PULSE-SMART: Pulse-Based Arrhythmia Discrimination Using a Novel Smartphone Application

External validation of a machine learning-based classification algorithm for ambulatory heart rhythm diagnostics in pericardioversion atrial fibrillation patients using smartphone photoplethysmography: the SMARTBEATS-ALGO study

AIMS

The aim of this study was to perform an external validation of an automatic machine learning (ML) algorithm for heart rhythm diagnostics using smartphone photoplethysmography (PPG) recorded by patients with atrial fibrillation (AF) and atrial flutter (AFL) pericardioversion in an unsupervised ambulatory setting.

METHODS AND RESULTS

Patients undergoing cardioversion for AF or AFL performed 1-min heart rhythm recordings pericardioversion at least twice daily for 4-6 weeks, using an iPhone 7 smartphone running a PPG application (CORAI Heart Monitor) simultaneously with a single-lead electrocardiogram (ECG) recording (KardiaMobile). The algorithm uses support vector machines to classify heart rhythm from smartphone-PPG. The algorithm was trained on PPG recordings made by patients in a separate cardioversion cohort. Photoplethysmography recordings in the external validation cohort were analysed by the algorithm. Diagnostic performance was calculated by comparing the heart rhythm classification output to the diagnosis from the simultaneous ECG recordings (gold standard). In total, 460 patients performed 34 097 simultaneous PPG and ECG recordings, divided into 180 patients with 16 092 recordings in the training cohort and 280 patients with 18 005 recordings in the external validation cohort. Algorithmic classification of the PPG recordings in the external validation cohort diagnosed AF with sensitivity, specificity, and accuracy of 99.7%, 99.7% and 99.7%, respectively, and AF/AFL with sensitivity, specificity, and accuracy of 99.3%, 99.1% and 99.2%, respectively.

CONCLUSION

A machine learning-based algorithm demonstrated excellent performance in diagnosing atrial fibrillation and atrial flutter from smartphone-PPG recordings in an unsupervised ambulatory setting, minimizing the need for manual review and ECG verification, in elderly cardioversion populations.

CLINICAL TRIAL REGISTRATION

Clinicaltrials.gov, NCT04300270.

Smartphone-Based Applications for Atrial Fibrillation Detection: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

Background: Atrial fibrillation (AF) burden is strongly associated with an increased risk of stroke, which, in most cases, can be prevented through earlier detection of AF and the timely initiation of anticoagulation therapy. Smartphone devices can provide a simple, non-invasive, cost-effective early AF detection solution. Methods: PubMed, Embase, and Scopus databases were searched for studies comparing smartphone-based photoplethysmography (PPG) with standard electrocardiogram for AF detection. A bivariate random-effects model with a 95% confidence interval (CI) was applied to generate the summary receiver operating characteristic (SROC) curve. Results: Fourteen studies were included, comprising 5,090 patients with an AF prevalence of 31.6%. The pooled sensitivity and specificity were 0.96 (95% CI, 0.93-0.97) and 0.97 (95% CI, 0.95-0.98). The area under the SROC curve was 0.98 (95% CI, 0.94-0.99). The diagnostic odds ratio was 960 (95% CI, 439-2,104), with significant heterogeneity (I2 = 51%). The projected positive and negative predictive values were 66.5% and 99.7%, respectively, in the elderly population aged >65 years and 39.2% and 99.9% in the general population. Conclusion: Smartphone-based PPG demonstrated relatively high sensitivity and specificity and appears capable of ruling out AF. Patients aged >65 are more likely to benefit from AF screening.

Wearables and Atrial Fibrillation: Advances in Detection, Clinical Impact, Ethical Concerns, and Future Perspectives

Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with a significantly increased risk of stroke, heart failure, and mortality. Early diagnosis and management are crucial to mitigating these risks. Wearable devices such as smartwatches and fitness bands, enhanced by advanced artificial intelligence (AI) algorithms, offer a promising solution for early AF detection due to their accessibility, ease of use, and cost-effectiveness. Although the ability of these algorithms to identify AF has been authorized, critical questions remain about their integration into clinical practice, ethical implications, and long-term benefits. This review uniquely explores the intersection of wearable technology and AF management, providing a detailed analysis of current evidence, emerging trends, and the challenges associated with these innovations.

Episode-level and clinical characterization of asymptomatic atrial fibrillation events

INTRODUCTION

Not all patients experience debilitating symptoms during Atrial Fibrillation (AF), some are asymptomatic. The reasons for this inter- and intrasubject variability is unknown.

PURPOSE

The study objective was NOAH characterize episode-level and clinical characteristics associated with symptomatic versus asymptomatic episodes of AF in patients with an implantable cardiac monitor (ICM).

METHODS

Patients with an AF episode detected on an ICM between 2007 and 2021 with overlapping clinical data from aggregated Electronic Health Records in the Optum® deidentified data set were included. Symptomatic episodes were labeled in real-time by the patient. Heart rate (HR) at onset, mean HR, AF Evidence Score (a measure of beat-to-beat irregularity), episode duration and Activity Index were evaluated for association with symptom status using multivariable regression modeling.

RESULTS

11 267 patients had AF episodes with clinical data available. The 1776 (15.8%) patients who reported symptomatic AF episodes were younger (67 ± 12 years vs. 71 ± 11 years old, p < .001) and had fewer cardiovascular co-morbidities than patients with asymptomatic AF exclusively. Symptomatic episodes were longer (5.5 [2.4, 14.4] h vs. 3.7 [1.7, 11] h, p < .001), had higher mean HR (103 ± 22 bpm vs. 88 ± 22 bpm, p < .001) and higher AF evidence scores (98 ± 27 vs. 82 ± 24, p < .001). These features were independently associated with symptomatic episodes on multivariable regression analysis and per-subject analysis in patients who had both symptomatic and asymptomatic episodes.

DISCUSSION

Episode-level characteristics differed between symptomatic AF episodes versus asymptomatic episodes in patients with ICMs. Symptomatic patients also had less comorbidities. These parameters may be useful in understanding variable symptomatic manifestation and remote stratification of AF episodes.

Considering User Experience and Behavioral Approaches in the Design of mHealth Interventions for Atrial Fibrillation: Systematic Review

BACKGROUND

Atrial fibrillation (AF) is a leading chronic cardiac disease associated with an increased risk of stroke, cardiac complications, and general mortality. Mobile health (mHealth) interventions, including wearable devices and apps, can aid in the detection, screening, and management of AF to improve patient outcomes. The inclusion of approaches that consider user experiences and behavior in the design of health care interventions can increase the usability of mHealth interventions, and hence, hopefully, yield an increase in positive outcomes in the lives of users.

OBJECTIVE

This study aims to show how research has considered user experiences and behavioral approaches in designing mHealth interventions for AF detection, screening, and management; the phases of designing complex interventions from the UK Medical Research Council (MRC) were referenced: namely, identification, development, feasibility, evaluation, and implementation.

METHODS

Studies published until September 7, 2022, that examined user experiences and behavioral approaches associated with mHealth interventions in the context of AF were extracted from multiple databases. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines were used.

