provides me the opportunity to discuss another issue, which the precision of predicting performance based on blood lactate data, regardless of how the latter is analyzed. As indicated previously, a number of studies have found that LT defined using the Dmax method is well correlated with endurance performance ability - in fact, it is often the very best predictor (compared to, e.g., OBLA, etc.). Despite this fact, when it comes to prescribing training for a given individual there can be significant differences between what data from blood lactate testing predicts that an athlete can do versus what that individual can actually do. For example, in the study referenced above pace at Dmax was closesest to 10 km pace in only six of the 13 athletes tested - the others raced 10 km either slower (n=4) or faster (n=3) than predicted based on the pace at Dmax. What this means is that if training were prescribed relative to Dmax pace, not all of the runners would be expected to perform at the same effort relative to their actual performance ability. Whether this would prove to be a significant issue in the long run would depend upon the exact circumstances, but recognition of this fact illustrates why for several decades I have ascribed to the belief that:

The best predictor of performance is performance itself.

(Note that the above consideration is not unique to running. For example, although the unpublished data of Drs. Jobson and Edwards that I presented previously indicates that there is no significant difference between power at Dmax and functional threshold power and that the two are highly (i.e., R^2 = 0.94) correlated, the standard error of the estimate when attempting to predict functional threshold power based on the blood lactate data is ±17 W. In turn, this means that only half the time would the predicted value be expected to fall within ±7 W of the true value. My point here is not to claim that blood lactate data cannot be successfully used to prescribe and guide training, as clearly it can - rather, I simply wish to point out the true magnitude of the uncertainty involved, so that people do not overemphasize or overinterpret such data.)

Friday, August 6, 2010

Estimation of functional threshold power based on blood lactate data: the Dmax method

by Andrew R. Coggan, Ph.D.

I am periodically asked, "what is the best way to collect and analyze blood lactate data to determine functional threshold power?" Since applied research of this nature has never been my focus as an exercise physiologist, the most honest answer I can possibly give to such a question is, "I don't really know". I do, though, at least have an opinion, which is based on the relevant literature as well as my personal experience with lactate testing of cyclists. It is this opinion that I would like to share in this post.

As many readers of this blog are undoubtly aware, there are multiple definitions or criteria used to establish "threshold" on the basis of lactate measurements, as well as multiple approachs to obtaining said data in the first place. The reason for this is that regardless of how the data are obtained or analyzed, all such thresholds tend to be highly correlated not only with endurance performance ability, physiological responses, etc., but also with one another, such that there is only a limited incentive in the scientific community for any form of standardization. In other words, from a purely research/conceptual perspective one method for determining lactate threshold (LT) is essentially just as good as any other, such that there isn't any great reason to favor any particular approach. On the other hand, when dealing directly with athletes some form of standardization is often quite helpful (e.g., so that a given individual's data are more "transportable"), especially when comparisons are to be made to some other reference measurement, e.g., functional threshold power.

In the latter context, then, the method for determining LT that I find most appealing from both a theoretical as well as a practical perspective is the Dmax approach first described by Cheng et al. in 1992 (1). The Dmax method consists of plotting the lactate data obtained during an incremental exercise test to fatigue against the exercise intensity (i.e., power or VO2), smoothing it using a 3rd order polynomial, then finding the point on that curve that yields the maximum perpendicular distance from a straight line formed by joining the lowest and highest points on the curve. Since that description probably doesn't immediately make sense to everyone, here is an example, using data from a category 1 cyclocross rider who was kindly willing to share his test results:


Figure 1. Blood lactate data from a cat. 1 cyclist analyzed using the Dmax method


In the above figure, the blue diamonds are the actual lactate data, the red curve is the fitted polynomial, the blue line joins the lowest and highest points on this curve, and the black arrows illustrate how the Dmax point and associated power output are determined.

I find this approach at least theoretically appealing because:

1) it is objective rather than subjective,

2) it is has a high test-retest reproducibility;

3) it can be applied to data obtained during a standard incremental exercise test that can also be used to determine cycling efficiency and VO2max,

4) compared to use of a fixed blood lactate concentration (e.g., use of 4 mmol/L to define the onset of blood lactate concentration, or OBLA), Dmax should be less influenced by, e.g., variations in glycogen stores or training status, use of different lactate analyzers, etc; and

5) it yields a value for LT that appears to be quite close to functional threshold power.

