Abstract
Objectives: This study aims to examine Internet addiction profiles, their associations with comorbid anxiety and depression, and characterize network architectures of anxiety and depression across profiles. Methods: From November 2022 to November 2023, we conducted a short-term cohort study including 2503 students. Latent profile analysis (LPA) and multinomial logistic regression analysis were employed to investigate the association between Internet addiction and comorbidity of anxiety and depression, and network analysis was used to characterize anxiety-depression network structure within each profile. Results: LPA identified three profiles of Internet addiction, which were labeled: "regular" (66.60%) profile, "risk" profile (23.09%), and "addiction" profile (10.31%). The incidence of comorbid anxiety and depression was 10.67%. Both the "risk" (adjusted OR = 1.76, 95% CI: 1.27-2.44) and "addiction" (adjusted OR = 2.12, 95% CI: 1.39-3.24) profiles were significantly associated with increased comorbidity risk. The "dass13" ("Downhearted and blue") emerged as a core symptom, and "dass15" ("Close to panic") was identified as a key bridge symptom across three network models. The edge weight for the dass05-dass21 (Lack of motivation-Meaninglessness of life) was higher in the "risk" profile than in the "addiction" profile. Conclusions: Children and adolescents in the "risk" and "addiction" profiles were significantly more likely to experience comorbid anxiety and depression. "dass13" ("Downhearted and blue") and "dass15" ("Close to panic") can be used as the key target during intervention. Targeted interventions can be implemented for children and adolescents in the "risk" and "addiction" profiles.