Risk level prediction for problematic internet use: A digital health perspective

Abstract

Problematic Internet Usage (PIU) research has long been a topic of interest across disciplines, and numerous theoretical and empirical studies have been conducted over the past decade. This study systematically reviews the existing literature to identify key research objectives, datasets, methodologies, and applications, and to highlight important gaps and challenges. To improve understanding and detection of PIU, we designed a comprehensive machine learning pipeline that combines detailed preprocessing, feature extraction, modeling, and performance validation strategies. Systematic evaluations demonstrate that model performance is significantly improved by addressing missing values and data imbalance. In particular, we identified key predictive features such as physiological indicators, physical activity, sleep quality, and Internet usage patterns, and clearly elucidated the differences in the positive or negative impact of these key features on PIU detection at different severity levels. These results have practical implications, especially for promoting early detection and enabling tailored interventions. Ultimately, this study contributes to digital health initiatives by providing actionable insights for the development of effective Internet addiction prevention and intervention programs.