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54th Annual Convention 2020 |
Invited Panel

Invited Panel | November 21 | 4:00 p.m. - 5:30 p.m., Grand Ballroom G&H

Personalizing Treatment to Improve CBT Outcomes

Moderator: Michelle G. Craske, Ph.D., University of California, Los Angeles


Robert J. DeRubeis, Ph.D., University of Pennsylvania

Aaron J. Fisher, Ph.D., University of California, Berkeley

Jacqueline B. Persons, Ph.D., Oakland Cognitive Behavior Therapy Center

Greg Siegle, Ph.D., University of Pittsburgh School of Medicine

Primary Categories: Treatment- CBT, Program/Treatment Design

Key Words: Case Conceptualization/Formulation, Evidence-Based Practice, Neuroscience

Participants earn 1.5 continuing education credits

This panel discussion focuses on innovations in research and practice related to using individual patient-level data to develop personalized interventions and improve clinical outcomes. Robert DeRubeis will describe recent trends and methods in the effort to promote evidence-based predictions of individual patients' mental health intervention outcomes. He will point to promising recent uses of these methods to promote evidence-based treatment selection, more powerful between-treatment comparisons, and improvements in the allocation of mental health resources. Aaron Fisher will discuss the collection, preparation, and analysis of person-level data for generating clinical insights. In addition, he will discuss specific methods for modeling and predicting individual behavior, with an eye toward building real-time adaptive interventions. Jacqueline Persons will provide a brief review of the evidence supporting the hypothesis that therapist use of an idiographic case formulation improves outcome and reduces dropout in CBT. Dr. Siegle will review evidence from neuroimaging studies suggesting brain mechanisms associated with depression, how they change in CBT, and how their assessment can be used to predict who is likely to respond to CBT. Given multiple obstacles to using assessment of neural mechanisms to guide treatment, he will also describe an alternate approach, in which targeted pretreatments are used to address predictive mechanisms, to make it more likely that individuals who are originally predicted to not respond to CBT are more likely to respond.

At the end of this session, the learner will be able to:

  • Identify how and why multivariable prediction models can be used to enhance personalized, or precision, mental health, beyond what can be achieved with research on moderators of treatment response.
  • Describe idiographic science, broadly, including applications of person-specific methods to issues of personalization and prediction.
  • Describe the state of the evidence supporting the hypothesis that therapist use of an idiographic case formulation improves outcome and reduces dropout in CBT.
  • Identify how to improve precision in cognitive behavioral therapy by working to target individuals' brain mechanisms.

Recommended Readings:

Delgadillo, J., & Lutz, W. (2020, Advance Online Publication). A developmental pathway towards precision mental health care. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2020.1048

DeRubeis, R. J. (2019). The history, current status, and potential future of precision mental health. Behaviour Research and Therapy, 123, 103506.

Fisher, A. J., Medaglia, J. D., & Jeronimus, B. F. (2018). Lack of group-to-individual generalizability is a threat to human subjects research. Proceedings of the National Academy of Sciences. June 2018, 201711978;

Persons, J. B., & Hong, J. J. (2016). Case formulation and the outcome of cognitive behavior therapy. In N. Tarrier & J. Johnson (Eds.), Case formulation in cognitive behaviour therapy (2nd ed., pp. 14-37). Routledge.

Siegle, G. J., Thompson, W. K., Collier, A., Berman, S. R., Feldmiller, J., Thase, M. E., & Friedman, E. S. (2012). Toward clinically useful neuroimaging in depression treatment: Prognostic utility of subgenual cingulate activity for determining depression outcome in cognitive therapy across studies, scanners, and patient characteristics. Archives of General Psychiatry, 69(9), 913-924. NIHMSID 524641. doi:10.1001/archgenpsychiatry.2012.65

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