Wireless Mobile Communication and Healthcare. 9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings

Research Article

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

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  • @INPROCEEDINGS{10.1007/978-3-030-70569-5_6,
        author={Han Yu and Asami Itoh and Ryota Sakamoto and Motomu Shimaoka and Akane Sano},
        title={Forecasting Health and Wellbeing for Shift Workers Using Job-Role Based Deep Neural Network},
        proceedings={Wireless Mobile Communication and Healthcare. 9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2021},
        month={7},
        keywords={Shift workers Health Wellbeing Wearables Mobile sensor Deep learning},
        doi={10.1007/978-3-030-70569-5_6}
    }
    
  • Han Yu
    Asami Itoh
    Ryota Sakamoto
    Motomu Shimaoka
    Akane Sano
    Year: 2021
    Forecasting Health and Wellbeing for Shift Workers Using Job-Role Based Deep Neural Network
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-030-70569-5_6
Han Yu1, Asami Itoh2, Ryota Sakamoto2, Motomu Shimaoka2, Akane Sano1
  • 1: Rice University
  • 2: Mie University

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.