Rationale and design of a large population study to validate software for the assessment of atrial fibrillation from data acquired by a consumer tracker or smartwatch: The Fitbit heart study

Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art

The objective of this review was to critically examine existing digital applications, tailored for use by citizens and professionals, to provide diet monitoring, diet planning, and precision nutrition. We sought to identify the strengths and weaknesses of such digital applications, while exploring their potential contributions to enhancing public health, and discussed potential developmental pathways. Nutrition is a critical aspect of maintaining good health, with an unhealthy diet being one of the primary risk factors for chronic diseases, such as obesity, diabetes, and cardiovascular disease. Tracking and monitoring one's diet has been shown to help improve health and weight management. However, this task can be complex and time-consuming, often leading to frustration and a lack of adherence to dietary recommendations. Digital applications for diet monitoring, diet generation, and precision nutrition offer the promise of better health outcomes. Data on current nutrition-based digital tools was collected from pertinent literature and software providers. These digital tools have been designed for particular user groups: citizens, nutritionists, and physicians and researchers employing genetics and epigenetics tools. The applications were evaluated in terms of their key functionalities, strengths, and limitations. The analysis primarily concentrated on artificial intelligence algorithms and devices intended to streamline the collection and organization of nutrition data. Furthermore, an exploration was conducted of potential future advancements in this field. Digital applications designed for the use of citizens allow diet self-monitoring, and they can be an effective tool for weight and diabetes management, while digital precision nutrition solutions for professionals can provide scalability, personalized recommendations for patients, and a means of providing ongoing diet support. The limitations in using these digital applications include data accuracy, accessibility, and affordability, and further research and development are required. The integration of artificial intelligence, machine learning, and blockchain technology holds promise for improving the performance, security, and privacy of digital precision nutrition interventions. Multidisciplinarity is crucial for evidence-based and accessible solutions. Digital applications for diet monitoring and precision nutrition have the potential to revolutionize nutrition and health. These tools can make it easier for individuals to control their diets, help nutritionists provide better care, and enable physicians to offer personalized treatment.

Sensor-Based Frailty Assessment Using Fitbit

This study evaluated the reliability of Fitbit in assessing frailty based on motor and heart rate (HR) parameters through a validated upper extremity function (UEF) test, which involves 20 s of rapid elbow flexion. For motor performance, participants completed six trials of full elbow flexion using their right arm, with and without weight. Fitbit and a commercial motion sensor were worn on the right arm. For HR measurements, an ECG system was placed on the left chest alongside the Fitbit on the left wrist. Motor parameters assessing speed, flexibility, weakness, exhaustion, and HR before, during, and after UEF were measured. A total of 42 participants (age = 22 ± 3) were recruited. For motor parameters, excellent agreement was observed between the wearable sensor and Fitbit, except for flexibility (ICC = 0.87 ± 0.09). For HR parameters, ICC values showed weak agreement between ECG and Fitbit for HR increase and recovery (ICC = 0.24 ± 0.11), while moderate to stronger agreement was seen for mean HR during baseline, task, and post-task (ICC = 0.81 ± 0.13). Fitbit is a reliable tool for assessing frailty through motor parameters and provides reasonably accurate HR estimates during baseline, task, and recovery periods. However, Fitbit's ability to track rapid HR changes during activity is limited.

Smart watch applications in atrial fibrillation detection: Current state and future directions

INTRODUCTION

Atrial fibrillation (Afib) is a prevalent chronic arrhythmia associated with severe complications, including stroke, heart failure, and increased mortality. This review explores the use of smartwatches for Afib detection, addressing the limitations of current monitoring methods and emphasizing the potential of wearable technology in revolutionizing healthcare.

RESULTS/OBSERVATION

Current Afib detection methods, such as electrocardiography, have limitations in sensitivity and specificity. Smartwatches with advanced sensors offer continuous monitoring, improving the chances of detecting asymptomatic and paroxysmal Afib. The review meticulously examines major clinical trials studying Afib detection using smartwatches, including the landmark Apple Heart Study and ongoing trials such as the Heart Watch, Heartline, and Fitbit Heart Study. Detailed summaries of participant numbers, smartwatch devices used, and key findings are presented. It also comments on the cost-effectiveness and scalability of smartwatch-based screening, highlighting the potential to reduce healthcare costs and improve patient outcomes.

CONCLUSION/RELEVANCE

The integration of wearable technology into healthcare can lead to earlier diagnosis, improved patient engagement, and enhanced cardiac health monitoring. Despite ethical considerations and disparities, the potential benefits outweigh the challenges. This review calls for increased awareness, collaboration with insurance companies, and ongoing research efforts to optimize smartwatch accuracy and encourage widespread adoption of Afib detection. With insights from major trials, this review serves as a comprehensive reference for healthcare professionals and policymakers, guiding future strategies in the early diagnosis and management of atrial fibrillation.

A Remote Patient Monitoring System With Feedback Mechanisms Using a Smartwatch: Concept, Implementation, and Evaluation Based on the activeDCM Randomized Controlled Trial

BACKGROUND

Technological advances allow for recording and sharing health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable close management of individual health trajectories, monitoring of risk factors, and asynchronous feedback. However, most remote patient monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack a patient-centric design.

OBJECTIVE

The objective of this study was to conceptualize and implement a user-friendly, reusable, interoperable, and secure RPM system incorporating asynchronous feedback mechanisms using a broadly available consumer wearable (Apple Watch). In addition, this study sought to evaluate factors influencing patient acceptance of such systems.

METHODS

The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was used as part of the activeDCM prospective randomized interventional study focusing on dilated cardiomyopathy. Each patient used the system for at least 12 months. The System Usability Scale was used to measure usability from a subjective patient perspective. In addition, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data classified into sensor-based health data (SHD) and patient-reported outcome measures (PROMs). Descriptive statistics using box plots and bootstrapped multiple linear regression with 95% CIs were used for evaluation analyzing the influence of age, sex, device experience, and intervention group membership.

RESULTS

The RPM system comprised 4 interoperable components: patient devices, a data server, a data viewer, and a notification service. The system was evaluated with 95 consecutive patients with dilated cardiomyopathy (28/95, 29% female; mean age 50, SD 12 y) who completed the activeDCM study protocol. The system's app achieved a mean System Usability Scale score of 78 (SD 17), which was most influenced by device experience. In total, 87% (83/95) of the patients could integrate the use of the app well or very well into their daily routine, and 71% (67/95) saw a benefit of the RPM system for management of their health condition. On average, patients interacted with the wearable on 61% (SD 26%) of days enrolled in the study. SHD were available on average for 78% (SD 23%) of days, and PROM data were available on 64% (SD 27%) of weeks enrolled in the study. Wearable interaction frequency, SHD, and PROM completeness were most influenced by intervention group membership.

CONCLUSIONS

Our results mark a first step toward integrating RPM systems based on a consumer wearable device for primary patient input into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable, and secure RPM system that can be integrated into patients' daily routines.

Wearable devices for out-of-hospital cardiac arrest: A population survey on the willingness to adhere

Objectives

When an out-of-hospital cardiac arrest (OHCA) occurs, the first step in the chain of survival is detection. However, 75% of OHCAs are unwitnessed, representing the largest barrier to activating the chain of survival. Wearable devices have the potential to be "artificial bystanders," detecting OHCA and alerting 9-1-1. We sought to understand factors impacting users' willingness for continuous use of a wearable device through an online survey to inform future use of these systems for automated OHCA detection.

