A new non exercise-based VO2max prediction equation for patients with chronic low back pain

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.

Non-exercise estimation of VO(2)max using the International Physical Activity Questionnaire

Non-exercise equations developed from self-reported physical activity can estimate maximal oxygen uptake (VO(2)max) as well as submaximal exercise testing. The International Physical Activity Questionnaire (IPAQ) is the most widely used and validated self-report measure of physical activity. This study aimed to develop and test a VO(2)max estimation equation derived from the IPAQ-Short Form (IPAQ-S). College-aged males and females (n = 80) completed the IPAQ-S and performed a maximal exercise test. The estimation equation was created with multivariate regression in a gender-balanced subsample of participants, equally representing five levels of fitness (n = 50) and validated in the remaining participants (n = 30). The resulting equation explained 43% of the variance in measured VO(2)max (SEE = 5.45 ml·kg(-1)·min(-1)). Estimated VO(2)max for 87% of individuals fell within acceptable limits of error observed with submaximal exercise testing (20% error). The IPAQ-S can be used to successfully estimate VO(2)max as well as submaximal exercise tests. Development of other population-specific estimation equations is warranted.