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Trends in Cardiovascular Medicine xxx (xxxx) xxx
Contents lists available at ScienceDirect
Trends in Cardiovascular Medicine
journal homepage: www.elsevier.com/locate/tcm
How useful is the smartwatch ECG?
✩
Nino Isakadze
a
, Seth S. Martin
b
,
∗
a
Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
b
Department of Medicine, Division of Cardiology, Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of
Medicine, 60 0N Wo lfe St , Carnegie 568, Baltimore, MD 21287, United States
a r t i c l e i n f o
Keywo rds:
mHealth
iECG
Atrial fibrillation
Apple Wa tch
a b s t r a c t
Apple launched a novel feature of the Apple Watch (Apple Inc.) series 4 that enables consumers to record
a rhythm strip and assist with self-diagnosis of atrial fibrillation (AF). The watch is paired with an app
that provides automatic classification of the rhythm. Ability of the algorithm to identify AF has received
Food and Drug Administration clearance. Given increasing use of direct-to-consumer devices, important
questio ns regarding the utilization of such devices and their features in clinical practice arise. It is un-
clear how the data obtained from these devices can be optimally incorporated in patient care and what
it means for patients. Safety and security of using wearables are also of concern. Furthermore, whether
data generated from the Electrocardiogram (ECG) feature will be beneficial to public health is to be deter-
mined. We discuss possible uses and challenges of Apple’s (Apple Inc.) newly launched ECG feature and
review an upcoming trial looking at clinical applications and outcomes using this technology. We also
review the literature on the Kardia (AliveCor Inc.) mobile and smartwatch ECG technology and briefly
discuss Apple Watch irregular heartbeat notifications along with the Apple Heart Study.
©2019 The Author(s). Published by Elsevier Inc.
This is an open access article under the CC BY-NC-ND license.
( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Screening for atrial fibrillation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Management of patients with atrial fibrillation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Research overview and opportunities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Challenges with use of smartwatch ECG feature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
✩ Declaration of Competing Interest: Dr. Isakadze reports no conflicts. Dr. Martin
reports personal fees for serving on scientific advisory boards for Amgen, Sanofi,
Regeneron, Esperion, Novo Nordisk, Quest Diagnostics, and Akcea Therapeutics, and
research support from Apple, Google, iHealth, Nokia, the Aetna Foundation, the
Maryland Innovation Initiative, Na tional Institutes of
Health, American Heart Asso-
ciation, PJ Schafer Memorial Fund , and David and June Trone Fam ily Foundation. Dr.
Martin is a founder of and holds equity in Corrie Health, which intends to further
develop the digital platform. This arrangement has been rev iewed and approved by
the Johns Hopkins Unive rsity in accordance with its conflict of interest policies.
∗Corresponding author.
E-mail address: smart100@jhmi.edu (S.S. Martin).
Introduction
The smartwatch market is thriving in the mobile technology
space as direct-to-consumer wearables and medical devices start
to blend in creating the possibility of monitoring personal health
metrics including cardiovascular health measures in real time [1] .
These devices now allow consumers to have access to a personal-
ized data report, which could help in prevention and management
of medical conditions.
Numerous smartwatches offer consumers heart rate monitor-
ing. Heart rate sensors on the majority of such devices, including
the Apple Watch (Apple Inc.), utilize photoplethysmography (PPG).
Using light beams and light sensitive sensors on the smartwatch,
changes in the blood volume passing through the wrist caused by
https://doi.org/10.1016/j.tcm.2019.10.010
1050-1738/© 2019 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.
( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Please cite this article as: N. Isakadze and S.S. Martin, How useful is the smartwatch ECG? Trends in Cardiovascular Medicine, https:
//doi.org/10.1016/j.tcm.2019.10.010
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Fig. 1. (A) Demonstration of the photoplethysmogram/Tachogram recording using Apple Wat ch using light beams to record changes in the blood volume passing through
the wrist (caused by peripheral pulse) (Apple Inc.). (B) Demonstration of the Lead I electrocardiogram recording though a circuit between the detector on the watch back
and the digital crown using an Apple Wa tch (Apple Inc.).
the peripheral pulse are measured to generate a PPG, which is then
used to estimate the heart rate. The “peak” to “peak interval be-
tween pulsations can be interpreted as the cardiac R–R interval [2] ,
and can be incorporated into an algorithm to detect atrial fibril-
lation (AF). Different groups have created AF detection algorithms
[3,4] and accuracy of such algorithms can be affected by ectopic
beats, motion, environmental conditions, as well as adequate blood
flow, among other factors [5] .
Using PPG technology, the Apple Watch records a tachogram
( Fig. 1 ) which is a plot of the time between heartbeats and then
applies its proprietary algorithm to determine pulse irregularity
and thus AF. In contrast to other available AF detection algo-
rithms, the Apple Watch algorithm is the first to receive Food
and Drug Administration (FDA) clearance for the consumer mar-
ket [6] . Rhythm classification as AF or sinus is only achievable
reliably during rest due to significant noise artifact with arm
movement [7] .
A single lead electrocardiogram (iECG) can be recorded through
a circuit between the detector on the watch back and the digital
crown ( Fig. 1 ). Rhythm analysis is reported after 30 s of record-
ing and is best done at rest. The app classifies an iECG as sinus
rhythm (SR), AF or inconclusive. Recordings from the watch are
saved in PDF format in the Health app [7] . According to publicly-
released letters from the FDA, the electrocardiogram (ECG) appli-
cation has received a de novo FDA clearance with Class II designa-
tion for over-the-counter use to determine the presence or absence
of AF [8] . The ECG app is recommended for information only, and
consultation with a healthcare professional is recommended prior
to taking action. While the feature may be useful for an individual
who is feeling symptoms to check his or her rhythm, the informa-
tion displayed in the app is careful to note to the user that this
feature is not intended to rule out a heart attack [7] .
ECG algorithm performance is limited in classifying other ar-
rhythmias. Unclassified rhythms such as second or third-degree AV
block, bigeminy, frequent ectopy, junctional rhythm, and low and
high heart rate (outside 50–150 bpm), and paced rhythms may not
be accurately identified by iECG [7] .
In an Apple sponsored multicenter study of 70 AF and 70 sinus
rhythm (SR) patients, it was determined that the ECG app gen-
erated waveform had morphologic equivalence to Lead I ECG for
98.4% of AF and 100% of SR patients; only 0.8% were excluded for
analysis due to artifact [7] . This highlights that Apple Watch iECG
recordings are reliable, have high fidelity and high performance.
In the second part of the study, sensitivity and specificity of the
rhythm classification algorithm were evaluated in 602 individuals.
Presence or absence of AF was determined by the ECG obtained
at the time of enrollment. There were 301 and 287 participants in
the AF and SR cohorts, respectively. With study personnel guidance
on the appropriate placement of the watch and arm positioning,
iECGs via the ECG app and conventional 12 ECGs were recorded
simultaneously. In paired strips generated by the ECG app and 12
lead ECG rhythm strips, 80.5% of strips were classifiable (exclud-
ing unreadable and unclassified rhythms). The ECG app algorithm
classification showed sensitivity of 95.5% (95% CI 92.2%, 97.8%) and
specificity of 97.1% (95% CI: 94.2%, 98.8%). There is a concern that
during everyday use the amount of unreadable or unclassifiable
rhythms will increase which may change overall performance of
the ECG app [7] .
