Forecasting Health and Wellbeing for Shift Workers Using Job-role Based Deep Neural Network

TL;DR

This paper presents a job-role based deep neural network that predicts the next day's health and wellbeing of shift workers using physiological, behavioral, and questionnaire data, outperforming existing models.

Contribution

Introduces a novel multitask, multilabel deep learning model tailored for nurses and doctors to forecast health and wellbeing based on wearable sensor data.

Findings

Model outperforms baseline and state-of-the-art in classification and regression tasks.

Features like heart rate, sleep, and shift timing are key predictors.

Differences in responses between nurses and doctors highlight the need for role-specific models.

Abstract

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.