Accuracy, Usability, and Adherence of Smartwatches for Atrial Fibrillation Detection in Older Adults After Stroke: Randomized Controlled Trial Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.2196/45137
Background Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers. Objective This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors. Methods Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period. Results A total of 120 participants were enrolled (mean age 65 years; 50/120, 41% female; 106/120, 88% White). The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days. Conclusions Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear. Trial Registration ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394 International Registered Report Identifier (IRRID) RR2-10.1016/j.cvdhj.2021.07.002
Related Topics
- Type
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- Language
- en
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- https://jmir.org/api/download?alt_name=cardio_v7i1e45137_app3.pdf&filename=1173e7cf5eb038d236057f01e0926909.pdf
- OA Status
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- Cited By
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- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389098722Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/45137Digital Object Identifier
- Title
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Accuracy, Usability, and Adherence of Smartwatches for Atrial Fibrillation Detection in Older Adults After Stroke: Randomized Controlled TrialWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-28Full publication date if available
- Authors
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Eric Ding, Khanh‐Van Tran, Darleen Lessard, Ziyue Wang, Dong Han, Fahimeh Mohagheghian, Edith Mensah Otabil, Kamran Noorishirazi, Jordy Mehawej, Andreas Filippaios, Syed Naeem, Matthew Gottbrecht, Timothy P. Fitzgibbons, Jane S. Saczynski, Bruce Barton, Ki H. Chon, David D. McManusList of authors in order
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https://doi.org/10.2196/45137Publisher landing page
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https://jmir.org/api/download?alt_name=cardio_v7i1e45137_app3.pdf&filename=1173e7cf5eb038d236057f01e0926909.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://jmir.org/api/download?alt_name=cardio_v7i1e45137_app3.pdf&filename=1173e7cf5eb038d236057f01e0926909.pdfDirect OA link when available
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Medicine, Usability, Smartwatch, Randomized controlled trial, Atrial fibrillation, Stroke (engine), Physical therapy, Physical medicine and rehabilitation, Internal medicine, Wearable computer, Computer science, Embedded system, Engineering, Mechanical engineering, Human–computer interactionTop concepts (fields/topics) attached by OpenAlex
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17Total citation count in OpenAlex
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2025: 10, 2024: 7Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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