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Published on 16.06.19 in Vol 7, No 6 (2019): June

Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/13641, first published Mar 01, 2019.

This paper is in the following e-collection/theme issue:

    Review

    Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review

    Health Economics, Department of Economics and Business, Justus Liebig University, Giessen, Germany

    Corresponding Author:

    Christian Gissel, PhD

    Health Economics

    Department of Economics and Business

    Justus Liebig University

    Licher Strasse 62

    Giessen, 35394

    Germany

    Phone: 49 641 99 22070

    Email: christian.gissel@wirtschaft.uni-giessen.de


    ABSTRACT

    Background: Mobile health (mHealth) devices can be used for the diagnosis of atrial fibrillation. Early diagnosis allows better treatment and prevention of secondary diseases like stroke. Although there are many different mHealth devices to screen for atrial fibrillation, their accuracy varies due to different technological approaches.

    Objective: We aimed to systematically review available studies that assessed the accuracy of mHealth devices in screening for atrial fibrillation. The goal of this review was to provide a comprehensive overview of available technologies, specific characteristics, and accuracy of all relevant studies.

    Methods: PubMed and Web of Science databases were searched from January 2014 until January 2019. Our systematic review was performed according to the Preferred Reporting Items for Systematic Review and Meta-Analyses. We restricted the search by year of publication, language, noninvasive methods, and focus on diagnosis of atrial fibrillation. Articles not including information about the accuracy of devices were excluded.

    Results: We found 467 relevant studies. After removing duplicates and excluding ineligible records, 22 studies were included. The accuracy of mHealth devices varied among different technologies, their application settings, and study populations. We described and summarized the eligible studies.

    Conclusions: Our systematic review identifies different technologies for screening for atrial fibrillation with mHealth devices. A specific technology’s suitability depends on the underlying form of atrial fibrillation to be diagnosed. With the suitable use of mHealth, early diagnosis and treatment of atrial fibrillation are possible. Successful application of mHealth technologies could contribute to significantly reducing the cost of illness of atrial fibrillation.

    JMIR Mhealth Uhealth 2019;7(6):e13641

    doi:10.2196/13641

    KEYWORDS



    Introduction

    Background

    Atrial fibrillation is a cardiac arrhythmia appearing in different forms. Globally, 33.5 million people are affected by atrial fibrillation [1]. This disease leads to a significantly increased risk of all-cause mortality, cardiovascular mortality, major cardiovascular events, stroke, ischemic stroke, ischemic heart disease, sudden cardiac death, heart failure, chronic kidney disease, and peripheral arterial disease [2].

    Atrial fibrillation can occur in five different forms: first diagnosed, paroxysmal, persistent, long-standing persistent, and permanent. For patients above 65 years of age, opportunistic screening for atrial fibrillation is recommended by pulse taking or using an electrocardiogram (ECG) rhythm strip. The gold standard for atrial fibrillation detection is the 12-lead ECG [3].

    Because atrial fibrillation is a serious risk factor for stroke and mortality, its treatment is inevitable for patients. Through medication with oral anticoagulation such as vitamin K antagonists or nonvitamin K antagonists, the risk for stroke and mortality in patients with atrial fibrillation can be markedly reduced [4,5].

    In addition to high health risks for patients with atrial fibrillation, the economic burden of the disease is vast. An investigation carried out by Johnson et al estimated an average of €20,403-€26,544 for the cost of illness caused by atrial fibrillation over 3 years in the Danish health care system [6]. For Sweden and Germany, the cost of illness amounts to €7,241 and €5,586 per year, respectively [7]. In this context, secondary diseases like stroke cause the majority of costs. The difference in costs between treated and untreated atrial fibrillation is significant. A stroke survivor with atrial fibrillation receiving oral anticoagulation costs €17,518, and the cost for a stroke survivor with atrial fibrillation not receiving oral anticoagulation is €19,143 [8]. Furthermore, there are several studies confirming the cost-effectiveness of screening for atrial fibrillation [9-12].

    One of the main challenges is detection of irregular forms of atrial fibrillation in an accurate way in order to start treatment as soon as possible. Even an ECG taken over a longer period (>24 hours) using a Holter monitor does not always lead to a reliable diagnosis of existing atrial fibrillation. In the case of paroxysmal atrial fibrillation, the occurrence of the disease often cannot be detected within the first 48 hours of ambulatory ECG monitoring [13].

    To manage the increasing number of patients with atrial fibrillation and to cope with the consequences of this disease, an early diagnosis is fundamental. In this context, mobile health (mHealth) has often been suggested as a possible solution.

    Among the reviews of the use of mHealth for the diagnosis, treatment, and prevalence estimation of arrhythmias [14-26], only one systematic review focused on outpatient cardiac rhythm monitoring in cryptogenic stroke [16]. Therefore, we conducted a systematic review focusing especially on the most recent and relevant noninvasive mHealth devices for the detection of atrial fibrillation. The aim of this article was to provide a systematic overview about the possible and real application of mHealth as well as to show its potentials and limitations by assessing the measurement quality.

