Bai Wei, Zhao Yanjie, An Fengrong, Zhang Qinge, Sha Sha, Cheung Teris, Cheng Calvin Pak-Wing, Ng Chee H, Xiang Yu-Tao
Unit of Psychiatry, Department of Public Health and Medicinal Administration, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, People's Republic of China.
Centre for Cognitive and Brain Sciences, University of Macau, Macao SAR, People's Republic of China.
Nat Sci Sleep. 2021 Oct 28;13:1921-1930. doi: 10.2147/NSS.S326880. eCollection 2021.
The coronavirus disease 2019 (COVID-19) pandemic is associated with increased risk of insomnia symptoms (insomnia hereafter) in health-care professionals. Network analysis is a novel approach in linking mechanisms at the symptom level. The aim of this study was to characterize the insomnia network structure in mental health professionals during the COVID-19 pandemic.
A total of 10,516 mental health professionals were recruited from psychiatric hospitals or psychiatric units of general hospitals nationwide between March 15 and March 20, 2020. Insomnia was assessed with the insomnia severity index (ISI). Centrality index (ie, strength) was used to identify symptoms central to the network. The stability of network was examined using a case-dropping bootstrap procedure. The network structures between different genders were also compared.
The overall network model showed that the item ISI7 (interference with daytime functioning) was the most central symptom in mental health professionals with the highest strength. The network was robust in stability and accuracy tests. The item ISI4 (sleep dissatisfaction) was connected to the two main clusters of insomnia symptoms (ie, the cluster of nocturnal and daytime symptoms). No significant gender network difference was found.
Interference with daytime functioning was the most central symptom, suggesting that it may be an important treatment outcome measure for insomnia. Appropriate treatments, such as stimulus control techniques, cognitive behavioral therapy and relaxation training, could be developed. Moreover, addressing sleep satisfaction in treatment could simultaneously ameliorate daytime and nocturnal symptoms.
2019冠状病毒病(COVID-19)大流行与医护人员失眠症状(以下简称失眠)风险增加有关。网络分析是一种在症状层面联系各种机制的新方法。本研究的目的是描述COVID-19大流行期间心理健康专业人员的失眠网络结构。
2020年3月15日至3月20日期间,从全国各精神病医院或综合医院的精神科招募了总共10516名心理健康专业人员。使用失眠严重程度指数(ISI)评估失眠情况。中心性指数(即强度)用于确定网络中的核心症状。使用逐个剔除的自助法检验网络的稳定性。还比较了不同性别的网络结构。
总体网络模型显示,条目ISI7(对日间功能的干扰)是心理健康专业人员中最核心的症状,强度最高。该网络在稳定性和准确性测试中表现稳健。条目ISI4(睡眠不满意)与失眠症状的两个主要集群相关(即夜间和日间症状集群)。未发现显著的性别网络差异。
对日间功能的干扰是最核心的症状,这表明它可能是失眠治疗效果的一项重要衡量指标。可以开发适当的治疗方法,如刺激控制技术、认知行为疗法和放松训练。此外,在治疗中解决睡眠满意度问题可以同时改善日间和夜间症状。