Patient-related barriers and enablers to the implementation of high-value physiotherapy for chronic pain: a systematic review.

Objective To identify and synthesize patient-related barriers to and enablers of the implementation of high-value physiotherapy (HVP) for chronic pain. Furthermore, to review what patient-related interventions have been used to facilitate the implementation of HVP for chronic pain, as well as their efficacy. Methods We systematically searched the APA PsycInfo, Embase, CINAHL, Medline, Scopus, and PEDro databases for peer-reviewed studies (published in English) of adults with chronic pain. We used the Theoretical Domains Framework of behavior change to synthesize identified themes relating to barriers and enablers. Outcomes from studies reporting on interventions were also qualitatively synthesized. Results Fourteen studies reported on barriers and enablers, 8 of which related to exercise adherence. Themes common to barriers and enablers included perceived efficacy of treatment, interrelationship with the physiotherapist, exercise burden, and the patient's understanding of exercise benefits. Other barriers included fear of movement, fragmented care, and cost. Ten studies explored interventions, 9 of which aimed to improve exercise adherence. Of these, evidence from 4 randomized controlled trials of technology-based interventions demonstrated improved exercise adherence among intervention groups compared with controls. Conclusion Patients with chronic pain experience barriers to HVP, including their beliefs, the nature of their interaction with their physiotherapist, perceived treatment efficacy, and cost. Enablers include rapport with their physiotherapist, achievable exercises, and seamless cost-effective care. Technology-based interventions have demonstrated effectiveness at increasing exercise adherence. Our findings suggest that interventions seeking to enhance implementation of HVP need to consider the multifactorial barriers experienced by patients with chronic pain. Study registration Open Science Framework (https://doi.org/10.17605/OSF.IO/AYGZV). [ABSTRACT FROM AUTHOR]