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Department of Psychiatry, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sleman, Indonesia
Specialized Residency Training, Department of Psychiatry, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sleman, Indonesia
Specialized Residency Training, Department of Psychiatry, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sleman, Indonesia
Specialized Residency Training, Department of Psychiatry, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sleman, Indonesia
Specialized Residency Training, Department of Psychiatry, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sleman, Indonesia
Specialized Residency Training, Department of Psychiatry, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sleman, Indonesia
Aim To examine several aspects, such as sociodemographic, psychological, personality, and parenting characteristics, that may contribute to the occurrence of PIGD in Yogyakarta, Indonesia. Additionally, the study intends to create a model that can accurately predict people who are at high risk of developing PIGD.
Methods. A cross-sectional study was conducted on 350 participants aged 15-25 years in Yogyakarta. The data was gathered through the use of a questionnaire that consisted of demographic measures: the Internet Gaming Disorder Scale—Short Form (IGDS9-SF), the Depression Anxiety Stress Scale (DASS-21), the Big Five Inventory (BFI), and the Parenting Style Questionnaire. Multiple logistic regression was utilized for data analysis in order to identify relevant risk factors and construct a risk prediction model.
Results. The study revealed that certain sociodemographic characteristics (male and student), psychological factors (depression, anxiety, and stress), personality traits (high neuroticism, low conscientiousness), and authoritarian parenting style were all significant predictors of the occurrence of PIGD. These characteristics led to the creation of the risk prediction model, which demonstrated strong performance with an area under the curve (AUC) of 0.85 (95% CI 0.80-0.90).
Conclusion. Multifaceted issue and variety of risk factors influence PIGD. This study's risk prediction model can effectively identify individuals at a heightened risk of developing PIGD. This allows for early and targeted preventative and treatment interventions to be implemented.
Conceptualization, A.F.K.; Funding acquisition, A.F.K.; Methodology, A.F.K. and R.H.; Supervision, A.F.K.; Validation, A.F.K., R.H. and H.S.; Writing – original draft, A.F.K. and A.P.A.; Formal Analysis, A.P.A.; Software, A.P.A. and M.D.H.; Investigation, M.D.H.; Resources, M.D.H. and M.J.D.; Writing – review & editing, M.D.H. and H.S.; Data curation, M.J.D.; Project administration, M.J.D.; Visualization, M.J.D. All authors have read and agreed to the published version of the manuscript.
No specific funding was received for this study.
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