Collecting symptoms and sensor data with consumer smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): protocol for a longitudinal, observational feasibility study

Research output: Contribution to journalArticle

Authors

  • Anna L Beukenhorst
  • Matthew J Parkes
  • Louise Cook
  • Rebecca Barnard
  • Sabine N van der Veer
  • Kelly Howells
  • Caroline Sanders
  • Jamie C Sergeant
  • Terence W O'Neill
  • John McBeth
  • William G Dixon

Colleges, School and Institutes

External organisations

  • University of Manchester
  • Aston University
  • Massachusetts Institute of Technology, Cambridge, MA, USA
  • Nuffield Department of Clinical Neurosciences, University of Oxford
  • The National Institute for Health Research School for Primary Care Research, Manchester Academic Health Science Centre, Mancheste
  • National Institute for Health Research Manchester Musculoskeletal Biomedical Research Centre, Manchester University National Health Service Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.
  • Health eResearch Centre, The United Kingdom Farr Institute of Health Informatics Research, Manchester
  • Department of Rheumatology, Salford Royal National Health Service Foundation Trust, Salford, United Kingdom.

Abstract

BACKGROUND: The Knee OsteoArthritis, Linking Activity and Pain (KOALAP) study is the first to test the feasibility of using consumer-grade cellular smartwatches for health care research.

OBJECTIVE: The overall aim was to investigate the feasibility of using consumer-grade cellular smartwatches as a novel tool to capture data on pain (multiple times a day) and physical activity (continuously) in patients with knee osteoarthritis. Additionally, KOALAP aimed to investigate smartwatch sensor data quality and assess whether engagement, acceptability, and user experience are sufficient for future large-scale observational and interventional studies.

METHODS: A total of 26 participants with self-diagnosed knee osteoarthritis were recruited in September 2017. All participants were aged 50 years or over and either lived in or were willing to travel to the Greater Manchester area. Participants received a smartwatch (Huawei Watch 2) with a bespoke app that collected patient-reported outcomes via questionnaires and continuous watch sensor data. All data were collected daily for 90 days. Additional data were collected through interviews (at baseline and follow-up) and baseline and end-of-study questionnaires. This study underwent full review by the University of Manchester Research Ethics Committee (#0165) and University Information Governance (#IGRR000060). For qualitative data analysis, a system-level security policy was developed in collaboration with the University Information Governance Office. Additionally, the project underwent an internal review process at Google, including separate reviews of accessibility, product engineering, privacy, security, legal, and protection regulation compliance.

RESULTS: Participants were recruited in September 2017. Data collection via the watches was completed in January 2018. Collection of qualitative data through patient interviews is still ongoing. Data analysis will commence when all data are collected; results are expected in 2019.

CONCLUSIONS: KOALAP is the first health study to use consumer cellular smartwatches to collect self-reported symptoms alongside sensor data for musculoskeletal disorders. The results of this study will be used to inform the design of future mobile health studies. Results for feasibility and participant motivations will inform future researchers whether or under which conditions cellular smartwatches are a useful tool to collect patient-reported outcomes alongside passively measured patient behavior. The exploration of associations between self-reported symptoms at different moments will contribute to our understanding of whether it may be valuable to collect symptom data more frequently. Sensor data-quality measurements will indicate whether cellular smartwatch usage is feasible for obtaining sensor data. Methods for data-quality assessment and data-processing methods may be reusable, although generalizability to other clinical areas should be further investigated.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/10238.

Details

Original languageEnglish
Pages (from-to)e10238
Number of pages9
JournalJMIR Research Protocols
Volume8
Issue number1
Publication statusPublished - 23 Jan 2019

Keywords

  • medical informatics computing, MHealth, patient-reported outcomes, musculoskeletal diseases, mobile phone