Leveraging Machine Learning Approaches to Understand Mechanisms of Exposure Therapy in Real-World Settings

Summary

Exposure therapy is the most effective treatment available for obsessive compulsive disorder, yet up to 50% of patients do not recover because the mechanisms underlying successful response are poorly understood, leading to significant variability in how clinicians conduct exposure therapy. The main purpose of this study is to determine which target mechanisms are most critical to engage in real-world exposure sessions to produce good treatment outcomes. Adult participants (N = 400) with Obsessive Compulsive Disorder (OCD) receiving exposure therapy from two sites (McLean Hospital, San Diego State University) across the continuum of care (outpatient, partial hospital, residential) will complete baseline clinical and demographic measures as well as weekly symptom reports. The project will measure exposure mechanisms across three levels of analysis (self-report, observer-rated behavior, physiology) during each exposure session. Mechanisms assessed will include a broad range of variables based on both habituation and inhibitory learning models of exposure. Self-report and observer-rated mechanisms will be measured with the Exposure Feedback Form, created and piloted by the study team. Physiological mechanisms will include skin conductance response, heart rate, and heart rate variability measured with a wristwatch. The current study will determine (1) which exposure mechanisms lead to favorable clinical outcomes, and (2) what makes a good exposure for whom. Results of this study have the potential to improve personalized care for the many patients who do not remit following exposure therapy for OCD.

Eligibility

Age range

18–65 years

Sex

All

Healthy volunteers

No

Interventions

• Behavioral
  Exposure therapy

  Participants will complete exposure therapy for up to 12 weeks, and coached exposure session will be approximately 50 minutes.

Locations (2)