Search

BackgroundMaximal oxygen consumption (VO2max) is one of the most predictive biometrics for cardiovascular health and overall mortality. However, VO2max is rarely measured in large-scale research studies or routine clinical care because of the high cost, participant burden, and requirement for specialized equipment and staff. ObjectiveTo overcome the limitations of clinical VO2max measurement, we aim to develop a digital VO2max estimation protocol that can be self-administered remotely using only the sensors within a smartphone. We also aim to validate this measure within a broadly representative population across a spectrum of smartphone devices. MethodsTwo smartphone-based VO2max estimation protocols were developed: a 12-minute run test (12-MRT) based on distance measured by GPS and a 3-minute step test (3-MST) based on heart rate recovery measured by a camera. In a 101-person cohort, balanced across age deciles and sex, participants completed a gold standard treadmill-based VO2max measurement, two silver standard clinical protocols, and the smartphone-based 12-MRT and 3-MST protocols in the clinic and at home. In a separate 120-participant cohort, the video-based heart rate measurement underlying the 3-MST was measured for accuracy in individuals across the spectrum skin tones while using 8 different smartphones ranging in cost from US $99 to US $999. ResultsWhen compared with gold standard VO2max testing, Lin concordance was pc=0.66 for 12-MRT and pc=0.61 for 3-MST. However, in remote settings, the 12-MRT was significantly less concordant with the gold standard (pc=0.25) compared with the 3-MST (pc=0.61), although both had high test-retest reliability (12-MRT intraclass correlation coefficient=0.88; 3-MST intraclass correlation coefficient=0.86). On the basis of the finding that 3-MST concordance was generalizable to remote settings whereas 12-MRT was not, the video-based heart rate measure within the 3-MST was selected for further investigation. Heart rate measurements in any of the combinations of the six Fitzpatrick skin tones and 8 smartphones resulted in a concordance of pc≥0.81. Performance did not correlate with device cost, with all phones selling under US $200 performing better than pc>0.92. ConclusionsThese findings demonstrate the importance of validating mobile health measures in the real world across a diverse cohort and spectrum of hardware. The 3-MST protocol, termed as heart snapshot, measured VO2max with similar accuracy to supervised in-clinic tests such as the Tecumseh (pc=0.94) protocol, while also generalizing to remote and unsupervised measurements. Heart snapshot measurements demonstrated fidelity across demographic variation in age and sex, across diverse skin pigmentation, and between various iOS and Android phone configurations. This software is freely available for all validation data and analysis code.

BackgroundNew electronic cohort (e-Cohort) study designs provide resource-effective methods for collecting participant data. It is unclear if implementing an e-Cohort study without direct, in-person participant contact can achieve successful participation rates. ObjectiveThe objective of this study was to compare 2 distinct enrollment methods for setting up mobile health (mHealth) devices and to assess the ongoing adherence to device use in an e-Cohort pilot study. MethodsWe coenrolled participants from the Framingham Heart Study (FHS) into the FHS–Health eHeart (HeH) pilot study, a digital cohort with infrastructure for collecting mHealth data. FHS participants who had an email address and smartphone were randomized to our FHS-HeH pilot study into 1 of 2 study arms: remote versus on-site support. We oversampled older adults (age ≥65 years), with a target of enrolling 20% of our sample as older adults. In the remote arm, participants received an email containing a link to enrollment website and, upon enrollment, were sent 4 smartphone-connectable sensor devices. Participants in the on-site arm were invited to visit an in-person FHS facility and were provided in-person support for enrollment and connecting the devices. Device data were tracked for at least 5 months. ResultsCompared with the individuals who declined, individuals who consented to our pilot study (on-site, n=101; remote, n=93) were more likely to be women, highly educated, and younger. In the on-site arm, the connection and initial use of devices was ≥20% higher than the remote arm (mean percent difference was 25% [95% CI 17-35] for activity monitor, 22% [95% CI 12-32] for blood pressure cuff, 20% [95% CI 10-30] for scale, and 43% [95% CI 30-55] for electrocardiogram), with device connection rates in the on-site arm of 99%, 95%, 95%, and 84%. Once connected, continued device use over the 5-month study period was similar between the study arms. ConclusionsOur pilot study demonstrated that the deployment of mobile devices among middle-aged and older adults in the context of an on-site clinic visit was associated with higher initial rates of device use as compared with offering only remote support. Once connected, the device use was similar in both groups.

COVID-19 increased the prevalence of clinically significant anxiety in the United States. To investigate contributing factors we analyzed anxiety, reported online via monthly Generalized Anxiety Disorders-7 (GAD-7) surveys between April 2020 and May 2022, in association with self-reported worry about the health effects of COVID-19, economic difficulty, personal COVID-19 experience, and subjective social status. 333,292 anxiety surveys from 50,172 participants (82% non-Hispanic white; 73% female; median age 55, IQR 42-66) showed high levels of anxiety, especially early in the pandemic. Anxiety scores showed strong independent associations with worry about the health effects of COVID-19 for oneself or family members (GAD-7 score +3.28 for highest vs. lowest category; 95% confidence interval: 3.24, 3.33; p

Cells interpret a variety of signals through G-protein-coupled receptors (GPCRs) and stimulate the generation of second messengers such as cyclic adenosine monophosphate (cAMP). A long-standing puzzle is deciphering how GPCRs elicit different physiological responses despite generating similar levels of cAMP. We previously showed that some GPCRs generate cAMP from both the plasma membrane and the Golgi apparatus. Here we demonstrate that cardiomyocytes distinguish between subcellular cAMP inputs to elicit different physiological outputs. We show that generating cAMP from the Golgi leads to the regulation of a specific protein kinase A (PKA) target that increases the rate of cardiomyocyte relaxation. In contrast, cAMP generation from the plasma membrane activates a different PKA target that increases contractile force. We further validated the physiological consequences of these observations in intact zebrafish and mice. Thus, we demonstrate that the same GPCR acting through the same second messenger regulates cardiac contraction and relaxation dependent on its subcellular location.

ObjectivePersons with HIV (PWH) have increased risk for atherosclerotic cardiovascular disease (CVD). Despite this increased risk, perceived cardiovascular risk among PWH is low, and interventions that are known to be beneficial in the general population, such as statins, have low uptake in this population. We sought to develop a bank of text messages about (1) the association between HIV and CVD and (2) advice on reducing cardiovascular risk.MethodsWe developed an initial bank of 162 messages. We solicited feedback from 29 PWH recruited from outpatient clinics providing HIV care at a large urban tertiary medical center and a public hospital in San Francisco, California. Participants reviewed 7-10 messages each and rated message usefulness, readability, and potential impact on behavior on a scale from 1 (least) to 5 (most). We also collected open-ended feedback on the messages and data on preferences about message timing.ResultsThe average score for the messages was 4.4/5 for usefulness, 4.4/5 for readability, and 4.0/5 for potential impact on behavior. The text messages were iteratively revised based on participant feedback, and lowest-rated messages were removed from the message bank. The final message bank included 116 messages on diet (30.2%), physical activity (24.8%), tobacco (11.2%), the association between HIV and cardiovascular disease (9.5%), general heart health (6.9%), cholesterol (5.2%), blood pressure (4.3%), blood sugar (2.6%), sleep (2.6%), and weight (2.6%).ConclusionWe describe an approach for developing educational text messages on primary prevention of cardiovascular disease among PWH.

