The metabolic power model in team sports: state of research, evaluation, optimization, and application
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In intermittent team sports, the monitoring of external (mechanical) and internal (metabolic) loads imposed on the athletes during training and matches plays a crucial role for training and recovery prescriptions. Especially the internal load is of importance, since the provoked stimuli are involved in regulating the gene expression required for all regeneration and adaptation processes. However, internal loads are difficult to examine in team sports as they usually require respiratory gas analyses and capillary blood lactate measures which are impractical for daily use. The metabolic power model offers the opportunity to simulate metabolic power, oxygen uptake, total energy expenditure, and aerobic-anaerobic supplies solely using speed and acceleration data. The general objective of this dissertation was to present the evidence of the metabolic power model in intermittent team sports as well as validate, optimize, and apply the model, including its prerequisites and fundamentals, accordingly. In five peer-reviewed publications, these individual aims were addressed step by step. For the first publication, a systematic review was conducted which revealed that the metabolic power model is methodologically valid but lacks physiological validity. Even though there was strong evidence of the metabolic power model underestimating total energy expenditure compared to measured oxygen uptake, the respective studies were often implemented incorrectly. This was due to the fact that they included technical activities and passive breaks which cannot be registered or were no data can be generated by the metabolic power model, respectively. Additionally, the anaerobic energy supply was neglected. The second publication addressed the detected flaws by focusing on intermittent running-based exercises that intended to primarily stress either the aerobic (continuous shuttle runs), anaerobic alactic (repeated accelerations), or lactic energy supply (repeated sprints). The 3-component model served as an established standard to validate the metabolic power model. The results showed large under- or overestimations in total energy expenditure (p ≤ .002), large underestimations in aerobic (p < .001), and large overestimations in anaerobic energy supply (p < .001). Additionally, the simulated oxygen uptakes revealed unphysiological and vertically shifted time courses compared to the measured oxygen uptakes. Based on the findings from the second publication, the third implemented various optimization strategies for improving the simulated oxygen uptake and reconsidered the calculation of the aerobic energy supply. With the best performing optimization model, which simulated oxygen uptake by machine learning strategies, it was possible to reduce the overall mean absolute error by approximately 45% compared to the measured oxygen uptake. Additionally, the discrepancy between the relative distribution of aerobic-anaerobic supplies was reduced by about 40%. Regarding total energy expenditure and aerobic supply, no significant differences were detected compared to the 3-component model (p ≥ .126), except during the repeated sprints (p ≤ .040). Still, the anaerobic energy supply remained largely overestimated (p = .003). While the third publication focused on optimizing the metabolic power model concerning the simulation of oxygen uptake, the fourth took the possible influence of different surfaces into account, which is also considered in the calculation basis of the metabolic power model. The results showed up to large metabolic differences between grass, tartan, and parquet, especially regarding the anaerobic energy supply. This provides a possible connection to the remaining overestimation of the anaerobic supply as shown by the third publication. Finally, the fifth publication addressed the valid assessment of the necessary input variables for the metabolic power model, namely speed and acceleration, by an inertial measurement unit (IMU) only tracking system operating independently of its environment. However, it was revealed that these variables cannot be validly be assessed yet (CCC = .71 to .92). Thus, the IMU only tracking system is currently unsuitable for the application of the metabolic power model. In conclusion, even though the conducted optimizations led to a more valid metabolic power model, concerning total energy expenditure and aerobic energy supply, anaerobic supply remains overestimated. The model’s prerequisites and fundamentals that were considered within this dissertation provide possible prospects for future studies. Until then, when implementing the metabolic power model into research and practice, it needs to be kept in mind what the model can and cannot actually and validly measure.
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