Toward Robust Stress Prediction in the Age of Wearables: Modeling Perceived Stress in a Longitudinal Study With Information Workers

Given the widespread adverse outcomes of stress – exacerbated by the current pandemic – wearable sensing provides unique opportunities for automated stress tracking to inform well-being interventions. However, its success in the wild and at scale depends on the robustness and validity of automated stress inference, which is limited in current systems. In this work, we enumerate the properties of robustness and validity necessary for achieving viable automated stress inference using wearable sensors, and we underscore present challenges to constructing and evaluating these systems. Using these criteria as guiding principles, we present automated stress inference results from a large (N=606) in situ longitudinal wearable and contextual sensing study of information workers. Using a multimodal approach encompassing a wearable sensor, relative location tracking, smartphone usage, and environmental sensing, we trained regression models to predict daily self-reported perceived stress in a participant-independent fashion. Our models significantly outperformed baseline variants with shuffled stress scores and were consistent with small-to-moderate effects. Our findings highlight the performance disparity between robust and valid approaches to automated perceived stress inference and current approaches and suggest that further performance gains might require additional sensing modalities and enhanced contextual awareness than existing approaches.