BackgroundArtificial intelligence (AI) applied to 12-lead electrocardiographs (ECGs) can detect hypertrophic cardiomyopathy (HCM).ObjectivesThe purpose of this study was to determine if AI-enhanced ECG (AI-ECG) can track longitudinal therapeutic response and changes in cardiac structure, function, or hemodynamics in obstructive HCM during mavacamten treatment.MethodsWe applied 2 independently developed AI-ECG algorithms (University of California-San Francisco and Mayo Clinic) to serial ECGs (n = 216) from the phase 2 PIONEER-OLE trial of mavacamten for symptomatic obstructive HCM (n = 13 patients, mean age 57.8 years, 69.2% male). Control ECGs from 2,600 age- and sex-matched individuals without HCM were obtained. AI-ECG output was correlated longitudinally to echocardiographic and laboratory metrics of mavacamten treatment response.ResultsIn the validation cohorts, both algorithms exhibited similar performance for HCM diagnosis, and exhibited mean HCM score decreases during mavacamten treatment: patient-level score reduction ranged from approximately 0.80 to 0.45 for Mayo and 0.70 to 0.35 for USCF algorithms; 11 of 13 patients demonstrated absolute score reduction from start to end of follow-up for both algorithms. HCM scores were significantly associated with other HCM-relevant parameters, including left ventricular outflow tract gradient at rest, postexercise, and with Valsalva, and NT-proBNP level, independent of age and sex (all P < 0.01). For both algorithms, the strongest longitudinal correlation was between AI-ECG HCM score and left ventricular outflow tract gradient postexercise (slope estimate: University of California-San Francisco 0.70 [95% CI: 0.45-0.96], P < 0.0001; Mayo 0.40 [95% CI: 0.11-0.68], P = 0.007).ConclusionsAI-ECG analysis longitudinally correlated with changes in echocardiographic and laboratory markers during mavacamten treatment in obstructive HCM. These results provide early evidence for a potential paradigm for monitoring HCM therapeutic response.

Studies have demonstrated that at least 20% of individuals infected with SARS-CoV-2 remain asymptomatic1-4. Although most global efforts have focused on severe illness in COVID-19, examining asymptomatic infection provides a unique opportunity to consider early immunological features that promote rapid viral clearance. Here, postulating that variation in the human leukocyte antigen (HLA) loci may underly processes mediating asymptomatic infection, we enrolled 29,947 individuals, for whom high-resolution HLA genotyping data were available, in a smartphone-based study designed to track COVID-19 symptoms and outcomes. Our discovery cohort (n = 1,428) comprised unvaccinated individuals who reported a positive test result for SARS-CoV-2. We tested for association of five HLA loci with disease course and identified a strong association between HLA-B*15:01 and asymptomatic infection, observed in two independent cohorts. Suggesting that this genetic association is due to pre-existing T cell immunity, we show that T cells from pre-pandemic samples from individuals carrying HLA-B*15:01 were reactive to the immunodominant SARS-CoV-2 S-derived peptide NQKLIANQF. The majority of the reactive T cells displayed a memory phenotype, were highly polyfunctional and were cross-reactive to a peptide derived from seasonal coronaviruses. The crystal structure of HLA-B*15:01-peptide complexes demonstrates that the peptides NQKLIANQF and NQKLIANAF (from OC43-CoV and HKU1-CoV) share a similar ability to be stabilized and presented by HLA-B*15:01. Finally, we show that the structural similarity of the peptides underpins T cell cross-reactivity of high-affinity public T cell receptors, providing the molecular basis for HLA-B*15:01-mediated pre-existing immunity.

BackgroundEngagement with self-monitoring of blood pressure (BP) declines, on average, over time but may vary substantially by individual.ObjectivesWe aimed to describe different 1-year patterns (groups) of self-monitoring of BP behaviors, identify predictors of those groups, and examine the association of self-monitoring of BP groups with BP levels over time.MethodsWe analyzed device-recorded BP measurements collected by the Health eHeart Study-an ongoing prospective eCohort study-from participants with a wireless consumer-purchased device that transmitted date- and time-stamped BP data to the study through a full 12 months of observation starting from the first day they used the device. Participants received no instruction on device use. We applied clustering analysis to identify 1-year self-monitoring, of BP patterns.ResultsParticipants had a mean age of 52 years and were male and White. Using clustering algorithms, we found that a model with three groups fit the data well: persistent daily use (9.1% of participants), persistent weekly use (21.2%), and sporadic use only (69.7%). Persistent daily use was more common among older participants who had higher Week 1 self-monitoring of BP frequency and was associated with lower BP levels than the persistent weekly use or sporadic use groups throughout the year.ConclusionWe identified three distinct self-monitoring of BP groups, with nearly 10% sustaining a daily use pattern associated with lower BP levels.

BackgroundCoffee is one of the most commonly consumed beverages in the world, but the acute health effects of coffee consumption remain uncertain.MethodsWe conducted a prospective, randomized, case-crossover trial to examine the effects of caffeinated coffee on cardiac ectopy and arrhythmias, daily step counts, sleep minutes, and serum glucose levels. A total of 100 adults were fitted with a continuously recording electrocardiogram device, a wrist-worn accelerometer, and a continuous glucose monitor. Participants downloaded a smartphone application to collect geolocation data. We used daily text messages, sent over a period of 14 days, to randomly instruct participants to consume caffeinated coffee or avoid caffeine. The primary outcome was the mean number of daily premature atrial contractions. Adherence to the randomization assignment was assessed with the use of real-time indicators recorded by the participants, daily surveys, reimbursements for date-stamped receipts for coffee purchases, and virtual monitoring (geofencing) of coffee-shop visits.ResultsThe mean (±SD) age of the participants was 39±13 years; 51% were women, and 51% were non-Hispanic White. Adherence to the random assignments was assessed to be high. The consumption of caffeinated coffee was associated with 58 daily premature atrial contractions as compared with 53 daily events on days when caffeine was avoided (rate ratio, 1.09; 95% confidence interval [CI], 0.98 to 1.20; P = 0.10). The consumption of caffeinated coffee as compared with no caffeine consumption was associated with 154 and 102 daily premature ventricular contractions, respectively (rate ratio, 1.51; 95% CI, 1.18 to 1.94); 10,646 and 9665 daily steps (mean difference, 1058; 95% CI, 441 to 1675); 397 and 432 minutes of nightly sleep (mean difference, 36; 95% CI, 25 to 47); and serum glucose levels of 95 mg per deciliter and 96 mg per deciliter (mean difference, -0.41; 95% CI, -5.42 to 4.60).ConclusionsIn this randomized trial, the consumption of caffeinated coffee did not result in significantly more daily premature atrial contractions than the avoidance of caffeine. (Funded by the University of California, San Francisco, and the National Institutes of Health; CRAVE ClinicalTrials.gov number, NCT03671759.).