    Mobile Health

    Smartphones, tablets, and mobile apps are widely used in many parts of the world. With an increasing rate of usage, two-thirds of the population in Europe and North America own at least one mobile device. Hence, there is already a basis for an mHealth approach in the context of atrial fibrillation, and the incremental costs for its use are relatively low.

    There are two possible stages for mHealth use in the context of atrial fibrillation. First, the treatment of atrial fibrillation should start even before the occurrence of arrhythmia, in the form of prevention. Obesity, physical inactivity, and hypertension are preventable risk factors [3]. Despite the fact that behavior does not change by purchasing a wearable device or smartphone, these devices can contribute to a healthier and more active lifestyle [15].

    Second, when atrial fibrillation has occurred, there are four possibilities to support the diagnosis and treatment: ECG or rhythm monitoring, heart rate monitoring, symptom and environmental annotation, and medication adherence [26].

    Diverse propositions exist in the field of medication support. One approach is to support patients through communication of general knowledge about the disease, the mechanism of medication, and medication adherence [27-29]. Other applications provide guidelines and risk scores to support decision making for treatments [30,31].

    In this review, the specific focus is on the diagnosis of atrial fibrillation by monitoring the heart rate and detection of arrhythmia by mHealth devices. For this purpose, event monitors or Holter devices are used. Monitoring can be done by either loop recorders or postevent recorders. The former is used over a long period, wherein electrodes are attached to the skin in order to monitor the heart activity when triggered by patients or an embedded algorithm. The patient-activated postevent ECG is not worn continuously, but used regularly or immediately after symptoms have occurred [32]. Nowadays, especially through the development in the field of mHealth and its simple use outside of health care, both approaches can record cardiac activities in an extensive way and thereby support the diagnosis of atrial fibrillation.

    Despite the high cost of illness of atrial fibrillation, there are few economic assessments for mHealth solutions [33,34].


    Methods

    Our systematic review is performed according to the guidelines for the Preferred Reporting Items for Systematic Reviews and Meta-Analyses [35].

    Article Retrieval

    We ran literature searches in PubMed MEDLINE and Web of Science databases in January 2019. Regarding time of publication, we considered the year 2014 as the baseline, because there are some reviews covering previous years [19,24,26]. As search keywords, we used the terms “mHealth,” “telemedicine,” “wearable,” “mobile health,” “app,” and “digital treatment” in combination with the term “atrial fibrillation.”

    Study Selection

    Eligible studies had to meet the following predefined criteria: original research, focus on the diagnosis of atrial fibrillation, interventions using mHealth devices, noninvasive, and published in English language. The following were used as exclusion criteria: focus on technical descriptions or algorithms and lack of information about the accuracy of the investigated device.

    Table 1. Overview of all studies included in the review.
    View this table

    Data Extraction and Analysis

    In the first step, we assessed the studies’ eligibility by focusing on the inclusion and exclusion criteria mentioned above. After searching for the reference method used in the study, we searched for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in relevant studies as indicators to evaluate the accuracy of the underlying mHealth device. In addition, we extracted characteristics about the study population and the size of the study population as well as recording duration and Food and Drug Administration (FDA) approval (Table 1).


    Results

    Literature Search

    We identified 461 articles through database searching. We added seven relevant studies either known by the authors, found by searching the reference lists of key studies, or found through a manual search in the Journal of Medical Internet Research and its sister journals. After removing duplicates, there were 352 articles. Of this pool, 22 studies were relevant for our review and included in our work (Figure 1).

    To present the results, we categorized the mHealth devices into three groups: apps (“app”), only smartphones or tablets used as a medium for diagnosis, and “wrist worn wearables” and “other devices.”

    Apps

    Smartphone or tablet apps are characterized by their usability and the fact that no additional device is needed for atrial fibrillation screening. In this field, a general distinction between direct and indirect photoplethysmography (PPG) can be made. Direct PPGs require direct contact between the user and device. Thus, it is possible to measure the pulse by putting a finger above the camera and flashlight while running the app. Indirect PPGs do not require direct contact; they measure the pulse by scanning a body part over a distance.

    Several apps use the direct method. One of the most common smartphone apps in this context is “Cardiio Rhythm,” which can be used either as a direct or an indirect heart rate monitor. Chan et al [39] and Rozen et al [53] investigated the direct use of this app and found a high accuracy in comparison with a single-lead ECG and a 12-lead ECG, respectively.

    Krivoshei et al [48] proposed an unnamed app using the direct PPG method. Comparison of the diagnostic results of the app with a heart rate monitor chest belt as a reference method showed high sensitivity and specificity.

    Fan et al [41] investigated atrial fibrillation detection through PPG with the aid of either one of two different smartphones or a smart band. Compared to 12-lead ECG, they found high accuracy in both smart phones but concluded that the final diagnosis should be based on ECG. Another study on atrial fibrillation screening with the aid of PPG showed that PPG-based algorithms can reach high accuracy; the authors recommended further investigation using population-based, large-scale atrial fibrillation screening studies [37].

