Understanding the complex network of anxiety, depression, sleep problems, and smartphone addiction among college art students using network analysis.

Background: This study employs a network analysis approach to explore the interconnections between anxiety, depression, and sleep problems and smartphone addiction among college students using network analysis, offering a new perspective on these prevalent mental health issues. Methods: A cross-sectional study was conducted among art students at a public university in the province of Fujian, China. Data were collected using the Generalized Anxiety Disorder Scale-7, Patient Health Questionnaire-9, Pittsburgh Sleep Quality Index, and Mobile Phone Addiction Index. The R package was used in the analysis for statistical analysis, and information was collected using multi-stage sampling as well as stratified sampling. Network analysis was utilized to identify bivariate associations between symptoms, core components, co-occurring patterns, and key nodes within the network. Network stability and accuracy were assessed using the bootstrap method, and network comparisons were conducted across subgroups based on gender, residential condition, and sibling status. Results: The study included 2,057 participants. The network analysis revealed uncontrollable worry as the most central symptom, with low energy and excessive worry also identified as key symptoms within the network. Bridge symptoms such as daytime dysfunction, self-harm or suicidal ideation, abnormal behavior and speech, and sensory fear were found to be critical in linking anxiety, depression, and sleep problems. The network of comorbid symptoms and smartphone addiction highlighted inefficiency and loss of control as central factors influencing mental health. No significant differences in network characteristics were found across the subgroups, suggesting the universality of the identified network structure. Conclusion: This study delineates the intricate network of anxiety, depression, sleep problems, and smartphone addiction among college students, identifying key symptomatic intersections and their implications for mental health.