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Background Adults 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. Objective We 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. Methods We 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. Results CVRF 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. Conclusions Self-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.

Background Heart 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). Methods We 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. Results LASSO 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). Conclusions Machine-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.

Background Falling is common in people with multiple sclerosis (MS) but tends to be under-ascertained and under-treated. Objective To evaluate fall risk in people with MS. Methods Ninety-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. Results 50/94 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 p = 0.003), and took fewer STEPS (mean difference − 1,979, p = 0.007) than Non-Fallers. Stepwise regression revealed MSWS-12 as a major predictor of future falls. Conclusions Falling is common in people with MS, under-reported, and under-ascertained by neurologists in clinic. Multimodal fall screening in clinic and remotely may help improve patient care by identifying those at greatest risk, allowing for timely intervention and referral to specialized physical rehabilitation.

Background The COVID-19 pandemic has catalyzed a global public response and innovation in clinical study methods. Objective The COVID-19 Citizen Science study was designed to generate knowledge about participant-reported COVID-19 symptoms, behaviors, and disease occurrence. Methods COVID-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. Results As 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. Conclusions Insights 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

Background: Sudden cardiac death (SCD) studies report higher incidence in men and Black people but presume cardiac cause. We sought to identify sex and racial differences in rates and causes of presumed SCDs in a prospective postmortem study in San Francisco County. Methods: All incident presumed SCDs meeting the World Health Organization definition ages 18 to 90 were autopsied via active surveillance of consecutive out-of-hospital deaths in the POST SCD study (Postmortem Systematic Investigation of Sudden Cardiac Death; February 1, 2011, to March 1, 2014). Autopsy-defined sudden arrhythmic deaths had no extracardiac cause or acute heart failure. Results: Among 541 presumed SCDs, 525 (97%) were autopsied; 362 (69%) were men, 110 Asian (21%), 81 Black (15%), 40 Hispanic (8%), 279 White (53%), and 15 other race (3%). Adjusted for age and race, women had more noncardiac causes of presumed SCD, including pulmonary emboli (8% versus 2%) and neurological causes (10% versus 3%, both P P =0.02) and nonischemic causes (53% versus 39%; P P P P P , all Conclusions: In this countywide postmortem study of presumed SCDs, women had more nonischemic and noncardiac causes. Black women had higher rates of autopsy-defined sudden arrhythmic death than White women while Asian and Hispanic men had lower rates than White men. These findings have implications for risk stratification and prevention of sudden mortality in women and minority populations.

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

Sidney Aung,1 Eric Vittinghoff,2 Gregory Nah,1 Noah D Peyser,1 Mark J Pletcher,2 Jeffrey E Olgin,1 Gregory M Marcus1 1Division of Cardiology, University of California, San Francisco, San Francisco, CA, USA; 2Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USACorrespondence: Gregory M MarcusUniversity of California, San Francisco, 505 Parnassus Ave, M1180B, San Francisco, CA, 94143, USATel +1 415-476-3450Fax +1 415-476-6260Email greg.marcus@ucsf.eduIntroduction: Severe 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.Methods: We 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.Results: Among 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.Conclusion: We 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.Keywords: COVID-19, epidemiology, cohort study, digital health, mobile applications

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 ration [OR] 1.75, 95% CI 1.39-2.20, pInterpretationWhile several immutable characteristics were associated with the risk of developing viral symptoms, multiple immediately modifiable exposures and habits that influence risk were also observed, potentially identifying readily accessible strategies to mitigate risk in the Covid-19 era.FundingThis study was funded by IU2CEB021881-01 and 3U2CEB021881-05S1 from the NIH/ NIBIB to Drs. Marcus, Olgin, and Pletcher.Research in contextEvidence before this studyPredictors of incident viral infection have been determined largely from cross-sectional studies prone to recall bias among individuals representing geographically constrained regions, and most were conducted before the era of the current Covid-19 pandemic.Added value of this studyWe conducted a world-wide, mobile application-based, longitudinal cohort study utilizing time-updated predictors and outcomes, providing novel and current information regarding risk-factors for incident viral symptoms based on real-time information in the era of Covid-19.Implications of all the available evidenceThese data suggest that certain immutable characteristics influence the risk for incident viral symptoms, while smoking cessation, physical distancing to avoid contact with individuals outside the household, regular exercise, and sanitizing one’s phone may each help mitigate the risk of viral infection.

