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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1097145/full |
| _version_ | 1811160329531424768 |
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| author | Mehmet Gülü Fatma Hilal Yagin Ishak Gocer Hakan Yapici Erdem Ayyildiz Filipe Manuel Clemente Filipe Manuel Clemente Luca Paolo Ardigò Ali Khosravi Zadeh Pablo Prieto-González Hadi Nobari Hadi Nobari |
| author_facet | Mehmet Gülü Fatma Hilal Yagin Ishak Gocer Hakan Yapici Erdem Ayyildiz Filipe Manuel Clemente Filipe Manuel Clemente Luca Paolo Ardigò Ali Khosravi Zadeh Pablo Prieto-González Hadi Nobari Hadi Nobari |
| author_sort | Mehmet Gülü |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T05:55:32Z |
| format | Article |
| id | doaj.art-d3aee62eded94c40b11ab224fccb6010 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-10T05:55:32Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-d3aee62eded94c40b11ab224fccb60102023-03-03T17:02:11ZengFrontiers Media S.A.Frontiers in Psychology1664-10782023-03-011410.3389/fpsyg.2023.10971451097145Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addictionMehmet Gülü0Fatma Hilal Yagin1Ishak Gocer2Hakan Yapici3Erdem Ayyildiz4Filipe Manuel Clemente5Filipe Manuel Clemente6Luca Paolo Ardigò7Ali Khosravi Zadeh8Pablo Prieto-González9Hadi Nobari10Hadi Nobari11Department of Coaching Education, Faculty of Sport Sciences, Kirikkale University, Kirikkale, TürkiyeDepartment of Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya, TürkiyeGraduate School of Health Sciences, Ankara University, Ankara, TürkiyeDepartment of Coaching Education, Faculty of Sport Sciences, Kirikkale University, Kirikkale, TürkiyeSports Management Department, Faculty of Sport Sciences, Tekirdağ Namık Kemal University, Tekirdağ, TürkiyeEscola Superior Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Álvares, Viana do Castelo, PortugalInstituto de Telecomunicações, Delegação da Covilhã, Lisboa, PortugalDepartment of Teacher Education, NLA University College, Oslo, NorwayDepartment of Exercise Physiology, Faculty of Sport Sciences, University of Isfahan, Isfahan, IranHealth and Physical Education Department, Prince Sultan University, Riyadh, Saudi Arabia0Faculty of Sport Sciences, University of Extremadura, Cáceres, Spain1Department of Motor Performance, Faculty of Physical Education and Mountain Sports, Transilvania University of Braşov, Braşov, RomaniaPrimary 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1097145/fullsedentary behaviorsobesitybody mass indexaddictionchildrenphysical inactivity |
| spellingShingle | Mehmet Gülü Fatma Hilal Yagin Ishak Gocer Hakan Yapici Erdem Ayyildiz Filipe Manuel Clemente Filipe Manuel Clemente Luca Paolo Ardigò Ali Khosravi Zadeh Pablo Prieto-González Hadi Nobari Hadi Nobari Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction Frontiers in Psychology sedentary behaviors obesity body mass index addiction children physical inactivity |
| 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 | sedentary behaviors obesity body mass index addiction children physical inactivity |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1097145/full |
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