    In addition to apps using PPG, there are two fundamentally different approaches. The first one, proposed by Lahdenoja et al [49], is the diagnosis of atrial fibrillation with a smartphone app using the integrated inertial measurement unit. The device is placed on the chest of the patient to measure movement triggered by the heart. Second is the app “CPstethoscope” presented by Kang et al [45] to auscultate the heart. Using this method, they found vast differences in sensitivity, specificity, PPV, and NPV depending on the smartphone model running the app.

    Figure 1. Flowchart of the systematic literature review process.
    View this figure

    Wrist-Worn Wearables

    The most popular wearables used to measure heart frequency and heart rhythm originate from the field of fitness. They appear either as simple bracelets or smartwatches. Besides their potential in supporting basic and clinical research by providing data [57], they have the capability to detect arrhythmias like atrial fibrillation.

    In the context of atrial fibrillation, use of such devices can be made in three different ways. First, they can promote healthy behaviors like an active lifestyle. Second, they can support the diagnosis of atrial fibrillation through permanent tracking of heart frequency and rhythm. Lastly, they are able to facilitate coping with the disease [15]. In this review, we focused on diagnosis of atrial fibrillation and the accuracy of mHealth devices.

    To detect pulse and heart rhythm, wrist-worn wearables use either PPGs or electrodes. In diagnosis of arrhythmias, the Apple Watch Series 4 is known as the most popular wrist-worn device. It uses both PPG and a two-lead ECG for detection of atrial fibrillation. For the ECG, the first electrode is installed in the digital crown, and the other one is installed on the back of the watch. Thus, this device allows both long-term surveillance of the heart rate through PPG and user-triggered ECG recording with a one-lead ECG.

    Koshy et al [47] investigated Apple Watch Series 1 and FitBit Blaze. They showed a high correlation between use of the devices and ECG in patients with sinus rhythm or atrial flutter, but the heart rate in patients with atrial fibrillation tended to be underestimated.

    While examining on the Cardiogram app using Apple Watch, Tison et al [55] noted a high accuracy in sedentary patients undergoing cardioversion, but lower accuracy in ambulatory participants.

    Another study showed a high accuracy of Apple Watch [38] in combination with the AliveCor KardiaBand, which is evaluated in the AliveCor section below.

    In patients with atrial fibrillation, Hochstadt et al [44] found a high correlation between use of such a PPG sensor and simultaneously recorded ECGs in the smartwatch CardiacSense.

    Bonomi et al [36] conducted a study on a wrist-worn device that includes a PPG sensor and an accelerometer. When comparing the measurements with either a Holter monitor or a single-lead ECG, the accuracy was high.

    Furthermore, while investigating the influence of various conditions on PPGs, Eerikäinen et al [40] found significant differences in sensitivity and specificity between the use in a hospital compared to the use under normal everyday conditions.

    Another wrist-worn device, HUAWEI Band 2, was compared to a 12-lead ECG by Fan et al [41]. They found that the PPG smart band is a convenient tool to detect AF at high accuracy.

    Other Devices

    Just like wrist-worn wearables, other wearable devices have the capability to measure either the pulse or heart rhythm to detect arrhythmias by using loop or event recording. Most recent articles about atrial fibrillation–diagnosing wearables are either about AliveCor or ECG devices integrated in patches like ZioPatch. Despite its FDA approval, we did not find eligible studies focusing on the accuracy of ZioPatch compared to a reference method. Therefore, it was not part of our review. Due to the high number of studies focusing on the accuracy of AliveCor, we first focused on AliveCor before analyzing studies about other devices.

    AliveCor

    AliveCor KardiaMobile is an event recorder that has been subject to various studies focusing on its accuracy. The device is an FDA-certified medical product that can record heart beat and rhythm by using a single-lead electrode. To measure heart rate and heart rhythm, the user has to put two fingers on the electrodes fixed to a small plastic plate, following which AliveCor KardiaMobile starts to write an ECG and transmits it to either a mobile phone or a tablet computer. It features a very high sensitivity and specificity. Koltowski et al [46] and Lowres et al [51] found high accuracy of this device compared to the standard 12-lead ECG. Furthermore, Selder et al [54] evaluated an arrhythmia program using AliveCor and reported its high accuracy compared to the reference method, which was a team assessing the device-recorded ECGs.

    In a study assessing the accuracy of AliveCor in patients with a history of either paroxysmal or persistent atrial fibrillation, William et al [56] found a very high accuracy in the form of sensitivity and specificity.

    Nevertheless, analyses with special populations like children [42] and elite athletes and cardiology clinic patients [43], or patients in either a cardiology or geriatric ward [33] showed slightly to significantly modified accuracy compared to the majority of studies focusing on AliveCor.

    In a study by Lown et al [50], AliveCore yielded high accuracy but was not superior to inexpensive consumer devices.

    A device related to KardiaMobile is KardiaBand. It is a watchband, but its function is similar to that of KardiaMobile; therefore, we deemed evaluation in combination with KardiaMobile appropriate, even though it is a wrist-worn device. A study by Bumgarner et al [38] found very high sensitivity of the KardiaBand used in combination with an Apple Watch (both FDA approved) compared to a 12-lead ECG.

