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Atrial fibrillation (AF) is the most common arrhythmia, and its prevalence is increasing. Early diagnosis is important to reduce the risk of stroke. Mobile health (mHealth) devices, such as single-lead electrocardiogram (ECG) devices, have been introduced to the worldwide consumer market over the past decade. Recent studies have assessed the usability of these devices for detection of AF, but it remains unclear if the use of mHealth devices leads to a higher AF detection rate.
The goal of the research was to conduct a systematic review of the diagnostic detection rate of AF by mHealth devices compared with traditional outpatient follow-up. Study participants were aged 16 years or older and had an increased risk for an arrhythmia and an indication for ECG follow-up—for instance, after catheter ablation or presentation to the emergency department with palpitations or (near) syncope. The intervention was the use of an mHealth device, defined as a novel device for the diagnosis of rhythm disturbances, either a handheld electronic device or a patch-like device worn on the patient’s chest. Control was standard (traditional) outpatient care, defined as follow-up via general practitioner or regular outpatient clinic visits with a standard 12-lead ECG or Holter monitoring. The main outcome measures were the odds ratio (OR) of AF detection rates.
Two reviewers screened the search results, extracted data, and performed a risk of bias assessment. A heterogeneity analysis was performed, forest plot made to summarize the results of the individual studies, and albatross plot made to allow the
A total of 3384 articles were identified after a database search, and 14 studies with a 4617 study participants were selected. All studies but one showed a higher AF detection rate in the mHealth group compared with the control group (OR 1.00-35.71), with all RCTs showing statistically significant increases of AF detection (OR 1.54-19.16). Statistical heterogeneity between studies was considerable, with a Q of 34.1 and an
Although the results of 13 of 14 studies support the effectiveness of mHealth interventions compared with standard care, study results could not be pooled due to considerable clinical and statistical heterogeneity. However, smartphone-connectable ECG devices provide patients with the ability to document a rhythm disturbance more easily than with standard care, which may increase empowerment and engagement with regard to their illness. Clinicians must beware of overdiagnosis of AF, as it is not yet clear when an mHealth-detected episode of AF must be deemed significant.
Atrial fibrillation (AF) is the most commonly diagnosed arrhythmia [
The worldwide prevalence of AF is increasing. This increase has been attributed to an aging population and increased prevalence of cardiovascular risk factors [
Early diagnosis of AF and prophylactic treatment for ischemic stroke with oral anticoagulants is therefore important, whether the AF is paroxysmal, persistent, or permanent and symptomatic or silent [
Traditionally, patients are diagnosed with AF using a 12-lead electrocardiogram (ECG). In case of suspected paroxysmal AF, it is possible to perform prolonged monitoring via Holter registration. However, as paroxysmal AF is often silent and patients can have vast periods of sinus rhythm, diagnosing paroxysmal AF is a challenge [
Over the last decade, consumer grade health monitoring devices have been developed and marketed as beneficial for personal health monitoring [
Studies have been done to assess the accuracy of mHealth devices compared with 12-lead ECGs. A recent systematic review suggests several mHealth devices are suitable in the use of detecting AF, based on the sensitivity and specificity of these devices [
A systematic literature review was conducted to evaluate the efficacy of mHealth devices using standard (traditional) care as the reference standard in people with an indication for follow-up for a suspected arrythmia (eg, after catheter ablation or electrical cardioversion) or in cases of an acute emergency department presentation with (near) syncope or palpitations where no arrhythmia could be found at the time of presentation. The efficacy of mHealth was defined as the detection rate of AF by a smartphone-connectable ECG device, either a handheld electronic device or patch-like device attached to the study subject’s chest or by requiring subject to send an ECG transtelephonically. Standard care was defined as follow-up via a general practitioner or regular outpatient clinic visit with a standard 12-lead ECG or Holter monitoring. This systematic review was conducted and reported by following the Cochrane Handbook for Systematic Reviews of Interventions [
The eligibility criteria for studies to be included in this systematic review were as follows:
Published studies comparing mHealth devices with standard care in patients with an indication for follow-up via ECG or Holter monitoring
Studies with AF detection as a primary or secondary outcome measure
Studies conducted in people aged 16 years and older reporting demographic data such as patient characteristics, study setting, sample size, and data points
Studies performed in a clinical or outpatient setting
Studies in patients without an internal cardioverter defibrillator, pacemaker, or ventricular assist device
Studies had to be published in English or Dutch to be selected. If a study has been indexed in multiple databases, only the PubMed version was included.
