A regression equation for the estimation of maximum oxygen uptake in nepalese adult females

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

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.

Developing new VO2max prediction models from maximal, submaximal and questionnaire variables using support vector machines combined with feature selection

Maximal oxygen uptake (VO₂max) is an essential part of health and physical fitness, and refers to the highest rate of oxygen consumption an individual can attain during exhaustive exercise. In this study, for the first time in the literature, we combine the triple of maximal, submaximal and questionnaire variables to propose new VO₂max prediction models using Support Vector Machines (SVM's) combined with the Relief-F feature selector to predict and reveal the distinct predictors of VO₂max. For comparison purposes, hybrid models based on double combinations of maximal, submaximal and questionnaire variables have also been developed. By utilizing 10-fold cross-validation, the performance of the models has been calculated using multiple correlation coefficient (R) and root mean square error (RMSE). The results show that the best values of R and RMSE, with 0.94 and 2.92mLkg^-1min^-1 respectively, have been obtained by combining the triple of relevantly identified maximal, submaximal and questionnaire variables. Compared with the results of the rest of hybrid models in this study and the other prediction models in literature, the reported values of R and RMSE have been found to be considerably more accurate. The predictor variables gender, age, maximal heart rate (MX-HR), submaximal ending speed (SM-ES) of the treadmill and Perceived Functional Ability (Q-PFA) questionnaire have been found to be the most relevant variables in predicting VO₂max. The results have also been compared with that of Multilayer Perceptron (MLP) and Tree Boost (TB), and it is seen that SVM significantly outperforms other regression methods for prediction of VO₂max.

Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances

Maximal oxygen uptake (VO2max) indicates how many milliliters of oxygen the body can consume in a state of intense exercise per minute. VO2max plays an important role in both sport and medical sciences for different purposes, such as indicating the endurance capacity of athletes or serving as a metric in estimating the disease risk of a person. In general, the direct measurement of VO2max provides the most accurate assessment of aerobic power. However, despite a high level of accuracy, practical limitations associated with the direct measurement of VO2max, such as the requirement of expensive and sophisticated laboratory equipment or trained staff, have led to the development of various regression models for predicting VO2max. Consequently, a lot of studies have been conducted in the last years to predict VO2max of various target audiences, ranging from soccer athletes, nonexpert swimmers, cross-country skiers to healthy-fit adults, teenagers, and children. Numerous prediction models have been developed using different sets of predictor variables and a variety of machine learning and statistical methods, including support vector machine, multilayer perceptron, general regression neural network, and multiple linear regression. The purpose of this study is to give a detailed overview about the data-driven modeling studies for the prediction of VO2max conducted in recent years and to compare the performance of various VO2max prediction models reported in related literature in terms of two well-known metrics, namely, multiple correlation coefficient (R) and standard error of estimate. The survey results reveal that with respect to regression methods used to develop prediction models, support vector machine, in general, shows better performance than other methods, whereas multiple linear regression exhibits the worst performance.

Criterion-Related Validity of the 20-M Shuttle Run Test for Estimating Cardiorespiratory Fitness: A Meta-Analysis

The main purpose of the present meta-analysis was to examine the criterion-related validity of the 20-m shuttle run test for estimating cardiorespiratory fitness. Relevant studies were searched from twelve electronic databases up to December 2014, as well as from several alternative modes of searching. The Hunter-Schmidt's psychometric meta-analysis approach was conducted to estimate the population criterion-related validity of the 20-m shuttle run test. From 57 studies that were included in the present meta-analysis, a total of 78 correlation values were analyzed. The overall results showed that the performance score of the 20-m shuttle run test had a moderate-to-high criterion-related validity for estimating maximum oxygen uptake (r p = 0.66-0.84), being higher when other variables (e.g. sex, age or body mass) were used (r p = 0.78-0.95). The present meta-analysis also showed that the criterion-related validity of Léger's protocol was statistically higher for adults (r p = 0.94, 0.87-1.00) than for children (r p = 0.78, 0.72-0.85). However, sex and maximum oxygen uptake level do not seem to affect the criterion-related validity values. When an individual's maximum oxygen uptake attained during a laboratory-based test is not feasible, the 20-m shuttle run test seems to be a useful alternative for estimating cardiorespiratory fitness. In adults the performance score only seems to be a strong estimator of cardiorespiratory fitness, in contrast among children the performance score should be combined with other variables. Nevertheless, as in the application of any physical fitness field test, evaluators must be aware that the performance score of the 20-m shuttle run test is simply an estimation and not a direct measure of cardiorespiratory fitness. Key pointsOverall the 20-m shuttle run test has a moderate-to-high mean criterion-related validity for estimating cardiorespiratory fitness.The criterion-related validity of the 20-m shuttle run test is significantly higher for adults than for children. However, when the performance score is combined with other variables, the criterion-related validity value increases considerably among children.Sex and maximum oxygen uptake level of individuals seem not to affect the criterion-related validity of the 20-m shuttle run test.When individuals' maximum oxygen uptake attained during a laboratory-based test is not feasible, the 20-m shuttle run test seems to be a useful alternative for estimating cardiorespiratory fitness.