Noise Reduction in Photoplethysmography Signals Using a Convolutional Denoising Autoencoder With Unconventional Training Scheme.

OBJECTIVE: We propose an efficient approach based on a convolutional denoising autoencoder (CDA) network to reduce motion and noise artifacts (MNA) from corrupted atrial fibrillation (AF) and non-AF photoplethysmography (PPG) data segments so that an accurate PPG-signal-derived heart rate can be obtained. Our method's main innovation is the optimization of the CDA performance for both rhythms using more AF than non-AF data for training the AF-specific CDA model and vice versa for the non-AF CDA network. METHODS: To evaluate this unconventional training scheme, our proposed network was trained and tested on 25-sec PPG data segments from 48 subjects from two different databases-the Pulsewatch dataset and Stanford University's publicly available PPG dataset. In total, our dataset contains 10,773 data segments: 7,001 segments for training and 3,772 independent segments from out-of-sample subjects for testing. RESULTS: Using real-life corrupted PPG segments, our approach significantly reduced the average heart rate root mean square error (RMSE) of the reconstructed PPG segments by 45.74% and 23% compared to the corrupted non-AF and AF data, respectively. Further, our approach exhibited lower RMSE, and higher sensitivity and PPV for detected peaks compared to the reconstructed data produced by the alternative methods. CONCLUSION: These results show the promise of our approach as a reliable denoising method, which should be used prior to AF detection algorithms for an accurate cardiac health monitoring involving wearable devices. SIGNIFICANCE: PPG signals collected from wearables are vulnerable to MNA, which limits their use as a reliable measurement, particularly in uncontrolled real-life environments.

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

BACKGROUND: Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers. OBJECTIVE: This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors. METHODS: Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period. RESULTS: A total of 120 participants were enrolled (mean age 65 years; 50/120, 41% female; 106/120, 88% White). The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days. CONCLUSIONS: Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear. TRIAL REGISTRATION: ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cvdhj.2021.07.002.

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

BACKGROUND: The prevalence of atrial fibrillation (AF) increases with age and can lead to stroke. Therefore, older adults may benefit the most from AF screening. However, older adult populations tend to lag more than younger groups in the adoption of, and comfort with, the use of mobile health (mHealth) apps. Furthermore, although mobile apps that can detect AF are available to the public, most are designed for intermittent AF detection and for younger users. No app designed for long-term AF monitoring has released detailed system design specifications that can handle large data collections, especially in this age group. OBJECTIVE: This study aimed to design an innovative smartwatch-based AF monitoring mHealth solution in collaboration with older adult participants and clinicians. METHODS: The Pulsewatch system is designed to link smartwatches and smartphone apps, a website for data verification, and user data organization on a cloud server. The smartwatch in the Pulsewatch system is designed to continuously monitor the pulse rate with embedded AF detection algorithms, and the smartphone in the Pulsewatch system is designed to serve as the data-transferring hub to the cloud storage server. RESULTS: We implemented the Pulsewatch system based on the functionality that patients and caregivers recommended. The user interfaces of the smartwatch and smartphone apps were specifically designed for older adults at risk for AF. We improved our Pulsewatch system based on feedback from focus groups consisting of patients with stroke and clinicians. The Pulsewatch system was used by the intervention group for up to 6 weeks in the 2 phases of our randomized clinical trial. At the conclusion of phase 1, 90 trial participants who had used the Pulsewatch app and smartwatch for 14 days completed a System Usability Scale to assess the usability of the Pulsewatch system; of 88 participants, 56 (64%) endorsed that the smartwatch app is "easy to use." For phases 1 and 2 of the study, we collected 9224.4 hours of smartwatch recordings from the participants. The longest recording streak in phase 2 was 21 days of consecutive recordings out of the 30 days of data collection. CONCLUSIONS: This is one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring. In this paper, we report on the system's usability and opportunities to increase the acceptability of mHealth solutions among older patients with cognitive impairment. TRIAL REGISTRATION: ClinicalTrials.gov NCT03761394; https://www.clinicaltrials.gov/ct2/show/NCT03761394. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cvdhj.2021.07.002.

