The use of Digital Patient-Generated Health Data to Support Clinical Care and Research in Musculoskeletal Disease

Student thesis: Phd


Mobile health is the application of mobile apps and sensors to obtain data pertinent to wellness and disease diagnosis, prevention, and management. It has the potential to monitor and intervene whenever and wherever as part of managing long-term conditions. With more than 85% of UK adults owning a smartphone and catalysed by the COVID-19 pandemic, there is an opportunity to achieve this in the foreseeable future. This thesis explores how digital patient-generated health data (PGHD) from mobile apps can advance clinical care and research in long-term conditions, using the example of rheumatoid arthritis (RA) – a chronic, disabling disease of the joints, characterised by fluctuating symptoms and disease severity through time. Infrequent outpatient visits mean that clinicians lack a clear picture of what happens between visits, because patients struggle to recall symptoms. This ultimately results in sub-optimal care. Longitudinal research into patterns of RA disease severity shares the limitation of sporadic data collection. Smartphones offer a unique opportunity to overcome this challenge for both clinical care and research by enabling patients to briefly report their symptoms regularly, integrated into their daily lives. An initial review of published studies on remote monitoring systems integrated into electronic health records (EHRs) to collect symptoms in long-term conditions found that there were few examples to inform future development of these systems (Chapter 2). Additionally, many of the anticipated benefits of remote monitoring had yet to be realised in practice. This suggests that creating and evaluating such systems is an ambitious achievement. The Remote Monitoring of Rheumatoid Arthritis (REMORA) programme aims to implement daily symptom monitoring from a smartphone app into the EHR to guide clinical decision-making. The first stage (REMORA1), conducted in 2015-17, was a feasibility study in 20 RA patients over three months. Through qualitative analysis of audio-recorded clinical consultations with REMORA1 patients, where visual summaries of PGHD over time were available for review, I aimed to enhance our understanding of how availability of PGHD influenced clinical care, and identified three distinct ways of using the data depending on when it was introduced (Chapter 3). As part of an essential update of the REMORA technical infrastructure, I set up an observational study to expand on the previous study by: 1) collecting data longer, 2) including a larger cohort of patients, 3) on-boarding without direct assistance, and 4) linking with contextual data collected from the EHR. I reflect on the challenges of setting up a mobile health study and insights gained through the process and from preliminary results (Chapter 4) that informed a current multi-centre trial. Analysis of daily symptoms allowed characterisation of self-reported RA flares (Chapter 5), where the frequency of flares and relationships with symptom changes were quantified. Building on this, I was able to demonstrate the feasibility of using daily PGHD to predict self-reported flares (Chapter 6), which opens up opportunities for timely interventions to avoid a flare or decrease its impact. This thesis demonstrates that building a sustainable infrastructure for the collection of daily PGHD on an app and integration into the EHR is complex but achievable. Smartphones make it possible to capture and characterise day-to-day variations in symptoms and occurrence of flares in real time, instead of relying on patient recall at infrequent clinical visits or at the discrete intervals of research - truly harnessing the potential of mobile health for both clinical care and longitudinal research.
Date of Award31 Dec 2022
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorJohn Mcbeth (Supervisor), William Dixon (Supervisor) & Sabine Van Der Veer (Supervisor)


  • Digital epidemiology
  • mHealth
  • Patient-generated health data

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