2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals

Abstract 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

R E FE R E N C E S S EC T I O N 1
1. Turakhia

| Ambulatory ECG monitoring
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.

| New mHealth-based modalities for arrhythmia monitoring
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
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.

| Contactless video plethysmography
Noncontact video monitoring of respiration and heart rate have been developed less than 15 years ago. 80

| Smart speakers
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.

| mHE ALTH APPLI C ATI ON S FOR ARRHY THMIA S
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

| Atrial fibrillation
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

Accuracy
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).

Outcomes
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

| Targeted identification in high-risk individuals
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

Other high-risk individuals
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.

CIEDS
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.

| Sudden cardiac death
See also section 4.1 Ischemia heart disease.

Ventricular arrhythmias
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.

Prediction
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.

Notification and reaction
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

| Clinical trial
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.

R E FE R E N C E S S EC T I O N 3
108. Svennberg

| COMORB IDITIE S
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).

| Ischemic heart disease
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.

| Emergency teams
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
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.

| Hybrid telerehabilitation in patients with heart failure
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.

N=100
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
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
Physical activity is any bodily movement from skeletal muscle contraction to increase energy expenditure above basal level (see

Competitive athletes
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.

| PATIENT S ELF-MANAG EMENT-INTEG R ATED CHRONI C C ARE
Generally, structured management programs inclusive of intensive patient education may improve outcomes. [326][327][328] These may be facilitated by mHealth.

| Patient engagement
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.

| Behavioral modification
Individual health status has been found to be a strong independent predictor of mortality and cardiovascular events. 340

| Patients as part of a community
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
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.

| Digital divide
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/).

| Screening
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.

| The Huwaei heart study
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.

| Fitbit study
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).

| Point of care
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

| Generalizability
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.

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

| Hacking strategies and methods in mHealth technologies
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).

| Recommendations to clinicians and administrators
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

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

| Reimbursement
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.

| Reduced costs
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.

| Remote monitoring of implanted devices
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

| Regulatory landscape for mHealth devices
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

| Implementation
• 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.

| Patient self-management
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.

| Manufacturer
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).

| Assign responsibilities
• 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

| Healthcare delivery
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).

CON CLUDING REMARK S
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.