    Other Devices

    Besides the devices mentioned above, there are some, less widespread forms of mHealth for the diagnosis of atrial fibrillation. Few of them have been assessed for accuracy.

    MyDiagnostick is similar to AliveCor in its functionality. It is a rod-like device with two electrodes on the endings. Desteghe et al [33] compared the device with either 6-lead or 12-lead ECG for its sensitivity, specificity, PPV, and NPV on patients in a cardiology ward and a geriatric ward. Compared to the algorithm, they found that manual interpretation of the device-recorded data led to increased sensitivity, but decreased specificity.

    To detect cardiac abnormalities in the home environment of elderly people residing in low and middle-income countries, Mena et al [52] designed and developed a loop recorder ECG sensor device. Two electrodes are attached to the chest and one to the right leg of the patient. The captured data are directly processed by a machine learning algorithm, and the patient receives feedback through his/her smartphone immediately. Furthermore, the data can be transmitted to health care providers. Tested on 100 older adults, the mobile ECG and the corresponding algorithm reached a very high accuracy (97%), sensitivity (100%), and specificity (96,6%). Thus, further development of the device seems useful.


    Discussion

    Overview

    In addition to the devices included in our review, there are many other kinds of mHealth devices to screen for atrial fibrillation, for example, ECG patches like the ZioPatch. Despite its positive evaluation in a multitude of studies [13,58-60], there is no eligible study about its accuracy compared to a reference method. Most of the studies about the ZioPatch compare the detection rate over a given period to the reference method.

    To provide an even more accurate diagnosis of atrial fibrillation through mHealth devices, Steijlen et al [61] presented a first approach to allow patients to record an accurate 12-lead ECG at home. They developed a device that can be worn within 8 minutes of first-time use. This device should be studied further.

    Another study focusing on the benefit of Apple Watch in the context of irregular heart rhythm detection is the Apple Heart Study [62]. Data about the heart rhythm are received from the Apple Watch and automatically evaluated. If there are irregularities, an app notifies the study participant. Furthermore, there is the possibility for some participants to receive an ePatch and to wear it up to 7 days. After returning the ePatch, the experts offer feedback and recommend further medical care from the participant’s own health care provider. The Apple Heart Study enrolls over 400,000 participants and is thus the largest ever study of its kind. The study results are not yet published.

    Despite the overall good evaluation of mHealth devices in the context of atrial fibrillation, there are some possible limitations. Shcherbina et al found that exogeneous factors like dark skin color, higher body mass index, and male gender as well as mechanical separating or shifting of PPG during physical activities led to higher device errors [63]. Furthermore, part of the recorded atrial fibrillation screenings were noninterpretable by algorithms [38,56].

    Principal Results

    In this review, we presented various possibilities to screen for atrial fibrillation. mHealth devices appearing in different forms like smartphone apps, wrist-worn devices, small plates such as the AliveCor, or rod-like devices were investigated for their accuracy. These devices mostly use either ECG or PPG technology to detect atrial fibrillation.

    Mobile apps provide a convenient way to screen for atrial fibrillation. Most common are apps using PPG, which allows detection of atrial fibrillation with a high accuracy compared to the gold standard. Furthermore, it is possible to develop apps that use the inertial measurement unit or can be used to auscultate the heart.

    Wrist-worn wearables appearing as bracelets or smart watches provide the possibility to measure heart rhythm in an unobtrusive way. The most effective way to guarantee atrial fibrillation detection is to combine PPG and ECG in a wrist worn-wearable device in order to screen over a long-term period and record an accurate user-triggered ECG. This is the case for Apple Watch or Apple Watch in combination with KardiaBand.

    The ECG-based AliveCor is one of the few FDA-approved devices. It reaches a very high overall accuracy and benefits from its ease of use. Overall, the use of mHealth devices is convenient [34,61,64,65]. Nevertheless, after atrial fibrillation detection through mHealth devices, the diagnosis should always be confirmed by standard 12-lead ECG Holter monitoring.

    Economic Aspects

    From an economic point of view, mHealth devices seem to be an eligible possibility to prevent expensive secondary diseases like stroke. Therefore, mobile apps have a high economic potential in screening for atrial fibrillation. Given the fact that smartphones are already widespread in many countries, the economic burden is low. Even if app accuracy does not reach the gold standard, mobile apps can provide a first approach to detect atrial fibrillation.

    The integration of atrial fibrillation screening methods in smartwatches and bracelets could be valuable. Smart watches, in particular, have gained popularity during the last few years. Further investigation on the economic effect of subsidizing wrist-worn wearables, which are able to screen for atrial fibrillation, should be performed. A special focus should be placed on the accuracy of these devices to avoid costs due to misdiagnosis.

    With a fundamentally different approach, AliveCor benefits from its ease of use. This device seems suitable to integrate in health care as already implemented in the Dutch Hartwacht program [54]. Orchard et al implemented a study to examine the cost-effectiveness of screening with AliveCor in a rural primary care setting. The aim was to screen 2000 patients aged ≥65 years for atrial fibrillation during 3-4 months and to evaluate the process through qualitative interviews as well as cost-effectiveness [56]. Especially for low-income countries, mHealth is a possible approach to screen for atrial fibrillation, which will reduce the economic burden [66,67]. Nevertheless, to assess the real economic potential of mHealth devices in the context of atrial fibrillation screening, further studies for all types of mHealth devices are needed.

