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Document Type

Open Access Thesis

Embargo Period

12-14-2018

Degree Program

Psychology

Degree Type

Master of Science (M.S.)

Year Degree Awarded

2019

Month Degree Awarded

February

Abstract

Change-talk (CT), or self-arguments for change, has been associated with favorable patient outcomes, while counter change-talk (CCT), or self-arguments against change, has been associated with poorer outcomes. Most studies on change language have focused on the prediction of distal posttreatment outcomes, while the prediction of more proximal outcomes has remained largely untested. Addressing this gap, we examined early treatment CT and CCT as predictors of worry change trajectories, “hazard” of clinically significant response, and time to response (i.e., outcome efficiency) in CBT and CBT integrated with MI (MI-CBT) for generalized anxiety disorder (GAD). We also explored whether treatment type moderated these associations. Data derived from a randomized controlled trial comparing CBT (n = 43) and MI-CBT (n = 42) for GAD. Independent observers reliably coded CT/CCT during session 1. Patients rated their worry after every session. Multilevel modeling revealed that, across both treatments, more CT associated with lower midtreatment worry level (p = .03), whereas more CCT associated with a slower rate of worry reduction at midtreatment (p = .04). However, treatment moderated the associations between CT and both midtreatment worry level (p = .004) and rate of change (p = .03). In CBT, patients with higher vs. lower CT had less worry and a faster rate of worry reduction; in MI-CBT, CT was unrelated to midtreatment worry level and the rate of worry change. Treatment did not moderate the CCT-worry relations. Survival analyses revealed that, across both treatments, more CT associated with a greater hazard of response (p = .004) and approached a faster time to response (p = .05), and more CCT associated with a lower hazard of response (p = .002) and approached a slower time to response (p = .06). Patient motivational language predicts proximal outcomes, and may be useful in differential treatment selection.

First Advisor

Michael J. Constantino

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