A deep learning quantification of patient specificity as a predictor of session attendance and treatment response to internet-enabled cognitive behavioural therapy for common mental health disorders.

Journal: Journal of affective disorders
Published Date: Apr 1, 2024

Abstract

BACKGROUND: Increasing an individual's ability to focus on concrete, specific detail, thus reducing the tendency toward overly broad, decontextualised generalisations about the self and world, is a target within cognitive behavioural therapy (CBT). However, empirical investigation of the impact of within-treatment specificity on treatment outcomes is scarce. We evaluated whether the specificity of patient dialogue predicted a) end-of-treatment symptoms and b) session completion for CBT for common mental health issues.

Authors

  • Caitlin Hitchcock
    Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
  • Julia Funk
    Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom; Department of Psychology, Ludwig-Maximilians-Universität München, Germany.
  • Ronan Cummins
    Clinical Science Laboratory, Ieso Digital Health, Cambridge, England.
  • Shivam D Patel
    Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom.
  • Ana Catarino
    Clinical Science Laboratory, Ieso Digital Health, Cambridge, England.
  • Keisuke Takano
    Center for Learning and Experimental Psychopathology, University of Leuven, Tiensestraat 102, Leuven, 3000, Belgium. Keisuke.Takano@ppw.kuleuven.be.
  • Tim Dalgleish
    Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom.
  • Michael Ewbank
    Ieso Digital Health, Jeffreys Building, Cowley Rd, Milton, Cambridge, United Kingdom.

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