key: cord-0986708-2zjtevws
authors: Varma, Niraj; Cygankiewicz, Iwona; Turakhia, Mintu; Heidbuchel, Hein; Hu, Yufeng; Chen, Lin Yee; Couderc, Jean‐Philippe; Cronin, Edmond M.; Estep, Jerry D.; Grieten, Lars; Lane, Deirdre A.; Mehra, Reena; Page, Alex; Passman, Rod; Piccini, Jonathan; Piotrowicz, Ewa; Piotrowicz, Ryszard; Platonov, Pyotr G.; Ribeiro, Antonio Luiz; Rich, Robert E.; Russo, Andrea M.; Slotwiner, David; Steinberg, Jonathan S.; Svennberg, Emma
title: 2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society
date: 2021-01-29
journal: Ann Noninvasive Electrocardiol
DOI: 10.1111/anec.12795
sha: 43739f5de9204e890a3230dd30d36187a60beaa9
doc_id: 986708
cord_uid: 2zjtevws

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self‐management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

However, results of remote monitoring of CIEDs may not be immediately generalizable to mHealth. For instance, the former is restricted to those with cardiac disease (largely arrhythmias and heart failure (HF)), that is, a group already defined as patients. The care pathways for CIED remote monitoring are also well defined, with billing and reimbursement in place in the United States and many other parts of the world. In comparison, mHealth differs: It is widely available in the form of consumer products that penetrate most sectors of society, including individuals without formal medical diagnoses; it may be applied to a wider group of medical conditions; data can be self-monitored rather than assessed by healthcare professionals (HCPs); and reimbursement models are not mature. Indeed, some heart rhythm tracking capabilities may be indirectly acquired in products purchased for different goals and then subsequently used for self-monitoring. Conversely, in the medical space, applications are largely not prescribed by HCPs, often lack validation for disease management use cases, and care pathways remain varied or poorly defined. Nevertheless, if properly implemented, the intersection of these two communities opens up a broad spectrum of opportunities, extending from population F I G U R E L E G E N D : mHealth tools for the individual. Sensors can be embedded in a variety of wearables. (IoT: Internet of thingsconnects from any location to hospital or cloud; See Table 1 ). Similarly, actions taken based on the monitored information should be transparent to all stakeholders. For example, for a patient who records and transmits an irregular heart rhythm via a wearable device, a designated decision process should be followed to confirm eg whether the rhythm is atrial fibrillation (AF) or not, whether confirmation by another diagnostic test is required, how that is arranged, and finally what therapy should be implemented and in what reasonable time frame? Clearly, there are risks of increasing cost from medical testing and provoking anxiety in consumers-who by virtue of seeking a medical verification become patients. Again, CIED experience sets a precedent. Studies that have shown improved outcome with telemonitoring succeeded when integrated into a clear logistical framework for a specific use case of disease management (e.g., IN-TIME for remote monitoring in patients receiving cardiac resynchronization therapy, CardioMEMS) (Abraham et al., 2011 , Hindricks et al., 2014 , Varma & Ricci, 2013 . Replicating this with mHealth creates challenges for healthcare providers and goes far beyond the technological capabilities of the monitoring and transmission equipment.

Implementation will require defined aims and fundamental changes to existing workflows and responsibilities. Such changes are always difficult. Apart from the organizational issues required to achieve such changes, reimbursement may drive or hinder such changes in the workplace. Awareness of these factors has been heightened by the SARS-CoV-2 pandemic, during which telemedicine solutions have been advocated to reduce patient contact with healthcare providers yet continue healthcare delivery . clinical trials; the patient perspective; and the issues that must be addressed in the future to permit useful application of mHealth technologies. Addtionally, discussion is extended to mHealth facilitation of those comorbidities increasingly recognized to influence arrhythmia management (e.g., obesity and sleep apnea) that are becoming the responsibility of heart rhythm professionals (Chung et al., 2020) .

Abraham, W. T., Adamson, P. B., Bourge, R. C., Aaron, M. F., Costanzo, M. R., Stevenson, L. W., … Yadav, J. S. (2011) . Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: A randomised controlled trial. 

F I G U R E 1 Application of digital health technologies in arrhythmias (Many of these sectors are interconnected).

palpitations, syncope) are present and how often they occur ( Figure 2 ). Since the XXI century has become the era of the AF epidemic, the emphasis has shifted to screen for asymptomatic patients at high risk of developing AF or in those with cryptogenic stroke, to enable early treatment with the hope of preventing stroke and other serious complications. Novel tools expand the time window in which information can be gathered and overcome existing limitations with traditional methods, that is, intermittent physical examination or ECG for the detection of a largely asymptomatic arrhythmia.

• Conventional ambulatory ECG devices with "continuous" or "intermittent" recording abilities (e.g., Holter, mobile cardiac telemetry (MCT)) increase the diagnostic yield for suspected arrhythmias, but limitations such as inadequate duration of monitoring, insufficient sensitivity or specificity for AF detection, cost, and patient discomfort and inconvenience remain important implementation barriers. Further details on these conventional systems are available in a prior expert consensus statement (Steinberg et al., 2017) .

• Implantable loop recorders (ILRs) continuously monitor cardiac rhythm, similar to traditional external loop recorders, but only record an ECG shortly before and after activation by either the patient or by an automated algorithm. The total monitoring period is limited only by battery longevity (ca. 2-5 years). Newer devices have dedicated algorithms resulting in increased interest in their use for AF detection, especially after cryptogenic stroke. Several approved ILR devices are available (Musat, Milstein, & Mittal, 2018 , Sakhi, Theuns, Szili-Torok, & Yap, 2019 , Tomson & Passman, 2015 , and several studies have been performed to evaluate the diagnostic accuracy of these devices (Ciconte et al., 2017 , Hindricks et al., 2010 , Mittal et al., 2016 , Nolker et al., 2016 , Sanders et al., 2016 . Since ILRs are invasive and costly, some functions may shift to mHealth.

These can be divided into technologies that:

• Record ECG tracings (single or multilead, in intermittent or continuous format, of various durations).

• Use non-ECG techniques such as pulse photoplethysmography (PPG) . . This is open to risks of not detecting significant events and/or overtreating-for F I G U R E 2 mHealth devices for arrhythmia monitoring according to indications. Traditional wearable monitors are used for defined, short periods of time. Advantages are continuous monitoring and ability to use multiple leads that may be important for arrhythmia differentiation. These have been used historically for evaluation of palpitations, syncope, and defining QRS morphology. mHealth extends monitoring time indefinitely, to be defined by the user, and to the possibility of monitoring other parameters simultaneously with the ECG and linking to machine learning. Typically, mHealth utilizes single-channel ECG or derived heart rate, and discontinuous monitoring. AF-atrial fibrillation, BP-blood pressure, BrS-Brugada syndrome, HF-heart failure, HR-heart rate, ILR-implantable loop recorder, LQT-long QT. (Desteghe et al., 2017 , Doliwa, Frykman, & Rosenqvist, 2009 , Hendrikx, Rosenqvist, Wester, Sandstrom, & Hornsten, 2014 , Kaasenbrood et al., 2016 , Poulsen et al., 2017 , Svennberg et al., 2017 , Tavernier et al., 2018 , Tieleman et al., 2014 , Vaes et al., 2014 (Table 2) .

Bumgarner, J. M., Lambert, C. T., Hussein, A. A., Cantillon, D. J., Baranowski, B., Wolski, K., … Tarakji et al., , Turakhia et al., 2013 . , Rosenberg, Samuel, Thosani, & Zimetbaum, 2013 .

As the patch has no external leads, it is perceived to be more comfortable to wear compared to conventional Holter monitors, with 94% of the patients preferring the patch over the Holter (Barrett et al., 2014) . In addition to the validation studies, the feasibility of two-week continuous monitoring to identify AF in an at-risk patient population has been examined by Turakhia et al. (2015) . It has also been used successfully to determine the prevalence of subclinical AF in the general population (Rooney et al., 2019) .

Newer patch-based systems add near-real-time analytics and by transmitting data continuously to the cloud. This may facilitate more rapid data collection and diagnosis. Multiparametric monitoring may be enabled with a patch worn for up to 3 months .

Textile-based systems for ECG monitoring were initially designed to ensure patients' comfort during daily activities and address the needs of active patients. These vests and elastic bands adapt easily to patients' movements that is particularly important for those performing physical activities that might be limited by the presence of wires. , Desteghe et al., 2017 , Lowres et al., 2014 , Tarakji et al., 2015 , and the device has been tested as a screening tool in at-risk populations , Lowres et al., 2014 . In Apple watch, the algorithm is effective when the heart rate is between 50 and 150 bpm, there are no or very few abnormal beats, and the shape, timing, and duration of each beat is considered normal for the patient ( Figure 4 ).

Sensitivity and specificity depend on the software (which can be calibrated to higher sensitivity or higher specificity), the population studied (e.g., elderly have more tremor and/or difficulty in holding the device leading to more unreadable tracings), and the prevalence of AF in the population. It indicates that use of such device always requires proper evaluation for every intended use case. There is also an accessory band for a smartwatch to allow ECG recording. The single-lead ECG with automatic AF detection is recorded by touching the band's integrated sensors that transmit data to a watch application. Recently, a new 6-lead case has been developed, allowing for 30 second recording of all 6 limb leads by touching each of the three electrodes. Also the QT interval may be derived from this (https:// cardi acrhy thmne ws.com/kardi amobi le-6l-can-be-used-to-measu re-qt-durat ion-in-covid -19-patie nts/ (Chung & Guise, 2015 . Information is limited; however, on how parameters such as QTc measured on a single-(or limited number) lead ECGs can reliably substitute for 12-lead ECG information. In one study, QT was underestimated by smartphone single-lead ECG (Koltowski et al., 2019) . Preliminary data indicate ability for ST monitoring for ischemia ( Figure 3 , Section 4.1). 

Consumer devices such as smartphones and smartwatches require accessories and often extra cost for conversion into rhythm monitoring tools. In contrast, the PPG technologies allow for the detection of arrhythmias using hardware already present on most consumer devices (smartwatches and fitness bands) through a downloadable application. PPG is an optical technique that can be used to detect AF by measuring and analyzing a peripheral pulse waveform. Using a light source and a photodetector, the pulse waveform can be measured by detecting changes in the light intensity, which reflects the tissue blood volume of a skin surface such as the fingertip, earlobe, or face (Conroy, Guzman, Hall, Tsouri, & Couderc, 2017 , McManus et al., 2013 ). An automated algorithm can subsequently analyze the generated pulse waveform to detect AF. PPG avoids the instability and motion artifacts of ECG sensors and can be passively and opportunistically measured.

This technology has been applied for use with smartphones using the phone's camera to measure a fingertip pulse waveform. Rapid irregularly conducted AF may produce variable pulse pressures that challenge detection (Choi & Shin, 2017) . The performance of algorithms interpreting these PPG signals has been proven to be in high agreement with ECG rhythm strips (McManus et al., 2013 . The smartphone-based PPG applications have been utilized in at-risk population to detect AF and as a screening tool in the general population (See Section 6).

The PPG technology has also been incorporated in smartwatches to measure heart rate and rhythm (Dorr et al., 2019, Guo

2019). Some have developed prototypes of a band that includes a single-channel ECG, multi-wavelength PPG, and tri-axial accelerometry recording simultaneously at 128 Hz (Nemati et al., 2016) , and others use a deep-neural network based on PPG sensors to detect AF (https://www.mobih ealth news.com/conte nt/study -apple -watch -paire d-deep-neura l-netwo rk-detec ts-atria l-fibri llati on-97-perce nt-accuracy; https://mrhyt hmstu dy.org). If PPG or optical sensors and detection algorithms can match the performance of ECG-based rhythm assessment, delivery of AF care may be expected to change substantially and drive a radical departure from relying on an office or ambulatory ECG for ascertainment of AF.

Blood pressure (BP) measurements can be erratic when the pulse is irregular. This characteristic is utilized by automatic oscillometric BP monitors that derive heart rhythm regularity algorithmically (Chen, Lei, & Wang, 2017) . Automated BP monitors have been used for opportunistic AF detection. Studies have shown that six devices from two manufacturers were reliable with sensitivities and specificities greater than 85% (Kane, Blake, McArdle, Langley, & Sims, 2016) . These studies suggested that BP devices with embedded algorithms for detecting arrhythmias show promise as screening tools for AF, comparing favorably with manual pulse palpation. Such capability could be added to continuous BP recording devices (Kario, 2016) . One device identifies possible AF when at least two of three consecutive measurements show pulse irregularity. Several studies addressed the diagnostic accuracy , Chen, Lei, & Wang, 2017 , Gandolfo, Balestrino, Bruno, Finocchi, & Reale, 2015 , Kearley et al., 2014 , Marazzi et al., 2012 , Stergiou, Karpettas, Protogerou, Nasothimiou, & Kyriakidis, 2009 , Wiesel, Fitzig, Herschman, & Messineo, 2009 , Wiesel, Arbesfeld, & Schechter, 2014 and the feasibility of this device as a screening tool , Omboni & Verberk, 2016 , Wiesel & Salomone, 2017 .

The following have undergone preliminary study:

Mechanocardiography uses accelerometers and gyroscopes to sense the mechanical activity of the heart. The accuracy of this technology to detect AF using a smartphone's built-in accelerometer and gyroscope sensors was assessed in a proof of concept study (Jaakkola et al., 2018) . A smartwatch (Sony Experia) was placed on the chest in supine patients to detect micro movements of the chest. Possibly, carrying this device in a pocket may have utility but is likely to be confounded by movement (e.g., walking) artifacts.

Noncontact video monitoring of respiration and heart rate have been developed less than 15 years ago (Takano & Ohta, 2007 , Verkruysse, Svaasand, & Nelson, 2008 . In 2014, a pioneering article described the concept of contactless video-based detection of AF (Couderc et al., 2015) . Deep learning of a video of a person's face can identify AF by examining irregularity of pulsatile facial perfusion (Yan et al., 2018) . It is a monitoring technique extracting the photoplethysmographic-like signals from a standard digital RGB video recording of the human skin and specifically of an individual's face. The videoplethysmographic signal describes the absorption peak of ambient light by the hemoglobin from the facial skin. Several studies have been performed to develop a method that is sensitive enough to detect each cardiac pulse and provide insights into variability on pulse on a beatto-beat basis. The HealthKam works using HUE color space from video cameras (Dautov, Savur, & Tsouri, 2018 , Tsouri & Li, 2015 and

can easily be integrated to any portable computer device with a camera (smartphone, tablet, etc.). By using mobile devices with cameras, the deployment of the technology is easy and scalable since it does not require the use and distribution of any physical devices. Such a system may change the approach to AF screening, which currently is only 1 patient at a time. High-throughput AF detection from multiple patients concurrently using a single digital camera and a pretrained deep convolutional neural network (DCNN) was feasible in a pilot study .

One requirement for these technologies is steady focus: Thus moving subjects present a challenge. It is important to avoid recording, sending, or communicating any video of the patient thus protecting privacy and dignity. Video-based technologies in telemedicine have raised a new set of societal and ethical concerns that are being continuously re-evaluated such as during the COVID-19 pandemic. Issues regarding privacy, confidentiality, and legal and ethical obligation to treat are crucial factors to be considered when these technologies are deployed at larger scale (Turakhia 2020).

There are preliminary reports on using commodity smart devices to identify agonal breathing , Wang, Sunshine, & Gollakota, 2019 . Identification of abnormal heart rate patterns may be made possible by converting smart speakers into a sonar device with emission of in-audible frequencies sound waves and receiving them to detect motion. These are not in consumer domain but potentially have wide scalability. 

Typically, most patients with palpitations and dizziness are evaluated using the various technologies reviewed in Section 2.1 (Steinberg et al., 2017) . Devices capable of recording at least one ECG lead allow the interpreting clinician to distinguish between wide-and narrowcomplex rhythms, bradycardia, and tachycardia, and thus distinguish between the various causative rhythms. Smart devices may be useful in pediatric patients (Gropler, Dalal, Van Hare, & Silva, 2018) .