RESULTS

A total of 2219 records were extracted, with only 55 records reporting on usability, user experiences, or behavioral approaches more widely for designing mHealth interventions in the context of AF. When mapping the studies onto the phases of the UK MRC's guidance for developing and evaluating complex interventions, the following was found: in the identification phase, there were significant differences between the needs of patients and health care workers. In the development phase, user perspectives guided the iterative development of apps, interfaces, and intervention protocols in 4 studies. Most studies (43/55, 78%) assessed the usability of interventions in the feasibility phase as an outcome, although the data collection tools were not designed together with users and stakeholders. Studies that examined the evaluation and implementation phase entailed reporting on challenges in user participation, acceptance, and workflows that could not be captured by studies in the previous phases. To realize the envisaged human behavior intended through treatment, review results highlight the scant inclusion of behavior change approaches for mHealth interventions across multiple levels of sociotechnical health care systems. While interventions at the level of the individual (micro) and the level of communities (meso) were found in the studies reviewed, no studies were found intervening at societal levels (macro). Studies also failed to consider the temporal variation of user goals and feedback in the design of long-term behavioral interventions.

CONCLUSIONS

In this systematic review, we proposed 2 contributions: first, mapping studies to different phases of the MRC framework for developing and evaluating complex interventions, and second, mapping behavioral approaches to different levels of health care systems. Finally, we discuss the wider implications of our results in guiding future mHealth research.

Enhancing Spinal Cord Injury Care: Using Wearable Technologies for Physical Activity, Sleep, and Cardiovascular Health

Wearable devices have the potential to advance health care by enabling real-time monitoring of biobehavioral data and facilitating the management of an individual's health conditions. Individuals living with spinal cord injury (SCI) have impaired motor function, which results in deconditioning and worsening cardiovascular health outcomes. Wearable devices may promote physical activity and allow the monitoring of secondary complications associated with SCI, potentially improving motor function, sleep, and cardiovascular health. However, several challenges remain to optimize the application of wearable technologies within this population. One is striking a balance between research-grade and consumer-grade devices in terms of cost, accessibility, and validity. Additionally, limited literature supports the validity and use of wearable technology in monitoring cardio-autonomic and sleep outcomes for individuals with SCI. Future directions include conducting performance evaluations of wearable devices to precisely capture the additional variation in movement and physiological parameters seen in those with SCI. Moreover, efforts to make the devices small, lightweight, and inexpensive for consumer ease of use may affect those with severe motor impairments. Overcoming these challenges holds the potential for wearable devices to help individuals living with SCI receive timely feedback to manage their health conditions and help clinicians gather comprehensive patient health information to aid in diagnosis and treatment.

Pulse oximetry in pediatric care: Balancing advantages and limitations

BACKGROUND

Pulse oximetry has become a cornerstone technology in healthcare, providing non-invasive monitoring of oxygen saturation levels and pulse rate. Despite its widespread use, the technology has inherent limitations and challenges that must be addressed to ensure accurate and reliable patient care.

AIM

To comprehensively evaluate the advantages, limitations, and challenges of pulse oximetry in clinical practice, as well as to propose recommendations for optimizing its use.

METHODS

A systematic literature review was conducted to identify studies related to pulse oximetry and its applications in various clinical settings. Relevant articles were selected based on predefined inclusion and exclusion criteria, and data were synthesized to provide a comprehensive overview of the topic.

RESULTS

Pulse oximetry offers numerous advantages, including non-invasiveness, real-time feedback, portability, and cost-effectiveness. However, several limitations and challenges were identified, including motion artifacts, poor peripheral perfusion, ambient light interference, and patient-specific factors such as skin pigmentation and hemoglobin variants. Recommendations for optimizing pulse oximetry use include technological advancements, education and training initiatives, quality assurance protocols, and interdisciplinary collaboration.

CONCLUSION

Pulse oximetry is crucial in modern healthcare, offering invaluable insights into patients' oxygenation status. Despite its limitations, pulse oximetry remains an indispensable tool for monitoring patients in diverse clinical settings. By implementing the recommendations outlined in this review, healthcare providers can enhance the effectiveness, accessibility, and safety of pulse oximetry monitoring, ultimately improving patient outcomes and quality of care.

Novel Technologies in the Detection of Atrial Fibrillation: Review of Literature and Comparison of Different Novel Technologies for Screening of Atrial Fibrillation

Atrial fibrillation (AF) is globally the most common arrhythmia associated with significant morbidity and mortality. It impairs the quality of the patient's life, imposing a remarkable burden on public health, and the healthcare budget. The detection of AF is important in the decision to initiate anticoagulation therapy to prevent thromboembolic events. Nonetheless, AF detection is still a major clinical challenge as AF is often paroxysmal and asymptomatic. AF screening recommendations include opportunistic or systematic screening in patients ≥65 years of age or in those individuals with other characteristics pointing to an increased risk of stroke. The popularities of well-being and taking personal responsibility for one's own health are reflected in the continuous development and growth of mobile health technologies. These novel mobile health technologies could provide a cost-effective solution for AF screening and an additional opportunity to detect AF, particularly its paroxysmal and asymptomatic forms.

Validation of a novel smartphone-based photoplethysmographic method for ambulatory heart rhythm diagnostics: the SMARTBEATS study

AIMS

In the current guidelines, smartphone photoplethysmography (PPG) is not recommended for diagnosis of atrial fibrillation (AF), without a confirmatory electrocardiogram (ECG) recording. Previous validation studies have been performed under supervision in healthcare settings, with limited generalizability of the results. We aim to investigate the diagnostic performance of a smartphone-PPG method in a real-world setting, with ambulatory unsupervised smartphone-PPG recordings, compared with simultaneous ECG recordings and including patients with atrial flutter (AFL).

METHODS AND RESULTS

Unselected patients undergoing direct current cardioversion for treatment of AF or AFL were asked to perform 1-min heart rhythm recordings post-treatment, at least twice daily for 30 days at home, using an iPhone 7 smartphone running the CORAI Heart Monitor PPG application simultaneously with a single-lead ECG recording (KardiaMobile). Photoplethysmography and ECG recordings were read independently by two experienced readers. In total, 280 patients recorded 18 005 simultaneous PPG and ECG recordings. Sufficient quality for diagnosis was seen in 96.9% (PPG) vs. 95.1% (ECG) of the recordings (P < 0.001). Manual reading of the PPG recordings, compared with manually interpreted ECG recordings, had a sensitivity, specificity, and overall accuracy of 97.7%, 99.4%, and 98.9% with AFL recordings included and 99.0%, 99.7%, and 99.5%, respectively, with AFL recordings excluded.

CONCLUSION

A novel smartphone-PPG method can be used by patients unsupervised at home to achieve accurate heart rhythm diagnostics of AF and AFL with very high sensitivity and specificity. This smartphone-PPG device can be used as an independent heart rhythm diagnostic device following cardioversion, without the requirement of confirmation with ECG.

Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing

BACKGROUND

Continuous monitoring for atrial fibrillation (AF) using photoplethysmography (PPG) from smartwatches or other wearables is challenging due to periods of poor signal quality during motion or suboptimal wearing. As a result, many consumer wearables sample infrequently and only analyze when the user is at rest, which limits the ability to perform continuous monitoring or to quantify AF.

OBJECTIVES

This study aimed to compare 2 methods of continuous monitoring for AF in free-living patients: a well-validated signal processing (SP) heuristic and a convolutional deep neural network (DNN) trained on raw signal.

