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Using latent growth curve modeling in clinical treatment research: Comparing guided self-change and cognitive behavioral therapy treatments for bulimia nervosa
Published online by Cambridge University Press: 16 April 2020
Abstract
The purpose of this study was to demonstrate the usefulness of multi-group piece-wise latent growth curve models (LGCM) in clinical research, particularly for assessing and comparing treatment effects. As an empirical example, this analytic technique was used to compare the effectiveness of Guided Self-Change (GSC) and Cognitive Behavioral Therapy (CBT) treatments for bulimia nervosa.
Sixty-two female patients (M age = 28.1, SD = 8.00) with bulimia nervosa were randomly assigned to a) a GSC treatment involving a self-care manual plus 8 bi-weekly sessions of CBT or b) 16 weekly sessions of CBT.
Both groups showed significant improvements in treatment outcomes across the treatment period, although the CBT group showed greater improvements. However, the GSC group evidenced more continued improvement post-treatment. CBT showed greater variability in effectiveness during the treatment period, while GSC showed greater variability during follow-up. For GSC patients, baseline levels on some treatment outcomes were related to follow-up improvement levels.
LGCM provided a rich analysis of these data, and addressed important questions regarding differences in the effectiveness of the two treatment programs. For example, CBT tended to show greater improvements during treatment, while GSC evidenced more continued improvements during follow-up.
- Type
- Poster Session 1: Eating Disorders
- Information
- European Psychiatry , Volume 22 , Issue S1: 15th AEP Congress - Abstract book - 15th AEP Congress , March 2007 , pp. S180
- Copyright
- Copyright © European Psychiatric Association 2007
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