Methods

Data were collected from October 2022 to June 2023 through voluntary response sampling. The survey investigated user convenience and perception of urgency to understand design preferences and willingness to adhere to continuous wearable use across different hypothetical risk levels. Associations between categorical variables and willingness were evaluated through nonparametric tests. Logistic models were fit to evaluate the association between continuous variables and willingness at different hypothetical risk levels.

Results

The survey was completed by 359 participants. Participants preferred hand-based devices (wristbands: 87%, watches: 86%, rings: 62%) and prioritized comfort (94%), cost (83%), and size (72%). Participants were more willing to adhere at higher levels of hypothetical risk. At the baseline risk of 0.1%, older individuals with prior wearable use were most willing to adhere to continuous wearable use.

Conclusion

Individuals were willing to continuously wear wearable devices for OHCA detection, especially at increased hypothetical risk of OHCA. Optimizing willingness is not just a matter of adjusting for user preferences, but also increasing perception of urgency through awareness and education about OHCA.

Recent Advances in the Wearable Devices for Monitoring and Management of Heart Failure

Heart failure (HF) is an acute and degenerative condition with high morbidity and mortality rates. Early diagnosis and treatment of HF can significantly enhance patient outcomes through admission and readmission reduction and improve quality of life. Being a progressive condition, the continuous monitoring of vital signs and symptoms of HF patients to identify any deterioration and to customize treatment regimens can be beneficial to the management of this disease. Recent breakthroughs in wearable technology have revolutionized the landscape of HF management. Despite the potential benefits, the integration of wearable devices into HF management requires careful consideration of technical, clinical, and ethical challenges, such as performance, regulatory requirements and data privacy. This review summarizes the current evidence on the role of wearable devices in heart failure monitoring and management, and discusses the challenges and opportunities in the field.

Long-term adherence to a wearable for continuous behavioural activity measuring in the SafeHeart implantable cardioverter defibrillator population

Aims

Wearable health technologies are increasingly popular. Yet, wearable monitoring only works when devices are worn as intended, and adherence reporting lacks standardization. In this study, we aimed to explore the long-term adherence to a wrist-worn activity tracker in the prospective SafeHeart study and identify patient characteristics associated with adherence.

Methods and results

This study enrolled 303 participants, instructed to wear a wrist-worn accelerometer day and night for 6 months. Long-term adherence was defined as valid days (≥22 h of wear time) divided by expected days, and daily adherence as mean hours of wear time per 24 h period. Optimal, moderate, and low long-term and daily adherence groups were defined as long-term adherence above or below 95 and 75% and daily adherence above or below 90 and 75%. Regression models were used to identify patient characteristics associated with long-term adherence. In total, 296 participants [median age 64 years; interquartile range (IQR) 57-72; 19% female] were found eligible, yielding 44 003 days for analysis. The median long-term adherence was 88.2% (IQR 74.6-96.5%). A total of 83 (28%), 127 (42.9%), and 86 (29.1%) participants had optimal, moderate, and low long-term adherence, and 163 (55.1%), 87 (29.4%), and 46 (15.5%) had optimal, moderate, and low daily adherence, respectively. Age and smoking habits differed significantly between adherence levels, and increasing changeover intervals improved the degree of long-term adherence.

Conclusion

Long-term adherence to a wearable activity tracker was 88.2% over a 6-month period. Older age and longer changeover interval were positively associated with long-term adherence. This serves as a benchmark for future studies that rely on wearable devices.

Trial registration number

The National Trial Registration number: NL9218 (https://onderzoekmetmensen.nl/).

Substantiation and Effectiveness of Remote Monitoring System Based on IoMT Using Portable ECG Device

Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of a portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial involving 2000 participants who wore the HiCardi device while engaging in hiking activities. The device monitored their ECG, heart rate, respiration, and body temperature in real-time. If an abnormal signal was detected while a physician was remotely monitoring the ECG at the IoMT monitoring center, he notified the clinical research coordinator (CRC) at the empirical research site, and the CRC advised the participant to visit a hospital. Follow-up calls were made to determine compliance and outcomes. Of the 2000 participants, 318 showed abnormal signals, and 182 were advised to visit a hospital. The follow-up revealed that 139 (76.37%) responded, and 30 (21.58% of those who responded) sought further medical examination. Most visits (80.00%) occurred within one month. Diagnostic approaches included ECG (56.67%), ECG and ultrasound (20.00%), ultrasound alone (16.67%), ECG and X-ray (3.33%), and general treatment (3.33%). Seven participants (23.33% of those who visited) were diagnosed with cardiovascular disease, including conditions such as arrhythmia, atrial fibrillation, and stent requirements. The portable ECG device using the patch-type electrocardiograph detected abnormal cardiovascular signals, leading to timely diagnoses and interventions, demonstrating its potential for broad applications in preventative healthcare.

Assessing the current utilization status of wearable devices in clinical research

BACKGROUND/AIMS

Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations.

METHODS

As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov-a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords "ActiGraph,""Apple Watch,""Empatica,""Fitbit,""Garmin," and "wearable devices" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables.

RESULTS

Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations.

CONCLUSIONS

Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future.

Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives

Atrial fibrillation (AF) significantly increases the risk of stroke and heart failure, but is frequently asymptomatic and intermittent; therefore, its timely diagnosis poses challenges. Early detection in selected patients may aid in stroke prevention and mitigate structural heart complications through prompt intervention. Smartwatches, coupled with powerful artificial intelligence (AI)-enabled algorithms, offer a promising tool for early detection due to their widespread use, easiness of use, and potential cost-effectiveness. Commercially available smartwatches have gained clearance from the FDA to detect AF and are becoming increasingly popular. Despite their promise, the evolving landscape of AI-enabled smartwatch-based AF detection raises questions about the clinical value of this technology. Following the ongoing digital transformation of healthcare, clinicians should familiarize themselves with how AI-enabled smartwatches function in AF detection and navigate their role in clinical settings to deliver optimal patient care. In this review, we provide a concise overview of the characteristics of AI-enabled smartwatch algorithms, their diagnostic performance, clinical value, limitations, and discuss future perspectives in AF diagnosis.

Early warning of atrial fibrillation using deep learning

Atrial fibrillation (AF), the most prevalent cardiac rhythm disorder, significantly increases hospitalization and health risks. Reverting from AF to sinus rhythm (SR) often requires intensive interventions. This study presents a deep-learning model capable of predicting the transition from SR to AF on average 30.8 min before the onset appears, with an accuracy of 83% and an F1 score of 85% on the test data. This performance was obtained from R-to-R interval signals, which can be accessible from wearable technology. Our model, entitled Warning of Atrial Fibrillation (WARN), consists of a deep convolutional neural network trained and validated on 24-h Holter electrocardiogram data from 280 patients, with 70 additional patients used for testing and further evaluation on 33 patients from two external centers. The low computational cost of WARN makes it ideal for integration into wearable technology, allowing for continuous heart monitoring and early AF detection, which can potentially reduce emergency interventions and improve patient outcomes.

Remote monitoring of atrial fibrillation recurrence using mHealth technology (REMOTE-AF)

Aims

This proof-of-concept study sought to evaluate changes in heart rate (HR) obtained from a consumer wearable device and compare against implantable loop recorder (ILR)-detected recurrence of atrial fibrillation (AF) and atrial tachycardia (AT) after AF ablation.

