Evaluating the Utility of mHealth ECG Heart Monitoring for the Detection and Management of Atrial Fibrillation in Clinical Practice

Atrial fibrillation: epidemiology, screening and digital health

Atrial fibrillation (AF) is highly prevalent with a lifetime risk of about 1 in 3-5 individuals after the age of 45 years. Between 2010 and 2019, the global prevalence of AF has risen markedly from 33.5 million to 59 million individuals living with AF. Early detection of AF and implementation of appropriate treatment could reduce the frequency of complications associated with AF. International AF management guidelines recommend opportunistic and systematic screening for AF, but additional data are needed. Digital approaches and pathways have been proposed for early detection and for the transition to early AF management. Mobile health (mHealth) devices provide an opportunity for digital screening and should be part of novel models of care delivery based on integrated AF care pathways. For a broad implementation of mHealth-based, integrated care for patients with chronic diseases as AF, further high quality evidence is necessary. In this review, we present an overview of the present data on epidemiology, screening techniques, and the contribution of digital health solutions to the integrated management of AF. We also provide a systemic review on current data of digital and integrated AF management.

Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review

BACKGROUND

Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments.

OBJECTIVE

This scoping review aimed to provide a comprehensive overview of the current landscape of mobile ECG devices, including the technology used, intended clinical use, and available clinical evidence.

METHODS

We conducted a scoping review to identify studies concerning mobile ECG devices in the electronic database PubMed. Secondarily, an internet search was performed to identify other ECG devices available in the market. We summarized the devices' technical information and usability characteristics based on manufacturer data such as datasheets and user manuals. For each device, we searched for clinical evidence on the capabilities to record heart disorders by performing individual searches in PubMed and ClinicalTrials.gov, as well as the Food and Drug Administration (FDA) 510(k) Premarket Notification and De Novo databases.

RESULTS

From the PubMed database and internet search, we identified 58 ECG devices with available manufacturer information. Technical characteristics such as shape, number of electrodes, and signal processing influence the capabilities of the devices to record cardiac disorders. Of the 58 devices, only 26 (45%) had clinical evidence available regarding their ability to detect heart disorders such as rhythm disorders, more specifically atrial fibrillation.

CONCLUSIONS

ECG devices available in the market are mainly intended to be used for the detection of arrhythmias. No devices are intended to be used for the detection of other cardiac disorders. Technical and design characteristics influence the intended use of the devices and use environments. For mobile ECG devices to be intended to detect other cardiac disorders, challenges regarding signal processing and sensor characteristics should be solved to increase their detection capabilities. Devices recently released include the use of other sensors on ECG devices to increase their detection capabilities.

Examining standardized tools used for the evaluation of mobile health applications for cardiovascular disease

Cardiovascular disease is one of the leading causes of death worldwide. Scarce resources and rising costs are pushing healthcare systems to their limits. There is an urgency to develop, optimize and evaluate technologies that provide more effective care for patients. Modern technologies, such as mobile health (mHealth) applications, can provide relief as a key strategy. To integrate digital interventions into care structures, a detailed impact assessment of all professional mHealth applications is needed. The aim of this study is to analyze the standardized tools used in the field of cardiovascular disease. The results show that questionnaires, usage logs, and key indicators are predominantly used. Although the identified mHealth interventions are specific to cardiovascular disease and thus require particular questions to evaluate apps, the user readiness, usability, or quality of life criteria are non-specific. Therefore, the results contribute to understanding how different mHealth interventions can be assessed, categorized, evaluated, and accepted.

Advantages of using a portable cardiac monitor in outpatients with paroxysmal atrial fibrillation

Aim. To study the advantages of using a portable electrocardiographic (ECG) monitor in outpatients with paroxysmal atrial fibrillation (AF).

