Returning cardiac rhythm data to patients: opportunities and challenges

1. Introduction

Atrial fibrillation (AF) is highly prevalent, affecting an estimated 37 million people globally.¹ Its prevalence continues to climb dramatically; it is estimated that 12 million individuals in the U.S. alone will have AF by 2030.² AF is a complex chronic cardiac condition that requires routine self-management to reduce risk of stroke and mortality.^3–10 AF represents a major public health burden, accounting for more than 750,000 hospitalizations, 130,000 deaths, and $6 billion in costs each year.²

Timely detection of AF, initiation of thromboembolic prophylaxis, and often restoration of normal sinus rhythm critical to improve disease management through medication and lifestyle adjustments and reduce negative health outcomes of AF, such as hospitalization, stroke, or death.^11,12 Timely detection also has the potential to improve quality of life and reduce public health burden of AF.^11,12 The most common approaches for detecting and managing AF typically include brief windows of electrocardiogram (ECG) monitoring and 12-lead ECGs at prescheduled health visits.¹³ These approaches are inadequate given the sporadic, unpredictable nature of the arrhythmia, so that AF often goes undetected and thus untreated.^11,13

Across healthcare sectors, digital health tools are transforming access that patients have to their own health data, as well as empowering them to assume a more central role in detecting symptoms and self-managing their condition. In the field of cardiology, novel digital cardiac devices represent a convergence between healthcare and emerging digital technologies, and include tools intended for healthcare providers, patients, or both. In the field of cardiac electrophysiology, digital health tools for AF fall into four primary categories: cardiac implantable electronic devices (CIEDs), consumer electrocardiogram (ECG) devices, mobile health applications (mHealth apps) and medical grade telemetry monitors. The 2019 clinical guidelines for the management of patients with AF include recommendations for the use of CIEDs to detect silent AF in patients with cryptogenic stroke over age 40.¹⁴ The guidelines also acknowledge the potential role that consumer ECG devices and mHealth apps have for screening and timely detection of arrhythmias, including silent AF.

The growth of consumer ECG devices and mHealth apps represents novel opportunities to assist AF patients with AF self-management. Consumer ECG devices such as the AliveCor™device allow individuals with AF to easily record and transmit an ECG to their healthcare provider for review using a device that is interoperable with both iOS and Android smartphones. The AliveCor™ algorithm in the mHealth app that accompanies the device attempts to identify arrhythmias such as AF.¹⁵ The SEARCH-AF study found that use of this technology in community settings was both cost-effective and feasible.¹⁶ The integration of ECG technology with mHealth apps can support both population screening and timely detection of AF.

The Apple Watch is another example of a consumer ECG device that enables individuals to record a single-lead ECG in an easy, accurate, and timely manner.¹⁷ As these devices improve in precision, they also facilitate earlier time to diagnose AF recurrence¹⁸ and population screening for AF among currently undiagnosed (estimated to be 13%)¹⁹ and asymptomatic patients with AF.

From the patient perspective, individuals with AF perceive a need for ECG mHealth technology. In a recent survey, most individuals living with a cardiac condition, such as AF, reported a need for technology-based support to increase health knowledge, decrease travel and accessibility restraints, and better utilize peer support.²⁰ Given the increased accessibility of the digital health tools for population screening and timely detection, and interest among patients to use these devices, there are significant opportunities and challenges for the future of patient-facing heart rhythm data.

2. The history and evolution of patient-facing data in electrophysiology

Whereas the use of medical and consumer technologies to monitor patients with a range of chronic conditions has become increasingly popular in recent years²¹, cardiac electrophysiology has been at the forefront of remote patient monitoring for decades. However, since its introduction in the 1970s, remote monitoring has primarily been used to inform healthcare providers about patients’ cardiac rhythm and function outside of clinical settings and guide medical decision-making.

