Association of Wearable Device Use With Pulse Rate and Health Care Use in Adults With Atrial Fibrillation

Wearable Devices, Health Care Use, and Psychological Well-Being in Patients With Atrial Fibrillation

BACKGROUND

Wearables are increasingly used by patients with atrial fibrillation (AF) for symptom monitoring and health management, but their impact on patient health care use and psychological well-being is not well understood.

METHODS AND RESULTS

In this retrospective, propensity-matched study of patients with AF, survey and electronic health record data were merged to compare AF-specific health care use (outpatient/inpatient visits, rhythm-related testing, and procedures) and informal health care use (telephone calls and patient portal messages) over a 9-month period between wearable users and nonusers. We also examined the effects of wearable cardiac monitoring features (eg, heart rate alerts, irregular rhythm notification, and ECG) on patient behavior and well-being. Of 172 patients with AF in this analysis (age, 72.6±9.0 years; 42% women), 83 used a wearable. Compared with nonusers, wearable users reported higher rates of symptom monitoring and preoccupation (P=0.03) and more AF treatment concerns (P=0.02). Moreover, 20% of wearable users experienced anxiety and always contacted their doctors in response to irregular rhythm notifications. After matching, AF-specific health care use was significantly greater among wearable users compared with nonusers (P=0.04), including significantly higher rates of ECGs, echocardiograms/transesophageal echocardiogram, and ablation. Wearable users were also significantly more likely to use informal health care resources compared with nonusers (P=0.05).

CONCLUSIONS

Wearables were associated with higher rates of symptom monitoring and preoccupation, AF treatment concerns, AF-specific health care use, and use of informal health care resources. Prospective, randomized studies are needed to understand the net effects of wearables and their alerts on patients, providers, and the health care system.

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.

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 .

Study protocol for ADHERE (Applying Directly observed therapy to HydroxyurEa to Realize Effectiveness): Using small business partnerships to deliver a scalable and novel hydroxyurea adherence solution to youth with sickle cell disease

Sickle cell disease (SCD) is an inherited blood disorder that affects approximately 100,000 Americans, primarily from underrepresented racial minority populations, and results in costly, multi-organ complications. Hydroxyurea, the primary disease-modifying therapy for SCD, is effective at reducing most complications; however, adherence to hydroxyurea remains suboptimal and is the primary barrier to clinical effectiveness. Video directly observed therapy (VDOT) has shown promise as an adherence-promoting intervention for hydroxyurea, yet previous VDOT trials were limited by high attrition from gaps in technology access, use of unvalidated adherence measures, and healthcare system limitations of delivering VDOT to patients. As such, we fostered a small business partnership to compare VDOT for hydroxyurea to attention control to address previous shortcomings, promote equitable trial participation, and maximize scalability. VDOT will be administered by Scene Health (formerly emocha Health) and adherence monitoring will be performed using a novel electronic adherence monitor developed to meet the unique needs of the target population. Adolescent and young adult patients as well as caregivers of younger patients (<11 years of age) will be recruited. In addition to visit incentives, all participants will be offered a smartphone with a data plan to ensure all participants have equal opportunity to complete study activities. The primary objectives of this pilot, multi-center, randomized controlled trial (RCT) are to assess retention and sustained engagement and to explore needs and preferences for longer-term adherence monitoring and interventions. This RCT is registered with the National Institutes of Health (NCT06264700). Findings will inform a future efficacy RCT applying VDOT to hydroxyurea to address adherence gaps and improve outcomes within this vulnerable population.

Wearable health monitoring: wave of the future or waste of time?

BACKGROUND

Atrial fibrillation is responsible for over 400,000 hospitalizations in the United States (US) each year. This costs the US health system over 4 billion each year. New smartwatches can constantly monitor pulse, oxygen saturation, and even heart rhythm. The FDA has provided clearance for select smartwatches to detect arrhythmias, including atrial fibrillation.