METHODS

We collected 4 weeks of continuous PPG and electrocardiography signals in 204 free-living patients. Both SP and DNN models were developed and validated both on holdout patients and an external validation set.

RESULTS

The results show that the SP model demonstrated receiver-operating characteristic area under the curve (AUC) of 0.972 (sensitivity 99.6%, specificity: 94.4%), which was similar to the DNN receiver-operating characteristic AUC of 0.973 (sensitivity 92.2, specificity: 95.5%); however, the DNN classified significantly more data (95% vs 62%), revealing its superior tolerance of tracings prone to motion artifact. Explainability analysis revealed that the DNN automatically suppresses motion artifacts, evaluates irregularity, and learns natural AF interbeat variability. The DNN performed better and analyzed more signal in the external validation cohort using a different population and PPG sensor (AUC, 0.994; 97% analyzed vs AUC, 0.989; 88% analyzed).

CONCLUSIONS

DNNs perform at least as well as SP models, classify more data, and thus may be better for continuous PPG monitoring.

Malignant arrhythmias detection using a synthesis-by-analysis modeling method of arterial blood pressure signal

Extreme bradycardia, extreme tachycardia, ventricular flutter fib, and ventricular tachycardia are four malignant arrhythmias (MAs) that lead to sudden cardiac death. It is very important to detect them in daily life. The arterial blood pressure (ABP) signal contains abundant pathological information about four MAs and is easy to be recorded under domestic conditions. Thus, a synthesis-by-analysis (SA) modeling method for ABP signal was proposed to detect the four MAs in this study. The average models of MAs and healthy subjects were obtained by SA modeling, and the change of each ABP wave was quantitively described by twelve parameters of wave models. Then, the probabilistic neural network (PNN) and random forest (RF) are trained to detect the MAs. The experimental data were employed from Fantasia and the 2015 PhysioNet/CinC Challenge databases. The SA modeling results show that some pathological and physiological changes could be extracted from the average models. The two-sample ks-test results between different groups are markedly different (h = 1, p < 0.05). The detection results show that the performances of PPN classifiers are less than that of RF. The kappa coefficients (KC) for the RF classifiers are 97.167 ± 1.46 %, 97.888 ± 0.808 %, 99.895 ± 0.545 %, 98.575 ± 1.683 % and 92.241 ± 1.517 %, respectively. The mean KC is 97.083 ± 0.67 %. Compared to the performance of some existing studies, the proposed method has better performance and is potential to diagnose MAs in m-health.

Analysis of the Chaotic Component of Photoplethysmography and Its Association with Hemodynamic Parameters

Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic conditions like blood pressure (BP), cardiac output (CO), vascular compliance (C), and peripheral resistance (R). Using irregular-resampling auto-spectral analysis (IRASA), we quantified chaotic components in PPG signals and employed different methods to measure the fractal dimension (FD) and entropy. Our findings revealed that in surgery patients, the power of chaotic components increased with vascular stiffness. As the intensity of CO fluctuations increased, there was a notable strengthening in the correlation between most complexity measures of PPG and these parameters. Interestingly, some conventional morphological features displayed a significant decrease in correlation, indicating a shift from a static to dynamic scenario. Healthy subjects exhibited a higher percentage of chaotic components, and the correlation between complexity measures and hemodynamics in this group tended to be more pronounced. Causal analysis showed that hemodynamic fluctuations are main influencers for FD changes, with observed feedback in most cases. In conclusion, understanding chaotic patterns in PPG signals is vital for assessing cardiovascular health, especially in individuals with unstable hemodynamics or during ambulatory testing. These insights can help overcome the challenges faced by wearable technologies and enhance their usage in real-world scenarios.

Batch Fabrication of Paper-Based Waterproof Flexible Pressure Sensors Enabled by Roll-to-Roll Lamination

Paper is a green and porous material that has been widely used in flexible pressure sensors due to its flexibility, renewability, and lightness. However, these sensors are often susceptible to environmental factors such as moisture and chemicals, leading to degradation or failure of their reliability for practical applications. Herein, we present a roll-to-roll lamination strategy for batch fabrication of paper-based waterproof flexible pressure sensors with good consistency based on single-walled carbon nanotube (SWCNT) coated tissue paper pieces. The pieces are sandwiched between poly(ethylene glycol) terephthalate (PET) films with a hot melt adhesive and screen-printed electrodes, and the layers are bonded reliably using roll-to-roll lamination. This process allows for the rapid fabrication of a batch of waterproof, flexible pressure sensors with high stability over 5000 loading/unloading cycles, an ultrashort response time of 8 ms, and a wide measurement range (450 kPa). These features enable our sensor to be utilized for human physiological signal detection, motion tracking, and drowning detection. Furthermore, the process also allows for the fabrication of sensor arrays for spatial pressure mapping and real-time human-machine interaction, expanding the application field of paper-based pressure sensors. This proposed batch fabrication strategy greatly enhances the consistency and reliability of paper-based pressure sensors, demonstrating endless possibilities for paper-based pressure sensors to be used for various applications.

Validity of MENARI plus (self-pulse assessment and clinical scoring) mobile apps for detecting atrial fibrillation in high-risk population

Background

Even before it is clinically diagnosed, atrial fibrillation (AF) can cause a stroke. This study validates self-pulse assessment and clinical scoring (MENARI Plus) based on android apps.

Objective

The aim of this study was to examine the validity of AF screening using MENARI Plus compared with an ECG recording.

Methods

We collected a total of 1385 subjects from high-risk population according to CHA2DS2-VASc score ≥2, attending 8 primary care centers (PCCs) in Malang between July 2021 and December 2021. Every participant underwent self-pulse assessment, and then was evaluated for MENARI Plus Score on android Apps. These cases had been classified as low or high probability for AF (cut-off score 7). After that, electrocardiography examinations were performed and classified with AF and Sinus Rhythm group.

Results

In this study, the mean age of these patients was 61.5 ± 6.9 years old. We found that 156/1385 (11%) patients had AF. There were 68/156 (43.5%) new cases of AF. The sensitivity for self-pulse palpation was 73.1% (95% CI: 68%-76%) and specificity was 68.3% (95% CI: 65%-72%). MENARI Plus had an area under the receiver operating curve (AUC) of 0.86 (95% CI: 0.82-0.89) with sensitivity per measurement occasion was (84%, 95% CI: 82%-88%) and specificity was (87.9%, 95% CI: 82%-90%).

Conclusion

In this study, we found that MENARI Plus has high sensitivity and specificity for AF. It is therefore useful for ruling out AF. It may also be a useful screen that can be applied opportunistically for previously undetected AFs.

The use of digital health in heart rhythm care

INTRODUCTION

Digital health is a broad term that includes telecommunication technologies to collect, share and manipulate health information to improve patient health and health care services. With the growing use of wearables, artificial intelligence, machine learning, and other novel technologies, digital health is particularly relevant to the field of cardiac arrhythmias, with roles pertinent to education, prevention, diagnosis, management, prognosis, and surveillance.

AREAS COVERED

This review summarizes information on the clinical use of digital health technology in arrhythmia care and discusses its opportunities and challenges.

