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.authoridClemente, Filipe Manuel/0000-0001-9813-2842
dc.authoridPrieto-González, Pablo/0000-0002-0668-4031
dc.authoridNobari, Hadi/0000-0001-7951-8977
dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authoridGulu, Mehmet/0000-0001-7633-7900
dc.authoridArdigò, Luca Paolo/0000-0001-7677-5070
dc.authorwosidClemente, Filipe Manuel/G-2625-2012
dc.authorwosidPrieto-González, Pablo/T-9113-2018
dc.authorwosidyapici, hakan/ACK-3335-2022
dc.authorwosidNobari, Hadi/AAO-9721-2021
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.authorwosidAyyıldız, Erdem/AER-8707-2022
dc.authorwosidQuispe Calcina, Willian/JRX-9094-2023
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.accessioned2024-08-04T20:53:32Z
dc.date.available2024-08-04T20:53:32Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
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.en_US
dc.identifier.doi10.3389/fpsyg.2023.1097145
dc.identifier.issn1664-1078
dc.identifier.pmid36936011en_US
dc.identifier.scopus2-s2.0-85150475721en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3389/fpsyg.2023.1097145
dc.identifier.urihttps://hdl.handle.net/11616/101235
dc.identifier.volume14en_US
dc.identifier.wosWOS:000953504400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherFrontiers Media Saen_US
dc.relation.ispartofFrontiers in Psychologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectsedentary behaviorsen_US
dc.subjectobesityen_US
dc.subjectbody mass indexen_US
dc.subjectaddictionen_US
dc.subjectchildrenen_US
dc.subjectphysical inactivityen_US
dc.titleExploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addictionen_US
dc.typeArticleen_US

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