The first smartwatch accessory cleared by the FDA for detection
of AF via its ability to record a single-lead ECG signal was the Kar-
dia Band (KB). It utilizes a paired iPhone and Apple Watch to func-
tion [9] . KB can be used with an earlier generation Apple Watch
(series 1–3), which do not have built-in iECG capability. iECG is
recorded through a circuit between the detector on the inner and
outer sides of the watch band, instead of the watch back and dig-
ital crown, as in the Series 4 Apple Watch. However, it was an-
nounced on August 19th , 2019 that AliveCor will end sales of KB
as the company released an FDA cleared Kardia Mobile 6 L, the first
available six lead personal ECG device [10] .
The ability of KB’s automated interpretation algorithm to cor-
rectly identify AF was tested in a group of 10 0 patients undergo-
ing cardioversion, demonstrating impressive sensitivity (93%) and
specificity (84% specificity). Accuracy was further improved with
physician interpretation of the KB recordings (99% sensitivity, 83%
specificity, K coefficient of 0.83). Of note, approximately 33% of
recordings were non-interpretable by the KB automated algorithm
[9] .
Lastly, another smartwatch ECG technology cleared by the FDA
is Verily’s study watch, which has received FDA clearance and is
intended for recording, storing and displaying ECG waveforms. In-
dividuals enrolled in the study using the Verily watch do not have
direct access to the data and the watch is only intended for re-
search purposes [11] .
Screening for atrial fibrillation
AF is the most frequently encountered arrhythmia, with an es-
timated prevalence of > 3% in the adult population [12] . It is as-
sociated with increased morbidity and mortality with a 5-fold in-
creased risk of stroke [13] . Subclinical AF (SCAF) represents approx-
imately a third of the total AF population [14] and is associated
with an increased risk of stroke [15] . In Sweden, mass screening
Please cite this article as: N. Isakadze and S.S. Martin, How useful is the smartwatch ECG? Trends in Cardiovascular Medicine, https:
//doi.org/10.1016/j.tcm.2019.10.010
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for AF in an elderly population by intermittent EKG for 2 weeks
identified a significant proportion of individuals with untreated AF
in a cost-effective manner [16,17] . A prospective randomized trial
of AF screening using AliveCor’s handheld single-lead ECG device
Kardia Mobile (KM) in a high-risk population (CHADS-VASc score
≥2) led to the diagnosis of AF in 3.8% of participants compared to
1% in the usual care group [18] .
Anticoagulation initiation after device detected AF varies widely
among practices, with low overall treatment rates [19] . Perino
et al. recently showed in a retrospective cohort study that an-
ticoagulation use was associated with stroke reduction if AF
episodes lasted > 24 h [19] . Although suggestive of benefit
from anticoagulation initiation for device detected, subclinical AF,
randomized trials are needed to further evaluate whether antico-
agulation in these patients prevents stroke. Apixaban for the Re-
duction of Thrombo-Embolism in Patients With Device-Detected
Sub-Clinical Atrial Fibrillation (ARTESiA) [20] and Non-vitamin K
Antagonist Oral Anticoagulants in Patients with Atrial High Rate
Episodes (NOAH) are underway to answer this important question
[21] . Although a 2018 USPSTF statement concluded that evidence
was insufficient to recommend screening for AF with ECG [22] , the
2019 American Heart Association (AHA)/American College of Car-
diology (ACC)/Heart Rhythm Society (HRS) Focused Update of the
2014 AHA/ACC/HRS Guideline for the Management of Patients with
Atrial Fibrillation notes that smart worn or handheld WiFi-enabled
devices with remote interpretation may have a role in screening
for silent AF [11] and the European Society of Cardiology recom-
mends opportunistic screening for AF in patients > 65 years of age
as class I recommendation [23] .
The Apple Heart Study examined the performance of an AF
detection algorithm based on pulse wave irregularity detected by
PPG. We will briefly discuss the study and its implications as
pulse irregularity notification is closely linked with Apple Watch
ECG feature. As mentioned above, consumers alerted with irregu-
lar heart rhythm notification now have the option to obtain iECG
to confirm presence or absence of AF. Through e-consent, the Ap-
ple Heart Study rapidly enrolled ~40 0,0 0 0 individuals aged 22
and above. The primary endpoint was detection of AF longer than
30 s on subsequent ECG patch monitoring among those who re-
ceived an AF notification and simultaneous AF on ECG patch and
tachogram [24] .
