Profiling Physical Fitness of Physical Education Majors Using Unsupervised Machine Learning

Diego A. Bonilla, Isabel A. Sánchez-Rojas (), Darío Mendoza-Romero, Yurany Moreno, Jana Kočí, Luis M. Gómez-Miranda, Daniel Rojas-Valverde, Jorge L. Petro and Richard B. Kreider
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Diego A. Bonilla: Research Division, Dynamical Business & Science Society—DBSS International SAS, Bogotá 110311, Colombia
Isabel A. Sánchez-Rojas: Grupo de Investigación Ciencias Aplicadas al Ejercicio, Deporte y Salud—GICAEDS, Universidad Santo Tomás, Bogotá 205070, Colombia
Darío Mendoza-Romero: Grupo de Investigación Ciencias Aplicadas al Ejercicio, Deporte y Salud—GICAEDS, Universidad Santo Tomás, Bogotá 205070, Colombia
Yurany Moreno: Research Division, Dynamical Business & Science Society—DBSS International SAS, Bogotá 110311, Colombia
Jana Kočí: Research Division, Dynamical Business & Science Society—DBSS International SAS, Bogotá 110311, Colombia
Luis M. Gómez-Miranda: Sports Faculty, Autonomous University of Baja California, Tijuana 22390, Mexico
Daniel Rojas-Valverde: Núcleo de Estudios para el Alto Rendimiento y la Salud (NARS-CIDISAD), Escuela Ciencia del Movimiento Humano y Calidad de Vida (CIEMHCAVI), Universidad Nacional, Heredia 863000, Costa Rica
Jorge L. Petro: Research Division, Dynamical Business & Science Society—DBSS International SAS, Bogotá 110311, Colombia
Richard B. Kreider: Exercise & Sport Nutrition Laboratory, Human Clinical Research Facility, Texas A&M University, College Station, TX 77843, USA

IJERPH, 2022, vol. 20, issue 1, 1-13

Abstract: 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 VO 2max (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: cardiorespiratory fitness; physical endurance; muscle power; sprint speed; range of motion; unsupervised machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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