New prediction models for the maximal oxygen uptake of collegeaged students using non-exercise data

Abstract

Maximal oxygen uptake (VO2max) refers to the maximal amount of oxygen that an individual can utilize during intense or maximal exercise. VO2max plays a significant role in sport science, education and research. The direct measurement of VO2max is time-consuming, requires expensive laboratory equipment and trained staff. Because of these disadvantages of direct measurement, numerous VO2max prediction models for a variety of subject groups have been developed. The purpose of this study is to develop new Multiple Linear Regression based on VO2max prediction models for Turkish college students by using physiological and questionnaire variables. The dataset includes the data of 62 (28 females and 34 males) students, ranging in age from 18 to 27 years, from the College of Physical Education and Sports Science at Gazi University. Seven different models consisting of the predictor variables gender, age, weight, height, Perceived Functional Ability scores (PFA1 and PFA-2), and Physical Activity Rating score (PA-R) have been used to predict VO2max. The performance of the prediction models has been evaluated by calculating their standard error of estimates (SEE’s) and multiple correlation coefficients (R’s). The prediction model including Gender, Age, Height, Weight, PFA-1 and PAR yields the lowest SEE with 5.14 mL.kg-1.min-1 and highest R with 0.93. It can be concluded that in situations where it is difficult to measure VO2max, the given model with MLR equation can be used to predict the VO2max of college students with acceptable error rates.
 
Keywords: Maximum oxygen uptake, machine learning, multiple linear regression.