It is primarily these last two points that I will focus on in the rest of this article.

Point #4: "Real world" reproducibility of LT determinations using various criteria

One of the motivations behind the original development of the Dmax method is the fact that when studying athletes who are actively training for competition, it can be difficult to standardize testing conditions as carefully as desirable. For example, on one occasion an athlete may be tested towards the end of a recovery week, when their muscle glycogen stores are high, whereas on another they may be tested at the end of a heavy block of training or at a training camp, when their muscle glycogen stores are low. Since large differences in muscle glycogen concentration will influence blood lactate levels, this can lead to under- or overestimation of an athlete's "true" LT if the latter is defined based on a fixed blood lactate concentration. Similarly, endurance training itself, via an increase in muscle respiratory capacity and a suppression of glycolytic enzyme activities, especially lactate dehydrogenase, tends to alter the vertical position of the blood lactate-exercise intensity curve, as well as shift it left or right. Again, this can create problems when using a fixed blood lactate concentration to define LT, as the individual's actual performance ability may not increase or decrease as much as implied by changes in their blood lactate threshold.

Again, a picture is probably worth a thousand words:

Figure 2. Determination of power output corresponding to OBLA using data from the 1st test shown in Figure 1 along with data from a 2nd test performed approximately 2 mo later.


In this plot, the blue diamonds and red curve are the same as in Figure 1, whereas the olive triangles and purple curve show the results of a follow-up test from the same athlete performed approximately 2 mo later. As can be seen in the figure, on the 2nd occasions their lactate concentration-exercise intensity curve was not only shifted to the left, but was also shifted quite a bit downward, especially at higher exercise intensities. Without additional information, it cannot be determined whether this is due to differences in exercise patterns and/or dietary carbohydrate intake on the days preceeding each test, a true training effect, or perhaps a difference in the calibration and hence accuracy of the lactate analyzer(s) that was/were employed. Clearly, however, the difference between the two tests in the location of curve with respect to the X and Y axes would influence the calculated magnitude of the difference in "threshold" when the latter is calculated using a fixed blood lactate concentration. For example, as shown by the heavy black arrows this athlete's OBLA seemingly increased from 290 W at the time of the 1st test to 308 W at the time of the 2nd test. At least some of this apparent improvement, however, seems likely to be related to the significant difference in absolute blood lactate levels, as there were no differences between the two tests (which were performed in the late fall and early winter, respectively) in either their heart rate-power output relationship or the power at which they ultimately fatigued during the incremental exercise tests. That the change in their OBLA likely overestimates the true increase in their functional threshold power is also suggested by the fact that it falls outside of the 72-77% of the final power ("MAP") normally found, especially during the 2nd test (i.e., 308 W / 360 W x 100% = 86%).

Converse to the above, when LT is defined using the Dmax method, thus taking into account the large differences in absolute blood lactate concentration, the results indicate that the athlete's LT changed minimally between tests, being 271 W on the 1st occasion and 263 W on the 2nd occasion (analysis not shown). This result is in keeping with the fact that there were no significant changes in their heart rate-power output relationship (data not shown), or in the maximal power they achieved during each test. Finally, these results are consistent with their MAP of 360 W on each occasion (i.e., 271 W / 360 W x 100% = 75%, 263 W / 360 W x 100% = 73%). Thus, although I lack any other information on this individual and thus can't state with certainty, based on their test results I would predict that there were minimal changes in their endurance performance ability between the two tests.


Point #5: Agreement/non-agreement between LT and functional threshold power


As stated before, in scientific studies a high correlation has almost invariably been found between various measures of LT and various measures of endurance performance ability, including the maximal power than a cyclist can maintain for 1 h (e.g., 2-4), which is what I have defined as "functional threshold power". A high correlation, however, is not the same as exact agreement - for example, when LT is defined as the exercise intensity corresponding to a 1 mmol/L increase in exercise baseline, the resultant value is highly correlated (i.e., R = 0.93) with "hour power", but the absolute values differ by, on average, 12% (range 4-19%) (4). This sort of offset or bias can be confusing to athletes using powermeters, and can complicate the prescription of training levels or zones based on laboratory-type testing. Thus, in an ideal world you would want to define LT in such a manner that it is not only highly correlated with functional threshold power, but it is at least on average the same as functional threshold power.

