A Metascientific Review of the Evidential Value of Acceptance and Commitment Therapy for Depression

Abstract

In the past three-and-a-half decades, nearly 500 randomized controlled trials (RCTs) have examined Acceptance and Commitment Therapy (ACT) for a range of health problems, including depression. However, emerging concerns regarding the replicability of scientific findings across psychology and mental health treatment outcome research highlight a need to re-examine the strength of evidence for treatment efficacy. Therefore, we conducted a metascientific review of the evidential value of ACT in treating depression. Whereas reporting accuracy was generally high across all trials, we found important differences in evidential value metrics corresponding to the types of control conditions used. RCTs of ACT compared to weaker controls (e.g., no treatment, waitlist) were well-powered, with sample sizes appropriate for detecting plausible effect sizes. They typically yielded stronger Bayesian evidence for (and larger posterior estimates of) ACT efficacy, though there was some evidence of significance inflation among these effects. RCTs of ACT against stronger controls (e.g., other psychotherapies), meanwhile, were poorly powered, designed to detect implausibly large effect sizes, and yielded ambiguous-if not contradicting-Bayesian evidence and estimates of efficacy. Although our review supports a view of ACT as efficacious for treating depression compared to weaker controls, future RCTs must provide more transparent reporting with larger groups of participants to properly assess the difference between ACT and competitor treatments such as behavioral activation and other forms of cognitive behavioral therapy. Clinicians and health organizations should reassess the use of ACT for depression if costs and resources are higher than for other efficacious treatments. Clinical trials contributing effects to our synthesis can be found at https://osf.io/qky35.

Keywords: Acceptance and Commitment Therapy; depression; evidence-based therapy; evidential value; metascience.