Using Network Analysis to Subgroup Risk Factors for Depressive Symptoms in College Students.

Abstract

Purpose: Network modeling has been suggested as an effective method to explore intricate relationships among antecedents, mediators, and symptoms. In this study, we aimed to investigate whether the severity of depressive symptoms in college students affects the multivariate relationships among anhedonia, smartphone addiction, and mediating factors.

Methods: A survey was conducted among 1347 Chinese college students (587 female) to assess depressive symptoms, anhedonia, addictive behaviors, anxiety, and insomnia. The participants were categorized the non-depressive symptom (NDS) and depressive symptom (DS) groups based on a cut-off score of 5 on the 9-item Patient Health Questionnaire-9. Network analysis was performed to investigate the symptom-to-symptom influences of symptoms in these two groups.

Results: The network of the DS group was more densely connected than that of the NDS group. Social anticipatory anhedonia was a central factor for DS, while withdraw/escape (one factor of smartphone addiction) was a central factor for NDS. The DS group exhibited greater strength between the PHQ9 score and social anticipatory anhedonia, as well as between the PHQ9 score and alcohol misuse score, compared to the NDS group. On the other hand, the NDS group had higher strength between anxiety and feeling lost, as well as between anxiety and withdraw/escape, compared to the DS group.

Conclusion: The findings suggest that there is a close relationship between social anhedonia, smartphone addiction, and alcohol consumption in the DS group. Addressing on ameliorating social anhedonia and smartphone addiction may be effective in preventing and managing depression in college students.