Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation

Determining the recurrence rate of premature ventricular complexes and idiopathic ventricular tachycardia after radiofrequency catheter ablation with the help of designing a machine-learning model

Ventricular arrhythmias increase cardiovascular morbidity and mortality. Recurrent PVCs and IVT are generally considered benign in the absence of structural heart abnormalities. Artificial intelligence is a rapidly growing field. In recent years, medical professionals have shown great interest in the potential use of ML, an integral part of AI, in various disciplines, including diagnostic applications, decision-making, prognostic stratification, and solving complex pathophysiological aspects of diseases from these data at extraordinary complexity, scale, and acquisition rate. The aim of this study was to design an ML model to predict the probability of PVC and IVT recurrence after RF ablation. Data of patients were collected and manipulated using traditional analysis and various artificial intelligence models, namely MLP, Gradient Boosting Machines, Random Forest, and Logistic Regression. Hypertension, male sex, and the use of non-irrigate catheters were associated with less freedom from arrhythmia. All these results were obtained through traditional analytic methods, and according to AI, none of the variables had a clear effect on the recurrence of arrhythmia. Each AI model presents unique strengths and weaknesses, and further optimization and fine-tuning of these models are necessary to increase their clinical utility. By expanding the dataset, improved predictions can be fostered to ultimately increase the clinical utility of AI in predicting PVC erosion outcomes.

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.

The War Against Heart Failure Hospitalizations: Remote Monitoring and the Case for Expanding Criteria

Successful remote patient monitoring depends on bidirectional interaction between patients and multidisciplinary clinical teams. Invasive pulmonary artery pressure monitoring has been shown to reduce heart failure (HF) hospitalizations, facilitate guideline-directed medical therapy optimization, and improve quality of life. Cardiac implantable electronic device-based multiparameter monitoring has shown encouraging results in predicting future HF-related events. Potential expanded indications for remote monitoring include guideline-directed medical therapy optimization, application to specific populations, and subclinical detection of HF. Voice analysis, inferior vena cava diameter monitoring, and artificial intelligence-based remote electrocardiogram show potential to gain some merit in remote patient monitoring in HF.

Smartwatch interventions in healthcare: A systematic review of the literature

OBJECTIVE

The use of smartwatches has attracted considerable interest in developing smart digital health interventions and improving health and well-being during the past few years. This work presents a systematic review of the literature on smartwatch interventions in healthcare. The main characteristics and individual health-related outcomes of smartwatch interventions within research studies are illustrated, in order to acquire evidence of their benefit and value in patient care.

METHODS

A literature search in the bibliographic databases of PubMed and Scopus was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, in order to identify research studies incorporating smartwatch interventions. The studies were grouped according to the intervention's target disease, main smartwatch features, study design, target age and number of participants, follow-up duration, and outcome measures.

RESULTS

The literature search identified 13 interventions incorporating smartwatches within research studies with people of middle and older age. The interventions targeted different conditions: cardiovascular diseases, diabetes, depression, stress and anxiety, metastatic gastrointestinal cancer and breast cancer, knee arthroplasty, chronic stroke, and allergic rhinitis. The majority of the studies (76%) were randomized controlled trials. The most used smartwatch was the Apple Watch utilized in 4 interventions (31%). Positive outcomes for smartwatch interventions concerned foot ulcer recurrence, severity of symptoms of depression, utilization of healthcare resources, lifestyle changes, functional assessment and shoulder range of motion, medication adherence, unplanned hospital readmissions, atrial fibrillation diagnosis, adherence to self-monitoring, and goal attainment for emotion regulation. Challenges in using smartwatches included frequency of charging, availability of Internet and synchronization with a mobile app, the burden of using a smartphone in addition to a patient's regular phone, and data quality.

CONCLUSION

The results of this review indicate the potential of smartwatches to bring positive health-related outcomes for patients. Considering the low number of studies identified in this review along with their moderate quality, we implore the research community to carry out additional studies in intervention settings to show the utility of smartwatches in clinical contexts.

Management of atrial fibrillation in older adults

Most people with atrial fibrillation are older adults, in whom atrial fibrillation co-occurs with other chronic conditions, polypharmacy, and geriatric syndromes such as frailty. Yet most randomized controlled trials and expert guidelines use an age agnostic approach. Given the heterogeneity of aging, these data may not be universally applicable across the spectrum of older adults. This review synthesizes the available evidence and applies rigorous principles of aging science. After contextualizing the burden of comorbidities and geriatric syndromes in people with atrial fibrillation, it applies an aging focused approach to the pillars of atrial fibrillation management, describing screening for atrial fibrillation, lifestyle interventions, symptoms and complications, rate and rhythm control, coexisting heart failure, anticoagulation therapy, and left atrial appendage occlusion devices. Throughout, a framework is suggested that prioritizes patients' goals and applies existing evidence to all older adults, whether atrial fibrillation is their sole condition, one among many, or a bystander at the end of life.