Methods and results

REMOTE-AF (NCT05037136) was a prospectively designed sub-study of the CASA-AF randomized controlled trial (NCT04280042). Participants without a permanent pacemaker had an ILR implanted at their index ablation procedure for longstanding persistent AF. Heart rate and step count were continuously monitored using photoplethysmography (PPG) from a commercially available wrist-worn wearable. Photoplethysmography-recorded HR data were pre-processed with noise filtration and episodes at 1-min interval over 30 min of HR elevations (Z-score = 2) were compared with corresponding ILR data. Thirty-five patients were enrolled, with mean age 70.3 ± 6.8 years and median follow-up 10 months (interquartile range 8-12 months). Implantable loop recorder analysis revealed 17 out of 35 patients (49%) had recurrence of AF/AT. Compared with ILR recurrence, wearable-derived elevations in HR ≥ 110 beats per minute had a sensitivity of 95.3%, specificity 54.1%, positive predictive value (PPV) 15.8%, negative predictive value (NPV) 99.2%, and overall accuracy 57.4%. With PPG-recorded HR elevation spikes (non-exercise related), the sensitivity was 87.5%, specificity 62.2%, PPV 39.2%, NPV 92.3%, and overall accuracy 64.0% in the entire patient cohort. In the AF/AT recurrence only group, sensitivity was 87.6%, specificity 68.3%, PPV 53.6%, NPV 93.0%, and overall accuracy 75.0%.

Conclusion

Consumer wearable devices have the potential to contribute to arrhythmia detection after AF ablation.

Study Registration

ClinicalTrials.gov Identifier: NCT05037136 https://clinicaltrials.gov/ct2/show/NCT05037136.

Detection of Arrhythmias Using Smartwatches-A Systematic Literature Review

Smartwatches represent one of the most widely adopted technological innovations among wearable devices. Their evolution has equipped them with an increasing array of features, including the capability to record an electrocardiogram. This functionality allows users to detect potential arrhythmias, enabling prompt intervention or monitoring of existing arrhythmias, such as atrial fibrillation. In our research, we aimed to compile case reports, case series, and cohort studies from the Web of Science, PubMed, Scopus, and Embase databases published until 1 August 2023. The search employed keywords such as "Smart Watch", "Apple Watch", "Samsung Gear", "Samsung Galaxy Watch", "Google Pixel Watch", "Fitbit", "Huawei Watch", "Withings", "Garmin", "Atrial Fibrillation", "Supraventricular Tachycardia", "Cardiac Arrhythmia", "Ventricular Tachycardia", "Atrioventricular Nodal Reentrant Tachycardia", "Atrioventricular Reentrant Tachycardia", "Heart Block", "Atrial Flutter", "Ectopic Atrial Tachycardia", and "Bradyarrhythmia." We obtained a total of 758 results, from which we selected 57 articles, including 33 case reports and case series, as well as 24 cohort studies. Most of the scientific works focused on atrial fibrillation, which is often detected using Apple Watches. Nevertheless, we also included articles investigating arrhythmias with the potential for circulatory collapse without immediate intervention. This systematic literature review provides a comprehensive overview of the current state of research on arrhythmia detection using smartwatches. Through further research, it may be possible to develop a care protocol that integrates arrhythmias recorded by smartwatches, allowing for timely access to appropriate medical care for patients. Additionally, continuous monitoring of existing arrhythmias using smartwatches could facilitate the assessment of the effectiveness of prescribed therapies.

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection

Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.

Precordial electrocardiographic recording and QT measurement from a novel wearable ring device

Background

The availability of portable and wearable electrocardiographic (ECG) devices has increased secondary to technological development. Single-lead ECG recordings have been shown to reliably detect and characterize cardiac rhythms such as atrial fibrillation. Acquisition of precordial electrodes for full 12-lead ECG reconstruction from bipolar recordings is complicated by the absence of a body ground/Wilson central terminal electrode. The extent of difference between standard precordial leads and those from a wearable bipolar ECG recorder has not been characterized.

Objective

The purpose of this study was to characterize the precordial ECG lead set from sequential bipolar recordings from an ECG ring wearable device.

Methods

In 70 patients who wore an ECG device on a right-hand finger, sequential precordial leads (CR1-CR6) were obtained along with chest electrodes (V1-V6). During acquisition of the modified precordial lead CR6, a full standardized 12-lead ECG capture was obtained. Signal quality was assessed using automated analysis software, and correlation values between the ring-derived ECG precordial leads and standard ECG leads were compared with regard to QRS duration, QT width, and RR interval.

Results

High concordance in the morphologies of precordial ECG leads obtained in a standard fashion and those recorded through an ECG ring was observed. Morphologic alignment improved with increasing laterality of the precordial lead with chest to right arm ring recording (CR5, CR6) compared with anterior chest leads to right arm (CR1, CR2). Segmental measurements of QRS duration and QT segment were well aligned and of high correlation.

Conclusion

Wearable ring-based ECG technology is capable of high-fidelity recordings of precordial leads for nonsimultaneous reconstruction of complete ECG sets. These recordings correlate highly with surface-obtained QRS and QT duration measurements and have significant implications for clinical applications. Uninterpretable tracings were primarily due to electrode noise from poor electrode contact.

Cardiac Remote Monitoring Devices and Technologies: A Review for the Perioperative Physician and Telemedicine Providers

Cardiovascular complications are a major cause of morbidity and mortality after surgery, necessitating adequate and thorough preoperative risk stratification and screening. Several technological advances in cardiac remote monitoring have improved the assessment and diagnosis of cardiovascular disease in patients before and after surgery. These devices perform measurements of physiological function, including vital signs, and more advanced functions, such as electrocardiograms and heart sound recordings. Some of the currently available devices include Fitbit® (Google LLC, Mountain View, CA, USA), BodyGuardian® (Preventive Inc., Rochester, MN, USA), ZephyrTM Performance Systems (Zephyr Inc., Annapolis, MD, USA), Sensium® (The Surgical Company, Amersfoort, UT, The Netherlands), KardiaMobile® (AliveCor, Mountain View, CA, USA), Coala® Heart Monitor (Coala Life Inc., Uppsala, Sweden), Smartex® Wearable Wellness System (Smartex, Porto, LX, Portugal), Eko® CORE and DUO (Eko Health, Emeryville, CA, USA), and TytoCareTM (TytoCare Ltd., New York, USA). Early studies have applied these devices to asymptomatic individuals and those with known cardiovascular disease with good sensitivity and specificity for electrophysiologic diagnosis. These devices carry several technical and other limitations, somewhat restricting the generalization of their use to all patients. However, information gathered from these devices can further guide anesthetic technique, operative timing, and postoperative follow-up, among other variables. As telehealth becomes more prevalent and comprehensive, it is paramount for the perioperative physician to be familiar with the available cardiac remote monitoring technologies.

Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring?

Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.

Self-reported efficacy in patient-physician interaction in relation to anxiety, patient activation, and health-related quality of life among stroke survivors

BACKGROUND

Early detection of AF is critical for stroke prevention. Several commercially available smartwatches are FDA cleared for AF detection. However, little is known about how patient-physician relationships affect patients' anxiety, activation, and health-related quality of life when prescribed smartwatch for AF detection.

METHODS

Data were used from the Pulsewatch study (NCT03761394), which randomized adults (>50 years) with no contraindication to anticoagulation and a CHA2DS2-VASc risk score ≥2 to receive a smartwatch-smartphone app dyad for AF monitoring vs. conventional monitoring with an ECG patch (Cardea SoloTM) and monitored participants for up to 45 days. The Perceived Efficacy in Patient-Physician Interactions survey was used to assess patient confidence in physician interaction at baseline with scores ≥45 indicating high perceived efficacy in patient-provider interactions. Generalized Anxiety Disorder-7 Scale, Consumer Health Activation Index, and Short-Form Health Survey were utilized to examine anxiety, patient activation, and physical and mental health status, at baseline, 14, and 44 days, respectively. We used mixed-effects repeated measures linear regression models to assess changes in psychosocial outcomes among smartwatch users in relation to self-reported efficacy in physician interaction over the study period.

