Profiling Physical Fitness of Physical Education Majors Using Unsupervised Machine Learning
Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
36612474
PubMed Central
PMC9819558
DOI
10.3390/ijerph20010146
PII: ijerph20010146
Knihovny.cz E-zdroje
- Klíčová slova
- cardiorespiratory fitness, muscle power, physical endurance, range of motion, sprint speed, unsupervised machine learning,
- MeSH
- cvičení MeSH
- index tělesné hmotnosti MeSH
- lidé MeSH
- průřezové studie MeSH
- strojové učení bez učitele * MeSH
- tělesná výchova * MeSH
- tělesná výkonnost MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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.
Clínica de Lesiones Deportivas Universidad Nacional Heredia 863000 Costa Rica
Department of Education Faculty of Education Charles University 11636 Prague Czech Republic
Sports Faculty Autonomous University of Baja California Tijuana 22390 Mexico
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