The patients’ important role in ensuring continued device communication in remote monitoring, troubleshooting device problems, attending follow-up appointments for interrogation, understanding what to do in an emergency because of delays in transmission has been acknowledged in recent years. This has led to expert recommendations from the Heart Rhythm Society to educate the patient about remote monitoring and their unique responsibilities in the process.²² As the trend of engaging patients in remote monitoring has continued to gain popularity, and technologies and platforms to monitor cardiac rhythm and shared health data electronically have become increasingly available to patients, there has been increasing interest and efforts to return cardiac rhythm data and other data about health status to patients. While the use of digital health tools to monitor and share cardiac rhythm data with patients has been mostly conducted for AF screening and management, the return of these data may also benefit patients with a number of other arrhythmias in the future.²²

3. The rationale for returning cardiac rhythm data to patients

The impetus for returning cardiac rhythm data has experienced both a “push” from the federal government and professional societies, as well as a “pull” from patients to share CIED data with more transparency about their own health information. Together these forces support a strong rationale for returning CIED data to patients. The Office of the National Coordinator for Health Information Technology (ONC), a branch of the Department of Health and Human Services, released the final rule from the 21st Century Cures Act, effective June 2020.²³ The new provisions in the final rule support interoperability and support access, exchange, and use of electronic health information by patients. This ruling is consistent with expert recommendations from the HRS to support interoperability of data from CIEDs.²⁴

Currently, if patients receive any information about their CIEDs, it is typically limited to device status indicators (such as battery status and functioning of the mobile communicator).²⁵ Patients report that they would like to receive information about arrhythmia episodes, changes in health status over time, and more routine and detailed information about their device status and functioning.²⁵ Increasing transparency by providing access to their own health information gives patients insight and more control over their care, including attending follow-up appointments for interrogation, and preventing emergencies by being more prepared for events such as a low battery or other errors with data transmission.

The primary reasons for returning data back to patients include the improved ability for patients to manage their own health for both primary and secondary disease prevention, as well as increased patient satisfaction. Studies of digital health information exchange from other domains have demonstrated positive health outcomes when patients have access to their health information.^26,27 Patients report improved understanding of their disease and factors that contribute to it because they are able to collect and visualize their data more efficiently with digital technologies.^28,29 Patients also report that remote monitoring is acceptable, convenient, and associated with good quality of life.³⁰ This is critical, because patient satisfaction and acceptance of remote follow-up is critical for the successful implementation of remote monitoring.³⁰ Furthermore, the latest thinking from the digital health and research communities at large is that there exists an ethical imperative to return this data to patients as the original source of the data and key stakeholder in the care delivery process.^31,32

In conclusion, the combination of the push from the ONC final rule and the HRS which represents electrophysiologists, as well as the pull from patients who want access to their own health information, mean that medical device companies and healthcare systems alike are re-examining existing models of remote patient monitoring and moving towards providing relevant cardiac health data back to patients.²⁵

4. Avenues for returning patient data

4.1. Consumer applications and devices to monitor cardiac rhythm

Digital health tools that are directly available to consumers are dramatically changing the health technology landscape across every discipline. Cardiology is no exception; consumer applications and devices offer patients the greatest access to their cardiac rhythm data and other relevant data about health compared to other technologies that collect or contain cardiac rhythm data. These devices allow patients to record and directly view cardiac information without requiring a healthcare professional to initiate or mediate data access. In this way, these devices act like a “window” into one’s cardiac rhythm that is not often available to them. The two main areas of growth are in mHealth apps, which leverage native features and functionalities of the phone itself to monitor health, and other consumer ECG devices, which explicitly monitor cardiac rhythm.

mHealth apps may include functionalities to record, view, and share health data. They may also leverage the phone itself in these functionalities--most notably, they can create photoplethysmography (PPG) waveforms by using the light-emitting diode (LED) flash from a smartphone camera to detect changes in tissue blood volume in a user’s finger.³³ An example of an mHealth app available to consumers for PPG waveform monitoring is the Qardio app, is shown in Figure 1. Several recent systematic reviews have noted an increasing number of applications being developed for atrial fibrillation, many of which include PPG waveform monitoring as their primary functionality.^34–36 These apps may also support medication adherence and symptom monitoring, motivate positive lifestyle behaviors, and provide relevant patient education.