BackgroundRemote activity monitoring has the potential to evaluate real-world, motor function, and disability at home. The relationships of daily physical activity with spinal cord white matter and gray matter (GM) areas, multiple sclerosis (MS) disability and leg function, are unknown.ObjectiveEvaluate the association of structural central nervous system pathology with ambulatory disability.MethodsFifty adults with progressive or relapsing MS with motor disability who could walk >2 minutes were assessed using clinician-evaluated, patient-reported outcomes, and quantitative brain and spinal cord magnetic resonance imaging (MRI) measures. Fitbit Flex2, worn on the non-dominant wrist, remotely assessed activity over 30 days. Univariate and multivariate analyses were performed to assess correlations between physical activity and other disability metrics.ResultsMean age was 53.3 years and median Expanded Disability Status Scale (EDSS) was 4.0. Average daily step counts (STEPS) were highly correlated with EDSS and walking measures. Greater STEPS were significantly correlated with greater C2-C3 spinal cord GM areas (ρ = 0.39, p = 0.04), total cord area (TCA; ρ = 0.35, p = 0.04), and cortical GM volume (ρ = 0.32, p = 0.04).ConclusionThese results provide preliminary evidence that spinal cord GM area is a neuroanatomical substrate associated with STEPS. STEPS could serve as a proxy to alert clinicians and researchers to possible changes in structural nervous system pathology.

BackgroundFew prospective studies of Long COVID risk factors have been conducted. The purpose of this study was to determine whether sociodemographic factors, lifestyle, or medical history preceding COVID-19 or characteristics of acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are associated with Long COVID.MethodsIn March 26, 2020, the COVID-19 Citizen Science study, an online cohort study, began enrolling participants with longitudinal assessment of symptoms before, during, and after SARS-CoV-2 infection. Adult participants who reported a positive SARS-CoV-2 test result before April 4, 2022 were surveyed for Long COVID symptoms. The primary outcome was at least 1 prevalent Long COVID symptom greater than 1 month after acute infection. Exposures of interest included age, sex, race/ethnicity, education, employment, socioeconomic status/financial insecurity, self-reported medical history, vaccination status, variant wave, number of acute symptoms, pre-COVID depression, anxiety, alcohol and drug use, sleep, and exercise.ResultsOf 13 305 participants who reported a SARS-CoV-2 positive test, 1480 (11.1%) responded. Respondents' mean age was 53 and 1017 (69%) were female. Four hundred seventy-six (32.2%) participants reported Long COVID symptoms at a median 360 days after infection. In multivariable models, number of acute symptoms (odds ratio [OR], 1.30 per symptom; 95% confidence interval [CI], 1.20-1.40), lower socioeconomic status/financial insecurity (OR, 1.62; 95% CI, 1.02-2.63), preinfection depression (OR, 1.08; 95% CI, 1.01-1.16), and earlier variants (OR = 0.37 for Omicron compared with ancestral strain; 95% CI, 0.15-0.90) were associated with Long COVID symptoms.ConclusionsVariant wave, severity of acute infection, lower socioeconomic status, and pre-existing depression are associated with Long COVID symptoms.

BackgroundThe emergence of a plethora of new tobacco products marketed as being less harmful than smoking, such as electronic cigarettes and heated tobacco products, and the increased popularity of recreational marijuana have raised concerns about the potential cardiovascular risk associated with their use.ObjectiveThe purpose of this study was to investigate whether the use of novel tobacco products or marijuana can cause the development of proarrhythmic substrate and eventually lead to arrhythmias.MethodsRats were exposed to smoke from tobacco, marijuana, or cannabinoid-depleted marijuana, to aerosol from electronic cigarettes or heated tobacco products, or to clean air once per day for 8 weeks, following by assays for blood pressure, cardiac function, ex vivo electrophysiology, and histochemistry.ResultsThe rats exposed to tobacco or marijuana products exhibited progressively increased systolic blood pressure, decreased cardiac systolic function with chamber dilation, and reduced overall heart rate variability, relative to the clean air negative control group. Atrial fibrillation and ventricular tachycardia testing by ex vivo optical mapping revealed a significantly higher susceptibility to each, with a shortened effective refractory period and prolonged calcium transient duration. Histological analysis indicated that in all exposure conditions except for air, exposure to smoke or aerosol from tobacco or marijuana products caused severe fibrosis with decreased microvessel density and higher level of sympathetic nerve innervation.ConclusionThese pathophysiological results indicate that tobacco and marijuana products can induce arrhythmogenic substrates involved in cardiac electrical, structural, and neural remodeling, facilitating the development of arrhythmias.

Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a fully automated approach to interpret angiographic coronary artery stenosis from standard coronary angiograms. Using 13,843 angiographic studies from 11,972 adult patients at University of California, San Francisco (UCSF), between April 1, 2008 and December 31, 2019, we train neural networks to accomplish four sequential necessary tasks for automatic coronary artery stenosis localization and estimation. Algorithms are internally validated against criterion-standard labels for each task in hold-out test datasets. Algorithms are then externally validated in real-world angiograms from the University of Ottawa Heart Institute (UOHI) and also retrained using quantitative coronary angiography (QCA) data from the Montreal Heart Institute (MHI) core lab. The CathAI system achieves state-of-the-art performance across all tasks on unselected, real-world angiograms. Positive predictive value, sensitivity and F1 score are all ≥90% to identify projection angle and ≥93% for left/right coronary artery angiogram detection. To predict obstructive CAD stenosis (≥70%), CathAI exhibits an AUC of 0.862 (95% CI: 0.843-0.880). In UOHI external validation, CathAI achieves AUC 0.869 (95% CI: 0.830-0.907) to predict obstructive CAD. In the MHI QCA dataset, CathAI achieves an AUC of 0.775 (95%. CI: 0.594-0.955) after retraining. In conclusion, multiple purpose-built neural networks can function in sequence to accomplish automated analysis of real-world angiograms, which could increase standardization and reproducibility in angiographic coronary stenosis assessment.