A landscape analysis of digital health technology in medical schools: preparing students for the future of health care

Although Digital Health Technology is increasingly implemented in hospitals and clinics, physicians are not sufficiently equipped with the competencies needed to optimize technology utilization. Medical schools seem to be the most appropriate channel to better prepare future physicians for this development. The purpose of this research study is to investigate the extent to which top-ranked medical schools equip future physicians with the competencies necessary for them to leverage Digital Health Technology in the provision of care. This research work relied on a descriptive landscape analysis, and was composed of two phases: Phase I aimed at investigating the articulation of the direction of the selected universities and medical schools to identify any expressed inclination towards teaching innovation or Digital Health Technology. In phase II, medical schools' websites were analyzed to discover how innovation and Digital Health Technology are integrated in their curricula. Among the 60 medical schools that were analyzed, none mentioned any type of Digital Health Technology in their mission statements (that of the universities, in general, and medical schools, specifically). When investigating the medical schools' curricula to determine how universities nurture their learners in relation to Digital Health Technology, four universities covering different Digital Health Technology areas were identified. The results of the current study shed light on the untapped potential of working towards better equipping medical students with competencies that will enable them to leverage Digital Health Technology in their future practice and in turn enhance the quality of care.

Artificial intelligence-enhanced patient evaluation: bridging art and science

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

Remote data collection of infant activity and sleep patterns via wearable sensors in the HEALthy Brain and Child Development Study (HBCD)

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Wearable and remote sensing technologies have advanced data collection outside of laboratory settings to enable exploring, in more detail, the associations of early experiences with brain development and social and health outcomes. In the HBCD Study, the Novel Technology/Wearable Sensors Working Group (WG-NTW) identified two primary data types to be collected: infant activity (by measuring leg movements) and sleep (by measuring heart rate and leg movements). These wearable technologies allow for remote collection in the natural environment. This paper illustrates the collection of such data via wearable technologies and describes the decision-making framework, which led to the currently deployed study design, data collection protocol, and derivatives, which will be made publicly available. Moreover, considerations regarding actual and potential challenges to adoption and use, data management, privacy, and participant burden were examined. Lastly, the present limitations in the field of wearable sensor data collection and analysis will be discussed in terms of extant validation studies, the difficulties in comparing performance across different devices, and the impact of evolving hardware/software/firmware.

Research Priorities for Atrial Fibrillation in Australia: A Statement From the Australian Cardiovascular Alliance Clinical Arrhythmia Theme

Atrial fibrillation (AF) is highly prevalent in the Australian community, ranking amongst the highest globally. The consequences of AF are significant. Stroke, dementia and heart failure risk are increased substantially, hospitalisations are amongst the highest for all cardiovascular causes, and Australians living with AF suffer from substantial symptoms that impact quality of life. Australian research has made a significant impact at the global level in advancing the care of patients living with AF. However, new strategies are required to reduce the growing incidence of AF and its associated healthcare demand. The Australian Cardiovascular Alliance (ACvA) has led the development of an arrhythmia clinical theme with the objective of tackling major research priorities to achieve a reduction in AF burden across Australia. In this summary, we highlight these research priorities with particular focus on the strengths of Australian research and the strategies needed to move forward in reducing incident AF and improving outcomes for those who live with this chronic condition.

The Digital Platform and Its Emerging Role in Decentralized Clinical Trials

Decentralized clinical trials (DCTs) are becoming increasingly popular. Digital clinical trial platforms are software environments where users complete designated clinical trial tasks, providing investigators and trial participants with efficient tools to support trial activities and streamline trial processes. In particular, digital platforms with a modular architecture lend themselves to DCTs, where individual trial activities can correspond to specific platform modules. While design features can allow users to customize their platform experience, the real strengths of digital platforms for DCTs are enabling centralized data capture and remote monitoring of trial participants and in using digital technologies to streamline workflows and improve trial management. When selecting a platform for use in a DCT, sponsors and investigators must consider the specific trial requirements. All digital platforms are limited in their functionality and technical capabilities. Integrating additional functional modules into a central platform may solve these challenges, but few commercial platforms are open to integrating third-party components. The lack of common data standardization protocols for clinical trials will likely limit the development of one-size-fits-all digital platforms for DCTs. This viewpoint summarizes the current role of digital platforms in supporting decentralized trial activities, including a discussion of the potential benefits and challenges of digital platforms for investigators and participants. We will highlight the role of the digital platform in the development of DCTs and emphasize where existing technology is functionally limiting. Finally, we will discuss the concept of the ideal fully integrated and unified DCT and the obstacles developers must address before it can be realized.

Scientific statement from the French neurovascular and cardiac societies for improved detection of atrial fibrillation after ischaemic stroke and transient ischaemic attack