RESULTS

A total of 93 participants (average age 64.1 ± 8.9 years; 43.0% female; 88.2% non-Hispanic white) were included in this analysis. At baseline, fifty-six (60%) participants reported high perceived efficacy in patient-physician interaction. In the fully adjusted models, high perceived efficacy (vs. low) at baseline was associated with greater patient activation and perceived mental health (β 12.0, p-value <0.001; β 3.39, p-value <0.05, respectively). High perceived self-efficacy was not associated with anxiety or physical health status (β - 0.61, p-value 0.46; β 0.64, p-value 0.77) among study participants.

CONCLUSIONS

Higher self-efficacy in patient-physician interaction was associated with higher patient activation and mental health status among stroke survivors using smartwatches. Furthermore, we found no association between anxiety and smartwatch prescription for AF in participants with high self-efficacy in patient-physician interaction. Efforts to improve self-efficacy in patient-physician interaction may improve patient activation and self-rated health and subsequently may lead to better clinical outcomes.KEY MESSAGESHigher self-efficacy in patient-physician interaction was associated with higher patient activation and mental health status among stroke survivors using smartwatches.No association between anxiety and smartwatch prescription for AF in participants with high self-efficacy in patient-physician interaction.Efforts to improve self-efficacy in patient-physician interaction may improve patient activation and self-rated health and subsequently may lead to better clinical outcomes.

Accuracy, Usability, and Adherence of Smartwatches for Atrial Fibrillation Detection in Older Adults After Stroke: Randomized Controlled Trial

BACKGROUND

Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers.

OBJECTIVE

This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors.

METHODS

Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period.

RESULTS

A total of 120 participants were enrolled (mean age 65 years; 50/120, 41% female; 106/120, 88% White). The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days.

CONCLUSIONS

Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1016/j.cvdhj.2021.07.002.

Progression of stroke risk in patients aged <65 years diagnosed with atrial fibrillation: a cohort study in general practice

BACKGROUND

As a result of new technologies, atrial fibrillation (AF) is more likely to be diagnosed in people aged <65 years.

AIM

To investigate the risk of someone diagnosed with AF aged <65 years developing an indication for anticoagulation before they reach 65 years.

DESIGN AND SETTING

Population-based cohort study of patients from English practices using the Clinical Practice Research Datalink, a primary care database of electronic medical records.

METHOD

The study included patients aged <65 years newly diagnosed with AF. The CHA2DS2-VASc score was derived at time of diagnosis based on patients' medical records. Patients not eligible for anticoagulation were followed up until they became eligible or turned 65 years old. The primary outcome of interest was development of a risk factor for stroke in AF.

RESULTS

Among 18 178 patients aged <65 years diagnosed with AF, 9188 (50.5%) were eligible for anticoagulation at the time of diagnosis. Among the 8990 patients not eligible for anticoagulation, 1688 (18.8%) developed a risk factor during follow-up before reaching 65 years of age or leaving the cohort for other reasons, at a rate of 6.1 per 100 patient-years. Hypertension and heart failure were the most common risk factors to occur, with rates of 2.65 (95% CI = 2.47 to 2.84) and 1.58 (95% CI = 1.45 to 1.72) per 100 patient-years, respectively. The rate of new diabetes was 0.95 (95% CI = 0.85 to 1.06) per 100 patient-years.

CONCLUSION

People aged <65 years with AF are at higher risk of developing hypertension, heart failure, and diabetes than the general population, so may warrant regular review to identify new occurrence of such risk factors.

CardinalKit: open-source standards-based, interoperable mobile development platform to help translate the promise of digital health

Smartphone devices capable of monitoring users' health, physiology, activity, and environment revolutionize care delivery, medical research, and remote patient monitoring. Such devices, laden with clinical-grade sensors and cloud connectivity, allow clinicians, researchers, and patients to monitor health longitudinally, passively, and persistently, shifting the paradigm of care and research from low-resolution, intermittent, and discrete to one of persistent, continuous, and high resolution. The collection, transmission, and storage of sensitive health data using mobile devices presents unique challenges that serve as significant barriers to entry for care providers and researchers alike. Compliance with standards like HIPAA and GDPR requires unique skills and practices. These requirements make off-the-shelf technologies insufficient for use in the digital health space. As a result, budget, timeline, talent, and resource constraints are the largest barriers to new digital technologies. The CardinalKit platform is an open-source project addressing these challenges by focusing on reducing these barriers and accelerating the innovation, adoption, and use of digital health technologies. CardinalKit provides a mobile template application and web dashboard to enable an interoperable foundation for developing digital health applications. We demonstrate the applicability of CardinalKit to a wide variety of digital health applications across 18 innovative digital health prototypes.

"Pill-in-Pocket" anticoagulation for stroke prevention in atrial fibrillation

Uninterrupted anticoagulation for atrial fibrillation (AF), regardless of AF burden, is deeply rooted in practice since the early anticoagulation trials. However, uninterrupted anticoagulation is not without risks, and may not be beneficial for allcomers with a history of AF. Indeed, contemporary data that support a critical duration threshold of AF that benefits from anticoagulation, and a temporal association between stroke and multihour AF episodes, compel the study of a more targeted approach to AF anticoagulation. In this review, we discuss data that support further investigation of "pill in the pocket" anticoagulation for AF, and introduce the pivotal Rhythm Evaluation for Anticoagulation Therapy for Atrial Fibrillation (REACT-AF) trial that will robustly evaluate this strategy.

Data-Driven and Technology-Enabled Trial Innovations Toward Decentralization of Clinical Trials: Opportunities and Considerations

Traditional trial designs have well-recognized inefficiencies and logistical barriers to participation. Decentralized trials and digital health solutions have been suggested as potential solutions and have certainly risen to the challenge during the pandemic. Clinical trial designs are now increasingly data driven. The use of distributed clinical data networks and digitization has helped to fundamentally upgrade existing research systems. A trial design may vary anywhere from fully decentralized to hybrid to traditional on-site. Various decentralization components are available for stakeholders to increase the reach and pace of their trials, such as electronic informed consent, remote interviews, administration, outcome assessment, monitoring, and laboratory and imaging modalities. Furthermore, digital health technologies can be included to enrich study conduct. However, careful consideration is warranted, including assessing verification and validity through usability studies and having various contingencies in place through dedicated risk assessment. Selecting the right combination depends not just on the ability to handle patient care and the medical know-how but also on the availability of appropriate technologic infrastructure, skills, and human resources. Throughout this process, quality of evidence generation and physician-patient relation must not be undermined. Here we also address some knowledge gaps, cost considerations, and potential impact of decentralization and digitization on inclusivity, recruitment, engagement, and retention. Last, we mention some future directions that may help drive the necessary change in the right direction.

"Click and mortar" opportunities for digitization and consumerism in trials

BACKGROUND

Digitization (using novel digital tools and strategies) and consumerism (taking a consumer-oriented approach) are increasingly commonplace in clinical trials, but the implications of these changes are not well described.

METHODS

We assembled a group of trial experts from academia, industry, non-profit, and government to discuss implications of this changing trial landscape and provide guidance.