BackgroundLittle is known about whether people who use both tobacco and cannabis (co-use) are more or less likely to have mental health disorders than single substance users or non-users. We aimed to examine associations between use of tobacco and/or cannabis with anxiety and depression.MethodsWe analyzed data from the COVID-19 Citizen Science Study, a digital cohort study, collected via online surveys during 2020-2022 from a convenience sample of 53,843 US adults (≥ 18 years old) nationwide. Past 30-day use of tobacco and cannabis was self-reported at baseline and categorized into four exclusive patterns: tobacco-only use, cannabis-only use, co-use of both substances, and non-use. Anxiety and depression were repeatedly measured in monthly surveys. To account for multiple assessments of mental health outcomes within a participant, we used Generalized Estimating Equations to examine associations between the patterns of tobacco and cannabis use with each outcome.ResultsIn the total sample (mean age 51.0 years old, 67.9% female), 4.9% reported tobacco-only use, 6.9% cannabis-only use, 1.6% co-use, and 86.6% non-use. Proportions of reporting anxiety and depression were highest for the co-use group (26.5% and 28.3%, respectively) and lowest for the non-use group (10.6% and 11.2%, respectively). Compared to non-use, the adjusted odds of mental health disorders were highest for co-use (Anxiety: OR = 1.89, 95%CI = 1.64-2.18; Depression: OR = 1.77, 95%CI = 1.46-2.16), followed by cannabis-only use, and tobacco-only use. Compared to tobacco-only use, co-use (OR = 1.35, 95%CI = 1.08-1.69) and cannabis-only use (OR = 1.17, 95%CI = 1.00-1.37) were associated with higher adjusted odds for anxiety, but not for depression. Daily use (vs. non-daily use) of cigarettes, e-cigarettes, and cannabis were associated with higher adjusted odds for anxiety and depression.ConclusionsUse of tobacco and/or cannabis, particularly co-use of both substances, were associated with poor mental health. Integrating mental health support with tobacco and cannabis cessation may address this co-morbidity.

Chest pain is a common clinical complaint for which myocardial injury is the primary concern and is associated with significant morbidity and mortality. To aid providers' decision-making, we aimed to analyze the electrocardiogram (ECG) using a deep convolutional neural network (CNN) to predict serum troponin I (TnI) from ECGs. We developed a CNN using 64,728 ECGs from 32,479 patients who underwent ECG within 2 h prior to a serum TnI laboratory result at the University of California, San Francisco (UCSF). In our primary analysis, we classified patients into groups of TnI

BackgroundFalling is common in people with multiple sclerosis (MS) but tends to be under-ascertained and under-treated.ObjectiveTo evaluate fall risk in people with MS.MethodsNinety-four people with MS, able to walk > 2 min with or without an assistive device (Expanded Disability Status Scale (EDSS ≤ 6.5) were recruited. Clinic-based measures were recorded at baseline and 1 year. Patient-reported outcomes (PROs), including a fall survey and the MS Walking Scale (MSWS-12), were completed at baseline, 1.5, 3, 6, 9, and 12 months. Average daily step counts (STEPS) were recorded using a wrist-worn accelerometer.Results50/94 participants (53.2%) reported falling at least once. Only 56% of participants who reported a fall on research questionnaires had medical-record documented falls. Fallers had greater disability [median EDSS 5.5 (IQR 4.0-6.0) versus 2.5 (IQR 1.5-4.0), p

ImportanceAtrial fibrillation (AF) is the most common arrhythmia. Although patients have reported that various exposures determine when and if an AF event will occur, a prospective evaluation of patient-selected triggers has not been conducted, and the utility of characterizing presumed AF-related triggers for individual patients remains unknown.ObjectiveTo test the hypothesis that n-of-1 trials of self-selected AF triggers would enhance AF-related quality of life.Design, setting, and participantsA randomized clinical trial lasting a minimum of 10 weeks tested a smartphone mobile application used by symptomatic patients with paroxysmal AF who owned a smartphone and were interested in testing a presumed AF trigger. Participants were screened between December 22, 2018, and March 29, 2020.Interventionsn-of-1 Participants received instructions to expose or avoid self-selected triggers in random 1-week blocks for 6 weeks, and the probability their trigger influenced AF risk was then communicated. Controls monitored their AF over the same time period.Main outcomes and measuresAF was assessed daily by self-report and using a smartphone-based electrocardiogram recording device. The primary outcome comparing n-of-1 and control groups was the Atrial Fibrillation Effect on Quality-of-Life (AFEQT) score at 10 weeks. All participants could subsequently opt for additional trigger testing.ResultsOf 446 participants who initiated (mean [SD] age, 58 [14] years; 289 men [58%]; 461 White [92%]), 320 (72%) completed all study activities. Self-selected triggers included caffeine (n = 53), alcohol (n = 43), reduced sleep (n = 31), exercise (n = 30), lying on left side (n = 17), dehydration (n = 10), large meals (n = 7), cold food or drink (n = 5), specific diets (n = 6), and other customized triggers (n = 4). No significant differences in AFEQT scores were observed between the n-of-1 vs AF monitoring-only groups. In the 4-week postintervention follow-up period, significantly fewer daily AF episodes were reported after trigger testing compared with controls over the same time period (adjusted relative risk, 0.60; 95% CI, 0.43- 0.83; P

BackgroundAdults with cardiovascular disease risk factors (CVRFs) are also at increased risk of developing cognitive decline and dementia. However, it is often difficult to study the relationships between CVRFs and cognitive function because cognitive assessment typically requires time-consuming in-person neuropsychological evaluations that may not be feasible for real-world situations.ObjectiveWe conducted a proof-of-concept study to determine if the association between CVRFs and cognitive function could be detected using web-based, self-administered cognitive tasks and CVRF assessment.MethodsWe recruited 239 participants aged ≥50 years (mean age 62.7 years, SD 8.8; 42.7% [n=102] female, 88.7% [n=212] White) who were enrolled in the Health eHeart Study, a web-based platform focused on cardiac disease. The participants self-reported CVRFs (hypertension, high cholesterol, diabetes, and atrial fibrillation) using web-based health surveys between August 2016 and July 2018. After an average of 3 years of follow-up, we remotely evaluated episodic memory, working memory, and executive function via the web-based Posit Science platform, BrainHQ. Raw data were normalized and averaged into 3 domain scores. We used linear regression models to examine the association between CVRFs and cognitive function.ResultsCVRF prevalence was 62.8% (n=150) for high cholesterol, 45.2% (n=108) for hypertension, 10.9% (n=26) for atrial fibrillation, and 7.5% (n=18) for diabetes. In multivariable models, atrial fibrillation was associated with worse working memory (β=-.51, 95% CI -0.91 to -0.11) and worse episodic memory (β=-.31, 95% CI -0.59 to -0.04); hypertension was associated with worse episodic memory (β=-.27, 95% CI -0.44 to -0.11). Diabetes and high cholesterol were not associated with cognitive performance.ConclusionsSelf-administered web-based tools can be used to detect both CVRFs and cognitive health. We observed that atrial fibrillation and hypertension were associated with worse cognitive function even in those in their 60s and 70s. The potential of mobile assessments to detect risk factors for cognitive aging merits further investigation.