Preliminary results of this study were presented at the ACC’s
68th Annual Scientific Sessions; however, a full manuscript has
not yet been published. Irregular heartbeat notification was re-
ceived by 0.52% of participants, with the lowest rate of notification
among those under 40 (0.16%). Of those who received the notifi-
cation ( n = 161), 15% acknowledged having a diagnosis of AF be-
fore enrollment. There was a higher than anticipated drop off af-
ter notification with only 44% of those notified completing the
first virtual study visit. Overall, patches were sent to 658 partic-
ipants and 68% (450 participants) returned them for analysis. AF
was identified in one-third (34%) of participants who had received
an irregular heartbeat notification and subsequently wore the ECG
patch. This finding is not entirely surprising as particularly in the
early course of the disease, AF can be paroxysmal and with mean
ECG patch wear time of 6.3 days a portion of participants with AF
could have been missed. In the 450 participants who wore both
the watch and ECG patch, the irregular heartbeat notification was
found to have a positive predictive value (PPV) of 0.84 and individ-
ual tachogram had PPV of 0.71 [25] .
The study had several limitations, in addition to the above-
mentioned high dropout rate after notification, it is important to
note that the original assumption during design was that the anal-
ysis would include 503 returned patches; with fewer returned
patches there is a concern for decreased precision. Further lim-
itations include reliance on self-reported data from participants
and missing the target enrollment (the original plan was to en-
roll 50 0,0 0 0 participants, with 75,0 0 0 aged 65 or older). Addition-
ally, Apple Watc h owners may not be representative of the broader
population as 13% had CHADSVASC > 2, 5% had diabetes, 1% had a
prior stroke, 21% had high blood pressure, and 38% were obese.
It also was skewed towards young, white and male participants,
with 52% being between 22 and 39 years old, 68% white and 58%
male. Adverse reactions were minimal, mostly related to anxiety.
While the study is groundbreaking in how clinical studies can be
conducted and sets the groundwork for future digitally powered
AF studies, more research and preferably randomized studies are
needed to evaluate the impact on clinical medicine and public
health [25] .
With growing direct-to-consumer device sales and use, the Ap-
ple Watch ECG capability combined with irregular heartbeat no-
tification is promising as a means to identify more AF patients.
Until the results of trials evaluating the benefit of AC in stroke
prevention for subclinical AF are available, we must manage these
patients by applying existing evidence, use of best judgment and
shared decision making with the patient. This represents an enor-
mous public health and economic opportunity to influence the life
expectancy and quality of life of the affected population. As dis-
cussed below, on the other hand, mass screening for AF can lead
to false positive and false negative results, which can lead to anxi-
ety and unnecessary further testing.
Lastly, advertising in the news that the Apple Watc h ECG fea-
ture (as well as other smartwatches with ECG capability) can de-
tect AF increases public awareness about this arrhythmia.
Management of patients with atrial fibrillation
AF in a majority of patients is a persistent or recurrent medi-
cal condition. The rhythm and heart rate information derived from
the Apple Watch iECG recordings could be important to inform
the type of interventions offered for management and evaluate the
success of treatment [26] . It is important to note that tachogram
based irregular rhythm notification use is not recommended for
patients with known AF, while the ECG function for a point-of-care
use for known AF is promising [27] .
In addition, probing the correlation between symptoms and
heart rate and rhythm will be possible. Many AF patients are tak-
ing antiarrhythmic therapy with a “pill in the pocket” method,
and prompt rhythm identification via the Apple ECG application
could be helpful in these patients. It may also allow identifi-
cation of AF recurrence after catheter ablation or cardioversion.