The search strategy is presented in
A 2-stage process was used for inclusion in the review. Two reviewers (TB, RT) first independently screened all titles and abstracts of the identified studies to find potentially relevant studies. The same reviewers then assessed the full-text articles independently for the eligibility criteria. Any disagreements were resolved by consensus.
Risk of bias was assessed with the RoB 2 (Risk of Bias 2) tool for randomized controlled trials (RCTs) and the ROBINS-I (Risk of Bias in Nonrandomized Studies of Interventions) tool for nonrandomized studies [
The primary outcome measure of this systematic review was the odds ratio (OR) of AF detection, comparing mHealth devices to standard care. The PATCH-ED (Patch Monitor in Patients With Unexplained Syncope After Initial Evaluation in the Emergency Department) and IPED (Investigation of Palpitations in the Emergency Department) study groups reported no events in the control groups [
As of October 19, 2020, a total of 3384 articles were obtained from the database searches. Two investigators (TB and RT) excluded 3350 studies based on the title and abstract. A total of 34 abstracts meeting the eligibility criteria were identified. After reviewing the full text, the reviewers chose 14 studies with a total of 4617 study subjects. The selection process is shown in
Study search and selection process.
The 14 selected studies consist of 8 cohort studies, 4 RCTs, and 2 case-control studies [
Study characteristics.
Author, year, country | Study type | Patient charactertistics | Sample size; drop out; mean age; male | Intervention | Control | Follow-up | Primary outcome |
Liu et al (2010), China [ |
Prospective cross-sectional | Catheter ablation patients | 92; 0 (0%); 54 ya; 78% male | Transtelephonic ECGb once daily | 24 h Holter+ at complaints | 90 dc | AFd detection |
Rosenberg et al (2013), US [ |
Prospective cross-sectional | Patients who are managed for AF, no definition was given | 74; 0 (0%); 65 y; 55% male | ZioPatch | 24 h Holter | 14 d | AF detection |
Barrett et al (2013), US [ |
Prospective cross-sectional | Outpatients with indication for Holter monitoring | 146; 4 (2.7%); n/ae; n/a | ZioPatch | 24 h Holter | 14 d | Arrhythmia detection |
Hendrikx et al (2014), Sweden [ |
Prospective cross-sectional | Patients with unexplained palpitations or presyncope | 95; 0 (0%); 54 y; 44% male | Zenicor twice daily + 24 hf Holter | 24 h Holter | 28 d | Arrhythmia detection |
Kimura et al (2016), Japan [ |
Prospective cross-sectional | Catheter ablation patients | 28; 2 (6.7%); 59 y; 87% male | CardioPhone twice daily | Monthly 24 h Holter | 6 mog | AF detection |
Busch et al (2017), Germany [ |
Retrospective cross-sectional | Volunteers to join in an mHealthh study | 1678; n/a; 51 y; 48% male | SensorMobile twice daily | Single 12-lead ECG | 28 d | AF detection |
Halcox et al (2017), UK [ |
Single center, open label RCTi | ≥65 y patients without AF at a GPj practice | 1001; 5 (0.5%); 73 y; 47% male | AliveCor Kardia twice a week | Follow-up at the GP | 1 y | Time to diagnosis of AF |
Hickey et al (2017), US [ |
Prospective, matched cohort study | Patients with a history of AF | 46; 0 (0%); 55 y; 65% male | AliveCor Kardia once daily | Standard care (no added care) | 6 mo | Atrial arrhythmia detection |
Narasimha et al (2018), US [ |
Prospective cross-sectional | Patients with unexplained palpitations who underwent previous Holter monitoring | 33; 5 (13.2%); 48 y; 42% male | AliveCor Kardia at complaints | External loop recorder | 30 d | Arrhythmia detection |
Reed et al (2018), Scotland [ |
Prospective, unmatched case-control study | ≥16 y ER patients with unexplained syncope | 689; 0 (0%); 67 y; 47% male | ZioPatch | Standard care (no added care) | 14 d | Symptomatic rhythm detection |
Reed et al (2019), Scotland [ |
Multicenter, open label RCT | ≥16 y ER patients with unexplained palpitations or (pre)syncope | 240; 2 (0.8%); 40 y; 44% male | Alivecor Kardia at complaints | Standard care (no added care) | 90 d | Symptomatic rhythm detection |
Goldenthal et al (2019), US [ |
Single center, open label RCT | Patients with documented AF, undergoing ablation or ECVk | 238; 5 (2.1%); 61 y; 76% male | AliveCor Kardia daily and at complaints | Standard care (no added care) | 6 mo | AF detection |
Karunadas et al (2019), India [ |
Prospective cross-sectional | Admitted patients to cardiology ward who required monitoring | 141; 0 (0%); 44 y; 53% male | WebCardio (patch) | 24 h Holter | 1 d | Arrhythmia detection |
Kaura et al (2019), UK [ |
Multicenter, open label RCT | Non-AF patients with nonlacunar stroke or TIAl | 116; 26 (22.4%); 70 y; 47% male | ZioPatch | 24 h Holter | 14 d | AF detection |
ay: year.