The disease is often intermittent and asymptomatic, which may delay diagnosis (McCabe, Chamberlain, Rhudy, & DeVon, 2015 , Strickberger, Ip, Saksena, Curry, Bahnson, & Ziegler, 2005 , Verma et al., 2013 , lead to incorrect estimation of AF burden (Boriani et al., 2015 , Garimella et al., 2015 , and pose management challenges to healthcare services, thereby exposing the patient to the consequences of untreated AF. New digital health and sensor technologies have the potential for early identification of AF, opening up opportunities for screening, which then can be tied to evidence-based management. These may be directed to several broad groups: for screening the general population or managing the already diagnosed, for following responses to treatment, and increasingly to managing comorbidities and lifestyle modification (See Section 4) ( Figure 5 ).

mHealth mechanisms may facilitate understanding the relation between AF burden, its progression, and cardiovascular risk . 

Classical epidemiological data point to the notion that early identification of AF has the potential to improve morbidity and possibly mortality.

(1) AF is associated with a 5-fold increased risk of stroke (Wolf, Abbott, & Kannel, 1991) and doubled mortality ; (2) The prevalence of undiagnosed AF is at least 1.5% for patients > 65 years ; (3) In about a quarter of all AF-related strokes, the stroke is the first manifestation of the arrhythmia (Friberg et al., 2014) while other AF patients present first with congestive HF; (4) Stroke risk is independent of symptoms (Xiong, Prioietti, Senoo, & Lip, 2015) ; (5) Diagnosis often requires repeated or prolonged ECG monitoring; and (6) Oral anticoagulants (OACs) are highly effective in reducing the risk of cardioembolic stroke, mortality, and possibly dementia in the setting of AF (Ding & Qiu, 2018 , Friberg & Rosenqvist, 2018 .

Atrial fibrillation identification depends on factors having to do with the arrhythmia itself, that is the combination of AF prevalence and density , and factors associated with detection such as the frequency and duration of monitoring and diagnostic test performance (Ramkumar et al., 2018) . Several studies including patients with variable stroke risk factors have used mHealth technologies to identify undiagnosed AF (Tables 2 and 3 ), but these may require gold-standard ECG confirmation.

General Practice, 70(695), e427-e433. https://doi.org/10.3399/bjgp2 0X708161 Kemp Gudmundsdottir, K., Fredriksson, T., Svennberg, E., Al-Khalili, F., Friberg, L., Frykman, V., . Stepwise mass screening for atrial fibrillation using N-terminal B-type natriuretic peptide: The STROKESTOP II study. Europace, 22, 24-32. https://doi. org/10.1093/europ ace/euz255 Lowres, N., Neubeck, L., Salkeld, G., Krass, I., McLachlan, A. J., Redfern, J., . Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study. 

The positive predictive value of an AF event will differ according to pretest probability of AF in a given population (e.g., those with an established diagnosis or one or more risk factors). This is especially relevant to "healthy consumers." Many technologies to identify AF are readily available directly to those without defined disease and are not deployed as individual or public health interventions. Rather, consumers who possess these technologies, such as smartwatches or smartphone-connected ECG recorders, opt into the use of these technologies. Therefore, consumer-driven AF identification is not the same as healthcare-initiated AF screening. AF identification by these devices requires confirmation, since these AF screening tools have variable specificity (Table 2) , raising the potential of a high false-positive rate in a low prevalence population, and risks of unnecessary treatment.

There have been almost 500 studies assessing accuracy of mHealth devices for AF detection, as described in recent systematic reviews (Giebel & Gissel, 2019 , O'Sullivan et al., 2020 . Their capabilities varied according to technologies utilized, settings, and study populations. Two large-scale screening trials were reported recently (See Section 6).

No large outcome trial of screen detected AF and hard endpoints of stroke and death has been conducted as yet.

Although an incidental diagnosis of AF seems to be associated with increased risk of stroke and protection by OAC therapy (Freedman, Potpara, & Lip, 2016 , Martinez, Katholing, & Freedman, 2014 , clinical trials to determine any benefit for opportunistically detected AF have not yet been completed but are underway , Steinhubl et al., 2018 , Heartline study https://www.heart line.com). This effort addresses the concern that AF detected by screening may identify inherently lower-risk patients so that efficacy of anticoagulation (and its risk/benefit ratio) requires recalibration. This is necessary prior to issuance of any recommendations. (Currently, no consensus exists yet on how to treat these arrhythmias, even in those with high CHA 2 DS 2 -VASc scores).

The European and American guidelines do recommend opportunistic screening for early identification of undiagnosed AF in patients aged ≥65 years (Freedman et al., 2017 . On the other hand, the U. S. Preventive Services Task Force has presently given an "insufficient" recommendation for systematic screening for AF with electrocardiograms (Jonas et al., 2018) .

Cryptogenic stroke/TIA Up to one-third of ischemic strokes is attributed to AF mediated embolism to the brain (Hannon et al., 2010) . Further, the risk of recurrent thromboembolism is high if AF is left undetected and untreated (Furie et al., 2012 , Kolominsky-Rabas et al., 2001 . Hence, prolonged monitoring for AF poststroke has been recommended in recent guidelines , Schnabel et al., 2019 .

Detection of AF poststroke depends not only on the monitoring device used and the duration of the monitoring period, but also on stroke type and patient selection; thus, the results of AF detection have been heterogenous (Kishore et al., 2014 , Sanna et al., 2014 , Zungsontiporn & Link, 2018 . A meta-analysis showed that a stepwise approach to AF detection in poststroke patients led to AF detection in 23.7% of patients (Sposato et al., 2015) , while a combined analysis of two randomized and two observational studies showed a 55% reduction in recurrent stroke following prolonged cardiac monitoring . However, the optimal AF duration threshold for initiating anticoagulation is currently unknown and may be lower in a poststroke population compared to those with fewer cardiovascular risk factors .

The risk of undiagnosed AF and other sources of thrombi has been considered high in embolic strokes of unknown source (ESUS), prompting studies that evaluated whether empiric NOAC therapy is more effective than antiplatelet therapy without a requirement of AF detection. Two of these studies, NAVIGATE ESUS F I G U R E 5 mHealth and AF. Applications include screening for AF in general or high-risk populations, managing comorbidities and lifestyles important for prevention and control, as well as managing treatment of known AF. AF-atrial fibrillation, DM-diabetes, HTNhypertension, NSR-normal sinus rhythm. (Hart et al., 2018) and RESPECT-ESUS (Diener et al., 2018) , have not shown a reduction in recurrent stroke in patients receiving NOACs.

It should be emphasized that the mere detection of AF after ESUS is not necessarily proof of positive causation. A third study is ongoing, including patients with suggested atrial myopathy (enlarged atria, increased levels of NT-proBNP, or enlarged P waves) (Kamel et al., 2019) .

These findings underscore the need for AF detection prior to initiation of OAC therapy in patients with cryptogenic stroke, ESUS, or ischemic stroke of known origin, and mHealth devices can ease the process of detection (Zungsontiporn & Link, 2018) .

The threshold of AF burden may very well differ in patients who have had a suspected cardioembolic event and those who have not .

The key to making AF identification feasible, efficient and clinically valuable is the selection of patients with an increased likelihood of harboring undiagnosed AF, rather than general screening in unselected populations. mHealth ECG recorders can facilitate frequent brief (e.g., 30 seconds) recordings over prolonged periods of time by the very ubiquity of devices (including smartphone-based apps or watches). These devices are particularly well suited to capture intermittent or nonpersistent arrhythmias; however, it is likely that frequent sampling would be necessary to capture infrequent paroxysmal AF and even daily "snapshot" ECG monitoring may miss half of AF episodes , Yano et al., 2016 . AF burden, increasingly recognized as a powerful independent predictor of stroke , though accurately measured by implanted devices , cannot be readily calculated from intermittent ECG data. The use of smartwatches with passive intermittent surveillance using PPG monitoring plus ECG confirmation may be a more effective screening tool and is currently being evaluated (Heartline study https://www.heart line. com).

Formal screening with mHealth ECG recordings has yielded meaningful incidences of newly diagnosed AF, statistically greater than if diagnosis relied only on the office ECG (Table 3 ). The yield generally is enhanced by the presence of risk factors, such as older age and higher CHA 2 DS 2 -VASc scores. Several studies (Chan & Choy, 2017 , Proietti et al., 2016 . Lowres 

Current guidelines for anticoagulation are based principally on the presence of risk factors and a diagnosis of clinical AF, regardless of AF duration, symptomatology, or burden . This applies even if the AF has been quiescent for long periods or eliminated altogether as the result of rhythm control interventions including antiarrhythmic drugs, ablation, or risk factor modification . However, there is increasing recognition that AF burden matters; for example, paroxysmal events have less thromboembolic risk than persistent AF . This understanding has been extended during continuous monitoring from CIEDs which depict AF with high granularity, and first advanced the metrics of "AF days" and burden in terms of cumulative load (hours/day) and concentration (density of AF days) . This measure is likely to be important for understanding mHealth discovered AF.

AF burden can be characterized as %/time monitored, longest duration, and density. Retrieved data provide an insight into natural history and associated sequelae (Healey et al., 2012 , Van Gelder et al., 2017 . This led to oral anticoagulation intervention trials to determine the ability to reduce stroke on the basis of AF duration (Lopes et al., 2017 , Martin et al., 2015 . These suggest that a threshold exists below which the risk of thromboembolic stroke is low and risk-benefit ratio may not justify chronic administration of oral anticoagulants. For instance, CIED data indicate that short subclinical AF events have lesser risk than more prolonged (and therefore more likely to be symptomatic) events (Al-Turki, Marafi, Russo, Proietti, & Essebag, 2019) . Device-detected, "subclinical" atrial high-rate episodes (AHRE) lasting 6 minutes to 24 hours are associated with increased stroke risk, but the absolute risk is considerably lower than expected based on risk factors alone (Glotzer et al., 2003 , Healey et al., 2012 , van Gelder et al., 2017 . Whether these require anticoagulation in high-risk individuals is the subject of ongoing studies (Kirchoff et al., 2017 , Lopes et al., 2017 , van Gelder et al., 2017 . Importantly, very short AF episodes (episodes in which both the onset and offset of AT/AF were present within a single EGM recording) were not associated with adverse outcomes (Swiryn et al., 2016) which may be important for mHealth monitoring.

mHealth AF detection using digital health tools offers further insights in patients without indication for implantable devices. mHealth extends AF screening to younger patients without cardiovascular disease and thromboembolic potential may be low. Those with high AF burden (defined by ≥ 11.4%; mean duration 11.7 hours) detected on a 14-day patch monitor had an increased thromboembolic event rate compared to those with lower AF burdens (Go et al., 2018) . There remains significant treatment variation in use of OAC, especially for device-detected AF (Perino et al., 2019) . This may be due to a large clinical uncertainty regarding the optimal cutpoint, even though observational data indicate that OAC is associated with a decreased risk of stroke for episodes > 24 hours and possibly for episodes 6-24

hours (Perino et al., 2019) .

Currently, there are no prospectively validated cutpoints or risk models that incorporate AF burden into decision-making for stroke prevention therapies.

Key knowledge gap:

• Identify characteristics (duration, episode number/ density) and risk factors that justify anticoagulation for mHealth detected AF.

• Rhythm While we await data on OAC treatment for mHealth detected AF, the finding of the arrhythmia should initiate mHealth monitoring of NSR retention, QT intervals (important for those on some antiarrhythmic drugs (Garebelli et al., 2016) , and discussion of cardiovascular risk factor modification and lifestyle changes, since AF coexists with comorbidities that may influence its occurrence and natural history (See Section 4). Thus, alcohol reduction, treatment of OSA, moderate exercise, and weight loss have been shown to reduce AF burden (Congrete et al., 2018 , Kanagala et al., 2003 , Voskoboinik et al., 2020 . 

(See also section 4.1 Ischemic heart disease).

The use of mHealth technology to diagnose ventricular arrhythmias lags behind its application to AF (See section 3.1). Detection of symptomatic VT has been reported using the AliveCor cardiac monitor (AliveCor, San Francisco, USA) and SmartWatch (Ringwald, Crich, & Beysard, 2019 , Waks, Fein, & Das, 2015 . Sophisticated automated analysis of a 2-minute PPG recording by the camera of a commercially available smartphone (iPhone 4S, Apple) can distinguish between AF, PACs, and PVCs from sinus rhythm, with a sensitivity of 0.733 and specificity of 0.976 for PVCs (Chong, Esa, McManus, & Chon, 2015 . PVCs may challenge to PPG-based systems, as many PVCs are nonperfusing (Billet et al., 2019) . An ECG tracing is therefore essential in order to facilitate rhythm diagnosis and avoid misclassification of "slow PPG pulse rates" (bradysphygmia) simply as "bradycardia."

Syncope presents unique challenges for mHealth applications. While prolonged ambulatory monitoring using medical-grade devices (wearable and implantable) has been the mainstay of cardiac rhythm diagnosis during episodes of syncope, user-activated systems must either be activated by the patient during prodromal symptoms (if present and time permits) in anticipation of syncope, or else incorporate loop recording to allow postsyncope activation (Steinberg et al., 2017) . This capability is not incorporated in currently popular consumer-grade wearable devices. However, a randomized controlled trial of AliveCor versus usual care in participants presenting with palpitations or presyncope showed a faster and increased rate of detection of symptomatic arrhythmias in the intervention group, suggesting that at least in presyncope, patient-activated rhythm detection using a commercially available mHealth device is productive (Reed et al., 2019) . Rhythms reported by devices that rely on heart rates will likely require validation with a medical-grade system to provide an ECG tracing during an event to allow determination of the causative rhythm.

There is a significant overlap between transient loss of consciousness and mechanical falls due to orthostatic intolerance, neurologic, or orthopedic problems. This is particularly disabling in elderly subjects and often unwitnessed , Heinrich, Rapp, Rissmann, Becker, & Konig, 2010 . Mobile applications that combine analysis of heart rate monitoring together with fall detection, GPS positioning, video recording with display of patients' surroundings, and the capability to send alerts either triggered by patients in case of symptoms or automatically in case of detected falls, may be useful.

The detection and response to sudden cardiac arrest (SCA) is an area where mHealth applications may prove lifesaving. As rapid treatment for cardiac arrest has consistently been associated with improved survival, pre-emptive identification of at-risk persons, detection of cardiac arrests, alerting of nearby lay and professional first responders, and coaching or quality assurance in the performance of cardiopulmonary resuscitation (CPR) are ideally suited to the mHealth paradigm in societies where mobile smartphones are ubiquitous.

It is possible that mHealth devices which continuously monitor heart rhythm and other physiologic data may be able to better predict impending SCA, even using measures which have not shown sufficient specificity or sensitivity when measured intermittently, such as heart rate variability (Lee, Shin, Seo, Nam, & Joo, 2016) . However, such continuous monitoring is present already in CIEDs and has not yet proven to be sufficiently predictive to be clinically useful (Au-Yeung, Reinhall, Bardy, & Brunton, 2018) . Therefore, the prediction of SCA by mHealth devices, while a tantalizing prospect, remains to be realized.

Once cardiac arrest occurs, rapid identification is essential to trigger a response by emergency responders. Wearable devices that combine physiologic monitoring, GPS, and a method of communication with emergency services such as cellular service are well positioned to provide almost instantaneous alert as well as location information (Kwon, Lee, Lee, Lee, & Park, 2018 , Praveen Kumar, Amgoth, & Annavarapu, 2019 ). An early device using a piezoelectric sensor to detect the pulse was capable of transmitting an alert to emergency medical system or other responders when a pulse was not detected and the watch (and thus the wearer) was still (Rickard et al., 2011) .

Preliminary reports indicate that smart speakers in commodity smart devices may be able to identify agonal breath patterns for sudden cardiac death detection . Widespread diffusion of such technology to patients at elevated risk of SCA will be necessary before any potential benefits can be tested.

The ubiquity of mobile phones in society leads to more rapid notification of emergency services, and the possibility of a dispatcher gathering information from a bystander at the patient's side and delivering instructions on care, such as CPR. This was associated with improved outcomes for a variety of emergencies (Wu et al., 2012) . Notification of lay first responders in the vicinity of a cardiac arrest is also feasible with current technology. A blinded, randomized trial conducted in Stockholm, Sweden, demonstrated that such a system improved the rate of bystander CPR (Ringh et al., 2015) . However, almost 10,000 volunteers were recruited over approximately 18 months, during which 667 activations occurred, emphasizing the large resources needed and the low rate of utilization of trained volunteers, even when alerted by mobile phone.