Rhythm evaluation after catheter ablation can be important to
guide further management and determine the success of the pro-
cedure [28] . Early recurrence, in the “blanking period” may predict
who will ultimately require further antiarrhythmic management
[28] .
In the only clinical study wherein wristband iECG was evalu-
ated for AF detection among patients undergoing cardioversion, KB
recordings were found to identify patients in AF vs. SR with rea-
sonable accuracy. Use of iECG in this setting could prevent un-
necessary visits to the hospital for cardioversion if sinus rhythm
is restored prior to arrival [9] . Similarly, it could help identify the
rhythm of patients who are coming in for catheter ablation and
allow early determination regarding the need for transesophageal
echocardiogram prior to the procedure which could improve the
workflow of the electrophysiology lab.
Lastly, incorporating mHealth app technology and single-lead
ECG technology could improve patient experience with AF, en-
hance self-management behaviors and education. By addressing
cardiovascular health modification, this would add another dimen-
sion to and personalize care for AF patients.
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Research overview and opportunities
Wearable technology with single-lead ECG recording capability
is an attractive space for research. First, we discuss past and
ongoing studies using single lead ECG recordings; all studies
discussed below except for the HEARTLINE study utilize AliveCor’s
KM Device. Given a comparable concept between technologies, the
studies discussed here could provide guidance and inspiration for
larger studies evaluating a similar hypothesis using smartwatch
iECG.
The iTransmit study evaluated the performance of single lead
ECG obtained via the KM. Patients were also provided with tra-
ditional transtelephonic monitor (TTM). They were instructed to
perform recordings when having symptoms or at least once a week
over 3–4-month period post-procedure. Agreement between KM
and traditional monitor recordings was excellent with a κstatistic
of 0.82 with sensitivity of 10 0% and specificity of 97% for detection
of AF and atrial flutter. Notably, most of the patients (92%) favored
the use of the KM over TTM. This study shows impressive accuracy
of the KM algorithm and highlights its ability to identify patients
with recurrence after ablation. Although some studies show lower
accuracy with sensitivity of 66.7% and specificity of > 98% for
the KM AF detection algorithm [29] , a more recent study again
demonstrated remarkable accuracy [30] . Smartwatch ECG, arguably
more user-friendly than a handheld device, can similarly be eval-
uated and potentially be used for detection of AF recurrence after
ablation.
Intermittent vs. Continuous Anticoagulation theRapy in patiEnts
with Atrial Fibrillation (iCARE-AF), was a pilot study of 58 patients
with a CHADSVASC of < 3. It evaluated intermittent anticoagulation
triggered by smartphone-detected AF vs. continuous anticoagula-
tion. Results suggest that smartphone rhythm monitoring guided
anticoagulation is feasible and intermittent anticoagulation in low-
risk patients confers no apparent increased in stroke, while it may
reduce bleeding risk. Major limitations include the small study size
and primary design to show feasibility [31] . The study provides
background for future larger studies with adequate power to ad-
dress the safety and efficacy of intermittent anticoagulation guided
by iECG technology. Such results could have an important impact
on public health and transform care for AF patients.
Other potential areas where iECG has shown promise include
evaluation for QTc interval changes [32 , 33] , as well as measure-
ment of blood electrolyte concentration, specifically potassium
level estimation [34] , but further research is needed in this area
to define clinical utility. iPhone (Apple Inc., California) Helping
Evaluate Atrial fibrillation Rhythm through Technology (iHEART)
study is a randomized study of 300 patients with recent-onset AF
whose rhythm is restored to SR upon enrollment. Participants in
the intervention group will receive motivational text messages and
KM to record daily ECGs, with the primary endpoint being de-
tection of AF recurrence. Secondary endpoints include treatment
changes as a result of early detection of AF recurrence, study ques-
tionnaire scores at baseline and six months, including quality of
life and improvement in cardiovascular measures (blood pressure,
glucose levels) and AF knowledge from baseline to 6 months [35] .