bECG: electrocardiogram.
cd: day.
dAF: atrial fibrillation.
eNot applicable.
fh: hour.
gmo: month.
hmHealth: mobile health.
iRCT: randomized controlled trial.
jGP: general practice.
kECV: electrical cardioversion.
lTIA: transient ischemic attack.
A total of 9 studies used handheld devices such as the Kardia (AliveCor Inc) or Zenicor-ECG (Zenicor Medical Systems AB) as an intervention, while 5 studies used a patch such as the Zio (iRhythm Technologies Inc), which was placed on the participant’s chest [
A total of 6 studies used 24-hour Holter monitoring as standard care, with 1 study adding another 24-hour Holter monitoring when study patients experienced an episode of palpitations and another study adding another 24-hour Holter monitoring every month, 6 times in total. However, 5 studies only saw patients back in the outpatient clinic or general practitioner. One study used an external loop recorder as standard care, activated at complaints during the entire follow-up duration, and the final study documented one extra standard ECG as standard care. Holter timing was at the start of the study in 4 of 6 studies that used Holter monitoring. In the other 2 studies, the timing of the Holter monitoring was unclear.
Study outcomes.
Author | Sample size, n | Intervention group, n | Control group, n | Events (intervention), n (%) | Events (control), n (%) | Odds ratio (95% CI) | |
|
|||||||
|
Liu et al, 2010 [ |
92 | —a | — | 39 (42.4) | 27 (29.2) | 1.77 (0.96-3.26) |
|
Hendrikx et al, 2014 [ |
95 | — | — | 9 (9.5) | 2 (2.1) | 4.87 (1.02-23.16) |
|
Kimura et al, 2016 [ |
28 | — | — | 15 (53.6) | 6 (21.4) | 4.23 (1.31-13.62) |
|
Busch et al, 2017 [ |
1678 | — | — | 42 (2.6) | 21 (1.3) | 2.03 (1.19-3.44) |
|
Halcox et al, 2017 [ |
1001 | 500 | 501 | 19 (3.8) | 5 (1.0) | 3.92 (1.45-10.58) |
|
Hickey et al, 2017 [ |
46 | 23 | 23 | 14 (60.9) | 7 (30.4) | 3.56 (1.05-12.05) |
|
Narasimha et al, 2018 [ |
33 | — | — | 6 (18.2) | 3 (9.1) | 2.22 (0.51-9.76) |
|
Reed et al, 2019 [ |
240 | 124 | 116 | 9 (7.3) | 0 (0) | 19.16b (1.10-333.12) |
|
Goldenthal et al, 2019 [ |
238 | 115 | 123 | 58 (50.4) | 49 (41.5) | 1.54 (0.92-2.57) |
|
|||||||
|
Rosenberg et al, 2013 [ |
74 | — | — | 38 (51.3) | 21 (28.4) | 2.66 (1.35-5.26) |
|
Barrett et al, 2013 [ |
146 | — | — | 41 (28.1) | 27 (18.5) | 1.72 (0.99-2.99) |
|
Reed et al, 2018 [ |
689 | 86 | 603 | 2 (2.3) | 0 (0) | 35.71b (1.70-750.18) |
|
Karunadas et al, 2019 [ |
141 | — | — | 3 (2.1) | 3 (2.1) | 1.00 (0.20-5.04) |
|
Kaura et al, 2019 [ |
116 | 56 | 60 | 7 (16.3) | 1 (2.1) | 8.43 (1.00-70.87) |
aNot applicable.
bHaldane correction applied.
