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Lookup NU author(s): Dr Stephen Barton, Peter Armstrong, Dr Lucy RobinsonORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
BACKGROUND: Cognitive behavioural therapy (CBT) is an effective treatment for depression but a significant minority of clients do not complete therapy, do not respond to it, or subsequently relapse. Non-responders, and those at risk of relapse, are more likely to have adverse childhood experiences, early-onset depression, co-morbidities, interpersonal problems and heightened risk. This is a heterogeneous group of clients who are currently difficult to treat. AIM: The aim was to develop a CBT model of depression that will be effective for difficult-to-treat clients who have not responded to standard CBT. METHOD: The method was to unify theory, evidence and clinical strategies within the field of CBT to develop an integrated CBT model. Single case methods were used to develop the treatment components. RESULTS: A self-regulation model of depression has been developed. It proposes that depression is maintained by repeated interactions of self-identity disruption, impaired motivation, disengagement, rumination, intrusive memories and passive life goals. Depression is more difficult to treat when these processes become interlocked. Treatment based on the model builds self-regulation skills and restructures self-identity, rather than target negative beliefs. A bespoke therapy plan is formed out of ten treatment components, based on an individual case formulation. CONCLUSIONS: A self-regulation model of depression is proposed that integrates theory, evidence and practice within the field of CBT. It has been developed with difficult-to-treat cases as its primary purpose. A case example is described in a concurrent article (Barton et al., 2022) and further empirical tests are on-going.
Author(s): Barton SB, Armstrong PV, Robinson LJ, Bromley EHC
Publication type: Article
Publication status: Published
Journal: Behavioural and Cognitive Psychotherapy
Year: 2023
Volume: 51
Issue: Special Issue 6
Pages: 543-558
Print publication date: 01/11/2023
Online publication date: 12/05/2023
Acceptance date: 15/06/2022
Date deposited: 19/02/2024
ISSN (print): 1352-4658
ISSN (electronic): 1469-1833
Publisher: Cambridge University Press
URL: https://doi.org/10.1017/S1352465822000273
DOI: 10.1017/S1352465822000273
Data Access Statement: This article introduces a theoretical model: data availability is not applicable as no new data were created or analysed.
PubMed id: 37170824
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