EXPERT OPINION

Digital health has begun to play an essential role in arrhythmia care regarding diagnostics, long-term monitoring, patient education and shared decision making, management, medication adherence, and research. Despite remarkable advances, integrating digital health technologies into healthcare faces challenges, including patient usability, privacy, system interoperability, physician liability, analysis and incorporation of the huge amount of real-time information from wearables, and reimbursement. Successful implementation of digital health technologies requires clear objectives and deep changes to existing workflows and responsibilities.

Potential of electronic devices for detection of health problems in older adults at home: A systematic review and meta-analysis

OBJECTIVE

The aim of this review was to evaluate the overall diagnostic performance of e-devices for detection of health problems in older adults at home.

METHODS

A systematic review was conducted following the PRISMA-DTA guidelines.

RESULTS

31 studies were included with 24 studies included in meta-analysis. The included studies were divided into four categories according to the signals detected: physical activity (PA), vital signs (VS), electrocardiography (ECG) and other. The meta-analysis showed the pooled estimates of sensitivity and specificity were 0.94 and 0.98 respectively in the 'VS' group. The pooled sensitivity and specificity were 0.97 and 0.98 respectively in the 'ECG' group.

CONCLUSIONS

All kinds of e-devices perform well in diagnosing the common health problems. While ECG-based health problems detection system is more reliable than VS-based ones. For sole signal detection system has limitation in diagnosing specific health problems, more researches should focus on developing new systems combined of multiple signals.

Photoplethysmography-Based Smart Devices for Detection of Atrial Fibrillation

Atrial fibrillation is the most commonly experienced type of cardiac arrhythmia and is the most associated with substantial clinical occurrences and expenses. This arrhythmia often occurs in its "silent" asymptomatic form, revealed only after complications such as a stroke or congestive heart failure have transpired. New smart devices confer effective advantages in the detection of this heart arrhythmia, of which photoplethysmography-based smart devices have shown great potential, according to previous research. However, the solution becomes a problem as widespread use and high availability of various applications and smart devices may lead to substantial amounts of false and misleading recordings and information, causing unnecessary anxiety regarding arrhythmic occurrences diagnosed by the devices but not professionally confirmed. Thus, with most of the devices being photoplethysmography based for detection of atrial fibrillation, it is important to research devices studied up to this point to find the best smart device to detect the aforementioned arrhythmias.

Detection and categorization of severe cardiac disorders based solely on heart period measurements

Cardiac disorders are common conditions associated with a high mortality rate. Due to their potential for causing serious symptoms, it is desirable to constantly monitor cardiac status using an accessible device such as a smartwatch. While electrocardiograms (ECGs) can make the detailed diagnosis of cardiac disorders, the examination is typically performed only once a year for each individual during health checkups, and it requires expert medical practitioners to make comprehensive judgments. Here we describe a newly developed automated system for alerting individuals about cardiac disorders solely by measuring a series of heart periods. For this purpose, we examined two metrics of heart rate variability (HRV) and analyzed 1-day ECG recordings of more than 1,000 subjects in total. We found that a metric of local variation was more efficient than conventional HRV metrics for alerting cardiac disorders, and furthermore, that a newly introduced metric of local-global variation resulted in superior capacity for discriminating between premature contraction and atrial fibrillation. Even with a 1-minute recording of heart periods, our new detection system had a diagnostic performance even better than that of the conventional analysis method applied to a 1-day recording.

Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis

OBJECTIVES

Timely diagnosis of atrial fibrillation (AF) is essential to reduce complications from this increasingly common condition. We sought to assess the diagnostic accuracy of smartphone camera photoplethysmography (PPG) compared with conventional electrocardiogram (ECG) for AF detection.

METHODS

This is a systematic review of MEDLINE, EMBASE and Cochrane (1980-December 2020), including any study or abstract, where smartphone PPG was compared with a reference ECG (1, 3 or 12-lead). Random effects meta-analysis was performed to pool sensitivity/specificity and identify publication bias, with study quality assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) risk of bias tool.

RESULTS

28 studies were included (10 full-text publications and 18 abstracts), providing 31 comparisons of smartphone PPG versus ECG for AF detection. 11 404 participants were included (2950 in AF), with most studies being small and based in secondary care. Sensitivity and specificity for AF detection were high, ranging from 81% to 100%, and from 85% to 100%, respectively. 20 comparisons from 17 studies were meta-analysed, including 6891 participants (2299 with AF); the pooled sensitivity was 94% (95% CI 92% to 95%) and specificity 97% (96%-98%), with substantial heterogeneity (p<0.01). Studies were of poor quality overall and none met all the QUADAS-2 criteria, with particular issues regarding selection bias and the potential for publication bias.

CONCLUSION

PPG provides a non-invasive, patient-led screening tool for AF. However, current evidence is limited to small, biased, low-quality studies with unrealistically high sensitivity and specificity. Further studies are needed, preferably independent from manufacturers, in order to advise clinicians on the true value of PPG technology for AF detection.

Glucometer Usability for 65+ Type 2 Diabetes Patients: Insights on Physical and Cognitive Issues

Background: Self-monitoring of blood glucose (SMBG) is of paramount relevance for type 2 diabetes mellitus (T2DM) patients. However, past evidence shows that there are physical and cognitive issues that might limit the usage of glucometers by T2DM patients aged 65 years and over. Objective: Our aim was to investigate the physical and cognitive issues related to the usage of glucometers by T2DM patients aged 65 years and over. Materials and Methods: The extant literature was analysed to define an original framework showing the logical nexus between physical and cognitive issues and quality of life. Then we collected evidence addressing the specific case of the Accu-Chek^® Instant glucometer produced by Roche Diabetes Care GmbH, which implements new features claiming to improve usability. We conducted 30 interviews with T2DM patients aged 65 years and over, three interviews with senior nurses, and a focus group with three senior physicians and three senior nurses. Results: From the interviews, both patients and nurses declared that they were generally satisfied with the Accu-Chek^® Instant glucometer's characteristics. In the focus group, the results were commented on and, in the light of some diverging answers, improvements have been set up for future implementation. Conclusions: Our study produces evidence and future suggestions about the usage of glucometers by type 2 diabetes patients aged 65 years and over.

Wearables in Sports Cardiology

The expanding array and adoption of consumer health wearables is creating a new dynamic to the patient-health-care provider relationship. Providers are increasingly tasked with integrating the biometric data collected from their patients into clinical care. Further, a growing body of evidence is supporting the provider-driven utility of wearables in the screening, diagnosis, and monitoring of cardiovascular disease. Here we highlight existing and emerging wearable health technologies and the potential applications for use within sports cardiology. We additionally highlight how wearables can advance the remote cardiovascular care of patients within the context of the COVID-19 pandemic. Finally, despite these promising advances, we acknowledge some of the significant challenges that remain before wearables can be routinely incorporated into clinical care.

Automated detection of atrial fibrillation based on vocal features analysis

INTRODUCTION

Early detection of atrial fibrillation (AF) is desirable but challenging due to the often-asymptomatic nature of AF. Known screening methods are limited and most of them depend of electrocardiography or other techniques with direct contact with the skin. Analysis of voice signals from natural speech has been reported for several applications in medicine. The study goal was to evaluate the usefulness of vocal features analysis for the detection of AF.