Forest plot of the study results. No pooling due to heterogeneity.
Albatross plot, with plotted odds ratio lines.
All studies showed a higher AF detection rate in the mHealth group compared with the control group except the study by Karunadas, which showed an equal number of events (3; 2.1%) in both groups [
All RCTs showed a statistically significant improvement of AF detection with mHealth devices. ORs were 3.92 (95% CI 1.45-10.58) for the REHEARSE-AF (Assessment of Remote Heart Rhythm Sampling Using the AliveCor Heart Monitor to Screen for Atrial Fibrillation) trial, 19.16 (95% CI 1.10-333.12) for IPED, 1.54 (95% CI 0.92-2.57) in the iHeart (Information Technology Approach to Implementing Depression Treatment in Cardiac Patients) trial, and 8.43 (95% CI 1.00-70.87) in the EPACS (Early Prolonged Ambulatory Cardiac Monitoring in Stroke) trial.
The 14 selected studies showed a variety of populations, interventions, and outcomes and are therefore considerably clinically heterogenic. A chi-square test was conducted to assess statistical heterogeneity, which showed a Q of 34.1 and an
Risk of bias assessment. Randomized trials were assessed with the ROB 2 (Risk of Bias 2) tool, while ROBINS-I was used for nonrandomized studies. ROBINS-I: Risk of Bias in Nonrandomized Studies of Interventions.
Of the nonrandomized studies, the studies by Liu et al [
Two studies showed a high risk of bias. Busch et al [
The main finding of this systematic review of 14 studies is the increased AF detection rate when using mHealth devices compared with standard follow-up. Moreover, the 4 RCTs included all showed a statistically significant difference. However, there was a considerable clinical and statistical interstudy heterogeneity. The results of all studies but one show that mHealth devices lead to an increased detection of AF.
An argument can be made that conducting more (spot) measurements will automatically lead to more diagnoses of any illness. However, as AF is often only present for a short period of time and untraceable once sinus rhythm is restored, the clinical implications of the opportunity for conducting more spot measurements could be of importance with regard to stroke risk, for example. Following standard care does not allow patients to record their ECG without a delay, as they must visit their care provider or call an ambulance. Meanwhile, a paroxysm of AF may already have disappeared. Smartphone-connectable ECG devices could therefore provide patients with the opportunity to act immediately by documenting their rhythm disturbance. This is not only true for AF but also for other paroxysmal arrhythmias.
Although both handheld devices and patches lead to an increased AF detection rate, there may be a different use case to both groups of devices. Patches could be seen as prolonged Holter monitoring. The Zio patch can remain on the body for up to 14 days [
Smartphone-connectable ECG devices cannot only be used in patients with a suspected paroxysmal rhythm disturbance but also for screening purposes. As stroke has been found to be the first symptom of AF in 37% of patients aged younger than 75 years with no history of cardiovascular diseases, secondary prevention in the form of screening risk groups for AF de novo may be of clinical relevance [
In this era of mHealth, patients are increasingly able to take (spot) measurements by using smartphone-connectable ECG devices, as those devices are commercially available. However, no consensus exists within the scientific community whether each episode of AF should be seen as clinically significant. AF is traditionally defined as an irregular arrhythmia without visible P waves lasting 30 seconds or more or documented on a standard 10-second 12-lead ECG [
Due to considerable clinical and statistic heterogeneity, with an
This systematic review reflects on 14 studies with different populations, interventions, and (primary) outcomes. A total of 13 studies found an increased number of AF diagnoses with the use of an mHealth intervention compared with standard care, with the remaining study by Karunadas et al [
Search strategy.
atrial fibrillation
congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke or transient ischemic attack (TIA), vascular disease, age 65 to 74 years, sex category
electrocardiogram
Early Prolonged Ambulatory Cardiac Monitoring in Stroke
An Information Technology Approach to Implementing Depression Treatment in Cardiac Patients
Investigation of Palpitations in the Emergency Department
mobile health
odds ratio
premature atrial contraction
Patch Monitor in Patients With Unexplained Syncope After Initial Evaluation in the Emergency Department
randomized controlled trial
Assessment of Remote Heart Rhythm Sampling Using the AliveCor Heart Monitor to Screen for Atrial Fibrillation
Risk of Bias 2
Risk Of Bias in Nonrandomized Studies of Interventions
None declared.