    Conclusions

    The main advantage of mHealth in atrial fibrillation detection is its use in addition to standard care. Even if its accuracy is not yet as high as expected, it is an additional possibility to diagnose atrial fibrillation, especially in its silent, paroxysmal form. Economic assessment of mHealth devices should be further explored.

    Conflicts of Interest

    None declared.

    References

    1. Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation 2014 Feb 25;129(8):837-847 [FREE Full text] [CrossRef] [Medline]
    2. Odutayo A, Wong CX, Hsiao AJ, Hopewell S, Altman DG, Emdin CA. Atrial fibrillation and risks of cardiovascular disease, renal disease, and death: systematic review and meta-analysis. BMJ 2016 Sep 06;354:i4482 [FREE Full text] [CrossRef] [Medline]
    3. Kirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B, ESC Scientific Document Group. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur Heart J 2016 Dec 07;37(38):2893-2962. [CrossRef] [Medline]
    4. Hart RG, Pearce LA, Aguilar MI. Meta-analysis: antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med 2007 Jun 19;146(12):857-867. [Medline]
    5. Ruff CT, Giugliano RP, Braunwald E, Hoffman EB, Deenadayalu N, Ezekowitz MD, et al. Comparison of the efficacy and safety of new oral anticoagulants with warfarin in patients with atrial fibrillation: a meta-analysis of randomised trials. Lancet 2014 Mar 15;383(9921):955-962. [CrossRef] [Medline]
    6. Johnsen SP, Dalby LW, Täckström T, Olsen J, Fraschke A. Cost of illness of atrial fibrillation: a nationwide study of societal impact. BMC Health Serv Res 2017 Nov 10;17(1):714 [FREE Full text] [CrossRef] [Medline]
    7. Jönsson L, Eliasson A, Kindblom J, Almgren O, Edvardsson N. Cost of illness and drivers of cost in atrial fibrillation in Sweden and Germany. Appl Health Econ Health Policy 2010;8(5):317-325. [CrossRef] [Medline]
    8. Reinhold T, Belke R, Hauser T, Grebmer C, Lennerz C, Semmler V, et al. Cost Saving Potential of an Early Detection of Atrial Fibrillation in Patients after ICD Implantation. Biomed Res Int 2018;2018:3417643 [FREE Full text] [CrossRef] [Medline]
    9. Jacobs MS, Kaasenbrood F, Postma MJ, van Hulst M, Tieleman RG. Cost-effectiveness of screening for atrial fibrillation in primary care with a handheld, single-lead electrocardiogram device in the Netherlands. Europace 2016 Oct 12. [CrossRef] [Medline]
    10. Levin L, Husberg M, Sobocinski PD, Kull VF, Friberg L, Rosenqvist M, et al. A cost-effectiveness analysis of screening for silent atrial fibrillation after ischaemic stroke. Europace 2015 Feb;17(2):207-214. [CrossRef] [Medline]
    11. Aronsson M, Svennberg E, Rosenqvist M, Engdahl J, Al-Khalili F, Friberg L, et al. Cost-effectiveness of mass screening for untreated atrial fibrillation using intermittent ECG recording. Europace 2015 Jul;17(7):1023-1029. [CrossRef] [Medline]
    12. Mayer F, Stahrenberg R, Gröschel K, Mostardt S, Biermann J, Edelmann F, et al. Cost-effectiveness of 7-day-Holter monitoring alone or in combination with transthoracic echocardiography in patients with cerebral ischemia. Clin Res Cardiol 2013 Dec;102(12):875-884 [FREE Full text] [CrossRef] [Medline]
    13. Solomon MD, Yang J, Sung SH, Livingston ML, Sarlas G, Lenane JC, et al. Incidence and timing of potentially high-risk arrhythmias detected through long term continuous ambulatory electrocardiographic monitoring. BMC Cardiovasc Disord 2016 Feb 17;16:35 [FREE Full text] [CrossRef] [Medline]
    14. Bansal A, Joshi R. Portable out-of-hospital electrocardiography: A review of current technologies. J Arrhythm 2018 Dec;34(2):129-138 [FREE Full text] [CrossRef] [Medline]
    15. McConnell MV, Turakhia MP, Harrington RA, King AC, Ashley EA. Mobile Health Advances in Physical Activity, Fitness, and Atrial Fibrillation: Moving Hearts. J Am Coll Cardiol 2018 Jun 12;71(23):2691-2701. [CrossRef] [Medline]
    16. Afzal MR, Gunda S, Waheed S, Sehar N, Maybrook RJ, Dawn B, et al. Role of Outpatient Cardiac Rhythm Monitoring in Cryptogenic Stroke: A Systematic Review and Meta-Analysis. Pacing Clin Electrophysiol 2015 Oct;38(10):1236-1245. [CrossRef] [Medline]
    17. Guzik P, Malik M. ECG by mobile technologies. J Electrocardiol 2016;49(6):894-901. [CrossRef] [Medline]
