Clinical Utility of Wearable Devices in Cardiac Rhythm Monitoring

In a Viewpoint article published in JAMA, James E. Ip, MD, from the Division of Cardiology at Cornell University Medical Center, New York City, discussed the clinical use of consumer-grade wearable devices for cardiac rhythm diagnosis.¹

Study data from 2017 estimate that more than 50 million people in the United States wear a device to track physical activity. The introduction of smart watches is expected to increase this figure to more than 160 million during the next several years.² The accuracy of wearable devices for arrhythmia detection varies by technology type; smart watches, for example, use photoplethysmographic (PPG) sensors to estimate heart rate. PPG sensors are an optical technology that detect blood flow by applying light signals to the skin.³ The accuracy of this specific system is affected by several external factors, including patient movement and environment. In addition, PPG-based sensors are not readily sensitive to the distinction between atrial fibrillation and premature beats. As such, irregularities detected with PPG-based sensors must be validated with electrocardiographic recordings.

In 2014, the KardiaMobile device received over-the-counter approval from the US Food and Drug Administration as a single-lead cardiac event monitor. In November 2017, the KardiaBand, an alternative KardiaMobile configuration that integrates with the Apple Watch, became available for consumer use. The KardiaBand detects atrial rate and rhythm, atrioventricular block, and QRS delay with accuracy comparable to that of a standard 12-lead electrocardiographic recording.⁴ Additional study data have supported the efficacy of the KardiaBand as a cardiac screening device: In a 2013 trial, KardiaBand displayed an overall accuracy level of 97% for detecting atrial fibrillation.⁵ A 2018 study that assigned 2659 patients to a self-applied continuous electrocardiographic patch found a higher rate of atrial fibrillation diagnosis after 4 months, greater initiation of anticoagulants, and increased healthcare use at 1 year.⁶ Per these data, Dr Ip wrote, wearable devices for electrocardiographic monitoring have comparable efficacy to that of 12-lead readings performed in clinics. In addition, wearable devices offer greater accessibility than technology available only in clinics.

Even so, the widespread use of wearable devices may present challenges to physicians. The quality of data from self-applied devices often varies: A 2018 study of 100 patients using KardiaBand found that 34% of recordings were “unclassifiable” by device algorithm.⁷ Poor signal quality can result in misinterpretation and false alarms, which subsequently cause unnecessary medical referrals and testing. In addition, confidentiality may become an issue in a device industry not yet strictly regulated. In the digital age, Dr Ip wrote, wearable devices can be used to diagnose cardiac rhythm abnormalities, but further investigation is necessary to optimize and integrate such technology into health care.

References

1. Ip JE. Wearable devices for cardiac rhythm diagnosis and management. JAMA. 2019;321(4):337-338.
2. Steinberg JS, Varma N, Cygankiewicz I, et al. 2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry. Heart Rhythm. 2017;14(7): e55-e96.
3. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas. 2007;28(3):R1-R39.
4. Haberman ZC, Jahn RT, Bose R, et al. Wireless smartphone ECG enables large-scale screening in diverse populations. J Cardiovasc Electrophysiol. 2015;26(5):520-526.
5. Lau JK, Lowres N, Neubeck L, et al. iPhone ECG application for community screening to detect silent atrial fibrillation: a novel technology to prevent stroke. Int J Cardiol. 2013;165(1):193-194.
6. Steinhubl SR, Waalen J, Edwards AM, et al. Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation. JAMA. 2018;320(2):146-155.
7. Bumgarner JM, Lambert CT, Hussein AA, et al. Smartwatch algorithm for automated detection of atrial fibrillation. J Am Coll Cardiol. 2018;71(21):2381-2388.