Group-specific models of healthcare workers’ well-being using iterative participant clustering

Healthcare workers often experience stress and burnout due to the demanding job responsibilities and long work hours. Ambulatory monitoring devices, such as wearable and environmental sensors, combined with machine learning algorithms can afford us a better understanding of the naturalistic onset and evolution of stress and emotional reactivity in real-life with valuable implications in behavioral interventions. However, the typically large degree of inter-subject variability, due to individual differences in responses and behaviors, makes it difficult for machine learning models to robustly learn behavioral signal patterns and adequately generalize to unseen individuals. In this study, we design group-specific models of well-being (i.e., stress, sleep, positive affect, negative affect) and contextual outcomes (i.e., type of activity) based on real-life multimodal longitudinal data collected in situ from healthcare workers in a hospital environment. Group-specific models are constructed by learning an initial model based on all individuals and subsequently refining the model for a specific group of participants. Participants are originally grouped based on the feature space constructed by the multimodal data, while the original grouping is iteratively refined using the learned multimodal representations of the group-specific models. The results from this study indicate that in the majority of cases the proposed group-specific models, learned through iterative participant clustering, outperform the baseline systems, which involve general models learned based on all participants, as well as group-specific models without iterative participant clustering. This study provides promising results for predicting psychological and behavioral factors that affect the well-being of healthcare workers and lays the foundation toward ambulatory real-life assessment and interventions.