Literature DB >> 25241627

Survival modeling of discontinuation from psychotherapy: a consumer decision-making perspective.

Partha Krishnamurthy1, Adwait Khare, Suzanne C Klenck, Peter J Norton.   

Abstract

OBJECTIVE: This study examines discontinuation of psychotherapy from a consumer decision-making perspective. Two plausible predictors, the level of illness and rate of progress from where the patient started, were examined as predictors of treatment discontinuation.
METHOD: Using data from 139 patients (45.5% women; mean age = 32.18 years) participating in a 12-week transdiagnostic cognitive-behavioral therapy program for anxiety, weekly assessments of anxiety severity were examined to investigate the extent to which level of anxiety and rate of improvement predicted treatment discontinuation.
RESULTS: Support was found for a significant interaction effect wherein at higher anxiety levels, rate of progress was less associated with discontinuation than at lower anxiety levels.
CONCLUSION: Faster rates of anxiety reduction are associated with greater likelihood of discontinuation when the client is at a lower level of anxiety, whereas rate of improvement is less associated with discontinuation if there remains continued impairment and room for improvement. As such, clinicians should monitor rates of improvement throughout treatment to help identify and evaluate patients at increased risk of premature discontinuation.
© 2014 Wiley Periodicals, Inc.

Entities:  

Keywords:  CBT; anxiety reduction; decision-making; transdiagnostic; treatment dropout

Mesh:

Year:  2014        PMID: 25241627     DOI: 10.1002/jclp.22122

Source DB:  PubMed          Journal:  J Clin Psychol        ISSN: 0021-9762


  5 in total

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Journal:  J Consult Clin Psychol       Date:  2015-12-21

2.  Predictors of amounts of child and adolescent mental health service use.

Authors:  Julian Edbrooke-Childs; Anisatu Rashid; Benjamin Ritchie; Jessica Deighton
Journal:  Eur Child Adolesc Psychiatry       Date:  2022-09-16       Impact factor: 5.349

3.  Seeing the forest for the trees: Predicting attendance in trials for co-occurring PTSD and substance use disorders with a machine learning approach.

Authors:  Teresa López-Castro; Yihong Zhao; Skye Fitzpatrick; Lesia M Ruglass; Denise A Hien
Journal:  J Consult Clin Psychol       Date:  2021-10

4.  Predictors of dropout in concurrent treatment of posttraumatic stress disorder and alcohol dependence: Rate of improvement matters.

Authors:  Laurie J Zandberg; David Rosenfield; Elizabeth Alpert; Carmen P McLean; Edna B Foa
Journal:  Behav Res Ther       Date:  2016-03-03

5.  Analysis of the Emails From the Dutch Web-Based Intervention "Alcohol de Baas": Assessment of Early Indications of Drop-Out in an Online Alcohol Abuse Intervention.

Authors:  Wouter A C Smink; Anneke M Sools; Marloes G Postel; Erik Tjong Kim Sang; Auke Elfrink; Lukas B Libbertz-Mohr; Bernard P Veldkamp; Gerben J Westerhof
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  5 in total

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