Network analysis of smartphone addiction and sleep disorder symptoms in Chinese college students

OBJECTIVE: This study aims to examine the comorbid relationship between smartphone addiction and sleep disorders in Chinese college students. By constructing a comorbidity network, identifying core and bridge symptoms, and exploring potential directional associations among symptoms, this research intends to establish a theoretical foundation for targeted intervention strategies. METHODS: A total of 1842 Chinese college students were recruited through convenience sampling. The smartphone addiction and sleep disorder symptoms were assessed using the Smartphone Addiction Scale-Short Version (SAS-SV) and the Pittsburgh Sleep Quality Index (PSQI), respectively. The data analysis was conducted in three steps. First, an undirected comorbidity network was constructed using the Gaussian Graphical Model (GGM) to identify core and bridge symptoms. Second, a Bayesian network approach was employed to generate Directed Acyclic Graphs (DAGs) that explored potential directional associations among symptoms. Finally, network comparison tests and community detection analyses were performed to examine gender differences in the comorbidity network structure. RESULTS: The GGM comorbidity network exhibited a connection density of 0.80 and a global strength of 9.39. Within this network, PSQI2 (sleep latency), SA2 (difficulty concentrating), and SA5 (impatience without phone) were identified as core symptoms. PSQI2 (sleep latency), PSQI1 (subjective sleep quality), and SA9 (longer use than intended) were identified as bridge symptoms. Further analysis using the DAGs suggested statistical directionality from sleep disorder symptoms toward smartphone addiction symptoms. Notably, SA5 (impatience without phone) served as an initial node in the DAGs. Finally, network comparison tests indicated no significant differences in the GGM network structure between genders; however, distinct gender differences were observed in the community clustering patterns of symptoms. CONCLUSION: In college students, smartphone addiction and sleep disorder symptoms interact to form a structurally stable comorbidity network. Consequently, interventions targeting core symptoms, bridge symptoms, and initial node could effectively interrupt the maintenance of this comorbidity.