Figure 1:

The freely available Qardio provides PPG waveform monitoring to consumers

mHealth apps offer the unique advantages of being free or low cost, and leverage the smartphone technology that 81% of Americans³⁷ and over 5 billion adults worldwide³⁸ already own. However, because cardiac rhythm monitoring is a functionality that can be supported with the technology but is not the sole or explicit purpose of it, the accuracy of mHealth apps to monitor cardiac rhythm is not as high as is it for consumer ECG devices that are explicitly designed for this purpose. Some consumer devices, such as the Apple Watch™, have numerous functionalities and applications, but have been engineered to allow users to directly measure and store their cardiac rate and rhythm. Others, such as AliveCor’s Kardia™ device, are simply hardware that directly records cardiac rate and rhythm, but communicates with an mHealth app to visualize the rhythm to users and allow data to be stored and shared.

The inclusion of the cardiac rhythm monitoring feature in newer generations of the Apple Watch has generated much excitement because of its popularity and widespread adoption. The Apple Watch uses PPG technology to monitor pulse rate over time and contains an algorithm that can automatically identify irregular waveforms as a possible sign of AF.³⁹ The incorporation of this technology into the popular consumer device has already been leveraged for population screening efforts on an unprecedented scale. The Apple Heart Study is likely the most dramatic example of this; in this study, the authors enrolled nearly 420,000 individuals over 8 months to use the Apple Watch for cardiac rhythm screening.⁴⁰ For those participants receiving irregular rhythm alerts, more intensive monitoring and cardiologist follow-up were initiated to confirm a diagnosis of AF.

Another device that has been available to patients for a longer period of time (it was first approved by the Food and Drug Administration [FDA] in 2012) is the Kardia™ device made by AliveCor.⁴¹ The device is a piece of lightweight hardware that attaches or can be placed next to a smartphone or tablet. The user places their fingertips on the device to record a 30-second single lead (lead II) ECG. Using Wi-Fi or cellular network transmission, the device communicates with a Kardia smartphone application. In this application, users can view their ECGs as they are recording them, review prior ECGs, and share ECGs. Furthermore, the application employs a proprietary algorithm to detect AF. Research has shown that the ECG tracing is highly sensitive (100%) and specific (94%), but the algorithm interpreting the ECG is somewhat lower (sensitivity: 55–70%, specificity: 60–69%).⁴² Studies have found that patients find AliveCor easy to use.^43,44

It is worth noting that there are also a number of mHealth apps that do not directly measure cardiac rhythm but that nonetheless collect and return valuable information to patients with arrhythmias. These applications are focused more generally on self-management and symptom monitoring, and may be beneficial to patients with chronic arrhythmias such as AF. For example, a number of applications are designed to allow patients to track symptoms over time, visualize their data, and uncover trends that may help them better understand their symptoms. One example is the Symple application (https://www.sympleapp.com/), which allows users to track a wide range of symptoms and includes sophisticated visualizations of longitudinal symptom data (Figure 2). Another example is the Medly application (https://medly.ca/), which allows patients to record daily weight, blood pressure, heart rate and symptoms. It includes an algorithm that generates personalized self-care messages (i.e., adjust diuretic medications) and sends real-time alerts to healthcare providers as needed. This application is currently being evaluated in the Medly-AID trial, a randomized, controlled trial in Canada.⁴⁵

Figure 2:

The freely available Symple application allows users to track multiple symptoms and visualize longitudinal trends

4.2. Patient-facing applications with CIEDs

Some CIEDs come with patient portals or other mechanisms of showing patients information about their CIED. The implications for returning data from CIEDs are distinct from other consumer technologies like mHealth apps and consumer ECG devices. By nature of being implantable, access to cardiac rhythm data requires more technical steps, as devices may need to be interrogated, or transmissions may only be available to healthcare providers and not the patient. In contrast, many consumer ECG devices and mHealth apps make cardiac rhythm data immediately viewable, offering opportunities for population-level screening of arrhythmias, in addition to ongoing monitoring and management.⁴⁶

Therefore, attention has been focused on providing timely access to device data along with non-technical summaries of device functionality and relevant clinical implications.⁴⁷ Most often, this information includes device battery status and functioning of the communicator. Recently there has been increased interest in sharing information about cardiac events and other health data collected with these devices. For example, the USC Center for Body Computing has developed a patient-facing, device-agnostic mHealth app for patients to visualize complex cardiac rhythm data in simplified ways, communicate with healthcare providers, and learn more about their arrhythmia.^48,49