METHODS

This prospective study was performed in two medical centers. Patients with persistent AF admitted for cardioversion were enrolled. The patients pronounced the vowels "Ahh" and "Ohh" were recorded synchronously with an ECG tracing. An algorithm was developed to provide an "AF indicator" for detection of AF from the speech signal.

RESULTS

A total of 158 patients were recruited. The final analysis of "Ahh" and "Ohh" syllables was performed on 143 and 142 patients, respectively. The mean age was 71.4 ± 9.3 and 43% of patients were females. The developed AF indicator was reliable. Its numerical value decreased significantly in sinus rhythm (SR) after the cardioversion ("Ahh": from 13.98 ± 3.10 to 7.49 ± 1.58; "Ohh": from 11.39 ± 2.99 to 2.99 ± 1.61). The values at SR were significantly more homogenous compared to AF as indicated by a lower standard deviation. The area under the receiver operating characteristic curve was >0.98 and >0.89 ("Ahh" and "Ohh," respectively, p < .001). The AF indicator sensitivity is 95% with 82% specificity.

CONCLUSION

This study is the first report to demonstrate feasibility and reliability of the identification of AF episodes using voice analysis with acceptable accuracy, within the identified limitations of our study methods. The developed AF indicator has higher accuracy using the "Ahh" syllable versus "Ohh."

Comparison of 2 Smart Watch Algorithms for Detection of Atrial Fibrillation and the Benefit of Clinician Interpretation: SMART WARS Study

BACKGROUND

Smart watches and wearable technology capable of heart rhythm assessment have increased in use in the general population. The Apple Watch Series 4 (AW4) and KardiaBand (KB) are devices capable of obtaining single-lead electrocardiographic recordings, presenting a novel opportunity for the detection of paroxysmal arrhythmias.

OBJECTIVES

The aim of this study was to assess the diagnostic utility of the AW4 and KB in an elderly outpatient population.

METHODS

Consecutive recordings were taken from patients attending cardiology outpatient clinic from the AW4 and KB concurrently with 12-lead electrocardiography. Automated diagnoses and blinded single-lead electrocardiographic tracing interpretations by 2 cardiologists were analyzed. Analysis was also conducted to assess the effect of combined device and clinician interpretation.

RESULTS

One hundred twenty-five patients were prospectively recruited (mean age 76 ± 7 years, 62% men). The accuracy of the automated rhythm assessment was higher with the KB than the AW4 (74% vs 65%). For the detection of atrial fibrillation, the sensitivity and negative predictive value of the KB were 89% and 97%, respectively, and of the AW4 were 19% and 82%, respectively. Using hybrid automated and clinician interpretation, the overall accuracy of the KB and AW4 was 91% and 87%, respectively.

CONCLUSIONS

The KB automated algorithm outperformed the AW4 in its accuracy and sensitivity for detecting atrial fibrillation in the outpatient setting. Clinician assessment of the single-lead electrocardiogram improved accuracy. These findings suggest that although these devices' tracings are of sufficient quality, automated diagnosis alone is not sufficient for making clinical decisions about atrial fibrillation diagnosis and management.

Visual Reassessment with Flux-Interval Plot Configuration after Automatic Classification for Accurate Atrial Fibrillation Detection by Photoplethysmography

Atrial fibrillation (AFib) is a common type of arrhythmia that is often clinically asymptomatic, which increases the risk of stroke significantly but can be prevented with anticoagulation. The photoplethysmogram (PPG) has recently attracted a lot of attention as a surrogate for electrocardiography (ECG) on atrial fibrillation (AFib) detection, with its out-of-hospital usability for rapid screening or long-term monitoring. Previous studies on AFib detection via PPG signals have achieved good results, but were short of intuitive criteria like ECG p-wave absence or not, especially while using interval randomness to detect AFib suffering from conjunction with premature contractions (PAC/PVC). In this study, we newly developed a PPG flux (pulse amplitude) and interval plots-based methodology, simply comprising an irregularity index threshold of 20 and regression error threshold of 0.06 for the precise automatic detection of AFib. The proposed method with automated detection on AFib shows a combined sensitivity, specificity, accuracy, and precision of 1, 0.995, 0.995, and 0.952 across the 460 samples. Furthermore, the flux-interval plot configuration also acts as a very intuitive tool for visual reassessment to confirm the automatic detection of AFib by its distinctive plot pattern compared to other cardiac rhythms. The study demonstrated that exclusive 2 false-positive cases could be corrected after the reassessment. With the methodology’s background theory well established, the detection process automated and visualized, and the PPG sensors already extensively used, this technology is very user-friendly and convincing for promoted to in-house AFib diagnostics.

Comparative effectiveness of smartphone healthcare applications for improving quality of life in lung cancer patients: study protocol

BACKGROUND

Although pulmonary rehabilitation is helpful for patients following lung cancer surgery, rehabilitation is not widely available, due in part to a lack of medical resources. Recent developments in digital health care have overcome the space limitations associated with in-person health care. This study will evaluate and compare the efficacy of three different smartphone healthcare systems in patients with lung cancer.

METHODS

This single center randomized controlled study is designed to evaluate the efficacy of digital healthcare applications for lung cancer patients after thoracoscopic lung resection. A total of 320 patients will be enrolled and randomized 1:1:1:1 into four different groups, with one group each using the smartphone applications NOOM, Walkon, and Efilcare and the fourth being the control group without intervention. Questionnaires will be administered to patients at baseline and after 3, 6, and 12 months. The primary endpoint will be the score on the EuroQol five-dimension index. Secondary endpoints will include other questionnaires about quality of life and dyspnea.

DISCUSSION

This prospective randomized controlled study may allow assessments and comparisons of the efficacy of various smartphone applications in patients who undergo lung cancer surgery. This process may enable the introduction of healthcare interventions that maintain quality of life in patients with lung cancer. Trial registration CRIS, KCT0005447. Registered 06 October 2020, https://cris.nih.go.kr/cris/search/detailSearch.do/19346.

Mobile health technology in atrial fibrillation

INTRODUCTION

Mobile health (mHealth) solutions in atrial fibrillation (AF) are becoming widespread, thanks to everyday life devices such as smartphones. Their use is validated both in monitoring and in screening scenarios. In the published literature, the diagnostic accuracy of mHealth solutions wide differs, and their current clinical use is not well established in principal guidelines.

AREAS COVERED

mHealth solutions have progressively built an AF-detection chain to guide patients from the device's alert signal to the health care practitioners' (HCPs) attention. This review aims to critically evaluate the latest evidence regarding mHealth devices and the future possible patient's uses in everyday life.

EXPERT OPINION

The patients are the first to be informed of the rhythm anomaly, leading to the urgency of increasing the patients' AF self-management. Furthermore, HCPs need to update themselves about mHealth devices use in clinical practice. Nevertheless, these are promising instruments in specific populations, such as post-stroke patients, to promote an early arrhythmia diagnosis in the post-ablation/cardioversion period, allowing checks on the efficacy of the treatment or intervention.

