key: cord-0950220-37j2x9ds
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: J Arrhythm
DOI: 10.1002/joa3.12461
sha: 3182085e84cbdd4ff38dcf73a0335b1cf52ee352
doc_id: 950220
cord_uid: 37j2x9ds

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.

goe/publi catio ns/goe_mheal th_web.pdf;https://apps.who.int/gb/ ebwha/ pdf_files/ WHA71/ A71_20-en.pdf?ua=1). 2,3 Utilization of these devices has proliferated among health-conscious consumers in recent years and is likely to continue rapid expansion and integration into more formalized medical settings. mHealth flows intuitively to health professionals in the field of arrhythmia management from experience gained through remote monitoring of cardiovascular implantable electronic devices (CIEDs), such as pacemakers and implantable cardioverter-defibrillators (ICDs). 4 A wealth of data garnered from many studies over the last 10-15 years have confirmed the benefits of remote technologyassisted follow-up and established it as standard of care. 5,6 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 selfmonitored 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 selfmonitoring. 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 screening and surveillance for undiagnosed disease to longitudinal disease management, and importantly, engaging patients in their own cycle of care, allowing much health care to be asynchronous and virtualized. Its value and degree of integration will depend on different healthcare systems in different countries. Table 1 ). 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. 10

1. Turakhia 

This is the cornerstone diagnostic method, and the choice of technique and time frame depend on whether symptoms (e.g., 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;

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

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.

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. 21

• 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, [22] [23] [24] and several studies have been performed to evaluate the diagnostic accuracy of these devices. [25] [26] [27] [28] [29] 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). allow the user to perform a "spot check" single-lead ECG strip, usually of up to 30 seconds or longer by placing a finger of each hand on the two electrodes, usually located on the phone case or external card (Figure 3 ). The ECG electrical signal is transmitted wirelessly to a smartphone with an integrated interpretation app.

The tracings can be reviewed on the smartphone, electronically stored, or transmitted for review by the user's provider if desired.

These have been directed largely to AF.

Automated algorithms can label the recording as "Possible AF" on the basis of criteria for the presence and absence of a P wave and the irregularity of the RR interval; "Normal" or "Sinus Rhythm" and "Unreadable" when the detector indicates there was too much interference for an adequate recording, whether from too much movement, or poor contact between the electrodes and the patient's skin.

Several versions of the AliveCor's automated algorithms have been evaluated, 16,51-54 and the device has been tested as a screening tool in at-risk populations. 52, 55 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 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. 61 The performance of algorithms interpreting these PPG signals has been proven to be in high agreement with ECG rhythm strips. 60, 62, 63 The smartphone-based PPG applications have been utilized in at-risk population to detect AF and as a screening tool in the general population 64 (See Section 6).

The PPG technology has also been incorporated in smartwatches to measure heart rate and rhythm. 65 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. 79 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. 80 

There are preliminary reports on using commodity smart devices to identify agonal breathing. 88,89 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. 90 Devices capable of recording at least one ECG lead allow the interpreting clinician to distinguish between wide-and narrow-complex rhythms, bradycardia, and tachycardia, and thus distinguish between the various causative rhythms. Smart devices may be useful in pediatric patients. 91

The disease is often intermittent and asymptomatic, which may delay diagnosis. 92-94 lead to incorrect estimation of AF burden, 95, 96 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. Atrial fibrillation identification depends on factors having to do with the arrhythmia itself, that is the combination of AF prevalence and density, 105 

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. [123] [124] [125] 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, 126 

Cryptogenic stroke/TIA Up to one-third of ischemic strokes is attributed to AF mediated embolism to the brain. 132 Further, the risk of recurrent thromboembolism is high if AF is left undetected and untreated. 133, 134 Hence, prolonged monitoring for AF poststroke has been recommended in recent guidelines. 99, 130, 135 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. [136] [137] [138] A metaanalysis showed that a stepwise approach to AF detection in poststroke patients led to AF detection in 23.7% of patients, 139 while a combined analysis of two randomized and two observational studies showed a 55% reduction in recurrent stroke following prolonged cardiac monitoring. 129 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. 140 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 141 and RESPECT-ESUS, 142 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). 143 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. 138 The threshold of AF burden may very well differ in patients who have had a suspected cardioembolic event and those who have not. 140

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 par ticularly well suited to capture intermittent or nonpersistent arrhy thmias; however, it is likely that frequent sampling would be necessar y to capture infrequent parox ysmal AF and even daily "snapshot" ECG monitoring may miss half of AF episodes. 105, 144 AF burden, increasingly recognized as a power ful independent predictor of stroke, 145 and burden in terms of cumulative load (hours/day) and concentration (density of AF days). 146 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. 140, 146, 152, 153 This led to oral anticoagulation intervention trials to determine the ability to reduce stroke on the basis of AF duration. 154,155 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. 156 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. 

