Network analysis of internet addiction and depression among Chinese college students during the COVID-19 pandemic: A longitudinal study. (January 2023)

• Record Type: Journal Article
• Authors: Zhao, Yue; Qu, Diyang; Chen, Shiyun; Chi, Xinli
• Is Part Of: Computers in human behavior. Volume 138(2023)
• Publication Date: 2023-01
• Subjects: Internet addiction -- Depression -- Network analysis -- Central symptoms -- Bridge symptoms -- Longitudinal data; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2022.107424
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: Background: There has been growing evidence of comorbidity between internet addiction and depression in youth during the COVID-19 period. According to the network theory, this may arise from the interplay of symptoms shared by these two mental disorders. Therefore, we examined this underlying process by measuring the changes in the central and bridge symptoms of the co-occurrence networks across time. Methods: A total of 852 Chinese college students were recruited during two waves (T1: August 2020; T2: November 2020), and reported their internet addiction symptoms and depressive symptoms. Network analysis was utilized for the statistical analysis. Results: The internet addiction symptoms "escape" and "irritable, " and depression symptoms "energy" and "guilty" were the central symptoms for both waves. At the same time, "guilty" and "escape" were identified as bridge symptoms. Notably, the correlation between "anhedonia" and "withdrawal" significantly increased, and that between "guilty" and "escape" significantly decreased over time. Conclusions: This study provides novel insights into the central features of internet addiction and depression during the two stages. Interestingly, "guilty" and "escape, " two functions of the defense mechanism, are identified as bridge symptoms. These two symptoms are suggested to activate the negative feedback loop and further contribute to the comorbidity between internet addiction and depression. Thus, targeting interventions on these internalized symptoms may contribute to alleviating the level of comorbidity among college students. Highlights: The central symptoms of depression in the network analysis are energy and guilty overtimes. The central symptoms of internet addiction in the network analysis are escape and irritable overtimes. Guilty and escape are the bridge symptoms in the comorbidity networks overtimes. The decreased correlation of bridge symptoms may help to alleviate the occurrence of comorbidity.