| Literature DB >> 32590755 |
Zhi-Hao Tu1, Jing-Wen He2, Na Zhou3.
Abstract
The aim of this study was to investigate the prevalence of sleep problems, depression and anxiety symptoms among conscripted frontline nurses fighting coronavirus disease 2019 (COVID-19) in Wuhan.This study was a cross-sectional study conducted with 100 frontline nurses. Sleep quality, depression, and anxiety symptoms were measured using the Pittsburgh sleep quality index (PSQI), the Generalized Anxiety Disorder 7-Item Scale (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9), respectively.Mean sleep duration was 5.71 hours (SD = 1.09) and mean sleep latency was 33.49 minutes (SD = 28.87). A total of 76%, 81%, 45%, and 19% reported difficulty initiating sleep (DIS), difficulty maintaining sleep (DMS) or early morning awakening (EMA), nightmares and using hypnotics respectively. Among 100 participants in this study, 60 (60%) had poor sleep quality, 46 (46%) suffered depression symptoms and 40 (40%) reported anxiety symptoms. Sleep quality (OR = 3.16, 95% CI: 1.17-8.52) and anxiety symptoms (OR = 8.07, 95% CI: 2.92-22.33) were significantly associated with depression symptoms. Depression symptoms (OR = 7.92, 95% CI: 2.89-21.73) were related to anxiety symptoms. Similarly, depression symptoms (OR = 3.24, 95% CI: 1.19-8.79) were associated with poor sleep quality.Sleep disturbance, depression, and anxiety symptoms are very common among frontline nurses who treating patients with COVID-19 in Wuhan, China. Comprehensive measures that involve psychosocial and personal behaviors should be implemented to improve sleep quality and prevent depression and anxiety symptoms.Entities:
Mesh:
Year: 2020 PMID: 32590755 PMCID: PMC7328950 DOI: 10.1097/MD.0000000000020769
Source DB: PubMed Journal: Medicine (Baltimore) ISSN: 0025-7974 Impact factor: 1.889
Descriptive data of 100 frontline nurses.
Sleep disturbance of 100 frontline nurses.
The prevalence, degrees and scores of depression, anxiety, and sleep quality in 100 frontline nurses.
Multivariate logistic regression analyses between socio-demographic variables, anxiety, depression, and sleep quality.