ABSTRACT
Objectives: Globally, demand outstrips capacity in rheumatology services, making Mobile Health (mHealth) attractive, with the potential to improve access, empower patient self-management and save costs. Existing mHealth interventions have poor uptake by end users. This study was designed to understand existing challenges, opportunities and barriers for computer technology in the RA care pathway. Methods: People with RA were recruited from Barts Health NHS Trust rheumatology clinics to complete paper questionnaires and clinicians were recruited from a variety of centres in the UK to complete an online questionnaire. Data collected included demographics, current technology use, challenges managing RA, RA medications and monitoring, clinic appointments, opportunities for technology and barriers to technology. Results: A total of 109 patient and 41 clinician questionnaires were completed. A total of 83.5% of patients and 93.5% of clinicians use smartphones daily. However, only 25% had ever used an arthritis app and only 5% had persisted with one. Both groups identified managing pain, flares and RA medications as areas of existing need. Access to care, medication support and disease education were mutually agreeable opportunities; however, discrepancies existed between groups with clinicians prioritizing education over access, likely due to concerns of data overwhelm (80.6% considered this a barrier). Conclusions: In spite of high technology use and willingness from both sides, our cohort did not utilize technology to support care, suggesting inadequacies in the existing software. The lack of an objective biomarker for RA disease activity, existing challenges in the healthcare system and the need for integration with existing technical systems were identified as the greatest barriers. Trial registration: Registered on the Clinical Research Network registry (IRAS ID: 264690).
ABSTRACT
Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease, that can lead to joint damage but also affects quality of life (QoL) including aspects such as self-esteem, fatigue, and mood. Current medical management focuses on the fluctuating disease activity to prevent progressive disability, but practical constraints mean periodic clinic appointments give little attention to the patient's experience of managing the wider consequences of chronic illness. The main aim of this study is to explore how to use patient-derived data both for clinical decision-making and for personalisation, with the first steps towards a platform for tailoring self-management advice to patients' lifestyle changes. As a result, we proposed a Bayesian network model for personalisation and have obtained promising outcomes.
