Prediction of the Wingate Anaerobic Mechanical Power Outputs from a Maximal Incremental Cardiopulmonary Exercise Stress Test using Machine-Learning Approach

Background: The Wingate Anaerobic Test (WAnT) is a short-term maximal intensity cycle ergometer test, which provides anaerobic mechanical power output variables. Despite the physiological significance of the variables extracted from the WAnT, the test is very intense, and the reliability of the results significantly decreases after the fourth decade. Our goal, in this paper, was to develop a new approach to predict the anaerobic mechanical power outputs using maximal incremental cardiopulmonary exercise stress test (CPET). We hypothesized that maximal incremental exercise stress test hold hidden information about the anaerobic components, which can be directly translated into mechanical power outputs.

Methods: We designed a computational model that included aerobic variables (features), and used a new computational \ predictive algorithm, which enabled the prediction of the anaerobic mechanical power outputs. We analyzed the chosen predicted features using clustering on a network.

Results: For peak power (PP) and mean power (MP) outputs, the equations included six features and four features, respectively. The combination of these features produced a prediction model of r=0.94 and r=0.9, respectively, on the validation set between the real and predicted PP/MP values (P< 0.001).

Conclusion: The newly predictive model allows the accurate prediction of the anaerobic mechanical power outputs at high accuracy. The assessment of additional tests is desired for the development of a robust application for athletes, older individuals, and/or non-healthy populations