Concurrent Associations of Gaming Time and Problematic Gaming with Depressive Symptoms in Children and Adolescents: A Three-Wave Cohort Study with Complementary Machine Learning Classification

Background/Objectives: Digital media use is integral to the daily lives of children and adolescents, intensifying concerns regarding potential associations with mental health. However, much of the literature linking gaming and depression is based on cross-sectional designs and does not adequately distinguish within-person change from between-person differences. This study examined concurrent associations between gaming use patterns, problematic gaming behavior, and depressive symptoms across three repeated assessment waves and, as a complementary analysis, evaluated the concurrent classification performance of gaming-related indicators for elevated depressive symptoms. Methods: Three-wave data collected at baseline, 12 months, and 24 months were obtained from the Internet, Game, and Smartphone User Cohort established by the National Center for Mental Health. Depressive symptoms were assessed using the Children’s Depression Inventory (CDI), and problematic gaming behavior was assessed using the Internet Game Use-Elicited Symptom Screen (IGUESS). Linear mixed-effects models, including within–between decomposition and change-score models, were applied. Logistic Regression, Random Forest, and XGBoost were used to classify concurrent elevated depressive symptoms (CDI ≥ 22). Results: The primary mixed-effects analyses included 2194 of 2294 enrolled participants. Across models, higher IGUESS scores were consistently associated with greater concurrent depressive symptoms, whereas gaming time showed weak or inconsistent associations. Machine learning models yielded ROC-AUC values of 0.85–0.88; however, precision and F1-scores were modest, indicating a substantial false-positive burden in this low-prevalence setting. Conclusions: Overall, problematic gaming behavior, rather than gaming time alone, may be more closely associated with depressive symptoms in children and adolescents.

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Publication Details

Journal
Healthcare
Published
2026-10-05
DOI
https://doi.org/10.3390/healthcare14193315
Primary Topic
Impact of Technology on Adolescents
Type
article
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article

Concurrent Associations of Gaming Time and Problematic Gaming with Depressive Symptoms in Children and Adolescents: A Three-Wave Cohort Study with Complementary Machine Learning Classification

Hyo Jung Kim, Yeseo Lee, Jun Seok Her
Healthcare
Impact of Technology on Adolescents
article

Concurrent Associations of Gaming Time and Problematic Gaming with Depressive Symptoms in Children and Adolescents: A Three-Wave Cohort Study with Complementary Machine Learning Classification

Hyo Jung Kim, Yeseo Lee, Jun Seok Her
article en

Abstract

Background/Objectives: Digital media use is integral to the daily lives of children and adolescents, intensifying concerns regarding potential associations with mental health. However, much of the literature linking gaming and depression is based on cross-sectional designs and does not adequately distinguish within-person change from between-person differences. This study examined concurrent associations between gaming use patterns, problematic gaming behavior, and depressive symptoms across three repeated assessment waves and, as a complementary analysis, evaluated the concurrent classification performance of gaming-related indicators for elevated depressive symptoms. Methods: Three-wave data collected at baseline, 12 months, and 24 months were obtained from the Internet, Game, and Smartphone User Cohort established by the National Center for Mental Health. Depressive symptoms were assessed using the Children’s Depression Inventory (CDI), and problematic gaming behavior was assessed using the Internet Game Use-Elicited Symptom Screen (IGUESS). Linear mixed-effects models, including within–between decomposition and change-score models, were applied. Logistic Regression, Random Forest, and XGBoost were used to classify concurrent elevated depressive symptoms (CDI ≥ 22). Results: The primary mixed-effects analyses included 2194 of 2294 enrolled participants. Across models, higher IGUESS scores were consistently associated with greater concurrent depressive symptoms, whereas gaming time showed weak or inconsistent associations. Machine learning models yielded ROC-AUC values of 0.85–0.88; however, precision and F1-scores were modest, indicating a substantial false-positive burden in this low-prevalence setting. Conclusions: Overall, problematic gaming behavior, rather than gaming time alone, may be more closely associated with depressive symptoms in children and adolescents.

HealthcareVol. 14(19)
Catholic University of Korea (KR)
Openalex Percentile: Top 5%
Impact of Technology on Adolescents
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