FINDINGS

These devices are not currently widely implemented as diagnostic tools. In this review, we delve into the mechanism of how smartwatches work as healthcare tools and how they capture health data. Additionally, we analyze the reliability of the data collected by smartwatches and the accuracy of their sensors in monitoring health parameters. Moreover, we explore the accessibility of smartwatches as healthcare tools and their potential to promote self-care among individuals. Finally, we assess the outcomes of using smartwatches in healthcare, including the limited studies on the clinical effects and barriers to uptake by the community.

CONCLUSION

Although smartwatches are accurate for the detection of atrial fibrillation, they still face many hurdles, including access to aging populations and trust in the medical community.

Exploring Disparities in Healthcare Wearable Use among Cardiovascular Patients: Findings from a National Survey

Background

Use of healthcare wearable devices holds significant potential for improving the prevention and management of cardiovascular diseases (CVD). However, we have limited knowledge on the actual use of wearable devices by CVD patients and the key factors associated with their use. This study aims to assess wearable device use and willingness to share health data among CVD patients, while identifying socio-demographic, health, and technology-related factors associated with wearable technology use.

Methods

Using a national survey of 933 CVD patients, we assess use of wearable healthcare devices (use, frequency of use and willingness to share health data from wearable with a provider), and a set of socio-demographic factors (age, gender, race, education and household income), health-related variables (general health, presence of comorbid conditions: diabetes and high blood pressure, attitude towards exercise) and technology self-efficacy using logistic regression.

Results

Of the 933 CVD patients, 18.34% reported using a healthcare wearable device in the prior 12 months. Of those, 41.92% indicated using it every day and another 19.76% indicated using it 'almost every day'. 83.54% of wearable users indicated their willingness to share health data with their healthcare providers. Female CVD patients are more likely to use wearables compared to men (odds ratio (OR) = 1.65, 95% confidence interval (CI) = 1.04-2.63). The odds decrease with age, and are significantly high in patients with higher income levels. In comparison with non-Hispanic White, Hispanic (OR = 0.14, 95% CI = 0.03-0.70) and African Americans (OR = 0.17, 95% CI = 0.04-0.86) are less likely to use healthcare wearables. CVD patients who perceive their general health to be better (OR = 1.45, 95% CI = 1.11-1.89) and those who enjoy exercising (OR = 1.76, 95% CI = 1.22-2.55) are more likely to use wearables. CVD patients who use the internet for searching for medical information (OR = 2.10, 95% CI = 1.17-3.77) and those who use electronic means to make appointments with their providers (OR = 2.35, 95% CI = 1.48-3.74) are more inclined to use wearables.

Conclusions

Addressing low wearable device usage among CVD patients requires targeted policy interventions to ensure equitable access. Variations in gender, age, race/ethnicity, and income levels emphasize the need for tailored strategies. Technological self-efficacy, positive health perceptions, and exercise enjoyment play significant roles in promoting wearable use. These insights should guide healthcare leaders in designing effective strategies for integrating wearables into cardiovascular care.

Validation of a Deep Learning Algorithm for Continuous, Real-Time Detection of Atrial Fibrillation Using a Wrist-Worn Device in an Ambulatory Environment

BACKGROUND

Wearable devices may be useful for identification, quantification and characterization, and management of atrial fibrillation (AF). To date, consumer wrist-worn devices for AF detection using photoplethysmography-based algorithms perform only periodic checks when the user is stationary and are US Food and Drug Administration cleared for prediagnostic uses without intended use for clinical decision-making. There is an unmet need for medical-grade diagnostic wrist-worn devices that provide long-term, continuous AF monitoring.

