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=Volume 13 - 2022

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=Depression often triggers addictive behaviors such as Internet addiction (IA). In this study we examined the relationship between IA and residual depressive symptoms (RDS) in clinically stable major depressive disorder (MDD) patients using network analysis. A total of 1,267 clinical stable patients with MDD were included. IA and RDS were measured using the Internet Addiction Test (IAT) and the Patient Health Questionnaire-2 (PHQ-2), respectively. Central symptoms and bridge symptoms were identified via centrality indices. Network stability was examined using the case-dropping procedure. Network analysis revealed the nodes IAT-15 (“Preoccupation with the Internet”), IAT-13 (“Snap or act annoyed if bothered without being online”) and IAT-2 (“Neglect chores to spend more time online”) were the most influential symptoms in the IA and RDS model. Additionally, bridge symptoms included the node PHQ-1 (“Anhedonia”), followed by PHQ-2 (“Sad mood”) and IAT-3 (“Prefer the excitement online to the time with others”). Gender did not significantly influence the network structure. Central symptoms and key bridge symptoms identified in this network analysis may be potential targets in prevention and the treatments for MDD patients with comorbid IA and RDS.