Profiling Physical Fitness of Physical Education Majors Using Unsupervised Machine Learning - Bonilla, Diego A; Sánchez-Rojas, Isabel A; Mendoza-Romero, Darío; Moreno, Yurany; Kočí, Jana; Gómez-Miranda, Luis M; Rojas-Valverde, Daniel; Petro, Jorge L; Kreider, Richard B (International journal of environmental research and public health, 2022)
The academic curriculum has shown to promote sedentary behavior in college students. This study aimed to profile the physical fitness of physical education majors using unsupervised machine learning and to identify the differences between sexes, academic years, socioeconomic strata, and the generated profiles. A total of 542 healthy and physically active students (445 males, 97 females; 19.8 [2.2] years; 66.0 [10.3] kg; 169.5 [7.8] cm) participated in this cross-sectional study. Their indirect VO2max (Cooper and Shuttle-Run 20 m tests), lower-limb power (horizontal jump), sprint (30 m), agility (shuttle run), and flexibility (sit-and-reach) were assessed. The participants were profiled using clustering algorithms after setting the optimal number of clusters through an internal validation using R packages. Non-parametric tests were used to identify the differences (p < 0.05). The higher percentage of the population were freshmen (51.4%) and middle-income (64.0%) students. Seniors and juniors showed a better physical fitness than first-year students. No significant differences were found between their socioeconomic strata (p > 0.05). Two profiles were identified using hierarchical clustering (Cluster 1 = 318 vs. Cluster 2 = 224). The matching analysis revealed that physical fitness explained the variation in the data, with Cluster 2 as a sex-independent and more physically fit group. All variables differed significantly between the sexes (except the body mass index [p = 0.218]) and the generated profiles (except stature [p = 0.559] and flexibility [p = 0.115]). A multidimensional analysis showed that the body mass, cardiorespiratory fitness, and agility contributed the most to the data variation so that they can be used as profiling variables. This profiling method accurately identified the relevant variables to reinforce exercise recommendations in a low physical performance and overweight majors.
Keywords: tělesná výchova; index tělesné hmotnosti; Male; Body Mass Index; Cross-Sectional Studies; Humans; cvičení; ženské pohlaví; lidé; tělesná výkonnost; Physical Education and Training; strojové učení bez učitele; mužské pohlaví; Unsupervised Machine Learning; Female; Physical Fitness; Exercise; průřezové studie