METHODS AND RESULTS

We evaluated the performance of a wrist-worn device with lead-I ECG and continuous photoplethysmography (Verily Study Watch) and photoplethysmography-based convolutional neural network for AF detection and burden estimation in a prospective multicenter study that enrolled 117 patients with paroxysmal AF. A 14-day continuous ECG monitor (Zio XT) served as the reference device to evaluate algorithm sensitivity and specificity for detection of AF in 15-minute intervals. A total of 91 857 intervals were contributed by 111 subjects with evaluable reference and test data (18.3 h/d median watch wear time). The watch was 96.1% sensitive (95% CI, 92.7%-98.0%) and 98.1% specific (95% CI, 97.2%-99.1%) for interval-level AF detection. Photoplethysmography-derived AF burden estimation was highly correlated with the reference device burden (R2=0.986) with a mean difference of 0.8% (95% limits of agreement, -6.6% to 8.2%).

CONCLUSIONS

Continuous monitoring using a photoplethysmography-based convolutional neural network incorporated in a wrist-worn device has clinical-grade performance for AF detection and burden estimation. These findings suggest that monitoring can be performed with wrist-worn wearables for diagnosis and clinical management of AF.

REGISTRATION INFORMATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT04546763.

Improved 1-year outcomes after active cooling during left atrial radiofrequency ablation

BACKGROUND

Active esophageal cooling during pulmonary vein isolation (PVI) with radiofrequency (RF) ablation for the treatment of atrial fibrillation (AF) is increasingly being utilized to reduce esophageal injury and atrioesophageal fistula formation. Randomized controlled data also show trends towards increased freedom from AF when using active cooling. This study aimed to compare 1-year arrhythmia recurrence rates between patients treated with luminal esophageal temperature (LET) monitoring versus active esophageal cooling during left atrial ablation.

METHOD

Data from two healthcare systems (including 3 hospitals and 4 electrophysiologists) were reviewed for patient rhythm status at 1-year follow-up after receiving PVI for the treatment of AF. Results were compared between patients receiving active esophageal cooling (ensoETM, Attune Medical, Chicago, IL) and those treated with traditional LET monitoring using Kaplan-Meier estimates.

RESULTS

A total of 513 patients were reviewed; 253 received LET monitoring using either single or multi-sensor temperature probes; and 260 received active cooling. The mean age was 66.8 (SD ± 10) years, and 36.8% were female. Arrhythmias were 60.1% paroxysmal AF, 34.3% persistent AF, and 5.6% long-standing persistent AF, with no significant difference between groups. At 1-year follow-up, KM estimates for freedom from AF were 58.2% for LET-monitored patients and 72.2% for actively cooled patients, for an absolute increase in freedom from AF of 14% with active esophageal cooling (p = .03). Adjustment for the confounders of patient age, gender, type of AF, and operator with an inverse probability of treatment weighted Cox proportional hazards model yielded a hazard ratio of 0.6 for the effect of cooling on AF recurrence (p = 0.045).

CONCLUSIONS

In this first study to date of the association between esophageal protection strategy and long-term efficacy of left atrial RF ablation, a clinically and statistically significant improvement in freedom from atrial arrhythmia at 1 year was found in patients treated with active esophageal cooling when compared to patients who received LET monitoring. More rigorous prospective studies or randomized studies are required to validate the findings of the current study.

Successes and challenges of artificial intelligence in cardiology

In the past decades there has been a substantial evolution in data management and data processing techniques. New data architectures made analysis of big data feasible, healthcare is orienting towards personalized medicine with digital health initiatives, and artificial intelligence (AI) is becoming of increasing importance. Despite being a trendy research topic, only very few applications reach the stage where they are implemented in clinical practice. This review provides an overview of current methodologies and identifies clinical and organizational challenges for AI in healthcare.