RESULTS

Digitization and consumerism can increase the volume and diversity of trial participants and expedite recruitment. However, downstream bottlenecks, challenges with retention, and serious issues with equity, ethics, and security can result. A "click and mortar" approach, combining approaches from novel and traditional trials with the thoughtful use of technology, may optimally balance opportunities and challenges facing many trials.

CONCLUSION

We offer expert guidance and three "click and mortar" approaches to digital, consumer-oriented trials. More guidance and research are needed to navigate the associated opportunities and challenges.

Incidence of clinical atrial fibrillation and related complications using a screening algorithm at a nationwide level

AIMS

In a recent position paper, the European Heart Rhythm Association (EHRA) proposed an algorithm for the screening and management of arrhythmias using digital devices. In patients with prior stroke, a systematic screening approach for atrial fibrillation (AF) should always be implemented, preferably immediately after the event. Patients with increasing age and with specific cardiovascular or non-cardiovascular comorbidities are also deemed to be at higher risk. From a large nationwide database, the aim was to analyse AF incidence rates derived from this new EHRA algorithm.

METHODS AND RESULTS

Using the French administrative hospital discharge database, all patients hospitalized in 2012 without a history of AF, and with at least a 5-year follow-up (FU) (or if they died earlier), were included. The yearly incidence of AF was calculated in each subgroup defined by the algorithm proposed by EHRA based on a history of previous stroke, increasing age, and eight comorbidities identified via International Classification of Diseases 10th Revision codes. Out of the 4526 104 patients included (mean age 58.9 ± 18.9 years, 64.5% women), 1% had a history of stroke. Among those with no history of stroke, 18% were aged 65-74 years and 21% were ≥75 years. During FU, 327 012 patients had an incidence of AF (yearly incidence 1.86% in the overall population). Implementation of the EHRA algorithm divided the population into six risk groups: patients with a history of stroke (group 1); patients > 75 years (group 2); patients aged 65-74 years with or without comorbidity (groups 3a and 3b); and patients < 65 years with or without comorbidity (groups 4a and 4b). The yearly incidences of AF were 4.58% per year (group 2), 6.21% per year (group 2), 3.50% per year (group 3a), 2.01% per year (group 3b), 1.23% per year (group 4a), and 0.35% per year (group 4b). In patients aged < 65 years, the annual incidence of AF increased progressively according to the number of comorbidities from 0.35% (no comorbidities) to 9.08% (eight comorbidities). For those aged 65-75 years, the same trend was observed, i.e. increasing from 2.01% (no comorbidities) to 11.47% (eight comorbidities).

CONCLUSION

These findings at a nationwide scale confirm the relevance of the subgroups in the EHRA algorithm for identifying a higher risk of AF incidence, showing that older patients (>75 years, regardless of comorbidities) have a higher incidence of AF than those with prior ischaemic stroke. Further studies are needed to evaluate the usefulness of algorithm-based risk stratification strategies for AF screening and the impact of screening on major cardiovascular event rates.

Digital health-high tech or high touch?

Digital transformation in medicine refers to the implementation of information technology-driven developments in the healthcare system and their impact on the way we teach, share, and practice medicine. We would like to provide an overview of current developments and opportunities but also of the risks of digital transformation in medicine. Therefore, we examine the possibilities wearables and digital biomarkers provide for early detection and monitoring of diseases and discuss the potential of artificial intelligence applications in medicine. Furthermore, we outline new opportunities offered by telemedicine applications and digital therapeutics, discuss the aspects of social media in healthcare, and provide an outlook on "Health 4.0."

Early Changes in Acute Myocardial Infarction in Pigs: Achieving Early Detection with Wearable Devices

We examined the changes in variables that could be recorded on wearable devices during the early stages of acute myocardial infarction (AMI) in an animal model. Early diagnosis of AMI is important for prognosis; however, delayed diagnosis is common because of patient hesitation and lack of timely evaluations. Wearable devices are becoming increasingly sophisticated in the ability to track indicators. In this study, we retrospectively reviewed the changes in four variables during AMI in a pig model to assess their ability to help predict AMI onset. AMI was created in 33 pigs by 90-min balloon occlusion of the left anterior descending artery. Blood pressure, EKG, and lactate and cardiac troponin I levels were recorded during the occlusion period. Blood pressure declined significantly within 15 min after balloon inflation (mean arterial pressure, from 61 ± 8 to 50 ± 8 mmHg) and remained at this low level. Within 5 min of balloon inflation, the EKG showed ST-elevation in precordial leads V1–V3. Blood lactate levels increased gradually after occlusion and peaked at 60 min (from 1.48 to 2.53 mmol/L). The continuous transdermal troponin sensor demonstrated a gradual increase in troponin levels over time. Our data suggest that significant changes in key indicators (blood pressure, EKG leads V1–V3, and lactate and troponin levels) occurred at the onset of AMI. Monitoring of these variables could be used to develop an algorithm and alert patients early at the onset of AMI with the help of a wearable device.

Prevalence and Prognostic Significance of Bradyarrhythmias in Patients Screened for Atrial Fibrillation vs Usual Care: Post Hoc Analysis of the LOOP Randomized Clinical Trial

Importance

There is increasing interest in heart rhythm monitoring and technologies to detect subclinical atrial fibrillation (AF), which may lead to incidental diagnosis of bradyarrhythmias.

Objective

To assess bradyarrhythmia prevalence and prognostic significance in persons screened for AF using implantable loop recorder (ILR) compared with unscreened persons.

Design, Setting, and Participants

This was a post hoc analysis of the Implantable Loop Recorder Detection of Atrial Fibrillation to Prevent Stroke (LOOP) randomized clinical trial, which took place in 4 sites in Denmark. Participants were 70 years or older without known AF but diagnosed with at least 1 of the following: hypertension, diabetes, heart failure, or prior stroke. Participants were recruited by letter invitation between January 31, 2014, and May 17, 2016. The median (IQR) follow-up period was 65 (59-70) months. Analysis took place between February and June 2022.

Interventions

ILR screening for AF with treatment of any bradyarrhythmia left to the discretion of the treating physician (ILR group) vs usual care (control group).

Main Outcomes and Measures

Adjudicated bradyarrhythmia episodes, pacemaker implantation, syncope, and sudden cardiovascular death.

Results

A total of 6004 participants were randomized (mean [SD] age, 75 [4.1] years; 2837 [47.3%] female; 5444 [90.7%] with hypertension; 1224 [20.4%] with prior syncope), 4503 to control and 1501 to ILR. Bradyarrhythmia was diagnosed in 172 participants (3.8%) in the control group vs 312 participants (20.8%) in the ILR group (hazard ratio [HR], 6.21 [95% CI, 5.15-7.48]; P < .001), and these were asymptomatic in 41 participants (23.8%) vs 249 participants (79.8%), respectively. The most common bradyarrhythmia was sinus node dysfunction followed by high-grade atrioventricular block. Risk factors for bradyarrhythmia included higher age, male sex, and prior syncope. A pacemaker was implanted in 132 participants (2.9%) vs 67 (4.5%) (HR, 1.53 [95% CI, 1.14-2.06]; P < .001), syncope occurred in 120 (2.7%) vs 33 (2.2%) (HR, 0.83 [95% CI, 0.56-1.22]; P = .34), and sudden cardiovascular death occurred in 49 (1.1%) vs 18 (1.2%) (HR, 1.11 [95% CI, 0.64-1.90]; P = .71) in the control and ILR groups, respectively. Bradyarrhythmias were associated with subsequent syncope, cardiovascular death, and all-cause death, with no interaction between bradyarrhythmia and randomization group.

