Prediction of the wingate anaerobic mechanical power outputs from a maximal incremental cardiopulmonary exercise stress test using machine-learning approach

Efrat Leopold, Dalya Navot-Mintzer, Eyal Shargal, Sharon Tsuk, Tamir Tuller, Mickey Scheinowitz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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 generally applies for athletes. 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. We therefore 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. 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). 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.

Original languageEnglish
Article numbere0212199
JournalPLoS ONE
Volume14
Issue number3
DOIs
StatePublished - Mar 2019

Fingerprint

Dive into the research topics of 'Prediction of the wingate anaerobic mechanical power outputs from a maximal incremental cardiopulmonary exercise stress test using machine-learning approach'. Together they form a unique fingerprint.

Cite this