Usability of a smartwatch for atrial fibrillation detection in older adults after stroke.

Background: Smartwatches can be used for atrial fibrillation (AF) detection, but little is known about how older adults at risk for AF perceive their usability. Methods: We employed a mixed-methods study design using data from the ongoing Pulsewatch study, a randomized clinical trial (NCT03761394) examining the accuracy of a smartwatch-smartphone app dyad (Samsung/Android) compared to usual care with a patch monitor (Cardea SOLO™ ECG System) for detection of AF among older stroke survivors. To be eligible to participate in Pulsewatch, participants needed to be at least 50 years of age, have had an ischemic stroke, and have no major contraindications to anticoagulation therapy should AF be detected. After 14 days of use, usability was measured by the System Usability Scale (SUS) and investigator-generated questions. Qualitative interviews were conducted, transcribed, and coded via thematic analysis. Results: Ninety participants in the Pulsewatch trial were randomized to use a smartwatch-smartphone app dyad for 14 days (average age: 65 years, 41% female, 87% White), and 46% found it to be highly usable (SUS ≥68). In quantitative surveys, participants who used an assistive device (eg, wheelchair) and those with history of anxiety or depression were more likely to report anxiety associated with watch use. In qualitative interviews, study participants reported wanting a streamlined system that was more focused on rhythm monitoring and a smartwatch with a longer battery life. In-person training and support greatly improved their experience, and participants overwhelmingly preferred use of a smartwatch over traditional cardiac monitoring owing to its comfort, appearance, and convenience. Conclusion: Older adults at high risk for AF who were randomized to use a smartwatch-app dyad for AF monitoring over 14 days found it to be usable for AF detection and preferred their use to the use of a patch monitor. However, participants reported that a simpler device interface and longer smartwatch battery life would increase the system's usability.

Online health information seeking, low atrial fibrillation-related quality of life, and high perceived efficacy in patient-physician interactions in older adults with atrial fibrillation.

Background: Little is known about online health information-seeking behavior among older adults with atrial fibrillation (AF) and its association with self-reported outcomes. Objective: To examine patient characteristics associated with online health information seeking and the association between information seeking and low AF-related quality of life and high perceived efficacy in patient-physician interaction. Methods: We used data from the SAGE-AF (Systematic Assessment of Geriatric Elements in AF) study, which includes older participants aged ≥65 years with AF and a CHA2DS2-VASc risk score ≥2. To assess online health information seeking, participants who reported using the Internet were asked at baseline if they used the Internet to search for advice or information about their health in the past 4 weeks (not at all vs at least once). Atrial Fibrillation Effect on Quality of Life and Perceived Efficacy in Patient-Physician Interactions questionnaires were used to examine AF-related quality of life (QOL) and patient-reported confidence in physicians. Logistic regression models were used to examine demographic and clinical factors associated with online health information seeking and associations between information seeking and low AF-related QOL (AFEQT <80) and high perceived efficacy for patient-physician interactions (PEPPI ≥45). Results: A total of 874 online participants (mean age 74.5 years, 51% male, 91% non-Hispanic White) were studied. Approximately 60% of participants sought health information online. Participants aged 74 years or older and those on anticoagulation were less likely, while those with a college degree were more likely, to seek online health information after adjusting for potential confounders. Participants who sought health information online, compared to those who did not, were significantly more likely to have a low AF-related QOL, but less likely to self-report confidence in patient-physician interaction (aOR = 1.56, 95% CI: 1.15-2.13; aOR = 0.68, 95% CI: 0.49-0.93, respectively). Conclusion: Clinicians should consider barriers to patient-physician interaction in older adults who seek health information online, encourage shared decision-making, and provide patients with a list of online resources for AF in addition to disease education plans to help patients manage their health.

Cumulative exposure to state-level structural sexism and risk of disordered eating: Results from a 20-year prospective cohort study.