Conclusions and Relevance

More than 1 in 5 persons older than 70 years with cardiovascular risk factors can be diagnosed with bradyarrhythmias when long-term continous monitoring for AF is applied. In this study, ILR screening led to a 6-fold increase in bradyarrhythmia diagnoses and a significant increase in pacemaker implantations compared with usual care but no change in the risk of syncope or sudden death.

Development of a data platform for monitoring personal health records in Japan: The Sustaining Health by Integrating Next-generation Ecosystems (SHINE) Study

BACKGROUND

The Sustaining Health by Integrating Next-generation Ecosystems (SHINE) Study was developed as a data platform that incorporates personal health records (PHRs) into health-related data at the municipal level in Japan. This platform allows analyses of the associations between PHRs and future health statuses, and supports the production of evidence for developing preventive care interventions. Herein, we introduce the SHINE Study's profile and describe its use in preliminary analyses.

METHODS

The SHINE Study involves the collection of participants' health measurements and their addition to various health-related data from the Longevity Improvement & Fair Evidence (LIFE) Study. With cooperation from municipal governments, measurements can be acquired from persons enrolled in government-led long-term care prevention classes and health checkups who consent to participate in the SHINE Study. For preliminary analyses, we collected salivary test measurements, lifelog measurements, and gait measurements; these were linked with the LIFE Study's database. We analyzed the correlations between these measurements and the previous year's health care expenditures.

RESULTS

We successfully linked PHR data of 33 participants for salivary test measurements, 44 participants for lifelog measurements, and 32 participants for gait measurements. Only mean torso speed in the gait measurements was significantly correlated with health care expenditures (r = -0.387, P = 0.029).

CONCLUSION

The SHINE Study was developed as a data platform to collect and link PHRs with the LIFE Study's database. The analyses undertaken with this platform are expected to contribute to the development of preventive care tools and promote health in Japan.

A Smartwatch System for Continuous Monitoring of Atrial Fibrillation in Older Adults After Stroke or Transient Ischemic Attack: Application Design Study

BACKGROUND

The prevalence of atrial fibrillation (AF) increases with age and can lead to stroke. Therefore, older adults may benefit the most from AF screening. However, older adult populations tend to lag more than younger groups in the adoption of, and comfort with, the use of mobile health (mHealth) apps. Furthermore, although mobile apps that can detect AF are available to the public, most are designed for intermittent AF detection and for younger users. No app designed for long-term AF monitoring has released detailed system design specifications that can handle large data collections, especially in this age group.

OBJECTIVE

This study aimed to design an innovative smartwatch-based AF monitoring mHealth solution in collaboration with older adult participants and clinicians.

METHODS

The Pulsewatch system is designed to link smartwatches and smartphone apps, a website for data verification, and user data organization on a cloud server. The smartwatch in the Pulsewatch system is designed to continuously monitor the pulse rate with embedded AF detection algorithms, and the smartphone in the Pulsewatch system is designed to serve as the data-transferring hub to the cloud storage server.

RESULTS

We implemented the Pulsewatch system based on the functionality that patients and caregivers recommended. The user interfaces of the smartwatch and smartphone apps were specifically designed for older adults at risk for AF. We improved our Pulsewatch system based on feedback from focus groups consisting of patients with stroke and clinicians. The Pulsewatch system was used by the intervention group for up to 6 weeks in the 2 phases of our randomized clinical trial. At the conclusion of phase 1, 90 trial participants who had used the Pulsewatch app and smartwatch for 14 days completed a System Usability Scale to assess the usability of the Pulsewatch system; of 88 participants, 56 (64%) endorsed that the smartwatch app is "easy to use." For phases 1 and 2 of the study, we collected 9224.4 hours of smartwatch recordings from the participants. The longest recording streak in phase 2 was 21 days of consecutive recordings out of the 30 days of data collection.

CONCLUSIONS

This is one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring. In this paper, we report on the system's usability and opportunities to increase the acceptability of mHealth solutions among older patients with cognitive impairment.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03761394; https://www.clinicaltrials.gov/ct2/show/NCT03761394.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1016/j.cvdhj.2021.07.002.

Grains of Sand to Clinical Pearls: Realizing the Potential of Wearable Data

Despite the rapid growth of wearables as a consumer technology sector and a growing evidence base supporting their use, they have been slow to be adopted by the health system into clinical care. As regulatory, reimbursement, and technical barriers recede, a persistent challenge remains how to make wearable data actionable for clinicians-transforming disconnected grains of wearable data into meaningful clinical "pearls". In order to bridge this adoption gap, wearable data must become visible, interpretable, and actionable for the clinician. We showcase emerging trends and best practices that illustrate these 3 pillars, and offer some recommendations on how the ecosystem can move forward.

Health Techequity: Opportunities for Digital Health Innovations to Improve Equity and Diversity in Cardiovascular Care

Purpose of Review

In this review, we define health equity, disparities, and social determinants of health; the different components of digital health; the barriers to digital health equity; and cardiovascular digital health trials and possible solutions to improve health equity through digital health.

Recent Findings

Digital health interventions show incredible potential to improve cardiovascular diseases by obtaining longitudinal, continuous, and actionable patient data; increasing access to care; and by decreasing delivery barriers and cost. However, certain populations have experienced decreased access to digital health innovations and decreased representation in cardiovascular digital health trials.

Summary

Special efforts will need to be made to expand access to the different elements of digital health, ensuring that the digital divide does not exacerbate health disparities. As the expansion of digital health technologies continues, it is vital to increase representation of minoritized groups in all stages of the process: product development (needs findings and screening, concept generation, product creation, and testing), clinical research (pilot studies, feasibility studies, and randomized control trials), and finally health services deployment.

The next generation of evidence-based medicine

Recently, advances in wearable technologies, data science and machine learning have begun to transform evidence-based medicine, offering a tantalizing glimpse into a future of next-generation 'deep' medicine. Despite stunning advances in basic science and technology, clinical translations in major areas of medicine are lagging. While the COVID-19 pandemic exposed inherent systemic limitations of the clinical trial landscape, it also spurred some positive changes, including new trial designs and a shift toward a more patient-centric and intuitive evidence-generation system. In this Perspective, I share my heuristic vision of the future of clinical trials and evidence-based medicine.

Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability-What We Know and What Is Coming

Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a measure of variation in time between each heartbeat, representing the balance between the parasympathetic and sympathetic nervous system and may predict adverse cardiovascular events. With advances in technology and increasing commercial interest, the scope of remote monitoring health systems has expanded. In this review, we discuss the concepts behind cardiac signal generation and recording, wearable devices, pros and cons focusing on accuracy, ease of application of commercial and medical grade diagnostic devices, which showed promising results in terms of reliability and value. Incorporation of artificial intelligence and cloud based remote monitoring have been evolving to facilitate timely data processing, improve patient convenience and ensure data security.

Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study

BACKGROUND

Morbidity from undiagnosed atrial fibrillation (AF) may be preventable with early detection. Many consumer wearables contain optical photoplethysmography (PPG) sensors to measure pulse rate. PPG-based software algorithms that detect irregular heart rhythms may identify undiagnosed AF in large populations using wearables, but minimizing false-positive detections is essential.

METHODS

We performed a prospective remote clinical trial to examine a novel PPG-based algorithm for detecting undiagnosed AF from a range of wrist-worn devices. Adults aged ≥22 years in the United States without AF, using compatible wearable Fitbit devices and Android or iOS smartphones, were included. PPG data were analyzed using a novel algorithm that examines overlapping 5-minute pulse windows (tachograms). Eligible participants with an irregular heart rhythm detection (IHRD), defined as 11 consecutive irregular tachograms, were invited to schedule a telehealth visit and were mailed a 1-week ambulatory ECG patch monitor. The primary outcome was the positive predictive value of the first IHRD during ECG patch monitoring for concurrent AF.