Author(s): Infeld, Margaret; Avram, Robert; Wahlberg, Kramer; Silverman, Daniel N; Habel, Nicole; Lustgarten, Daniel L; Pletcher, Mark J; Olgin, Jeffrey E; Marcus, Gregory M; Meyer, Markus

Author(s): Tison, Geoffrey H; Avram, Robert; Kuhar, Peter; Abreau, Sean; Marcus, Greg M; Pletcher, Mark J; Olgin, Jeffrey E

Background Regular 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. Objective The 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. Methods Daily 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. Results Among 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). Conclusion Objective, 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.

Background:Technological advancements of remote-monitoring used in clinical-care and research require validation of model updates. Objectives:To compare the output of a newer consumer-grade accelerometer to a previous model in people with multiple sclerosis (MS) and to the ActiGraph, a waist-worn device widely used in MS research. Methods:Thirty-one individuals with MS participated in a 7-day validation by the Fitbit Flex (Flex), Fitbit Flex-2 (Flex2) and ActiGraph GT3X. Primary outcome was step count. Valid epochs of 5-min block increments, where there was overlap of ≥1 step/min for both devices were compared and summed to give a daily total for analysis. Results:Bland-Altman plots showed no systematic difference between the Flex and Flex2; mean step-count difference of 25 more steps-per-day more recorded by Flex2 (95% confidence intervals (CI) = 2, 48; p = 0.04),interclass correlation coefficient (ICC) = 1.00. Compared to the ActiGraph, Flex2 (and Flex) tended to record more steps (808 steps-per-day more than the ActiGraph (95% CI= -2380, 765; p

Background: Conventional definitions of sudden cardiac death (SCD) presume cardiac cause. We studied the World Health Organization–defined SCDs autopsied in the POST SCD study (Postmortem Systematic Investigation of SCD) to determine whether premortem characteristics could identify autopsy-defined sudden arrhythmic death (SAD) among presumed SCDs. Methods: Between January 2, 2011, and January 4, 2016, we prospectively identified all 615 World Health Organization–defined SCDs (144 witnessed) 18 to 90 years in San Francisco County for medical record review and autopsy via medical examiner surveillance. Autopsy-defined SADs had no extracardiac or acute heart failure cause of death. We used 2 nested sets of premortem predictors—an emergency medical system set and a comprehensive set adding medical record data—to develop Least Absolute Selection and Shrinkage Operator models of SAD among witnessed and unwitnessed cohorts. Results: Of 615 presumed SCDs, 348 (57%) were autopsy-defined SAD. For witnessed cases, the emergency medical system model (area under the receiver operator curve 0.75 [0.67–0.82]) included presenting rhythm of ventricular tachycardia/fibrillation and pulseless electrical activity, while the comprehensive (area under the receiver operator curve 0.78 [0.70–0.84]) added depression. If only ventricular tachycardia/fibrillation witnessed cases (n=48) were classified as SAD, sensitivity was 0.46 (0.36–0.57), and specificity was 0.90 (0.79–0.97). For unwitnessed cases, the emergency medical system model (area under the receiver operator curve 0.68 [0.64–0.73]) included black race, male sex, age, and time since last seen normal, while the comprehensive (area under the receiver operator curve 0.75 [0.71–0.79]) added use of β-blockers, antidepressants, QT-prolonging drugs, opiates, illicit drugs, and dyslipidemia. If only unwitnessed cases Conclusions: Our models identify premortem characteristics that can better specify autopsy-defined SAD among presumed SCDs and suggest the World Health Organization definition can be improved by restricting witnessed SCDs to ventricular tachycardia/fibrillation or nonpulseless electrical activity rhythms and unwitnessed cases to