    18. Singh N, Chun S, Hadley D, Froelicher V. Clinical Implications of Technological Advances in Screening for Atrial Fibrillation. Prog Cardiovasc Dis 2018;60(4-5):550-559. [CrossRef] [Medline]
    19. Bruining N, Caiani E, Chronaki C, Guzik P, van der Velde E, Task Force of the e-Cardiology Working. Acquisition and analysis of cardiovascular signals on smartphones: potential, pitfalls and perspectives: by the Task Force of the e-Cardiology Working Group of European Society of Cardiology. Eur J Prev Cardiol 2014 Nov;21(2 Suppl):4-13. [CrossRef] [Medline]
    20. Fung E, Järvelin M, Doshi RN, Shinbane JS, Carlson SK, Grazette LP, et al. Electrocardiographic patch devices and contemporary wireless cardiac monitoring. Front Physiol 2015;6:149 [FREE Full text] [CrossRef] [Medline]
    21. Dunn J, Runge R, Snyder M. Wearables and the medical revolution. Per Med 2018 Sep;15(5):429-448 [FREE Full text] [CrossRef] [Medline]
    22. Shrivastav M, Padte S, Arora V, Biffi M. Pilot evaluation of an integrated monitor-adhesive patch for long-term cardiac arrhythmia detection in India. Expert Rev Cardiovasc Ther 2014 Jan;12(1):25-35. [CrossRef] [Medline]
    23. Ajijola OA, Boyle NG, Shivkumar K. Detecting and monitoring arrhythmia recurrence following catheter ablation of atrial fibrillation. Front Physiol 2015;6:90 [FREE Full text] [CrossRef] [Medline]
    24. Garabelli P, Stavrakis S, Po S. Smartphone-based arrhythmia monitoring. Curr Opin Cardiol 2017 Jan;32(1):53-57. [CrossRef] [Medline]
    25. Cheung CC, Krahn AD, Andrade JG. The Emerging Role of Wearable Technologies in Detection of Arrhythmia. Can J Cardiol 2018 Aug;34(8):1083-1087 [FREE Full text] [CrossRef] [Medline]
    26. Turakhia MP, Kaiser DW. Transforming the care of atrial fibrillation with mobile health. J Interv Card Electrophysiol 2016 Oct;47(1):45-50. [CrossRef] [Medline]
    27. Stephan LS, Almeida ED, Guimarães RB, Ley AG, Mathias RG, Assis MV, et al. Oral Anticoagulation in Atrial Fibrillation: Development and Evaluation of a Mobile Health Application to Support Shared Decision-Making. Arq Bras Cardiol 2018 Jan;110(1):7-15 [FREE Full text] [CrossRef] [Medline]
    28. Hirschey J, Bane S, Mansour M, Sperber J, Agboola S, Kvedar J, et al. Evaluating the Usability and Usefulness of a Mobile App for Atrial Fibrillation Using Qualitative Methods: Exploratory Pilot Study. JMIR Hum Factors 2018 Mar 15;5(1):e13 [FREE Full text] [CrossRef] [Medline]
    29. Desteghe L, Kluts K, Vijgen J, Koopman P, Dilling-Boer D, Schurmans J, et al. The Health Buddies App as a Novel Tool to Improve Adherence and Knowledge in Atrial Fibrillation Patients: A Pilot Study. JMIR Mhealth Uhealth 2017 Jul 19;5(7):e98 [FREE Full text] [CrossRef] [Medline]
    30. Kotecha D, Chua WWL, Fabritz L, Hendriks J, Casadei B, Schotten U, European Society of Cardiology (ESC) Atrial Fibrillation Guidelines Taskforce‚ the CATCH ME consortiumthe European Heart Rhythm Association (EHRA). European Society of Cardiology smartphone and tablet applications for patients with atrial fibrillation and their health care providers. Europace 2018 Dec 01;20(2):225-233 [FREE Full text] [CrossRef] [Medline]
    31. Guo Y, Chen Y, Lane DA, Liu L, Wang Y, Lip GYH. Mobile Health Technology for Atrial Fibrillation Management Integrating Decision Support, Education, and Patient Involvement: mAF App Trial. Am J Med 2017 Dec;130(12):1388-1396.e6 [FREE Full text] [CrossRef] [Medline]
    32. Marouf M, Vukomanovic G, Saranovac L, Bozic M. Multi-purpose ECG telemetry system. Biomed Eng Online 2017 Jun 19;16(1):80 [FREE Full text] [CrossRef] [Medline]
    33. Desteghe L, Raymaekers Z, Lutin M, Vijgen J, Dilling-Boer D, Koopman P, et al. Performance of handheld electrocardiogram devices to detect atrial fibrillation in a cardiology and geriatric ward setting. Europace 2017 Jan;19(1):29-39. [CrossRef] [Medline]
    34. Halcox JPJ, Wareham K, Cardew A, Gilmore M, Barry JP, Phillips C, et al. Assessment of Remote Heart Rhythm Sampling Using the AliveCor Heart Monitor to Screen for Atrial Fibrillation: The REHEARSE-AF Study. Circulation 2017 Nov 07;136(19):1784-1794. [CrossRef] [Medline]
