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

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dc.authoridPrieto-González, Pablo/0000-0002-0668-4031
dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authorwosidPrieto-González, Pablo/T-9113-2018
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.contributor.authorGülü, Mehmet
dc.contributor.authorYağin, Fatma Hilal
dc.contributor.authorGöçer, İshak
dc.contributor.authorYapıcı, Hakan
dc.contributor.authorAyyıldız, Erdem
dc.contributor.authorClemente, Filipe Manuel
dc.contributor.authorArdigo, Luca Paolo
dc.date.accessioned2023-05-06T17:22:10Z
dc.date.available2023-05-06T17:22:10Z
dc.date.issued2023
dc.departmentFakülteler, Spor Bilimleri Fakültesi, Spor Yöneticiliği Bölümü
dc.description.abstractPrimary 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.
dc.identifier.doi10.3389/fpsyg.2023.1097145
dc.identifier.issn1664-1078
dc.identifier.pmid36936011
dc.identifier.scopus2-s2.0-85150475721
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3389/fpsyg.2023.1097145
dc.identifier.urihttps://hdl.handle.net/20.500.11776/12083
dc.identifier.volume14
dc.identifier.wosWOS:000953504400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAyyıldız, Erdem
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers In Psychology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectsedentary behaviors
dc.subjectobesity
dc.subjectbody mass index
dc.subjectaddiction
dc.subjectchildren
dc.subjectphysical inactivity
dc.subjectInternet Addiction
dc.subjectPrevalence
dc.subjectHealth
dc.subjectStudents
dc.subjectOverweight
dc.subjectAssociation
dc.subjectChildhood
dc.subjectChildren
dc.subjectWeight
dc.subjectHead
dc.titleExploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction
dc.typeArticle

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