Exploring the comorbidity mechanism of internet addiction, insomnia, depression, and suicidality among Chinese college students through network analysis

Background: Internet addiction is a common concern among college students, often resulting in negative mental outcomes such as insomnia, depression, and suicidality. However, the variability in internet addiction patterns among college students and its connection to mental disorders remains insufficiently explored. This study investigated the comorbidity network of internet addiction, insomnia, depression, and suicidality in 3,127 Chinese college students using latent profile analysis (LPA) and network analysis. Methods: A total of 3,127 Chinese college students provided their data on internet use, insomnia, depression, and suicidality using the Internet Addiction Test (IAT), Insomnia Severity Index (ISI), Patient Health Questionnaire-9 (PHQ-9), and Suicide Behavior Questionnaire-Revised (SBQ-R). Latent profile analysis (LPA) was employed to identify subgroups of students exhibiting similar patterns of internet addiction. Network structures relating to internet addiction and its association with mental disorders were constructed among addicted users. The stability of the network was assessed through a case drop bootstrap procedure, and a network comparison test (NCT) was conducted to evaluate differences in network characteristics across the identified subgroups. Results : LPA identified three distinct groups of college students based on their internet usage patterns: regular users, moderate users, and addicted users. Network analysis revealed that the central symptom of internet addiction was “lack of self-control when online”. Furthermore, “trouble sleeping”, “frequency of suicidal ideation over the past year” and “sleep maintenance (middle)” were identified as bridge symptoms, connecting insomnia, depression, and suicidality with internet addiction. The NCT showed no significant gender differences in the global strength of the internet addiction network. Conclusions: Interventions focused on improving self-regulation and addressing sleep problems should be prioritized to help reduce internet addiction, insomnia, depression, and suicidality in this population.