Whether a trained or novice bystander responds, mobile devices may be further useful to provide voice (or video) instructions from a dispatcher or from the device itself. Studies of prerecorded audio, live video, and animation-based instruction have shown improvements in some aspects of CPR delivery and AED use, although technology continues to evolve (Bolle, Scholl, & Gilbert, 2009 , Choa et al., 2008 , Merchant et al., 2010 , You, Park, Chung & Park, 2008 . One limitation is that as such apps are unregulated, many do not convey current basic life support algorithms and may have poor usability (Kalz et al., 2014) . In addition, delay in commencing CPR and in calling emergency services due to distraction of the rescuer by using an app is a concern (Paal et al., 2012) .

Automated external defibrillator (AED) use in cardiac arrest is associated with improved survival, but AED use remains low (Weisfeldt et al., 2010) . Mobile devices have the potential to increase this by assisting with the retrieval and use of AEDs.

Multiple apps have been created to locate AEDs in the vicinity of the user, although with mixed results in simulations (Sakai et al., 2011 , Hatakeyama et al., 2018 , Neves Briard et al., 2019 . Barriers include the accuracy of AED location databases, size of the user base, app interface, and the availability of multiple apps instead of a single validated regional, national, or international standard. An emerging approach to circumvent these limitations is the dispatch of an AED via a drone to the location of the cardiac arrest, which is expected to reduce time to defibrillation, especially in rural areas (Boutilier et al., 2017) . Feasibility has been demonstrated (Claesson et al., 2017) .

The complete chain from activation of citizen responders was tested in the Heartrunner trial (Andelius et al., 2020) in a region of almost 2 million inhabitants. Results showed that citizen responders arrived before emergency services 42% of out of hospital cardiac arrests, accompanied by a threefold increase in bystander defibrillation with a trend to improved 30-day survival. Results were more pronounced when emergency arrival times were longer, for example, in rural areas. 

A large proportion of arrhythmias are influenced by coexisting conditions. Their management may directly affect arrhythmia recurrence and outcome. Thus, lifestyle modifications and management of comorbid conditions ( Figure 5) is becoming an objective of arrhythmia management (Chung et al., 2020) and received a Class 1 recommendation in most recent guidelines . mHealth has significant potential for facilitating these interventions ( Figure 6 ).

Early management (e.g., primary angioplasty) of acute ischemic syndromes may reduce infarct territory and ventricular arrhythmias, thereby improving outcome. AF after myocardial infarction worsens prognosis (Pizzetti et al., 2001) .

ST segment monitoring technology embedded in conventionally indicated ICDs when tested in a randomized cross-over study suggested a reduction in the time from the onset of ischemia to presentation to hospital . The AngelMed

Guardian system (Angel Medical Systems, Eatontown, New Jersey)

is approved for use in the United States for patients with prior acute coronary syndrome (ACS) who remain at high risk for recurrent ACS.

For lower-risk patients, mHealth may improve symptom recognition and earlier presentation, that is, "symptom-to-door time" (Moser et al., 2006) .

Wearable devices that continuously monitor physiologic data promise detection, and possibly pre-emption, of the early stages of MI, by alerting patient and/or healthcare team early. A noninvasive device consisting of a three-lead ECG linked wirelessly to a dedicated mobile device has recently been described (Van Heuverswyn et al., 2019) . Three lead ECG tracings (as well as derived augmented limb leads) can be recorded with commercially available smartwatches (Avila, 2019) . Limitations of this approach are the need for the patient or a bystander to possess the device or app, and be familiar with its use, before the onset of symptoms.

An emerging technology (www.heart beam.com) uses a credit card sized device that is pressed against the user's chest (Figure 3 ).

It collects ECG signals using a novel 3D vector approach. The signals are sent to the cloud, where they are analyzed and compared to the patient's asymptomatic baseline reading. A proprietary algorithm combines the signal analysis with the patient's history and reported symptoms. This information, along with a diagnostic recommendation and ECG waveforms, is sent to the patient's physician, who makes a final determination and informs the patient This system is used by patients in the telehealth setting to assess whether chest pain is the result of an myocardial infarction.

The next step of patient care involved transmission of ECGs by emergency responders in the field to hospitals for review and triage and was shown to result in shorter door-to-balloon time, lower peak troponin and creatine phosphokinase levels, higher postinfarction left ventricular ejection fraction, and shorter length of stay compared with control patients whose ECGs were not transmitted (Clemmensen, Loumann-Nielson, & Sejersten, 2010 , Sanchez-Ross et al., 2011 . This paradigm has now been widely implemented. Technical factors, such as transmission failure and lack of network coverage, are the main impediments to adoption of such systems.

This is often confusing for patients, who often exhibit a poor understanding of their medications, follow-up procedures, and future appointments (Horwitz et al., 2013; Ziaeian, Araujo, Van Ness, & Horwitz, 2012) . This contributes to frequent hospital readmissions. Mobile technologies may enable individualized contact between patients and healthcare providers. Phone calls led to a modest improvement in medication adherence in patients with F I G U R E 6 Digital applications can integrate patient relayed information of sensor and clinical information with automatic remote analysis, but also permit patients to receive advice and treatment adjustments from physicians directly. coronary artery disease in one large randomized controlled trial (Vollmer et al., 2014) . Text messaging was shown to increase medication adherence and improved cardiovascular risk factors Unal, Giakoumidakis, Khan, & Patelarou, 2018) . Available evidence is limited by short-term follow-up and self-reported adherence (Shariful Islam et al., 2019) . Success may depend on personalized messages with tailored advice, the ability to respond to texts, timing messages to coincide with medication doses, higher frequency of messages, and the use of additional apps or websites . Interoperability with the EMR may facilitate this approach.

This was shown to improve health outcomes among patients with heart disease, but is underutilized. The Million Hearts Cardiac Rehabilitation Collaborative aims to increase participation rates to ≥70% by 2022 (Ritchey et al., 2020) . Mobile apps and linked sensors to measure heart rate, respiration rate, and exercise parameters may overcome traditional limitations of availability, cost, and convenience and be more acceptable to some patients (Zwisler et al., 2016) . A randomized controlled trial center-based and mobile rehabilitation found improved uptake, adherence, and completion with home-based cardiac rehabilitation in postinfarction patients (See also 4.2.2.)

Heart failure is widely prevalent, costly to manage, and degrades patient outcomes (Benjamin et al., 2017 , Albert & Estep, 2019 Despite promise, most large, multicenter randomized trials failed to demonstrate improved outcomes of remote monitoring in HF patients (Table 4 ) , Dickinson et al., 2018 .

Combination algorithms based on multiple parameters may be valuable (Ono & Varma, 2017) . One trial stands out. The TIM-HF2 trial TA B L E 4 Randomized trials with neutral results based on external-device remote patient monitoring (RPM) Electronic device to assess symptoms and educate patients with HF. Abnormal symptoms directed to a monitoring nurse. Device tailored itself to patient's knowledge.

Excellent adherence with use of the device. Planned and unplanned face-to-face HF nurse visits were higher in the control group. Event rates for both groups were lower than expected. Primary limitation appeared to be the excellent outcomes in the control group.

LINK-HF (Stehlik, CIrc HF 2020)

Disposable multisensor chest patch for 3 months linked via smartphone to cloud analytics. Apply machine-learning algorithm.

Pilot study, compliance eroded. However, this detected precursors of hospitalization for HF exacerbation with 76% to 88% sensitivity and 85% specificity.

Abbreviations: BP, blood pressure; HF, heart failure; HR, heart rate; PC, primary care; RPM, remote patient monitoring.

randomized HF patients to either remote patient management plus usual care or to usual care only and were followed up for over a year (Koehler et al., 2018) . The results showed reduction in the combined endpoint of percentage of days lost due to unplanned hospitalization and all-cause mortality. However cardiovascular mortality was similar between remote monitoring and standard care groups. Implanted devices that monitor pulmonary arterial pressure may be beneficial in select patients when used in structured programs (Dickinson et al., 2018) . The positive findings of the CHAMPION trial (CardioMEMS Heart Sensor Allows Monitoring of Pressure to Improve Outcomes in NYHA Functional Class III Heart Failure Patients) trial and subsequent FDA approval has renewed interest in remote patient management for HF patients (Abraham et al., 2016 , Desai et al., 2017 .

This requires daily download of hemodynamic data and a prespecified medical treatment plan. An app is also available which illustrates patient compliance with monitoring, alerts the patient when transmissions are not received, shows medication reminders, and allows for medication reconciliation and titration.

The concept of coupling remote monitoring and mobile cellular technologies is attractive for the HF community , Cipresso et al., 2012 . Heart rate (ECG), BP, and weight were the most frequently monitored parameters. Sensors that detect respiratory rate and pattern by detecting movement of the chest wall, via pressure, stretch, or accelerometry, may have applications in HF. Detecting breathing via microphone (sounds), change in impedance, or pulse oximetry are other possible means to monitor respiratory function. Some of these modalities could be integrated into smart clothing (Molinaro et al., 2018) .

Some trials included also alert reminders of medication use, voice messages on educational tips, video education, and tracking of physical activity (See Section 4.6.1). Patients were mostly monitored daily and followed for an average of 6 months. A reduction was seen in HF-related hospital days . High rates of patient engagement, acceptance, usage and adherence have been reported in some trials but not others , Hamilton, Mills, Birch, & Thompson, 2018 .

Preliminary results using a disposable multisensor chest patch in the LINK-HF study were encouraging , detecting precursors of hospitalization for HF exacerbation with 76% to 88% sensitivity and 85% specificity, 1 week before clinical manifestations.

Exercise training is recommended for all stable HF patients (Piepoli et al., 2011 . Hybrid cardiac telerehabilitation is a novel approach. Telerehabilitation is the supervision and performance of comprehensive cardiac rehabilitation at a distance, encompassing: telemonitoring (minimally intrusive, often involving sensors), teleassessment (active remote assessment), telesupport (supportive televisits by nurses, psychological support), teletherapy (actual interactive therapy), telecoaching (support and instruction for therapy), and teleconsulting and telesupervision of exercise training (Piotrowicz, Piepoli, et al. 2016) . Various devices have been described, from heart rate monitoring (Smart, Haluska, Jeffriess, & Marwick, 2005) and transtelephonic electrocardiographic monitoring (Kouidi, Farmakiotis, Kouidis, & Deligiannis, 2006) to tele-ECG-monitoring via a remote device (Piotrowicz, Zieliński, et al., 2015) and real-time ECG and voice transtelephonic monitoring (Ades et al., 2000) .

Home-based telerehabilitation was demonstrated to be safe, effective with high adherence among HF patients. It improves physical capacity (Piotrowicz, Buchner, Piotrowski, & Piotrowicz, 2015) and psychological status (Piotrowicz, Piotrowski, & Piotrowicz, 2016) , with similar QoL improvement to standard rehabilitation (Piotrowicz, Stepnowska, et al., 2015) . The first randomized, prospective, multicenter study (TELEREH-HF) showed that hybrid telerehabilitation and telecare in HF patients was more effective than usual care in improving peak VO2, 6-minute walk distance, and QoL, although not associated with reduction of 24-month mortality and hospitalization except in the most experienced centers .

The recent Scientific Statement from the American Association of Cardiovascular and Pulmonary Rehabilitation, the AHA, and the ACC indicates that home-based rehabilitation using telemedicine is a promising new direction (Thomas et al., 2019) .

Diabetes mellitus is a strong risk factor for the development of morbidity and mortality associated with a range of cardiovascular (Cosentino et al., 2020) .

Glycemic control may reduce AF development and recurrence (Chao et al., 2012; Chang et al., 2014; Gu et al., 2011 , Otake, Suzuki, Honda, & Maruyama, 2009 ).

Mobile apps can facilitate self-management by reminding regular assessment of required parameters and medications to take and provide educational tools and motivational support. Regular Stand-alone diabetes management apps have recently been reviewed (Fleming et al., 2020) . Short-term measures, such as HbA1c, may be improved by such apps in conjunction with clinical support, but many have suboptimal usability (Veazie et al., 2018) . Phonebased interventions were associated with improved glycemic control as compared to standard care (Fokkert et al., 2019 , Liang et al., 2011 , Pillay et al., 2015 , Saffari, Ghanizadeh, & Koenig, 2014 . Efficacy for improving glycemic control in randomized controlled trials has shown mixed results (Agarwal et al., 2019 , Quinn et al., 2011 . Metaanalyses indicate that mobile phone interventions for self-management reduced HbA1c modestly by 0.2-0.5% over a median of 6-month follow-up duration, with a greater reduction in patients with type 2 compared to type 1 diabetes (Pal et al., 2014) . A significant impact on clinical outcomes may affect healthcare expenditures by reducing the need for in-person contact with healthcare providers, preventing hospital admissions, and improving prognosis.

In a retrospective study, the use of mHealth technologies was associated with a 21.9% reduction in medical spending than a control group during the first year (Whaley et al., 2019) . Key determinants to successful uptake of decision-support apps will be their user-friendliness and complexity and the delivery of electronic communications and feedback to the patient.

Hypertension, because of its high prevalence, provides the highest attributable risk for the development of AF (Huxley et al., 2011) . respectively (Duan et al., 2017) . BP telemonitoring nested in a more complex intervention, including additional support, as faceto-face counseling, telecounseling, education, behavioral management, medication management, and adherence contracts, led to additional and more sustainable benefit (Duan et al., 2017; Tucker et al., 2017) mHealth has the potential to promote patient self-management, as a complement to the doctor's intervention, and encourage greater participation in medical decision-making. Indeed, the TASMINH4 unblinded randomized controlled trial showed that patients who used self-monitoring of BP to titrate antihypertensives, with or without telemonitoring, achieved better BP control than those assigned to usual care . The self-monitoring group that used telemonitoring achieved lower BP quicker than the self-monitoring group not receiving telemonitoring support, but readings were not significantly different at 1 year of follow-up. Cost-effectiveness analysis suggests that self-monitoring in this context is cost-effective by NICE criteria, that is, costing well under £20,000 per QALY (Monahan et al., 2019) .

Although mHealth options may aid hypertension management, technological barriers, high costs, heterogeneity of solutions and technologies, and lack of standards challenge clinical implementation. The 2019 ESC guidelines on hypertension stress the importance of self-monitoring and underline the potential use of smartphone-based solutions. Nevertheless, they do not recommend the use of mobile apps as independent mean of BP measurements (Williams et al., 2018) .

Sleep disorders are widely prevalent and contribute to cardiovascular risk and arrhythmias, especially AF (Daghlas et al., 2019 , Hirshkowitz et al., 2015 , Mehra et al., 2006 , May et al., 2016 , May, Van Wagoner, & Mehra, 2017 • Treating sleep apnea may reduce AF burden (Qureshi et al., 2015 , Youssef et al., 2018 .

Consumer technology directed to sleep medicine may revolutionize the detection and treatment of sleep disorders. Since such apps are preinstalled on many smartphones, sleep tracking may be among the most widely applied facets of mHealth (Khosla et al., 2018) . Applications include mobile device applications, wearable devices, embedded devices (in the individual's sleep environment), rings (https://bodim etrics.com/produ ct/circu l-sleep -and-fitne ssring), integration of accessory diagnostic monitoring (e.g., oximetry, ECG monitoring), and sleep therapy adherence monitoring. Several commercially available wearable devices measure total sleep time accurately, but not more detailed parameters such as sleep efficiency and different sleep stages (Mantua, Gravel, & Spencer, 2016) .

Preliminary data suggest that wearable devices may be capable of detecting sleep apnea with good accuracy compared to gold-standard polysomnography (Selvaraj et al., 2014) and transform the approach to sleep disorder screening, diagnosis, and treatment. Sleep irregularity diagnosed by 7 day wrist actigraphy was linked to risk of cardiovascular events (Huang, Mariani, & Redline, 2020) . Preliminary studies indicated that use of wearables may permit behavior modifications that improve sleep quality (Berryhill et al., 2020) . In this regard, mHealth applications to sleep diagnosis and treatment promise facilitation of rhythm control. Figure 5) 

Physical activity is any bodily movement from skeletal muscle contraction to increase energy expenditure above basal level. Athletic activity varies from recreational sports to competitive events. There is a compelling evidence that regular aerobic exercise at the levels recommended by Physical Activity Guidelines Advisory Committee reduces the risk of a variety of cardiovascular conditions, including AF (Everett et al., 2011 , Mozaffarian, Furberg, Psaty, & Siscovick, 2008 , Piercy et al., 2018 . However, the majority of the population is not engaged in physical activity at the recommended levels (Piercy 2018 ). Among patients with cardiovascular disease, patient activity measured automatically by ICDs correlated with survival following ICD implantation (Kramer et al., 2015) . • Cardiorespiratory fitness has an inverse relationship to AF burden (Faselis et al., 2016) .

