Current Ongoing Research Projects
"Brave": A Mobile App to Guide Users in Self-Administration of Exposure Therapy for Anxiety Disorders
Brave is a mobile-based exposure treatment program for social, panic, and generalized anxiety disorders. I am currently in the process of developing the mobile app in preparation for a clinical trial testing feasibility and acceptability of the Brave program. Brave will use cutting edge machine learning methods to personalize a user's exposure hierarchy. Further, Brave will capitalize on principles that enhance inhibitory learning such as affect labeling, positive affect, and fear toleration, to enhance effectiveness of the exposure exercises.
This project is supported by the UCSF Resource Allocation Program Digital Mental Health Award (https://rap.ucsf.edu/home).
REsolving Psychological Stress (REPS) Remote Randomized Clinical Trial
REPS is a mobile-based treatment program that aims to reduce symptoms of hypervigilance and threat reactivity in people with symptoms of posttraumatic stress disorder. Developed by my mentor, Dr. Aoife O’Donovan, and I, REPS is a two-week attention bias modification training program that is completed entirely on one’s mobile device, requiring just five minutes of engagement per day.
In this clinical trial, conducted through the THRIVE Lab at UCSF, we are testing the effectiveness of the REPS program when completed entirely remotely, meaning that participants never come into the lab. Further, I developed an algorithm to personalize the content of the REPS training, and we are testing whether personalized training content leads to better outcomes compared to non-personalized content. Findings from this study will help us better understand the feasibility and acceptability of mobile-based treatments involving minimal contact with study personnel and will inform the effectiveness of mobile-based attention bias modification for posttraumatic stress disorder.
BRIGHTEN Study – Testing Personalized Treatment Selection for Depression
Using data previously collected, we are using machine learning to develop an algorithm to select the mobile intervention that will be most effective for an individual based on baseline characteristics. We are testing whether assignment to a personalized intervention using the algorithm can improve treatment outcome. Findings from this project will help us confirm methodological approaches to treatment personalization and identify the extent to which personalized treatment assignment can improve treatment outcome.
Scenario Personalization for Interpretation Bias Modification
In collaboration with Dr. Bethany Teachman and the Program for Anxiety, Cognition, and Treatment (PACT) Lab at the University of Virginia, we are collecting data to develop an algorithm to select personalized scenarios for use in interpretation bias modification. People with anxiety disorders tend to interpret ambiguous situations as threatening, which plays a role in the onset and maintenance of and recovery from anxiety problems.
Interpretation bias modification is a form of cognitive training that can help people form healthier interpretations of anxiety-provoking situations. By selecting situations that have personal relevance for the person engaging in training, we may be able to improve training effects. Findings from this project will inform methods for and effects of personalization of stimuli in cognitive training paradigms for anxiety disorders.