False Atrial Fibrillation Alerts from Smartwatches are Associated with Decreased Perceived Physical Well-being and Confidence in Chronic Symptoms Management

Wrist-based wearables have been FDA approved for AF detection. However, the health behavior impact of false AF alerts from wearables on older patients at high risk for AF are not known. In this work, we analyzed data from the Pulsewatch (NCT03761394) study, which randomized patients (≥50 years) with history of stroke or transient ischemic attack to wear a patch monitor and a smartwatch linked to a smartphone running the Pulsewatch application vs to only the cardiac patch monitor over 14 days. At baseline and 14 days, participants completed validated instruments to assess for anxiety, patient activation, perceived mental and physical health, chronic symptom management self-efficacy, and medicine adherence. We employed linear regression to examine associations between false AF alerts with change in patient-reported outcomes. Receipt of false AF alerts was related to a dose-dependent decline in self-perceived physical health and levels of disease self-management. We developed a novel convolutional denoising autoencoder (CDA) to remove motion and noise artifacts in photoplethysmography (PPG) segments to optimize AF detection, which substantially reduced the number of false alerts. A promising approach to avoid negative impact of false alerts is to employ artificial intelligence driven algorithms to improve accuracy.

Contemporary Considerations in the Evolution of Wearable Technology for Arrhythmia Detection

Arrhythmias are an increasingly common cause of hospital admissions worldwide. Late detection of arrhythmias is associated with a significantly increased risk of cardiovascular complications. Early identification and management of life-threatening arrhythmias is paramount to reduce mortality. Wearable technologies are now widespread among the general population, providing a continuous output of healthcare data. However, this data are not routinely integrated into clinical practice. Here, we begin by outlining the current landscape in wearable technology for aiding arrhythmia detection; we then consider the clinical impact of wearable technology for both clinicians and patients; we further highlight the latest and emerging trials in wearable technology for arrhythmia detection and finally postulate the wider implications of the expansion of such cardiac devices.

Patient-initiated cardiovascular monitoring with commercially available devices: How useful is it in a cardiology outpatient setting? Mixed methods, observational study

BACKGROUND

The availability, affordability and utilisation of commercially available self-monitoring devices is increasing, but their impact on routine clinical decision-making remains little explored. We sought to examine how patient-generated cardiovascular data influenced clinical evaluation in UK cardiology outpatient clinics and to understand clinical attitudes and experiences with using data from commercially available self-monitoring devices.

METHODS

Mixed methods study combining: a) quantitative and qualitative content analysis of 1373 community cardiology clinic letters, recording consultations between January-September 2020 including periods with different Covid-19 related restrictions, and b) semi-structured qualitative interviews and group discussions with 20 cardiology-affiliated clinicians at the same NHS Trust.

RESULTS

Patient-generated cardiovascular data were described in 185/1373 (13.5%) clinic letters overall, with the proportion doubling following onset of the first Covid-19 lockdown in England, from 8.3% to 16.6% (p < 0.001). In 127/185 (69%) cases self-monitored data were found to: provide or facilitate cardiac diagnoses (34/127); assist management of previously diagnosed cardiac conditions (55/127); be deployed for cardiovascular prevention (16/127); or be recommended for heart rhythm evaluation (10/127). In 58/185 (31%) cases clinicians did not put the self-monitored data to any evident use and in 12/185 (6.5%) cases patient-generated data prompted an unnecessary referral. In interviews and discussions, clinicians expressed mixed views on patient-generated data but foresaw a need to embrace and plan for this information flow, and proactively address challenges with integration into traditional care pathways.

CONCLUSIONS

This study suggests patient-generated data are being used for clinical decision-making in ad hoc and opportunistic ways. Given shifts towards remote monitoring in clinical care, accelerated by the pandemic, there is a need to consider how best to incorporate patient-generated data in clinical processes, introduce relevant training, pathways and governance frameworks, and manage associated risks.

Wearables in Cardiovascular Disease

Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.

Impact of Device Miniaturization on Insertable Cardiac Monitor Use in the Pediatric Population: An Analysis of the MarketScan Commercial and Medicaid Databases

Background

Insertable cardiac monitors (ICMs) are effective in the detection of paroxysmal arrhythmias. In 2014, the first miniaturized ICM was introduced with a less invasive implant technique. The impact of this technology on ICM use in pediatric patients has not been evaluated. We hypothesized an increase in annual pediatric ICM implants starting in 2014 attributable to device miniaturization.