BackgroundIt is increasingly recognized that policies have played a role in both alleviating and exacerbating the health and economic consequences of the COVID-19 pandemic. There has been limited systematic evaluation of variation in U.S. local COVID-19-related policies. This study introduces the U.S. COVID-19 County Policy (UCCP) Database, whose objective is to systematically gather, characterize, and assess variation in U.S. county-level COVID-19-related policies.MethodsIn January-March 2021, we collected an initial wave of cross-sectional data from government and media websites for 171 counties in 7 states on 22 county-level COVID-19-related policies within 3 policy domains that are likely to affect health: (1) containment/closure, (2) economic support, and (3) public health. We characterized the presence and comprehensiveness of policies using univariate analyses. We also examined the correlation of policies with one another using bivariate Spearman's correlations. Finally, we examined geographical variation in policies across and within states.ResultsThere was substantial variation in the presence and comprehensiveness of county policies during January-March 2021. For containment and closure policies, the percent of counties with no restrictions ranged from 0% (for public events) to more than half for public transportation (67.8%), hair salons (52.6%), and religious gatherings (52.0%). For economic policies, 76.6% of counties had housing support, while 64.9% had utility relief. For public health policies, most were comprehensive, with 70.8% of counties having coordinated public information campaigns, and 66.7% requiring masks outside the home at all times. Correlations between containment and closure policies tended to be positive and moderate (i.e., coefficients 0.4-0.59). There was variation within and across states in the number and comprehensiveness of policies.ConclusionsThis study introduces the UCCP Database, presenting granular data on local governments' responses to the COVID-19 pandemic. We documented substantial variation within and across states on a wide range of policies at a single point in time. By making these data publicly available, this study supports future research that can leverage this database to examine how policies contributed to and continue to influence pandemic-related health and socioeconomic outcomes and disparities. The UCCP database is available online and will include additional time points for 2020-2021 and additional counties nationwide.

BackgroundAmbulatory disability is common in people with multiple sclerosis (MS). Remote monitoring using average daily step count (STEPS) can assess physical activity (activity) and disability in MS. STEPS correlates with conventional metrics such as the expanded disability status scale (Expanded Disability Status Scale; EDSS), Timed-25 Foot walk (T25FW) and timed up and go (TUG). However, while STEPS as a summative measure characterizes the number of steps taken over a day, it does not reflect variability and intensity of activity.ObjectivesNovel analytical methods were developed to describe how individuals spends time in various activity levels (e.g., continuous low versus short bouts of high) and the proportion of time spent at each activity level.Methods94 people with MS spanning the range of ambulatory impairment (unaffected to requiring bilateral assistance) were recruited into FITriMS study and asked to wear a Fitbit continuously for 1-year. Parametric distributions were fit to minute-by-minute step data. Adjusted R2 values for regressions between distributional fit parameters and STEPS with EDSS, TUG, T25FW and the patient-reported 12-item MS Walking scale (MSWS-12) were calculated over the first 4-weeks, adjusting for sex, age and disease duration.ResultsDistributional fits determined that the best statistically-valid model across all subjects was a 3-compartment Gaussian Mixture Model (GMM) that characterizes the step behavior within 3 levels of activity: high, moderate and low. The correlation of GMM parameters for baseline step count measures with clinical assessments was improved when compared with STEPS (adjusted R2 values GMM vs. STEPS: TUG: 0.536 vs. 0.419, T25FW: 0.489 vs. 0.402, MSWS-12: 0.383 vs. 0.378, EDSS: 0.557 vs. 0.465). The GMM correlated more strongly (Kruskal-Wallis: p = 0.0001) than STEPS and gave further information not included in STEPS.ConclusionsIndividuals' step distributions follow a 3-compartment GMM that better correlates with clinic-based performance measures compared with STEPS. These data support the existence of high-moderate-low levels of activity. GMM provides an interpretable framework to better understand the association between different levels of activity and clinical metrics and allows further analysis of walking behavior that takes step distribution and proportion of time at three levels of intensity into account.

Cardiac arrhythmias have been observed among patients hospitalised with acute COVID-19 infection, and palpitations remain a common symptom among the much larger outpatient population of COVID-19 survivors in the convalescent stage of the disease. To determine arrhythmia prevalence among outpatients after a COVID-19 diagnosis. Adults with a positive COVID-19 test and without a history of arrhythmia were prospectively evaluated with 14-day ambulatory electrocardiographic monitoring. Participants were instructed to trigger the monitor for palpitations. A total of 51 individuals (mean age 42±11 years, 65% women) underwent monitoring at a median 75 (IQR 34-126) days after a positive COVID-19 test. Median monitoring duration was 13.2 (IQR 10.5-13.8) days. No participant demonstrated atrial fibrillation, atrial flutter, sustained supraventricular tachycardia (SVT), sustained ventricular tachycardia or infranodal atrioventricular block. Nearly all participants (96%) had an ectopic burden of

ImportanceLittle is known about the factors associated with COVID-19 vaccine adverse effects in a real-world population.ObjectiveTo evaluate factors potentially associated with participant-reported adverse effects after COVID-19 vaccination.Design, setting, and participantsThe COVID-19 Citizen Science Study, an online cohort study, includes adults aged 18 years and older with a smartphone or internet access. Participants complete daily, weekly, and monthly surveys on health and COVID-19-related events. This analysis includes participants who provided consent between March 26, 2020, and May 19, 2021, and received at least 1 COVID-19 vaccine dose.ExposuresParticipant-reported COVID-19 vaccination.Main outcomes and measuresParticipant-reported adverse effects and adverse effect severity. Candidate factors in multivariable logistic regression models included age, sex, race, ethnicity, subjective social status, prior COVID-19 infection, medical conditions, substance use, vaccine dose, and vaccine brand.ResultsThe 19 586 participants had a median (IQR) age of 54 (38-66) years, and 13 420 (68.8%) were women. Allergic reaction or anaphylaxis was reported in 26 of 8680 participants (0.3%) after 1 dose of the BNT162b2 (Pfizer/BioNTech) or mRNA-1273 (Moderna) vaccine, 27 of 11 141 (0.2%) after 2 doses of the BNT162b2 or mRNA-1273 vaccine or 1 dose of the JNJ-78436735 (Johnson & Johnson) vaccine. The strongest factors associated with adverse effects were vaccine dose (2 doses of BNT162b2 or mRNA-1273 or 1 dose of JNJ-78436735 vs 1 dose of BNT162b2 or mRNA-1273; odds ratio [OR], 3.10; 95% CI, 2.89-3.34; P

BackgroundHeart failure (HF) is a leading cause of cardiac morbidity among women, whose risk factors differ from those in men. We used machine-learning approaches to develop risk- prediction models for incident HF in a cohort of postmenopausal women from the Women's Health Initiative (WHI).MethodsWe used 2 machine-learning methods-Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Trees (CART)-to perform variable selection on 1227 baseline WHI variables for the primary outcome of incident HF. These variables were then used to construct separate Cox proportional hazard models, and we compared these results, using receiver-operating characteristic (ROC) curve analysis, against a comparator model built using variables from the Atherosclerosis Risk in Communities (ARIC) HF prediction model. We analyzed 43,709 women who had 2222 incident HF events; median follow-up was 14.3 years.ResultsLASSO selected 10 predictors, and CART selected 11 predictors. The highest correlation between selected variables was 0.46. In addition to selecting well-established predictors such as age, myocardial infarction, and smoking, novel predictors included physical function, number of pregnancies, number of previous live births and age at menopause. In ROC analysis, the CART-derived model had the highest C-statistic of 0.83 (95% confidence interval [CI], 0.81-0.85), followed by LASSO 0.82 (95% CI, 0.81-0.84) and ARIC 0.73 (95% CI, 0.70-0.76).ConclusionsMachine-learning approaches can be used to develop HF risk-prediction models that can have better discrimination compared with an established HF risk model and may provide a basis for investigating novel HF predictors.

