Patient motivational language in the prediction of symptom change, clinically significant response, and time to response in psychotherapy for generalized anxiety disorder. Academic Article uri icon

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abstract

  • Existing research demonstrates that patient change-talk (CT), or self-arguments for change, associates variably with favorable outcomes, whereas counter change-talk (CCT), or self-arguments against change, associates consistently with poorer outcomes. However, most studies on change language have focused on posttreatment versus more proximal outcomes. Addressing this gap, we examined Session 1 CT and CCT as predictors of during-treatment worry change, likelihood of clinically significant response during treatment, and time to response across cognitive-behavioral therapy (CBT; n = 43) and CBT integrated with motivational interviewing (MI; n = 42) for generalized anxiety disorder. We also explored whether treatment moderated these associations. Multilevel modeling revealed that, across both treatments, more CT associated with lower midtreatment worry level (p = .04), whereas more CCT associated with lower worry level (p = .048) and a slower rate of worry reduction at midtreatment (p = .04). Treatment moderated only the associations between CT and both midtreatment worry level (p = .03) and rate of change (p = .03). CBT patients with higher versus lower CT had lower worry and a faster rate of worry reduction; in MI-CBT, CT was unrelated to these outcomes. Survival analyses revealed that, across both treatments, more CT associated with a greater likelihood of response (p = .004) and approached a faster time to response (p = .05), and more CCT associated with a lower likelihood of response (p = .001) and approached a slower time to response (p = .06). Motivational language predicts proximal outcomes and may be useful in treatment selection. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

authors

  • Goodwin, Brien J
  • Constantino, Michael J
  • Westra, Henny A
  • Button, Melissa L
  • Antony, Martin

publication date

  • December 2019