BACKGROUND: Gendered inequities in disordered eating are well-documented, yet few studies have examined their structural drivers. To help fill this gap, we investigated whether cumulative exposure to state-level structural sexism from childhood through young adulthood potentiates differences in disordered eating risk between cisgender girls/women and boys/men. METHODS: Participants came from the Growing Up Today Study (N = 16,875), a cohort of children aged 9-14 years in 1996 who we followed through 2016. Using a composite index of relevant state policies and social inequalities from the Institute for Women's Policy Research, we categorized states as having high or low levels of structural sexism and summed the number of years participants had lived in a high structural sexism state during the study period to quantify their cumulative exposure. We fit sequential conditional mean models to estimate the effect of cumulative exposure on risk of four outcomes (chronic dieting, purging, binge eating, and overeating), controlling for individual- and state-level confounders via propensity scores. We then tested whether effects differed between girls/women and boys/men by including cumulative-exposure-by-gender-identity interaction terms and calculating the relative excess risk due to interaction (RERI). RESULTS: In the full sample, each additional year of living in a high structural sexism state was associated with a 5% increased risk of purging (95% confidence interval (CI): 3%, 7%), an 8% increased risk of binge eating (95% CI: 6%, 10%), and a 9% increased risk of overeating (95% CI: 8%, 11%). Risk increases were larger on average for girls/women than for boys/men, and girls/women who had lived in a high structural sexism state for four or more years had excess risk of chronic dieting (RERI: 0.64, 95% CI: 0.18, 1.10), purging (RERI: 2.64, 95% CI: 1.24, 4.30), and binge eating (RERI: 2.21, 95% CI: 0.93, 3.50). CONCLUSIONS: Structural sexism may contribute to inequities in disordered eating between cisgender girls/women and boys/men. Future research should include transgender and gender diverse participants, explore intersectional effects, and identify underlying mechanisms to inform policy-oriented interventions.

Complicating Narratives of Sexual Minority Mental Health: An Intersectional Analysis of Frequent Mental Distress at the Intersection of Sexual Orientation, Gender Identity, and Race/Ethnicity.

Purpose: Research indicates that sexual minority populations experience mental health inequities. However, few studies have examined mental health outcomes in sexual minority populations while including intersecting dimensions of social identity. This study had two objectives: (1) to quantify the prevalence of frequent mental distress among U.S. adults across intersecting social identity categories and (2) to evaluate the contribution of intersectional interactions to observed inequities. Methods: Using data from the Behavioral Risk Factor Surveillance System 2014-2019 (N = 1,024,261), we performed an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA). Participants were nested in 45 intersectional groups defined by combining 3 sexual orientation (gay/lesbian, bisexual, and heterosexual), 5 gender identity (transgender women, transgender men, gender nonconforming, cisgender women, and cisgender men), and 3 racial/ethnic (non-Hispanic Black, Hispanic/Latinx, and non-Hispanic White) categories. We estimated the predicted probability of frequent mental distress for each stratum. We then calculated the variance partition coefficient (VPC) and proportional change in variance (PCV). Results: We found that multiply marginalized groups tended to have the highest prevalence of frequent mental distress. Groups with racial/ethnic minority individuals were equally represented among low- and high-prevalence groups. The VPC indicated that slightly over 10% of observed variance in prevalence was attributable to group-level differences, while the PCV revealed that a small but meaningful amount of observed heterogeneity in prevalence was due to intersectional interactions between the dimensions of social identity. Conclusion: I-MAIHDA is a promising method for examining the patterning of sexual orientation-based mental health inequities at the population level.

Wearable device use and technology preferences in cancer survivors with or at risk for atrial fibrillation.

