Abstract
Purpose: 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.
Patients and Methods: 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.
Results: 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.
Conclusion: Interference with daytime functioning was the most central symptom, suggest-ing 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.
Original language | English |
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Pages (from-to) | 1921-1930 |
Number of pages | 10 |
Journal | Nature and Science of Sleep |
Volume | 13 |
DOIs | |
Publication status | Published - Oct 2021 |
Keywords
- COVID-19
- Insomnia
- Physicians
- SARS-CoV-2
- Sleep
ASJC Scopus subject areas
- Applied Psychology
- Behavioral Neuroscience