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To extract, prioritize, and model the highly conserved variations in mean-maximal power (MMP) data in cyclists utilizing functional principal component (FPC) analysis.
Models for human running performances of various complexities and underlying principles have been proposed, often combining data from world record performances and bio-energetic facts of human physiology. The purpose of this work is to develop a novel, minimal and universal model for human running performance that employs a relative metabolic power scale.
Despite the fact that an increase of lactate at a certain submaximal level of power was known long before the idea of “anaerobic threshold” (AT) was introduced by Wasserman et al. as the point at which a change from exclusively aerobic to partially anaerobic energy supply by formation and accumulation of lactate occurs in graded exercise tests, the mechanism of this change in a metabolic pattern is not well understood and therefore the subject of various speculation.
As an alternative to the Wbal models, a methodology based on maximum mean power profiling (MMP) is presented here to predict intermittent exercise performance potential.
Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling.
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