Int J Sports Med 2011; 32(6): 422-428
DOI: 10.1055/s-0031-1271676
Training & Testing

© Georg Thieme Verlag KG Stuttgart · New York

Anaerobic Capacity: Effect of Computational Method

D. A. Noordhof1 , A. M. T. Vink1 , J. J. de Koning1, , 2 , C. Foster1, , 2
  • 1VU University, Faculty of Human Movement Sciences, Amsterdam, The Netherlands
  • 2University of Wisconsin La Crosse, Department of Exercise and Sports Science, La Crosse, Wisconsin, USA
Further Information

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.

References

  • 1 Bangsbo J. Is the O2 deficit an accurate quantitative measure of the anaerobic energy production during intense exercise?.  J Appl Physiol. 1992;  73 1207-1209
  • 2 Bangsbo J. Oxygen deficit: a measure of the anaerobic energy production during intense exercise?.  Can J Appl Physiol. 1996;  21 350-363 discussion 359–364
  • 3 Bangsbo J, Gollnick PD, Graham TE, Juel C, Kiens B, Mizuno M, Saltin B. Anaerobic energy production and O2 deficit-debt relationship during exhaustive exercise in humans.  J Physiol. 1990;  422 539-559
  • 4 Bickham D, Le Rossignol P, Gibbons C, Russell AP. Re-assessing accumulated oxygen deficit in middle-distance runners.  J Sci Med Sport. 2002;  5 372-382
  • 5 Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement.  Lancet. 1986;  1 307-310
  • 6 Bosquet L, Duchene A, Delhors PR, Dupont G, Carter H. A comparison of methods to determine maximal accumulated oxygen deficit in running.  J Sports Sci. 2008;  26 663-670
  • 7 Buck D, Mc Naughton L. Maximal accumulated oxygen deficit must be calculated using 10-min time periods.  Med Sci Sports Exerc. 1999;  31 1346-1349
  • 8 de Koning JJ, de Groot G, Van Ingen Schenau GJ. A power equation for the sprint in speed skating.  J Biomechanics. 1992;  25 573-580
  • 9 de Koning JJ, Foster C, Lampen J, Hettinga F, Bobbert MF. Experimental evaluation of the power balance model of speed skating.  J Appl Physiol. 2005;  98 227-233
  • 10 Ettema G, Lorås HW. Efficiency in cycling: a review.  Eur J Appl Physiol. 2009;  106 1-14
  • 11 Foster C, de Koning JJ, Hettinga F, Lampen J, La Clair KL, Dodge C, Bobbert M, Porcari JP. Pattern of energy expenditure during simulated competition.  Med Sci Sports Exerc. 2003;  35 826-831
  • 12 Garby L, Astrup A. The relationship between the respiratory quotient and the energy equivalent of oxygen during simultaneous glucose and lipid oxidation and lipogenesis.  Acta Physiol Scand. 1987;  129 443-444
  • 13 Green S, Dawson BT, Goodman C, Carey MF. Anaerobic ATP production and accumulated O2 deficit in cyclists.  Med Sci Sports Exerc. 1996;  28 315-321
  • 14 Harriss DJ, Atkinson G. International Journal of Sports Medicine – Ethical Standards in Sport and Exercise Science Research.  Int J Sports Med. 2009;  30 701-702
  • 15 Hettinga FJ, de Koning JJ, Broersen FT, Van Geffen P, Foster C. Pacing strategy and the occurrence of fatigue in 4 000-m cycling time trials.  Med Sci Sports Exerc. 2006;  38 1484-1491
  • 16 Hettinga FJ, de Koning JJ, de Vrijer A, Wust RC, Daanen HA, Foster C. The effect of ambient temperature on gross-efficiency in cycling.  Eur J Appl Physiol. 2007;  101 465-471
  • 17 Hopkins WG. A new view of statistics.   http://www.sportsci.org/resource/stats/index In 2000; 
  • 18 Lucia A, Hoyos J, Perez M, Santalla A, Chicharro JL. Inverse relationship between VO2max and economy/efficiency in world-class cyclists.  Med Sci Sports Exerc. 2002;  34 2079-2084
  • 19 Maxwell NS, Nimmo MA. Anaerobic capacity: a maximal anaerobic running test versus the maximal accumulated oxygen deficit.  Can J Appl Physiol. 1996;  21 35-47
  • 20 Medbø JI, Mohn AC, Tabata I, Bahr R, Vaage O, Sejersted OM. Anaerobic capacity determined by maximal accumulated O2 deficit.  J Appl Physiol. 1988;  64 50-60
  • 21 Medbø JI, Tabata I. Relative importance of aerobic and anaerobic energy release during short-lasting exhausting bicycle exercise.  J Appl Physiol. 1989;  67 1881-1886
  • 22 Medbø JI, Tabata I. Anaerobic energy release in working muscle during 30 s to 3 min of exhausting bicycling.  J Appl Physiol. 1993;  75 1654-1660
  • 23 Moseley L, Jeukendrup AE. The reliability of cycling efficiency.  Med Sci Sports Exerc. 2001;  33 621-627
  • 24 Noordhof DA, de Koning JJ, Foster C. The maximal accumulated oxygen deficit method: a valid and reliable measure of anaerobic capacity?.  Sports Med. 2010;  40 285-302
  • 25 Noordhof DA, de Koning JJ, van Erp T, van Keimpema B, de Ridder D, Otter R, Foster C. The between and within day variation in gross efficiency.  Eur J Appl Physiol. 2010;  109 1209-1218
  • 26 Ransom V, Clark A, van Langen FA, Uitslag TP, Hettinga FJ, de Koning JJ, Foster C. Constant value of gross mechanical efficiency at high exercise intensity.  Med Sci Sports Exerc. 2008;  40 S67
  • 27 Russell AP, Le Rossignol P, Lo SK. The precision of estimating the total energy demand: implications for the determination of the accumulated oxygen deficit.  J Exerc Physiol. 2000;  3 55-63
  • 28 Seresse O, Lortie G, Bouchard C, Boulay MR. Estimation of the contribution of the various energy systems during maximal work of short duration.  Int J Sports Med. 1988;  9 456-460
  • 29 Stainsby WN, Gladden LB, Barclay JK, Wilson BA. Exercise efficiency: validity of base-line subtractions.  J Appl Physiol. 1980;  48 518-522
  • 30 Van Ingen Schenau GJ, Cavanagh PR. Power equations in endurance sports.  J Biomechanics. 1990;  23 865-881

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 (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=yfixedyintercept

Sxx xi 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

    >