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Problematic internet use (PIU) is a growing concern, especially among adolescents, with significant impacts on mental and physical health. This study aims to predict the severity of PIU, measured by the Severity Impairment Index (SII), using a combination of physical activity, demographic, and behavioral data. Machine learning models, including XGBoost, CatBoost, TabNet, and LightGBM, were employed to classify participants into SII categories: none, mild, moderate, and severe. Data were sourced from the Healthy Brain Network (HBN) dataset, which includes accelerometer data, internet usage, fitness assessments, and physiological measures from over 3000 participants aged 5–22 years. Key feature engineering steps included creating interaction terms (e.g., BMI \(\times \) Age) and applying autoencoders for dimensionality reduction on the high-dimensional actigraphy data. The results indicated that CatBoost performed best in predicting minority SII categories, handling imbalanced data effectively. XGBoost and LightGBM demonstrated stable performance, while TabNet provided interpretability but lower overall predictive power. Evaluation metrics, particularly Quadratic Weighted Kappa (QWK), were used to assess model performance, with QWK offering insights into the ordinal nature of misclassifications. This study highlights the value of combining physical activity and behavioral data in predicting PIU severity. The findings underscore the potential of machine learning in identifying individuals at risk for severe PIU and suggest avenues for future interventions to reduce the negative impacts of excessive internet use.

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Predicting Problematic Internet Use Severity: A Machine Learning Approach Using Physical Activity and Behavioral Data

  • 会议论文

  • 发表日期：

    2026年

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  • Aisha Karigar,
  • Mohammed Qadir Ternikar,
  • Harsh Nesari,
  • N. Vanashree,
  • Prema T. Akkasaligar

摘要

Problematic internet use (PIU) is a growing concern, especially among adolescents, with significant impacts on mental and physical health. This study aims to predict the severity of PIU, measured by the Severity Impairment Index (SII), using a combination of physical activity, demographic, and behavioral data. Machine learning models, including XGBoost, CatBoost, TabNet, and LightGBM, were employed to classify participants into SII categories: none, mild, moderate, and severe. Data were sourced from the Healthy Brain Network (HBN) dataset, which includes accelerometer data, internet usage, fitness assessments, and physiological measures from over 3000 participants aged 5–22 years. Key feature engineering steps included creating interaction terms (e.g., BMI \(\times \) Age) and applying autoencoders for dimensionality reduction on the high-dimensional actigraphy data. The results indicated that CatBoost performed best in predicting minority SII categories, handling imbalanced data effectively. XGBoost and LightGBM demonstrated stable performance, while TabNet provided interpretability but lower overall predictive power. Evaluation metrics, particularly Quadratic Weighted Kappa (QWK), were used to assess model performance, with QWK offering insights into the ordinal nature of misclassifications. This study highlights the value of combining physical activity and behavioral data in predicting PIU severity. The findings underscore the potential of machine learning in identifying individuals at risk for severe PIU and suggest avenues for future interventions to reduce the negative impacts of excessive internet use.