The role of machine learning methods in physiological explorations of endurance trained athletes: a mini-review

Abstract

Endurance-trained athletes require physiological explorations that have evolved throughout the history of exercise physiology with technological advances. From the use of the Douglas bag to measure gas exchange to the development of wearable connected devices, advances in physiological explorations have enabled us to move from the classic but still widely used cardiopulmonary exercise test (CPET) to the collection of data under real conditions on outdoor endurance or ultra-endurance events. However, such explorations are often costly, time-consuming, and complex, creating a need for efficient analysis methods. Machine Learning (ML) has emerged as a powerful tool in exercise physiology, offering solutions to these challenges. Given that exercise physiologists may be unfamiliar with ML, this mini-review provides a concise overview of its relevance to the field. It introduces key ML methods, highlights their ability to predict important physiological parameters (e.g., heart rate variability and exercise-induced hypoxemia), and discusses their strengths and limitations. Finally, it outlines future directions based on the challenges identified, serving as an initial reference for physiologists exploring the application of ML in endurance exercise.

Keywords: endurance trained athletes; exercise physiology; exploration; machine learning (ML); performance.

Figure 1

Supervised learning workflow used to predict exercise-induced hypoxemia (EIH) in endurance-trained athletes, without oxygen saturation measurements, based on previous cardiopulmonary exercise test parameters (not all included): oxygen consumption (VO₂), ventilation (VE), carbon dioxide production (VCO₂), heart rate (HR). The machine learning classifies athletes as either having or not having EIH [adapted from Boudry et al. (39)].

Figure 2

SHAP summary plot illustrating feature importance and effects in predicting maximal oxygen consumption (VO₂max) based on submaximal cardiopulmonary exercise test (CPET) data [adapted from Rosoł et al. (51)].