Predictive Modeling of VO2max Based on 20 m Shuttle Run Test for Young Healthy People

Abstract

This study presents mathematical models for predicting VO2max based on a 20 m shuttle run and anthropometric parameters. The research was conducted with data provided by 308 young healthy people (aged 20.6 ± 1.6). The research group includes 154 females (aged 20.3 ± 1.2) and 154 males (aged 20.8 ± 1.8). Twenty-four variables were used to build the models, including one dependent variable and 23 independent variables. The predictive methods of analysis include: the classical model of ordinary least squares (OLS) regression, regularized methods such as ridge regression and Lasso regression, artificial neural networks such as the multilayer perceptron (MLP) and radial basis function (RBF) network. All models were calculated in R software (version 3.5.0, R Foundation for Statistical Computing, Vienna, Austria). The study also involved variable selection methods (Lasso and stepwise regressions) to identify optimum predictors for the analysed study group. In order to compare and choose the best model, leave-one-out cross-validation (LOOCV) was used. The paper presents three types of models: for females, males and the whole group. An analysis has revealed that the models for females ( RMSE C V = 4.07 mL·kg−1·min−1) are characterised by a smaller degree of error as compared to male models ( RMSE C V = 5.30 mL·kg−1·min−1). The model accounting for sex generated an error level of RMSE C V = 4.78 mL·kg−1·min−1.