Material and methods. We examined 88 patients (26 men, 62 women, mean age, 63 [57;68] years) with paroxysmal AF with an average frequency of arrhythmia episodes of 1,0 [0.3;3.75] per month. Forty three (49%) patients were included in the remote monitoring (RM) group using the ECG Dongle monitor, and 45 (51%) patients were included in the conventional monitoring (CM) group. Patients underwent scheduled examinations 2, 6 and 12 months after the start of therapy. Unscheduled consultations were carried out in case of AF recurrence. Patients from the RM group, using portable cardiac monitor, additionally self-registered ECG 1 time per week and in case of symptoms suggestive of AF. ECG strips were sent to the clinical investigator for analysis.

Results. With the help of portable cardiac monitor, 2477 ECG strips were collected and analyzed: 2159 (87,2%) — sinus rhythm, 318 (12,8%) — AF and atypical atrial flutter. Forty four episodes of AF in 3 patients were asymptomatic. In addition, 211 (8,5%) sinus rhythm ECG strips were recorded by 13 patients with symptoms suggestive of AF. In the RM group, 7 (16%) patients were hospitalized due to recurrent AF, while in the CM group, 20 (44%) patients (p=0,004). The total number of emergency hospitalizations was 8 and 29 in the RM and CM groups, respectively. The number of patients who called an ambulance was significantly lower in the RM group: in the CM group, 37 calls for an ambulance were made by 22 patients, while in the RM group — 12 patients made 15 calls (p=0,044). The proportion of calls followed by hospitalization (40 vs 70%, p=0,043) was significantly lower in the RM group.

Conclusion. Remote ECG monitoring using the ECG Dongle system is a useful way to monitor patients with paroxysmal AF, which make it possible to register asymptomatic AF episodes, differentiate the causes of palpitations, and significantly reduce the frequency of emergency calls and hospitalizations.

Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection

The analysis of the electrocardiogram (ECG) is critical for the diagnosis of arrhythmias. Recent advances in information and communications technology (ICT) have led to the development of wearable ECG devices and arrhythmia-detection algorithms. This study aimed to develop an ICT-based clinical trial service platform using a self-reporting technique for real-time arrhythmia detection. To establish a clinical-trial service platform, a mobile application (app), a demilitarized zone (DMZ), an internal network, and Amazon web services virtual private cloud (AWS-VPC) were developed. The ECG data acquired by a wearable device were transmitted to the mobile app, which collected the participants’ self-reported information. The mobile app transmitted raw ECG and self-reported data to the AWS-VPC and DMZ, respectively. In the AWS-VPC, the live-streaming and playback-reviewer services were operational to display the currently and previously acquired ECG data to clinicians through the web client. All the measured data were transmitted to the internal network, in which the arrhythmia-detection algorithm was executed and all the data were saved. The self-reporting technique and arrhythmia-detection algorithm are the key elements of this platform. In particular, subjective information of participants can be easily collected using a self-reporting technique. These features are particularly of critical importance for treating painless, sparsely occurring arrhythmias.

Daily ECG transmission versus serial 6-day Holter ECG for the assessment of efficacy of ablation for atrial fibrillation - the AGNES-ECG study

Purpose

To compare daily ECG transmissions using trans-telephonic monitoring (TTM) with repeated 6-day Holter ECG in detecting atrial fibrillation (AF) episodes following ablation.

Methods

Each patient underwent two types of recordings: daily ECG TTM lasting 30 s and standard 6-day ambulatory ECG monitoring performed 3, 6, and 12 months after ablation. Number of patients with detected AF recurrences, time to first detected recurrence of AF, and AF burden were assessed.