See also section 4.1 Ischemia heart disease.

The use of mHealth technology to diagnose ventricular arrhythmias lags behind its application to AF (See Section 3.1). 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. 172 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. 

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. 175 However, such continuous monitoring is present already in CIEDs and has not yet proven to be sufficiently predictive to be clinically useful. 176 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. 177,178 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. 179 Preliminary reports indicate that smart speakers in commodity smart devices may be able to identify agonal breath patterns for sudden cardiac death detection. 180 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. 181 [183] [184] [185] [186] One limitation is that as such apps are unregulated, many do not convey current basic life support algorithms and may have poor usability. 187 In addition, delay in commencing CPR and in calling emergency services due to distraction of the rescuer by using an app is a concern. 188 Automated external defibrillator (AED) use in cardiac arrest is associated with improved survival, but AED use remains low. 189 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. [190] [191] [192] 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. 193 Feasibility has been demonstrated. 194

The complete chain from activation of citizen responders was tested in the Heartrunner trial 195 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.

108. Svennberg 

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 196 and received a Class 1 recommendation in most recent guidelines. 130 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. 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 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.

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. 203, 204 This paradigm has now been widely implemented. 

Heart failure is widely prevalent, costly to manage, and degrades patient outcomes. 215 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. 228 High rates of patient engagement, acceptance, usage and adherence have been reported in some trials but not others. 231, 232 Preliminary results using a disposable multisensor chest patch in the LINK-HF study were encouraging, 44 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. 233, 234 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. 235 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) 44

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. 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. 242, 243 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. 244

Diabetes mellitus is a strong risk factor for the development of morbidity and mortality associated with a range of cardiovascular diseases. Stand-alone diabetes management apps have recently been reviewed. 259 Short-term measures, such as HbA1c, may be improved by such apps in conjunction with clinical support, but many have suboptimal usability. 260 Phone-based interventions were associated with improved glycemic control as compared to standard care. [261] [262] [263] [264] Efficacy for improving glycemic control in randomized controlled trials has shown mixed results. 265, 266 Meta-analyses 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. 267 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. 268 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. 

Physical activity is any bodily movement from skeletal muscle contraction to increase energy expenditure above basal level (see 

These are a unique category. Endurance athletes may have increased AF risk. 303 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. 319 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. 320

By contrast, a visual image-based app, such as the Technology-Assisted Dietary Assessment (TADA) system, directly addresses the aforementioned shortcomings. [321] [322] [323] This is in research phase.

The TADA system consists of two main components: (a) A smartphone app that runs on either iPhones (iOS) or Android devices: the Mobile Food Record (mFR), and (b) 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. 324 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. 325 There remains a need for further evidence development before specific apps or other mHealth technology can be recommended or prescribed. 

Generally, structured management programs inclusive of intensive patient education may improve outcomes. [326] [327] [328] 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. 329 In this way, mHealth may facilitate information sharing and interaction between patients and HCPs without the need for an elaborate infrastructure ( Figure 6) . 330 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. 340 

Incorporation of a patient as part of a wider community may offer benefits. Social networking is widely used for health. 358 Online communities enable individuals to "meet," share their experiences, discuss treatment, and receive and provide support from peers, patient organizations, or HCPs. [350] [351] [352] [353] [354] [355] [356] [357] [358] [359] [360] 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. 361

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, [362] [363] [364] [365] [366] [367] [368] [369] [370] webEndevaour, Apple Heart Study).

