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.authoridGulu, Mehmet/0000-0001-7633-7900
dc.authoridYAGIN, Fatma Hilal/0000-0002-9848-7958
dc.authoridPrieto Gonzalez, Dr. Pablo/0000-0002-0668-4031
dc.authoridNobari, Hadi/0000-0001-7951-8977
dc.authoridClemente, Filipe Manuel/0000-0001-9813-2842
dc.contributor.authorGulu, Mehmet
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorGocer, Ishak
dc.contributor.authorYapici, Hakan
dc.contributor.authorAyyildiz, Erdem
dc.contributor.authorClemente, Filipe Manuel
dc.contributor.authorArdigo, Luca Paolo
dc.date.accessioned2025-01-21T16:41:07Z
dc.date.available2025-01-21T16:41:07Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
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.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3389/fpsyg.2023.1097145
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24801
dc.identifier.volume14
dc.identifier.wosWOS:000953504400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers In Psychology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectsedentary behaviors; obesity; body mass index; addiction; children; physical inactivity
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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