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.

Despite proper sleep hygiene being critical to our health, guidelines for improving sleep habits often focus on only a single component, namely, sleep duration. Recent works, however, have brought to light the importance of another aspect of sleep: bedtime regularity, given its ties to cognitive and metabolic health outcomes. To further our understanding of this often-neglected component of sleep, the objective of this work was to investigate the association between bedtime regularity and resting heart rate (RHR): an important biomarker for cardiovascular health. Utilizing Fitbit Charge HRs to measure bedtimes, sleep and RHR, 255,736 nights of data were collected from a cohort of 557 college students. We observed that going to bed even 30 minutes later than one’s normal bedtime was associated with a significantly higher RHR throughout sleep (Coeff +0.18; 95% CI: +0.11, +0.26 bpm), persisting into the following day and converging with one’s normal RHR in the early evening. Bedtimes of at least 1 hour earlier were also associated with significantly higher RHRs throughout sleep; however, they converged with one’s normal rate by the end of the sleep session, not extending into the following day. These observations stress the importance of maintaining proper sleep habits, beyond sleep duration, as high variability in bedtimes may be detrimental to one’s cardiovascular health.

Social networks influence health-related behavior, such as obesity and smoking. While researchers have studied social networks as a driver for diffusion of influences and behavior, it is less understood how the structure or topology of the network, in itself, impacts an individual’s health behavior and wellness state. In this paper, we investigate whether the structure or topology of a social network offers additional insight and predictability on an individual’s health and wellness. We develop a method called the Network-Driven health predictor (NetCARE) that leverages features representative of social network structure. Using a large longitudinal data set of students enrolled in the NetHealth study at the University of Notre Dame, we show that the NetCARE method improves the overall prediction performance over the baseline models—that use demographics and physical attributes—by 38%, 65%, 55%, and 54% for the wellness states—stress, happiness, positive attitude, and self-assessed health—considered in this paper.