A representative patient's experience is described below: CIEDs. 378 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. 379 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. 380 However, the lack of familiarity with the technology and access to mobile devices, rather than lack of engagement per se, remain the principal barriers. [381] [382] [383] Older users of mHealth prefer personalized information, which is clearly presented and is easy to navigate. 384 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. 385 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. 386 mHealth permits information and apps to be tailored appropriately for language, literacy levels (including "text to speech" technology), and cultural differences to promote engagement. 381, 387, 388 There is heterogeneity of mHealth availability among different countries. 389 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 smartphonebased 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 resourcelimited areas. 390 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 sitebased study visits. Examples include remote rhythm assessment with single-or multilead ECGs from smartphone or smartwatchbased technologies and automatic ascertainment of hospitalizations using smartphone-based geofencing. 393 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 sitefree 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.

6.1.1 | The Apple heart study 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. 87, 394 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. 395 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.

A similar study was performed using smart device-based (Huawei fitness band or smartwatch) PPG technology. 396 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. 397 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.

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

(HRS 2020 7 May 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. 398 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. 399

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

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. 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 414

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, 415 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 cam- 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 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. 417 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. 418 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. 419 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). 420

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. 421 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. 422

Together, these may reduce the burden of chronic disease and associated long-term disability. However, assessment of these longerterm cost advantages is challenging, and value will vary according to country and healthcare system. Healthcare providers will also be required to spend time reviewing and interpreting potentially voluminous results (and associated phone calls) prior to making additional evaluation and management decisions. This requires financial compensation in order to maintain a viable practice.

This provides valuable experience. RCTs conducted over many years that demonstrated safe and effective replacement of traditional inclinic evaluations, and more effective discovery of asymptomatic clinical events. 423 Health-economic studies like EuroEco (ICD patients) showed that clinic time needed for checking web-based 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. 424 From a payer perspective, there was a trend for cost-saving given fewer and shorter hospitalizations, seen also in other trials. [425] [426] [427] [428] 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, 427 

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 (ht tps://aasmo rg/fitbi t-scien tist s -revea l-resul t s-analy sis-6-billi on-night s-sleep -data). 440 Mobile health with Internet connection enables cloud-based predictive analytics from individual-level information. [441] [442] [443] Cardiology has been an early area of investigation in AI due to the abundance of data well suited for classification and prediction. 444 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. 445 

• Cost-effectiveness Eg impact of improved clinical workflow and enhance clinical care, according to condition. 429 Impact on healthcare system and reimbursement.

Impact on costs to patient or consumer.

• Public health and professional society initiatives Education, awareness

Bring together stakeholders Guidelines. 

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. 462 • 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, smarttechnology 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 decisionmaking on acquired data. Any advance toward medical application (beyond toys for the worried well/wealthy well) will require manufacturers to:

• Facilitate accessibility and affordability

• 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 misdiagnosis 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.

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.

• Ethical and societal issues with multiple screening. 463,464

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. 465 • Pooled population screening-altering the paradigm of individual screening. 464, 466 • 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. [467] [468] [469] 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, FDA). [470] [471] [472] 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. (used for reporting on a Rhythm ECG, 1-3 leads, without interpretation and report) would not be appropriate for patient initiated mobile device events as this would require an order that is triggered by an event followed by a separate signed and retrievable report. However, there must be a clinically relevant reason for the physician to need to review the data each month.

Validation and clinical use of a novel diagnostic device for screening of atrial fibrillation

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

Intermittent short ECG recording is more effective than 24-hour Holter ECG in detection of arrhythmias

Yield of screening for atrial fibrillation in primary care with a hand-held, single-lead electrocardiogram device during influenza vaccination

Performance of short ECG recordings twice daily to detect paroxysmal atrial fibrillation in stroke and transient ischemic attack patients

Safe automatic one-lead electrocardiogram analysis in screening for atrial fibrillation

Screening for atrial fibrillation in hospitalised geriatric patients

The diagnostic accuracy of the MyDiagnostick to detect atrial fibrillation in primary care

Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring

Diagnostic utility of a novel leadless arrhythmia monitoring device

Use of a noninvasive continuous monitoring device in the management of atrial fibrillation: A pilot study

Feasibility of extended ambulatory electrocardiogram monitoring to identify silent atrial fibrillation in high-risk patients: The Screening Study for Undiagnosed Atrial Fibrillation (STUDY-AF)

Prevalence and characteristics of subclinical atrial fibrillation in a community-dwelling elderly population: The ARIC study

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

Validity and reliability of the hexoskin wearable biometric vest during maximal aerobic power testing in elite cyclists

