Development and Internal Validation of a Model for Predicting Internet Gaming Disorder Risk in Adolescents and Children

BACKGROUND: The high prevalence of Internet gaming disorder among children and adolescents and its severe psychological, health, and social consequences have become a public emergency. A high efficiency and cost-effective early recognition method are urgently needed. OBJECTIVE: We aim to develop and internally validate a nomogram model for predicting Internet gaming disorder (IGD) risk in Chinese adolescents and children. METHODS: Through an online survey, 780 children and adolescents aged 7–18 years who participated in the survey from June to August 2021 were selected. The least absolute shrinkage and selection operator regression model was used to filter the factors. Multivariate logistic regression analysis was used to establish the prediction model and generate nomograms and a website calculator. The area under the receiver operating characteristic curve, calibration plot, and decision curve analysis were used to evaluate the model's discrimination, calibration, and clinical utility. Bootstrapping validation was used to verify the model internally. RESULTS: Male sex and experience of game consumption were the two most important predictors. Both models exhibited good discrimination, with an area under the curve >0.80. The calibration plots were both close to the diagonal line (45°). Decision curve analyses revealed that two nomograms were clinically useful when the threshold probability for the intervention was set to 5–75%. CONCLUSION: Two prediction models appear to be reliable tools for Internet gaming disorder screening in children and adolescents, which can also help clinicians to personalize treatment plans. Moreover, from the standpoint of simplification and cost, Model 2 appears to be a better alternative.