<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>As a critical transition period from adolescence to youth, college students’ health behaviors, and physical forms are gradually maturing, and nowadays, college students’ obsession with smartphones largely causes great harm to their body function metabolism. This paper mainly evaluates and analyzes the physical function metabolic risk of college students in physical education institutions themselves by linear regression model and least squares method, studies the correlation between body fat percentage and physical fitness and functional metabolic risk of college students, analyzes the mediating effect of body fat percentage between physical fitness and physical function metabolic risk, and finally analyzes the body fat percentage, physical fitness and joint indexes of different genders. The results showed that the physical fitness index, including grip strength index and maximal oxygen uptake, could significantly diagnose the functional metabolic risk of college students. There may be professional and gender differences in grip strength index and maximal oxygen uptake to diagnose functional metabolic risk in college students. This study provides a reliable theoretical basis for coaches’ training programs.</p>
</abstract>

As a critical transition period from adolescence to youth, college students’ health behaviors, and physical forms are gradually maturing, and nowadays, college students’ obsession with smartphones largely causes great harm to their body function metabolism. This paper mainly evaluates and analyzes the physical function metabolic risk of college students in physical education institutions themselves by linear regression model and least squares method, studies the correlation between body fat percentage and physical fitness and functional metabolic risk of college students, analyzes the mediating effect of body fat percentage between physical fitness and physical function metabolic risk, and finally analyzes the body fat percentage, physical fitness and joint indexes of different genders. The results showed that the physical fitness index, including grip strength index and maximal oxygen uptake, could significantly diagnose the functional metabolic risk of college students. There may be professional and gender differences in grip strength index and maximal oxygen uptake to diagnose functional metabolic risk in college students. This study provides a reliable theoretical basis for coaches’ training programs.

Analysis of the factors influencing the metabolic risk of students’ body functions in physical education institutions and the construction of a diagnostic model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

As a critical transition period from adolescence to youth, college students’ health behaviors, and physical forms are gradually maturing, and nowadays, college...

Analysis of the factors influencing the metabolic risk of students’ body functions in physical education institutions and the construction of a diagnostic model

Publié en ligne: 26 juil. 2023

Pages: -

Reçu: 25 août 2022

Accepté: 01 févr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00083

Mots clés
Big data, Linear regression, Physical education institutions, Physical function metabolism, Diagnostic models

© 2023 Kaiyin Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of the factors influencing the metabolic risk of students’ body functions in physical education institutions and the construction of a diagnostic model

Publié en ligne: 26 juil. 2023

Pages: -

Reçu: 25 août 2022

Accepté: 01 févr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00083

Mots clésBig data, Linear regression, Physical education institutions, Physical function metabolism, Diagnostic models

© 2023 Kaiyin Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Big data, Linear regression, Physical education institutions, Physical function metabolism, Diagnostic models