Results

Fifty patients (9 females, mean age 57 ± 11 years) were included. The mean duration of the follow-up was 382 ± 38 days. A total of 17,573 (mean 351 ± 111 per patient) TTM recordings were performed and 99.95% of recordings were of quality sufficient to assess cardiac rhythm. Altogether, 14 (28%) patients had AF recurrence. Holter ECG detected AF recurrence in 7 (14%) patients whereas TTM — in 12 (24%) patients, p = 0.0416 (TTM only — 7 (14%), Holter ECG only — 2 (4%), and both methods — 5 (10%)). Time to the first AF recurrence tended to be shorter using TTM than Holter ECG (156 ± 91 vs 204 ± 121 days, p = 0.0819). There was no significant difference in AF burden assessed by TTM versus Holter ECG recordings 3.1 ± 0.14% vs 4.8 ± 0.2%, p = 0.21.

Conclusions

Compared with Holter ECG, daily 30-s ECG recordings detected more patients with AF recurrences. Time to first detected AF episode tended to be shorter using TTM. Daily ECG recordings transmitted using smartphone may replace standard Holter ECG in detecting AF after ablation.

Trial registration

Clinical Trials Identifier: NCT03877913

Methods Used to Evaluate mHealth Applications for Cardiovascular Disease: A Quasi-Systematic Scoping Review

In the face of demographic change and constantly increasing health care costs, health care system decision-makers face ever greater challenges. Mobile health applications (mHealth apps) have the potential to combat this trend. However, in order to integrate mHealth apps into care structures, an evaluation of such apps is needed. In this paper, we focus on the criteria and methods of evaluating mHealth apps for cardiovascular disease and the implications for developing a widely applicable evaluation framework for mHealth interventions. Our aim is to derive substantiated patterns and starting points for future research by conducting a quasi-systematic scoping review of relevant peer-reviewed literature published in English or German between 2000 and 2021. We screened 4066 articles and identified n = 38 studies that met our inclusion criteria. The results of the data derived from these studies show that usability, motivation, and user experience were evaluated primarily using standardized questionnaires. Usage protocols and clinical outcomes were assessed primarily via laboratory diagnostics and quality-of-life questionnaires, and cost effectiveness was tested primarily based on economic measures. Based on these findings, we propose important considerations and elements for the development of a common evaluation framework for professional mHealth apps, including study designs, data collection tools, and perspectives.

Mobile Electrocardiogram Monitoring and Health-Related Quality of Life in Patients With Atrial Fibrillation: Findings From the iPhone Helping Evaluate Atrial Fibrillation Rhythm Through Technology (iHEART) Study

BACKGROUND

Atrial fibrillation (AF) is associated with high recurrence rates and poor health-related quality of life (HRQOL) but few effective interventions to improve HRQOL exist.

OBJECTIVE

The aim of this study was to examine the impact of the "iPhone Helping Evaluate Atrial Fibrillation Rhythm through Technology" (iHEART) intervention on HRQOL in patients with AF.

METHODS

We randomized English- and Spanish-speaking adult patients with AF to receive either the iHEART intervention or usual care for 6 months. The iHEART intervention used smartphone-based electrocardiogram monitoring and motivational text messages. Three instruments were used to measure HRQOL: the Atrial Fibrillation Effect on Quality of Life (AFEQT), the 36-item Short-Form Health survey, and the EuroQol-5D. We used linear mixed models to compare the effect of the iHEART intervention on HRQOL, quality-adjusted life-years, and AF symptom severity.

RESULTS

A total of 238 participants were randomized to the iHEART intervention (n = 115) or usual care (n = 123). Of the participants, 77% were men and 76% were white. More than half (55%) had an AF recurrence. Both arms had improved scores from baseline to follow-up for AFEQT and AF symptom severity scores. The global AFEQT score improved 18.5 and 11.2 points in the intervention and control arms, respectively (P < .05). There were no statistically significant differences in HRQOL, quality-adjusted life-years, or AF symptom severity between groups.

CONCLUSIONS

We found clinically meaningful improvements in AF-specific HRQOL and AF symptom severity for both groups. Additional research with longer follow-up should examine the influence of smartphone-based interventions for AF management on HRQOL and address the unique needs of patients diagnosed with different subtypes of AF.