RESULTS

A total of 455 699 participants enrolled (median age 47 years, 71% female, 73% White) between May 6 and October 1, 2020. IHRDs occurred for 4728 (1%) participants, and 2070 (4%) participants aged ≥65 years during a median of 122 (interquartile range, 110-134) days at risk for an IHRD. Among 1057 participants with an IHRD notification and subsequent analyzable ECG patch monitor, AF was present in 340 (32.2%). Of the 225 participants with another IHRD during ECG patch monitoring, 221 had concurrent AF on the ECG and 4 did not, resulting in an IHRD positive predictive value of 98.2% (95% CI, 95.5%-99.5%). For participants aged ≥65 years, the IHRD positive predictive value was 97.0% (95% CI, 91.4%-99.4%).

CONCLUSIONS

A novel PPG software algorithm for wearable Fitbit devices exhibited a high positive predictive value for concurrent AF and identified participants likely to have AF on subsequent ECG patch monitoring. Wearable devices may facilitate identifying individuals with undiagnosed AF.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT04380415.

Stroke Prevention in Atrial Fibrillation

PURPOSE OF REVIEW

Atrial fibrillation is associated with a significant increase in stroke and systemic embolism. This review explores the areas of stroke prevention.

RECENT FINDINGS

In the last decade, NOAC has overtaken warfarin as the anticoagulant of choice for stroke prevention in AF. For patients unable to take anticoagulation, LAA closure has proven to be a valid option. The use of digital devices has led to widespread consumer-directed AF screening. It remains to be determined if all device detect AF pose the same amount of risk as recent studies have shown that short and infrequent episodes of AF may not benefit from anticoagulation. Stroke prevention is paramount in the management of AF. In this review we describe the risk factors contributing to stroke, recent advances in antithrombotic therapies, and the increasing role of digital health in guiding AF detection and stroke prevention.

Smartphone and wearable detected atrial arrhythmias in Older Adults: Results of a fully digital European Case finding study

Aims

Simplified detection of atrial arrhythmias via consumer-electronics would enable earlier therapy in at-risk populations. Whether this is feasible and effective in older populations is not known.

Methods and results

The fully remote, investigator-initiated Smartphone and wearable detected atrial arrhythmia in Older Adults Case finding study (Smart in OAC-AFNET 9) digitally enrolled participants ≥65 years without known atrial fibrillation, not receiving oral anticoagulation in Germany, Poland, and Spain for 8 weeks. Participants were invited by media communications and direct contacts. Study procedures adhered to European data protection. Consenting participants received a wristband with a photoplethysmography sensor to be coupled to their smartphone. The primary outcome was the detection of atrial arrhythmias lasting 6 min or longer in the first 4 weeks of monitoring. Eight hundred and eighty-two older persons (age 71 ± 5 years, range 65-90, 500 (57%) women, 414 (47%) hypertension, and 97 (11%) diabetes) recorded signals. Most participants (72%) responded to adverts or word of mouth, leaflets (11%) or general practitioners (9%). Participation was completely remote in 469/882 persons (53%). During the first 4 weeks, participants transmitted PPG signals for 533/696 h (77% of the maximum possible time). Atrial arrhythmias were detected in 44 participants (5%) within 28 days, and in 53 (6%) within 8 weeks. Detection was highest in the first monitoring week [incidence rates: 1st week: 3.4% (95% confidence interval 2.4-4.9); 2nd-4th week: 0.55% (0.33-0.93)].

Conclusion

Remote, digitally supported consumer-electronics-based screening is feasible in older European adults and identifies atrial arrhythmias in 5% of participants within 4 weeks of monitoring (NCT04579159).

The Role of Contemporary Wearable and Handheld Devices in the Diagnosis and Management of Cardiac Arrhythmias

Cardiac arrhythmias are associated with significant morbidity, mortality and economic burden on the health care system. Detection and surveillance of cardiac arrhythmias using medical grade non-invasive methods (electrocardiogram, Holter monitoring) is the accepted standard of care. Whilst their accuracy is excellent, significant limitations remain in terms of accessibility, ease of use, cost, and a suboptimal diagnostic yield (up to ∼50%) which is critically dependent on the duration of monitoring. Contemporary wearable and handheld devices that utilise photoplethysmography and the electrocardiogram present a novel opportunity for remote screening and diagnosis of arrhythmias. They have significant advantages in terms of accessibility and availability with the potential of enhancing the diagnostic yield of episodic arrhythmias. However, there is limited data on the accuracy and diagnostic utility of these devices and their role in therapeutic decision making in clinical practice remains unclear. Evidence is mounting that they may be useful in screening for atrial fibrillation, and anecdotally, for the diagnosis of other brady and tachyarrhythmias. Recently, there has been an explosion of patient uptake of such devices for self-monitoring of arrhythmias. Frequently, the clinician is presented such information for review and comment, which may influence clinical decisions about treatment. Further studies are needed before incorporation of such technologies in routine clinical practice, given the lack of systematic data on their accuracy and utility. Moreover, challenges with regulation of quality standards and privacy remain. This state-of-the-art review summarises the role of novel ambulatory, commercially available, heart rhythm monitors in the diagnosis and management of cardiac arrhythmias and their expanding role in the diagnostic and therapeutic paradigm in cardiology.

ESC Working Group on e-Cardiology Position Paper: accuracy and reliability of electrocardiogram monitoring in the detection of atrial fibrillation in cryptogenic stroke patients : In collaboration with the Council on Stroke, the European Heart Rhythm Association, and the Digital Health Committee

The role of subclinical atrial fibrillation as a cause of cryptogenic stroke is unambiguously established. Long-term electrocardiogram (ECG) monitoring remains the sole method for determining its presence following a negative initial workup. This position paper of the European Society of Cardiology Working Group on e-Cardiology first presents the definition, epidemiology, and clinical impact of cryptogenic ischaemic stroke, as well as its aetiopathogenic association with occult atrial fibrillation. Then, classification methods for ischaemic stroke will be discussed, along with their value in providing meaningful guidance for further diagnostic efforts, given disappointing findings of studies based on the embolic stroke of unknown significance construct. Patient selection criteria for long-term ECG monitoring, crucial for determining pre-test probability of subclinical atrial fibrillation, will also be discussed. Subsequently, the two major classes of long-term ECG monitoring tools (non-invasive and invasive) will be presented, with a discussion of each method's pitfalls and related algorithms to improve diagnostic yield and accuracy. Although novel mobile health (mHealth) devices, including smartphones and smartwatches, have dramatically increased atrial fibrillation detection post ischaemic stroke, the latest evidence appears to favour implantable cardiac monitors as the modality of choice; however, the answer to whether they should constitute the initial diagnostic choice for all cryptogenic stroke patients remains elusive. Finally, institutional and organizational issues, such as reimbursement, responsibility for patient management, data ownership, and handling will be briefly touched upon, despite the fact that guidance remains scarce and widespread clinical application and experience are the most likely sources for definite answers.

Cost-effectiveness of Screening for Atrial Fibrillation Using Wearable Devices

Question

Is population-based atrial fibrillation (AF) screening using wearable devices cost-effective?