• Improvement in exercise capacity of 2 METs in overweight individuals may double freedom from AF .

trackers that can stand alone, a fitness tracker that is coupled with a companion app, or an app that can be downloaded onto a smartphone, which then utilizes various features of the smartphone to measure activity and sleep. The accuracy of these measurements varies between different products and between measures within the same product (Rosenberger, Buman, Haskell, McConnell, & Carstensen, 2016) . Furthermore, while step-counting is long established, measuring the intensity of exercise is more complex. Although fitness technology has the exciting potential to increase physical activity by promoting goal setting and providing feedback, its effectiveness in motivating positive behavioral change remains unclear (Sullivan & Lachman, 2017) .

One cautionary tale is the study by Jakicic et al. that examined the effectiveness of a lifestyle intervention with or without a fitness tracker (Jakicic et al., 2016) . Two groups received instruction to promote physical activity and dietary restriction. Six months into the intervention, half of the participants were provided with an upper arm fitness tracker and web-based support accompanying the device.

The other half logged and tracked their activity and diet on a study website. Of note, the group that wore the tracker lost less weight than the group who did not. Moreover, changes in physical activity between the two groups were not significantly different. These results cast doubt on the effectiveness of fitness trackers in promoting greater physical activity, and thus, further data are required to assess the impact of this approach (See Section 5).

These are a unique category. Endurance athletes may have increased AF risk (Abdulla & Nielson, 2009 , Anderson et al., 2013 . Remote evaluation of ECG recordings may be useful in countries that perform preparticipation ECG screening (Brunetti et al., 2014 , Orchard et al., 2019 . Mobile devices and apps provide complex data which can be used as a self-monitoring tool for managing training (Aroganam, Manivannan, & Harrison, 2019 , Li et al., 2016 , Peake, Kerr, & Sullivan, 2018 , Peart, Balsalobre-Fernandez, & Shaw, 2019 , Seshadri et al., 2019 .

Exercise load and performance level can be accessed on a regular basis by coaches as well as athletes. Training guided by daily monitoring of HRV parameters has also been proposed, but data are limited (Coppetti et al., 2017 , Dobbs et al., 2019 , Singh et al., 2018 .

Mobile devices provide the possibility of online real-time monitoring during indoor and outdoor training and competitions. Monitoring of heart rate provides both information on performance and level of training but can also provide valuable information regarding heart rhythm irregularity suggestive of arrhythmias. Any kind of paroxysmal arrhythmia related to sport participation and detected by mobile devices designed merely for heart rate assessment should trigger further cardiological evaluation. Having in mind data indicating that sports participation may be associated with higher risk of development of AF mobile devices may serve as valuable screening tool for AF detection.

Importantly, mHealth solutions enable easy access to athletes' medical data. The latter approach can be of special interest in management of athletes' health during competitions abroad.

In 2010, the American Heart Association promulgated "Life's Simple 7" as a public health strategy to improve cardiovascular health with the motto: "7 Small Steps to Big Changes. It's easy and simple. Anyone can do it. Start with one or two!" Unfortunately, research has shown that this strategy is anything but simple: virtually, no adults (<1%) are compliant with all recommendations and 42% are compliant with only 0-2 recommendations . Although there is ample evidence that weight loss and maintaining an ideal weight are beneficial in reducing AF burden and symptoms, compliance with this recommendation is poor; the reasons include among others, inability to track food intake (Abed et al., 2013 , Donnellan et al., 2019 .

• Weight loss combined with risk factor modification is a Class 1 (B-R) recommendation in treatment of AF • >10% weight reduction/ target BMI <27 kg/m 2 reduces AF burden .

There are currently many consumer-oriented mobile phonebased applications (apps) designed for tracking food intake, but their utility for use in carbohydrate counting is limited due their design (El-Gayar, Timsina, & Nawar, 2013) . Commonly, these consumer-oriented apps require multiple steps. As an example, the user types in the food consumed and then scrolls through the search results to match with the program's food and nutrient database.

Next, after finding a matching food type, the user must estimate and enter an amount. These apps require significant user input and time burden along with high possibility of error. In addition, they are also plagued by uncertain accuracy. Recently, research has shown that nutrient calculations from leading nutrition tracking apps tended to be lower than results from using 24-hour recall with analysis by the Nutrition Data System for Research (NDSR), a research-level dietary analysis software (Griffiths, Harnack, & Pereira, 2018) .

By contrast, a visual image-based app, such as the Technology-Assisted Dietary Assessment (TADA) system, directly addresses the aforementioned shortcomings (Boushey, Spoden, Zhu, Delp, & Kerr, 2017 , Six et al., 2010 . This is in research phase. The TADA system consists of two main components: (1) A smartphone app that runs on either iPhones (iOS) or Android devices: the Mobile Food Record (mFR), and (2) Cloud-based server that communicates with the mFR, processes, and stores the food images. Using the TADA system, a person takes a photograph of the meal they are planning to eat using their smartphone's camera.

The use of geometric models has permitted the TADA system to use a single image of a meal to estimate portion size to within 15% of the actual amount (Fang, Liu, Zhu, Delp, & Boushey, 2015) . Hence, smartphone-based technology such as the TADA system can facilitate tracking of food intake, which in turn can potentially help with weight management.

Despite the profusion of diet-and weight-related apps, and the interest in weight loss in the community, there remains a dearth of high-quality evidence that these apps are actually effective (Dounavi & Tsoumani, 2019) . There remains a need for further evidence development before specific apps or other mHealth technology can be recommended or prescribed.

Chung 

Generally, structured management programs inclusive of intensive patient education may improve outcomes (Hendriks et al., 2012; Lin et al., 2014; Angaran et al., 2015) . These may be facilitated by mHealth.

mHealth offers the opportunity to reach more patients more effectively. It may promote patient engagement through ease of access and wider dissemination to regions and communities who may not access health care through traditional modes due to cost, time, distance, embarrassment/stigma, marginalized groups, health inequities, etc. In this way, mHealth may facilitate information sharing and interaction between patients and HCPs without the need for an elaborate infrastructure , Walsh, Topol, & Steinhubl, 2014 ) ( Figure 6 ). Apps may aid HCPs to explain the condition and treatment options, utilizing videos, avatars, and individualized risk scores, enabling greater patient understanding and encouraging a two-way exchange of information to achieve a concordant decision about treatment.

A recent HRS statement advocates for transparent and secure access by patients to their digital data . This enables active participation and appropriate self-management.

For instance, many patients with AF are interested in seeing their AF burden and physiologic data, similarly to patients with hypertension tracking their BP or patients with diabetes tracking their glucose. Recent systematic reviews of technology-based patient-directed interventions for cardiovascular disease suggest that engaging elements include self-monitoring of symptoms and measurements, daily tracking of health behaviors, disease education, reminders, and interaction with HCPs . In some cardiovascular conditions, self-management (without any HCP input) improved key outcomes (Hagglund et al., 2015 .

The model requires that patients assume responsibility and accountability for tracking conditions effectively and taking corrective measures. Possibly, this may be facilitated by data organization to present salient elements in a format comprehensible to the lay public. Active role of patients in decision-making regarding the choice of treatment has been underlined by AF clinical guidance documents.

Patients with AF are encouraged to be involved in decision-taking through better understanding of their disease, which helps to improve communication between patients, their families, and doctors and improves patients' adherence to prescribed therapy. Two applications in AF-one for patients and the other for healthcare providers-have been developed by CATCH ME Consortium in collaboration with European Society of Cardiology (Kotecha et al., 2018) , but these have yet to be formally tested. In China, Guo and colleagues (Guo, Chen, Lane, Liu, Wang, & Lip, 2017) demonstrated that the mobile atrial fibrillation (mAFA) app, incorporating decision support, education, and patient engagement, significantly improved AF patients' knowledge, medication adherence, quality of life, and satisfaction to anticoagulation compared to usual care.

Limitations should be recognized:

• Demands of self-management may be excessive for even well intentioned patients required to be facile with setting up their own medical monitoring device, assessing frequency of download, interpreting and acting on data when required, and troubleshooting. These are not trivial challenges.

Individual health status has been found to be a strong independent predictor of mortality and cardiovascular events (Rumsfeld et al., 2013) .

mHealth may catalyze positive behavioral change and facilitate health care. An induced healthy-user effect was likely the basis of survival benefit among CIED patients adhering more closely to remote management (Varma et al., 2015) . mHealth may support patients with text messaging or mobile applications to remind patients of medication doses and times, as well as medical appointments (but synchronization with healthcare providers and/ or EMR is generally lacking). The "just-in-time adaptive intervention"

(JITAI) premise is to provide the appropriate type and amount of support to an individual at the correct time, with the ability to adjust depending on the person's current internal and situational factors (Nahum-Shani et al., 2018) . mHealth technology is an ideal platform to facilitate JITAIs by providing "real-time" personalized information, which can be utilized to inform the intervention delivered. JITAIs have been widely employed for health promotion and to support behavior change, but evidence of their efficacy is limited (Gustafson et al., 2014 , Patrick et al., 2009 , Riley, Obermayer, & Jean-Mary, 2008 .

Timing is integral to the perception of benefit, as is receptivity to accept and use the support (Nahum-Shani et al., 2015) . Bespoke, multi-faceted mHealth tools, with motivational messages and incorporating gamification, are most engaging .

Incorporation of gamification strategies (e.g., rewards, prizes, avatars, performance feedback, leader-boards, competitions, and social connection) into mHealth promotes patient engagement and sustains healthy behaviors (Blondon, Meyer, Lovis, & Ehrler, 2017 , Cugelman, 2013 , Edwards et al., 2016 , Johnson et al., 2016 , Sardi, Idri, & Fernandez-Aleman, 2017 . However, a recent systematic review demonstrated that only 4% (64/1680) of English-language "top-rated" health apps incorporated ≥1 gaming feature (Edwards et al., 2016) . There are limited hypothesis-generated data for these mHealth interventions, and their efficacy in this context is as yet unmeasured. Self-regulatory behavior change techniques, such as feedback and monitoring (including self-monitoring), comparison of behavior, rewards, incentives and threats, and social support, are the most common behavior change techniques employed in gamification apps and are frequently utilized in successful nongaming apps targeting health promotion and secondary prevention (Conroy, Yang, & Maher, 2014 , Direito et al., 2014 , Edwards et al., 2016 . Engaging with apps involving gamification can also improve emotional well-being through feelings of accomplishment and social connectivity (Johnson et al., 2016) .

Incorporation of a patient as part of a wider community may offer benefits. Social networking is widely used for health (Fox 2011) .

Online communities enable individuals to "meet," share their experiences, discuss treatment, and receive and provide support from peers, patient organizations, or HCPs (Fox, 2011 , Swan, 2009 , Swan, 2012 .

While crowdsourcing via the Internet and social networks allows collective sharing and exchange of information from a large number of people, the integrity and accuracy of such information remains largely un-vetted and as such may be unreliable (Besaleva & Weaver, 2014) .

Sustaining healthy behaviors and minimizing intervention fatigue is paramount to long-term maintenance. Although mHealth may help to maintain motivation, available data demonstrate significant attrition with mHealth interventions targeting risk factors and chronic conditions, even when people report liking the intervention and have purchased it , Flores Mateo, Granado-Font, Ferré-Grau, Férre-Grau, & Montaña-Carreras, 2015 , Fukuoka, Gay, Haskell, Arai, & Vittinghoff, 2015 , Morgan et al., 2017 , Owen et al., 2015 , Simblett et al., 2018 , Whitehead & Seaton, 2016 , Endeavour Partners, 2017 .

A representative patient's experience is described below:

A few years ago (2017) Understanding the basis for health-protective behavior is vital (Dunton, 2018) . Many apps, including those from national heart foundations (American Heart Association, , British Heart Foundation, 2014 , Canadian Heart and Stroke Foundation, 2014 , National Heart Foundation of Australia, 2014 , are available to support healthy lifestyle choices, but their efficacy remains largely untested or is limited by design features (i.e., small sample sizes, selection bias, etc.). Cost, service connectivity, and credibility of information sources are important factors. However, patient engagement may be jeopardized by worries about privacy and personal data security (Burke et al., 2015 , Kumar et al., 2013 ).

The level and duration of clinic support needed will likely depend on condition monitored and goals for treatment. Reduction in compulsory routine in-clinic evaluations and reliance on continuous remote monitoring improved retention to long-term follow-up of patients with CIEDs (Varma, Michalski, Stambler, Pavri, & TRUST Investigators, 2014) . In one HF trial, gain was related to the period of remote instruction. Whether this indicates that efficacy of the active program had peaked and stabilized or that it needed to be sustained is unclear . Ideally, a training program should be finite in time but its effects durable.

Although mHealth is highly promising in transforming health care, it can potentially exacerbate disparities in health care along sociodemographic lines.

Older people are perceived to engage less with mHealth. A 2017

Pew Research Center survey found that 92% of 18-29 year olds and 74% of age 50-64 year olds own a smartphone (Smith, 2017) .

However, the lack of familiarity with the technology and access to mobile devices, rather than lack of engagement per se, remain the principal barriers . Older users of mHealth prefer personalized information, which is clearly presented and is easy to navigate (Neubeck et al., 2015) .

There is also disparity across the educational spectrum, with smartphone usage in 57% of the population with less than high school education and 91% of the population who graduated from college.

Smartphone use differs by income, with smartphone usage in 67% of the population with income annual ≤ $30,000 and 93% of the population with income ≥ $75,000 (Mobile Fact Sheet, 2018) . Limited evidence from the USA suggests that, although there is some variation in the mHealth use related to ethnicity, black and Hispanic

Americans are not disadvantaged (Martin, 2012) . mHealth permits information and apps to be tailored appropriately for language, literacy levels (including "text to speech" technology), and cultural differences to promote engagement , Neubeck, Cartledge, Dawkes, & Gallagher, 2017 .

There is heterogeneity of mHealth availability among different countries . Even some of the best studied and FDA and CE approved technologies described here may be currently unavailable due to regulatory or marketing rules or simply unaffordable to either individuals or healthcare systems in many other countries.

As healthcare systems leverage and incorporate smartphone-based technology in their workflow and processes, a strategy is needed in parallel to ensure that those who do not have access to smartphone-based technology will continue to receive appropriate high-quality care. This critical initiative will require consensus and action among all stakeholders including HCPs, hospital systems, insurance providers, and state and federal government agencies.

Thus enabled, mHealth promises improved patient outcomes in resource-limited areas (Bhavnani et al., 2018) .

Nurse-led care vs. usual care for patients with atrial fibrillation: results of a randomized trial of integrated chronic care vs. routine clinical care in ambulatory patients with atrial fibrillation. European Heart Journal, 33 (21), 2692-2699. https://doi.org/10.1093/eurhe artj/ehs071

mHealth may have particular impact on trials of heart rhythm disorders. Traditionally, clinical trials testing drugs and devices for arrhythmias utilized time-to-event outcomes and analyses, such as first recurrence of AF after a blanking period (Piccini et al., 2017) . Patients randomized to the control and intervention would be monitored intermittently, either with ambulatory devices and/or in-clinic visit.

Such monitoring had limited sensitivity for recurrent arrhythmias, including symptomatic and asymptomatic episodes. Furthermore, time-to-first event may not accurately capture reductions in arrhythmia burden, which have also been shown to be beneficial in recent randomized trials (Andrade et al., 2019) . While CIEDs such as pacemakers and defibrillators can be leveraged for continuous monitoring , these studies do not generalize to broader CIED-free populations. ILRs may have a potential role, but are costly and unless used for clinical indications, difficult to justify simply for study event ascertainment.

There are a variety of free-standing handheld ECG monitors, some of which have automated AF detection (Table 1 ). However, many do not have cellular or networking capability and therefore generally cannot transmit data or findings in real time. This is where smart-or mobile-connected arrhythmia and pulse detection technologies have significant promise. These may enhance detection and measurement of clinical outcomes while also allowing for remote or virtual data collection without the need for site-based study visits. Examples include remote rhythm assessment with single-or multilead ECGs from smartphone or smartwatch-based technologies and automatic ascertainment of hospitalizations using smartphone-based geofencing (Nguyen et al., 2017) . These operational enhancements, in turn, can improve participant satisfaction, reduce cost, improve study efficiency, and facilitate or expand enrollment. An example is the ongoing Health eHeart study, a site-free cardiovascular research study that leverages self-reported data, data from wearable sensors, electronic health records, and other importable "big data" to enable rapid-cycle, low-cost interventional and observational cardiovascular research (https:// www.healt h-ehear tstudy.org/).