Taking into consideration all of the available data as well as simplicity or practicality of the testing, at least at the present time it appears to me that the Dmax method comes closest to satisfying this criteria. For example, in two separate studies of female cyclists and triathletes Bishop et al. found that, of the six methods they examined, not only was LT defined using the Dmax criteria most closely correlated with maximal 1 h power, the two differed, on average, by only 1% (2,3). (In contrast, power at OBLA was almost 10% higher, whereas other measures, such as LT defined as the breakpoint in the blood lactate-vs.-power curve when both are plotted on a log scale, were significantly lower.) Much more recently, Drs. Simon Jobson, now at the University of Kent, and Lindsay Edwards, now at the University of Tasmania, have obtained similar results (cf. Figure 3).

Figure 3. Relationship between functional threshold power and power at LT determined using the Dmax method in six male cyclists (Jobson and Edwards, unpublished observations).


Once again, power LT as determined using the Dmax method and functional threshold power were within 1% of each other, averaging 303 +/ -53 W and 300 +/- 61 W, respectively.

On the other hand, Van Schuylenbergh, Vanden Eynde, and Hespel (5) have concluded that the Dmax approach cannot be used to precisely predict the maximal steady-state power of professional cyclists. In this study, however, maximal steady-state power was determined using a 30 min instead of a 60 min test, which likely accounts for much of the 7% difference in the mean values. Moreover, as found by others there was a good correlation (i.e., R=0.85) between 30 min power and power at LT as defined using the Dmax approach when the stages of the incremental exercise test were 6 min long. However, no correlation was found when the stages were only 30 s in length, presumably because insufficient time was allowed for diffusion of lactate from the exercising muscles into the blood. On the other hand, McNaughton, Roberts, and Bentley (6) found no difference in LT determined using the Dmax method when comparing incremental exercise tests using 3 min vs. 5 min steps, implying that a minimum of 3 min should be used.


This blog entry is not intended to be a "how to" guide to lactate testing, as it is impossible to cover the numerous details (e.g., blood sampling site, measurement of blood vs. plasma lactate, etc.) potentially impacting the results. As well, I do not mean to imply that the Dmax method is markedly superior to other approachs, and that indviduals currently conducting lactate testing should change their methodology (there is quite a bit to be said for sticking to a consistent protocol and establishing a database of knowledge from which to draw, even if newer/ostensibly better methods happen to come along). Rather, my goal here has simply been to draw people's attention to this alternative in case they wish to try it themselves, as well as to highlight some of the considerations and complications associated with the use of lactate data to, e.g, prescribe training intensities.

(I would like to sincerely thank the anonymous cyclist whose data are shown in Figures 1 and 2 and also Drs. Jobson and Edwards following me to share their results.)



References

1. Cheng B, Kuipers H, Snyder AC, Keizer HA, Jeukendrup A, Hesselink M. A new approach for the determination of ventilatory and lactate thresholds.Int J Sports Med 13:518-522, 1992.


2. Bishop D, Jenkins DG, Mackinnon LT. The relationship between plasma lactate parameters, Wpeak and 1-h cycling performance in women. Med Sci Sports Exerc 30:1270-1275, 1998.


3. Bishop D, Jenkins DG, McEniery M, Carey MF. Relationship between plasma lactate parameters and muscle characteristics in female cyclists. Med Sci Sports Exerc 32:1088-1093, 2000.


4. Coyle EF, Feltner ME, Kautz SA, Hamilton MT, Montain SJ, Baylor AM, Abraham LD, Petrek GW. Physiological and biomechanical factors associated with elite endurance cycling performance. Med Sci Sports Exerc 23:93-107, 1991.


5. Van Schuylenbergh R, Vanden Eynde B, and Hespel P. Correlations between lactate and ventilatory thresholds and maximal lactate steady state in elite cyclists. Int J Sports Med 25:403-408, 2004.


6. McNaughton LR, Roberts S, Bentley DJ. The relationship among peak power output, lactate threshold, and short-distance cycling performance: effect of incremental exercise test design. J Strength Cond Res 20:157-161, 2006.