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.
Keywords: BP neural network; fatigue; nurse; shift work; sleep disturbances.
© 2019 John Wiley & Sons Ltd.