Author + information
- Received August 27, 2018
- Revision received October 1, 2018
- Accepted October 11, 2018
- Published online February 18, 2019.
- Marcus Dörr, MDa,b,∗ (, )
- Vivien Nohturfft, MSca,
- Noé Brasier, MScc,
- Emil Bosshard, BScc,
- Aleksandar Djurdjevic, MScc,
- Stefan Gross, PhDa,b,
- Christina J. Raichle, MDc,
- Mattias Rhinisperger, BScc,
- Raphael Stöckli, BScc and
- Jens Eckstein, MD, PhDc,d
- aDepartment of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- bGerman Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
- cChief Medical Information Officer (CMIO) Office, University Hospital Basel, Basel, Switzerland
- dDepartment of Internal Medicine, University Hospital Basel, Basel, Switzerland
- ↵∗Address for correspondence:
Dr. Marcus Dörr, Department of Internal Medicine B, University Medicine Greifswald, Ferdinand-Sauerbruch-Strasse, 17475 Greifswald, Germany.
Objectives The WATCH AF (SmartWATCHes for Detection of Atrial Fibrillation) trial compared the diagnostic accuracy to detect atrial fibrillation (AF) by a smartwatch-based algorithm using photoplethysmographic (PPG) signals with cardiologists’ diagnosis by electrocardiography (ECG).
Background Timely detection of AF is crucial for stroke prevention.
Methods In this prospective, 2-center, case-control trial, a PPG pulse wave recording using a commercially available smartwatch was obtained along with Internet-enabled mobile ECG in 672 hospitalized subjects. PPG recordings were analyzed by a novel automated algorithm. Cardiologists’ diagnoses were available for 650 subjects, although 142 (21.8%) datasets were not suitable for PPG analysis, among them 101 (15.1%) that were also not interpretable by the automated Internet-enabled mobile ECG algorithm, resulting in a sample size of 508 subjects (mean age 76.4 years, 225 women, 237 with AF) for the main analyses.
Results For the PPG algorithm, we found a sensitivity of 93.7% (95% confidence interval [CI]: 89.8% to 96.4%), a specificity of 98.2% (95% CI: 95.8% to 99.4%), and 96.1% accuracy (95% CI: 94.0% to 97.5%) to detect AF.
Conclusions The results of the WATCH AF trial suggest that detection of AF using a commercially available smartwatch is in principle feasible, with very high diagnostic accuracy. Applicability of the tested algorithm is currently limited by a high dropout rate as a result of insufficient signal quality. Thus, achieving sufficient signal quality remains challenging, but real-time signal quality checks are expected to improve signal quality. Whether smartwatches may be useful complementary tools for convenient long-term AF screening in selected at-risk patients must be evaluated in larger population-based samples. (SmartWATCHes for Detection of Atrial Fibrillation [WATCH AF]:; NCT02956343)
The trial was supported by unrestricted research grants from Preventicus GmbH and the University Hospital Basel. The funders had no influence on the design or conduct of the trial and were not involved in data collection or statistical analysis, in the writing of the manuscript, or in the decision to submit it for publication. Dr. Dörr holds 0.5% virtual shares in Preventicus. Dr. Eckstein holds 0.5% virtual shares in Preventicus; and has received a travel grant from Preventicus. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
All authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the JACC: Clinical Electrophysiology author instructions page.
- Received August 27, 2018.
- Revision received October 1, 2018.
- Accepted October 11, 2018.
- 2019 American College of Cardiology Foundation
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