The Atrial Fibrillation health Literacy Information Technology Trial
(AF-LITT) is a randomized controlled trial wherein the intervention
group will receive a smartphone-based intervention inclusive of an
embodied conversational agent as well as the KM. The initial aims
are to evaluate effects of this intervention on quality of life and
self-reported adherence to anticoagulation. Study investigators also
plan to explore the efficacy of this intervention on healthcare uti-
lization in individuals with chronic AF [36] . Results of these studies
will be very important as comprehensive cardiac risk factor modi-
fication is suggested to be the “4th pillar” in addition to prevention
of thromboembolism, rhythm control, and rate control in manage-
ment of patients with AF [37] .
The I STOP Afib study plans to enroll approximately 500 AF pa-
tients in a randomized fashion where intervention patients will
receive an AliveCor device to evaluate daily ECGs. They will also
receive the Eureka mobile application through which participants
will be evaluating exposure and elimination of different AF trig-
gers. Eureka is an NIH-funded, scalable, nimble resource for con-
ducting research using mobile technology, which allows multi-
model data collection (connected apps and devices, surveys, EMR
integration) [38] . It allows participants to track daily AF duration
and severity, daily mood and sleep quality, daily AliveCor tracings
and daily trigger exposure. Based on what they “learn” they will
implement these changes for four weeks during which they will
continue to monitor AF episode duration and severity via the app.
The primary aim of the study is to evaluate the intervention’s ef-
fect on AF episode frequency and severity and quality of life for
AF patients [39] . This has the opportunity to personalize care and
improve AF patient experience.
To the best of our knowledge, the HEARTLINE Study will be the
first randomized clinical trial utilizing the iECG app feature of the
Apple Watch. The trial is starting in late 2019 and will enroll pa-
tients above age 65 without known AF to through a virtual enroll-
ment system. The aim is to determine if a broad health focused en-
gagement program (which will include general health and AF ed-
ucation via an iPhone app) paired with the PPG and ECG system
of the Apple Wa tch can increase the clinically confirmed diagnosis
of AF vs. standard care. Investigators envision the primary outcome
as the number (%) of clinically confirmed AF diagnoses at a defined
time point and key secondary endpoints are planned to be CV out-
comes defined as major adverse cardiac events. As a second objec-
tive, participants age 65 and older with known AF who are already
on anticoagulation will be enrolled to determine if an anticoagula-
tion adherence module administered via an app on iPhone and Ap-
ple Watch will lead to improved adherence [40] . If the HEARTLINE
Study shows that in addition to accurate diagnosis of AF, the Apple
Watch PPG and iECG system can lead to reduction in stroke or car-
diovascular mortality, this will have tremendous clinical and public
health impact and could lead to reimbursement of the watch as a
medical device.
Lastly, artificial intelligence (AI) may reveal yet unknown ECG
characteristics that have clinical significance. For example, re-
searchers from the Mayo Clinic were able to apply AI to ECGs to
determine age and sex of patients [41] . Though age and sex are
known clinical variables, this indicates the potential of the ECG sig-
nal to serve as a global measure of health.
Challenges with use of smartwatch ECG feature
In the current era we face a new social health experience.
The general public is learning, sharing and acting on health in-
formation differently because of social network systems and new
technologies. As the Apple Watch is a direct-to-consumer product,
there is a socioeconomic (SEC) and demographic disparity in the
adoption of this technology. In addition, it is likely that motivated
consumers with healthier lifestyles will purchase the smartwatch,
while low SEC individuals are less likely to have access to these
tools, despite being a population that could benefit most. Lower
income is associated with higher AF risk and AF related mortal-
ity [ 42 , 43 ]. It is incumbent upon hospitals and health systems, in
partnership with payers, technology companies, and professional
societies such as AHA and ACC, to help digital health tools reach
low SEC individuals to promote health equity.
