Title: MovIng to Support Sustained Improvement of Outcomes iN Rheumatoid Arthritis – MISSION-RA
Funder: Department of Health and Social Care

Abstract: Background: Rheumatoid Arthritis (RA) is an autoimmune disease characterised by high-grade systemic inflammation. Physical activity (PA) is recommended for management of RA, and interventions typically focus on encouraging moderate-to-vigorous intensity PA (MVPA) via structured exercise. However, symptoms (e.g. pain, fatigue) are common barriers to exercise in RA, suggesting alternative approaches to PA promotion are required. The health benefits of increasing overall volume of PA have been underlined (2019 UK PA guidelines), and are achievable by increasing activities of daily living (e.g. walking, housework, stair-climbing), and reducing sedentary time. People with RA may consider this approach more acceptable than exercise interventions. Importantly, tailored interventions, which consider the unique characteristics and requirements of the individual and the broader target population (e.g. PA needs/preferences, disease-specific movement patterns), will optimise PA behaviour change. Aims: Harnessing advances in Health Data science, I will; 1) design the first tailored digital behaviour change intervention (DBCI) to support people with RA to increase their overall volume of PA, and 2) assess the feasibility of a full-scale trial to test the intervention. Methods: The intervention will consist of a mobile-health (mHealth) app and linked commercial activity tracker. The app will use machine learning to tailor the intervention, and will be developed through four, linked work-packages (WP);WP1 (years 1-2): Ecological Momentary Assessment (using body-worn cameras and accelerometers), will be employed to develop RA-specific machine learning models that classify specific types of PA (e.g. walking, housework, stair-climbing), from accelerometer data. The app will integrate these models to tailor the intervention based on; RA-specific movement patterns and their health impacts (in WP2, population tailoring) Users 'real-time' PA behaviours (acquired from linked activity tracker), to provide personalised feedback/recommendations to support PA (individual tailoring)WP2 (years 1-3): Machine learning models will be validated and applied to UK Biobank data, to explore RA-specific movement patterns and their health impacts. Results will inform the volume and type(s) of behaviours to be recommended to patients in the intervention (via the app, population tailoring). WP3 (years 1-3): App content and functions will be developed using the Person-based Approach to developing DBCIs. This involves iterative, in-depth, qualitative research (focus groups, interviews, usability study), with a wide-range of people with RA.WP4 (years 4-5): A randomised feasibility study (intervention vs. control) will assess the feasibility of testing the intervention/app in a full-scale Randomised Controlled Trial (RCT). Results will be used to optimise engagement and acceptability of the intervention/app, and inform the future RCT design. Impact: This research will produce; 1) an intervention with potential to increase PA in RA, and in the long-term, reduce NHS costs; 2) a future RCT design to test its efficacy; and 3) a pioneering approach/framework for developing tailored PA DBCIs (joining machine learning and the Person-based Approach) to inform future research in RA, and other populations. Dissemination: Findings will be disseminated by conventional (e.g. conferences, publications), and newer approaches (e.g. National Rheumatoid Arthritis Society and fellowship/study websites, podcasts, videos), to engage a range of audiences (e.g. patients, researchers, NHS commissioners/professionals, and international Rheumatology organisations).

Lay abstract: Background: Rheumatoid Arthritis (RA) is a condition where the immune system attacks our own joints. Physical activity, and specifically moderate-to-vigorous 'exercise', is recommended to help manage RA symptoms and reduce the risk of developing other health problems (e.g. cardiovascular disease). However, people with RA often say joint pain and fatigue stop them from engaging in exercise. Another approach is to increase overall levels of physical activity, without focussing on exercise. This may improve health in RA and can be done by increasing everyday activities, such as walking and housework, and reducing sitting time. However, ways of using this approach to support physical activity have not been developed specifically for people with RA yet.Research aims: I will develop a new way (an 'intervention') to help people with RA increase their overall physical activity. The intervention will be personalised ('tailored') to people with RA, by considering their unique physical activity patterns, needs and preferences. This will help make sure the intervention is more effective. To achieve this, I will answer the following important questions: 1: Can I use advances in technology, such as artificial intelligence, to develop an intervention tailored to people with RA?First, people with RA will wear a body-camera and physical activity trackers (like a Fitbit) for 24-hours. Using this data, I will develop a computer programme that uses the unique activity patterns of people with RA to automatically recognise specific types of physical activity from activity tracker data (e.g. walking, housework, sitting time). This computer programme will; Help explore if certain activities are better than others for improving health in RA (e.g. walking vs. stair-climbing). This information will be used to recommend types of activity to patients as part of the intervention. Be built into a mobile phone app which links to the activity tracker. This app and activity tracker will become the intervention. The app will use activity tracker data to provide personalised feedback on physical activity behaviour, and suggest changes to help people with RA increase their physical activity.2: How can I ensure the intervention is designed to meet the needs of people with RA? I will interview 50 people with RA about their experiences of physical activity. Using this information, I will work with a Patient Research Committee (8 people with RA) to co-design the app. Then, another 40 people with RA from across the UK will try the app and activity tracker in the real world, and their feedback used to improve it. 3: How can I test if the intervention works in the future? I will then run a study where 50 RA patients will be given the app and activity tracker and 50 will receive routine NHS advice about physical activity. I will use this study to see if it is possible to run a larger clinical trial, to test if the app is effective at increasing overall physical activity in RA. I will also interview participants and use their feedback to further improve the app, to make it even more acceptable and engaging. Patient Involvement: The National Rheumatoid Arthritis Society (NRAS) have helped develop this research, and 16 people with RA have joined focus groups to advise on research questions and methods (e.g. use of body-camera). NRAS will lead the Patient Research Committee who will help to co-design the app, and communicate research findings.Dissemination: I will share research findings through scientific and public avenues (e.g. conferences, scientific papers, study website, social media) to engage patients, researchers, NHS professionals/managers and key NHS decision makers. NRAS will communicate findings via their website, member events, and involvement in international Rheumatology organisations.

Research activity codes: 6.3 MEDICAL DEVICES;6.7 PHYSICAL
Health categories: INFLAMMATORY AND IMMUNE SYSTEM