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This page is an AI-assisted Turkish summary of the study presented at the 26th World Congress of Psychiatry. The study belongs to the presenting authors; please refer to the original source when citing it.

EV0037

Modeling daily sleep and gaming processes in high-risk gamers: a dynamic structural equation approach combining wearable monitoring and self-report data

MODELING DAILY SLEEP–GAMING PROCESSES IN HIGH-RISK GAMERS: A DYNAMIC STRUCTURAL EQUATION APPROACH COMBINING WEARABLE TRACKING AND SELF-REPORT DATA

In high-risk gamers, the previous night's subjective sleep quality, rather than objective sleep duration, predicts next-day gaming duration, and this association is not explained by physical activity.

The aim was to examine daily-level associations between sleep and gaming duration in individuals with high-risk gaming behavior and to test whether daily physical activity, operationalized as step count, statistically accounted for these associations. Forty high-risk university students identified using the Gaming Disorder Screening Scale (GDSS) were recruited from universities in Shanghai and Hunan, China, between September 2023 and February 2024 (age range 17–25 years, mean 18.43, SD 1.30; 92.5% male, 97.5% undergraduates, 97.5% mobile gamers, and 72.5% competitive multiplayer gamers). Over 28 days, daily gaming duration and the previous night's sleep quality (rated from 0 to 10) were assessed using one item each, while objective sleep duration and daily step count were recorded using Huawei wearable devices. Data were analyzed using dynamic structural equation modeling (DSEM), which separates within-person and between-person components. In the first model, higher subjective sleep quality the previous night predicted longer next-day gaming duration (β = 0.091, 95% confidence interval [0.034, 0.148]), higher objectively measured physical activity on the same day was associated with shorter gaming duration (β = -0.064, [-0.109, -0.019]), and gaming duration showed significant day-to-day persistence (β = 0.421, [0.360, 0.482]). In the second model, objective sleep duration predicted neither next-day gaming duration nor subsequent physical activity; same-day physical activity was again associated with shorter gaming duration (β = -0.055, [-0.101, -0.010]), and the day-to-day persistence of gaming duration remained (β = 0.423, [0.363, 0.482]). The indirect effect through physical activity was not significant in either model. In conclusion, sleep quality appears more relevant than sleep duration to understanding gaming behavior, and interventions and programs targeting sleep may benefit from considering broader behavioral patterns that include alternative activities such as physical exercise.

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