Two recent large-scale studies highlight the potential advantages of mHealth for AF screening and treatment.

This was a highly pragmatic, single-arm investigational device exemption study designed to test the performance and safety of a PPG-based irregular rhythm detection algorithm on the Apple Watch for identification of AF . The study was a siteless "bring your own device" study, such that participants needed their own compatible smartphone and watch to enroll online. All study procedures, including eligibility verification, onboarding, enrollment, and data collection, were performed via the study app, which could be downloaded from the app store. If a participant received an irregular pulse notification, then subsequent study visits were done via video conferencing to study physicians directly with the app. The study enrolled over 419,000 participants without pre-existing AF in just an eight-month period, in large part due to the pragmatic, virtual design, and easy accessibility (Figure 4) . The algorithm was found to have a positive predictive value of simultaneous ECG-confirmed AF of 0.84 . Only 0.5% of the enrolled population received any irregular pulse notification, but 3.2% of those age ≥ 65 years received notifications. However, only 153/450 (34%) patients had AF detected by a subsequent single ECG patches after the irregular rhythm notification was received. This may reflect the paroxysmal nature of early-stage AF rather than explicit false positives. Because the study only administered ECG patch morning to those with irregular rhythm notification rather than then entire cohort or to negative controls, the negative predictive value was not estimated. It should be noticed that the Apple Heart Study was in a population without diagnosed AF; test performance and diagnostic yield could be considerably different in a population with known AF, and this software is not approved for use for AF surveillance in established AF.

• The Huawei heart study A similar study was performed using smart device-based (Huawei fitness band or smartwatch) PPG technology . The algorithm had been validated with over 29 485 PPG signals before commencement of the trial. More than 246,000 people downloaded the PPG screening app, of which about 187 000 individuals monitored their pulse rhythm for 7 months. AF was found in 0.23% (slightly lower than Apple Heart, possibly due to a younger and healthier enrolled cohort). Validation was achieved in 87% (PPV >90%) compared to 34% in Apple Heart. The results indicated that this was a feasible frequent continuous monitoring approach for the screening and early detection of AF in a large population.

A significant observation was that clinical decision-support tools provided enabled management decisions, for example, almost 80%

high-risk patients were anticoagulated. Subsequent enrollment into the mAFA II trial showed significantly reduced risk of rehospitalization and clinical adverse events (Guo et al., 2020) . These trial results encourage incorporation of such technology effectively into the AF management pathways at multiple levels, that is, screening and detection of AF, as well as early interventions to reduce stroke and other AF-related complications.

• Fitbit study

Another large-scale virtual study to identify episodes of irregular heart rhythm suggestive of AF was announced by Fitbit in May 2020 (MobiHealthNews, 2020 .

The next step beyond parameterizing safety could be to actionably guide therapy at the point of care (Figure 6 ). For example, patients could obtain ECGs before and after taking "pill-in-the-pocket" antiarrhythmic drug therapy such as flecainide to confirm AF, ensure no QRS widening, and confirm restoration of sinus rhythm. A similar approach has been proposed for rhythm-guided use of direct OACs in lower-risk AF patients with infrequent episodes either spontaneously or as the result of a rhythm control intervention including drugs and ablation; a randomized trial is in development (Passman et al., 2016) . The use of smartwatch-guided rate control as a treatment strategy could also be tested, as this may provide a more personalized approach rather than prior randomized trials of lenient versus strict rate control that used population level rather than personalized heart rate treatment thresholds (Van Gelder et al., 2010) .

This is key to application of results from trials. mHealth is widely available and often simple to apply and wear.

a. Older individuals and those with low health literacy may find technologies difficult to use (5.5 Digital Divide), and this may be compounded by disease state, for example, previous stroke.

b. Cost and service plans associated with smartphones and smartwatches may preclude their use in lower socioeconomic populations who are already under-represented in clinical trials and in many geographies.

Thus, patients who volunteer in mHealth studies in the USA are more likely to be a white/non-Hispanic, more educated, and less likely to have disease.

• Adherence mHealth-based evaluation of clinical endpoints may be confounded if adherence is low, particularly if there are no secondary means of endpoint assessments (Guo, Vittinghoff, Olgin, Marcus, & Pletcher, 2017) . Virtual designs may be more susceptible to the loss of participant engagement. For example, if monitoring is completely reliant upon mobile health technology and there are no traditional measures or in-person visits to assess arrhythmia, then significant missing data due to low-adherence may become a major limitation that could imperil the validity and generalizability of the findings. For example, among the 2,161 of the 419,297 that received an irregular pulse notification in the Apple Heart Study, only 945 completed a subsequent protocoled first study visit. Of these 658 ambulatory ECG patches shipped, there were only 450 with returned and analyzable data .

Development of effective strategies to increase retention and maintain high engagement remains an unmet need and is an area ripe for more research.

These are key to adoption and reimbursement. More specifically, , Glotzer et al., 2009 .

Does pill-in-the-pocket DOAC treatment of PAF adequately cover the risk of stroke? Some measures remain less well studied, like the occurrence of irregularity with a wearable pulse-based monitor system, particularly without ECG confirmation.

Since these mHealth prediagnostic or diagnostic tools may then be directly tied to initiation or termination of treatment, rigorous evaluation of clinical safety and efficacy will be required and, in some cases, warrant a combined drug-device regulatory approval.

Despite these challenges, there is enormous potential for patients to use these technologies to self-monitor their arrhythmia treatment and extend this to manage comorbidities (See Section 4). The process of data transparency and accessibility to the patient may improve the patient's engagement with their overall care, even if the data are not directly actionable by the patient.

The restrictions to clinic access during the SARS-Cov-2 pandemic have accelerated the adoption of mHealth solutions . ECGs for clinical trials were recorded by smart devices and assessed at virtual visits instead of routine in-person evaluations. In some cases, the entire management of clinical trials went online.

A fundamental but as yet unresolved challenge of incorporating mHealth into clinical practice is the channel of data communication between patient and provider. This may differ depending upon whether the data are physician-facing (e.g., for CIEDs) or patient-facing (consumer digital health products, e.g., the Apple Watch (Apple Inc., Cupertino, CA)).

CIEDs: Experience with CIEDs provides a framework. CIEDs generate voluminous quantities of eHealth data. In a single patient, this may be generated from distinct sources, that is, remote monitoring and in-person interrogations. Transmission from remote monitoring has been well worked out: data flow from the CIED to the remote transceiver and then to the manufacturer's server for access by individual practices. Unfortunately, this is usually retrieved in an image format rendering the granular data uninterpretable by the practice's electronic health record (EHR). When shared with the patient, the image file is posted on the EHR's patient portal. These files are difficult for physicians to interpret and practically uninterpretable by the lay public. In order to engage patients and caregivers, the data will need to be provided in a format that enables the lay public to get a high-level summary of key features (such as battery status and remote monitor function status) with explanations and the ability to drill down to the more granular details for those individuals who wish to do so.

Consumer digital health product data: Consumers are rapidly adopting products to monitor their health status for early detection of abnormalities as well as for managing chronic diseases. These tools empower and engage patients in managing their health, but the very basic task of sharing the data with their healthcare provider presents challenges. From a technical standpoint, many EHR portals do not permit patients to send attachments. Therefore, the patient and provider are left using email, which is not considered secure or HIPPA or GDPR compliant. Even if the EHR portal accepts attachments, incorporating the digital health data into the EHR remains ad hoc and inconsistent. The logistical and practical concerns frighten many care providers into discouraging their patients from using these devices. Concerns among providers include the fear of being inundated with unnecessary transmissions to review as well as the concern that patients may send inappropriate data, for example, BP or glucose monitoring data to their electrophysiologist. Cloud-based storage may avoid some of these challenges.

Assimilating the data obtained from digital health tools, whether implantable or wearable, is proving to be one of the greatest clinical challenges. Clinicians feel increasingly burdened as both the volume of data as well as the sources of data increase. Creating the nomenclature and data models that would enable the information to be incorporated in the electronic medical record is less a technical chal- 

There is interest in mHealth to support patients with text messaging or mobile applications to remind patients of medication doses and times or medical appointments. To be effective, this requires synchronization with healthcare providers, ideally by integration with the EMR, allowing changes in medications and doses, as well as appointments, to flow between patients and clinicians in an accurate and bidirectional manner (Spaulding et al., 2019) .

However, EMR systems software is lacking such functionality and interoperability at this point (Ratwani et al., 2018) .

Interconnection of medical devices and clinical data promises facilitation of clinical care but also creates opportunities for intrusions by maleficent actors (i.e., hackers) to disable systems and/or access private health information (PHI) (Jalali, Russell, Razak, & Gordon, 2019 , Kruse, Frederick, Jacobson, & Monticone, 2017 2. Theft and sale of patient data (i.e., PHI).

3. Company attack. A hacker may identify flaws in a system or device, short the company's stock, and then make the flaws public.

Alternatively, a maleficent user may try to harvest insider information from a breached company's network. Attackers may compromise a company, but not take any of the above actions. Instead, they may sell their methods or credentials to another group who will use them (Perakslis, 2014) Scenarios where a cyber attack results in the deaths of individuals or groups (e.g., by corrupting the firmware of a pacemaker or insulin pump) can be easily imagined and have been demonstrated by researchers (Klonoff, 2015) , but to date, no such attack is known to have occurred in the real world. It is possible that that this is because attacks against organizations yield greater gain than attacks against individuals.

It is essential therefore to establish best practice methods to maintain patient safety and privacy in this new ecosystem of remotely managed devices and mass data collection.

Often times, attackers will not directly compromise the system that they are after; they will instead start by compromising a weaker link.

For example, if the goal is to obtain PHI about a specific patient, they may attempt to get the patient (or a staff member) to install a malicious app, compromising the rest of the phone, including email and other credentials. From this point, the attacker is in a better position to attack the actual target. The process of chaining exploits to work through a system is called pivoting. Each pivot or "hop" enables new privileges that bring the hacker closer to desired goals.

The easiest thing to exploit is often a person with phishing campaigns. A compromised email account can be used to reset passwords for other services and to distribute more realistic phishing messages.

More technical attack pathways are used to compromise the remote-monitoring components of a healthcare system, for example, wireless links (bluetooth, wifi, etc.), Internet and local network communications or servers (databases, web frontends, file servers, etc.)

It is not possible to create systems that cannot be hacked. However, systems/devices should be designed to fail gracefully in conjunction with a plan. This enables rapid correction in the event of intrusion.

Business decisions (e.g., budget, timeline) should not override security which should be the priority. Attempting to close or obscure devices/protocols is not a solution, and the so called security through obscurity, as a defensive measure, has long been rejected as inadequate (Shanon, 1949) . A balance between usability and security has to be struck carefully. Securing devices against attackers, while keeping them open to clinicians is a difficult task. In mHealth, this difficulty can be amplified by the dependence on the patient's devices (e.g., smartphone) and practices, which are outside the control of a healthcare IT system. An example of an engineering compromise in implantable cardiac devices is the requirement for important wireless communications to only work at very short ranges. These communications could be made more secure but less usable (e.g., requiring wires), or less secure but more usable (e.g., using Bluetooth).

The organization should be designed with security in layers (also called defense in depth), where each system is protected with more than one layer of security. Hence, a breach in one layer will not necessarily result in total compromise. For example, a database may 1) require a password, 2) only grant a minimum level of access to each user, and 3) only accept internal connections. Thus, if a user's password is compromised (#1 failed), an attacker still cannot use it remotely. If the server is accidentally opened to remote access (#3 failed), the attacker can still only access that one user's data. Other innovative solutions include delegating security to a personal base station to use a novel radio design that can act as a jammer-cumreceiver (Gollakota et al., 2011) .

When recommending devices for patients, it is important to consider the potential privacy/security weaknesses compared to alternatives, ensure the patient is informed about these tradeoffs, and review how the manufacturer has responded to security incidents in the past (Saxon, Varma, Epstein, Ganz, & Epstein, 2018) . However, the lack of outcome data, combined with the lack of documented real-world instances of actual cybersecurity intrusions to these devices or to peripheral products that support device connectivity (programmer, home communicator, database, communication protocols) , pose a difficult risk-benefit assessment for clinicians and patients alike.

Regulatory frameworks around cybersecurity are changing rapidly (Voelker, 2018) . The FDA (as well as other regulatory agencies worldwide) now includes security as a part of device safety/efficacy checks, and we encourage readers to report security issues to manufacturers and the government (e.g., through FDA Medwatch) .

Clear advice to patients concerning cybersecurity should be followed by a formal patient informed consent.

Reimbursement is a powerful driver of adoption of new clinical pathways and typically instituted once an intervention has been proven scientifically valid and cost-effective (Treskes, van der Velde, Barendse, & Bruining, 2016) . This process has only just started in mHealth and may be more complex to measure given the wide scope of telemedicine.

•

This technology may promote an effective means for early diagnosis and treatment of arrhythmias and associated comorbidities, leading to benefits of screening, prevention, and early treatment, thereby reducing adverse effects related to delayed therapy and utilization of costly healthcare resources (e.g., ER visits or hospitalizations). mHealth may help individuals adhere to health recommendations, empower active participation in lifestyle changes to modify cardiovascular risk profile, and promote adherence to medical therapy (Feldman et al., 2018) . Together, these may reduce the burden of chronic disease and associated long-term disability. However, assessment of these longer-term cost advantages is challenging, and value will vary according to country and healthcare system. •

This provides valuable experience. RCTs conducted over many years that demonstrated safe and effective replacement of traditional in-clinic evaluations, and more effective discovery of asymptomatic clinical events (Varma, Epstein, Irimpen, Schweikert, Love, & TRUST Investigators, 2010) . Health-economic studies like EuroEco (ICD patients) showed that clinic time needed for checking webbased information, telephone contacts, and in-clinic discussion when required was balanced by fewer planned in-office visits with remote monitoring, resulting in a similar cost for hospitals vs. purely in-office follow-up (Heidbuchel et al., 2014) . From a payer perspective, there was a trend for cost-saving given fewer and shorter hospitalizations, seen also in other trials (Crossley et al., 2011 , Guedon-Moreau et al., 2014 , Hindricks et al., 2014 , Mabo et al., 2012 . However, in systems with fee-for-service reimbursement, less in-office visits (and hospitalizations) will lead to less income for the providers (i.e., physicians and hospitals) without adaption of the new remote-monitoring paradigm. This illustrates the complexities in reimbursement.

Currently, remote-monitoring reimbursement (e.g., USA, Germany, France, UK) is implemented in a discrete way following the protocols of randomized trials like TRUST or IN-TIME (Hindricks et al., 2014 , Varma, Epstein, Irimpen, Schweikert, Love, & TRUST Investigators, 2010 , Varma, Michalski, Epstein, & Schweikert, 2010 , with billing after demonstration of a remote contact, with a maximum number per year. Given the technological trend toward more continuous transmissions, and decision-support server systems that alert healthcare providers of potentially relevant information, possibly a subscription-based system providing a lump sum per

year per followed patient may be more effective. This should cover costs of hardware, software, and other services (like potential use of third-party data monitoring centers) and would result in a much better prospective budgeting for both healthcare insurers and providers. This scheme may be apt for mobile technology.

It is anticipated that mobile health technology may provide a more efficient and cost-effective approach to healthcare delivery that could improve clinical workflow and enhance clinical care when integrated into clinical practice . Linking this to improved outcome will be an important driver of reimbursement, for example, for a process leading to an arrhythmia management decision (but not when monitoring the large asymptomatic population without risk factors). Ongoing studies evaluating mobile technology, such as use of a smartphone ECG for AF screening in the AF SMART II (Atrial Fibrillation Screen, Management and Guideline Recommended Therapy) study, include a cost-effectiveness analysis . Responsibilities for reimbursement may extend beyond traditional parties in health care and drive novel pathways. Mobile device companies are clearly interested in reimbursement issues, evidenced by contact between Apple health executives and insurance companies (Bruining et al., 2014) .

Initiatives undertaken in the USA are described in Appendix.

The pace of changes and improvement of digital technology is furiously fast. With the release and spread of the 5G cellular technology, this growth will probably be strengthened, and new frontiers around data streaming and associated analytics will be crossed.