4.3. Inpatient portals

Transparent access to personal health information has been championed for hospitalized patients in an effort to increase transparency and engage vulnerable patients with their health information in real-time.^50,51 Providing patients with access to their health information through a patient portal while in the hospital is termed inpatient portals. Inpatient portals include access to medications being administered, short videos explaining the purpose of each medication as well as potential side effects, links to comprehensive medication information from MedlinePlus, documented allergies, diagnostic test orders and results, current documented diet, and vital signs and weight.⁵⁰ Access has also been hypothesized to improve patient activation, safety, and satisfaction with hospitals and healthcare providers.^52–59 The results of a randomized clinical trial among patients with heart failure did not result in improving patient activation, but it was associated with patients looking up health information online, a lower 30-day hospital readmission rate, without increasing burden on healthcare professionals, and without having a negative impact on direct healthcare delivery.⁵⁰ Overall, this study supports the growing body of evidence supporting transparent access to health information.

5. Opportunities to improve outcomes through patient-facing data

5.1. Screening for atrial fibrillation

There exists a significant opportunity to screen for arrhythmias using mHealth apps and consumer ECG devices. Many of these tools leverage PPG, which involves using light-based sensors to detect changes in tissue blood volume that result from peripheral pulses in order to create pulse waveforms. The light-emitting diode [LED] flash from a smartphone camera can create PPG waveforms. As a result, smartphones can be used to record PPG waveforms; this creates significant potential to monitor for arrhythmias, particularly AF, on a population level.¹³ The fact that many of the mHealth apps that use PPG technology are freely available to patients improves their ability to be adopted and used by a wide range of individuals, including those who may lack financial resources to use more expensive technologies for rhythm monitoring. For example, in a recent study, 12,000 individuals were screened for AF over 7 days using PPG, and 136 individuals (1%) screened positive for AF and then received a confirmatory clinical diagnosis of AF.⁶⁰

However, a recent app review found the reviewed apps had wide variability in quality, functionality, and adherence to self-management behaviors.³⁶ Moreover, several studies have reported widely variability in the positive predictive values of these technologies for population screening, which suggests their performance may still vary between populations.^61–64 PPG waveforms are also prone to false positive findings because they cannot distinguish from among AF, atrial or ventricular premature contractions, and variable atrioventricular conduction (atrioventricular block).⁶⁰ Ultimately, patient and provider education needs to focus on the strengths and limitations of PPG waveforms for AF screening. These technologies should be used for screening but not confirmation of a diagnosis of a heart rhythm disorder; a 12-lead ECG or other guideline-recommended monitoring technology is still clinically necessary for diagnosis.

5.2. Self-management and self-monitoring of disease

AF is particularly difficult to manage even with medication, lifestyle changes, and interventional procedures, such as ablations or the placement of internal monitors.^51,52 Even after procedures to restore normal sinus rhythm, AF recurrence is high. For example, the CABANA trial recently evaluated over 2,200 symptomatic patients undergoing ablation and found that AF recurred in 50% of cases, and 17% needed a repeat ablation.⁵⁸

Lifestyle-related risk factors for AF include stress, obesity, high cholesterol, poor nutrition, low exercise, hypertension and smoking. The iHEART trial is an example of a randomized control trial that swas designed to support prevention of AF recurrence and behavioral lifestyle interventions, including medication adherence, increasing exercise, losing weight and eating a healthy diet.⁴⁴ Participants were given access to both Alivecor and behavioral altering text messages that were centered around the American Heart Association Life’s Simple Seven educational materials, and sent to patients with AF risk factors three times per week in English or Spanish.

Overall, the relationship between AF episodes and symptoms is complex and not well understood.^65–67 The reasons why some patients experience a heavy burden of symptoms but not a heavy AF burden, or the converse, is poorly understood.^65,66 Nonetheless, symptoms impact quality of life and are a primary indicator for ablation. As such, monitoring and managing symptoms is a critical aspect of AF management that can be facilitated with digital health tools. Common symptoms of AF include both physical and psychological symptoms, including dyspnea, chest pain, fatigue, anxiety, and palpitations.⁶⁵ For many patients with AF, symptoms limit daily functioning and impact their quality of life.⁶⁷ Furthermore, patients have expressed a desire to better understand the relationship between their AF symptoms and cardiac rhythm.^25,68 Therefore, appropriate management of AF symptoms can improve quality of life and mitigate psychological distress.