A randomized trial evaluating the accuracy of AF detection by four external ambulatory ECG monitors compared to permanent pacemaker AF detection

Evaluation of a new shirt-based electrocardiogram device for cardiac screening in soccer players: Comparative study with treadmill ergospirometry

The use of a smart-textile garment during high-intensity functional training: A pilot study

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

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

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

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

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

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

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

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

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

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

Detection of atrial fibrillation using an earlobe photoplethysmographic sensor

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

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

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

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

The WATCH AF trial: SmartWATCHes for detection of atrial fibrillation

Mobile photoplethysmographic technology to detect atrial fibrillation

Monitoring and detecting atrial fibrillation using wearable technology

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

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

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

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

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

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

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

Detection of atrial fibrillation using a modified microlife blood pressure monitor

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

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)

Heart rate measurement based on a time-lapse image

Remote plethysmographic imaging using ambient light

Detection of atrial fibrillation using contactless facial video monitoring

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

On the Effect of Face Detection on Heart Rate Estimation inVideoplethysmography

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

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

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

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

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

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

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

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)

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

High prevalence of atrial fibrillation among patients with ischemic stroke

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

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

Less dementia with oral anticoagulation in atrial fibrillation

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

Accuracy of mHealth devices for atrial fibrillation screening: Systematic review

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

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

Stroke prevention in atrial fibrillation

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

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

A report of the AF-SCREEN international collaboration

AHA/ACC/HRS focused update of the

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

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

Council on Cardiovascular Nursing; Council on Clinical Cardiology; 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

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

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

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

Cryptogenic stroke and underlying atrial fibrillation

Newer technologies for detection of atrial fibrillation

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

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

RE-SPECT ESUS Steering Committee and Investigators. Dabigatran for prevention of stroke after embolic stroke of undetermined source

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

Simulation of daily snapshot rhythm monitoring to identify atrial fibrillation in continuously monitored patients with stroke risk factors

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

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

Screening for atrial fibrillation

Hong Kong citizens with smartphone electrocardiogram

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

Belgian Heart Rhythm Week Investigators. A population

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

Contactless cardiac arrest detection using smart devices

Mobile phone use for contacting emergency services in life threatening circumstances

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

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

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

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

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

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

Mobile phone-assisted basic life support augmented with a metronome

Mobile phone-assisted basic life support augmented with a metronome

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

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

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

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

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

Lifestyle and risk factor modification for reduction of atrial fibrillation: A scientific statement from the

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

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

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

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

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

Telemedicine fighting acute coronary syndromes

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

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

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

Improving adherence to cardiovascular disease medications with information technology

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

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

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

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

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 transitionheart failure

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

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

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

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

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

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

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

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

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

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

Telemonitoring in patients with heart failure

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

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

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

Telerehabilitation in heart failure patients: The evidence and the pitfalls

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

Transtelephonic electrocardiographic monitoring of an outpatient cardiac rehabilitation programme

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

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

Homebased 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

Positive effects of the reversion of depression on the sympathovagal balance after telerehabilitation 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

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 inter vention

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

Prevalence and prevention of cardiovascular disease and diabetes mellitus

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

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

Atrial fibrillation and diabetes mellitus

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

Impact of lifestyle modification on atrial fibrillation

ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 selfmonitoring of blood pressure for antihypertensive titration in primary care (TASMINH4)

ESC/ESH Guidelines for the management of arterial hypertension

Sleep duration and myocardial infarction

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

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

US); 2006. 3, Extent and Health Consequences of Chronic Sleep Loss and Sleep Disorders

Sleep and circadian influences on cardiac autonomic nervous system activity

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

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

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

Reliability of sleep measures from four personal health monitoring devices compared to researchbased actigraphy and polysomnography

Automated prediction of the apneahypopnea index using a wireless patch sensor

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

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

Physical activity and the risk of incident atrial fibrillation in women

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

The physical activity guidelines for Americans

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

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

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

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

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

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

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

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

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

Risk of arrhythmias in 52 755 longdistance crosscountry skiers: A cohort study

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

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

Review on wearable technology sensors used in consumer sport applications

Wearable performance devices in sports medicine

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

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

Accuracy of smartphone apps for heart rate measurement

The accuracy of acquiring heart rate variability from portable devices: A systematic review and metaanalysis

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

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

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

Outcomes of atrial fibrillation ablation in morbidly obese patients following bariatric surgery compared with a nonobese cohort

