Profiling Physical Fitness of Physical Education Majors using Unsupervised Machine Learning
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Autor
BONILLA, D
SÁNCHEZ-ROJAS, I
MENDOZA-ROMERO, D
Datum vydání
2022Publikováno v
International Journal of Environmental Research and Public HealthRočník / Číslo vydání
1 (146)ISBN / ISSN
ISSN: 1661-7827ISBN / ISSN
eISSN: 1660-4601Metadata
Zobrazit celý záznamKolekce
Tato publikace má vydavatelskou verzi s DOI 10.3390/ijerph20010146
Abstrakt
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.
Klíčová slova
cardiorespiratory fitness, physical endurance, muscle power, sprint speed, range of motion, unsupervised machine learning
Trvalý odkaz
https://hdl.handle.net/20.500.14178/2452Licence
Licence pro užití plného textu výsledku: Creative Commons Uveďte původ 4.0 International