A prediction model of sleep disturbances among female nurses by using the BP-ANN.

Abstract

To describe sleep disturbances and fatigue among female registered nurses in Beijing and to develop a prediction model for sleep disturbances. Chinese nurses are required to work rotating shifts on a weekly basis, which could negatively impact their sleep and well-being. 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. 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. 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.