The comparison of Kardia Mobile and Hartmann Veroval 2 in 1 in detecting first diagnosed atrial fibrillation

BACKGROUND

Atrial fibrillation (AF) is the leading cause of stroke. The European Society of Cardiology (ESC) advises opportunistic AF screening among patients aged ≥ 65 years. Considering this, the aim herein, was compare the feasibility of two different systems of smartphone-based electrocardiogram (ECG) recordings to identify AF among those without a previous arrhythmia history.

METHODS

Prospective AF screening was conducted at six pharmacies using Kardia Mobile and Hartmann Veroval 2 in 1. A single-lead ECG was acquired by the placement of fingers on the pads. A cardiologist evaluated findings from both devices.

RESULTS

Atrial fibrillation was identified in 3.60% and previously unknown AF was detected in 1.92% of the study participants. Sensitivity and specificity of the Kardia application in detecting AF were 66.7% (95% confidence interval [CI] 38.4-88.2%) and 98.5% (95% CI 96.7-99.5%), and for Veroval 10.0% (95% CI 0.23-44.5%) and 94.96% (95% CI 92.15-96.98%), accordingly. Inter-rater agreement was k = 0.088 (95% CI 1.59-16.1%).

CONCLUSIONS

Mobile devices can detect AF, but each finding must be verified by a professional. The Kardia application appeared to be more user-friendly than Veroval. Cardiovascular screening using mobile devices is feasible at pharmacies. Hence it might be considered for routine use.

Education of Self-Radial Pulse Palpation and Atrial Fibrillation Signs and Symptoms

The purpose of this evidence-based education initiative was to increase A Fib detection in a cardiovascular diseased population in which participants were taught radial pulse palpation and signs/symptoms of A Fib. Participants were evaluated after initial education on performance of radial pulse palpation and teach back of signs/symptoms of the arrhythmia and again at three weeks on adherence to daily self-screening and recall of signs/symptoms of A Fib. Post initial education, 88% of eligible participants learned pulse-palpation and 93% could verbalize at minimum one sign/symptom. After three weeks, 94.7% reported daily self-screening, but only 44.7% could verbalize at minimum one sign/symptom. This indicated that participants were continuing to self-screen, but in need of booster education sessions. Educating individuals on self-screening and signs/symptoms of A Fib is clinically feasible.

Impact of Mobile Health Devices for the Detection of Atrial Fibrillation: Systematic Review

Background

Atrial fibrillation (AF) is the most common arrhythmia, and its prevalence is increasing. Early diagnosis is important to reduce the risk of stroke. Mobile health (mHealth) devices, such as single-lead electrocardiogram (ECG) devices, have been introduced to the worldwide consumer market over the past decade. Recent studies have assessed the usability of these devices for detection of AF, but it remains unclear if the use of mHealth devices leads to a higher AF detection rate.

Objective

The goal of the research was to conduct a systematic review of the diagnostic detection rate of AF by mHealth devices compared with traditional outpatient follow-up. Study participants were aged 16 years or older and had an increased risk for an arrhythmia and an indication for ECG follow-up—for instance, after catheter ablation or presentation to the emergency department with palpitations or (near) syncope. The intervention was the use of an mHealth device, defined as a novel device for the diagnosis of rhythm disturbances, either a handheld electronic device or a patch-like device worn on the patient’s chest. Control was standard (traditional) outpatient care, defined as follow-up via general practitioner or regular outpatient clinic visits with a standard 12-lead ECG or Holter monitoring. The main outcome measures were the odds ratio (OR) of AF detection rates.

Methods

Two reviewers screened the search results, extracted data, and performed a risk of bias assessment. A heterogeneity analysis was performed, forest plot made to summarize the results of the individual studies, and albatross plot made to allow the P values to be interpreted in the context of the study sample size.

