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

Area of Science:

• Psychology
• Mental Health Research
• Network Analysis

Background:

• Network modeling offers insights into complex relationships between symptoms.
• Understanding these connections is crucial for mental health interventions in college students.

Purpose of the Study:

• To investigate how depressive symptom severity impacts multivariate relationships among anhedonia, smartphone addiction, and mediating factors in Chinese college students.
• To compare network structures between students with and without depressive symptoms.

Main Methods:

• Survey of 1347 Chinese college students assessing depressive symptoms, anhedonia, smartphone addiction, anxiety, and insomnia.
• Categorization into non-depressive symptom (NDS) and depressive symptom (DS) groups based on PHQ-9 scores.
• Network analysis to examine symptom-to-symptom influences within each group.

Main Results:

• The DS group exhibited a more densely connected network than the NDS group.
• Social anticipatory anhedonia was central in the DS group; withdraw/escape (smartphone addiction) was central in the NDS group.
• Stronger associations were found between PHQ-9 scores and social anhedonia/alcohol misuse in the DS group, and between anxiety and feeling lost/withdraw/escape in the NDS group.

Conclusions:

• A significant link exists between social anhedonia, smartphone addiction, and alcohol use in college students with depressive symptoms.
• Interventions targeting social anhedonia and smartphone addiction may be effective for depression prevention and management in this population.