Impact of recording length and other arrhythmias on atrial fibrillation detection from wrist photoplethysmogram using smartwatches

This study aimed to evaluate whether quantitative analysis of wrist photoplethysmography (PPG) could detect atrial fibrillation (AF). Continuous electrocardiograms recorded using an electrophysiology recording system and PPG obtained using a wrist-worn smartwatch were simultaneously collected from patients undergoing catheter ablation or electrical cardioversion. PPG features were extracted from 10, 25, 40, and 80 heartbeats of the split segments. Machine learning with a support vector machine and random forest approach were used to detect AF. A total of 116 patients were evaluated. We annotated > 117 h of PPG. A total of 6475 and 3957 segments of 25-beat pulse-to-pulse intervals (PPIs) were annotated as AF and sinus rhythm, respectively. The accuracy of the 25 PPIs yielded a test area under the receiver operating characteristic curve (AUC) of 0.9676, which was significantly better than the AUC for the 10 PPIs (0.9453; P < .001). PPGs obtained from another 38 patients with frequent premature ventricular/atrial complexes (PVCs/PACs) were used to evaluate the impact of other arrhythmias on diagnostic accuracy. The new AF detection algorithm achieved an AUC of 0.9680. The appropriate data length of PPG for optimizing the PPG analytics program was 25 heartbeats. Algorithm modification using a machine learning approach shows robustness to PVCs/PACs.

mHealth Apps for Self-Management of Cardiovascular Diseases: A Scoping Review

The use of mHealth apps for the self-management of cardiovascular diseases (CVDs) is an increasing trend in patient-centered care. In this research, we conduct a scoping review of mHealth apps for CVD self-management within the period 2014 to 2021. Our review revolves around six main aspects of the current status of mHealth apps for CVD self-management: main CVDs managed, main app functionalities, disease stages managed, common approaches used for data extraction, analysis, management, common wearables used for CVD detection, monitoring and/or identification, and major challenges to overcome and future work remarks. Our review is based on Arksey and O'Malley's methodological framework for conducting studies. Similarly, we adopted the PRISMA model for reporting systematic reviews and meta-analyses. Of the 442 works initially retrieved, the review comprised 38 primary studies. According to our results, the most common CVDs include arrhythmia (34%), heart failure (32%), and coronary heart disease (18%). Additionally, we found that the majority mHealth apps for CVD self-management can provide medical recommendations, medical appointments, reminders, and notifications for CVD monitoring. Main challenges in the use of mHealth apps for CVD self-management include overcoming patient reluctance to use the technology and achieving the interoperability of mHealth applications with other systems.

2021 Asia Pacific Heart Rhythm Society (APHRS) practice guidance on atrial fibrillation screening

In this paper, the Asia Pacific Heart Rhythm Society (APHRS) sought to provide practice guidance on AF screening based on recent evidence, with specific considerations relevant to the Asia-Pacific region. A key recommendation is opportunistic screening for people aged ≥65 years (all countries), with systematic screening to be considered for people aged ≥75 years or who have additional risk factors (all countries).

Atrial fibrillation: a geriatric perspective on the 2020 ESC guidelines

BACKGROUND

The Task Force for the diagnosis and management of atrial fibrillation (AF) of the European Society of Cardiology (ESC) published in 2020 the updated Guidelines for the Diagnosis and Management of Atrial Fibrillation with the contribution of the European Heart Rhythm Association (EHRA) of the ESC and the European Association for Cardiothoracic Surgery (EACTS).

METHODS AND RESULTS

In this narrative viewpoint, we approach AF from the perspective of aging medicine and try to provide the readers with information usually neglected in clinical routine, mainly due to the fact that while the large majority of AF patients in real life are older, frail and cognitively impaired, these are mostly excluded from clinical trials, and physicians' attitudes often prevail over standardized algorithms.

CONCLUSIONS

On the basis of existing evidence, (1) opportunistic AF screening by pulse palpation or ECG rhythm strip is cost-effective, and (2) whereas advanced chronological age by itself is not a contraindication to AF treatment, a Comprehensive Geriatric Assessment (CGA) including frailty, cognitive impairment, falls and bleeding risk may assist in clinical decision making to provide the best individualized treatment.

[Mobile health technologies in cardiology]

Digital medicine is becoming an essential part of the healthcare system. The intense development of mobile technologies, the global coverage of mobile networks, and the growing attachment in the society to mobile devices have prompted the creation of mobile healthcare (mHealth). At present, mobile healthcare technologies have been tested in various cardiovascular diseases. Among the main tasks set for telemedicine, it is necessary to note improvements of general medical care, monitoring of patients' condition, accuracy of clinical diagnoses, timely correction of therapy, and improvement of emergency care. Clinical studies are performed in parallel with active work in the field of informational technologies to provide safety of data storage and intellectual processing. Finally, despite the broad public support for the development of this area of medicine, the search continues for methods to improve patients' compliance with the prescribed therapy. This article presents current information about the use of mHealth in cardiology, study results, prospects of mobile healthcare, and major difficulties in implementing projects in this area.

Contactless facial video recording with deep learning models for the detection of atrial fibrillation

Atrial fibrillation (AF) is often asymptomatic and paroxysmal. Screening and monitoring are needed especially for people at high risk. This study sought to use camera-based remote photoplethysmography (rPPG) with a deep convolutional neural network (DCNN) learning model for AF detection. All participants were classified into groups of AF, normal sinus rhythm (NSR) and other abnormality based on 12-lead ECG. They then underwent facial video recording for 10 min with rPPG signals extracted and segmented into 30-s clips as inputs of the training of DCNN models. Using voting algorithm, the participant would be predicted as AF if > 50% of their rPPG segments were determined as AF rhythm by the model. Of the 453 participants (mean age, 69.3 ± 13.0 years, women, 46%), a total of 7320 segments (1969 AF, 1604 NSR & 3747others) were analyzed by DCNN models. The accuracy rate of rPPG with deep learning model for discriminating AF from NSR and other abnormalities was 90.0% and 97.1% in 30-s and 10-min recording, respectively. This contactless, camera-based rPPG technique with a deep-learning model achieved significantly high accuracy to discriminate AF from non-AF and may enable a feasible way for a large-scale screening or monitoring in the future.

Detection of Common Arrhythmias by the Watch-PAT: Expression of Electrical Arrhythmias by Pulse Recording

BACKGROUND

The WatchPAT (WP) device was shown to be accurate for the diagnosis of sleep apnea and is widely used worldwide as an ambulatory diagnostic tool. While it records peripheral arterial tone (PAT) and not electrocardiogram (ECG), the ability of it to detect arrhythmias is unknown and was not studied previously. Common arrhythmias such as atrial fibrillation (AF) or premature beats may be uniquely presented while recording PAT/pulse wave.

PURPOSE

To examine the potential detection of common arrhythmias by analyzing the PAT amplitude and pulse rate/volume changes.

PATIENTS AND METHODS

Patients with suspected sleep disordered breathing (SDB) were recruited with preference for patients with previously diagnosed AF or congestive heart failure (CHF). They underwent simultaneous WP and PSG studies in 11 sleep centers. A novel algorithm was developed to detect arrhythmias while measuring PAT and was tested on these patients. Manual scoring of ECG channel (recorded as part of the PSG) was blinded to the automatically analyzed WP data.

