Network analysis of depression, insomnia and internet addiction symptoms in Chinese college students

Excessive internet usage is a global issue, often associated with depressive and insomnia symptoms (depression and insomnia hereafter). This study aimed to examine the inter-relationships among symptoms of Internet Addiction (IA), depression, and insomnia among Chinese college students using network analysis.

This cross-sectional study was conducted with college students in Guangzhou, China. All participants completed the 20-item Internet Addiction Test (IAT), the Insomnia Severity Index (ISI), and the Patient Health Questionnaire (PHQ-9). Network analysis was employed to investigate the network structure, identify central symptoms and bridge symptoms, and assess the network stability. Centrality was measured using expected influence and bridge expected influence.

A total of 1130 college students participated in the study. Network analysis revealed that node IAT20 ('Depress/moody/nervous only while being offline', node EI = 1.214) was the most central symptom in the co-occurrence network. The connection between ISI1 and ISI2 ('Falling asleep'- 'Staying asleep', edge weight = 0.456) was the strongest link between different communities. Several sleep-related symptoms, such as PHQ3 ('Sleep') and ISI1 ('Falling Asleep'), acted as key bridging symptoms connecting communities of depression, insomnia, and IA. The model demonstrated a high level of stability.

IAT20 ('Depress/moody/nervous only while being offline') was the most central symptom in the IA-depression-insomnia network. Sleep problems, particularly difficulties in falling asleep, played a key role in the interactions among different symptom clusters. Addressing sleep issues may help mitigate co-occurring depression and excessive internet use among college students.