Results

A total of 3384 articles were identified after a database search, and 14 studies with a 4617 study participants were selected. All studies but one showed a higher AF detection rate in the mHealth group compared with the control group (OR 1.00-35.71), with all RCTs showing statistically significant increases of AF detection (OR 1.54-19.16). Statistical heterogeneity between studies was considerable, with a Q of 34.1 and an I² of 61.9, and therefore it was decided to not pool the results into a meta-analysis.

Conclusions

Although the results of 13 of 14 studies support the effectiveness of mHealth interventions compared with standard care, study results could not be pooled due to considerable clinical and statistical heterogeneity. However, smartphone-connectable ECG devices provide patients with the ability to document a rhythm disturbance more easily than with standard care, which may increase empowerment and engagement with regard to their illness. Clinicians must beware of overdiagnosis of AF, as it is not yet clear when an mHealth-detected episode of AF must be deemed significant.

Smartphone ECG Monitoring System Helps Lower Emergency Room and Clinic Visits in Post-Atrial Fibrillation Ablation Patients

Aim:

To evaluate the effectiveness of using a smartphone-based electrocardiography (ECG) monitoring device (ECG Check) on the frequency of clinic or emergency room visits in patients who underwent ablation of atrial fibrillation (AF).

Methods:

Two groups of patients were identified and compared: The conventional monitoring group (CM group) included patients who were prescribed conventional event monitoring or Holter monitoring systems. The ECG Check group (EC group) included patients who were prescribed the ECG Check device for continuous monitoring in addition to conventional event monitoring. The primary outcome was the number of patient visits to clinic or emergency room. The feasibility, accuracy, and detection rate of mobile ECG Check were also evaluated.

Results:

Ninety patients were studied (mean age: 66.2 ± 11 years, 64 males, mean CHA₂DS₂-VASc score: 2.6 ± 2). In the EC group, forty-five patients sent an average of 52.8 ± 6 ECG records for either routine monitoring or symptoms of potential AF during the follow-up period. The rhythm strips identified sinus rhythm (84.7%), sinus tachycardia (8.4%), AF (4.2%), and atrial flutter (0.9%). Forty-two EC transmissions (1.8%) were uninterpretable. Six patients (13%) in the EC group were seen in the clinic or emergency room over a 100-day study period versus 16 (33%) in the standard care arm (P value < 0.001).

Conclusions:

Use of smartphone-based ECG monitoring led to a significant reduction in AF-related visits to clinic or emergency department in the postablation period.

Feasibility of Atrial Fibrillation Screening With Mobile Health Technologies at Pharmacies

BACKGROUND

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Thus, the aim of our study was to evaluate the smartphone-based electrocardiogram (ECG) recordings aimed at AF screening at Polish pharmacies.

METHODS

Prospective AF screening among patients aged ≥65 years was conducted at 10 pharmacies using Kardia Mobile with a dedicated application (Kardia app). Prior AF was a study exclusion criterion. CHA₂DS₂-VASc score (congestive heart failure, hypertension, age, diabetes mellitus, previous stroke/transient ischemic attack, female sex, and vascular disease) has been collected from every patient. A single-lead ECG has been acquired by the placement of fingers from each hand on the pads. Kardia app diagnosis has been evaluated by the cardiologist.

RESULTS

A total of 525 ECGs were performed. Kardia app diagnosis was provided in 490 cases. In 437 (89.18%) cases, it was "normal" rhythm, in 17 (3.47%) recordings "possible AF," in 23 (4.69%) ECGs "unreadable," and in 13 (2.65%) "unclassified". After the cardiologist reevaluation, the new AF was identified in 7 (1.33%) patients. Sensitivity and specificity of Kardia app in detecting AF was 100% (95% confidence interval [CI]: 71.5%-100%) and 98.7% (95% CI: 97.3%-99.5%), respectively. The positive predictive value was 64.7% (95% CI: 38.3%-85.7%) and the negative predictive value was 100% (95% CI: 99.2%-100%). CHA₂DS₂-VASc score was 2.14 ± 0.69 for those with new AF and 3.33 ± 1.26 in the non-AF group.