RESULTS

A total of 84 patients aged 57±16 (54 males) participated in this study. Their BMI was 30±5.7Kg/m2. Of them, 41 had heart failure (49%) and 17 (20%) had AF. The sensitivity and specificity of the WP to detect AF segments (of at least 60 seconds) were 0.77 and 0.99, respectively. The correlation between the WP derived detection of premature beats (events/min) to that of the PSG one was 0.98 (p<0.001).

CONCLUSION

The novel automatic algorithm of the WP can reasonably detect AF and premature beats. We suggest that when the algorithm raises a flag for arrhythmia, the patients should shortly undergo ECG and/or Holter ECG study.

Study on the Types of Elderly Intelligent Health Management Technology and the Influencing Factors of Its Adoption

[Background]: In recent years, aging has become a global social problem. Intelligent health management technology (IHMT) provides solutions for the elderly to deal with various health risks. However, the elderly are facing many difficulties in using IHMT. Studying the application types of IHMT and the influencing factors of the elderly’s acceptance of it will help to improve the use behavior of the elderly. [Methods]: This paper summarizes the application types of IHMT, identifies the influencing factors of the elderly’s adaption of IHMT, and makes a systematic comment on the influencing factors. [Results]: We divide the different functions of IHMT for the elderly into four types: self-monitoring, medical care, remote monitoring, and health education. The influencing factors are divided into three types: individual, social, and technology. [Conclusions]: This study finds that IHMT’s application covers all aspects of the health services of the elderly. Among these applications, self-monitoring is the most used. We divided the influencing factors of the elderly’s acceptance of IHMT into three categories and nine subcategories, having 25 variables.

Predictive performance and impact of algorithms in remote monitoring of chronic conditions: A systematic review and meta-analysis

BACKGROUND

The use of telehealth interventions, such as the remote monitoring of patient clinical data (e.g. blood pressure, blood glucose, heart rate, medication use), has been proposed as a strategy to better manage chronic conditions and to reduce the impact on patients and healthcare systems. The use of algorithms for data acquisition, analysis, transmission, communication and visualisation are now common in remote patient monitoring. However, their use and impact on chronic disease management has not been systematically investigated.

OBJECTIVES

To investigate the use, impact, and performance of remote monitoring algorithms across various types of chronic conditions.

METHODS

A literature search of MEDLINE complete, CINHAL complete, and EMBASE was performed using search terms relating to the concepts of remote monitoring, chronic conditions, and data processing algorithms. Comparable outcomes from studies describing the impact on process measures and clinical and patient-reported outcomes were pooled for a summary effect and meta-analyses. A comparison of studies reporting the predictive performance of algorithms was also conducted using the Youden Index.

RESULTS

A total of 89 articles were included in the review. There was no evidence of a positive impact on healthcare utilisation [OR 1.09 (0.90 to 1.31); P = .35] and mortality [OR 0.83 (0.63 to 1.10); P = .208], but there was a positive effect on generic health status [SDM 0.2912 (0.06 to 0.51); P = .010] and diabetes control [SDM -0.53 (-0.74 to -0.33); P < .001; I² = 15.71] (with two of the three diabetes studies being identified as having a high risk of bias). While the majority of impact studies made use of heuristic threshold-based algorithms (n = 27,87%), most performance studies (n = 36, 62%) analysed non-sequential machine learning methods. There was considerable variance in the quality, sample size and performance amongst these studies. Overall, algorithms involved in diagnosis (n = 22, 47%) had superior performance to those involved in predicting a future event (n = 25, 53%). Detection of arrythmia and ischaemia utilising ECG data showed particularly promising results.

CONCLUSION

The performance of data processing algorithms for the diagnosis of a current condition, particularly those related to the detection of arrythmia and ischaemia, is promising. However, there appears to exist minimal testing in experimental studies, with only two included impact studies citing a performance study as support for the intervention algorithm used. Because of the disconnect between performance and impact studies, there is currently limited evidence of the effect of integrating advanced inference algorithms in remote monitoring interventions. If the field of remote patient monitoring is to progress, future impact studies should address this disconnect by evaluating high performance validated algorithms in robust clinical trials.

Mobile health solutions for atrial fibrillation detection and management: a systematic review

Aim

We aimed to systematically review the available literature on mobile Health (mHealth) solutions, including handheld and wearable devices, implantable loop recorders (ILRs), as well as mobile platforms and support systems in atrial fibrillation (AF) detection and management.

Methods

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The electronic databases PubMed (NCBI), Embase (Ovid), and Cochrane were searched for articles published until 10 February 2021, inclusive. Given that the included studies varied widely in their design, interventions, comparators, and outcomes, no synthesis was undertaken, and we undertook a narrative review.

Results

We found 208 studies, which were deemed potentially relevant. Of these studies included, 82, 46, and 49 studies aimed at validating handheld devices, wearables, and ILRs for AF detection and/or management, respectively, while 34 studies assessed mobile platforms/support systems. The diagnostic accuracy of mHealth solutions differs with respect to the type (handheld devices vs wearables vs ILRs) and technology used (electrocardiography vs photoplethysmography), as well as application setting (intermittent vs continuous, spot vs longitudinal assessment), and study population.

Conclusion

While the use of mHealth solutions in the detection and management of AF is becoming increasingly popular, its clinical implications merit further investigation and several barriers to widespread mHealth adaption in healthcare systems need to be overcome.

Graphic abstract

Mobile health solutions for atrial fibrillation detection and management: a systematic review.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00392-021-01941-9.

Mobile health applications for the detection of atrial fibrillation: a systematic review

AIMS

Atrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart failure. We aimed to conduct a systematic review of the literature and summarize the performance of mobile health (mHealth) devices in diagnosing and screening for AF.

METHODS AND RESULTS

We conducted a systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Forty-three studies met the inclusion criteria and were divided into two groups: 28 studies aimed at validating smart devices for AF diagnosis, and 15 studies used smart devices to screen for AF. Evaluated technologies included smartphones, with photoplethysmographic (PPG) pulse waveform measurement or accelerometer sensors, smartbands, external electrodes that can provide a smartphone single-lead electrocardiogram (iECG), such as AliveCor, Zenicor and MyDiagnostick, and earlobe monitor. The accuracy of these devices depended on the technology and the population, AliveCor and smartphone PPG sensors being the most frequent systems analysed. The iECG provided by AliveCor demonstrated a sensitivity and specificity between 66.7% and 98.5% and 99.4% and 99.0%, respectively. The PPG sensors detected AF with a sensitivity of 85.0-100% and a specificity of 93.5-99.0%. The incidence of newly diagnosed arrhythmia ranged from 0.12% in a healthy population to 8% among hospitalized patients.

CONCLUSION

Although the evidence for clinical effectiveness is limited, these devices may be useful in detecting AF. While mHealth is growing in popularity, its clinical, economic, and policy implications merit further investigation. More head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness.

Use of Smartphones and Wearables for Arrhythmia Monitoring

Smartphones and other wearable electronic devices increasingly are used for ambulatory cardiac rhythm assessment. These consumer technologies have been evaluated in several studies for diagnosis and management of atrial fibrillation. Diverse mobile health applications, including management of other arrhythmias and medical conditions, are expanding alongside advances in technology. Electronic devices owned by millions of consumers have the potential to alter health care delivery as well as research design and implementation. This review provides an up-to-date discussion of the available mobile health technologies, specific applications and limitations for arrhythmia evaluation, their impact on health care systems, and key areas for future investigation.