    35. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 2009;339:b2700 [FREE Full text] [Medline]
    36. Bonomi AG, Schipper F, Eerikäinen LM, Margarito J, van Dinther R, Muesch G, et al. Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo-Plethysmography at the Wrist. J Am Heart Assoc 2018 Aug 07;7(15):e009351. [CrossRef] [Medline]
    37. Brasier N, Raichle CJ, Dörr M, Becke A, Nohturfft V, Weber S, et al. Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO). Europace 2018 Jul 31. [CrossRef] [Medline]
    38. Bumgarner JM, Lambert CT, Hussein AA, Cantillon DJ, Baranowski B, Wolski K, et al. Smartwatch Algorithm for Automated Detection of Atrial Fibrillation. J Am Coll Cardiol 2018 May 29;71(21):2381-2388. [CrossRef] [Medline]
    39. Chan P, Wong C, Poh YC, Pun L, Leung WW, Wong Y, et al. Diagnostic Performance of a Smartphone-Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting. J Am Heart Assoc 2016 Dec 21;5(7) [FREE Full text] [CrossRef] [Medline]
    40. Eerikäinen L, Dekker L, Bonomi A, Vullings R, Schipper F, Margarito J. Validating Feature for Atrial Fibrillation Detection from Photoplethysmogram under Hospital and Free-living Conditions. In: Computing in Cardiology. 2017 Presented at: 44th Computing in Cardiology Conference, CinC 2017; Sept 24-27, 2017; Rennes, France p. 1-4   URL: https:/​/research.​tue.nl/​en/​publications/​validating-features-for-atrial-fibrillation-detection-from-photop-2 [CrossRef]
    41. Fan Y, Li Y, Li J, Cheng W, Shan Z, Wang Y, et al. Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry). JMIR Mhealth Uhealth 2019 Mar 05;7(3):e11437 [FREE Full text] [CrossRef] [Medline]
    42. Gropler MRF, Dalal AS, Van Hare GF, Silva JNA. Can smartphone wireless ECGs be used to accurately assess ECG intervals in pediatrics? A comparison of mobile health monitoring to standard 12-lead ECG. PLoS One 2018;13(9):e0204403 [FREE Full text] [CrossRef] [Medline]
    43. Haberman ZC, Jahn RT, Bose R, Tun H, Shinbane JS, Doshi RN, et al. Wireless Smartphone ECG Enables Large-Scale Screening in Diverse Populations. J Cardiovasc Electrophysiol 2015 May;26(5):520-526. [CrossRef] [Medline]
    44. Hochstadt A, Chorin E, Viskin S, Schwartz AL, Lubman N, Rosso R. Continuous heart rate monitoring for automatic detection of atrial fibrillation with novel bio-sensing technology. J Electrocardiol 2019;52:23-27. [CrossRef] [Medline]
    45. Kang S, Joe B, Yoon Y, Cho G, Shin I, Suh J. Cardiac Auscultation Using Smartphones: Pilot Study. JMIR Mhealth Uhealth 2018 Feb 28;6(2):e49 [FREE Full text] [CrossRef] [Medline]
    46. Koltowski L, Balsam P, Glłowczynska R, Rokicki JK, Peller M, Maksym J, et al. Kardia Mobile applicability in clinical practice: A comparison of Kardia Mobile and standard 12-lead electrocardiogram records in 100 consecutive patients of a tertiary cardiovascular care center. Cardiol J 2019 Jan 15 [FREE Full text] [CrossRef] [Medline]
    47. Koshy AN, Sajeev JK, Nerlekar N, Brown AJ, Rajakariar K, Zureik M, et al. Smart watches for heart rate assessment in atrial arrhythmias. Int J Cardiol 2018 Sep 01;266:124-127. [CrossRef] [Medline]
    48. Krivoshei L, Weber S, Burkard T, Maseli A, Brasier N, Kühne M, et al. Smart detection of atrial fibrillation†. Europace 2017 May 01;19(5):753-757 [FREE Full text] [CrossRef] [Medline]
    49. Lahdenoja O, Hurnanen T, Iftikhar Z, Nieminen S, Knuutila T, Saraste A, et al. Atrial Fibrillation Detection via Accelerometer and Gyroscope of a Smartphone. IEEE J Biomed Health Inform 2018 Jan;22(1):108-118. [CrossRef] [Medline]
    50. Lown M, Yue AM, Shah BN, Corbett SJ, Lewith G, Stuart B, et al. Screening for Atrial Fibrillation Using Economical and Accurate Technology (From the SAFETY Study). Am J Cardiol 2018 Oct 15;122(8):1339-1344. [CrossRef] [Medline]
    51. Lowres N, Neubeck L, Salkeld G, Krass I, McLachlan AJ, Redfern J, et al. Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study. Thromb Haemost 2014 Jun;111(6):1167-1176. [CrossRef] [Medline]
    52. Mena LJ, Félix VG, Ochoa A, Ostos R, González E, Aspuru J, et al. Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly. Comput Math Methods Med 2018;2018:9128054 [FREE Full text] [CrossRef] [Medline]