ObjectivesTo examine the association of atrial fibrillation (AF) with incident dementia in older veterans and the effect of anticoagulation on that association.MethodsAround 407,871 veterans aged ≥55 years receiving care from US Veterans Health Administration between August 2003 and September 2015 were included in our retrospective study. AF and incident dementia were determined according to ICD-9-CM codes. Logistic regressions with veterans grouped into high-dimensional propensity scores deciles were used, and a mediation analysis was employed to examine the extent of cardio/cerebrovascular diseases that may also account for that association.ResultsAF was associated with greater dementia risk (odds ratio = 1.14; 95% confidence interval 1.07-1.22), partially mediated by cardio/cerebrovascular disease. Among veterans with AF taking anticoagulants, the risk of dementia was 44% higher (odds ratio =1.44; 95% CI 1.27-1.63) compared to those without anticoagulants, likely related to AF severity.ConclusionOur findings underscore the importance of considering cognitive function in the management of AF patients.

ObjectiveUntil effective treatments and vaccines are made readily and widely available, preventative behavioural health measures will be central to the SARS-CoV-2 public health response. While current recommendations are grounded in general infectious disease prevention practices, it is still not entirely understood which particular behaviours or exposures meaningfully affect one's own risk of incident SARS-CoV-2 infection. Our objective is to identify individual-level factors associated with one's personal risk of contracting SARS-CoV-2.DesignProspective cohort study of adult participants from 26 March 2020 to 8 October 2020.SettingThe COVID-19 Citizen Science Study, an international, community and mobile-based study collecting daily, weekly and monthly surveys in a prospective and time-updated manner.ParticipantsAll adult participants over the age of 18 years were eligible for enrolment.Primary outcome measureThe primary outcome was incident SARS-CoV-2 infection confirmed via PCR or antigen testing.Results28 575 unique participants contributed 2 479 149 participant-days of data across 99 different countries. Of these participants without a history of SARS-CoV-2 infection at the time of enrolment, 112 developed an incident infection. Pooled logistic regression models showed that increased age was associated with lower risk (OR 0.98 per year, 95% CI 0.97 to 1.00, p=0.019), whereas increased number of non-household contacts (OR 1.10 per 10 contacts, 95% CI 1.01 to 1.20, p=0.024), attending events of at least 10 people (OR 1.26 per 10 events, 95% CI 1.07 to 1.50, p=0.007) and restaurant visits (OR 1.95 per 10 visits, 95% CI 1.42 to 2.68, p

BackgroundFalling is common in people with multiple sclerosis (MS) but tends to be under-ascertained and under-treated.ObjectiveTo evaluate fall risk in people with MS.MethodsNinety-four people with MS, able to walk > 2 min with or without an assistive device (Expanded Disability Status Scale (EDSS ≤ 6.5) were recruited. Clinic-based measures were recorded at baseline and 1 year. Patient-reported outcomes (PROs), including a fall survey and the MS Walking Scale (MSWS-12), were completed at baseline, 1.5, 3, 6, 9, and 12 months. Average daily step counts (STEPS) were recorded using a wrist-worn accelerometer.Results50/94 participants (53.2%) reported falling at least once. Only 56% of participants who reported a fall on research questionnaires had medical-record documented falls. Fallers had greater disability [median EDSS 5.5 (IQR 4.0-6.0) versus 2.5 (IQR 1.5-4.0), p

ObjectivesThe goal of this study was to investigate the characteristics of mitral valve prolapse (MVP) in a post-mortem study of consecutive sudden cardiac deaths (SCDs) in subjects up to 90 years of age.BackgroundUp to 2.3% of subjects with MVPs experience SCD, but by convention SCD is rarely confirmed by autopsy. In a post-mortem study of persons 1,000 consecutive World Health Organization-defined (presumed) cases of SCD in subjects aged 18 to 90 years since 2011; a total of 603 were adjudicated. Autopsy-defined sudden arrhythmic death (SAD) required absence of nonarrhythmic cause; MVP diagnosis required leaflet billowing. One hundred antemortem echocardiograms were revised to identify additional MVPs missed on autopsy.ResultsAmong the 603 presumed SCDs, 339 (56%) were autopsy-defined SADs, with MVP identified in 7 (1%). Six additional MVPs were identified by review of echocardiograms, for a prevalence of at least 2% among 603 presumed SCDs and 4% among 339 SADs (vs. 264 non-SADs; p = 0.02). All 6 additional MVPs had monoleaflet rather than bileaflet involvement and mild mitral regurgitation, ruling out hemodynamic cause. Less than one-half had MAD with replacement fibrosis, but all had multisite interstitial fibrosis.ConclusionsIn a countywide post-mortem study of all adult cases of SCD, MVP prevalence was at least 4% of SADs, but one-half were missed on autopsy. Monoleaflet MVP was often underdiagnosed post-mortem. Compared with young cases of SCD, lethal MVP in older cases of SCD did not consistently have bileaflet anatomy, replacement fibrosis, or MAD.

BackgroundThe incidence of sudden cardiac death and sudden death caused by arrhythmia, as determined by autopsy, in persons with human immunodeficiency virus (HIV) infection has not been clearly established.MethodsBetween February 1, 2011, and September 16, 2016, we prospectively identified all new deaths due to out-of-hospital cardiac arrest among persons 18 to 90 years of age, with or without known HIV infection, for comprehensive autopsy and toxicologic and histologic testing. We compared the rates of sudden cardiac death and sudden death caused by arrhythmia between groups.ResultsOf 109 deaths from out-of-hospital cardiac arrest among 610 unexpected deaths in HIV-positive persons, 48 met World Health Organization criteria for presumed sudden cardiac death; of those, fewer than half (22) had an arrhythmic cause. A total of 505 presumed sudden cardiac deaths occurred between February 1, 2011, and March 1, 2014, in persons without known HIV infection. Observed incidence rates of presumed sudden cardiac death were 53.3 deaths per 100,000 person-years among persons with known HIV infection and 23.7 deaths per 100,000 person-years among persons without known HIV infection (incidence rate ratio, 2.25; 95% confidence interval [CI], 1.37 to 3.70). Observed incidence rates of sudden death caused by arrhythmia were 25.0 and 13.3 deaths per 100,000 person-years, respectively (incidence rate ratio, 1.87; 95% CI, 0.93 to 3.78). Among all presumed sudden cardiac deaths, death due to occult drug overdose was more common in persons with known HIV infection than in persons without known HIV infection (34% vs. 13%). Persons who were HIV-positive had higher histologic levels of interstitial myocardial fibrosis than persons without known HIV infection.ConclusionsIn this postmortem study, the rates of presumed sudden cardiac death and myocardial fibrosis were higher among HIV-positive persons than among those without known HIV infection. One third of apparent sudden cardiac deaths in HIV-positive persons were due to occult drug overdose. (Supported by the National Heart, Lung, and Blood Institute.).

