AUTHOR=Cai Hong , Bai Wei , Yue Yan , Zhang Ling , Mi Wen-Fang , Li Yu-Chen , Liu Huan-Zhong , Du Xiangdong , Zeng Zhen-Tao , Lu Chang-Mou , Zhang Lan , Feng Ke-Xin , Ding Yan-Hong , Yang Juan-Juan , Jackson Todd , Cheung Teris , An Feng-Rong , Xiang Yu-Tao TITLE=Mapping network connectivity between internet addiction and residual depressive symptoms in patients with depression JOURNAL=Frontiers in Psychiatry VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2022.997593 DOI=10.3389/fpsyt.2022.997593 ISSN=1664-0640 ABSTRACT=Background and aims

Depression often triggers addictive behaviors such as Internet addiction. In this network analysis study, we assessed the association between Internet addiction and residual depressive symptoms in patients suffering from clinically stable recurrent depressive disorder (depression hereafter).

Materials and methods

In total, 1,267 depressed patients were included. Internet addiction and residual depressive symptoms were measured using the Internet Addiction Test (IAT) and the two-item Patient Health Questionnaire (PHQ-2), respectively. Central symptoms and bridge symptoms were identified via centrality indices. Network stability was examined using the case-dropping procedure.

Results

The prevalence of IA within this sample was 27.2% (95% CI: 24.7–29.6%) based on the IAT cutoff of 50. IAT15 (“Preoccupation with the Internet”), IAT13 (“Snap or act annoyed if bothered without being online”) and IAT2 (“Neglect chores to spend more time online”) were the most central nodes in the network model. Additionally, bridge symptoms included the node PHQ1 (“Anhedonia”), followed by PHQ2 (“Sad mood”) and IAT3 (“Prefer the excitement online to the time with others”). There was no gender difference in the network structure.

Conclusion

Both key central and bridge symptoms found in the network analysis could be potentially targeted in prevention and treatment for depressed patients with comorbid Internet addiction and residual depressive symptoms.