Atrial fibrillation (AF) is the primary cause of ischaemic stroke and transient ischaemic attack (TIA). AF is associated with a high risk of recurrence, which can be reduced using optimal prevention strategies, mainly anticoagulant therapy. The availability of effective prophylaxis justifies the need for a significant, coordinated and thorough transdisciplinary effort to screen for AF associated with stroke. A recent French national survey, initiated and supported by the Société française neurovasculaire (SFNV) and the Société française de cardiologie (SFC), revealed many shortcomings, such as the absence or inadequacy of telemetry equipment in more than half of stroke units, insufficient and highly variable access to monitoring tools, delays in performing screening tests, heterogeneous access to advanced or connected ambulatory monitoring techniques, and a lack of dedicated human resources. The present scientific document has been prepared on the initiative of the SFNV and the SFC with the aim of helping to address the current shortcomings and gaps, to promote efficient and cost-effective AF detection, and to improve and, where possible, homogenize the quality of practice in AF screening among stroke units and outpatient post-stroke care networks. The working group, composed of cardiologists and vascular neurologists who are experts in the field and are nominated by their peers, reviewed the literature to propose statements, which were discussed in successive cycles, and maintained, either by consensus or by vote, as appropriate. The text was then submitted to the SFNV and SFC board members for review. This scientific statement document argues for the widespread development of patient pathways to enable the most efficient AF screening after stroke. This assessment should be carried out by a multidisciplinary team, including expert cardiologists and vascular neurologists.

A Crowdsourced AI Framework for Atrial Fibrillation Detection in Apple Watch and Kardia Mobile ECGs

Background: Atrial fibrillation (AFib) detection via mobile ECG devices is promising, but algorithms often struggle to generalize across diverse datasets and platforms, limiting their real-world applicability. Objective: This study aims to develop a robust, generalizable AFib detection approach for mobile ECG devices using crowdsourced algorithms. Methods: We developed a voting algorithm using random forest, integrating six open-source AFib detection algorithms from the PhysioNet Challenge. The algorithm was trained on an AliveCor dataset and tested on two disjoint AliveCor datasets and one Apple Watch dataset. Results: The voting algorithm outperformed the base algorithms across all metrics: the average of sensitivity (0.884), specificity (0.988), PPV (0.917), NPV (0.985), and F1-score (0.943) on all datasets. It also demonstrated the least variability among datasets, signifying its highest robustness and effectiveness in diverse data environments. Moreover, it surpassed Apple's algorithm on all metrics and showed higher specificity but lower sensitivity than AliveCor's Kardia algorithm. Conclusions: This study demonstrates the potential of crowdsourced, multi-algorithmic strategies in enhancing AFib detection. Our approach shows robust cross-platform performance, addressing key generalization challenges in AI-enabled cardiac monitoring and underlining the potential for collaborative algorithms in wearable monitoring devices.

Wearable technology in vascular surgery: Current applications and future perspectives

The COVID-19 pandemic exposed the vulnerabilities of global health care systems, underscoring the need for innovative solutions to meet the demands of an aging population, workforce shortages, and rising physician burnout. In recent years, wearable technology has helped segue various medical specialties into the digital era, yet its adoption in vascular surgery remains limited. This article explores the applications of wearable devices in vascular surgery and explores their potential outlets, such as enhancing primary and secondary prevention, optimizing perioperative care, and supporting surgical training. The integration of artificial intelligence and machine learning with wearable technology further expands its applications, enabling predictive analytics, personalized care, and remote monitoring. Despite the promising prospects, challenges such as regulatory complexities, data security, and interoperability must be addressed. As the digital health movement unfolds, wearable technology could play a pivotal role in reshaping vascular surgery while offering cost-effective, accessible, and patient-centered 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.

"An excellent servant but a terrible master": Understanding the value of wearables for self-management in people with cystic fibrosis and their healthcare providers - A qualitative study

BACKGROUND

Wearables hold potential to improve chronic disease self-management in conditions like cystic fibrosis (CF) through remote monitoring, early detection of illness and motivation. Little is known about the acceptability and sustainability of integrating wearables into routine care from the perspectives of people with CF (pwCF) and their treating clinicians.

METHODS

A cross-sectional qualitative study involving semi-structured interviews with adult pwCF and focus groups comprising members of a CF multidisciplinary team (MDT) were conducted at a specialist CF centre in Australia. A phenomenological orientation underpinned the study. Inductive thematic analysis was performed using the Framework method. The study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist.

RESULTS

Nine pwCF and eight members of a CF MDT, representing six clinical disciplines, participated in the study. Eight themes were inductively generated from the data, of which four were identified from each group. PwCF valued wearables for providing real-time data to motivate healthy behaviours and support shared goal-setting with healthcare providers. Wearables did not influence adherence to CF-specific self-management practices and had some hardware limitations. Members of the CF MDT recognised potential benefits of remote monitoring and shared goal-setting, but advised caution regarding data accuracy, generating patient anxiety in certain personality traits, and lack of evidence supporting use in CF self-management.

CONCLUSIONS

Perspectives on integrating wearables into CF care were cautiously optimistic, with emerging risks related to patient anxiety and lack of evidence moderating acceptance.

The potential for large language models to transform cardiovascular medicine

Cardiovascular diseases persist as the leading cause of death globally and their early detection and prediction remain a major challenge. Artificial intelligence (AI) tools can help meet this challenge as they have considerable potential for early diagnosis and prediction of occurrence of these diseases. Deep neural networks can improve the accuracy of medical image interpretation and their outputs can provide rich information that otherwise would not be detected by cardiologists. With recent advances in transformer models, multimodal AI, and large language models, the ability to integrate electronic health record data with images, genomics, biosensors, and other data has the potential to improve diagnosis and partition patients who are at high risk for primary preventive strategies. Although much emphasis has been placed on AI supporting clinicians, AI can also serve patients and provide immediate help with diagnosis, such as that of arrhythmia, and is being studied for automated self-imaging. Potential risks, such as loss of data privacy or potential diagnostic errors, should be addressed before use in clinical practice. This Series paper explores opportunities and limitations of AI models for cardiovascular medicine, and aims to identify specific barriers to and solutions in the application of AI models, facilitating their integration into health-care systems.