Findings

In this economic evaluation of 30 million simulated individuals with an age, sex, and comorbidity profile matching the US population aged 65 years or older, AF screening using wearable devices was cost-effective, with the overall preferred strategy identified as wearable photoplethysmography, followed conditionally by wearable electrocardiography with patch monitor confirmation (incremental cost-effectiveness ratio, $57 894 per quality-adjusted life-year). The cost-effectiveness of screening was consistent across multiple scenarios, including strata of sex, screening at earlier ages, and with variation in the association of anticoagulation with risk of stroke associated with screening-detected AF.

Meaning

This study suggests that contemporary AF screening using wearable devices may be cost-effective.

Importance

Undiagnosed atrial fibrillation (AF) is an important cause of stroke. Screening for AF using wrist-worn wearable devices may prevent strokes, but their cost-effectiveness is unknown.

Objective

To evaluate the cost-effectiveness of contemporary AF screening strategies, particularly wrist-worn wearable devices.

Design, Setting, and Participants

This economic evaluation used a microsimulation decision-analytic model and was conducted from September 8, 2020, to May 23, 2022, comprising 30 million simulated individuals with an age, sex, and comorbidity profile matching the US population aged 65 years or older.

Interventions

Eight AF screening strategies, with 6 using wrist-worn wearable devices (watch or band photoplethysmography, with or without watch or band electrocardiography) and 2 using traditional modalities (ie, pulse palpation and 12-lead electrocardiogram) vs no screening.

Main Outcomes and Measures

The primary outcome was the incremental cost-effectiveness ratio, defined as US dollars per quality-adjusted life-year (QALY). Secondary measures included rates of stroke and major bleeding.

Results

In the base case analysis of this model, the mean (SD) age was 72.5 (7.5) years, and 50% of the individuals were women. All 6 screening strategies using wrist-worn wearable devices were estimated to be more effective than no screening (range of QALYs gained vs no screening, 226-957 per 100 000 individuals) and were associated with greater relative benefit than screening using traditional modalities (range of QALYs gained vs no screening, −116 to 93 per 100 000 individuals). Compared with no screening, screening using wrist-worn wearable devices was associated with a reduction in stroke incidence by 20 to 23 per 100 000 person-years but an increase in major bleeding by 20 to 44 per 100 000 person-years. The overall preferred strategy was wearable photoplethysmography, followed conditionally by wearable electrocardiography with patch monitor confirmation, which had an incremental cost-effectiveness ratio of $57 894 per QALY, meeting the acceptability threshold of $100 000 per QALY. The cost-effectiveness of screening was consistent across multiple scenarios, including strata of sex, screening at earlier ages (eg, ≥50 years), and with variation in the association of anticoagulation with risk of stroke in the setting of screening-detected AF.

Conclusions and Relevance

This economic evaluation of AF screening using a microsimulation decision-analytic model suggests that screening using wearable devices is cost-effective compared with either no screening or AF screening using traditional methods.

High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

BACKGROUND

Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized.

OBJECTIVE

We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk.

METHODS

We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events.

RESULTS

We found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers.

CONCLUSIONS

High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management.

Telemedicine: Benefits for Cardiovascular Patients in the COVID-19 Era

The recent pandemic with SARS-CoV-2 raises questions worldwide regarding telemedicine for housebound patients, including those with cardiovascular conditions. The need for further investigation, monitoring and therapeutic management are advancing practical issues which had not been identified for consideration prior to the pandemic. Using the marketing assessment, we identified the needs of the patients and evaluated the future steps necessary in the short term to meet them. The research found progress made via telemedicine in monitoring and conducting minor decisions (like up-titrating the doses of different medication regimens) in patients with several cardiovascular diseases (heart failure, atrial fibrillation, high blood pressure), as there is a worldwide trend to develop new telemonitoring biosensors and devices based on implantable delivered transcatheter. The worldwide telemedicine trend encourages a switch from small and hesitating steps to a more consistent assessment of the patients, based on high technology and Interventional Cardiology. Cardiovascular telemedicine, although made a sustainable effort in managing patients' health, has many obstacles to overcome before meeting all their needs. Data security, confidentiality and reimbursement are the top priorities in developing remote Cardiology. The regulatory institutions need to play an integrative role in leading the way for defining the framework of future telemedicine activities. The SARS-CoV-2 outbreak with all its tragedy served to reinforce the message that telemedicine services can be life-saving for cardiovascular patients. Once the Covid-19 era will fade away, telemedicine is likely to remain a complementary service of standard care. There is still room to improve the remote identification and investigation of heart disease, provide an accurate diagnosis and therapeutic regimen, and update regulations and guidelines to the new realities of technological progress in the field.

Pandemic-proof recruitment and engagement in a fully decentralized trial in atrial fibrillation patients (DeTAP)

The Coronavirus Disease 2019 (COVID-19) pandemic curtailed clinical trial activity. Decentralized clinical trials (DCTs) can expand trial access and reduce exposure risk but their feasibility remains uncertain. We evaluated DCT feasibility for atrial fibrillation (AF) patients on oral anticoagulation (OAC). DeTAP (Decentralized Trial in Afib Patients, NCT04471623) was a 6-month, single-arm, 100% virtual study of 100 AF patients on OAC aged >55 years, recruited traditionally and through social media. Participants enrolled and participated virtually using a mobile application and remote blood pressure (BP) and six-lead electrocardiogram (ECG) sensors. Four engagement-based primary endpoints included changes in pre- versus end-of-study OAC adherence (OACA), and % completion of televisits, surveys, and ECG and BP measurements. Secondary endpoints included survey-based nuisance bleeding and patient feedback. 100 subjects (mean age 70 years, 44% women, 90% White) were recruited in 28 days (traditional: 6 pts; social media: 94 pts in 12 days with >300 waitlisted). Study engagement was high: 91% televisits, 85% surveys, and 99% ECG and 99% BP measurement completion. OACA was unchanged at 6 months (baseline: 97 ± 9%, 6 months: 96 ± 15%, p = 0.39). In patients with low baseline OACA (<90%), there was significant 6-month improvement (85 ± 16% to 96 ± 6%, p < 0.01). 86% of respondents (69/80) expressed willingness to continue in a longer trial. The DeTAP study demonstrated rapid recruitment, high engagement, and physiologic reporting via the integration of digital technologies and dedicated study coordination. These findings may inform DCT designs for future cardiovascular trials.

Lessons learned in the Apple Heart Study and implications for the data management of future digital clinical trials

The digital clinical trial is fast emerging as a pragmatic trial that can improve a trial's design including recruitment and retention, data collection and analytics. To that end, digital platforms such as electronic health records or wearable technologies that enable passive data collection can be leveraged, alleviating burden from the participant and study coordinator. However, there are challenges. For example, many of these data sources not originally intended for research may be noisier than traditionally obtained measures. Further, the secure flow of passively collected data and their integration for analysis is non-trivial. The Apple Heart Study was a prospective, single-arm, site-less digital trial designed to evaluate the ability of an app to detect atrial fibrillation. The study was designed with pragmatic features, such as an app for enrollment, a wearable device (the Apple Watch) for data collection, and electronic surveys for participant-reported outcomes that enabled a high volume of patient enrollment and accompanying data. These elements led to challenges including identifying the number of unique participants, maintaining participant-level linkage of multiple complex data streams, and participant adherence and engagement. Novel solutions were derived that inform future designs with an emphasis on data management. We build upon the excellent framework of the Clinical Trials Transformation Initiative to provide a comprehensive set of guidelines for data management of the digital clinical trial that include an increased role of collaborative data scientists in the design and conduct of the modern digital trial.