Despite the clear need for increased symptom management in AF, the symptom tracking and visualization functionality in mHealth apps or consumer ECG devices for AF are still in their nascent stages.³⁶ Future technology should offer functionality that allows patients to track a range of physical and psychological symptoms, correlate symptoms with cardiac rhythm, lifestyle changes, and various therapies (medications, catheter ablation), and generate insights about strategies for improved management.⁶⁸

5.3. Shared decision-making

Shared decision-making (SDM) is the process of discussing risks and benefits of treatment options in the context of a patient’s values, expectations, and preferences with the goal of selecting a treatment that aligns with these priorities.⁶⁹ SDM is an increasingly important and common practice that involves presenting health data to patients. SDM has been encouraged for a number of use cases in electrophysiology, and even mandated by the Centers for Medicare and Medicaid Services in specific cases, such as in anticoagulation decisions for AF patients with high stroke risk.³⁵

A central component of the SDM process is the use of decision aids. These are tools that present information about risks and benefits with the goals of improving patient knowledge, engagement, and satisfaction in the process.⁷⁰ Traditionally, decision aids have been paper-based pamphlets or educational materials that can be easily disseminated in clinical practices and other healthcare settings. Recently, many web-based decision aids have also been developed; these are often interactive, allowing the patient to specify details about themselves (demographics, medical history) and preferences for treatment so that the system can tailor the way information is displayed. Example - mayo. Drawbacks = access. Regardless of the medium, information visualization is frequently incorporated to support comprehension. For example, a number of visualization-based decision aids aimed at helping patients choose the right anticoagulant for stroke prevention in AF have been developed;^71–75 one example is shown in Figure 3.

Figure 3:

Pictogram showing the annual stroke risk of a patient with atrial fibrillation and a CHADS2 score of 3 (From Seaburg L, Hess E, Coylewright M, Ting H, McLeod C, Montori V. Shared Decision Making in Atrial Fibrillation. Circulation. 2014;129(6):704–710. doi:10.1161/circulationaha.113.004498; with permission)

6. Challenges and Recommendations

Returning data to patients is still in its nascent stages, with several key questions remaining unanswered. Below we highlight three major challenges in the field to date, and offer recommendations to address these challenges.

Challenges	Recommendations
1) Comprehension of information	Develop visualizations to support and aid comprehension of complex information.
2) Managing patient-healthcare professional communication to reduce patient-level anxiety	Provide patient education and support, set clear goals and expectations around data exchange, consult stakeholders when establishing goals and expectations for a specific practice or organization.
3) Sustaining patient engagement	Develop patient-facing digital health tools through participatory design with end users to align features with unique needs and preferences.

6.1. Comprehension of complex informational from remote monitoring tools

A significant challenge for the return of remote monitoring patient information is the exponential quantity of data that patients need to sort, interpret and then act upon in order for the information to be relevant and useful. A significant barrier is low graph literacy that varies by education level and socioeconomic status.⁷⁶ In the US, at least one-third of US adults cannot correctly interpret graphs.⁷⁷ This is problematic because the vast majority of cardiac health information is returned to patients in the form of line graphs.⁷⁸

In the context of the challenge of comprehension, information visualizations are a promising solution for supporting patients with interpreting their health information.^79–81 Visualizations support cognitive processing by leveraging human potential for perceiving differences in the sizes, shapes, colors, and spatial positions of objects, and giving meaning to those differences.^82,83 Visualizations are particularly meaningful because they can support comprehension of information without relying on high literacy or numeracy skills. Colors can facilitate interpretation instead of text.⁸¹ Real-time changes to visualizations are also easier to deliver when they are integrated into digital technologies.⁷⁹ Importantly patients are interpreting complex visualizations from a different mental model than health care professionals who have advanced statistical and medical knowledge.^84,85

