Symposia
Adult Depression
Jonah Meyerhoff, Ph.D. (he/him/his)
Research Assistant Professor
Northwestern University Feinberg School of Medicine
Chicago, IL, United States
Lyle Ungar, Ph.D.
Professor
Department of Computer Science, University of Pennsylvania
Philadelphia, PA, United States
Konrad Kording, PhD
Faculty
University of Pennsylvania
Philadelphia, PA, United States
David C. Mohr, Ph.D. (he/him/his)
Professor
Northwestern University Feinberg School of Medicine
Chicago, IL, United States
Tony Liu, PhD
Faculty
Mount Holyoke
South Hadley, MA, United States
Objective:
Personal sensing leverages signals from networked sensors embedded in smartphones and wearables to make inferences about peoples’ contexts, symptoms, and states of mind. In time, these tools promise to alter the digital mental health field by reducing the need for patients to complete long assessments and delivering optimal interventions when they are most needed. In the spring of 2020, our research group had started recruiting for the largest (n=1,200) personal sensing study of adults with depression to date. We aimed to understand if smartphone sensors such as movement and geolocation patterns, app use, communication patterns and styles, could reliably predict short term changes in symptoms of depression. When designing this study, we assumed adequate variability in each sensor signal within and across participants. COVID-19-related restrictions in geographic movement, app use, and communication mediums, however, meant substantially less variability than anticipated. Our research team, therefore, pursued the primary study aims while seeking to understand how abrupt pandemic-related changes affected people's wellbeing. 127 participants provided PHQ-8 data every 3 weeks and ecological momentary assessment data of mood and stress (9-point Likert scales) 3 times daily. We compared shifts before and after the national state of emergency declaration (3/13/2020) and subsequent social distancing guidelines (pre-pandemic data: 3/3/2020-3/9/2020, pandemic data: 3/24/2020-3/30/2020). Results During the pandemic, time spent at work sharply dropped, (Cohen’s d=0.33, p< 0.001) coinciding with an increase in homestay (Cohen’s d=0.38, p< 0.001). Stress increased (Cohen’s d=0.15, p=0.03) as did worsening mood (Cohen’s d=0.17, p< 0.01). These shifts were comparable to changes people experienced between the shift from a weekend to a weekday in the pre-pandemic period (stress: Cohen’s d=0.23; mood Cohen’s d=0.13). Changes in PHQ-8 were, on average, non-significant, but for 7 participants who lost their jobs in the week following the national emergency declaration, there was a PHQ-8 increase of 3 points, demonstrating that early pandemic effects may not have been uniformly experienced. The COVID-19 pandemic demanded flexibility of researchers, trial protocols, and research questions. These data demonstrate the utility of agile in-the-field studies to understand the impact of global events alongside a-priori defined research questions.
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Conclusion: