Internet Addiction and Depressive Symptoms in University Students: Latent Profiles, Network Structure, and Symptomatic Pathways to Suicide Risk

Abstract

Background: Internet addiction and depression frequently co-occur among university students, resulting in amplified functional deterioration and treatment resistance. Despite established bidirectional relationships, existing research has predominantly examined linear associations and treated these conditions as single global constructs. This study integrated person-centered and network-based approaches to identify distinct symptom profiles of Internet addiction and depressive symptoms, examine sociodemographic predictors of profile membership, and uncover interconnected symptom networks within high-risk populations among Chinese university students.

Methods: A multicenter cross-sectional study was conducted from April to July 2024. Data were collected through a web-based survey incorporating validated instruments for Internet addiction, depression, and suicide risk assessment. Latent profile analysis was employed to identify distinct symptom profiles, followed by multivariate logistic regression to examine sociodemographic predictors. Network analysis was performed within the high-risk profile to unveil symptom interactions, central symptoms, bridge symptoms, and symptomatic pathways to suicide risk.

Results: Among 30,992 participants, latent profile analysis identified three distinct groups: Healthy profile (59.31%), at-risk profile (35.06%), and comorbidity profile (5.63%). Students who were female, ethnic minorities, in higher grade levels, and had prolonged Internet use showed increased risks of problematic profiles. Conversely, enrollment in bachelor’s programs, science and medical majors, higher household income, and regular physical activity demonstrated protective effects. Network analysis revealed Internet preoccupation and fatigue as central symptoms, identified key bridge symptoms (e.g., offline negative affect, difficulty concentrating) linking the symptom clusters, and highlighted Internet withdrawal symptoms and depressed mood as critical pathways to suicide risk within the comorbidity profile.

Conclusion: This study identified distinct profiles of Internet addiction and depression comorbidity, with specific sociodemographic and lifestyle predictors informing targeted screening strategies. Network analysis revealed central symptoms and specific bridge symptoms connecting the conditions, while also identifying critical pathways to suicide risk in the Comorbidity profile, providing empirical evidence for developing precise and effective interventions.