ObjectivesPregnancy, menses and menopause are related to fluctuations in endogenous sex hormones in women, which cumulatively may alter cardiac electrical conduction. Therefore, we sought to study the association between number of pregnancies and reproductive period duration (RD, time from menarche to menopause) with ECG intervals in the Women’s Health Initiative Clinical Trials.DesignSecondary analysis of multicentre clinical trial.SettingUSA.Primary outcome measuresECGintervals: PR interval, P-wave duration, P-wave dispersion, QTc interval.Participantsn=40 687 women (mean age=62 years) participating in the Women’s Health Initiative Clinical Trials. 82.5% were white, 9.3% black, 4% Hispanic and 2.7% Asian.MethodsIn primary analysis, we employed multivariable linear regression models relating number of pregnancies and RD with millisecond changes in intervals from enrolment ECG. We studied effect modification by hormone therapy use.ResultsAmong participants, 5+ live births versus 0 prior pregnancies was associated with a 1.32 ms increase in PR interval (95% CI 0.25 to 2.38), with a graded association with longer QTc interval (ms) (none (prior pregnancy, no live births)=0.66 (–0.56 to 1.88), 1=0.15 (–0.71 to 1.02), 2–4=0.25 (–0.43 to 0.94) and 5+ live births=1.15 (0.33 to 1.98), p=0.008). RD was associated with longer PR interval and maximum P-wave duration (but not P-wave dispersion) among never users of hormone therapy: (PR (ms) per additional RD year: 0.10 (0.04 to 0.16); higher P-wave duration (ms): 0.09 (0.06 to 0.12)). For every year increase in reproductive period, QTc decreased by 0.04 ms (−0.07 to –0.01).ConclusionsAn increasing number of live births is related to increased and RD to decreased ventricular repolarisation time. Both grand multiparity and longer RD are related to increased atrial conduction time. Reproductive factors that alter midlife cardiac electrical conduction system remodelling in women may modestly influence cardiovascular disease risk in later life.Trial registration numberNCT00000611; Post-results.

Background Cigarette smoking is a risk factor for atrial fibrillation (AF), but whether secondhand smoke (SHS) impacts the risk of AF remains unknown. Objective To determine if SHS exposure is associated with an increased risk of AF. Methods We performed a cross-sectional analysis of data from participants enrolled in the Health eHeart Study, an internet-based, longitudinal cardiovascular cohort study, who completed baseline SHS exposure and medical conditions questionnaires. SHS was assessed through a validated 22-question survey, and prevalent AF was assessed by self-report, with validation of a subset (n = 42) by review of electronic medical records. Results Of 4976 participants, 593 (11.9%) reported having AF. In unadjusted analyses, patients with AF were more likely to have been exposed to SHS in utero, as a child, as an adult, at home, and at work. After multivariable adjustment for potential confounders, having had a smoking parent during gestational development (OR 1.37, 95% CI 1.08–1.73, P = .009) and residing with a smoker during childhood (OR 1.40, 95% CI 1.10–1.79, P = .007) were each significantly associated with AF. Both positive associations were more pronounced among patients without risk factors for AF ( P values for interaction Conclusions SHS exposure during gestational development and during childhood was associated with having AF later in life. This association was even stronger in the absence of established risk factors for AF. Our findings indicate that SHS in early life may be an important, potentially modifiable risk factor for the development of AF.

PurposeThe effect of the heart failure substrate on the initiation of ventricular fibrillation (VF) and its resulting mechanism is not known. The objective of this study was to determine the effects of substrate on VF initiation and its spatiotemporal organization in the heart failure model.MethodsOptical action potentials were recorded from LV wedge preparations either from structurally normal hearts (control, n = 11) or from congestive heart failure (CHF; n = 7), at the epicardial surface, endocardial surface which included a papillary muscle, and a transmural cross section. Action potential duration (APD(80)) was determined, and VF was initiated. A fast Fourier transform was calculated, and the dominant frequency (DF) was determined.ResultsThe CHF group showed increased VF vulnerability (69 vs 26 %, p

Background— Advanced age is the most important risk factor for atrial fibrillation (AF); however, the mechanism remains unknown. Telomeres, regions of DNA that shorten with cell division, are considered reliable markers of biological aging. We sought to examine the association between leukocyte telomere length (LTL) and incident AF in a large population-based cohort using direct LTL measurements and genetic data. To further explore our findings, we compared atrial cell telomere length and LTL in cardiac surgery patients. Methods and Results— Mean LTL and the TERT rs2736100 single nucleotide polymorphism were assessed as predictors of incident AF in the Cardiovascular Health Study (CHS). Among the surgical patients, within subject comparison of atrial cell telomere length versus LTL was assessed. Among 1639 CHS participants, we observed no relationship between mean LTL and incident AF before and after adjustment for potential confounders (adjusted hazard ratio, 1.09; 95% confidence interval: 0.92–1.29; P =0.299); chronologic age remained strongly associated with AF in the same model. No association was observed between the TERT rs2736100 single nucleotide polymorphism and incident AF (adjusted hazard ratio: 0.95; 95% confidence interval: 0.88–1.04; P =0.265). In 35 cardiac surgery patients (26 with AF), atrial cell telomere length was longer than LTL (1.19±0.20 versus 1.02±0.25 [T/S ratio], P Conclusions— Our study revealed no evidence of an association between LTL and incident AF and no evidence of relative atrial cell telomere shortening in AF. Chronological aging independent of biological markers of aging is the primary risk factor for AF.