Category: Technology
Jonah Meyerhoff, Ph.D.
Research Assistant Professor
Northwestern University
Chicago, Illinois
Caitlin Stamatis, Ph.D.
Clinical Intern
Northwestern University Feinberg School of Medicine
Jersey City, New Jersey
Bethany Teachman, Ph.D.
Professor
University of Virginia
Charlottesville, Virginia
Abhishek Pratap, Ph.D.
CAMH
Toronto, Ontario, Canada
Andrew Sherrill, Ph.D.
Emory University
Atlanta, Georgia
Susan Murphy, Ph.D.
Harvard University
Cambridge, Massachusetts
Caitlin Stamatis, Ph.D.
Clinical Intern
Northwestern University Feinberg School of Medicine
Jersey City, New Jersey
Mental health treatment gaps are growing worldwide, and rates of in-person mental health service use have not kept pace with rising rates of mental health conditions, particularly during the pandemic. One key avenue to increase access to care is through digital mental health interventions. These interventions can be delivered via online-connected devices, are effective for reducing symptoms in myriad disorders, and overcome many of the structural and attitudinal barriers that delay treatment for common mental health conditions. Moreover, these tools are low cost, can be used in private, and are delivered on-demand. However, the biggest challenge facing the success of digital mental health interventions remains low rates of sustained engagement, with the vast majority of digital mental health tools abandoned within the first 2 weeks of initiating use. Promoting engagement requires bridging gaps in fit between a digital mental health intervention’s offerings and an individual’s specific needs.
Sensors embedded within digital devices offer promise to help customize, tailor, and deliver digital mental health interventions that are matched to a users’ specific needs, treatment targets, and availability, offering a potential solution to the engagement problem. Passively-sensed behavioral changes are associated with symptoms of mental health conditions; for instance, a reduction in activities and movement through space has been linked with anhedonia in people with depression, and psychophysiological changes may signal increased psychomotor agitation in individuals with generalized anxiety disorder. Promising but less studied areas include how the sentiment in text messages may signal changes in affective states, and how online search data may indicate risk. Better understanding links between such passively-sensed data and psychological symptoms offers opportunities to increase the fit between a digital mental health intervention and a user’s needs via just-in-time, idiographic interventions.
In this panel, we will hear from a series of speakers on how passively sensed data can be used to optimize treatment delivery, improve symptom detection, and enhance engagement. The first two talks will highlight the impact of personal sensing and machine learning methods in clinical trials. Presenter 1 will share results from a trial testing the use of sensor data during prolonged exposure in an applied clinical treatment setting for PTSD. Presenter 2 will discuss statistical methods for determining the impact of artificial intelligence-based treatment personalization on clinical outcomes. The second two talks will focus on text and language-based personal sensing to improve risk prediction. Presenter 3 will share novel findings on the assessment of self-harm risk based on online search data. Presenter 4 will discuss the differential prediction of depression and anxiety symptoms based on in-vivo text messaging data. The Discussant, a leading expert in digital mental health, will synthesize findings to highlight how passively sensed data can enhance personalized treatment, improve access to care, and promote the scalability of evidence-based care in the context of mental health treatment gaps that have widened during the pandemic.
Presenter: Abhishek Pratap, Ph.D. – CAMH
Co-author: Patricia Areán, Ph.D. – Department of Psychiatry and Behavioral Sciences, University of Washington
Co-author: Honor Hsin, MD PHD – Kaiser Permanente
Co-author: Patrick J Heagerty, PhD – Univ of Washington
Co-author: Trevor Cohen, MBChB, PhD, FACMI – University of Washington
Co-author: Courtney Bagge, PhD – University of Michigan
Co-author: Katherine Comtois, PhD, MPH – University of Washington
Presenter: Andrew M. Sherrill, Ph.D. – Emory University
Co-author: Barbara Rothbaum, Ph.D., ABPP – Emory University School of Medicine
Co-author: Sheila Rauch, ABPP, Ph.D. – Emory University School of Medicine
Co-author: Hayley Evans, PhD – Georgia Institute of Technology
Co-author: Rosa Arriaga, PhD – Georgia Institute of Technology
Presenter: Susan Murphy, Ph.D. – Harvard University
Presenter: Caitlin A. Stamatis, Ph.D. – Northwestern University Feinberg School of Medicine
Co-author: Jonah Meyerhoff, Ph.D. – Northwestern University
Co-author: Tingting Liu, Ph.D. – Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH)
Co-author: Garrick Sherman, Ph.D. – Department of Computer Science, University of Pennsylvania
Co-author: Harry Wang, B.S. – Department of Computer Science, University of Pennsylvania
Co-author: Tony Liu, M.S. – Department of Computer Science, University of Pennsylvania
Co-author: Brenda Curtis, Ph.D., MsPH – Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH)
Co-author: Lyle Ungar, Ph.D. – Department of Computer Science, University of Pennsylvania
Co-author: David Mohr, PhD – Northwestern University