Sleep disorders among frontline nurses after the COVID-19 outbreak: a large-scale cross-sectional study.

This large-scale cross-sectional multicenter study aims to investigate the prevalence of sleep disorders among frontline nurses in China after the COVID-19 pandemic and to identify potential influencing factors contributing to these sleep disturbances. A total of 2065 frontline nurses from 27 provinces in China participated in an online survey conducted through the Wenjuan Xing platform. Data on demographic characteristics, work-related factors, and mental health assessments, including the Pittsburgh Sleep Quality Index (PSQI), Zung Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS), were collected. Statistical analyses, including chi-square tests, t-tests, binary logistic regression, and ROC analysis, were conducted to explore the relationships between various factors and sleep disorders. Over half (52.7%) of the surveyed nurses exhibited sleep disorders, reflecting a considerable post-pandemic impact on sleep quality. Factors such as nursing titles, personality traits, COVID-19 infection status, and exercise frequency showed statistically significant associations with sleep disorders. Extraverted nurses and those who had recovered from COVID-19 displayed a lower risk of sleep disorders, while anxiety was identified as an independent risk factor. The study also identified a nuanced relationship between exercise frequency and sleep quality. The study highlights a high prevalence of sleep disorders among frontline nurses post-COVID-19, emphasizing the need for targeted interventions. Factors such as nursing titles, personality traits, COVID-19 infection status, exercise habits, and anxiety levels were found to influence sleep quality. Comprehensive support strategies addressing these factors are essential for improving the overall well-being of frontline nurses and, subsequently, sustaining a resilient healthcare workforce. Further research is recommended to explore additional influencing factors and consider diverse nurse populations.