Comparison of physiological and clinical reactions to COVID-19 and influenza vaccination

BACKGROUND

Public reluctance to receive COVID-19 vaccination is associated with safety concerns. By contrast, the seasonal influenza vaccine has been administered for decades with a solid safety record and a high level of public acceptance. We compare the safety profile of the BNT162b2 COVID-19 booster vaccine to that of the seasonal influenza vaccine.

METHODS

We study a prospective cohort of 5079 participants in Israel and a retrospective cohort of 250,000 members of MHS selected randomly. We examine reactions to BNT162b2 mRNA COVID-19 booster and to influenza vaccinations. All prospective cohort participants wore a smartwatch and completed a daily digital questionnaire. We compare pre-vaccination and post-vaccination smartwatch heart-rate data, and a stress measure based on heart-rate variability. We also examine adverse events from electronic health records.

RESULTS

In the prospective cohort, 1905 participants receive the COVID-19 booster vaccine; 899 receive influenza vaccination. Focusing on those who receive both vaccines yields a total of 689 participants in the prospective cohort and 31,297 members in the retrospective cohort. Individuals reporting a more severe reaction after influenza vaccination tend to likewise report a more severe reaction after COVID-19 vaccination. In paired analysis, the increase in both heart rate and stress measure for each participant is higher for COVID-19 than for influenza in the first 2 days after vaccination. No elevated risk of hospitalization due to adverse events is found following either vaccine. Except for Bell's palsy after influenza vaccination, no elevated risk of adverse events is found.

CONCLUSIONS

The more pronounced side effects after COVID-19 vaccination may explain the greater concern associated with it. Nevertheless, our comprehensive analysis supports the safety profile of both vaccines.

Feasibility of a using chest strap and dry electrode system for longer term cardiac arrhythmia monitoring: Results from a pilot observational study

BACKGROUND AND AIM

Cardiac arrhythmia diagnostic yield improves with increased duration of monitoring. We investigated patient comfort, diagnostic quality of ECG, and arrhythmia diagnostic yield using a single lead longer term external cardiac monitor (ECM).

METHODS

The observational ECM feasibility study enrolled patients with increased risk of cardiac arrhythmia. The ECM investigational prototype was designed using a chest strap with dry electrodes connected to module capable of triggered loop recording of ECG, and automatic detection of arrhythmia. In group-A of study (24-h inpatient), patients wore ECM and Holter that recorded ECG from the ECM and adhesive electrodes. In group-B of study (12-weeks ambulatory), at monthly follow-ups patients filled out a comfort survey and device stored arrhythmia episodes were reviewed.

RESULTS

The study enrolled 34 patients (38 % females, average age 57.5 years, 65 % had palpitations, 12 % had syncope). Diagnostic quality ECG was recorded on 76.5 % of the monitoring duration in 12 of 20 patients with reviewable data in group-A, with motion artifacts causing loss in ECG signal for 18.7 % of the time. In 14 patients in group-B, 94.9 % of the survey responses indicated that ECM was comfortable to wear. Cardiac arrhythmia was observed in 4 of 17 patients (24 %) in group-A and 9 of 14 patients (64 %) in group-B in device recorded episodes. All ECM detected pause and tachycardia were inappropriate detections due to motion artifacts and temporary device removal.

CONCLUSION

The chest strap-based ECM device was mostly comfortable to wear and recorded diagnostic quality ECG in three-fourth of monitoring period. Cardiac arrhythmia was observed in 64 % of patients over 3-month monitoring along with large number of motion artifact induced inappropriate detections.

Approaches for the Use of AI in Workplace Health Promotion and Prevention: Systematic Scoping Review

BACKGROUND

Artificial intelligence (AI) is an umbrella term for various algorithms and rapidly emerging technologies with huge potential for workplace health promotion and prevention (WHPP). WHPP interventions aim to improve people's health and well-being through behavioral and organizational measures or by minimizing the burden of workplace-related diseases and associated risk factors. While AI has been the focus of research in other health-related fields, such as public health or biomedicine, the transition of AI into WHPP research has yet to be systematically investigated.

OBJECTIVE

The systematic scoping review aims to comprehensively assess an overview of the current use of AI in WHPP. The results will be then used to point to future research directions. The following research questions were derived: (1) What are the study characteristics of studies on AI algorithms and technologies in the context of WHPP? (2) What specific WHPP fields (prevention, behavioral, and organizational approaches) were addressed by the AI algorithms and technologies? (3) What kind of interventions lead to which outcomes?

