JOURNAL ARTICLE

Predicting response to and relapse after treatment of trichotillomania with the Comprehensive Behavioral model (ComB).

  • Published In: Bulletin of the Menninger Clinic, 2024, v. 88, n. 1. P. 81 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Coyne, Allison F.; Carlson, Emily J.; Malloy, Elizabeth J.; Haaga, David A. F. 3 of 3

Abstract

Prior studies of behavior therapy for trichotillomania (TTM) have shown that response is variable, and relapse after treatment discontinuation is common. Little information is available concerning prognostic factors capable of predicting individual differences in response or maintenance of improvement. The present study is a secondary analysis of a randomized controlled trial (N = 36) of the Comprehensive Behavioral (ComB) model of treatment for TTM (Carlson et al., 2021). We investigated age, disorder history, pre-treatment symptom severity, longest prior period of abstinence from pulling, and Emotion and Intention hair pulling styles as predictors of initial response. We studied age, disorder history, pre-treatment symptom severity, longest prior period of abstinence from pulling, and post-treatment symptom severity or hair-pulling abstinence as predictors of relapse following treatment. Older age significantly predicted lower TTM severity following treatment. Lower pre-treatment severity significantly predicted lower severity of TTM at the 3-month follow-up. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Bulletin of the Menninger Clinic. 2024/01, Vol. 88, Issue 1, p81
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:0025-9284
  • DOI:10.1521/bumc.2024.88.1.81
  • Accession Number:176212544
  • Copyright Statement:Copyright of Bulletin of the Menninger Clinic is the property of Guilford Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.