Mapping Depression and Problematic Internet Use Symptoms in Chinese College Students: Insights from a Large-Scale Network Analysis

The rapidly growing rate of Internet use globally is associated with an increased risk of both depression and Problematic Internet Use (PIU) in young people. This study aimed to analyze the inter-relationships between depression and PIU among college students from the perspective of network analysis. A total of 6514 college students were recruited nationwide from September to December 2023 in China. Depression and PIU were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and the Internet Addiction Test, respectively. Expected Influence (EI) and bridge EI were used as centrality indices to characterize the network structure of the symptoms. Based on self-reported questionnaires, the percentage of college students with depressive symptoms and PIU were 53.7% [95% confidence interval (CI): 52.4%–54.9%] and 27.9% (95%CI: 26.8%–29.0%), respectively. The network analysis identified IAT2 (“Neglect chores to spend more time online”) as the most central symptom, followed by IAT16 (“Request an extension for longer time”) and IAT8 (“Academic efficiency declines”). Additionally, PHQ8 (“Motor disturbances”), IAT20 (“Depress/moody/nervous being offline”) and PHQ9 (“Suicide ideation”) were the key bridge nodes linking the communities of depression and PIU. Depression and PIU were common among university students in China. Appropriate measures should target the central and bridge symptoms identified in this network model to effectively address such mental health problems in those affected.

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Publication Details

Journal
International Journal of Mental Health and Addiction
Published
2025-09-29
DOI
https://doi.org/10.1007/s11469-025-01519-w
Citations
1
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
2.12

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article

Mapping Depression and Problematic Internet Use Symptoms in Chinese College Students: Insights from a Large-Scale Network Analysis

Yi Ma, Zhaohui Su, Mengyi Chen, A Li-Ya et al.
1 citations
International Journal of Mental Health and Addiction
Mental Health Research Topics
2.12
article

Mapping Depression and Problematic Internet Use Symptoms in Chinese College Students: Insights from a Large-Scale Network Analysis

Yi Ma, Zhaohui Su, Mengyi Chen, A Li-Ya, Yu‐Tao Xiang, Qingyong Meng, Teris Cheung, Huiting Huang, Chee H. Ng, Yuan-Yuan Jiang, Shou Liu
article en
1 citations

Abstract

The rapidly growing rate of Internet use globally is associated with an increased risk of both depression and Problematic Internet Use (PIU) in young people. This study aimed to analyze the inter-relationships between depression and PIU among college students from the perspective of network analysis. A total of 6514 college students were recruited nationwide from September to December 2023 in China. Depression and PIU were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and the Internet Addiction Test, respectively. Expected Influence (EI) and bridge EI were used as centrality indices to characterize the network structure of the symptoms. Based on self-reported questionnaires, the percentage of college students with depressive symptoms and PIU were 53.7% [95% confidence interval (CI): 52.4%–54.9%] and 27.9% (95%CI: 26.8%–29.0%), respectively. The network analysis identified IAT2 (“Neglect chores to spend more time online”) as the most central symptom, followed by IAT16 (“Request an extension for longer time”) and IAT8 (“Academic efficiency declines”). Additionally, PHQ8 (“Motor disturbances”), IAT20 (“Depress/moody/nervous being offline”) and PHQ9 (“Suicide ideation”) were the key bridge nodes linking the communities of depression and PIU. Depression and PIU were common among university students in China. Appropriate measures should target the central and bridge symptoms identified in this network model to effectively address such mental health problems in those affected.

International Journal of Mental Health and Addiction
Macau University of Science and Technology (MO), Qinghai University (CN), Hong Kong Polytechnic University (HK), The University of Melbourne (AU), The University of Western Australia (AU), University of Macau (MO), Guangdong Polytechnic of Science and Technology (CN), St Vincent's Hospital Melbourne (AU), Melbourne Clinic (AU), Zhuhai Institute of Advanced Technology (CN), Southeast University (CN), The University of Notre Dame Australia (AU)
Universidade de Macau
Openalex Percentile: Top 11%
Mental Health Research Topics
2.12
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