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Demographic and clinical predictors of response to internet-enabled cognitive–behavioural therapy for depression and anxiety

BACKGROUND: Common mental health problems affect a quarter of the population. Online cognitive–behavioural therapy (CBT) is increasingly used, but the factors modulating response to this treatment modality remain unclear. AIMS: This study aims to explore the demographic and clinical predictors of re...

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Autores principales: Catarino, Ana, Bateup, Sarah, Tablan, Valentin, Innes, Katherine, Freer, Stephen, Richards, Andy, Stott, Richard, Hollon, Steven D., Chamberlain, Samuel Robin, Hayes, Ann, Blackwell, Andrew D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6171334/
https://www.ncbi.nlm.nih.gov/pubmed/30294451
http://dx.doi.org/10.1192/bjo.2018.57
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author Catarino, Ana
Bateup, Sarah
Tablan, Valentin
Innes, Katherine
Freer, Stephen
Richards, Andy
Stott, Richard
Hollon, Steven D.
Chamberlain, Samuel Robin
Hayes, Ann
Blackwell, Andrew D.
author_facet Catarino, Ana
Bateup, Sarah
Tablan, Valentin
Innes, Katherine
Freer, Stephen
Richards, Andy
Stott, Richard
Hollon, Steven D.
Chamberlain, Samuel Robin
Hayes, Ann
Blackwell, Andrew D.
author_sort Catarino, Ana
collection PubMed
description BACKGROUND: Common mental health problems affect a quarter of the population. Online cognitive–behavioural therapy (CBT) is increasingly used, but the factors modulating response to this treatment modality remain unclear. AIMS: This study aims to explore the demographic and clinical predictors of response to one-to-one CBT delivered via the internet. METHOD: Real-world clinical outcomes data were collected from 2211 NHS England patients completing a course of CBT delivered by a trained clinician via the internet. Logistic regression analyses were performed using patient and service variables to identify significant predictors of response to treatment. RESULTS: Multiple patient variables were significantly associated with positive response to treatment including older age, absence of long-term physical comorbidities and lower symptom severity at start of treatment. Service variables associated with positive response to treatment included shorter waiting times for initial assessment and longer treatment durations in terms of the number of sessions. CONCLUSIONS: Knowledge of which patient and service variables are associated with good clinical outcomes can be used to develop personalised treatment programmes, as part of a quality improvement cycle aiming to drive up standards in mental healthcare. This study exemplifies translational research put into practice and deployed at scale in the National Health Service, demonstrating the value of technology-enabled treatment delivery not only in facilitating access to care, but in enabling accelerated data capture for clinical research purposes. DECLARATION OF INTEREST: A.C., S.B., V.T., K.I., S.F., A.R., A.H. and A.D.B. are employees or board members of the sponsor. S.R.C. consults for Cambridge Cognition and Shire. Keywords: Anxiety disorders; cognitive behavioural therapies; depressive disorders; individual psychotherapy
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spelling pubmed-61713342018-10-05 Demographic and clinical predictors of response to internet-enabled cognitive–behavioural therapy for depression and anxiety Catarino, Ana Bateup, Sarah Tablan, Valentin Innes, Katherine Freer, Stephen Richards, Andy Stott, Richard Hollon, Steven D. Chamberlain, Samuel Robin Hayes, Ann Blackwell, Andrew D. BJPsych Open Papers BACKGROUND: Common mental health problems affect a quarter of the population. Online cognitive–behavioural therapy (CBT) is increasingly used, but the factors modulating response to this treatment modality remain unclear. AIMS: This study aims to explore the demographic and clinical predictors of response to one-to-one CBT delivered via the internet. METHOD: Real-world clinical outcomes data were collected from 2211 NHS England patients completing a course of CBT delivered by a trained clinician via the internet. Logistic regression analyses were performed using patient and service variables to identify significant predictors of response to treatment. RESULTS: Multiple patient variables were significantly associated with positive response to treatment including older age, absence of long-term physical comorbidities and lower symptom severity at start of treatment. Service variables associated with positive response to treatment included shorter waiting times for initial assessment and longer treatment durations in terms of the number of sessions. CONCLUSIONS: Knowledge of which patient and service variables are associated with good clinical outcomes can be used to develop personalised treatment programmes, as part of a quality improvement cycle aiming to drive up standards in mental healthcare. This study exemplifies translational research put into practice and deployed at scale in the National Health Service, demonstrating the value of technology-enabled treatment delivery not only in facilitating access to care, but in enabling accelerated data capture for clinical research purposes. DECLARATION OF INTEREST: A.C., S.B., V.T., K.I., S.F., A.R., A.H. and A.D.B. are employees or board members of the sponsor. S.R.C. consults for Cambridge Cognition and Shire. Keywords: Anxiety disorders; cognitive behavioural therapies; depressive disorders; individual psychotherapy Cambridge University Press 2018-10-02 /pmc/articles/PMC6171334/ /pubmed/30294451 http://dx.doi.org/10.1192/bjo.2018.57 Text en © The Royal College of Psychiatrists 2018 http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
spellingShingle Papers
Catarino, Ana
Bateup, Sarah
Tablan, Valentin
Innes, Katherine
Freer, Stephen
Richards, Andy
Stott, Richard
Hollon, Steven D.
Chamberlain, Samuel Robin
Hayes, Ann
Blackwell, Andrew D.
Demographic and clinical predictors of response to internet-enabled cognitive–behavioural therapy for depression and anxiety
title Demographic and clinical predictors of response to internet-enabled cognitive–behavioural therapy for depression and anxiety
title_full Demographic and clinical predictors of response to internet-enabled cognitive–behavioural therapy for depression and anxiety
title_fullStr Demographic and clinical predictors of response to internet-enabled cognitive–behavioural therapy for depression and anxiety
title_full_unstemmed Demographic and clinical predictors of response to internet-enabled cognitive–behavioural therapy for depression and anxiety
title_short Demographic and clinical predictors of response to internet-enabled cognitive–behavioural therapy for depression and anxiety
title_sort demographic and clinical predictors of response to internet-enabled cognitive–behavioural therapy for depression and anxiety
topic Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6171334/
https://www.ncbi.nlm.nih.gov/pubmed/30294451
http://dx.doi.org/10.1192/bjo.2018.57
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