Si Sun’s research while affiliated with IBM Research - Thomas J. Watson Research Center and other places

Recent advancements in mediated communication technologies enable unfettered communication between patients and their providers. We explore patients' utilization of different direct and mediated communication modalities in a Direct Primary Care (DPC) clinic and the patient characteristics that predict their communication behavior patterns. Based on this knowledge, we developed 2 patient personas that explicate the nuances of patients who tend to prefer visiting the clinic in person versus those who use mediated modalities more often. We hope this study may inform future work in understanding and supporting patient-provider communication in a new technical environment. The results suggest that patients and their health team alike may be incentivized to voluntarily adopt and utilize multi-modality communication in a DPC setting.

BACKGROUND Technology-enabled ecological momentary assessment (EMA) facilitates the calibration of physiological signals against self-reported data and contexts. However, research using this method rarely considers the impact that user experience (UX) has on the quality of data. OBJECTIVE The purpose of this study is to explore the biases that UX factors induce in self-reported data and physiological signals collected through EMA and the UX factors that have the largest impact on the data. METHODS A retrospective analysis on data from a field feasibility study is conducted. The study uses an application on a smartwatch device to measure heart rate variability (HRV) and collect self-reported stress levels. We collected data on event types, age, sex, personality traits, and engineered 66 UX features (e.g., number of screens viewed, perception of notification frequency). We use a series of random forest models, conditional forest models, linear regression models, and correlation analysis to predict self-reported stress, HRV, and their discrepancies. We then use iterated comparative analysis to confirm the effects of UX factors. RESULTS Analysis on 1240.6 hours of data from 29 participants reveal that self-reported stress is correlated with the HRV signal collected after EMA notification (HRV2) but not with the HRV signal collected before the notification (HRV1) or after user interaction starts (HRV3). UX factors explain 6.6% - 10% (P < .001) of the variation in self-reported stress. UX factors do not significantly predict HRV signals but explain 63.8% (P < .001) of the difference between self-reported stress and the HRV signal collected after the EMA notification. In addition, UX factors have a significant but smaller delayed effect on self-reported stress and HRV signals collected in the next user interaction cycle. In almost all models, UX features rank higher in terms of feature importance than the other confounding factors (i.e., age, sex, personality traits) and in some models rank higher than the main effect (i.e., event types). We discuss specific symptoms of UX-induced biases related to EMA instrument design and study design, mere measurement effect and observer effect, and propose topics of examination for future studies. CONCLUSIONS User experience may induce biases in data collected through technology-enabled EMA method. In some cases, the impact of the biases may be larger than that of the main effect, other confounding factors, and the corresponding data used for calibration.

The advances in mobile and wearable sensing have led to a myriad of approaches for stress detection in both laboratory and free-living settings. Most of these methods, however, rely on the usage of some combination of physiological signals measured by the sensors to detect stress. While these solutions work great in a lab or a controlled environment, the performance in free-living situations leaves much to be desired. In this work, we explore the role of context of the user in free-living conditions, and how that affects users' perceived stress levels. To this end, we conducted an 'in-the-wild' study with 23 participants, where we collected physiological data from the users, along with 'high-level' contextual labels, and perceived stress levels. Our analysis shows that context plays a significant role in the users' perceived stress levels, and when used in conjunction with physiological signals leads to much higher stress detection results, as compared to relying on just physiological data.

In modern life, the nonstop and pervasive stress tends to keep us on long-lasting high alert, which over time, could lead to a broad range of health problems from depression, metabolic disorders to heart diseases. However, there is a stunning lack of practical tools for effective stress management that can help people navigate through their daily stress. This paper presents the feasibility evaluation of StressHacker, a smartwatch-based system designed to continuously and passively monitor one's stress level using bio-signals obtained from the on-board sensors. With the proliferation of smartwatches, StressHacker is highly accessible and suited for daily use. Our preliminary evaluation is based on 300 hours of data collected in a real-life setting (12 subjects, 29 days). The result suggests that StressHacker is capable of reliably capturing daily stress dynamics (precision = 86.1%, recall = 91.2%), thus with great potential to enable seamless and personalized stress management.