Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features.

ABSTRACT:

Purpose

Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical characteristics of each individual. This study aimed to use machine learning models to determine individualized ramp protocols based on non-exercise features. We hypothesized that machine learning models will predict peak oxygen uptake ( V˙ O_2peak) and peak power output (PPO) more accurately than conventional multiple linear regression (MLR).

Methods

The cross-sectional study was conducted with 274 (♀168, ♂106) participants who performed CPET on a cycle ergometer. Machine learning models and multiple linear regression w

SUBMITTER: Wenzel C 

PROVIDER: S-EPMC11519113 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature