Key result
Adding wearable sleep and heart rate rhythms to machine learning improves shift worker burnout prediction.
Why the study?
Direct prediction of burnout risk remains limited, and interpreting machine learning models in healthcare poses challenges.
Can machine learning models utilizing wearable and survey data predict burnout risk in shift workers?
Can machine learning models utilizing wearable and survey data predict burnout risk in shift workers?
Machine learning models using wearable data, particularly sleep and heart rate rhythm features, can effectively predict burnout risk in shift workers.
May support wearable ML for burnout screening in shift workers; leaves open need for prospective validation.
Shift work disrupts sleep and causes chronic stress, resulting in burnout syndrome characterized by emotional exhaustion, depersonalization, and decreased personal accomplishment. Continuous biometric data collected through wearable devices contributes to mental health research. However, direct prediction of burnout risk is still limited, and interpreting machine learning (ML) models in healthcare poses challenges. In this paper, we develop machine learning models that utilize wearable and survey data, including rhythm features, to predict burnout risk among shift workers. Additionally, we employ the DiCE (Diverse Counterfactual Explanations) framework to generate interpretable explanations for the ML model, aiding in the management of burnout risks. Our experiments on the AMED dataset show that incorporating rhythm features significantly enhances the predictive performance of our models. Specifically, sleep and heart rate features have emerged as significant indicators for accurately predicting burnout risk.
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Tang et al. (2023) studied Burnout syndrome. Machine learning models utilizing wearable and survey data was evaluated on Burnout risk prediction. Incorporating sleep and heart rate rhythm features from wearable data into machine learning models significantly enhanced the accurate prediction of burnout risk among shift workers.
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