Usability of a smartwatch for atrial fibrillation detection in older adults after stroke.

Acceptability Atrial fibrillation Smartwatch Stroke Usability

Journal

Cardiovascular digital health journal
ISSN: 2666-6936
Titre abrégé: Cardiovasc Digit Health J
Pays: United States
ID NLM: 101771268

Informations de publication

Date de publication:
Jun 2022
Historique:
entrez: 20 6 2022
pubmed: 21 6 2022
medline: 21 6 2022
Statut: epublish

Résumé

Smartwatches can be used for atrial fibrillation (AF) detection, but little is known about how older adults at risk for AF perceive their usability. We employed a mixed-methods study design using data from the ongoing Pulsewatch study, a randomized clinical trial (NCT03761394) examining the accuracy of a smartwatch-smartphone app dyad (Samsung/Android) compared to usual care with a patch monitor (Cardea SOLO™ ECG System) for detection of AF among older stroke survivors. To be eligible to participate in Pulsewatch, participants needed to be at least 50 years of age, have had an ischemic stroke, and have no major contraindications to anticoagulation therapy should AF be detected. After 14 days of use, usability was measured by the System Usability Scale (SUS) and investigator-generated questions. Qualitative interviews were conducted, transcribed, and coded via thematic analysis. Ninety participants in the Pulsewatch trial were randomized to use a smartwatch-smartphone app dyad for 14 days (average age: 65 years, 41% female, 87% White), and 46% found it to be highly usable (SUS ≥68). In quantitative surveys, participants who used an assistive device (eg, wheelchair) and those with history of anxiety or depression were more likely to report anxiety associated with watch use. In qualitative interviews, study participants reported wanting a streamlined system that was more focused on rhythm monitoring and a smartwatch with a longer battery life. In-person training and support greatly improved their experience, and participants overwhelmingly preferred use of a smartwatch over traditional cardiac monitoring owing to its comfort, appearance, and convenience. Older adults at high risk for AF who were randomized to use a smartwatch-app dyad for AF monitoring over 14 days found it to be usable for AF detection and preferred their use to the use of a patch monitor. However, participants reported that a simpler device interface and longer smartwatch battery life would increase the system's usability.

Sections du résumé

Background UNASSIGNED
Smartwatches can be used for atrial fibrillation (AF) detection, but little is known about how older adults at risk for AF perceive their usability.
Methods UNASSIGNED
We employed a mixed-methods study design using data from the ongoing Pulsewatch study, a randomized clinical trial (NCT03761394) examining the accuracy of a smartwatch-smartphone app dyad (Samsung/Android) compared to usual care with a patch monitor (Cardea SOLO™ ECG System) for detection of AF among older stroke survivors. To be eligible to participate in Pulsewatch, participants needed to be at least 50 years of age, have had an ischemic stroke, and have no major contraindications to anticoagulation therapy should AF be detected. After 14 days of use, usability was measured by the System Usability Scale (SUS) and investigator-generated questions. Qualitative interviews were conducted, transcribed, and coded via thematic analysis.
Results UNASSIGNED
Ninety participants in the Pulsewatch trial were randomized to use a smartwatch-smartphone app dyad for 14 days (average age: 65 years, 41% female, 87% White), and 46% found it to be highly usable (SUS ≥68). In quantitative surveys, participants who used an assistive device (eg, wheelchair) and those with history of anxiety or depression were more likely to report anxiety associated with watch use. In qualitative interviews, study participants reported wanting a streamlined system that was more focused on rhythm monitoring and a smartwatch with a longer battery life. In-person training and support greatly improved their experience, and participants overwhelmingly preferred use of a smartwatch over traditional cardiac monitoring owing to its comfort, appearance, and convenience.
Conclusion UNASSIGNED
Older adults at high risk for AF who were randomized to use a smartwatch-app dyad for AF monitoring over 14 days found it to be usable for AF detection and preferred their use to the use of a patch monitor. However, participants reported that a simpler device interface and longer smartwatch battery life would increase the system's usability.

Identifiants

pubmed: 35720675
doi: 10.1016/j.cvdhj.2022.03.003
pii: S2666-6936(22)00028-7
pmc: PMC9204791
doi:

Types de publication

Journal Article

Langues

eng

Pagination

126-135

Subventions

Organisme : NCATS NIH HHS
ID : UL1 TR001453
Pays : United States

Informations de copyright

© 2022 Heart Rhythm Society.

Références

Arch Intern Med. 2006 May 22;166(10):1092-7
pubmed: 16717171
JMIR Mhealth Uhealth. 2019 Mar 26;7(3):e10044
pubmed: 30912756
Stroke. 1996 Oct;27(10):1760-4
pubmed: 8841325
JMIR Mhealth Uhealth. 2020 Apr 20;8(4):e15704
pubmed: 32310149
JMIR Mhealth Uhealth. 2018 Nov 08;6(11):e11066
pubmed: 30409767
J Am Geriatr Soc. 2005 Apr;53(4):695-9
pubmed: 15817019
Circulation. 2014 Feb 25;129(8):837-47
pubmed: 24345399
JACC Clin Electrophysiol. 2018 May;4(5):618-625
pubmed: 29798789
PLoS One. 2018 Apr 12;13(4):e0195088
pubmed: 29649277
J Am Coll Cardiol. 2014 Dec 2;64(21):e1-76
pubmed: 24685669
Curr Cardiol Rev. 2008 Feb;4(1):41-8
pubmed: 19924276
Ann Intern Med. 2007 Jun 19;146(12):857-67
pubmed: 17577005
Arrhythm Electrophysiol Rev. 2016 Aug;5(2):136-43
pubmed: 27617093
Am J Med. 2003 Feb 15;114(3):206-10
pubmed: 12637135
Circ Cardiovasc Qual Outcomes. 2021 May;14(5):e000103
pubmed: 33793309
PLoS One. 2016 Dec 9;11(12):e0168010
pubmed: 27936187
Int J Inf Manage. 2020 Dec;55:102209
pubmed: 32834339
J Gen Intern Med. 2001 Sep;16(9):606-13
pubmed: 11556941
BMJ. 2010 Sep 17;341:c4587
pubmed: 20851841
Lancet. 2014 Mar 15;383(9921):955-62
pubmed: 24315724

Auteurs

Eric Y Ding (EY)

Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.

Maira CastañedaAvila (M)

Department of Population and Quantitative Health Sciences at the University of Massachusetts Medical School, Worcester, Massachusetts.

Khanh-Van Tran (KV)

Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.

Jordy Mehawej (J)

Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.

Andreas Filippaios (A)

Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.

Tenes Paul (T)

Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.

Edith Mensah Otabil (EM)

Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.

Kamran Noorishirazi (K)

Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.

Dong Han (D)

Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut.

Jane S Saczynski (JS)

Department of Pharmacy and Health Systems Sciences, Northeastern University, Boston, Massachusetts.

Bruce Barton (B)

Department of Population and Quantitative Health Sciences at the University of Massachusetts Medical School, Worcester, Massachusetts.

Kathleen M Mazor (KM)

Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.

Ki Chon (K)

Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut.

David D McManus (DD)

Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts.

Classifications MeSH