Utilising Smartphone-Based Apps and Wearable Accelerometer Sensors in Idiopathic Inflammatory Myopathies to Improve Treatment and Research

Student thesis: Phd


Clinical management and longitudinal research of chronic diseases are hampered by infrequency of data collection. For example, a clinician may assess a patient with a chronic condition at six-monthly intervals, thus basing their management plan on "data" collected on two days out of 365, equating to only 0.5% of days. It is plausible that increasing the frequency of data collection may enhance clinical care and the accuracy of longitudinal research. The idiopathic inflammatory myopathies (IIMs) are a group of chronic multi-system inflammatory conditions that exemplify the limitation of infrequent data collection. Recent technological advances have made the prospect of the "digital healthcare revolution" a possible reality. Digital healthcare technology includes smartphone-based apps and wearable sensors. Combined, these two technologies offer two key opportunities over "traditional" approaches of data collection: 1) the ability to measure novel parameters, and 2) the ability to collect frequent longitudinal "free-living" data outside the confines of a clinical/research facility. During my PhD I carried out the Myositis Physical Activity Device (MyoPAD) study, which aimed to 1) investigate the need for more frequent monitoring in the IIMs and 2) to carry out a "mobile-health" (mHealth) study investigating engagement with and utility of daily symptom collection via a smartphone-based app and continuous remote gait pattern measurement via a thigh-worn accelerometer sensor. An initial review of accelerometer data collection in IIM study populations was carried out (Chapter 2). This review indicated that accelerometer data collection in IIM populations was in its infancy. Further, no previous study has used such data to quantify gait patterns, rather accelerometer data was used to quantify physical activity levels instead. Qualitative interviews were carried out with MyoPAD participants (Chapter 3). Themes identified include 1) pain and fatigue as predominant symptoms, 2) day-to-day symptom variation, 3) IIM flare characterisation, and 4) limitations of disease activity methods. A 91 day trial of the MyoPAD app and sensor in 20 adult IIM participants revealed high engagement. Qualitative interviews facilitated identification of enablers/barriers to engagement (Chapter 4). Analysis of daily symptom data allowed characterisation of IIM flares, which have not been previously defined or investigated (Chapter 5). The frequency of flares and their relationships with symptom changes were quantified. Finally, a method of processing accelerometer data for individual participant gait parameter assessment was developed (Chapter 6). The relationships between gait pattern and IIM disease activity were quantified, providing preliminary insights useful for focusing future research. Overall, this thesis has demonstrated that collection of daily symptom data and continuous accelerometer data is feasible and practical. Daily symptom and gait pattern data can provide novel insights, potentially useful for both IIM clinical and research applications. These results pave the way for completion of future steps necessary for translation into clinical/research settings, which include economic analysis and regulatory approval. It is possible that, in the not too distant future, remote daily/continuous data collection will become the norm, thus relegating infrequent data collection to the past and revolutionising clinical management and research for the IIMs and other diseases.
Date of Award31 Aug 2021
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorWilliam Dixon (Supervisor) & Hector Chinoy (Supervisor)

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