METHODS

A systematic scoping literature review (PRISMA-ScR [Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews]) was conducted in the 3 academic databases PubMed, Institute of Electrical and Electronics Engineers, and Association for Computing Machinery in July 2023, searching for papers published between January 2000 and December 2023. Studies needed to be (1) peer-reviewed, (2) written in English, and (3) focused on any AI-based algorithm or technology that (4) were conducted in the context of WHPP or (5) an associated field. Information on study design, AI algorithms and technologies, WHPP fields, and the patient or population, intervention, comparison, and outcomes framework were extracted blindly with Rayyan and summarized.

RESULTS

A total of 10 studies were included. Risk prevention and modeling were the most identified WHPP fields (n=6), followed by behavioral health promotion (n=4) and organizational health promotion (n=1). Further, 4 studies focused on mental health. Most AI algorithms were machine learning-based, and 3 studies used combined deep learning algorithms. AI algorithms and technologies were primarily implemented in smartphone apps (eg, in the form of a chatbot) or used the smartphone as a data source (eg, Global Positioning System). Behavioral approaches ranged from 8 to 12 weeks and were compared to control groups. Additionally, 3 studies evaluated the robustness and accuracy of an AI model or framework.

CONCLUSIONS

Although AI has caught increasing attention in health-related research, the review reveals that AI in WHPP is marginally investigated. Our results indicate that AI is promising for individualization and risk prediction in WHPP, but current research does not cover the scope of WHPP. Beyond that, future research will profit from an extended range of research in all fields of WHPP, longitudinal data, and reporting guidelines.

TRIAL REGISTRATION

OSF Registries osf.io/bfswp; https://osf.io/bfswp.

Study of Postacute Sequelae of COVID-19 Using Digital Wearables: Protocol for a Prospective Longitudinal Observational Study

BACKGROUND

Postacute sequelae of COVID-19 (PASC) remain understudied in nonhospitalized patients. Digital wearables allow for a continuous collection of physiological parameters such as respiratory rate and oxygen saturation that have been predictive of disease trajectories in hospitalized patients.

OBJECTIVE

This protocol outlines the design and procedures of a prospective, longitudinal, observational study of PASC that aims to identify wearables-collected physiological parameters that are associated with PASC in patients with a positive diagnosis.

METHODS

This is a single-arm, prospective, observational study of a cohort of 550 patients, aged 18 to 65 years, male or female, who own a smartphone or a tablet that meets predetermined Bluetooth version and operating system requirements, speak English, and provide documentation of a positive COVID-19 test issued by a health care professional within 5 days before enrollment. The primary end point is long COVID-19, defined as ≥1 symptom at 3 weeks beyond the first symptom onset or positive diagnosis, whichever comes first. The secondary end point is chronic COVID-19, defined as ≥1 symptom at 12 weeks beyond the first symptom onset or positive diagnosis. Participants must be willing and able to consent to participate in the study and adhere to study procedures for 6 months.

RESULTS

The first patient was enrolled in October 2021. The estimated year for publishing the study results is 2025.

CONCLUSIONS

This is a fully decentralized study investigating PASC using wearable devices to collect physiological parameters and patient-reported outcomes. The study will shed light on the duration and symptom manifestation of PASC in nonhospitalized patient subgroups and is an exemplar of the use of wearables as population-level monitoring health tools for communicable diseases.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04927442; https://clinicaltrials.gov/study/NCT04927442.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/57382.

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.

Artificial intelligence and atrial fibrillation: A bibliometric analysis from 2013 to 2023

Background

In the study of atrial fibrillation (AF), a prevalent cardiac arrhythmia, the utilization of artificial intelligence (AI) in diagnostic and therapeutic strategies holds the potential to address existing limitations. This research employs bibliometrics to objectively investigate research hotspots, development trends, and existing issues in the application of AI within the AF field, aiming to provide targeted recommendations for relevant researchers.

Methods

Relevant publications on the application of AI in AF field were retrieved from the Web of Science Core Collection (WoSCC) database from 2013 to 2023. The bibliometric analysis was conducted by the R (4.2.2) "bibliometrix" package and VOSviewer(1.6.19).

Results

Analysis of 912 publications reveals that the field of AI in AF is currently experiencing rapid development. The United States, China, and the United Kingdom have made outstanding contributions to this field. Acharya UR is a notable contributor and pioneer in the area. The following topics have been elucidated: AI's application in managing the risk of AF complications is a hot mature topic; AI-electrocardiograph for AF diagnosis and AI-assisted catheter ablation surgery are the emerging and booming topics; smart wearables for real-time AF monitoring and AI for individualized AF medication are niche and well-developed topics.

Conclusion

This study offers comprehensive analysis of the origin, current status, and future trends of AI applications in AF, aiming to advance the development of the field.

A Wrist-Worn Wearable Device Can Identify Frailty in Middle-Aged and Older Adults: The UK Biobank Study

OBJECTIVES

Digital gait biomarkers collected from body-worn devices can remotely and continuously collect movement types, quantity, and quality in real life. This study assessed whether digital gait biomarkers from a wrist-worn device could identify people with frailty in a large sample of middle-aged and older adults.

DESIGN

Cross-sectional study.

SETTING AND PARTICIPANTS

A total of 5822 middle-aged (43-64 years) and 4344 older adults (65-81 years) who participated in the UK Biobank study.