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

Mobile applications for diabetes self-management: Status and potential

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

New mobile methods for dietary assessment: Review of image-assisted and image-based dietary assessment methods. The Proceedings of the Nutrition Society

Evidence-based development of a mobile telephone food record

The use of mobile devices in aiding dietary assessment and evaluation

Single-view food portion estimation based on geometric models

Mobile health applications in weight management: A systematic literature review

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 TaskForce

Agency for Healthcare Research and Quality(US); 2014. U.S. Preventive Services Task Force Evidence Syntheses, formerly Systematic Evidence Reviews

Nurseled 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

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

mHealth in cardiovascular health care

Novel wireless devices for cardiac monitoring

Transparent sharing of digital health data: A call to action

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

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

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

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

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

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

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

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

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

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

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

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

Internet and mobile phone text messaging intervention for college smokers

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

Mobile phone interventions for the secondary prevention of cardiovascular disease

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

The effectiveness of mobile-health behaviour change interventions for

A systematic review of gamification in e-Health

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

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

The Social Life of Health 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

CrowdHelp: m-Health application for emergency response improvement through crowdsourced and sensor-detected information

Telemonitoring in patients with heart failure

Mobile phone apps to promote weight loss and increase physical activity: A systematic review and metaanalysis

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

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

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

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

Heart and stroke etools for a healthier you

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

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

National Heart Foundation of Australia. My heart, my life

Sustaining healthy behaviours (AHA Simple 7)

Our healthy recipe finder app

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

Mobile health technology evaluation: The mHealth evidence workshop

The emerging field of mobile health

Superiority of automatic remote monitoring compared with inperson 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? Lancet Digital Health

Record shares of Americans now own smartphones, have home broadband. Pew Research Center

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

Success of pacemaker remote monitoring using app-based technology: Does patient age matter?

The mobile revolution-using smartphone apps to prevent cardiovascular disease

Mobile FactSheet

Assessing mHealth: Opportunities and barriers to patient engagement

Is there an app for that? Mobile phones and secondary prevention of cardiovascular disease. Current Opinion Cardiology

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?

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

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

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

Smartphone-based geofencing to ascertain hospitalizations

Large-scale assessment of a smartwatch to identify atrial fibrillation

Large-scale assessment of a smartwatch to identify atrial fibrillation

Mobile photoplethysmographic technology to detect atrial fibrillation

Mobile health technology to improve care for patients with atrial fibrillation

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

Alings AM, …Van den Berg MP. Lenient versus strict rate control in patients with atrial fibrillation

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

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

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

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

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

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

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

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

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

EARS to cyber incidents in health care

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

Taking businesses hostage

Hospitals become major target for ransomware

Cybersecurity in health care

Cybersecurity for connected diabetes devices

Communication theory of secrecy systems

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

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

FDA joins new effort to strengthen medical device cybersecurity

FDA regulation of mobile medical apps

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

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

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

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

Implant-based multiparameter telemonitoring of patients with heart failure (IN-TIME): A randomised controlled trial

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

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

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

Task Force of the e-Cardiology Working Group. 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

FDA regulation of mobile medical apps

S. Food and Drug Administration

Food and Drug Administration precertification pilot program for digital health software: Weighing the benefits and risks

Artificial intelligence: Practical primer for clinical research in cardiovascular disease

The potential for artificial intelligence in healthcare

The emerging field of mobile health

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

Smartwatch algorithm for automated detection of atrial fibrillation

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

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

Application of mobile health, telemedicine and artificial intelligence to echocardiography

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

Automatic diagnosis of the short-duration 12-lead ECG using a deep neural network: TheCODE study

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

Fully automated echocardiogram interpretation in clinical practice

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

An artificial intelligenceenabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction

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

Handheld echocardiography: Current state and future perspectives

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

Health informatics via machine learning for the clinical management of patients

Prediction of 30-day allcause 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

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

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

Toward a patient-centered, datadriven cardiology

Can machinelearning improve cardiovascular risk prediction using routine clinical data?

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

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

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

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

Personalized and automated remote monitoring of atrial fibrillation

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

Telesurgery: Past, present, and future

Surgery in space: The future of robotic telesurgery

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

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

Notification of enforcement discretion for telehealth remote communications during the COVID-19 nationwide public healthemergency

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