Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study

Eric Y Ding; Dong Han; Cody Whitcomb; Syed Khairul Bashar; Oluwaseun Adaramola; Apurv Soni; Jane Saczynski; Timothy P Fitzgibbons; Majaz Moonis; Steven A Lubitz; Darleen Lessard; Mellanie True Hills; Bruce Barton; Ki Chon; David D McManus

doi:10.2196/13850

Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study

JMIR Cardio. 2019 May 15;3(1):e13850. doi: 10.2196/13850.

Authors

Affiliations

¹ Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States.
² Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States.
³ Division of Cardiology, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States.
⁴ Department of Pharmacy and Health Systems Sciences, Northeastern University, Boston, MA, United States.
⁵ Department of Neurology, University of Massachusetts Medical School, Worcester, MA, United States.
⁶ Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, United States.
⁷ StopAfib.org, American Foundation for Women's Health, Decatur, TX, United States.

PMID: 31758787
PMCID: PMC6834225
DOI: 10.2196/13850

Abstract

Background: Atrial fibrillation (AF) is often paroxysmal and minimally symptomatic, hindering its diagnosis. Smartwatches may enhance AF care by facilitating long-term, noninvasive monitoring.

Objective: This study aimed to examine the accuracy and usability of arrhythmia discrimination using a smartwatch.

Methods: A total of 40 adults presenting to a cardiology clinic wore a smartwatch and Holter monitor and performed scripted movements to simulate activities of daily living (ADLs). Participants' clinical and sociodemographic characteristics were abstracted from medical records. Participants completed a questionnaire assessing different domains of the device's usability. Pulse recordings were analyzed blindly using a real-time realizable algorithm and compared with gold-standard Holter monitoring.

Results: The average age of participants was 71 (SD 8) years; most participants had AF risk factors and 23% (9/39) were in AF. About half of the participants owned smartphones, but none owned smartwatches. Participants wore the smartwatch for 42 (SD 14) min while generating motion noise to simulate ADLs. The algorithm determined 53 of the 314 30-second noise-free pulse segments as consistent with AF. Compared with the gold standard, the algorithm demonstrated excellent sensitivity (98.2%), specificity (98.1%), and accuracy (98.1%) for identifying irregular pulse. Two-thirds of participants considered the smartwatch highly usable. Younger age and prior cardioversion were associated with greater overall comfort and comfort with data privacy with using a smartwatch for rhythm monitoring, respectively.

Conclusions: A real-time realizable algorithm analyzing smartwatch pulse recordings demonstrated high accuracy for identifying pulse irregularities among older participants. Despite advanced age, lack of smartwatch familiarity, and high burden of comorbidities, participants found the smartwatch to be highly acceptable.

Keywords: atrial fibrillation; electrocardiography; mHealth; mobile health; photoplethysmography; screening; smartwatch.

©Eric Y Ding, Dong Han, Cody Whitcomb, Syed Khairul Bashar, Oluwaseun Adaramola, Apurv Soni, Jane Saczynski, Timothy P Fitzgibbons, Majaz Moonis, Steven A Lubitz, Darleen Lessard, Mellanie True Hills, Bruce Barton, Ki Chon, David D McManus. Originally published in JMIR Cardio (http://cardio.jmir.org), 15.05.2019.

Abstract

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