MEASURES

Frailty was assessed using a modified Fried's frailty assessment and was defined as having ≥3 of the 5 frailty criteria (weakness, low activity levels, slowness, exhaustion, and weight loss). Fourteen digital gait biomarkers were extracted from accelerometry data collected from wrist-worn sensors worn continuously by participants for up to 7 days.

RESULTS

A total of 238 (4.1%) of the middle-aged group and 196 (4.5%) of the older group were categorized as frail. Multivariable logistic regression analysis revealed that less daily walking (as assessed by step counts), slower maximum walking speed, and increased step time variability best-identified people with frailty in the middle-aged group [area under the curve (95% CI): 0.70 (0.66-0.73)]. Less daily walking, slower maximum walking speed, increased step time variability, and a lower proportion of walks undertaken with a manual task best-identified people with frailty in the older group [0.73 (0.69-0.76)].

CONCLUSIONS AND IMPLICATIONS

Our findings indicate that measures obtained from wrist-worn wearable devices worn in everyday life can identify individuals with frailty in both middle-aged and older people. These digital gait biomarkers may facilitate screening programs and the timely implementation of frailty-prevention interventions.

Multidisciplinary Approach in Atrial Fibrillation: As Good as Gold

Atrial fibrillation (AF) represents the most common sustained arrhythmia necessitating dual focus: acute complication management and sustained longitudinal oversight to modulate disease progression and ensure comprehensive patient care over time. AF is a multifaceted disorder; due to such a great number of potential exacerbating conditions, a multidisciplinary team (MDT) should manage AF patients by cooperating with a cardiologist. Effective management of AF patients necessitates the implementation of a well-coordinated and tailored care pathway aimed at delivering optimized treatment through collaboration among various healthcare professionals. Management of AF should be carefully evaluated and mutually agreed upon in consultation with healthcare providers. It is crucial to recognize that treatment may evolve due to the emergence of new risk factors, symptoms, disease progression, and advancements in treatment modalities. In the context of multidisciplinary AF teams, a coordinated approach involves assembling a diverse team tailored to meet individual patients' unique needs based on local services' availability.

Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care

Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.

Diagnostic accuracy of Apple Watch Series 6 recorded single-lead ECGs for identifying supraventricular tachyarrhythmias: a comparative analysis with invasive electrophysiological study

BACKGROUND

The advancements in wearable technology have made the detection of arrhythmias more accessible. While smartwatches are commonly used to detect patients with atrial fibrillation, their effectiveness in the differential diagnosis of supraventricular tachycardias (SVT) lacks consensus.

METHODS

A study was conducted on 47 patients with documented SVTs on a 12-lead ECG. All patients in the cohort underwent electrophysiology study with induction of SVT. A 6th generation Apple Watch was used to record ECG tracings during baseline sinus rhythm and during induced SVT. Cardiology residents and attending cardiologists evaluated these recordings to diagnose the differential diagnosis of SVT.

RESULTS

The evaluation revealed 27 cases of typical atrioventricular nodal reentrant tachycardia (AVNRT), 11 cases of atrioventricular reentrant tachycardia (AVRT), and 9 cases of atrial tachycardia/atrial flutter (AT/AFL) among the induced tachycardias. Attending physicians achieved an accuracy of 66.0 to 76.6%, and residents demonstrated accuracy rates between 68.1 and 74.5%. Interrater reliability was assessed using Fleiss's Kappa method, resulting in a moderate level of agreement between residents (Kappa = 0.465, p < 0.001, 95% CI 0.30-0.63) and attendings (Kappa = 0.519, p < 0.001, 95% CI 0.35-0.68). The overall Kappa value was 0.417 (p < 0.001, 95% CI 0.34-0.49).

CONCLUSIONS

Smartwatch recordings demonstrate moderate feasibility in diagnosing SVT when following a pre-specified algorithm. However, this diagnostic performance was lower than the accuracy obtained from 12-lead ECG tracings when blinded to procedure outcomes.

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.

Digital health in stroke: a narrative review

Digital health is significantly transforming stroke care, particularly in remote and economically diverse regions, by harnessing mobile and wireless technologies, big data, and artificial intelligence (AI). Despite the promising advancements, a notable gap exists in the formal clinical validation of many digital health applications, raising concerns about their efficacy and safety in real-world clinical settings. Our review systematically explores the landscape of digital health in stroke care, assessing the development, validation, and implementation of various digital tools. We adopted a comprehensive search strategy, scrutinizing peer-reviewed articles published between January 2015 and January 2024, to gather evidence on the effectiveness of digital health interventions. A rigorous quality assessment was conducted to ensure the reliability of the included studies, with findings synthesized to underscore key technological innovations and their clinical outcomes. Ethical considerations were meticulously observed to maintain data confidentiality and integrity. Our findings highlight the transformative potential of mobile health technologies, AI, and telemedicine in improving diagnostic accuracy, treatment efficacy, and patient outcomes in stroke care. Our paper delves into the evolution and impact of digital health in cerebrovascular prevention, diagnosis, rehabilitation and stroke treatment, emphasizing the digital health's role in enhancing access to expert care, mitigating treatment delays and improving outcomes. However, the review also underscores the critical need for rigorous clinical validation and ethical considerations in the development and deployment of digital health technologies to ensure their safe and effective integration into stroke care practices.