Displaying raw results without interpretation or contextualization at best is ineffective,^50,86 and at worst perpetuates intervention-generated inequity,⁸⁷ a phenomena where well-intentioned interventions worsen existing health disparities rather than reduce them,^50,86–90 and miscommunication.⁹¹ Appropriate use of visualizations for returning patient information is needed to ensure that patients are able to understand and,when appropriate, act upon health data in a safe and effective manner.⁷⁸

An example of an application of visualization of complex cardiac data from implantable devices is found in a participatory design study by Ahmed and colleagues (Figure 4).^92–94 Participatory design approaches are an important method for ensuring that visualizations are acceptable to patients. In this work, the authors have conducted participatory design to create a dashboard displaying complex data from cardiac resynchronization therapy (CRT) devices. Patients reported wanting to visualize a range of data elements, including daily pacing reports, symptoms, and health tips, in addition to device functioning (battery life and recorded events).⁹⁵

Figure 4:

An example patient-facing visualization of complex cardiac data from implantable devices published by Ahmed and colleagues (From Ahmed R, Toscos T, Rohani Ghahari R et al. Visualization of Cardiac Implantable Electronic Device Data for Older Adults Using Participatory Design. Appl Clin Inform. 2019;10(04):707–718. doi:10.1055/s-0039-1695794; with permission)

6.2. Managing patient-healthcare professional communication to reduce patient-level anxiety about patient health information

The possibility of a cardiac arrhythmia recurring, often with little to no warning, causes many patients with cardiac rhythm disorders to develop symptoms of anxiety.⁹⁶ For example, in the recent “iPhone Helping Evaluate Atrial Fibrillation Rhythm through Technology” (iHEART) trial, over 30% of the 171 adults with AF enrolled in the trial reported clinically significant anxiety.⁹⁷ Furthermore, this study found the severity of anxiety was associated with the severity of AF. In a separate qualitative study with iHEART trial participants, anxiety was commonly reported as both a reason for using digital technology (specifically, the AliveCor Kardia device), and a reason for discontinuing use.⁶⁸ Specifically, some participants reported that the ability to view their heart rhythm ameliorated questions about their cardiac rhythm, particularly if they did not typically experience symptomatic AF. Other patients and many providers reported that viewing their cardiac rhythm data so frequently only caused them to focus more on their rhythm, which increased anxiety and ultimately proved unhelpful to the patient.

As a result of this anxiety, patients’ natural reaction is to reach out to providers to explain their data and ameliorate concerns. However, this creates a number of workflow and logistical challenges. Current clinical workflows are not designed to accommodate patients reaching out about their health data, and critically important questions about reimbursement, time, staffing, roles, and scope of practice have yet to be fully answered.²⁹ Additionally, it may be difficult for providers to appraise the quality of health data patients collect using consumer devices; this relates to variability in both patient skill in collecting and recording health measurements, and in the quality of the technologies being used.²⁷ Privacy and confidentiality regulations around patient data (e.g., Health Insurance Portability and Accountability Act, or HIPAA) also complicate these interactions. Patients and developers of different consumer technologies alike may not fully understand or comply with these regulations.⁹⁸ Furthermore, any health data patients may want to share can rarely be integrated into the EHR.⁹⁹ While a lack of EHR integration limits the ability of providers to review and respond to these health data, efforts to advance EHR integration are complicated by interoperability requirements and concerns about data quality and security.

The literature suggests that certain basic practices may be beneficial to implement in clinical practice to improve expectations and communication about health data and reduce patient anxiety. These include providing education and support for patients to correctly collect and interpret health data, and setting clear goals and expectations around data exchange, including establishing routines for data sharing, expected timeframes for responses about the data, and instructions on what to do in the event of an emergency.²¹ However, several studies also note that goals and expectations need to be tailored to the exact clinical use case; therefore, organizations seeking to return health data to patients should carefully consider a range of stakeholder perspectives, including different types of healthcare providers and patients, when creating policies and procedures for the exchange of health data.

