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Randomized Controlled Trial
. 2015 Apr;83(2):395-404.
doi: 10.1037/a0038305. Epub 2014 Nov 17.

Decentering as a potential common mechanism across two therapies for generalized anxiety disorder

Affiliations
Randomized Controlled Trial

Decentering as a potential common mechanism across two therapies for generalized anxiety disorder

Sarah A Hayes-Skelton et al. J Consult Clin Psychol. 2015 Apr.

Abstract

Objective: The purpose of this study was to examine decentering as a potential mechanism of action across 2 treatments for generalized anxiety disorder: an acceptance-based behavioral therapy (ABBT) and applied relaxation (AR).

Method: Sixty-four individuals who completed at least half of the 16 total sessions of either ABBT or AR (65.6% female; 79.7% identified as White; average age = 34.41 years) completed measures of decentering (Experiences Questionnaire) and of symptoms of anxiety (Depression Anxiety Stress Scale-Stress subscale) at 5 time points over the course of therapy, and a measure of worry (Penn State Worry Questionnaire) at pre- and posttreatment.

Results: Initial growth curve models showed that decentering increased significantly over therapy (z = 7.09), and this increase was associated with a decrease in worry symptoms (Penn State Worry Questionnaire) at posttreatment (z = -8.51). The rate of change did not significantly vary across treatments, Δχ2/Δdf = 0.16/1, p = .69. Further, a series of bivariate latent difference score models indicated that the best-fitting model was one in which decentering was a leading indicator of change in symptoms (DASS-Stress). Allowing this coupling to vary across treatments did not significantly improve the fit of the model, Δχ2/Δdf = 0.71/1, p = .40.

Conclusions: In this sample, results suggest that increased decentering was associated with decreases in anxiety and that changes in decentering appear to precede changes in symptoms within both ABBT and AR, indicating that decentering may be an important common mechanism of action. (PsycINFO Database Record

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Figures

Figure 1
Figure 1
A bivariate latent difference score model including couplings in both directions. Y[t] and X[t] = observed scores at time t. y[t] and x[t] = latent true scores at time t. Δy[t] and Δx[t] = latent change scores at t. yi and xi = initial scores. ys and xs = slopes. Triangle = constant. α = slope parameter (=1). β = autoproportional parameter. γ = coupling parameter.

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