Methods and Results

A retrospective observational study was conducted using administrative claims from MarketScan Medicaid and commercial insurance claims databases. Use of ICM between January 2013 and December 2018 was measured (normalized to the total enrolled population ≤18 years) and compared with balancing measures (Holter ambulatory monitors, cardiac event monitors, encounters with syncope diagnosis, implantation of implantable cardioverter‐defibrillator/pacemaker). Secondary analyses included evaluations of subsequent interventions and complications. The study cohort included 33 532 185 individual subjects, of which 769 (0.002%) underwent ICM implantation. Subjects who underwent ICM implantation were 52% male sex, with a median age of 16 years (interquartile range, 10–17 years). A history of syncope was present in 71%, palpitations in 43%, and congenital heart disease in 28%. Following release of the miniaturized ICM, use of ICMs increased from 5 procedures per million enrollees in 2013 to 11 per million between 2015 and 2018 (P<0.001), while balancing measures remained static. Of 394 subjects with ≥1 year of follow‐up after implantation, interventions included catheter ablation in 24 (6%), pacemaker implantation in 15 (4%), and implantable cardioverter‐defibrillator implantation in 7 (2%).

Conclusions

Introduction of the miniaturized ICM was followed by a rapid increase in pediatric use. The effects on outcomes and value deserve further attention.

Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study

Ventricular arrhythmia (VA) is a leading cause of sudden death and health deterioration. Recent advances in predictive analytics and wearable technology for behavior assessment show promise but require further investigation. Yet, previous studies have only assessed other health outcomes and monitored patients for short durations (7−14 days). This study explores how behaviors reported by a consumer wearable can assist VA risk prediction. An exploratory observational study was conducted with participants who had an implantable cardioverter-defibrillator (ICD) and wore a Fitbit Alta HR consumer wearable. Fitbit reported behavioral markers for physical activity (light, fair, vigorous), sleep, and heart rate. A case-crossover analysis using conditional logistic regression assessed the effects of time-adjusted behaviors over 1−8 weeks on VA incidence. Twenty-seven patients (25 males, median age 59 years) were included. Among the participants, ICDs recorded 262 VA events during 8093 days monitored by Fitbit (median follow-up period 960 days). Longer light to fair activity durations and a higher heart rate increased the odds of a VA event (p < 0.001). In contrast, lengthier fair to vigorous activity and sleep durations decreased the odds of a VA event (p < 0.001). Future studies using consumer wearables in a larger population should prioritize these outcomes to further assess VA risk.

A Clinician's Guide to Smartwatch "Interrogation"

Purpose of Review

Wearable technology is rapidly evolving and the data that it can provide regarding an individual’s health is becoming increasingly important for clinicians to consider. The purpose of this review is to help inform health care providers of the benefits of smartwatch interrogation, with a focus on reviewing the various parameters and how to apply the data in a meaningful way.

Recent Findings

This review details interpretation of various parameters found commonly in newer smartwatches such as heart rate, step count, ECG, heart rate recovery (HRR), and heart rate variability (HRV), while also discussing potential pitfalls that a clinician should be aware of.

Summary

Smartwatch interrogation is becoming increasingly relevant as the continuous data it provides helps health care providers make more informed decisions regarding diagnosis and treatment. For this reason, we recommend health care providers familiarize themselves with the technology and integrate it into clinical practice.

Assessing patient readiness for a patient portal implementation in the UAE

Purpose

This paper proposes a Web-based patient portal based on the electronic medical record. Such a portal can allow patients to manage their own health care, reduce health-care visits and significantly improve the quality of their health care.