    53. Rozen G, Vaid J, Hosseini SM, Kaadan MI, Rafael A, Roka A, et al. Diagnostic Accuracy of a Novel Mobile Phone Application for the Detection and Monitoring of Atrial Fibrillation. Am J Cardiol 2018 May 15;121(10):1187-1191. [CrossRef] [Medline]
    54. Selder JL, Breukel L, Blok S, van Rossum AC, Tulevski II, Allaart CP. A mobile one-lead ECG device incorporated in a symptom-driven remote arrhythmia monitoring program. The first 5,982 Hartwacht ECGs. Neth Heart J 2019 Jan;27(1):38-45 [FREE Full text] [CrossRef] [Medline]
    55. Tison GH, Sanchez JM, Ballinger B, Singh A, Olgin JE, Pletcher MJ, et al. Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch. JAMA Cardiol 2018 May 01;3(5):409-416. [CrossRef] [Medline]
    56. William AD, Kanbour M, Callahan T, Bhargava M, Varma N, Rickard J, et al. Assessing the accuracy of an automated atrial fibrillation detection algorithm using smartphone technology: The iREAD Study. Heart Rhythm 2018 Dec;15(10):1561-1565. [CrossRef] [Medline]
    57. Lim WK, Davila S, Teo JX, Yang C, Pua CJ, Blöcker C, et al. Beyond fitness tracking: The use of consumer-grade wearable data from normal volunteers in cardiovascular and lipidomics research. PLoS Biol 2018 Dec;16(2):e2004285 [FREE Full text] [CrossRef] [Medline]
    58. Turakhia MP, Ullal AJ, Hoang DD, Than CT, Miller JD, Friday KJ, et al. Feasibility of extended ambulatory electrocardiogram monitoring to identify silent atrial fibrillation in high-risk patients: the Screening Study for Undiagnosed Atrial Fibrillation (STUDY-AF). Clin Cardiol 2015 May;38(5):285-292 [FREE Full text] [CrossRef] [Medline]
    59. Steinhubl SR, Waalen J, Edwards AM, Ariniello LM, Mehta RR, Ebner GS, et al. Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial. JAMA 2018 Dec 10;320(2):146-155. [CrossRef] [Medline]
    60. Barrett PM, Komatireddy R, Haaser S, Topol S, Sheard J, Encinas J, et al. Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring. Am J Med 2014 Jan;127(1):95.e11-95.e17 [FREE Full text] [CrossRef] [Medline]
    61. Steijlen AS, Jansen KM, Albayrak A, Verschure DO, Van Wijk DF. A Novel 12-Lead Electrocardiographic System for Home Use: Development and Usability Testing. JMIR Mhealth Uhealth 2018 Jul 30;6(7):e10126 [FREE Full text] [CrossRef] [Medline]
    62. Turakhia MP, Desai M, Hedlin H, Rajmane A, Talati N, Ferris T, et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. Am Heart J 2019 Jan;207:66-75 [FREE Full text] [CrossRef] [Medline]
    63. Shcherbina A, Mattsson CM, Waggott D, Salisbury H, Christle JW, Hastie T, et al. Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort. J Pers Med 2017 May 24;7(2) [FREE Full text] [CrossRef] [Medline]
    64. Kropp C, Ellis J, Nekkanti R, Sears S. Monitoring Patients With Implantable Cardioverter Defibrillators Using Mobile Phone Electrocardiogram: Case Study. JMIR Cardio 2018 Feb 21;2(1):e5. [CrossRef]
    65. Veale EL, Stewart AJ, Mathie A, Lall SK, Rees-Roberts M, Savickas V, et al. Pharmacists detecting atrial fibrillation (PDAF) in primary care during the influenza vaccination season: a multisite, cross-sectional screening protocol. BMJ Open 2018 Dec 14;8(3):e021121 [FREE Full text] [CrossRef] [Medline]
    66. Soni A, Earon A, Handorf A, Fahey N, Talati K, Bostrom J, et al. High Burden of Unrecognized Atrial Fibrillation in Rural India: An Innovative Community-Based Cross-Sectional Screening Program. JMIR Public Health Surveill 2016 Oct 13;2(2):e159 [FREE Full text] [CrossRef] [Medline]
    67. Evans GF, Shirk A, Muturi P, Soliman EZ. Feasibility of Using Mobile ECG Recording Technology to Detect Atrial Fibrillation in Low-Resource Settings. Glob Heart 2017 Dec;12(4):285-289. [CrossRef] [Medline]


    Abbreviations

    AF: atrial fibrillation
    ECG: electrocardiogram
    FDA: Food and Drug Administration
    mHealth: mobile health
    NPV: negative predictive value
    PPG: photoplethysmography
    PPV: positive predictive value


    Edited by G Eysenbach; submitted 01.03.19; peer-reviewed by A Hernández, J Kors, L Saxon, R Casado; comments to author 20.03.19; revised version received 14.05.19; accepted 14.05.19; published 16.06.19.

    ©Godwin Denk Giebel, Christian Gissel. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 16.06.2019.

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