CONCLUSION

Kardia app is capable of fast screening and detecting AF with high sensitivity and specificity. The possible diagnosis of AF deserves additional cardiological evaluation. The results obtained in patients with low CHA₂DS₂-VASc score and "silent" AF confirm the importance of routine AF screening. Cardiovascular screening with the use of mobile health technology is feasible at pharmacies.

Mobile Health Advances in Physical Activity, Fitness, and Atrial Fibrillation: Moving Hearts

The growing recognition that "health" takes place outside of the hospital and clinic, plus recent advances in mobile and wearable devices, have propelled the field of mobile health (mHealth). Cardiovascular disease and prevention are major opportunities for mHealth, as mobile devices can monitor key physiological signals (e.g., physical activity, heart rate and rhythm) for promoting healthy behaviors, detecting disease, and aid in ongoing care. In this review, the authors provide an update on cardiovascular mHealth by highlighting recent progress and challenges with mobile and wearable devices for assessing and promoting physical activity and fitness, and for monitoring heart rate and rhythm for the detection and management of atrial fibrillation.

Safety and compatibility of smart device heart rhythm monitoring in patients with cardiovascular implantable electronic devices

INTRODUCTION

Emerging medical technology has allowed for monitoring of heart rhythm abnormalities using smartphone compatible devices. The safety and utility of such devices have not been established in patients with cardiac implantable electronic devices (CIEDs). We sought to assess the safety and compatibility of the Food and Drug Administration-approved AliveCor Kardia device in patients with CIEDs.

METHODS AND RESULTS

We prospectively recruited patients with CIED for a Kardia recording during their routine device interrogation. A recording was obtained in paced and nonpaced states. Adverse clinical events were noted at the time of recording. Electrograms (EGMs) from the cardiac device were obtained at the time of recording to assess for any electromagnetic interference (EMI) introduced by Kardia. Recordings were analyzed for quality and given a score of 3 (interpretable rhythm, no noise), 2 (interpretable rhythm, significant noise) or 1 (uninterpretable). A total of 251 patients were recruited (59% with a pacemaker and 41% with ICD). There were no adverse clinical events noted at the time of recording and no changes to CIED settings. Review of all EGMs revealed no EMI introduced by Kardia. Recordings were correctly interpreted in 90% of paced recordings (183 had a score of 3, 43 of 2, and 25 of 1) and 94.7% of nonpaced recordings (147 of 3, 15 of 2, and 9 of 1).

CONCLUSION

The AliveCor Kardia device has an excellent safety profile when used in conjunction with most CIEDs. The quality of recordings was preserved in this population. The device, therefore, can be considered for heart rhythm monitoring in patients with CIEDs.

An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal

BACKGROUND

Electromyography (EMG) signal processing and Muscle Onset Latency (MOL) are widely used in rehabilitation sciences and nerve conduction studies. The majority of existing software packages provided for estimating MOL via analyzing EMG signal are computerized, desktop based and not portable; therefore, experiments and signal analyzes using them should be completed locally. Moreover, a desktop or laptop is required to complete experiments using these packages, which costs.

OBJECTIVE

Develop a non-expensive and portable Android application (app) for estimating MOL via analyzing surface EMG.

MATERIAL AND METHODS

A multi-layer architecture model was designed for implementing the MOL estimation app. Several Android-based algorithms for analyzing a recorded EMG signal and estimating MOL was implemented. A graphical user interface (GUI) that simplifies analyzing a given EMG signal using the presented app was developed too.

RESULTS

Evaluation results of the developed app using 10 EMG signals showed promising performance; the MOL values estimated using the presented app are statistically equal to those estimated using a commercial Windows-based surface EMG analysis software (MegaWin 3.0). For the majority of cases relative error <10%. MOL values estimated by these two systems are linearly related, the correlation coefficient value ~ 0.93. These evaluations revealed that the presented app performed as well as MegaWin 3.0 software in estimating MOL.

