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Digital Phenotyping of High-Risk Gaming Behavior Using Wearable Devices.

Yijie Song¹, Yawen Shi², Yiqun Tu¹

¹Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200030 Shanghai, China.

Alpha Psychiatry

|July 8, 2026

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This summary is machine-generated.

Wearable sensors and self-reports improve early detection of high-risk gaming behaviors (HRGB). Depression, heart rate, and sleep patterns are key indicators for gaming disorder prevention.

Area of Science:

Digital Health
Mental Health Technology
Behavioral Science

Background:

Gaming addiction is a significant mental health concern.
Early identification of high-risk gaming behaviors (HRGB) is vital for prevention.
Research on using wearable data for HRGB detection is limited.

Purpose of the Study:

To analyze temporal dynamics of gaming behavior and health indicators in young adults with HRGB.
To evaluate the effectiveness of wearable data in identifying HRGB.
To identify key predictive factors for gaming disorder intervention.

Main Methods:

A 28-day longitudinal study using a self-designed system on the Huawei Research Platform.
Daily mobile app questionnaires (gaming time, mood, sleep) and continuous smart band data collection (physiological, activity).

Keywords:

digital health internet gaming disorder machine learning wearable electronic devices

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Machine learning models to compare active, passive, and combined data for HRGB identification.

Main Results:

Combined data showed superior predictive performance (AUROC 0.86) compared to individual data sources.
Wearable data significantly enhanced early classification of HRGB.
Depression severity (PHQ-9), heart rate, and sleep metrics emerged as key predictors.

Conclusions:

Wearable data integration improves the early classification of high-risk gaming behaviors.
Key predictors include PHQ-9 scores, heart rate, and sleep metrics, demonstrating clinical utility.
Findings support the development of targeted gaming disorder prevention and intervention strategies.