Quantifying training response in cycling based on cardiovascular drift using machine learning
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Date
Publisher
Frontiers
Abstract
Purpose: The most important parameter influencing performance in endurance
sports is aerobic fitness, the quality of the cardiovascular system for efficient
oxygen supply of working muscles to produce mechanical work. Each individual
athlete responds differently to training. However, for coaches it is not always
easy to see improvement, accumulated fatigue, or overreaching. In the new era
of technology, we propose an experimental method using machine learning (ML)
to measure response quantified as aerobic fitness level based on cardiovascular
drift and aerobic decoupling data.
Methods: Twenty well-trained athletes in cycling-based sports performed
monthly aerobic fitness tests over five months, riding at 75% of their functional
threshold power for 60 min. Based on aerobic decoupling (power-to-heart
rate ratio) and cardiovascular drift of each test ride, a prediction model was
created using ML (Logistic regression, Variational Gaussian Process models
and k-nearest neighbors algorithm) that indicated whether or not an athlete
was responding to the training. Athletes were spitted as responders (i.e., those
showing improvements in cardiovascular drift and aerobic decoupling) or nonresponders.
Results: Cardiovascular drift and aerobic decoupling demonstrated a significant
strong linear correlation. All ML models achieved good predictive performance
in classifying athletes as responders or non-responders, with cross-validation
accuracy ranging from 0.87 to 0.9. Average predictive accuracy of 0.86 was for
k-nearest neighbors, 0.91 for logistic regression, 0.93 for Variational Gaussian
Process model. The Variational Gaussian Process model achieved the highest
classification for training response.
Conclusion: Cardiovascular drift and aerobic decoupling are reliable indicators
of response to training stimulus. ML is a promising tool for monitoring training
response in endurance sports, offering early and sensitive insights into fitness
adaptations or fatigue that can support more personalized training decisions for
coaches and athletes.
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Except where otherwised noted, this item's license is described as Attribution 4.0 International