The prevalence of internet addiction and its association with quality of life among clinically stable patients with major depressive disorder

Yang, Juanjuan; Bai, Wei; Guo, Tong; Zhang, Ling; Li, Yuchen; Liu, Huan-Zhong; Du, Xiangdong; Cai, Hong; Balbuena, Lloyd; An, Feng-Rong; Xiang, Yu‐Tao

doi:10.1016/j.jad.2022.06.067

Cited by 5 publications

(1 citation statement)

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“…This study was part of a large-scale project on mental health status of clinically stable patients with depression during the COVID-19 pandemic conducted in outpatient departments of six tertiary psychiatric hospitals ( 31 ). Similar to other studies ( 32 – 35 ), the “Wenjuanxing” program embedded in the WeChat application was administered in this study.…”

Section: Methodsmentioning

confidence: 99%

Mapping network connectivity between internet addiction and residual depressive symptoms in patients with depression

et al. 2022

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Background and aimsDepression 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 methodsIn 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.ResultsThe 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.ConclusionBoth 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.

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Section: Methodsmentioning

confidence: 99%