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

Zheng-Chi Zhang; Yong-Yi He; Yi-Hui Liu; Kai-Min Yao; Wen-Gao Li; Ran Gao; Yuan Yang

doi:10.1016/j.jad.2025.119805

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

J Affect Disord. 2025 Jun 30:390:119805. doi: 10.1016/j.jad.2025.119805. Online ahead of print.

Authors

Zheng-Chi Zhang¹, Yong-Yi He², Yi-Hui Liu¹, Kai-Min Yao¹, Wen-Gao Li³, Ran Gao⁴, Yuan Yang⁵

Affiliations

¹ Guangdong Mental Health Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
² Department of Psychiatry, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
³ Department of Psychiatry, General Hospital of Southern Theatre Command, Guangzhou, China.
⁴ Department of Psychology, Guangdong Peizheng College, Guangzhou, China. Electronic address: 1903803@peizheng.edu.cn.
⁵ Guangdong Mental Health Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. Electronic address: yangyuan@gdph.org.cn.

PMID: 40602540
DOI: 10.1016/j.jad.2025.119805

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: College students; Depression; Insomnia; Internet addiction; Network analysis.