Remote monitoring of rheumatoid arthritis (REMORA): study protocol for a stepped wedge cluster randomized trial and process evaluation of an integrated symptom tracking intervention
Sabine N. van der Veer,
Deb Griffiths-Jones,
Matthew Parkes,
Katie L. Druce,
Paul Amlani-Hatcher,
Christopher J. Armitage,
Nicholas Bansback,
Peter Bower,
Dawn Dowding,
Benjamin Ellis,
Jill Firth,
Sean Gavan,
Elaine Mackey,
Caroline Sanders,
Charlotte A. Sharp,
Karen Staniland,
William G. Dixon,
the REMORA team
Affiliations
Sabine N. van der Veer
Centre for Health Informatics, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester Academic Health Science Centre
Deb Griffiths-Jones
Centre for Health Informatics, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester Academic Health Science Centre
Matthew Parkes
Centre for Biostatistics, Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, The University of Manchester
Katie L. Druce
Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester Academic Health Science Centre
Paul Amlani-Hatcher
Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester Academic Health Science Centre
Christopher J. Armitage
Manchester Centre for Health Psychology, Division of Psychology and Mental Health, The University of Manchester, Manchester Academic Health Science Centre
Nicholas Bansback
School of Population and Public Health, University of British Columbia
Peter Bower
Centre for Primary Care and Health Services Research, Division of Population Health, Health Services Research and Primary Care, The University of Manchester, Manchester Academic Health Science Centre
Dawn Dowding
Division of Nursing, Midwifery and Social Work, School of Health Sciences, Faculty of Biomedicine and Health, The University of Manchester
Benjamin Ellis
Imperial College Healthcare NHS Trust
Jill Firth
Pennine MSK Partnership, Integrated Care Centre
Sean Gavan
Manchester Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care, Manchester Academic Health Science Centre, The University of Manchester
Elaine Mackey
Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester Academic Health Science Centre
Caroline Sanders
Centre for Primary Care and Health Services Research, Division of Population Health, Health Services Research and Primary Care, The University of Manchester, Manchester Academic Health Science Centre
Charlotte A. Sharp
Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester Academic Health Science Centre
Karen Staniland
Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester Academic Health Science Centre
William G. Dixon
Centre for Health Informatics, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester Academic Health Science Centre
Abstract Background Management of rheumatoid arthritis (RA) relies on symptoms reported by patients during infrequent outpatient clinic visits. These reports are often incomplete and inaccurate due to poor recall, leading to suboptimal treatment decisions and outcomes. Asking people to track symptoms in-between visits and integrating the data into clinical pathways may improve this. However, knowledge on how to implement this into practice and its impact on services and outcomes remains scarce in RA. Therefore, we evaluate the comparative effectiveness and cost-effectiveness of integrated symptom tracking in people with RA over and above usual care, while generating insights on factors for successful implementation. Methods In this superiority stepped wedge cluster-randomized controlled trial with continuous recruitment short exposure design, 16 rheumatology outpatient departments (clusters) recruit a total of 732 people with active RA. They initially offer clinic visits according to standard of care before switching in pairs to visits with integrated symptom tracking. Clusters switch in randomized order every 3 weeks. Integrated symptom tracking consists of (1) a mobile app for patients to track their symptoms daily and other RA aspects weekly/monthly, and (2) an interactive dashboard visualizing the app data, which healthcare professionals access from their electronic health record system. Clinic visits happen according to usual practice, with tracked symptom data only reviewed during visits. Our primary outcome is a difference in marginal mean disease activity score at 12 ± 3 months between standard of care and integrated symptom tracking, after accounting for baseline values, cluster, and other covariates. Secondary outcomes include patient-reported disease activity, quality of life and quality-adjusted life-years, medication/resource use, consultation and decision-making experience, self-management, and illness perception. We also conduct interviews and observations as part of a parallel process evaluation to gather information on implementation. Discussion Our trial will generate high-quality evidence of comparative and cost-effectiveness of integrated symptom tracking compared to standard of care in people with RA, with our process evaluation delivering knowledge on successful implementation. This optimizes the chances of integrated symptom tracking being adopted more widely if we find it is (cost-) effective. Trial registration Registered 4-Jun-2024 on https://www.isrctn.com/ , ISRCTN51539448. Trial open science framework repository https://osf.io/sj9ha/ .
