Aim
To describe sleep disturbances and fatigue among female registered nurses in Beijing and to develop a prediction model for sleep disturbances.
Background
Chinese nurses are required to work rotating shifts on a weekly basis, which could negatively impact their sleep and well‐being.
Method
A total of 647 registered nurses participated in this study. Self‐reported sleep‐related data and selected physiological data were collected. Back propagation artificial neural networks was used to develop the prediction model by using the risk management and population health framework.
Results
Majority of them reported clinically significant poor sleep (69.4%) and fatigue (75.4%). A total of eight predictors were identified for sleep disturbances, and the top four normalized importance predictors are morning fatigue (100%), body mass index (30.5%), gastrointestinal symptoms (17.6%) and drinking caffeinated beverages at work (17.3%). The cross‐entropy error was 206.58, and the model accounted for 77.6% of the variance in sleep disturbances.
Conclusions and implications for nursing management
Female registered nurses in China experience clinically significant sleep disturbances. Morning fatigue severity along with seven significant influencing factors may be used to identify shift nurses who face a higher risk of sleep disturbances. The back propagation artificial neural networks model could be used as the foundation for health promotion interventions for registered nurses.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.