Predicting recovery oxygen consumption after cycle exercise

(Voraussage zum Sauerstoffverbrauch bei der Wiederherstellung nach zyklischen Belastungen)

The purpose of this study was to construct a mathematical model using the EPOC literature in order to predict "excess post-exercise oxygen consumption" magnitude (EPOC) and analyse the impact of its majors determinants: O2 deficit (DO2), Exercise O2 consumption (EOC), exercise intensity (P) (% VO2max and exercise duration (t) (min). The selected data included 15 different studies from international literature and 33 EPOC values (n = 33) measured after cycle exercise performed between 29 and 80% VO2max. EPOC was calculated as the time integral of the difference in O2 uptake and baseline (VO2o). In the controlled group (group(c)) (n = 8), VO2o value was measured during 12 h during a separate rest control experiment and in the no controlled group (group(nc)) (n = 25), baseline was a single measure determined on less than one hour before the exercise. The proposed model was: predicted EPOC (L) = (1 + x · ((VO2max - VO2o)/VO2max)^g * (t)^h * DO2 where x, g and h were constants determined thanks to group(c) (x = 0.045, g = 2.7 and h = 1.6), VO2max was expressed in L.min-1. DO2 had a low and no significant correlation coefficient with measured EPOC (n = 25, r = 0.33) contrary to EOC (n = 25, r = 0.71, p < 0.005). The model accounted for 95.6% and 77.7% of the variance in measured EPOC in the group(c) (n = 8, r = 0.98, p < 0.001) and in the group(nc) (n = 25, r = 0.88, p < 0.001) respectively. The model supported exercise intensity as the primary determinant of the EPOC magnitude. The interest of this model was to predict EPOC magnitude for a wide range of intensities and durations.
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Bibliographische Detailangaben
Schlagworte:
Notationen:Trainingswissenschaft Biowissenschaften und Sportmedizin
Veröffentlicht in:Movement & Sport Sciences - Science & Motricité
Sprache:Englisch
Veröffentlicht: 2012
Online-Zugang:http://doi.org/10.1051/sm/2011116
Heft:77
Seiten:5-12
Dokumentenarten:Artikel
Level:hoch