ImportanceActive SARS-CoV-2 (coronavirus) transmission continues in the US. It is unclear whether better access to coronavirus testing and more consistent use of testing could substantially reduce transmission.ObjectiveTo describe coronavirus testing in persons with new onset of febrile illness and analyze whether there are changes over time and differences by race and ethnicity.Design, setting, and participantsThis cohort study used data from the COVID-19 Citizen Science Study, launched in March 2020, which recruited participants via press release, word-of-mouth, and partner organizations. Participants completed daily surveys about COVID-19 symptoms and weekly surveys about coronavirus testing. All adults (aged at least 18 years) with a smartphone were eligible to join. For this analysis, US participants with new onset of febrile illness from April 2020 to October 2020 were included. Data analysis was performed from November 2020 to March 2021.Main outcomes and measuresReceipt of a coronavirus test result within 7 days of febrile illness onset.ResultsOf the 2679 participants included in this analysis, the mean (SD) age was 46.3 (13.4) years, 1983 were female (74%), 2017 were college educated (75%), and a total of 3865 distinct new febrile illness episodes were reported (300 episodes [7.8%] from Hispanic participants, 71 episodes [1.8%] from Black participants, and 3494 episodes [90.4%] from not Black, not Hispanic participants) between April 2 and October 23, 2020. In weekly surveys delivered during the 14 days after fever onset, 12% overall (753 participants) indicated receipt of a test result. Using serial survey responses and parametric time-to-event modeling, it was estimated that by 7 days after onset of febrile illness, a total of 20.5% (95% CI, 19.1%-22.0%) had received a test result. This proportion increased from 9.8% (95% CI, 7.5%-12.0%) early in the epidemic to 24.1% (95% CI, 21.5%-26.7%) at the end of July, but testing rates did not substantially improve since then, increasing to 25.9% (95% CI; 21.6%-30.3%) in late October at the start of the winter surge. Black participants reported receiving a test result about half as often as others (7% [7 of 103] of survey responses vs 12% [53 of 461] for Hispanic vs 13% [693 of 5516] for not Black, not Hispanic; P = .03). This association was not statistically significant in adjusted time-to-event models (hazard ratio = 0.59 vs not Black, not Hispanic participants; 95% CI, 0.26-1.34).Conclusions and relevanceSystematic underuse of coronavirus testing was observed in this cohort study through late October 2020, at the beginning of the winter COVID-19 surge, which may have contributed to preventable coronavirus transmission.

[Figure: see text].

ObjectivesThis study sought to identify acute changes in human atrial electrophysiology during alcohol exposure.BackgroundThe mechanism by which a discrete episode of atrial fibrillation (AF) occurs remains unknown. Alcohol appears to increase the risk for AF, providing an opportunity to study electrophysiologic effects that may render the heart prone to arrhythmia.MethodsIn this randomized, double-blinded, placebo-controlled trial, intravenous alcohol titrated to 0.08% blood alcohol concentration was compared with a volume and osmolarity-matched, masked, placebo in patients undergoing AF ablation procedures. Right, left, and pulmonary vein atrial effective refractory periods (AERPs) and conduction times were measured pre- and post-infusion. Isoproterenol infusions and burst atrial pacing were used to assess AF inducibility.ResultsOf 100 participants (50 in each group), placebo recipients were more likely to be diabetic (22% vs. 4%; p = 0.007) and to have undergone a prior AF ablation (36% vs. 22%; p = 0.005). Pulmonary vein AERPs decreased an average of 12 ms (95% confidence interval: 1 to 22 ms; p = 0.026) in the alcohol group, with no change in the placebo group (p = 0.98). Whereas no statistically significant differences in continuously assessed AERPs were observed, the proportion of AERP sites tested that decreased with alcohol (median: 0.5; interquartile range: 0.6 to 0.6) was larger than with placebo (median: 0.4; interquartile range: 0.2 to 0.6; p = 0.0043). No statistically significant differences in conduction times or in the proportion with inducible AF were observed.ConclusionsAcute exposure to alcohol reduces AERP, particularly in the pulmonary veins. These data demonstrate a direct mechanistic link between alcohol, a common lifestyle exposure, and immediate proarrhythmic effects in human atria. (How Alcohol Induces Atrial Tachyarrhythmias Study [HOLIDAY]; NCT01996943).

Excess alcohol use is an important determinant of death and disability. Machine learning (ML)-driven interventions leveraging smart-breathalyzer data may help reduce these harms. We developed a digital phenotype of long-term smart-breathalyzer behavior to predict individuals' breath alcohol concentration (BrAC) levels trained on data from a smart breathalyzer. We analyzed roughly one million datapoints from 33,452 users of a commercial smart-breathalyzer device, collected between 2013 and 2017. For validation, we analyzed the associations between state-level observed smart-breathalyzer BrAC levels and impaired-driving motor vehicle death rates. Behavioral, geolocation-based, and time-series-derived features were fed to an ML algorithm using training (70% of the cohort), development (10% of the cohort), and test (20% of the cohort) sets to predict the likelihood of a BrAC exceeding the legal driving limit (0.08 g/dL). States with higher average BrAC levels had significantly higher alcohol-related driving death rates, adjusted for the number of users per state B (SE) = 91.38 (15.16), p