A total of 16% of Americans now own a smartwatch and
according to research by the NPD Group, compared to 12% in
Please cite this article as: N. Isakadze and S.S. Martin, How useful is the smartwatch ECG? Trends in Cardiovascular Medicine, https:
//doi.org/10.1016/j.tcm.2019.10.010
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Fig. 2. Smartwatch ECG feature possible uses and challenges.
December 2017. Research also reports expected higher adoption
rates of smartwatch technology among elderly due to more health-
related features [44] .
Long-term use of the technology is a concern, as 20% of con-
sumers stop using their wearables after three months with < 50%
continuing to use it after 1.5 years [45] . As AF is a chronic con-
dition, long-term adherence to smartwatch technology could be-
come useful in guiding rate and rhythm control, and evaluating
results of interventions (e.g., ablation). Health apps with a user-
friendly interface developed with active participation from patients
can potentially help overcome the challenge of long-term user
engagement.
There are concerns about the safety and security of using
direct-to-consumer smartwatches in health care [46] . Companies
own the raw data and the user lacks control over the use of their
information. In addition, patients may feel that near-continuous
monitoring is intrusive. Although data are stored anonymously,
based on users activity and location, “digital traces” may still re-
veal the identity of an individual [47] .
Other concerns with the Apple Watch and KB ECG feature in-
clude inability to monitor for AF while active, lack of continuous
monitoring and limited accuracy at heart rates < 50 and > 150 bpm.
False positives and false negatives are of serious concern when it
comes to using the app in a low-risk healthy population. Detection
of atrial arrhythmias in such a population could create anxiety and
exposure to unnecessary therapies and possible complications.
Increasing the burden on clinicians to evaluate readings from
iECG recordings can be challenging and we will need to learn more
how to utilize these additional data efficiently. The Apple Watch
ECG app platform is not presently linked to any Electronic Health
Record (EHR) systems, while AliveCor’s Kardia devices are linked
to Practice Fusion EHR only [48] . The absence of interoperability
could be a major barrier to effective use. Integrating data from
the ECG app into the EHR, especially if incorporated into the clin-
ical workflow of telemedicine visits, could move patient care away
from the traditional model of the office visit. Creation of easy to
interpret ECG app data summaries with the help of AI in the EHR
might be one of the solutions to deal with abundance of the data.
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Before this is possible, the process of interpreting iECGs may re-
quire more clinician time for a given patient, which could lead to
burnout and make physicians less likely to prescribe these tools.
This could lead to low adoption and clinical impact.
Proper reimbursement models, billing and protected time to re-
view digital data will need to be implemented to appropriately re-
spond to advances in mHealth and recognize the value of clini-
cian time outside the clinic visit. As a step forward, the Centers for
Medicare and Medicaid Services (CMS) recently incorporated new
Current Procedural Terminology (CPT) billing codes that reimburse
for review of remotely collected digital data [49] . Without thought-
ful integration into clinical workflow and alignment of financial in-
centives for clinicians and health systems, adoption of the Apple
Watch ECG feature is likely to lag.
Summary
An era of mobile technology is here, and it is actively trans-
forming clinical practice, with potential impact on multiple areas
of cardiovascular health. The Apple Watch Series 4 ECG feature
is FDA cleared for detection of presence of AF. Incorporating this
feature in everyday life of consumers may help raise awareness
for AF and facilitate health promotion and preventative effort s.
The ECG feature also shows promise in AF detection and manage-
ment and may enable users to take a more active role in their
health care. There are significant challenges such as lack of out-
comes data, false positives, and concerns with data privacy requir-
ing more research as well as collaboration of regulatory bodies and
technology companies to support the implementation of mobile
technology in cardiovascular disease prevention and management
( Fig. 2 ). AF represents only one area where the Apple Watch iECG
shows promise to transform care, while other opportunities and
challenges remain to be explored.
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//doi.org/10.1016/j.tcm.2019.10.010