Network analysis of comorbid insomnia and depressive symptoms among psychiatric practitioners during the COVID-19 pandemic

被引:13
作者
Zhao, Na [1 ,2 ,3 ]
Zhao, Yan-Jie [1 ,2 ,4 ,5 ]
An, Fengrong [6 ,7 ]
Zhang, Qinge [6 ,7 ]
Sha, Sha [6 ,7 ]
Su, Zhaohui [8 ]
Cheung, Teris [9 ]
Jackson, Todd [10 ]
Zang, Yu-Feng [3 ]
Xiang, Yu-Tao [1 ,2 ,4 ,5 ,11 ]
机构
[1] Univ Macau, Dept Publ Hlth, Unit Psychiat, Med Adm, Zhuhai, Peoples R China
[2] Univ Macau, Inst Translat Med, Fac Hlth Sci, Zhuhai, Peoples R China
[3] Hangzhou Normal Univ, Inst Psychol Sci, Ctr Cognit & Brain Disorders, Hangzhou, Peoples R China
[4] Univ Macau, Ctr Cognit & Brain Sci, Macau, Peoples R China
[5] Univ Macau, Inst Adv Studies Humanities & Soc Sci, Macau, Peoples R China
[6] Capital Med Univ, Beijing Key Lab Mental Disorders, Beijing Anding Hosp, Natl Clin Res Ctr Mental Disorders,Natl Ctr Mental, Beijing, Peoples R China
[7] Capital Med Univ, Adv Innovat Ctr Human Brain Protect, Beijing, Peoples R China
[8] Southeast Univ, Sch Publ Hlth, Nanjing, Peoples R China
[9] Hong Kong Polytech Univ, Sch Nursing, Hong Kong, Peoples R China
[10] Univ Macau, Dept Psychol, Macau, Peoples R China
[11] Avenida Univ, Univ Macau, Fac Hlth Sci, 1-F,Bldg E12, Macau, Peoples R China
来源
JOURNAL OF CLINICAL SLEEP MEDICINE | 2023年 / 19卷 / 07期
关键词
insomnia; depression; network analysis; COVID-19; epidemiology; SEVERITY INDEX; MENTAL-HEALTH; FATIGUE; ASSOCIATION; PREVALENCE; DISORDERS; VALIDITY; SLEEP; MODEL; PHQ-9;
D O I
10.5664/jcsm.10586
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Study Objectives: Insomnia and depression are common mental health problems reported by mental health professionals during the COVID-19 pandemic. Network analysis is a fine-grained approach used to examine associations between psychiatric syndromes at a symptom level. This study was designed to elucidate central symptoms and bridge symptoms of a depression-insomnia network among psychiatric practitioners in China. The identification of particularly important symptoms via network analysis provides an empirical foundation for targeting specific symptoms when developing treatments for comorbid insomnia and depression within this population. Methods: A total of 10,516 psychiatric practitioners were included in this study. The Insomnia Severity Index (ISI) and 9-item Patient Health Questionnaire (PHQ-9) were used to estimate prevalence rates of insomnia and depressive symptoms, respectively. Analyses also generated a network model of insomnia and depression symptoms in the sample. Results: Prevalence rates of insomnia (ISI total score & GE;8), depression (PHQ-9 total score & GE;5) and comorbid insomnia and depression were 22.2% (95% confidence interval: 21.4-22.9%), 28.5% (95% confidence interval: 27.6-29.4%), and 16.0% (95% confidence interval: 15.3-16.7%), respectively. Network analysis revealed that "Distress caused by sleep difficulties" (ISI7) and "Sleep maintenance" (ISI2) had the highest strength centrality, followed by "Motor dysfunction" (PHQ8) and "Sad mood" (PHQ2). Furthermore, the nodes "Sleep dissatisfaction" (ISI4), "Fatigue" (PHQ4), and "Motor dysfunction" (PHQ8) had the highest bridge strengths in linking depression and insomnia communities. Conclusions: Both central and bridge symptoms (ie, Distress caused by sleep difficulties, Sleep maintenance, Motor dysfunction, Sad mood, Sleep dissatisfaction, and Fatigue) should be prioritized when testing preventive measures and specific treatments to address comorbid insomnia and depression among psychiatric practitioners during the COVID-19 pandemic.
引用
收藏
页码:1271 / 1279
页数:9
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