Design/methodology/approach

A patient portal prototype and an accompanying online survey were distributed to assess the adoption readiness among a group of people in the United Arab Emirates (UAE).

Findings

The results from 470 survey participants demonstrated an enhanced awareness of this technology, and support the study hypotheses indicating that both intrinsic and extrinsic factors are important when considering the implementation of a patient portal in the UAE.

Originality/value

This study adds value to the few research studies undertaken in the Middle East discussing online health information technology and its adoption and usage among the population at large. The extended technology acceptance model, which contains two additional constructs, had not been previously validated in terms of a patient portal in the UAE, according to the author’s knowledge, adding more value. The UAE’s health-care system must use the benefits from the available IT infrastructure to provide a user-friendly online portal to encourage patients to manage their health care and health information.

Will Apple devices' passive atrial fibrillation detection prevent strokes? Estimating the proportion of high-risk actionable patients with real-world user data

OBJECTIVE

Utilizing integrated electronic health record (EHR) and consumer-grade wearable device data, we sought to provide real-world estimates for the proportion of wearers that would likely benefit from anticoagulation if an atrial fibrillation (AFib) diagnosis was made based on wearable device data.

MATERIALS AND METHODS

This study utilized EHR and Apple Watch data from an observational cohort of 1802 patients at Cedars-Sinai Medical Center who linked devices to the EHR between April 25, 2015 and November 16, 2018. Using these data, we estimated the number of high-risk patients who would be actionable for anticoagulation based on (1) medical history, (2) Apple Watch wear patterns, and (3) AFib risk, as determined by an existing validated model.

RESULTS

Based on the characteristics of this cohort, a mean of 0.25% (n = 4.58, 95% CI, 2.0-8.0) of patients would be candidates for new anticoagulation based on AFib identified by their Apple Watch. Using EHR data alone, we find that only approximately 36% of the 1802 patients (n = 665.93, 95% CI, 626.0-706.0) would have anticoagulation recommended even after a new AFib diagnosis.

DISCUSSION AND CONCLUSION

These data suggest that there is limited benefit to detect and treat AFib with anticoagulation among this cohort, but that accessing clinical and demographic data from the EHR could help target devices to the patients with the highest potential for benefit. Future research may analyze this relationship at other sites and among other wearable users, including among those who have not linked devices to their EHR.

Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects

Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.

Cardiac Outpatient Care in a Digital Age: Remote Cardiology Clinic Visits in the Era of COVID-19

Purpose of Review

The worldwide pandemic caused by the novel coronavirus disease transformed healthcare in many ways. The impact of the pandemic was also noted in outpatient settings with various clinics adopting telehealth as the new normal. The goal of this paper is to investigate how the pandemic impacted the outpatient cardiology setting, specifically regarding the use of telehealth, and can the lessons learned from the adoption of telehealth in the backdrop of COVID-19 be applied to facilitate the wider and routine use of telemedicine in the outpatient cardiology clinic.

Recent Findings

Several studies have been conducted showcasing COVID-19’s impact on the telehealth field of cardiology. Studies showed advantages for patients. Among these advantages are reduction in wait and travel time, easier medication reconciliation, and convenience. They also showed a general comfortability with the transition to telehealth among cardiologists. Furthermore, the adoption of telehealth in the outpatient cardiology setting, specifically with respect to the management of common cardiac conditions of congestive heart failure, atrial fibrillation, and ischemic heart disease, revealed the potential of telemedicine to be used to adequately address these conditions. The transition to telehealth was not without its challenges, such as lack of a physical exam, barriers with certain patient populations to adopting the technology, and changes were noted in frequencies of medication ordering and cardiology-specific laboratory and diagnostic imaging.

