Forecasting Health and Wellbeing for Shift Workers Using Job-role Based Deep Neural Network
This work addresses health forecasting for shift workers like nurses and doctors, offering a tailored approach but is incremental as it builds on existing multitask learning methods.
The study tackled predicting multidimensional health and wellbeing for shift workers by analyzing wearable sensor and questionnaire data from nurses and doctors, proposing a job-role based multitask deep learning model that outperformed baseline and state-of-the-art models in classification and regression tasks.
Shift workers who are essential contributors to our society, face high risks of poor health and wellbeing. To help with their problems, we collected and analyzed physiological and behavioral wearable sensor data from shift working nurses and doctors, as well as their behavioral questionnaire data and their self-reported daily health and wellbeing labels, including alertness, happiness, energy, health, and stress. We found the similarities and differences between the responses of nurses and doctors. According to the differences in self-reported health and wellbeing labels between nurses and doctors, and the correlations among their labels, we proposed a job-role based multitask and multilabel deep learning model, where we modeled physiological and behavioral data for nurses and doctors simultaneously to predict participants' next day's multidimensional self-reported health and wellbeing status. Our model showed significantly better performances than baseline models and previous state-of-the-art models in the evaluations of binary/3-class classification and regression prediction tasks. We also found features related to heart rate, sleep, and work shift contributed to shift workers' health and wellbeing.