The potential role for artificial intelligence in fracture risk prediction

Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these therapies are underused in individuals at high risk for fracture, calling for better case-finding and fracture risk assessment strategies. Artificial intelligence (AI) and machine learning (ML) hold promise for enhancing identification of individuals at high risk for fracture by distilling useful features from high-dimensional data derived from medical records, imaging, and wearable devices. AI-ML could enable automated opportunistic screening for vertebral fractures and osteoporosis, home-based monitoring and intervention targeting lifestyle factors, and integration of multimodal features to leverage fracture prediction, ultimately aiding improved fracture risk assessment and individualised treatment. Optimism must be balanced with consideration for the explainability of AI-ML models, biases (including information inequity in numerically under-represented populations), model limitations, and net clinical benefit and workload impact. Clinical integration of AI-ML algorithms has the potential to transform osteoporosis management, offering a more personalised approach to reduce the burden of osteoporotic fractures.

Novel stroke prevention strategies following catheter ablation for atrial fibrillation

Stroke prevention following successful catheter ablation of atrial fibrillation remains a controversial topic. Oral anticoagulation is associated with a significant reduction in stroke risk in the general atrial fibrillation population but may be associated with an increased risk of major bleeding, and the benefit: risk ratio must be considered. Improvement in successful catheter ablation and widespread use of cardiac monitoring devices may allow for novel anticoagulation strategies in a subset of patients with atrial fibrillation, which may optimize stroke prevention while minimizing bleeding risk. In this review, we discuss stroke risk in atrial fibrillation and the effects of successful catheter ablation on thromboembolic risk. We also explore novel strategies for stroke prevention following successful catheter ablation.

Controversies in Diagnosis and Management of Atrial Fibrillation

Early detection of atrial fibrillation (AF) plays an important role in decreasing adverse cardiovascular outcomes. It is estimated, however, that one-third of those with AF are asymptomatic and may experience the adverse effects of the arrhythmia prior to being detected clinically. In the past, AF was diagnosed on 12-lead electrocardiogram or medically prescribed external monitors. The development of device-monitoring technologies capable of recording AF or AF-surrogates such as atrial high-rate episodes on cardiovascular implantable electronic devices or photoplethysmography/electrocardiogram on consumer-grade wearable devices, has resulted in increased recognition of device-detected, subclinical, AF. Recent studies reveal information about the stroke risk associated with these subclinical events and the response to anticoagulation and raise important questions about the use of both medical and direct-to-consumer AF detection devices for screening purposes. In addition to screening and detection of AF, emerging studies are also being conducted on different strategies for maintenance of sinus rhythm and stroke prevention including catheter ablation and left atrial appendage occlusion. This review aims to highlight recent developments and future studies in these areas.

Unlocking Tomorrow's Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence

As an integral aspect of health care, digital technology has enabled modelling of complex relationships to detect, screen, diagnose, and predict patient outcomes. With massive data sets, artificial intelligence (AI) can have marked effects on 3 levels: for patients, clinicians, and health systems. In this review, we discuss contemporary AI-enabled wearable devices undergoing research in the field of cardiovascular medicine. These include devices such as smart watches, electrocardiogram patches, and smart textiles such as smart socks and chest sensors for diagnosis, management, and prognostication of conditions such as atrial fibrillation, heart failure, and hypertension as well as monitoring for cardiac rehabilitation. We review the evolution of machine learning algorithms used in wearable devices from random forest models to the use of convolutional neural networks and transformers. We further discuss frameworks for wearable technologies such as the V3-stage process of verification, analytical validation, and clinical validation as well as challenges of AI integration in medicine such as data veracity, validity, and security and provide a reference framework to maintain fairness and equity. Last, clinician and patient perspectives are discussed to highlight the importance of considering end-user feedback in development and regulatory processes.

Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility

Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.

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.

The Risk of Atrial Fibrillation and Previous Ischemic Stroke in Cognitive Decline

Objectives: Our study investigated the inverse relationship between cognitive decline (CD) and the presence of documented atrial fibrillation (AFib), ischemic stroke, heart failure, lower extremity peripheral artery disease, and diabetes mellitus. Methods: We conducted a retrospective cross-sectional study between December 2016 and November 2019. A total of 469 patients were enrolled who underwent cognitive evaluation with three cognitive tests (Montreal Cognitive Assessment-MOCA, Mini-Mental State Examination-MMSE, and General Practitioner Assessment of Cognition-GPCOG). We used the standard cut-off values, and the optimal thresholds were obtained from the receiver operating characteristic curves. Results: The standard cut-off level of the MOCA (<26 points) was associated with the presence of AFib (OR: 1.83, 95% CI: 1.11-3.01) and the optimal cut-off level with <23 points with ischemic stroke (OR: 2.64, 95% CI: 1.47-4.74; p = 0.0011). The optimal cut-off value of the MMSE (<28 points) was associated with the presence of ischemic stroke (OR: 3.07, 95% CI: 1.56-6.07; p = 0.0012), AFib (OR: 1.65, 95% CI: 1.05-2.60; p = 0.0287), and peripheral artery disease (OR: 2.72, 95% CI: 1.38-5.36; p = 0.0039). GPCOG < 8 points were associated with ischemic stroke (OR: 2.18, 95% CI: 1.14-4.14; p = 0.0176) and heart failure (OR: 1.49, 95% CI: 1.01-2.21; p = 0.0430). Conclusions: Our research highlighted the broader utility of cognitive assessment. The MOCA and MMSE scores proved to be associated with documented AFib. Higher cognitive test results than the standard threshold for CD of the MMSE, GPCOG, and lower MOCA scores represented risk factors for the presence of previous ischemic stroke.

