Journal of Multimorbidity and Comorbidity (Nov 2021)

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

  • Syed Mustafa Ali,
  • David A Selby,
  • Kazi Khalid,
  • Katherine Dempsey,
  • Elaine Mackey,
  • Nicola Small,
  • Sabine N van der Veer,
  • Brian Mcmillan,
  • Peter Bower,
  • Benjamin Brown,
  • John McBeth,
  • William G Dixon

DOI
https://doi.org/10.1177/26335565211062791
Journal volume & issue
Vol. 11

Abstract

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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.