Background: Cancer survivors face increased risk of heart disease, including atrial fibrillation (AF). Certain types of technology, such as consumer wearable devices, can be useful to monitor for AF, but little is known about wearables and AF monitoring in cancer survivor populations. Objective: The purpose of this study was to understand technology usage and preferences in cancer survivors with or at risk for AF, and to describe demographic factors associated with wearable device ownership in this population. Methods: Eligible patients completed a remote survey assessment regarding use of commercial wearable devices. The survey contained questions designed to assess commercial wearable device use, electronic health communications, and perceptions regarding the participant's cardiac health. Results: A total of 424 cancer survivors (mean age 74.2 years; 53.1% female; 98.8% white) were studied. Although most participants owned a smartphone (85.9%), only 31.8% owned a wearable device. Over half (53.5%) of cancer survivors were worried about their heart health. Overall, patients believed arrhythmias (79.7%) were the most important heart condition for a wearable to detect. Survivors reported being most willing to share blood pressure (95.6%) and heart rate (95.3%) data with their providers and were least willing to share information about their diet, weight, and physical activity using these devices. Conclusion: Understanding factors such as device ownership, usage, and heart health concerns in cancer survivors can play an important role in improving cardiovascular monitoring and its accessibility. Long-term patient outcomes may be improved by incorporating wearable devices into routine care of cancer survivors.

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients.

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients' AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.

MI-PACE Home-Based Cardiac Telerehabilitation Program for Heart Attack Survivors: Usability Study.

BACKGROUND: Cardiac rehabilitation programs, consisting of exercise training and disease management interventions, reduce morbidity and mortality after acute myocardial infarction. OBJECTIVE: In this pilot study, we aimed to developed and assess the feasibility of delivering a health watch-informed 12-week cardiac telerehabilitation program to acute myocardial infarction survivors who declined to participate in center-based cardiac rehabilitation. METHODS: We enrolled patients hospitalized after acute myocardial infarction at an academic medical center who were eligible for but declined to participate in center-based cardiac rehabilitation. Each participant underwent a baseline exercise stress test. Participants received a health watch, which monitored heart rate and physical activity, and a tablet computer with an app that displayed progress toward accomplishing weekly walking and exercise goals. Results were transmitted to a cardiac rehabilitation nurse via a secure connection. For 12 weeks, participants exercised at home and also participated in weekly phone counseling sessions with the nurse, who provided personalized cardiac rehabilitation solutions and standard cardiac rehabilitation education. We assessed usability of the system, adherence to weekly exercise and walking goals, counseling session attendance, and disease-specific quality of life. RESULTS: Of 18 participants (age: mean 59 years, SD 7) who completed the 12-week telerehabilitation program, 6 (33%) were women, and 6 (33%) had ST-elevation myocardial infarction. Participants wore the health watch for a median of 12.7 hours (IQR 11.1, 13.8) per day and completed a median of 86% of exercise goals. Participants, on average, walked 121 minutes per week (SD 175) and spent 189 minutes per week (SD 210) in their target exercise heart rate zone. Overall, participants found the system to be highly usable (System Usability Scale score: median 83, IQR 65, 100). CONCLUSIONS: This pilot study established the feasibility of delivering cardiac telerehabilitation at home to acute myocardial infarction survivors via a health watch-based program and telephone counseling sessions. Usability and adherence to health watch use, exercise recommendations, and counseling sessions were high. Further studies are warranted to compare patient outcomes and health care resource utilization between center-based rehabilitation and telerehabilitation.

Cardiovascular Health Metrics in Patients Hospitalized with an Acute Coronary Syndrome.

BACKGROUND: The Life's Simple 7 (LS7) is a guiding metric for primordial/primary prevention of cardiovascular disease. However, little is known about the prevalence and distribution of LS7 metrics in patients with an acute coronary syndrome at the time of hospitalization. METHODS: Data were obtained from patients hospitalized for an acute coronary syndrome at 6 hospitals in Central Massachusetts and Georgia (2011-2013). The LS7 assessed patient's smoking, diet, and physical activity based on self-reported measures, and patients' body mass index, blood pressure, and serum cholesterol and glucose levels were abstracted from medical records. All items were operationalized into 3 categories: poor (0), intermediate (1), or ideal (2). A total summary cardiovascular health score (0-14) was obtained and categorized into tertiles (0-5, 6-7, and 8-14). RESULTS: The average age of study participants (n = 1110) was 59.6 years and 35% were women. Cardiovascular health scores ranged from 0-12 (mean = 6.2). Patients with higher scores were older, white, had lower burden of comorbidities, had fewer symptoms of anxiety, depression, and stress, better quality of life, more social support, and greater healthcare activation. One-third of patients had only 1 ideal cardiovascular health measure, less than 1% had 5, and no participant had more than 5 ideal factors. CONCLUSIONS: Our results indicate that patients with acute coronary syndrome have poor cardiovascular health. Sociodemographic, clinical, and psychosocial characteristics differed across cardiovascular health groups. These findings highlight potential areas for educational and therapeutic interventions to reduce the risk of cardiovascular disease and promote cardiovascular health in adult men and women.

