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Original Articles

The relation between cycling time to exhaustion and anaerobic threshold

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Pages 1027-1042 | Received 27 Jun 1989, Accepted 26 Mar 1990, Published online: 24 Oct 2007
 

Abstract

This study investigated whether the anaerobic threshold (AnT) could be used to predict prolonged work capacity measured as cycling time to exhaustion (= endurance time) and which factors, in addition to relative exercise intensity, could explain variation in endurance time. Theoretical exercise intensities corresponding to certain endurance times were also calculated. The hyperbolic and exponential functions between cycling time and relative work rate (WR[%]), as well as between cyling time and relative oxygen uptake ([Vdot]O2[%]) were fitted to the pooled data (n = 45) of 17 subjects. The WR(%) and [Vdot]O2 (%) were expressed as a percentage of the subject's own AnT- and maximum -values. At WR corresponding to AnT (i.e., 70% of WRmax) an average subject could cycle 60 min according to both AnT- or maximum-related exponential function. When prediction was done for an endurance time of 4 h, the AnT-related exponential function gave 2·9%-units ( = 11 W or ∼0·15 O21 · min−1) lower intensity level (51% of WRmax than the maximum-related function (54% of WRmax). The WR(%) alone explained 54% and 70% of the variation in endurance time of the AnT-related and maximum-related exponential functions, respectively. Muscle fibre composition and initial blood lactate or relative muscle glycogen depletion (change in muscle glycogen as percentage) increased significantly the explanatory power of these models. The differences between the observed and expected exercise times correlated with blood lactate accumulation (r = −0·42; p < 0·01), muscle fibre composition (r = 0·33; p < 0·05) and relative muscle glycogen depletion (r = 0·67; p < 0·01). It was concluded that the capacity for prolonged work measured as cycling time to exhaustion can be estimated by AnT-related power output, and that the exponential function model is the most suitable. Prediction power of the model can be improved by multiple regressions including muscle fibre composition, initial blood lactate level and relative muscle glycogen depletion.

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