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Are you looking for an article that shows the range of cycling efficiency in riders?That's exactly what I'm looking for!
I understand how the system works. Your calories burned are proportional to your efficiency and rear wheel power (with appropriate conversion factors applied). That part I don't think anyone can disagree with, and I certainly never have. I've only taken issue with the lack of evidence proving the range of efficiencies that everyone keeps quoting. Thanks to Devlin, I looked for cycling ergometry + efficiency and had much better luck. Thank you! Have a look at this one:
Efficiency in cycling: a review
Gertjan Ettema1
and Håvard Wuttudal Lorås1
[/url](1) Human Movement Science Programme, Faculty of Social Sciences and Technology Management, Norwegian University of Science and Technology, 7941 Trondheim, Norway
The executive summary is that the r^2 for the best fit line representing the data points connecting metabolic work rate and external power looks a lot better than that connecting GME and external power.
Figure
2a shows the data according to cadence. Even though most studies report a clear negative effect of cadence on gross efficiency, the overall picture shows a minimal effect. The inter-study variation is much larger than any visible trend, and some studies show the opposite (positive) effect or an inverted u-shape with an optimal cadence. The inter-study variation may easily be thought to be caused by methodological differences. However, when plotting the same pool of data against external power, a different picture is shown. A very consistent relationship between work rate and efficiency is found. This relationship is even more clearly demonstrated by plotting the metabolic rate against work rate (Fig.
2c). A linear relationship is found, which is not unexpected but merely reflecting what various studies have reported explicitly (e.g., Anton-Kuchly et al.
1984; Bijker et al.
2001,
2002; Chavarren and Calbet
1999; Coast and Welch
1985; Francescato et al.
1995; Gaesser and Brooks
1975; Hintzy-Cloutier et al.
2003; McDaniel et al.
2002; Moseley et al.
2004; Widrick et al.
1992). As stated before, the curved work rate–gross efficiency relationship is a consequence of the offset (y-intercept) of the work rate–metabolic rate relationship. Note, that this offset does not, per sé, indicate any fixed baseline energy cost that, physiologically, is independent of work rate. The rather surprising aspect of the result is the high consistency between the various studies regarding the work rate–metabolic rate relationship, where it seems to be lacking as a function of cadence. Although one should be cautious with the interpretation of correlations here, that between metabolic rate and external power amounts to 0.97 (
n = 93,
p < 0.0001; 26 studies, 29 conditions/subject groups, meaning that 94% of the variation among all (mean) energy expenditure values for all these situations is explained by absolute work rate. This outcome is only slightly more ambivalent when separate data for all different cadences at the same power output were entered (in 9 studies), as shown in Fig.
2e. Also when converting the data to work rate-efficiency curves, only small differences with the original calculations occur (Fig.
2f), with the correlation being reduced to 0.95 (
r 2 = 0.91). In other words, factors other than work rate, including cadence, explain less then 10% of the variation in energy expenditure. Adding cadence as a dependent factor, the explained variance is increased to 94% (cadence explains about 10% on its own). These findings, both correlation values as well as the absolute cost-work rate relationship, agree well with McDaniel et al. (
2002) (redrawn in grey in Fig.
2c, but not included in the analysis), who looked at cadence, work rate and movement speed (by altering crank length). In their study, 95% of all variation in metabolic cost, including all experimental conditions, was explained by work rate. In the present data pool, cadence and power are correlated to some extent (
r = 0.171,
p < 0.019; Fig.
3a), which complicates the interpretation somewhat as these two factors share some of their variance. Still, both factors seem reasonably evenly spread over all data considered in this overview (Fig.
3a). Therefore, it is unlikely that this correlation between work rate and cadence has a strong effect on the findings. Interestingly, the intercept of the two-dimensional regression at zero work rate and zero cadence (Fig.
3b), which would be the theoretical value for energy expenditure while sitting still on a bicycle, reaches a value of 40 W (not statistically significant from zero). This value is too low, but still physically possible, despite the rather large extrapolation range from the experimental data. Overall, it seems that the very original findings by Fenn (
1924) on isolated muscle also apply to the entire human body in cycling in a very consistent manner.
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Lucia et al. (
2002) reported rather high gross efficiency values for some top cyclists. The average for the group amounted to 24.5% (with a peak individual value at 28.1%). Jeukendrup et al. (
2003) argued that these results were extremely high from a theoretical point-of-view and must have been affected by errors in the measurements (see also below, next section). They furthermore concluded that if these data were correct, “some interesting physiological adaptations may exist…”. Coyle (
2005) reported an increase in efficiency over a period of 7 years of training and competing in one of the most outstanding cyclists of modern times from about 21–23%. Coyle proposed that biochemical adaptations may have caused this improvement (i.e., a greater contribution from aerobically-efficient type I fibres). When considering these data and their placement within the data derived from the literature (Fig.
2b, c; data enclosed in a grey square; only overall average is shown for both studies), these values do not seem extraordinary, although Lucia et al. (
2002) appear to show a slightly high efficiency value. This is supported by values from Sallet et al. (
2006) on elite and professional riders who score even higher efficiencies at powers above 400 W (data most to the left in Fig.
2b, c). The main reason why gross efficiency is relatively high is likely because of the high work rate. Also the improvement in efficiency reported by Coyle (
2005) may be explained by an increased power at which these values were determined. Nevertheless, the studies by Sallet et al. (
2006) and Lucia et al. (
2002) show metabolic rates below the regression line in Fig.
2c, which may indicate either measurement error or, indeed, some physiological changes that enhance efficiency above the increase that is directly linked to that for the work rate. It is interesting to note that the same group (Lucia et al.
2004) report a lower efficiency is reported (23.4 vs. 24.5%) at a slightly lower power (366 vs. 385 W).
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Irrespective of definitions and concepts, a framework for the accuracy of efficiency measurements can be established. It seems reasonable to allow for a 5% error in biological measurements with regard to studies on cycling efficiency. Figure
2d shows the ranges of efficiency calculations that arise from 5% error in both metabolic rate and external power going in opposite directions. The vertical bar shows the range near 300 W if only one of these measures has that same error. Only one data point falls clearly outside the range of 5% error (filled circle). This is the result from Luhtanen et al. (
1987) at the highest work rate, which was, as mentioned earlier, well above the lactate threshold and thus bound to result in a lower efficiency. Thus, the difference between studies may be partly explained by differences in (systematic) errors. This merely strengthens the notion that cycling is an extremely consistent exercise model with regard to the relationship between metabolic rate and external power. Thus, the situation presented in Fig.
2c may constitute a very solid framework for the interpretation of past, present and future
studies.
**Edit to add figures**
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