Unfortunately, this growth has been slower in the field of digital The potential synergy between AI and mHealth has excited the healthcare community since this may enable solutions to improve patient outcomes and increase efficiency with reduced costs in health care (Davenport & Kalakota, 2019; Marcolino et al., 2018) . Smartphone apps and wearable devices generate a huge amount of data that exceed the human capacity of integration and interpretation . Biometric datasets of astronomical proportions may be compiled. This knowledge may be directed to treat an individual or understand populations. For instance, 6 billion nights of surrogate sleep data reflecting global sleep deprivation may potentially inform public health initiatives (Pogue, 2020 ; https://aasmo rg/ fitbi t-scien tists -revea l-resul ts-analy sis-6-billi on-night s-sleep -data).

Mobile health with Internet connection enables cloud-based predictive analytics from individual-level information , Nascimento et al., 2018 .

Cardiology has been an early area of investigation in AI due to the abundance of data well suited for classification and prediction (Seetharam, Kagiyama, & Sengupta, 2019) . Neural networks have been tested, trained, and successfully validated to be at least as accurate,

if not more, than physicians in diagnosis or classification of 12-lead ECGs and recognition of arrhythmias in rhythm strips and ambulatory ECG recordings (Hannun et al., 2019 , Smith et al., 2019 . They have also been shown to successfully estimate ejection fraction, identify left ventricular dysfunction, and even diagnosis diagnose diseases such as hypertrophic cardiomyopathy from the echocardiogram (Zhang et al., 2018) . More recently, neural networks have also aided in gathering new dimensions of information, such as identifying left ventricular dysfunction (Attia, Kapa, et al., 2019) . These methods have the potential for a point-ofuse diagnosis of a wearable sensor or consumer device and without delays of requiring clinical conformation, although rigorous safety assessments of unsupervised use will be necessary. More recently, AI methods have also been applied to prediction, not just classification, for example, using 12-lead ECG to predict risk of AF from a sinus rhythm ECG (Attia, Noseworthy, et al., 2019b) .

Already, AI has been embedded in mHealth applications, such as smartwatch and smartphone-connect ECG semi-automated diagnosis of arrhythmias .

These diagnoses are intended to serve as prediagnostics rather than supplanting a physician interpretation. Application of artificial intelligence techniques to point-of-care ultrasound in the development of machine-learning systems may aid in the optimization of acquisition and interpretation of a high volume of images, reduce variability, and improve diagnostic accuracy (Chamsi-Pasha, Sengupta, & Zoghbi, 2017) . AI-based prediction models have been developed for HF and AF, although sometimes the accuracy of the AI-derived models seems to be rather limited or not superior than those derived from conventional methods (Awan, Bennamoun, Sohel, Sanfilippo, & Dwivedi, 2019 , Clifton, Niehaus, Charlton, & Colopy, 2015 , Frizzel et al., 2017 , Goto et al., 2019 , Safavi et al., 2019 , Tripoliti et al., 2019 . mHealth specific investigations are few. Results from the LINK-HF study were encouraging. A cloud-based analytics platform used a general machine-learning method of similarity-based modeling which models the behavior of complex systems (e.g., aircraft engines) to create a predictive algorithm for HF decompensation, using data streamed from a chest patch sensor.

Several limitations should be considered and roadblocks removed before AI-based mHealth strategies become routinely incorporated in clinical practice (Kagiyama, Shrestha, Farjo, & Sengupta, 2019 , Powell, 2019 . Studies on AI are still scarce and based on observational studies and secondary datasets. Validation in other clinical settings and a deeper evaluation of their meaning in every day practice are generally lacking. Thus, high-quality evidence that supports the adoption of many new technologies is not available. Most algorithms work with the "black box" principle, without allowing the user to know the reasons why a diagnosis or recommendation was generated, which can be a problem, especially if the algorithms were designed for a different environment than the one that the current patient is inserted , Weng, 2017 . Issues regarding cost-effectiveness, implementation, ethics, privacy, and safety are still unsolved.

mHealth is disruptive at multiple levels of health care but requires significant investment in validation, demonstration of clinical utility and value. Stakeholders, each with independent concerns and constraints, (Table 5 ) lack consensus or coordination with design, use cases, and implementation ( Figure 7) . Thus, formal recommendations for integration of mHealth into clinical practice cannot be made at this time.

This is exemplified by the US Preventative Services Task Forces statement that "evidence is insufficient to initiate therapy for AF detected by mHealth"-despite the fact that AF has been an early use case with strong patient and clinician interest (Curry et al., 2018) . Thus, mHealth devices are currently nonprescription devices marketed directly to consumers to track data without enabling interventions.

Some of the steps needed to standardize mHealth applications are outlined below.

• Promote standards and create tools for the comparative assessment of functionality, relative to a medical use device.

Results from different devices applied to the same condition may not match: for example, the diagnosis of AF by ECG or PPG based systems are made very differently. This has significant implications for medical decisions.

• Screening a. Assess value according to the population addressed b. Establish a uniform set of criteria for clinical actionability .

Screening should be medically directed and not driven by commercial interests. Caution should be exercised in extrapolating management strategies learned from cohorts with clinically diagnosed AF (usually from healthcare system data, trials or inpatient registries)

to AF detected with mHealth technologies ("healthy consumers").

Data from low-risk populations carry a relatively high risk of false positives, which may generate additional tests with resultant clinical risk to patient (even inducing anxiety rather than reassurance), risk from overtreatment, and costs to the payor. There is a risk that unless directed to a higher risk population, screening for AF using mHealth technologies may fail and follow the trajectory of many medical screening programs throughout history.

Key knowledge gap-Identify characteristics (duration, episode number/ density) and risk factors that justify anticoagulation for mHealth detected AF. 

• Cost-effectiveness Eg impact of improved clinical workflow and enhance clinical care, according to condition .

Impact on costs to patient or consumer.

• Public health and Professional society initiatives 

Patients control the intensity of monitoring and act on patient-facing data. Frequency of data acquisition is sporadic determined by, for example, convenience, or following symptoms, or recreational. This strategy is likely insensitive for events and rarely delivers rapid clinical actionability for life-threatening conditions.

What is required is as follows:

• Education on which data are clinically actionable in individual's clinical context and • Tailor monitoring schedule accordingly • Proof of safety.

In one recent example illustrates an on-demand use. The Fibricheck app was utilized by patients to monitor rate and rhythm for a week prior to teleconsultations during the COVID-19 pandemic to enable remote assessment of the disease state and support treatment decisions. This was regulated by a time-limited prescription to use the app for a predefined period, avoiding unnecessary data-load and additional follow-up patients-contacts (Pluymaekers et al., 2020) .

• Patients' legal right to their medical data to include data collected from nonmedical (i.e., consumer) products.

mHealth introduces the manufacturer as a party with significant responsibilities. mHealth tools largely have been developed as consumer-facing technologies accessible to a broader market through retail channels rather than through established medical supply channels. This may make business sense for the technology supplier,

given high community penetration of wearable, smart-technology devices (1 in 10 Americans (30 million total)). However, a direct to consumer healthcare delivery bypasses both the clinician, healthcare system, and insurer, without addressing the needs of health professionals-who remain responsible for clinical decision-making on acquired data. Any advance toward medical application (beyond toys for the worried well/ wealthy well) will require manufacturers to:

• Facilitate accessibility and affordability F I G U R E 7 Connectivity and Questions. Multiple levels of cooperation among a variety of stakeholders are needed to capitalize fully on the vast potential of mHealth, but many questions remain unanswered. Healthy consumers (increasing) predominate among mhealth users. Only a minority of patients are prescribed these digital tools. Potential health benefits of mHealth may be realized when manufacturer participates with clinic for validation in defined disease states. Parties responsible for data control-and therby predictive analytics-need to be defined. Ultimately, the payor and physician need to be convinced of benefits before digital tools are firmly embedded in clinical practice.

• Engage with clinicians to engineer devices according to clinical needs and partner in validation. This is vital, since physician carries ultimate responsibility for medical decisions and is best positioned to guide development and application

• Define role as data controllers (e.g., GDPR in Europe).

• Identify parties (manufacturer, hospital, third party) responsible for cybersecurity, data protection, and liability for mis-diagnosis or missed diagnosis • Define standard of care for clinic response time according to condition.

This assumes greater significance as clinical decisions become enabled in real time using cloud processing resources linked to enhanced data transmission rates (5G) and Internet of Things (IoT) and scalability increases.

• Ethical and societal issues with multiple screening , Turakhia, 2020 .

Interconnectedness between individual applications and with existing healthcare architectures may reshape the current environment.

• "Exception-based" ambulatory care, that is, see patients as they need to be seen • Centralized (cloud) based processing to forward only clinically relevant data to physician/clinic.

• Identify at-risk patients early (even before symptoms develop) and permit pre-emptive care (Boehmer 2017, Rosier et al., 2016) .

• Pooled population screening-altering the paradigm of individual screening (Yan et al., 2019 , Turakhia, 2020 • Extend the role of wearables from ambulatory to in-hospital care, a. Enable interventional procedures, for example, Tele-Robotic ablations models which could improve access to patients living in remote areas with highly skilled EPs operating remotely (Choi, Oskouian, & Tubbs, 2018 , Haidegger, Sándor, & Benyó, 2011 , Shinoda et al., 2020 .

b. Enable precision medicine by incorporation of the wider range of mobile signals seamlessly into genetic and clinical profile, with environmental and lifestyle data ("big data"). (https://ghr.nlm.nih.

gov/prime r/preci sionm edici ne/initi ative).

mHealth application is at different stages of evolution around the world. Few of the technologies described are universally approved and/or affordable in all countries. As a result, this document reflects largely US perspectives. The experience described may serve to guide other members of the international professional bodies endorsing this collaborative statement. The World Health Organization envisioned that increasing the capacity to implement and scale up cost-effective innovative digital health could play a major role in toward achieving universal health coverage and ensuring access to quality health services, at the same time recognizing barriers to implementation similar to those discussed in this document. Some of these can be resolved rapidly, as seen in response to the recent SARS-CoV-2 global pandemic that forced a need for contactless monitoring and thereby adoption of digital tools (DHSS, 2020 , FDA, 2020 . Regulatory bodies were responsive, approving technologies, relaxing rules confining use of telehealth services within borders and to certain patient populations, and creating a reimbursement structure, illustrating that appropriate solutions can be created when necessary.

Demonstration of the clinical utility of mHealth has the potential to revolutionize how populations interact with health services, worldwide.

Boehmer, J. P., Hariharan, R., Devecchi, F. G., Smith, A. L., Molon, G., Capucci, A., … Singh, J. P. 

CryptoAF investigators. Yield of atrial fibrillation detection with Textile Wearable Holter from the acute phase of stroke: Pilot study of Crypto-AF registry

Smartwatch algorithm for automated detection of atrial fibrillation

Diagnostic performance of a smartphone-based photoplethysmographic application for atrial fibrillation screening in a primary care setting

Head-to-Head Comparison of the AliveCor Heart Monitor and Microlife WatchBP Office AFIB for Atrial Fibrillation Screening in a Primary Care Setting

QTC intervals can be assessed with the AliveCor heart monitor in patients on dofetilide for atrial fibrillation

Performance of handheld electrocardiogram devices to detect atrial fibrillation in a cardiology and geriatric ward setting

Comparison of QT interval readings in normal sinus rhythm between a smartphone heart Monitor and a 12-Lead ECG for healthy volunteers and inpatients receiving sotalol or dofetilide

Assessment of remote heart rhythm sampling using the alivecor heart monitor to screen for atrial fibrillation: The REHEARSE-AF Study

Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study

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

Using a novel wireless system for monitoring patients after the atrial fibrillation ablation procedure: the iTransmit study

HRS/EHRA/APHRS/LAHRS/ACC/ AHA worldwide practical guidance for telehealth and arrhythmia monitoring during and after a pandemic

Detection of atrial fibrillation using an earlobe photoplethysmographic sensor

Photoplethysmography sampling frequency: Pilot assessment of how low can we go to analyze pulse rate variability with reliability?

The WATCH AF trial: SmartWATCHes for detection of atrial fibrillation

MAFA II Investigators. Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation

A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation

PULSE-SMART: Pulse-based arrhythmia discrimination using a novel smartphone application

Monitoring and detecting atrial fibrillation using wearable technology

Mobile phone-based use of the photoplethysmography technique to detect atrial fibrillation in primary care: Diagnostic accuracy study of the fibricheck app

Atrial fibrillation screening with photo-plethysmography through a smartphone camera

Head-to-head comparison of the AliveCor heart monitor and microlife WatchBP office AFIB for atrial fibrillation screening in a primary care setting

Atrial fibrillation screening during automated blood pressure measurement-Comment on "Diagnostic accuracy of new algorithm to detect atrial fibrillation in a home blood pressure monitor

Validation of a simple method for atrial fibrillation screening in patients with stroke

Opportunistic detection of atrial fibrillation using blood pressure monitors: A systematic review

Evidence and perspectives on the 24-hour management of hypertension: Hemodynamic biomarker-initiated 'anticipation medicine' for zero cardiovascular event

Triage tests for identifying atrial fibrillation in primary care: A diagnostic accuracy study comparing single-lead ECG and modified BP monitors

Comparison of Microlife BP A200 Plus and Omron M6 blood pressure monitors to detect atrial fibrillation in hypertensive patients

Opportunistic screening of atrial fibrillation by automatic blood pressure measurement in the community

Diagnostic accuracy of a home blood pressure monitor to detect atrial fibrillation

Comparison of the Microlife blood pressure monitor with the Omron blood pressure monitor for detecting atrial fibrillation

Detection of atrial fibrillation using a modified microlife blood pressure monitor

Screening for atrial fibrillation in patients >/=65 years using an automatic blood pressure monitor in a skilled nursing facility

Mobile phone detection of atrial fibrillation with mechanocardiography: The MODE-AF Study (mobile phone detection of atrial fibrillation)

Detection of atrial fibrillation using contactless facial video monitoring

On the Effect of Face Detection on Heart Rate Estimation in Videoplethysmography

Heart rate measurement based on a timelapse image

On the benefits of alternative color spaces for noncontact heart rate measurements using standard red-greenblue cameras

Remote plethysmographic imaging using ambient light

High-throughput, contact-free detection of atrial fibrillation from video with deep learning

Contact-free screening of atrial fibrillation by a smartphone using facial pulsatile photoplethysmographic signals

Contactless cardiac arrest detection using smart devices

Contactless infant monitoring using white noise

Prevalence of atrial fibrillation and cardiovascular risk factors in a 63-65 years old general population cohort: The Akershus Cardiac Examination (ACE) 1950 Study

Diagnostic performance of a smartphone-based photoplethysmographic application for atrial fibrillation screening in a primary care setting

Improved screening for silent atrial fibrillation after ischaemic stroke

Stepwise screening of atrial fibrillation in a 75-year-old population: Implications for stroke prevention

Mobile photoplethysmographic technology to detect atrial fibrillation

Assessment of Remote heart rhythm sampling using the alivecor heart monitor to screen for atrial fibrillation: The REHEARSE-AF Study

Yield and consistency of arrhythmia detection with patch electrocardiographic monitoring: The Multi-Ethnic Study of Atherosclerosis

Opportunistic screening versus usual care for diagnosing atrial fibrillation in general practice: A cluster randomised controlled trial

Asymptomatic atrial fibrillation: clinical correlates, management, and outcomes in the EORP-AF Pilot General Registry

Accuracy of patient perception of their prevailing rhythm: A comparative analysis of monitor data and questionnaire responses in patients with atrial fibrillation

Symptom representation and treatment-seeking prior to diagnosis of atrial fibrillation

Relationship between atrial tachyarrhythmias and symptoms

Discerning the incidence of symptomatic and asymptomatic episodes of atrial fibrillation before and after catheter ablation (DISCERN AF): A prospective, multicenter study

Progression of device-detected subclinical atrial fibrillation and the risk of heart failure

A comprehensive evaluation of rhythm monitoring strategies for the detection of atrial fibrillation recurrence: Insights from 647 continuously monitored patients and implications for monitoring after therapeutic interventions

Atrial fibrillation, cognitive decline, and dementia: An epidemiologic review

Stroke prevention in atrial fibrillation

A Report of the AF-SCREEN International Collaboration

Less dementia with oral anticoagulation in atrial fibrillation

High prevalence of atrial fibrillation among patients with ischemic stroke

Accuracy of mHealth devices for atrial fibrillation screening: Systematic review

Stepwise mass screening for atrial fibrillation using N-terminal B-type natriuretic peptide: the STROKESTOP II study

