Understanding Predictors of Outcome for Guided Self-Help Interventions for Anxiety

Demand for treatment of anxiety is growing and so services need to provide effective and efficient psychological interventions. This is driving the development of more low intensity (LI) interventions, but current evidence suggests efficacy is mixed. Guided self-help (GSH) interventions are designed to help patients manage symptoms via psychoeducational methods and brief contact time with interpersonal support of a facilitator. Given the uptake of LI interventions, it is important to understand what predicts outcome and how this information can be utilised to improve services.

The first chapter explores which psychosocial and treatment characteristics influence how people with anxiety respond to low intensity cognitive behavioural therapy (CBT-GSH). Identification of baseline characteristics supports treatment matching and identification of in-treatment variables supports understanding of how to adapt LI treatment. A search of the existing literature for published studies in this area was completed. Twenty-four studies were found which examined a total of one-hundred-and-sixteen predictor variables. There were no baseline characteristics that were individually consistently associated with CBT-GSH outcomes across studies, and so further research is needed to determine the most suitable candidate for CBT-GSH. Between session engagement and facilitator’s experience delivering GSH consistently predicted outcome. This fits with the wider literature on psychotherapy outcomes and suggests future research should focus on adapting CBT-GSH to increase engagement.

With a newly developed version of GSH informed by cognitive analytic therapy (CAT-GSH), the second chapter aims to understand common and differential predictors of treatment outcome following CBT-GSH or CAT-GSH for anxiety and develop a treatment matching algorithm. Considering the impact of patient preference on outcomes was a further aim, alongside understanding the influence of receiving the indicated-optimal treatment on outcomes. Pre-existing data from a patient preference trial completed in a National Health Service (NHS) Talking Therapies services was retrospectively analysed. Separate predictive models were developed for CAT-GSH and CBT-GSH using baseline sociodemographic, clinical and treatment preference variables. Patients were grouped into having optimal GSH vs not having optimal GSH using the patient advantage index (PAI). Receiving optimal GSH improved outcomes within a subgroup, but the PAI-recommended treatment was not influenced by patient preference. This study is the first to apply the PAI approach to recommend GSH interventions, indicating that treatment matching algorithms have the potential to improve outcomes via improved treatment allocation.