Non-Invasive Sensors Integration for NCDs with AIoT Based Telemedicine System

Thailand's hospitals face overcrowding, particularly with non-communicable disease (NCD) patients, due to a doctor shortage and an aging population. Most literature showed implementation merely on web or mobile application to teleconsult with physicians. Instead, in this work, we developed and implemented a telemedicine health kiosk system embedded with non-invasive biosensors and time-series predictors to improve NCD indicators over an eight-month period. Two cohorts were randomly selected: a control group with usual care and a telemedicine-using group. The telemedicine-using group showed significant improvements in average fasting blood glucose (148 to 130 mg/dL) and systolic blood pressure (152 to 138 mmHg). Data mining with the Apriori algorithm revealed correlations between diseases, occupations, and environmental factors, informing public health policies. Communication between kiosks and servers used LoRa, 5G, and IEEE802.11, which are selected based on the distance and signal availability. The results support telemedicine kiosks as effective for NCD management, significantly improving key NCD indicators, average blood glucose, and blood pressure.

Role of the Electrocardiogram for Identifying the Development of Atrial Fibrillation

Atrial fibrillation (AF), a prevalent cardiac arrhythmia, is associated with increased morbidity and mortality worldwide. Stroke, the leading cause of serious disability in the United States, is among the important complications of this arrhythmia. Recent studies have demonstrated that certain clinical variables can be useful in the prediction of AF development in the future. The electrocardiogram (ECG) is a simple and cost-effective technology that is widely available in various healthcare settings. An emerging body of evidence has suggested that ECG tracings preceding the development of AF can be useful in predicting this arrhythmia in the future. Various variables on ECG especially different P wave parameters have been investigated in the prediction of new-onset AF and found to be useful. Several risk models were also introduced using these variables along with the patient's clinical data. However, current guidelines do not provide a clear consensus regarding implementing these prediction models in clinical practice for identifying patients at risk of AF. Also, the role of intensive screening via ECG or implantable devices based on this scoring system is unclear. The purpose of this review is to summarize AF and various related terminologies and explain the pathophysiology and electrocardiographic features of this tachyarrhythmia. We also discuss the predictive electrocardiographic features of AF, review some of the existing risk models and scoring system, and shed light on the role of monitoring device for screening purposes.

Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review

Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.

Consumer wearable devices for evaluation of heart rate control using digoxin versus beta-blockers: the RATE-AF randomized trial

Consumer-grade wearable technology has the potential to support clinical research and patient management. Here, we report results from the RATE-AF trial wearables study, which was designed to compare heart rate in older, multimorbid patients with permanent atrial fibrillation and heart failure who were randomized to treatment with either digoxin or beta-blockers. Heart rate (n = 143,379,796) and physical activity (n = 23,704,307) intervals were obtained from 53 participants (mean age 75.6 years (s.d. 8.4), 40% women) using a wrist-worn wearable linked to a smartphone for 20 weeks. Heart rates in participants treated with digoxin versus beta-blockers were not significantly different (regression coefficient 1.22 (95% confidence interval (CI) -2.82 to 5.27; P = 0.55); adjusted 0.66 (95% CI -3.45 to 4.77; P = 0.75)). No difference in heart rate was observed between the two groups of patients after accounting for physical activity (P = 0.74) or patients with high activity levels (≥30,000 steps per week; P = 0.97). Using a convolutional neural network designed to account for missing data, we found that wearable device data could predict New York Heart Association functional class 5 months after baseline assessment similarly to standard clinical measures of electrocardiographic heart rate and 6-minute walk test (F1 score 0.56 (95% CI 0.41 to 0.70) versus 0.55 (95% CI 0.41 to 0.68); P = 0.88 for comparison). The results of this study indicate that digoxin and beta-blockers have equivalent effects on heart rate in atrial fibrillation at rest and on exertion, and suggest that dynamic monitoring of individuals with arrhythmia using wearable technology could be an alternative to in-person assessment. ClinicalTrials.gov identifier: NCT02391337 .

The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment

Atrial fibrillation (AF) is the most prevalent arrhythmia worldwide. Although the guidelines for AF have been updated in recent years, its gradual onset and associated risk of stroke pose challenges for both patients and cardiologists in real-world practice. Artificial intelligence (AI) is a powerful tool in image analysis, data processing, and for establishing models. It has been widely applied in various medical fields, including AF. In this review, we focus on the progress and knowledge gap regarding the use of AI in AF patients and highlight its potential throughout the entire cycle of AF management, from detection to drug treatment. More evidence is needed to demonstrate its ability to improve prognosis through high-quality randomized controlled trials.