Health Care Satisfaction in Relation to Gender Identity: Behavioral Risk Factor Surveillance Survey, 20 States (2014-2018).

BACKGROUND: Health care satisfaction is a key component of patient-centered care. Prior research on transgender populations has been based on convenience samples, and/or grouped all gender minorities into a single category. OBJECTIVE: The objective of this study was to quantify differences in health care satisfaction among transgender men, transgender women, gender nonconforming, and cisgender adults in a diverse multistate sample. RESEARCH DESIGN: Cross-sectional analysis of 2014-2018 Behavioral Risk Factor Surveillance System data from 20 states, using multivariable logistic models. SUBJECTS: We identified 167,468 transgender men, transgender women, gender-nonconforming people, cisgender women, and cisgender men and compared past year health care satisfaction across these groups. RESULTS: Transgender men and women had the highest prevalence of being "not at all satisfied" with the health care they received (14.6% and 8.6%, respectively), and gender-nonconforming people had the lowest prevalence of being "very satisfied" with their health care (55.7%). After adjustment for sociodemographic characteristics, transgender men were more likely to report being "not at all satisfied" with health care than cisgender men (odds ratio: 4.45, 95% confidence interval: 1.72-11.5) and cisgender women (odds ratio: 3.40, 95% confidence interval: 1.31-8.80). CONCLUSIONS: Findings indicate that transgender and gender-nonconforming adults report considerably less health care satisfaction relative to their cisgender peers. Interventions to address factors driving these differences are needed.

Design, deployment, and usability of a mobile system for cardiovascular health monitoring within the electronic Framingham Heart Study.

Background: The electronic Framingham Heart Study (eFHS) is an ongoing nested study, which includes FHS study participants, examining associations between health data from mobile devices with cardiovascular risk factors and disease. Objective: To describe application (app) design, report user characteristics, and describe usability and survey response rates. Methods: Eligible FHS participants were consented and offered a smartwatch (Apple Watch), a digital blood pressure (BP) cuff, and the eFHS smartphone app for administering surveys remotely. We assessed usability of the new app using 2 domains (functionality, aesthetics) of the Mobile App Rating Scale (MARS) and assessed survey completion rates at baseline and 3 months. Results: A total of 196 participants were recruited using the enhanced eFHS app. Of these, 97 (49.5%) completed the MARS instrument. Average age of participants was 53 ± 9 years, 51.5% were women, and 93.8% were white. Eighty-six percent of participants completed at least 1 measure on the baseline survey, and 50% completed the 3-month assessment. Overall subjective score of the app was 4.2 ± 0.7 on a scale from 1 to 5 stars. Of those who shared their health data with others, 46% shared their BP and 7.7% shared their physical activity with a health care provider. Conclusion: Participants rated the new, enhanced eFHS app positively overall. Mobile app survey completion rates were high, consistent with positive in-app ratings from participants. These mobile data collection modalities offer clinicians new opportunities to engage in conversations about health behaviors.

Feasibility of atrial fibrillation detection from a novel wearable armband device.

