Although post-exercise oxygen consumption (EPOC) has traditionally been investigated for its potential role in weight loss, EPOC may also be used as a measure of the time taken to reverse exercise-induced changes in homeostasis and how this changes with training. For example, trained individuals recover more rapidly following exercise at a set % of VO2max (Hagberg 1980, Short 1997). However, the effects of training are multi-faceted and it is unclear to what extent changes in metabolic recovery are related to changes in body composition, autonomic balance, muscle adaptation and absolute intensity of the exercise bout. Therefore the aim of this study was to investigate to what extent the half life (HL) of EPOC could be explained by participant characteristics, training status and responses associated with the exercise dose, following a standardized exercise bout. Methods: Untrained individuals (n=11) and moderately-trained (n=13) and well-trained (n=12) runners were recruited and visited the laboratory on 2 occasions for the measurement of height and body mass, a Dual X-ray absorptiometry scan and 2 Bruce protocol maximal treadmill tests, 1 per visit. The 3rd and final visit involved a standardized protocol of 15min rest, a 3km treadmill run at 70% of VO2max and 65min of resting recovery. Respiratory gases and heart rate were measured for each phase of the protocol. A one-phase exponential decay curve was fitted to recovery oxygen consumption measurements and the HL of the curve used as the dependent variable for multiple regression analysis. The following variables were investigated for their ability to predict HL: age, gender, mass and body fat% (participant characteristics); weekly training km, VO2max and Bruce protocol time to exhaustion (training status); absolute oxygen consumption (ml/kg/min), energy expenditure (cal/kg), RER, %maximum heart rate, total exercise heart beats (ex hb`s) and RPE (exercise dose). Results: Variables within the participant characteristics, training status and exercise dose categories were able to explain 58%, 53% and 68% of the variation in HL respectively. The strongest single predictor of HL was ex hb`s (R2= 0.56) and the best subset of 3-4 predictive variables consisted of body fat %, exercise RER and ex hb`s. Each variable contributed significantly to the outcome (p < 0.05) and together accounted for 71% of variation in HL (SEE = 4.0 s). Discussion: It may be concluded that decreased recovery HL with exercise training represents the integrated effect of altered body composition and substrate metabolism and decreased cardiovascular load during exercise. This study provides preliminary support for HL as an integrated measure of training adaptation. Understanding the remaining 29% unexplained variation could be the focus of further study as could the possible practical applications of the HL measurement for monitoring and assessment.
© Copyright 2012 17th Annual Congress of the European College of Sport Science (ECSS), Bruges, 4. -7. July 2012. Veröffentlicht von Vrije Universiteit Brussel. Alle Rechte vorbehalten. / Übersetzt mit https://www.DeepL.com/Translator