Frontiers in psychiatry · 2025
Sleep patterns and smartphone use among left-behind children: a latent class analysis and its association with depressive symptoms.
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Han Xue, Li Cheng-Han, Miao Heng, Xu Su, Yan Wen-Jing, Chen Juan
Аннотация
BACKGROUND: Left-behind children in China face challenges in sleep patterns, technology use, and mental health. This study uses an individual-centered approach to derive behavioral profiles associated with depressive symptoms.
METHODS: Data from 131,586 left-behind children aged 8 to 18 years from the Chinese Psychological Health Guard for Children and Adolescents Project were analyzed. Participants were recruited from 569 centers across schools, community institutes, orphanages, and children's hospitals throughout China. Latent class analysis was conducted using weekday and weekend sleep duration and smartphone use as indicators. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D).
RESULTS: Four distinct classes emerged: Sufficient Sleep Low Users (23.6%), Moderate Sleep Medium Users (25.2%), Limited Sleep High Users (22.1%), and Healthy Sleep Low Users (29.2%). Significant differences in CES-D scores were found between classes (F(3, 131579) = 4929, p <.001, η² = 0.101). The Limited Sleep High Users class reported the highest levels of depressive symptoms (M = 11.60, SE = 0.0658), while the Sufficient Sleep Low Users class reported the lowest (M = 3.67, SE = 0.0346). A linear relationship between sleep duration and depressive symptoms was observed. Significant weekday-weekend differences in smartphone use were noted in the unhealthy categories.
CONCLUSIONS: This study reveals complex associations between sleep patterns, smartphone use, and depressive symptoms among left-behind children. The identified behavioral profiles provide insights into population heterogeneity and inform targeted intervention strategies. Findings emphasize the importance of addressing both sleep and technology use in mental health initiatives for this vulnerable population.
METHODS: Data from 131,586 left-behind children aged 8 to 18 years from the Chinese Psychological Health Guard for Children and Adolescents Project were analyzed. Participants were recruited from 569 centers across schools, community institutes, orphanages, and children's hospitals throughout China. Latent class analysis was conducted using weekday and weekend sleep duration and smartphone use as indicators. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D).
RESULTS: Four distinct classes emerged: Sufficient Sleep Low Users (23.6%), Moderate Sleep Medium Users (25.2%), Limited Sleep High Users (22.1%), and Healthy Sleep Low Users (29.2%). Significant differences in CES-D scores were found between classes (F(3, 131579) = 4929, p <.001, η² = 0.101). The Limited Sleep High Users class reported the highest levels of depressive symptoms (M = 11.60, SE = 0.0658), while the Sufficient Sleep Low Users class reported the lowest (M = 3.67, SE = 0.0346). A linear relationship between sleep duration and depressive symptoms was observed. Significant weekday-weekend differences in smartphone use were noted in the unhealthy categories.
CONCLUSIONS: This study reveals complex associations between sleep patterns, smartphone use, and depressive symptoms among left-behind children. The identified behavioral profiles provide insights into population heterogeneity and inform targeted intervention strategies. Findings emphasize the importance of addressing both sleep and technology use in mental health initiatives for this vulnerable population.