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Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction

Primary study aim was defining prevalence of obesity, physical activity levels, digital game addiction level in adolescents, to investigate gender differences, relationships between outcomes. Second aim was predicting game addiction based on anthropometric measurements, physical activity levels. Cro...

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Detalles Bibliográficos
Autores principales: Gülü, Mehmet, Yagin, Fatma Hilal, Gocer, Ishak, Yapici, Hakan, Ayyildiz, Erdem, Clemente, Filipe Manuel, Ardigò, Luca Paolo, Zadeh, Ali Khosravi, Prieto-González, Pablo, Nobari, Hadi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10022696/
https://www.ncbi.nlm.nih.gov/pubmed/36936011
http://dx.doi.org/10.3389/fpsyg.2023.1097145
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author Gülü, Mehmet
Yagin, Fatma Hilal
Gocer, Ishak
Yapici, Hakan
Ayyildiz, Erdem
Clemente, Filipe Manuel
Ardigò, Luca Paolo
Zadeh, Ali Khosravi
Prieto-González, Pablo
Nobari, Hadi
author_facet Gülü, Mehmet
Yagin, Fatma Hilal
Gocer, Ishak
Yapici, Hakan
Ayyildiz, Erdem
Clemente, Filipe Manuel
Ardigò, Luca Paolo
Zadeh, Ali Khosravi
Prieto-González, Pablo
Nobari, Hadi
author_sort Gülü, Mehmet
collection PubMed
description Primary study aim was defining prevalence of obesity, physical activity levels, digital game addiction level in adolescents, to investigate gender differences, relationships between outcomes. Second aim was predicting game addiction based on anthropometric measurements, physical activity levels. Cross-sectional study design was implemented. Participants aged 9–14 living in Kirikkale were part of the study. The sample of the study consists of 405 adolescents, 231 girls (57%) and 174 boys (43%). Self-reported data were collected by questionnaire method from a random sample of 405 adolescent participants. To determine the physical activity levels of children, the Physical Activity Questionnaire for Older Children (PAQ-C). Digital Game addiction was evaluated with the digital game addiction (DGA) scale. Additionally, body mass index (BMI) status was calculated by measuring the height and body mass of the participants. Data analysis were performed using Python 3.9 software and SPSS 28.0 (IBM Corp., Armonk, NY, United States) package program. According to our findings, it was determined that digital game addiction has a negative relationship with physical activity level. It was determined that physical activity level had a negative relationship with BMI. In addition, increased physical activity level was found to reduce obesity and DGA. Game addiction levels of girl participants were significantly higher than boy participants, and game addiction was higher in those with obesity. With the prediction model obtained, it was determined that age, being girls, BMI and total physical activity (TPA) scores were predictors of game addiction. The results revealed that the increase in age and BMI increased the risk of DGA, and we found that women had a 2.59 times greater risk of DGA compared to men. More importantly, the findings of this study showed that physical activity was an important factor reducing DGA 1.51-fold. Our prediction model Logit (P) = 1/(1 + exp(−(−3.384 + Age*0.124 + Gender-boys*(−0.953) + BMI*0.145 + TPA*(−0.410)))). Regular physical activity should be encouraged, digital gaming hours can be limited to maintain ideal weight. Furthermore, adolescents should be encouraged to engage in physical activity to reduce digital game addiction level. As a contribution to the field, the findings of this study presented important results that may help in the prevention of adolescent game addiction.
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spelling pubmed-100226962023-03-18 Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction Gülü, Mehmet Yagin, Fatma Hilal Gocer, Ishak Yapici, Hakan Ayyildiz, Erdem Clemente, Filipe Manuel Ardigò, Luca Paolo Zadeh, Ali Khosravi Prieto-González, Pablo Nobari, Hadi Front Psychol Psychology Primary study aim was defining prevalence of obesity, physical activity levels, digital game addiction level in adolescents, to investigate gender differences, relationships between outcomes. Second aim was predicting game addiction based on anthropometric measurements, physical activity levels. Cross-sectional study design was implemented. Participants aged 9–14 living in Kirikkale were part of the study. The sample of the study consists of 405 adolescents, 231 girls (57%) and 174 boys (43%). Self-reported data were collected by questionnaire method from a random sample of 405 adolescent participants. To determine the physical activity levels of children, the Physical Activity Questionnaire for Older Children (PAQ-C). Digital Game addiction was evaluated with the digital game addiction (DGA) scale. Additionally, body mass index (BMI) status was calculated by measuring the height and body mass of the participants. Data analysis were performed using Python 3.9 software and SPSS 28.0 (IBM Corp., Armonk, NY, United States) package program. According to our findings, it was determined that digital game addiction has a negative relationship with physical activity level. It was determined that physical activity level had a negative relationship with BMI. In addition, increased physical activity level was found to reduce obesity and DGA. Game addiction levels of girl participants were significantly higher than boy participants, and game addiction was higher in those with obesity. With the prediction model obtained, it was determined that age, being girls, BMI and total physical activity (TPA) scores were predictors of game addiction. The results revealed that the increase in age and BMI increased the risk of DGA, and we found that women had a 2.59 times greater risk of DGA compared to men. More importantly, the findings of this study showed that physical activity was an important factor reducing DGA 1.51-fold. Our prediction model Logit (P) = 1/(1 + exp(−(−3.384 + Age*0.124 + Gender-boys*(−0.953) + BMI*0.145 + TPA*(−0.410)))). Regular physical activity should be encouraged, digital gaming hours can be limited to maintain ideal weight. Furthermore, adolescents should be encouraged to engage in physical activity to reduce digital game addiction level. As a contribution to the field, the findings of this study presented important results that may help in the prevention of adolescent game addiction. Frontiers Media S.A. 2023-03-03 /pmc/articles/PMC10022696/ /pubmed/36936011 http://dx.doi.org/10.3389/fpsyg.2023.1097145 Text en Copyright © 2023 Gülü, Yagin, Gocer, Yapici, Ayyildiz, Clemente, Ardigò, Zadeh, Prieto-González and Nobari. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Gülü, Mehmet
Yagin, Fatma Hilal
Gocer, Ishak
Yapici, Hakan
Ayyildiz, Erdem
Clemente, Filipe Manuel
Ardigò, Luca Paolo
Zadeh, Ali Khosravi
Prieto-González, Pablo
Nobari, Hadi
Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction
title Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction
title_full Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction
title_fullStr Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction
title_full_unstemmed Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction
title_short Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction
title_sort exploring obesity, physical activity, and digital game addiction levels among adolescents: a study on machine learning-based prediction of digital game addiction
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10022696/
https://www.ncbi.nlm.nih.gov/pubmed/36936011
http://dx.doi.org/10.3389/fpsyg.2023.1097145
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