Engagement with consumer smartwatches for tracking symptoms of individuals living with multiple long-term conditions (multimorbidity): A longitudinal observational study

Author:

Ali Syed Mustafa1ORCID,Selby David A1,Khalid Kazi1,Dempsey Katherine1,Mackey Elaine1,Small Nicola1,van der Veer Sabine N2,Mcmillan Brian3ORCID,Bower Peter4,Brown Benjamin23,McBeth John15,Dixon William G156

Affiliation:

1. Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Science Centre (MAHSC), University of Manchester, Manchester, UK

2. Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK

3. NIHR School for Primary Care Research, Centre for Primary Care and Health Services Research, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and health, Manchester Academic Health Science Centre Manchester, University of Manchester, Manchester, UK

4. NIHR Policy Research Unit for Older People and Frailty, Centre for Primary Care and Health Services Research, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and health, Manchester Academic Health Science Centre Manchester, University of Manchester, Manchester, UK

5. NIHR Manchester Biomedical Research Centre, Manchester NHS Foundation Trust, Manchester, UK

6. Salford Royal NHS Foundation Trust, Salford, UK

Abstract

Introduction People living with multiple long-term conditions (multimorbidity) (MLTC-M) experience an accumulating combination of different symptoms. It has been suggested that these symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices. Aim The aim of this study was to investigate longitudinal user engagement with a smartwatch application, collecting survey questions and active tasks over 90 days, in people living with MLTC-M. Methods ‘Watch Your Steps’ was a prospective observational study, administering multiple questions and active tasks over 90 days. Adults with more than one clinician-diagnosed long-term conditions were loaned Fossil® Sport smartwatches, pre-loaded with the study app. Around 20 questions were prompted per day. Daily completion rates were calculated to describe engagement patterns over time, and to explore how these varied by patient characteristics and question type. Results Fifty three people with MLTC-M took part in the study. Around half were male ( = 26; 49%) and the majority had a white ethnic background ( n = 45; 85%). About a third of participants engaged with the smartwatch app nearly every day. The overall completion rate of symptom questions was 45% inter-quartile range (IQR 23–67%) across all study participants. Older patients and those with greater MLTC-M were more engaged, although engagement was not significantly different between genders. Conclusion It was feasible for people living with MLTC-M to report multiple symptoms per day over 3 months. User engagement appeared as good as other mobile health studies that recruited people with single health conditions, despite the higher daily data entry burden.

Funder

University of Manchester/Medical Research Council

Publisher

SAGE Publications

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