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 and Blackwell, Andrew D. (2018) Demographic and clinical predictors of response to internet-enabled cognitive-behavioural therapy for depression and anxiety. BJPsych Open, 4 (5), 411-418. (doi:10.1192/bjo.2018.57).

Abstract

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.

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Contributors

Author: Samuel Robin Chamberlain

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