6.3. Sustaining patient engagement

However, both within and beyond cardiology contexts, sustained engagement with digital health tools remains one of the most significant barriers to their clinical utility. Studies measuring self-monitoring over an extended period of time show that many patients who are using mHealth to self-monitor discontinue use within three to six months of initiation, suggesting that patients are not engaged in the process for a sustained period of time.^100–102

Little is known about personalized approaches to improve sustained engagement. Much of the existing literature on user engagement focuses on strategies to improve initial uptake rather than sustained engagement. Many have attempted to bolster sustained engagement by including certain mHealth features, such as gamification and incentives, but have generally been unsuccessful.^103,104 A promising alternative approach is to design technologies based on individual user characteristics that may predict sustained engagement. Previous studies have demonstrated that individual characteristics, such as age and disease status, affect sustained engagement with mHealth.^102,105,106 This is supported by focus group findings suggesting that control over mHealth features, context provided with health data, and data shared with healthcare providers would improve sustained engagement.^107,108 Recent studies also suggest that intrinsic motivation to manage health plays a role in engagement with these technologies.^109,110 These reported factors demonstrate the need for mHealth technologies to be personalized.

The length of time that mHealth users must sustain engagement with the technology is pre-specified depending on the ultimate goal of use. The goal of these technologies is to better detect and treat cardiac arrhythmias in a timelier manner.¹³ For paroxysmal arrhythmias including AF, longitudinal data collection is often more valuable than brief, discrete periods of monitoring because of the spontaneous and unpredictable nature of the arrhythmia episodes. However, longitudinal data collection is only possible if patients remain engaged with self-monitoring for a sustained period of time. When captured longitudinally, these data can be shared with healthcare providers to diagnose, treat, and manage these conditions more quickly and efficiently.^11,13

A critical question for future research surrounding consumer ECG devices and mHealth apps specifically is what length of time, and what intensity of engagement, is appropriate and clinically beneficial. While AF burden can automatically be quantified using CIEDs, it cannot be quantified using most consumer ECG devices or mHealth apps as these do not provide continuous monitoring. Thus, questions of the appropriate frequency and duration of use for detecting and monitoring AF are of paramount importance for their clinical utility. Although previous work has identified the shortest time periods with the highest diagnostic yield for CIEDs and medical-grade wearable devices, such as the Zio Patch™, this question has yet to be explored for digital health tools.^111–113

KEY POINTS.

• The impetus for sharing data comes both from a push from the federal government through the 21st Century Cures Act, and the Heart Rhythm Society, as well as a pull from patients to share patient health data (such as arrhythmia episodes, changes in health status, and device status) with more transparency.

• Reasons for returning patient health data include: increasing transparency, deeper insights into personal health status, improved ability to schedule and manage follow-up care, and prevention of emergencies such as a low battery or data transmission errors.

• Data can be returned using consumer applications and devices to monitor cardiac rhythm, including mHealth apps and consumer ECG devices.

• Three major challenges for returning patient information include: patient comprehension of health data, management of patient-healthcare professional communication to reduce patient-level anxiety, and sustaining patient engagement over time.

• Three recommendations for addressing challenges includes: developing visualizations to support and aid comprehension of complex information, setting clear goals and expectations with patients about data exchange, and develop patient-facing digital tools with participatory design methods including end-users to align features with their unique needs and preferences.

SYNOPSIS.

Spurred by federal legislation, professional organizations, and patients themselves, patient access to data from electronic cardiac devices is increasingly transparent. Patients may now directly collect more data through consumer devices and applications, such as cardiac rhythm and changes in health status, and access data traditionally shared only with healthcare providers through portals made available by cardiac device companies and health systems. While these data have exciting potential to improve screening, self-management, and shared decision-making for cardiac arrhythmias, particularly atrial fibrillation, a number of challenges surrounding patient comprehension, communication with providers, and sustained engagement remain. Recommendations for addressing these challenges are presented, including leveraging visualizations that support comprehension, including patients in the design and development of patient-facing digital tools, and establishing clear practices and goals for data exchange with healthcare providers.

Clinical care points:

• Visual aids, including graphics and data visualizations, should be used when returning personal health data from remote monitoring tools to patients to improve comprehension.

• Conversations with patients should occur prior to initiating remote monitoring to provide education and set clear expectations about exchanging, reviewing, and responding to remote monitoring data.

• Clinicians should advocate for clear policies at their health institutions about exchanging and interacting with patients about their remote monitoring data.