Background: Atrial fibrillation (AF) is the world's most common heart rhythm disorder and even several minutes of AF episodes can contribute to risk for complications, including stroke. However, AF often goes undiagnosed owing to the fact that it can be paroxysmal, brief, and asymptomatic. Objective: To facilitate better AF monitoring, we studied the feasibility of AF detection using a continuous electrocardiogram (ECG) signal recorded from a novel wearable armband device. Methods: In our 2-step algorithm, we first calculate the R-R interval variability-based features to capture randomness that can indicate a segment of data possibly containing AF, and subsequently discriminate normal sinus rhythm from the possible AF episodes. Next, we use density Poincaré plot-derived image domain features along with a support vector machine to separate premature atrial/ventricular contraction episodes from any AF episodes. We trained and validated our model using the ECG data obtained from a subset of the MIMIC-III (Medical Information Mart for Intensive Care III) database containing 30 subjects. Results: When we tested our model using the novel wearable armband ECG dataset containing 12 subjects, the proposed method achieved sensitivity, specificity, accuracy, and F1 score of 99.89%, 99.99%, 99.98%, and 0.9989, respectively. Moreover, when compared with several existing methods with the armband data, our proposed method outperformed the others, which shows its efficacy. Conclusion: Our study suggests that the novel wearable armband device and our algorithm can be used as a potential tool for continuous AF monitoring with high accuracy.

Smartwatch monitoring for atrial fibrillation after stroke-The Pulsewatch Study: Protocol for a multiphase randomized controlled trial.

Background: Atrial fibrillation (AF) is a common heart rhythm disorder that elevates stroke risk. Stroke survivors undergo routine heart rhythm monitoring for AF. Smartwatches are capable of AF detection and potentially can replace traditional cardiac monitoring in stroke patients. Objective: The goal of Pulsewatch is to assess the accuracy, usability, and adherence of a smartwatch-based AF detection system in stroke patients. Methods: The study will consist of two parts. Part I will have 6 focus groups with stroke patients, caretakers, and physicians, and a Hack-a-thon, to inform development of the Pulsewatch system. Part II is a randomized clinical trial with 2 phases designed to assess the accuracy and usability in the first phase (14 days) and adherence in the second phase (30 days). Participants will be randomized in a 3:1 ratio (intervention to control) for the first phase, and both arms will receive gold-standard electrocardiographic (ECG) monitoring. The intervention group additionally will receive a smartphone/smartwatch dyad with the Pulsewatch applications. Upon completion of 14 days, participants will be re-randomized in a 1:1 ratio. The intervention group will receive the Pulsewatch system and a handheld ECG device, while the control group will be passively monitored. Participants will complete questionnaires at enrollment and at 14- and 44-day follow-up visits to assess various psychosocial measures and health behaviors. Results: Part I was completed in August 2019. Enrollment for Part II began September 2019, with expected completion by the end of 2021. Conclusion: Pulsewatch aims to demonstrate that a smartwatch can be accurate for real-time AF detection, and that older stroke patients will find the system usable and will adhere to monitoring.

NExUS-Heart: Novel examinations using smart technologies for heart health-Data sharing from commercial wearable devices and telehealth engagement in participants with or at risk of atrial fibrillation.

Background: Telemedicine and commercial wearable devices capable of detecting atrial fibrillation (AF) have revolutionized arrhythmia care during coronavirus disease 2019. However, not much is known about virtual patient-provider interactions or device sharing behaviors. Objective: The purpose of this study was to characterize how participants with or at risk of AF are engaging with their providers in the context of telemedicine and using commercially wearable devices to manage their health. Methods: We developed a survey to describe participant behaviors around telemedicine encounters and commercial wearable device use. The survey was distributed to participants diagnosed with AF or those at risk of AF (as determined by being at least 65 years old and having a CHA2DS2-VASc stroke risk score of >2) in the University of Massachusetts Memorial Health Care system. Results: The survey was distributed to 23,530 patients, and there were 1222 (5.19%) participant responses. Among the participants, 327 (26.8%) had AF and 895 (73.2%) were at risk of AF. Neither device ownership nor device type use differed by AF status. After adjusting for covariates that may influence surveyed participant communication patterns, we found that participants with AF were more likely to share their wearable device-derived data with providers (adjusted odds ratio 1.87; 95% confidence interval 1.02-3.41). Rates of sharing physical activity or sleep data were low for both groups and did not differ by AF status. Conclusion: Compared with participants at risk of developing AF, those with AF were more likely to share heart rate and rhythm data from their commercial wearable devices with providers. However, both groups had similar rates of sharing physical activity and sleep data, telemedicine engagement, and technology use and ownership.

