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DOI: 10.1055/s-0031-1271676
© Georg Thieme Verlag KG Stuttgart · New York
Anaerobic Capacity: Effect of Computational Method
Publication History
accepted after revision December 30, 2010
Publication Date:
11 May 2011 (online)
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
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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 |
Correspondence
Dionne Adriana NoordhofMSc
VU University
Faculty of Human Movement
Sciences
van der Boechorststraat 9
1081 BT Amsterdam
The Netherlands
Phone: + 31/20/59 82000
Fax: + 31/20/59 88529
Email: d.a.noordhof@vu.nl