BackgroundTailored Antiplatelet Initiation to Lessen Outcomes Due to Decreased Clopidogrel Response after Percutaneous Coronary Intervention (TAILOR-PCI) is the largest cardiovascular genotype-based randomized pragmatic trial (NCT#01742117) to evaluate the role of genotype-guided selection of oral P2Y12 inhibitor therapy in improving ischemic outcomes after PCI. The trial has been extended from the original 12- to 24-month follow-up, using study coordinator-initiated telephone visits. TAILOR-PCI Digital Study tests the feasibility of extending the trial follow-up in a subset of patients for up to 24 months using state-of-the-art digital solutions. The rationale, design, and approach of extended digital study of patients recruited into a large, international, multi-center clinical trial has not been previously described.MethodsA total of 930 patients from U.S. and Canadian sites previously enrolled in the 5,302 patient TAILOR-PCI trial within 23 months of randomization are invited by mail to the Digital Study website (http://tailorpci.eurekaplatform.org) and by up to 2 recruiting telephone calls. Eureka, a direct-to-participant digital research platform, is used to consent and collect prospective data on patients for the digital study. Patients are asked to answer health-related surveys at fixed intervals using the Eureka mobile app and or desktop platform. The likelihood of patients enrolled in a randomized clinical trial transitioning to a registry using digital technology, the reasons for nonparticipation and engagement rates are evaluated. To capture hospitalizations, patients may optionally enable geofencing, a process that allows background location tracking and triggering of surveys if a hospital visit greater than 4 hours is detected. In addition, patients answer digital hospitalization surveys every month. Hospitalization data received from the Digital Study will be compared to data collected from study coordinator telephone visits during the same time frame.ConclusionsThe TAILOR-PCI Digital Study evaluates the feasibility of transitioning a large multicenter randomized clinical trial to a digital registry. The study could provide evidence for the ability of digital technology to follow clinical trial patients and to ascertain trial-related events thus also building the foundation for conducting digital clinical trials. Such a digital approach may be especially pertinent in the era of COVID-19.

BackgroundIn the absence of universal testing, effective therapies, or vaccines, identifying risk factors for viral infection, particularly readily modifiable exposures and behaviors, is required to identify effective strategies against viral infection and transmission.MethodsWe conducted a world-wide mobile application-based prospective cohort study available to English speaking adults with a smartphone. We collected self-reported characteristics, exposures, and behaviors, as well as smartphone-based geolocation data. Our main outcome was incident symptoms of viral infection, defined as fevers and chills plus one other symptom previously shown to occur with SARS-CoV-2 infection, determined by daily surveys.FindingsAmong 14, 335 participants residing in all 50 US states and 93 different countries followed for a median 21 days (IQR 10-26 days), 424 (3%) developed incident viral symptoms. In pooled multivariable logistic regression models, female biological sex (odds ratio [OR] 1.75, 95% CI 1.39-2.20, p

IntroductionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (Covid-19), has been a serious threat to global health. Previous work has focused primarily on hospitalized patients or on identifying risk factors for disease severity and mortality once the infection has taken place. We sought to leverage the ubiquity of smartphones and mobile applications to study risk factors for Covid-19 infection in a large, geographically heterogenous cohort.MethodsWe analyzed data obtained from the Covid-19 Citizen Science (CCS) Study, a worldwide, mobile application-based cohort. After employing forward selection to identify variables with p values < 0.1, multivariable logistic regression models were utilized to identify independent risk factors associated with prevalent SARS-CoV-2 infection.ResultsAmong 36,041 participants in 113 countries and all 50 states in the US, 484 participants had prevalent SARS-CoV-2 infection. After multivariable adjustment, being a healthcare worker, living with at least one school-aged child, having pets at home, and having immunodeficiency were each associated with an increased odds of SARS-CoV-2. The association between pets and prevalent SARS-CoV-2 was driven by dog ownership. After adjustment for the same covariates, Asian or Pacific Islander race, receiving a flu shot within the past year, increased level of education, and smoking or vaping marijuana within the last 30 days were each associated with a lower odds of SARS-CoV-2.ConclusionWe identified various characteristics and behaviors, many of which are potentially modifiable, associated with prevalent SARS-CoV-2 infection in a world-wide mobile application-based cohort.

BackgroundThe COVID-19 pandemic has catalyzed a global public response and innovation in clinical study methods.ObjectiveThe COVID-19 Citizen Science study was designed to generate knowledge about participant-reported COVID-19 symptoms, behaviors, and disease occurrence.MethodsCOVID-19 Citizen Science is a longitudinal cohort study launched on March 26, 2020, on the Eureka Research Platform. This study illustrates important advances in digital clinical studies, including entirely digital study participation, targeted recruitment strategies, electronic consent, recurrent and time-updated assessments, integration with smartphone-based measurements, analytics for recruitment and engagement, connection with partner studies, novel engagement strategies such as participant-proposed questions, and feedback in the form of real-time results to participants.ResultsAs of February 2021, the study has enrolled over 50,000 participants. Study enrollment and participation are ongoing. Over the lifetime of the study, an average of 59% of participants have completed at least one survey in the past 4 weeks.ConclusionsInsights about COVID-19 symptoms, behaviors, and disease occurrence can be drawn through digital clinical studies. Continued innovation in digital clinical study methods represents the future of clinical research.International registered report identifier (irrid)DERR1-10.2196/28169.

The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes.

BackgroundVest Prevention of Early Sudden Death Trial did not demonstrate a significant reduction in arrhythmic death with the wearable cardioverter-defibrillator (WCD), but compliance with the device may have substantially affected the results. ThePletcher influence of WCD compliance on outcomes has not yet been fully evaluated.MethodsUsing linear and pooled logistic models, we performed as-treated analyses omitting person-time in the hospital and adjusted for correlates of WCD compliance. To assess the impact of early stopping of WCD, we performed a per-protocol Kaplan-Meier analysis, censoring after the last day the WCD was worn. Interactions of potential effect modifiers with treatment assignment and WCD compliance on outcomes were investigated. Finally, we used linear models to identify predictors of WCD compliance.ResultsA per-protocol analysis demonstrated a significant reduction in total (P

BackgroundRegular physical activity is an important determinant of cardiovascular health and quality of life. Previous investigations examining the association between exercise and atrial fibrillation (AF) have been limited by self-reported, retrospectively collected activity data.ObjectiveThe purpose of this study was to objectively quantify differences in daily physical activity among individuals with and those without AF using electronic wearable activity trackers.MethodsDaily exercise data were directly obtained from wrist-worn activity trackers (Fitbit, San Francisco, CA) among participants in the Health eHeart (HeH) study. Average daily step count was compared between individuals with and those without AF both before and after adjusting for comorbidities. AF severity was quantified using the Atrial Fibrillation Effect on QualiTy of Life (AFEQT) survey.ResultsAmong 171,284 HeH study participants, 3333 individuals (234 with AF [7%]) submitted activity data. In unadjusted analysis, AF participants ambulated an average of 723 fewer steps per day (95% confidence interval [CI] 292-1154; P = .001) compared to individuals without AF. After adjustment for demographics and comorbid diseases, participants with AF demonstrated 591 fewer steps per day (95% CI 149-1033; P = .009). Among AF patients, AF severity was associated with less physical activity. For each single point decrease in AFEQT score (corresponding to more symptomatic AF), physical activity decreased by a mean 24 steps per day (95% CI 1-46; P = .04).ConclusionObjective, automatically collected step count data demonstrate that individuals with AF engage in significantly less average daily physical activity. In addition, worsening AF symptom severity is associated with reduced daily exercise.