Summary

This transition to telehealth during the pandemic allowed for various studies to be conducted on how telehealth impacted the field of cardiology in the outpatient setting. While patient and practitioner advantages were revealed when compared to traditional outpatient cardiology visits, barriers to the adoption of the technology among specific patient populations were noted as were changes in practice among cardiologists. The use of telemedicine to adequately address common cardiac conditions was also shown. Further investigation into understanding the barriers of specific patient populations and overcoming these barriers, understanding the reason for the changes in practice of cardiologists with the use telemedicine, and investigating the use and incorporation of existing technology such as smart watches and patient portals or apps to make the transition to telehealth not only simpler, but to also optimize the cardiologist management of common cardiac conditions, have the potential to lead to the wider and routine use of telemedicine in the outpatient cardiology clinic.

A novel contact-free atrial fibrillation monitor: a pilot study

Aims

Atrial fibrillation (AF) is a major cause of morbidity and mortality. Current guidelines support performing electrocardiogram (ECG) screenings to spot AF in high-risk patients. The purpose of this study was to validate a new algorithm aimed to identify AF in patients measured with a recent FDA-cleared contact-free optical device.

Methods and results

Study participants were measured simultaneously using two devices: a contact-free optical system that measures chest motion vibrations (investigational device, 'Gili') and a standard reference bed-side ECG monitor (Mindray^®). Each reference ECG was evaluated by two board certified cardiologists that defined each trace as: regular rhythm, AF, other irregular rhythm or indecipherable/missing. A total of 3582, 30-s intervals, pertaining to 444 patients (41.9% with a history of AF) were made available for analysis. Distribution of patients with active AF, other irregular rhythm, and regular rhythm was 16.9%, 29.5%, and 53.6% respectively. Following application of cross-validated machine learning approach, the observed sensitivity and specificity were 0.92 [95% confidence interval (CI): 0.91-0.93] and 0.96 (95% CI: 0.95-0.96), respectively.

Conclusion

This study demonstrates for the first time the efficacy of a contact-free optical device for detecting AF.

Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform

The recent curriculum reform in China puts forward higher requirements for the development of physical education. In order to further improve students' physical quality and motor skills, the traditional model was improved to address the lack of accuracy in motion recognition and detection of physical condition so as to assist teachers to improve students' physical quality. First, the physical education teaching activities required by the new curriculum reform were studied with regard to the actual needs of China's current social, political, and economic development; next, the application of artificial intelligence technology to physical education teaching activities was proposed; and finally, deep learning technology was studied and a human movement recognition model based on a long short-term memory (LSTM) neural network was established to identify the movement state of students in physical education teaching activities. The designed model includes three components: data acquisition, data calculation, and data visualization. The functions of each layer were introduced; then, the intelligent wearable system was adopted to detect the status of students and a feedback system was established to assist teaching; and finally, the dataset was constructed to train and test the designed model. The experimental results demonstrate that the recognition accuracy and loss value of the training model meet the practical requirements; in the algorithm test, the motion recognition accuracy of the designed model for different subjects was greater than 97.5%. Compared with the traditional human motion recognition algorithm, the designed model had a better recognition effect. Hence, the designed model can meet the actual needs of physical education. This exploration provides a new perspective for promoting the intelligent development of physical education.

Smartwatch-based detection of cardiac arrhythmias: Beyond the differentiation between sinus rhythm and atrial fibrillation

Within the span of a few years, watches have functionally morphed from objects that tell time to wearable minicomputers that allow real-time recording of electrocardiograms (ECGs). Considerable information can be deduced from these single lead tracings, and it is now not uncommon to see patients in whom diagnostic tracings of clinically relevant but elusive arrhythmias are captured using a smartwatch. Empowering individuals to record their own ECG tracings in scenarios such as palpitations, syncope, and for risk stratification of sudden death intuitively has considerable potential, but its value remains to be robustly demonstrated. The main objective of this review is to describe the information that can be obtained from smartwatch-based single-lead ECG recordings beyond simply differentiating between sinus rhythm and atrial fibrillation. We also review the strengths and limitations of using these devices in clinical settings and offer potential solutions to address the latter.