2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society in Collaboration With the Society of Thoracic Surgeons

Screening for atrial fibrillation with electrocardiography: Evidence report and systematic review for the US preventive services task force

2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS: The Task Force for the management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Endorsed by the European Stroke Organisation (ESO)

Estimated stroke risk, yield, and number needed to screen for atrial fibrillation detected through single time screening: a multicountry patient-level meta-analysis of 141,220 screened individuals

Adverse prognosis of incidentally detected ambulatory atrial fibrillation. A cohort study

Atrial fibrillation screen, management and guideline recommended therapy (AF SMART II) in the rural primary care setting: An implementation study protocol

Accuracy of smartphone camera applications for detecting atrial fibrillation: A systematic review and meta-analysis

Atrial fibrillation detection using single lead portable electrocardiographic monitoring: A systematic review and meta-analysis

Effect of a home-based wearable continuous ecg monitoring patch on detection of undiagnosed atrial fibrillation: The mSToPS randomized clinical trial

Mass screening for untreated atrial fibrillation: The STROKESTOP study

Duration of implantable cardiac monitoring and detection of atrial fibrillation in ischemic stroke patients: A systematic review and meta-analysis

Atrial fibrillation as an independent risk factor for stroke: The Framingham Study

Asymptomatic versus symptomatic atrial fibrillation: A systematic review of age/ gender differences and cardiovascular outcomes

Screening for atrial fibrillation

Head-to-head comparison of the alivecor heart monitor and microlife WatchBP office AFIB for atrial fibrillation screening in a primary care setting

A comprehensive evaluation of rhythm monitoring strategies for the detection of atrial fibrillation recurrence: Insights from 647 continuously monitored patients and implications for monitoring after therapeutic interventions

Atrial Fibrillation Burden: Moving Beyond Atrial Fibrillation as a Binary Entity: A Scientific Statement From the

RE-SPECT ESUS Steering Committee and Investigators. Dabigatran for Prevention of Stroke after Embolic Stroke of Undetermined Source

Stepwise screening of atrial fibrillation in a 75-year-old population: Implications for stroke prevention

American Heart Association Stroke Council

Council on Peripheral Vascular Disease. Oral antithrombotic agents for the prevention of stroke in nonvalvular atrial fibrillation: a science advisory for healthcare professionals from the American Heart Association/American Stroke Association

Stepwise mass screening for atrial fibrillation using N-terminal B-type natriuretic peptide: The STROKESTOP II study

Stroke associated with atrial fibrillation-incidence and early outcomes in the north Dublin population stroke study

2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society in Collaboration With the Society of Thoracic Surgeons

The AtRial cardiopathy and antithrombotic drugs in prevention after cryptogenic stroke randomized trial: Rationale and methods

Stroke risk as a function of atrial fibrillation duration and CHA2DS2-VASc score

2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS: The Task Force for the management of atrial fibrillation of the European Society of Cardiology (ESC)Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC Endorsed by the European Stroke Organisation (ESO)

Detection of atrial fibrillation after ischemic stroke or transient ischemic attack: A systematic review and meta-analysis

Epidemiology of ischemic stroke subtypes according to TOAST criteria: Incidence, recurrence, and long-term survival in ischemic stroke subtypes: A population-based study

Estimated stroke risk, yield, and number needed to screen for atrial fibrillation detected through single time screening: A multicountry patient-level meta-analysis of 141,220 screened individuals

Belgian Heart Rhythm Week Investigators. A population screening programme for atrial fibrillation: A report from the Belgian Heart Rhythm Week screening programme

CRYSTAL AF Investigators. Cryptogenic stroke and underlying atrial fibrillation

Searching for atrial fibrillation poststroke: A white paper of the Af-SCREEN international collaboration

Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: A systematic review and meta-analysis

Mass Screening for Untreated Atrial Fibrillation: The STROKESTOP Study

Duration of implantable cardiac monitoring and detection of atrial fibrillation in ischemic stroke patients: A systematic review and meta-analysis

Detection of atrial fibrillation by implanted devices with wireless data transmission capability

Subclinical atrial fibrillation and risk of stroke: Past, present and future

Atrial fibrillation burden: Moving beyond atrial fibrillation as a binary entity: A Scientific Statement from the

Effect of obstructive sleep apnea and its treatment of atrial fibrillation recurrence after radiofrequency catheter ablation: A meta-analysis

Comparison of QT interval readings in normal sinus rhythm between a smartphone heart Monitor and a 12-lead ECG for healthy volunteers and inpatients receiving sotalol or dofetilide

Atrial high rate episodes detected by pacemaker diagnostics predict death and stroke: Report of the atrial diagnostics ancillary study of the MOde selection trial (MOST)

Association of burden of atrial fibrillation with risk of ischemic stroke in adults with paroxysmal atrial fibrillation: The KP-RHYTHM study

Subclinical atrial fibrillation and the risk of stroke

2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society in Collaboration With the Society of Thoracic Surgeons

Obstructive sleep apnea and the recurrence of atrial fibrillation

Stroke risk as a function of atrial fibrillation duration and CHA2DS2-VASc score

2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS: The Task Force for the management of atrial fibrillation of the European Society of Cardiology (ESC)Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Endorsed by the European Stroke Organisation (ESO)

Probing oral anticoagulation in patients with atrial high rate episodes: Rationale and design of the Non-vitamin K antagonist Oral anticoagulants in patients with Atrial High rate episodes (NOAH-AFNET 6) trial

Rationale and design of the apixaban for the reduction of thrombo-embolism in patients with device-detected sub-clinical atrial fibrillation (ARTESiA) trial

IMPACT investigators randomized trial of atrial arrhythmia monitoring to guide anticoagulation in patients with implanted defibrillator and cardiac resynchronization devices

Long-term effect of goal-directed weight management in an atrial fibrillation cohort: A long-term follow-up study (LEGACY)

Practice variation in anticoagulation prescription and outcomes after device-detected atrial fibrillation

Clinical implications of brief device-detected atrial tachyarrhythmias in a cardiac rhythm management device population: Results from the registry of atrial tachycardia and atrial fibrillation episodes

Duration of device-detected subclinical atrial fibrillation and occurrence of stroke in ASSERT

Detection of atrial fibrillation by implanted devices with wireless data transmission capability

Alcohol abstinence in drinkers with atrial fibrillation

Smartphone activation of citizen responders to facilitate defibrillation in out-of-hospital cardiac arrest

Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data

Hemodynamic consequences of premature ventricular contractions: Association of mechanical bradycardia and postextrasystolic potentiation with premature ventricular contraction-induced cardiomyopathy

Can video mobile phones improve CPR quality when used for dispatcher assistance during simulated cardiac arrest?

Optimizing a drone network to deliver automated external defibrillators

Time to delivery of an automated external defibrillator using a drone for simulated out-of-hospital cardiac arrests vs emergency medical services

Contactless cardiac arrest detection using smart devices

The effectiveness of cardiopulmonary resuscitation instruction: Animation versus dispatcher through a cellular phone

Arrhythmia discrimination using a smart phone

International comparison of cost of falls in older adults living in the community: A systematic review

A smartphone application to reduce the time to automated external defibrillator delivery after a witnessed out-of-hospital cardiac arrest: A randomized simulation-based study

Cost of falls in old age: A systematic review

Smartphone apps for cardiopulmonary resuscitation training and real incident support: A mixed-methods evaluation study

An algorithm based on deep learning for predicting in-hospital cardiac arrest

Prediction of ventricular tachycardia one hour before occurrence using artificial neural networks

PULSESMART: Pulse-based arrhythmia discrimination using a novel smartphone application

Cell phone cardiopulmonary resuscitation: Audio instructions when needed by lay rescuers: A randomized, controlled trial

Automated external defibrillator geolocalization with a mobile application, verbal assistance or no assistance: A pilot randomized simulation (AED G-MAP)

Mobile phone-assisted basic life support augmented with a metronome

Machine learning algorithms for wireless sensor networks: A survey

Multi-centre randomised controlled trial of a smartphone-based event recorder alongside standard care versus standard care for patients presenting to the emergency department with palpitations and pre-syncope

Utility of a novel watch-based pulse detection system to detect pulselessness in human subjects

Mobile-phone dispatch of laypersons for CPR in out-of-hospital cardiac arrest

Smart watch recording of ventricular tachycardia: Case study

Effectiveness of the new "Mobile AED Map" to find and retrieve an AED: A andomized controlled trial

2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry

Wide complex tachycardia recorded with a smartphone cardiac rhythm monitor

Survival after application of automatic external defibrillators before arrival of the emergency medical system: Evaluation in the resuscitation out-comes consortium population of 21 million

Mobile phone use for contacting emergency services in life threatening circumstances

Performance of cellular phones with video telephony in the use of automated external defibrillators by untrained laypersons

Tailored telemonitoring in patients with heart failure: Results of a multicentre randomized controlled trial

Telemonitoring in patients with heart failure

Telemedical Interventional Monitoring in Heart Failure Investigators. Impact of remote telemedical management on mortality and hospitalizations in ambulatory patients with chronic heart failure: The Telemedical Interventional Monitoring in Heart Failure study

Better effectiveness after transition-heart failure (BEAT-HF) research group. Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure: the better effectiveness after transition-heart failure (BEAT-HF) randomized clinical trial

A randomized controlled trial of telemonitoring in older adults with multiple health issues to prevent hospitalizations and emergency department visits

Continuous wearable monitoring analytics predict heart failure hospitalization the LINK-HF multicenter study

Novel use of apple watch 4 to obtain 3-lead electrocardiogram and detect cardiac ischemia

Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: A randomized clinical trial

Telemedicine fighting acute coronary syndromes

Implantable cardiac alert system for early recognition of ST-segment elevation myocardial infarction

Implanted monitor alerting to reduce treatment delay in patients with acute coronary syndrome events

Quality of discharge practices and patient understanding at an academic medical center

American Heart Association Council on Cardiovascular Nursing and Stroke Council. Reducing delay in seeking treatment by patients with acute coronary syndrome and stroke: a scientific statement from the American Heart Association Council on Cardiovascular Nursing and Stroke Council

Mobile phone interventions for the secondary prevention of cardiovascular disease

Incidence and prognostic significance of atrial fibrillation in acute myocardial infarction: the GISSI-3 data

Tracking cardiac rehabilitation participation and completion among medicare beneficiaries to inform the efforts of a national initiative

The STAT-MI (ST-Segment analysis using wireless technology in acute myocardial Infarction) trial improves outcomes

Mobile phone text-messaging interventions aimed to prevent cardiovascular diseases (Text2PreventCVD): Systematic review and individual patient data meta-analysis

Mobile phone text messaging for improving secondary prevention in cardiovascular diseases: A systematic review

Feasibility and performance of a device for automatic self-detection of symptomatic acute coronary artery occlusion in outpatients with coronary artery disease: A multicentre observational study

Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: Results from a randomised controlled trial

Improving adherence to cardiovascular disease medications with information technology

Medication reconciliation accuracy and patient understanding of intended medication changes on hospital discharge

Home-based cardiac rehabilitation for people with heart failure: A systematic review and meta-analysis

Sustained efficacy of pulmonary artery pressure to guide adjustment of chronic heart failure therapy: complete follow-up results from the CHAMPION randomized trial

Economic impact of chronic heart failure management in today's cost-conscious environment

Heart disease and stroke statistics-2017 update: A report from the

Tailored telemonitoring in patients with heart failure: Results of a multicentre randomized controlled trial

Mobile technologies for managing heart failure: a systematic review and meta-analysis. Telemedicine and e-Health

Telemonitoring in patients with heart failure

Ambulatory hemodynamic monitoring reduces heart failure hospitalizations in "real-world" clinical practice

Consensus Statement. Remote monitoring of patients with heart failure: A white paper from the Heart Failure Society of America Scientific Statements Committee

Efficacy of telemedical interventional management in patients with heart failure (TIM-HF2): A randomised, controlled, parallel-group, unmasked trial

Telemedical Interventional Monitoring in Heart Failure Investigators. Impact of remote telemedical management on mortality and hospitalizations in ambulatory patients with chronic heart failure: The Telemedical Interventional Monitoring in Heart Failure study

Better effectiveness after transition-heart failure (BEAT-HF) research group. Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure: the better effectiveness after transition-heart failure (BEAT-HF) randomized clinical trial

Remote monitoring to Improve long-term prognosis in heart failure patients with implantable cardioverter-defibrillators

A randomized controlled trial of telemonitoring in older adults with multiple health issues to prevent hospitalizations and emergency department visits

Mobile technologies for managing heart failure: A systematic review and meta-analysis. Telemedicine and e-Health

Telemonitoring in patients with heart failure

Is your phone so smart to affect your states? An exploratory study based on psychophysiological measures

Smartphones in the secondary prevention of cardiovascular disease: A systematic review

Wearable textile based on silver plated knitted sensor for respiratory rate monitoring

Continuous wearable monitoring analytics predict heart failure hospitalization the LINK-HF multicenter study

A controlled trial of cardiac rehabilitation in the home setting using electrocardiographic and voice transtelephonic monitoring

Transtelephonic electrocardiographic monitoring of an outpatient cardiac rehabilitation programme

Exercise training in heart failure: from theory to practice. A consensus document of the Heart Failure Association and the European Association for Cardiovascular Prevention and Rehabilitation

Influence of Home-based telemonitored Nordic walking training on autonomic nervous system balance in heart failure patients

Effects of a 9-Week Hybrid Comprehensive Telerehabilitation Program on Long-term Outcomes in Patients With Heart Failure. The Telerehabilitation in Heart Failure Patients (TELEREH-HF) Randomized Clinical Trial

Telerehabilitation in heart failure patients: The evidence and the pitfalls

Hybrid comprehensive telerehabilitation in heart failure patients (TELEREH-HF): A randomized, multicenter, prospective, open-label, parallel group controlled trial-Study design and description of the intervention

Positive effects of the reversion of depression on the sympathovagal balance after telerehabilitation in heart failure patients

Quality of life in heart failure patients undergoing home-based telerehabilitation versus outpatient rehabilitation -a randomized controlled study

Home-based telemonitored Nordic walking training is well accepted, safe, effective and has high adherence among heart failure patients, including those with cardiovascular implantable electronic devices -a randomized controlled study

ESC Scientific Document Group. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC

Predictors of a sustained response to exercise training in patients with chronic heart failure: A telemonitoring study

Home-based cardiac rehabilitation. A scientific statement from the American Association of Cardiovascular and Pulmonary Rehabilitation the American Heart Association, and the American College of Cardiology

Mobile app for improved self-management of type 2 diabetes: Multicenter pragmatic randomized controlled trial

Telemonitoring in diabetes: Evolution of concepts and technologies, with a focus on results of the more recent studies

Prevalence and prevention of cardiovascular disease and diabetes mellitus

Association of metformin with lower atrial fibrillation risk among patients with type 2 diabetes mellitus: A population-based dynamic cohort and in vitro studies

Thiazolidinediones can prevent new onset atrial fibrillation in patients with non-insulin dependent diabetes

ESC Scientific Document Group. 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD

Association between pre-ablation glycemic control and outcomes among patients with diabetes undergoing atrial fibrillation ablation

Diabetes digital app technology: Benefits, challenges, and recommendations. A Consensus Report by the European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) Diabetes Technology Working Group

Performance of the FreeStyle Libre Flash glucose monitoring system in patients with type 1 and 2 diabetes mellitus

Improved well-being and decreased disease burden after 1-year use of flash glucose monitoring (FLARE-NL4)

Mobile phone and smartphone technologies for diabetes care and self-management

Diabetes mellitus and atrial fibrillation: Pathophysiological mechanisms and potential upstream therapies

Beneficial effect of pioglitazone on the outcome of catheter ablation in patients with paroxysmal atrial fibrillation and type 2 diabetes mellitus

Effect of mobile phone intervention for diabetes on glycaemic control: A meta-analysis

Influences of autonomic nervous system on atrial arrhythmogenic substrates and the incidence of atrial fibrillation in diabetic heart

Computer-based interventions to improve self-management in adults with type 2 diabetes: A systematic review and meta-analysis

Behavioral programs for type 1 diabetes mellitus: A systematic review and meta-analysis

Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control

Health education via mobile text messaging for glycemic control in adults with type 2 diabetes: A systematic review and meta-analysis

Rapid evidence review of mobile applications for self-management of diabetes

Atrial fibrillation and diabetes mellitus

Reduced medical spending associated with increased use of a remote diabetes management program and lower mean blood glucose values

