Prevalence,Correlates,and Network Analysis Of Internet Addiction Symptoms Among Clinically Stable Patients With Major Depressive Disorder

Objective Internet addiction（IA）has been an important health challenge with rapid development of technology in recent years.Depression is usually associated with increased risk of addictive behaviors such as IA.Epidemiology of IA in depressed patients are not clear.This study examined the prevalence of IA and its association with quality of life（QOL）among clinically stable patients with major depressive disorder（MDD）and the related factors.Then,network analysis was used to examine the relationship between IA and residual depressive symptoms（RDS）.Methods This was a cross-sectional survey conducted between September 2020 to July2021.Basic demographic and clinical information were collected.The Young’s Internet Addiction Test（IAT）and the World Health Organization Quality of Life Brief version were used to assess IA and global QOL,respectively.RDS were measured using the Patient Health Questionnaire-2（PHQ-2）.Socio-demographic and clinical characteristics were compared and analyzed between groups with and without IA.Central symptoms and bridge symptoms were identified via centrality indices.Network stability was examined using the case-dropping procedure.Results Of 1,267 patients who were included,the prevalence of IA（IAT total scores≥50）was 27.2%（95%CI: 24.7%-29.6%）.Compared to patients without IA,patients with IA had lower QOL（F（1,1267）=43.1,P<0.001）.Binary logistic regression analysis revealed that higher education level（senior high school and above;OR=1.83,95%CI:1.12-2.98）,a family history of psychiatric disorders（OR=1.71,95%CI: 1.08-2.69）,a higher PHQ-2 total score（OR=1.23,95%CI:1.14-1.32）and older age（OR=0.93,95%CI: 0.90-0.95）were significantly associated with IA.Network analysis revealed the nodes IAT-15（“Preoccupation with the Internet”）,IAT-13（“Snap or act annoyed if bothered”）and IAT-2（“Neglect chores to spend more time online”）were the most influential symptoms in the IA and RDS model.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.Conclusion IA was common in clinically stable patients with MDD.Considering the negative influence on daily life and QOL,effective preventive measures should be developed for clinically stable patients with MDD.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.