Survey of current perspectives on consumer-available digital health devices for detecting atrial fibrillation.

BACKGROUND: Many digital health technologies capable of atrial fibrillation (AF) detection are directly available to patients. However, adaptation into clinical practice by heart rhythm healthcare practitioners (HCPs) is unclear. OBJECTIVE: To examine HCP perspectives on use of commercial technologies for AF detection and management. METHODS: We created an electronic survey for HCPs assessing practice demographics and perspectives on digital devices for AF detection and management. The survey was distributed electronically to all members of 3 heart rhythm professional societies. RESULTS: We received 1601 responses out of 73,563 e-mails sent, with 43.6% from cardiac electrophysiologists, 12.8% from fellows, and 11.6% from advanced practice practitioners. Most respondents (62.3%) reported having recommended patient use of a digital device for AF detection. Those who did not had concerns about their accuracy (29.6%), clinical utility of results (22.8%), and integration into electronic health records (19.8%). Results from a 30-second single-lead electrocardiogram were sufficient for 42.7% of HCPs to recommend oral anticoagulation for patients at high risk for stroke. Respondents wanted more data comparing the accuracy of digital devices to conventional devices for AF monitoring (64.9%). A quarter (27.3%) of HCPs had no reservations recommending digital devices for AF detection, and most (53.4%) wanted guidelines from their professional societies providing guidance on their optimal use. CONCLUSION: Many HCPs have already integrated digital devices into their clinical practice. However, HCPs reported facing challenges when using digital technologies for AF detection, and professional society recommendations on their use are needed.

Emerging Technologies for Identifying Atrial Fibrillation.

Atrial fibrillation (AF) is a major cause of morbidity and mortality globally, and much of this is driven by challenges in its timely diagnosis and treatment. Existing and emerging mobile technologies have been used to successfully identify AF in a variety of clinical and community settings, and while these technologies offer great promise for revolutionizing AF detection and screening, several major barriers may impede their effectiveness. The unclear clinical significance of device-detected AF, potential challenges in integrating patient-generated data into existing healthcare systems and clinical workflows, harm resulting from potential false positives, and identifying the appropriate scope of population-based screening efforts are all potential concerns that warrant further investigation. It is crucial for stakeholders such as healthcare providers, researchers, funding agencies, insurers, and engineers to actively work together in fulfilling the tremendous potential of mobile technologies to improve AF identification and management on a population level.

Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study.

BACKGROUND: Atrial fibrillation (AF) is often paroxysmal and minimally symptomatic, hindering its diagnosis. Smartwatches may enhance AF care by facilitating long-term, noninvasive monitoring. OBJECTIVE: This study aimed to examine the accuracy and usability of arrhythmia discrimination using a smartwatch. METHODS: A total of 40 adults presenting to a cardiology clinic wore a smartwatch and Holter monitor and performed scripted movements to simulate activities of daily living (ADLs). Participants' clinical and sociodemographic characteristics were abstracted from medical records. Participants completed a questionnaire assessing different domains of the device's usability. Pulse recordings were analyzed blindly using a real-time realizable algorithm and compared with gold-standard Holter monitoring. RESULTS: The average age of participants was 71 (SD 8) years; most participants had AF risk factors and 23% (9/39) were in AF. About half of the participants owned smartphones, but none owned smartwatches. Participants wore the smartwatch for 42 (SD 14) min while generating motion noise to simulate ADLs. The algorithm determined 53 of the 314 30-second noise-free pulse segments as consistent with AF. Compared with the gold standard, the algorithm demonstrated excellent sensitivity (98.2%), specificity (98.1%), and accuracy (98.1%) for identifying irregular pulse. Two-thirds of participants considered the smartwatch highly usable. Younger age and prior cardioversion were associated with greater overall comfort and comfort with data privacy with using a smartwatch for rhythm monitoring, respectively. CONCLUSIONS: A real-time realizable algorithm analyzing smartwatch pulse recordings demonstrated high accuracy for identifying pulse irregularities among older participants. Despite advanced age, lack of smartwatch familiarity, and high burden of comorbidities, participants found the smartwatch to be highly acceptable.