UMMS Affiliation

Department of Population and Quantitative Health Sciences; Division of Cardiovascular Medicine, Department of Medicine; Department of Neurology; Graduate School of Biomedical Sciences

Publication Date


Document Type



Analytical, Diagnostic and Therapeutic Techniques and Equipment | Biomedical Engineering and Bioengineering | Biotechnology | Cardiology | Cardiovascular Diseases | Health Services Research | Telemedicine


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.


atrial fibrillation, electrocardiography, mHealth, mobile health, photoplethysmography, screening, smartwatch, UMCCTS funding

Rights and Permissions

© 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 (, 15.05.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cardio, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.

DOI of Published Version



JMIR Cardio. 2019 May 15;3(1):e13850. doi: 10.2196/13850. Link to article on publisher's site

Journal/Book/Conference Title

JMIR cardio

PubMed ID


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Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.