Anaerobic Capacity: Effect of Computational Method

Abstract

Anaerobic capacity (AnC) can be estimated by subtracting VO₂ consumed from VO₂ demand, which can be estimated from multiple submaximal exercise bouts or by gross efficiency (GE), requiring one submaximal bout. This study compares AnC using the MAOD and GE method. The precision of estimated VO₂ demand and AnC, determined by MAOD using 3 power output – VO₂ regressions, based on VO₂ from min 8–10 (10 − Y), during min 4 without (4 − Y) and with forced y-intercept (4+Y), and from GE was evaluated by the 95% confidence interval (CI). Well-trained males (n=15) performed submaximal exercise tests to establish VO₂ demand with the MAOD and GE method. To determine AnC subjects completed a constant power output trial. The 3 MAOD procedures and GE method had no significant difference for VO₂ demand and AnC. The 4+Y MAOD procedure and GE method resulted in a smaller 95% CI of VO₂ demand and AnC than the 10 − Y (p<0.05; p<0.01) and 4 – Y (p<0.001; p<0.01) MAOD procedures. Therefore, the 4+Y MAOD procedure and GE method are preferred for estimating AnC, but as individual differences exist, they cannot be used interchangeably.

Key words

MAOD - gross efficiency - efficiency - performance

References

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Appendix

Calculating the 95% CI of a regression line

The observed data is represented as x _i and y _i (i=1, 2, …, n), which results in the below presented equations for the regression line without and with fixed value for the y-intercept.

Regression – without fixed y-intercept
Sxx =Σ(xi – mean(x))2	sum of squares of x
Sxy=Σ((xi – mean(x)) · (yi – mean(y)))	sum op products
b=Sxy/Sxx	slope
a=mean(y) – b · mean(x)	y-intercept
ŷi=a+b · xi	regression equation
εi=yi – ŷi	residuals
s ε= ((Σε i 2)/(n−2))	standard deviation of the residuals
seŷ=s ε · (1/n+(xi – mean(x)) 2/Sxx)	standard error of the estimate ŷ
95% CI=ŷi ±t(n–1)  · seŷ	95% confidence interval based on a t-distribution

Regression – with fixed y-intercept
y=y – fixedyintercept
Sxx =Σxi 2	sum of squares of x
S xy=Σ(xi  · yi)	sum op products
b=Sxy/Sxx	slope
ŷi =b · xi	regression equation
εi =yi – ŷi	residuals
s ε =√((Σε i 2)/(n−1))	standard deviation of the residuals
seŷ=s ε · √(xi 2/Sxx)	standard error of the estimate ŷ
95% CI=ŷi ±t(n−1)  · seŷ	95% confidence interval based on a t-distribution

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