Examining predictors of treatment effect in digital Acceptance and Commitment Therapy for chronic pain

Abstract

Digitally delivered behavioral interventions for chronic pain have been encouraging with effects similar to face-to-face treatment. Although many chronic pain patients benefit from behavioral treatment, a substantial proportion do not improve. To contribute to more knowledge about factors that predict treatment effects in digitally delivered behavioral interventions for chronic pain, the present study analyzed pooled data (N = 130) from three different studies on digitally delivered Acceptance and Commitment Therapy (ACT) for chronic pain. Longitudinal linear mixed-effects models for repeated measures were used to identify variables with significant influence on the rate of improvement in the main treatment outcome pain interference from pre- to post-treatment. The variables were sorted into six domains (demographics, pain variables, psychological flexibility, baseline severity, comorbid symptoms and early adherence) and analysed in a stepwise manner. The study found that shorter pain duration and higher degree of insomnia symptoms at baseline predicted larger treatment effects. The original trials from which data was pooled are registered at clinicaltrials.gov (registration number: NCT03105908 and NCT03344926).