CONCLUSION

Recent advances in smart portable devices such as mobile phones have shown the great capability of facilitating and decreasing the cost of analyzing biomedical signals, particularly in academic environments. Here, we developed an Android app for estimating MOL via analyzing the surface EMG signal. Performance is promising to use the app for teaching or research purposes.

Comparison of a smartphone-based ECG recording system with a standard cardiac event monitor in the investigation of palpitations in children

BACKGROUND

The AliveCor (Kardia) monitor attaches to a smartphone and allows a single-lead ECG to be recorded during symptoms. In 2016, we introduced the use of this smartphone device for investigating palpitations, without syncope, in children. The aim of our study was to review our experience with the smartphone device, comparing it with our previous standard conventional approach to cardiac event monitoring using the Cardiocall monitor, which uses skin electrodes and is given for a finite period.

METHODS

Over a period of 24 months, 80 smartphone monitors were issued and compared with the most recent 100 conventional event monitors. The number of ECG recordings received, arrhythmias documented, quality of ECG recordings and patient satisfaction were evaluated.

RESULTS

Median patient age was 11 years in the smartphone monitor group compared with 10 years in the conventional group. Seventy-nine of 80 (98%) patients with a smartphone monitor sent an ECG recorded during symptoms, compared with 62/100 (62%) from the conventional group. A total of 836 ECG recordings were sent from the smartphone monitors compared with 752 from the conventional group. Eight per cent of ECG recordings in each group were of inadequate quality for analysis. Twenty of 80 (25%) patients with a smartphone monitor had documented tachyarrhythmia compared with 6/100 (6%) patients with the conventional monitor (p<0.001). On comparison with the conventional approach, the smartphone monitor outperformed with respect to diagnostic yield and patient satisfaction.

CONCLUSIONS

A smartphone-based event monitor allows simple, effective, long-term ECG event monitoring in children that is highly acceptable to the patient and parent.

Detection and management of atrial fibrillation using remote monitoring

Atrial fibrillation (AF) is the most common dysrhythmia encountered in the United States. Symptoms may be similar to those of other cardiac conditions, which can delay the timely detection, diagnosis, and management of AF. This article provides an overview of AF and modalities used in remote monitoring.

Developing and Sustaining a Career as a Transdisciplinary Nurse Scientist

PURPOSE

The purpose of this article is to provide an overview of strategies to build and sustain a career as a nurse scientist. This article examines how to integrate technologies and precision approaches into clinical practice, research, and education of the next generation of nursing scholars.

DESIGN

This article presents information for shaping a sustainable transdisciplinary career. Programs of research that utilize self-management to improve quality of life are discussed throughout the article. The ongoing National Institute of Nursing Research-funded (R01 grant) iPhone Helping Evaluate Atrial Fibrillation Rhythm through Technology (iHEART) study is the first prospective, randomized controlled trial to evaluate whether electrocardiographic monitoring with the AliveCor™ device in the real-world setting will improve the time to detection and treatment of recurrent atrial fibrillation over a 6-month period as compared to usual cardiac care.

METHODS

Opportunities to sustain a career as a nurse scientist and build programs of transdisciplinary research are identified. These opportunities are focused within the area of research and precision medicine.

FINDINGS

Nurse scientists have the potential and ability to shape their careers and become essential members of transdisciplinary partnerships. Exposure to clinical research, expert mentorship, and diverse training opportunities in different areas are essential to ensure that contributions to nursing science are visible through publications and presentations as well as through securing grant funding to develop and maintain programs of research.

CONCLUSIONS

Transcending boundaries and different disciplines, nurses are essential members of many diverse teams.

CLINICAL RELEVANCE

Nurse scientists are strengthening research approaches, clinical care, and communication and improving health outcomes while also building and shaping the next generation of nurse scientists.

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.