The Auxiliary Diagnostic Value of a Novel Wearable Electrocardiogram-Recording System for Arrhythmia Detection: Diagnostic Trial

Background: AMAZFIT^®, a novel wearable electrocardiogram (ECG)-recording system is used for the measurement, acquisition, and storage of single-lead cardiac waveforms for adults. The aim of the study was to evaluate the accuracy of AMAZFIT^® for diagnosing arrhythmia in older patients.

Methods: From May to December 2019, we recruited 291 elderly individuals with an average age of 78±10 years old, and 41.9% women. All cardiac waveforms were obtained from the AMAZFIT^® which included limb and chest leads. Two trained technicians reviewed all ECG data to determine cardiac rhythm using standard diagnostic criteria. We evaluated the accuracy of AMAZFIT^® for identifying arrhythmia by comparing the sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and positive and negative likelihood ratios with those of a standard 12-lead ECG.

Results: Of the 291 participants, 197 older adults had arrhythmias, including AF (n = 119), first-degree AVB (n = 28), PACs (n = 25), and PVCs (n = 28). Three of these participants had arrhythmias of AF and PVCs. Chest lead data from 100% and limb lead data from 4.7% of the participants were analyzed. An evaluation of AMAZFIT^® for atrial fibrillation (AF) reported a sensitivity, specificity, PPV, NPV PLR, and negative likelihood ratio (NLR) of 93.28, 95.35, 93.28, 95.35, 20.06, and 0.07%, respectively. AMAZFIT^® also demonstrated excellent sensitivity for premature atrial contractions (PACs) (84.00%) and premature ventricular contractions (PVCs) (89.29%). However, the device demonstrated a low sensitivity for first-degree atrioventricular block (32.14%).

Conclusions: The AMAZFIT^® showed significantly higher sensitivity and specificity for AF, PACs, and PVCs. This portable ECG-recording device based on an algorithm has a potential auxiliary diagnostic value for identifying arrhythmia compared with a standard 12-lead ECG device.

Phenotypes of engagement with mobile health technology for heart rhythm monitoring

Objectives

Guided by the concept of digital phenotypes, the objective of this study was to identify engagement phenotypes among individuals with atrial fibrillation (AF) using mobile health (mHealth) technology for 6 months.

Materials and Methods

We conducted a secondary analysis of mHealth data, surveys, and clinical records collected by participants using mHealth in a clinical trial. Patterns of participants’ weekly use over 6 months were analyzed to identify engagement phenotypes via latent growth mixture model (LGMM). Multinomial logistic regression models were fitted to compute the effects of predictors on LGMM classes.

Results

One hundred twenty-eight participants (mean age 61.9 years, 75.8% male) were included in the analysis. Application of LGMM identified 4 distinct engagement phenotypes: “High-High,” “Moderate-Moderate,” “High-Low,” and “Moderate-Low.” In multinomial models, older age, less frequent afternoon mHealth use, shorter intervals between mHealth use, more AF episodes measured directly with mHealth, and lower left ventricular ejection fraction were more strongly associated with the High-High phenotype compared to the Moderate-Low phenotype (reference). Older age, more palpitations, and a history of stroke or transient ischemic attack were more strongly associated with the Moderate-Moderate phenotype compared to the reference.

Discussion

Engagement phenotypes provide a nuanced characterization of how individuals engage with mHealth over time, and which individuals are more likely to be highly engaged users.

Conclusion

This study demonstrates that engagement phenotypes are valuable in understanding and possibly intervening upon engagement within a population, and also suggests that engagement is an important variable to be considered in digital phenotyping work more broadly.

Digital health solutions in the screening of subclinical atrial fibrillation

Atrial fibrillation (AF) will become one of the biggest challenges in cardiovascular medicine in the near future. Attempting an improvement in future patient care calls explicitly for the screening of subclinical AF. Digital health solutions implementing communication technologies for the collection and analysis of digitally assessable data will most likely serve this need. Several new rapidly developing methods were introduced in the past decade. Although the vast majority still require scientific validation, the body of evidence is growing and several randomized controlled trials are planned. This review aims to give an overview of current technologies with a specific focus on mobile health (mHealth) and appraise their value with regard to the available scientific data.

Distinguishing atrial fibrillation from sinus rhythm using commercial pulse detection systems: The non-interventional BAYathlon study

Objective

Early diagnosis of atrial fibrillation (AFib) is a priority for stroke prevention. We sought to test four commercial pulse detection systems (CPDSs) for ability to distinguish AFib from normal sinus rhythm using a published algorithm (Zhou et al., PLoS One 2015;10:e0136544), compared with visual diagnosis by electrocardiogram inspection.

Methods

BAYathlon was a prospective, non-interventional, single-centre study. Adult cardiology patients with documented AFib or sinus rhythm who were due to have a routine 5-min electrocardiogram were randomized to undergo a parallel 5-min pulse assessment with a Polar V800, eMotion Faros 360, TomTom heart rate monitor, or Adidas miCoach Smart Run.

Results

144 patients (73 with AFib, 71 with sinus rhythm (based on electrocardiograms); median age: 73 years; 53.5% male) were analysed. Algorithm sensitivities (primary endpoint) and specificities for AFib when applied to CPDS recordings were 93.3% and 94.1% with the Polar V800, 90.0% and 84.2% with the eMotion Faros 360, and 0% and 100% with the other CPDSs (analysis period: 127 heart rate signals + 2 min). When applied to routine electrocardiograms, the algorithm correctly detected AFib in 71/73 patients. Different analysis periods (127 heart rate signals +1 or 3 min) only slightly changed the sensitivities with the Polar V800 and eMotion Faros 360 and had no effect on the sensitivities with the other CPDSs.

Conclusion

AFib screening using the applied algorithm is feasible with the Polar V800 and eMotion Faros 360 (which provide RR interval data) but not with the other CPDSs (which provide pre-processed heart rate time series).

ClinicalTrials.gov identifier: NCT02875106

2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

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.

2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

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.

2021 ISHNE / HRS / EHRA / APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society

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.

Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring

Remote monitoring of health can reduce frequent hospitalisations, diminishing the burden on the healthcare system and cost to the community. Patient monitoring helps identify symptoms associated with diseases or disease-driven disorders, which makes it an essential element of medical diagnoses, clinical interventions, and rehabilitation treatments for severe medical conditions. This monitoring can be expensive and time-consuming and provide an incomplete picture of the state of the patient. In the last decade, there has been a significant increase in the adoption of mobile and wearable devices, along with the introduction of smart textile solutions that offer the possibility of continuous monitoring. These alternatives fuel a technology shift in healthcare, one that involves the continuous tracking and monitoring of individuals. This scoping review examines how mobile, wearable, and textile sensing technology have been permeating healthcare by offering alternate solutions to challenging issues, such as personalised prescriptions or home-based secondary prevention. To do so, we have selected 222 healthcare literature articles published from 2007 to 2019 and reviewed them following the PRISMA process under the schema of a scoping review framework. Overall, our findings show a recent increase in research on mobile sensing technology to address patient monitoring, reflected by 128 articles published in journals and 19 articles in conference proceedings between 2014 and 2019, which represents 57.65% and 8.55% respectively of all included articles.