Heart failure and diabetes mellitus: Defining the problem and exploring the interrelationship

Impact of lifestyle modification on atrial fibrillation

Home blood pressure management and improved blood pressure control: results from a randomized controlled trial

Effectiveness of home blood pressure telemonitoring: a systematic review and meta-analysis of randomised controlled studies

Absolute and attributable risks of atrial fibrillation in relation to optimal and borderline risk factors: the Atherosclerosis Risk in Communities (ARIC) study

Randomized clinical trial to assess the effectiveness of remote patient monitoring and physician care in reducing office blood pressure

Telemonitoring and self-management in the control of hypertension (TASMINH2): A randomised controlled trial

Efficacy of self-monitored blood pressure, with or without telemonitoring, for titration of antihypertensive medication (TASMINH4): An unmasked randomised controlled trial

Cost-effectiveness of telemonitoring and self-monitoring of blood pressure for antihypertensive titration in primary care (TASMINH4)

Self-monitoring of blood pressure in hypertension: A systematic review and individual patient data meta-analysis

Guidelines for the management of arterial hypertension

Effect of wearables on sleep in healthy individuals: A randomized crossover trial and validation study

Sleep and circadian influences on cardiac autonomic nervous system activity

Sleep duration and myocardial infarction

National Sleep Foundation's updated sleep duration recommendations: Final report

Sleep irregularity and risk of cardiovascular events: The multi-ethnic study of atherosclerosis

Consumer sleep technology: An American Academy of Sleep Medicine Position Statement

Reliability of sleep measures from four personal health monitoring devices compared to research-based actigraphy and polysomnography

Central sleep-disordered breathing predicts incident atrial fibrillation in older men

OSA and cardiac arrhythmogenesis: Mechanistic insights

Sleep Heart Health Study. Association of nocturnal arrhythmias with sleep-disordered breathing: The Sleep Heart Health Study

Meta-analysis of continuous positive airway pressure as a therapy of atrial fibrillation in obstructive sleep apnea

Automated prediction of the apnea-hypopnea index using a wireless patch sensor

Obstructive sleep apnea as a risk factor for atrial fibrillation: A meta-analysis

3, Extent and Health Consequences of Chronic Sleep Loss and Sleep Disorders

Is the risk of atrial fibrillation higher in athletes thanin the general population? A systematic review and meta-analysis

Risk of arrhythmias in 52 755 long-distance crosscountry skiers: A cohort study

Review on wearable technology sensors used in consumer sport applications

Young football italian amateur players remote electrocardiogram screening with telemedicine (you first) study: Preliminary results

Accuracy of smartphone apps for heart rate measurement

The accuracy of acquiring heart rate variability from portable devices: A systematic review and meta-analysis

Physical activity and the risk of incident atrial fibrillation in women

Exercise capacity and atrial fibrillation risk in veterans: A cohort study

Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: The IDEA randomized clinical trial

Patient activity and survival following implantable cardioverter-defibrillator implantation: The ALTITUDE activity study

Wearable performance devices in sports medicine

Mobile health advances in physical activity, fitness, and atrial fibrillation: Moving hearts

Physical activity and incidence of atrial fibrillation in older adults -The cardiovascular health study

ECG-based cardiac screening programs: Legal, ethical, and logistical considerations

Impact of CARDIOrespiratory fitness on arrhythmia recurrence in obese individuals with atrial fibrillation: The CARDIO-FIT study

A Critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations

Use of mobile applications to collect data in sport, health, and exercise science: A narrative review

The physical activity guidelines for Americans

Twenty-four hours of sleep, sedentary behavior, and physical activity with nine wearable devices

Wearable sensors for monitoring the internal and external workload of the athlete

Heart rate variability: An old metric with new meaning in the era of using mhealth technologies for health and exercise training guidance. Part two: Prognosis and training

Behavior change with fitness technology in sedentary adults: A review of the evidence for increasing physical activity

The growing value of digital health: Evidence and impact on human health and the healthcare system

Effect of weight reduction and cardiometabolic risk factor management on symptom burden and severity in patients with atrial fibrillation a randomized clinical trial

New mobile methods for dietary assessment: Review of image-assisted and image-based dietary assessment methods

Association between pre-ablation bariatric surgery and atrial fibrillation recurrence in morbidly obese patients undergoing atrial fibrillation ablation

Mobile health applications in weight management: A systematic literature review

Mobile applications for diabetes self-management: Status and potential

Single-view food portion estimation based on geometric models

Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence

Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications

Long-term effect of goal-directed weight management in an atrial fibrillation cohort a long-term follow-up study (LEGACY)

Evidence-based development of a mobile telephone food record

The use of mobile devices in aiding dietary assessment and evaluation

The atrial fibrillation therapies after ER visit: Outpatient care for patients with acute AF: the AFTER3 study

Behavioral Counseling to Promote a Healthy Lifestyle for Cardiovascular Disease Prevention in Persons With Cardiovascular Risk Factors: An Updated Systematic Evidence Review for the U.S. Preventive Services Task Force

Agency for Healthcare Research and Quality

Preventive Services Task Force Evidence Syntheses, formerly Systematic Evidence Reviews

mHealth in cardiovascular health care

Effectiveness, acceptability and usefulness of mobile applications for cardiovascular disease self-management: Systematic review with meta-synthesis of quantitative and qualitative data

Effect of mobile health interventions on the secondary prevention of cardiovascular disease: Systematic review and meta-analysis

Mobile health technology for atrial fibrillation management integrating decision support, education and patient involvement: mAF App trial

Patient-centred home-based management of heart failure. Findings from a randomised clinical trial evaluating a tablet computer for self-care, quality of life and effects on knowledge

European Society of Cardiology (ESC) Atrial Fibrillation Guidelines Taskforce, the CATCH ME consortium and the European Heart Rhythm Association (EHRA)

Mobile phone interventions for the secondary prevention of cardiovascular disease

The effectiveness of mobile-health behaviour change interventions for cardiovascular disease self-management: A systematic review

Transparent sharing of digital health data: A call to action

Novel wireless devices for cardiac monitoring

Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: Results from a randomised controlled trial

Department of Veterans Affairs. VA to provide capability for veterans to access their VA health data on Apple iPhones

Gamification and mHealth: A model to bolster cardiovascular disease self-management

Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: A randomized clinical trial

Behavior change techniques in top-ranked mobile apps for physical activity

Effectiveness, acceptability and usefulness of mobile applications for cardiovascular disease self-management: Systematic review with meta-synthesis of quantitative and qualitative data

Gamification: What it is and why it matters to digital health behavior change developers

Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques?

Gamification for health promotion: systematic review of behaviour change techniques in smartphone apps

Effect of Mobile Health Interventions on the Secondary Prevention of Cardiovascular Disease: Systematic Review and Metaanalysis

A smartphone application to support recovery from alcoholism: A randomized clinical trial

Gamification for health and wellbeing: A systematic review of the literature

Just-in-Time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support

Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework

A text message-based intervention for weight loss: Randomized controlled trial

The effectiveness of mobile-health behaviour change interventions for cardiovascular disease self-management: A systematic review

Internet and mobile phone text messaging intervention for college smokers

American Heart Association Council on Quality of Care and Outcomes Research, Council on Cardiovascular and Stroke Nursing, Council on Epidemiology and Prevention, Council on Peripheral Vascular Disease, and Stroke Council. Cardiovascular Health: The Importance of Measuring Patient-Reported Health Status A Scientific Statement From the

A systematic review of gamification in e-Health

The relationship between level of adherence to automatic wireless remote monitoring and survival in pacemaker and defibrillator patients

CrowdHelp: m-Health Application for Emergency Response Improvement through Crowdsourced and Sensor-Detected Information

Emerging patient-driven health care models: An examination of health social networks, consumer personalized medicine and quantified self-tracking

Health 2050: The realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen

Current science on consumer use of mobile health for cardiovascular disease prevention: A scientific statement from the

Telemonitoring in patients with heart failure

mHealth in cardiovascular health care

Sustaining health-protective behaviors such as physical activity and healthy eating

Mobile phone apps to promote weight loss and increase physical activity: A systematic review and meta-analysis

Identifying factors associated with dropout during prerandomization run-in period from an mhealth physical activity education study: The mPED trial

Mobile health technology evaluation: the mHealth evidence workshop

Remote management of heart failure using implantable electronic devices

mHealth in the wild: Using novel data to examine the reach, use, and impact of PTSD coach

Large-scale assessment of a smartwatch to identify atrial fibrillation

Barriers to and facilitators of engagement with remote measurement technology for managing health: Systematic review and content analysis of findings

The emerging field of mobile health

The effectiveness of self-management mobile phone and tablet apps in long-term condition management: A systematic review

Superiority of automatic remote monitoring compared with in-person evaluation for scheduled ICD follow-up in the TRUST trial -testing execution of the recommendations

Remote Patent Management Of Heart Failure Patients -How Long Should It Go On?

Sustaining healthy behaviours (AHA Simple 7)

Our healthy recipe finder app

Inside wearables: how the science of human behavior change offers the secret to long-term

A randomized trial of pocket-echocardiography integrated mobile health device assessments in modern structural heart disease clinics

Effectiveness, acceptability and usefulness of mobile applications for cardiovascular disease self-manegement: Systematic review with meta-synthesis of quantitative and qualitattive data

Mobile technology use across age groups in patients eligible for cardiac rehabilitation: Survey study

Mobile Technology Use Across Age Groups in Patients Eligible for Cardiac Rehabilitation: Survey Study

Assessing mHealth: Opportunities and barriers to patient engagement

The mobile revolution-using smartphone apps to prevent cardiovascular disease

Is there an app for that? Mobile phones and secondary prevention of cardiovascular disease

Record shares of Americans now own smartphones, have home broadband

Success of pacemaker remote monitoring using appbased technology: Does patient age matter?

Factors influencing engagement, perceived usefulness and behavioral mechanisms associated with a text message support program

Remote patent management of heart failure patients -how long should it go on? Lancet Digital Health

Cryoballoon or radiofrequency ablation for atrial fibrillation assessed by continuous monitoring: A randomized clinical trial

Atrial fibrillation burden: moving beyond atrial fibrillation as a binary entity: A scientific statement from the

The relationship between daily atrial tachyarrhythmia burden from implantable device diagnostics and stroke risk: The TRENDS study. Circulation: Arrhythmia and Electrophysiology

Volunteer participation in the Health eHeart Study: A comparison with the US population

Mobile photoplethysmographic technology to detect atrial fibrillation

Mobile health technology to improve care for patients with atrial fibrillation

Stroke risk as a function of atrial fibrillation duration and CHA2DS2-VASc score

Smartphone-based geofencing to ascertain hospitalizations

Targeted anticoagulation for atrial fibrillation guided by continuous rhythm assessment with an insertable cardiac monitor: The rhythm evaluation for anticoagulation with continuous monitoring (REACT.COM) pilot study

Large-scale assessment of a smartwatch to identify atrial fibrillation

Long-term electrocardiographic safety monitoring in clinical drug development: A report from the Cardiac Safety Research Consortium

Atrial fibrillation burden, progression, and the risk of death: a case-crossover analysis in patients with cardiac implantable electronic devices

Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study

Lenient versus strict rate control in patients with atrial fibrillation

Detection of atrial fibrillation by implanted devices with wireless data transmission capability

HRS/EHRA/APHRS/LAHRS/ACC/ AHA Worldwide Practical Guidance for Telehealth and Arrhythmia Monitoring During and After a Pandemic

Progression of Device-Detected Subclinical Atrial Fibrillation and the Risk of Heart Failure

Fitbit launches large-scale consumer health study to detect a-fib via heart rate sensors

Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: A randomized clinical trial

A usability and safety analysis of electronic health records: A multi-center study

HRS White Paper on interoperability of data from cardiac implantable electronic devices (CIEDs)

Corrie health digital platform for self-management in secondary prevention after acute myocardial infarction

They can hear your heartbeats: Non-invasive security for implantable medical devices

EARS to cyber incidents in health care

Cybersecurity for connected diabetes devices

Cybersecurity in healthcare: A systematic review of modern threats and trends

Ransomware: taking businesses hostage

Hospitals become major target for ransomware

Cybersecurity in health care

Factors influencing the decision to proceed to firmware upgrades to implanted pacemakers for cybersecurity risk mitigation

Communication theory of secrecy systems

FDA regulation of mobile medical apps

FDA joins new effort to strengthen medical device cybersecurity

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

The CONNECT (Clinical Evaluation of Remote Notification to Reduce Time to Clinical Decision) trial: the value of wireless remote monitoring with automatic clinician alerts

Harnessing mHealth technologies to increase physical activity and prevent cardiovascular disease

Costs of remote monitoring vs. ambulatory follow-ups of implanted cardioverter defibrillators in the randomized ECOST study

EuroEco (European Health Economic Trial on Home Monitoring in ICD Patients): a provider perspective in five European countries on costs and net financial impact of follow-up with or without remote monitoring

Implant-based multiparameter telemonitoring of patients with heart failure

The Cost-Effectiveness of Digital Health Interventions on the Management of Cardiovascular Diseases: Systematic Review

A randomized trial of long-term remote monitoring of pacemaker recipients (the COMPAS trial)

Atrial Fibrillation Screen, Management and Guideline Recommended Therapy (AF SMART II) in the rural primary care setting: an implementation study protocol

Mobile health in cardiology: a review of currently available medical apps and equipment for remote monitoring

Efficacy and safety of automatic remote monitoring for implantable cardioverter-defibrillator follow-up: the Lumos-T Safely Reduces Routine Office Device Follow-up (TRUST) trial

Automatic remote monitoring of implantable cardioverter-defibrillator lead and generator performance: the Lumos-T Safely RedUceS RouTine Office Device Follow-Up (TRUST) trial

U.S. Food and Drug Administration Precertification Pilot Program for Digital Health Software: Weighing the Benefits and Risks

FDA regulation of mobile medical apps

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction

Machine learning-based prediction of heart failure readmission or death: Implications of choosing the right model and the right metrics

Smartwatch algorithm for automated detection of atrial fibrillation

Handheld echocardiography: Current state and future perspectives

Health informatics via machine learning for the clinical management of patients

The potential for artificial intelligence in healthcare

Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: Comparison of machine learning and other statistical approaches

New AI Prediction Model Using Serial PT-INR Measurements in AF Patients on VKAs: GARFIELD-AF

Assessment of remote heart rhythm sampling using the alivecor heart monitor to screen for atrial fibrillation: The REHEARSE-AF study

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

Artificial intelligence: Practical primer for clinical research in cardiovascular disease

The impact of mhealth interventions: Systematic review of systematic reviews

Integration of echocardiographic screening by non-physicians with remote reading in primary care

Trust Me, I'm a Chatbot: How Artificial Intelligence in Health Care Fails the Turing Test

Teleelectrocardiography and bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) study

Automatic Diagnosis of the Short-Duration 12-Lead ECG using a Deep Neural Network: the CODE Study

Toward a patient-centered, data-driven cardiology

Development and validation of a machine learning model to aid discharge processes for inpatient surgical care

Application of mobile health, telemedicine and artificial intelligence to echocardiography

A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation

The emerging field of mobile health

A knowledge management system targeting the management of patients with heart failure

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Fully automated echocardiogram interpretation in clinical practice

Exclusive: What Fitbit's 6 billion nights of sleep data reveals about us

Telesurgery: Past, present, and future

US Preventive Services Task Torce, screening for atrial fibrillation with electrocardiography: US preventive services task force recommendation statement

Surgery in space: The future of robotic telesurgery

The cost-effectiveness of digital health interventions on the management of cardiovascular diseases: Systematic review

On-demand app-based rate and rhythm monitoring to manage atrial fibrillation through tele-consultations during COVID-19

Personalized and automated remote monitoring of atrial fibrillation

Early clinical experience of radiofrequency catheter ablation using an audiovisual telesupport system

HRS White Paper on interoperability of data from cardiac implantable electronic devices (CIEDs)

Diagnosing with a camera from a distance-proceed cautiously and responsibly

HRS/EHRA/APHRS/LAHRS/ACC/ AHA worldwide practical guidance for telehealth and arrhythmia monitoring during and after a pandemic

High-throughput, contact-free detection of atrial fibrillation from video with deep learning

Enforcement policy for non-invasive remote monitoring devices used to support patient monitoring during the coronavirus disease-2019 (COVID-19) public health emergency