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Background When studied in community-based samples, the association of physical activity with blood pressure (BP) remains controversial and is perhaps dependent on the intensity of physical activity. Prior studies have not explored the association of smartwatch-measured physical activity with home BP. Objective We aimed to study the association of habitual physical activity with home BP. Methods Consenting electronic Framingham Heart Study (eFHS) participants were provided with a study smartwatch (Apple Watch Series 0) and Bluetooth-enabled home BP cuff. Participants were instructed to wear the watch daily and transmit BP values weekly. We measured habitual physical activity as the average daily step count determined by the smartwatch. We estimated the cross-sectional association between physical activity and average home BP using linear mixed effects models adjusting for age, sex, wear time, antihypertensive drug use, and familial structure. Results We studied 660 eFHS participants (mean age 53 years, SD 9 years; 387 [58.6%] women; 602 [91.2%] White) who wore the smartwatch 5 or more hours per day for 30 or more days and transmitted three or more BP readings. The mean daily step count was 7595 (SD 2718). The mean home systolic and diastolic BP (mmHg) were 122 (SD 12) and 76 (SD 8). Every 1000 increase in the step count was associated with a 0.49 mmHg lower home systolic BP (P=.004) and 0.36 mmHg lower home diastolic BP (P=.003). The association, however, was attenuated and became statistically nonsignificant with further adjustment for BMI. Conclusions In this community-based sample of adults, higher daily habitual physical activity measured by a smartwatch was associated with a moderate, but statistically significant, reduction in home BP. Differences in BMI among study participants accounted for the majority of the observed association.

Introduction: The prevalence of obesity is rising. Most previous studies that examined the relationship between body mass index (BMI) and physical activity measured BMI at a single time-point, ignoring the time-varying nature of BMI. The relationship between BMI trajectories and habitual physical activity in community settings remains unclear. Objective: To assess the relationship between BMI trajectories and habitual physical activity measured by daily steps from a smartwatch, among participants enrolled in the electronic Framingham Heart Study (eFHS). We hypothesized that participants whose BMI trajectories increased over a 14-year period prior to the step assessment take fewer daily steps, compared to participants who maintained stable BMI trajectories during the same time period. Methods: We used a semiparametric group-based modelling method to identify BMI trajectory patterns. Participants who attended exams 1, 2, and 3 were included in building the trajectories. Daily steps were recorded from the smartwatch provided at exam 3 with “active days” defined as days with ≥ 5watch wear-hours. We excluded participants with Results: We identified three trajectory groups for the 837 eFHS participants. Group 1 included 292 participants (mean age 54 years, 57% women) whose BMI was stable (slope: 0.005, p=0.75); Group 2 included 468 participants (mean age 53 years, 56% women) whose BMI increased slightly (slope: 0.123, p Conclusion: Participants whose BMI trajectory increased over time took significantly fewer steps compared to participants with more stable BMI trajectories. Our findings suggest that levels of physical activity may correlate with greater weight gain during adulthood.

Introduction: Indices of cardiac structure and function, such as left ventricular (LV) mass and ejection fraction (EF), have individually been associated with development of incident heart failure (HF). Identifying HF risk groups (or phenogroups) based on imaging parameters may help inform distinct imaging phenotypes to target for prevention. Objectives: To identify phenogroups based on cardiovascular magnetic resonance imaging (cMRI) parameters in participants free of cardiovascular disease at baseline and examine longitudinal risk of incident HF. Methods: We included participants from the Multi-Ethnic Study of Atherosclerosis (MESA) who had completed baseline cMRI between 2000 and 2002. We applied latent class analysis to define phenogroups based on cMRI parameters of right ventricular and LV structure and function (including circumferential strain) at baseline (using the Bayesian Information Criterion as well as visual inspection). We used Cox-proportional hazard models to assess the association between membership in a phenogroup and risk of incident HF adjusted for age, sex, race, hypertension, diabetes, and hyperlipidemia. Results: Of 1484 participants, 48% were female and mean age was 65 (10) years. Over a median follow-up of 13 years, a total of 91 incident HF events occurred. We identified 3 distinct phenogroups that differed in terms of cardiac structure and function. Adjusted hazards ratio (95% confidence interval) for incident HF was higher among participants in Group 3 (2.08 [1.17, 3.71]) and Group 2 (2.24 [1.15, 4.37]), compared with Group 1 as the referent ( Table ). Conclusion: Phenogroups based on cMRI were differentially associated with risk of HF. Classification of cardiovascular imaging phenogroups integrating traditional and strain parameters within the normal range may be useful in identifying risk for HF; further validation is required before implementation.

Introduction: Long-term use is critical for successful clinical or research applications of digital devices, but digital health studies are challenged by significant early discontinuation of use. Hypothesis: We sought to identify factors associated with long-term use of a smartwatch among participants enrolled in the electronic Framingham Heart Study (eFHS). We hypothesized that sociodemographic and health variables are associated with watch use. Methods: Participants were provided with a study smartwatch and were asked to wear the watch daily. We examined watch use over 12 months. Weekly watch use was defined as a binary response (yes=watch wear for ≥1 days for ≥ 5 hours per day, vs. no). We considered 19 different predictors including sociodemographic, health behaviors, and family relationship. We selected an individual predictor for watch use ( P Results: Among 1243 participants (mean age 53 years, 59% women), watch use was highest in the age-group ≥65 years, and decreased linearly over time in all participants (Figure). In GLMM adjusted for age, sex, and weeks, we found that self-reported health status (excellent vs good, fair or poor) (OR=2.7; 95%CI, 1.6-4.8), BMI (OR=0.9; 95%CI, 0.9-1.0) per 1 kg/m 2 increase, and depressive symptoms (OR=0.5; 95%CI, 0.3-0.8) were associated with watch use. In the model with all selected predictors, age-group > 65 (OR=4.0; 95%CI, 1.4 -11.6), excellent health (OR=2.3; 95%CI, 1.3-4.1), and depressive symptoms (OR=0.5; 95%CI, 0.3-0.9) remained significant. Conclusions: Older age, lack of depressive symptoms, and self-reported excellent health were associated with greater use of the smartwatch over the 12-month follow-up. Consideration of these factors in planning future digital studies may improve participation.

Introduction: Peripheral artery disease (PAD) affects 10-15% of people age 65 and older, and the prevalence is expected to rise as the population ages. People with PAD have greater functional impairment and faster decline in walking performance than people without PAD. Objectives: To determine the association between 1-year change in walking performance and mobility loss. We hypothesized that greater declines in walking performance over one year would be associated with higher rates of mobility loss. Methods: Participants underwent measurement of 6-minute walk and 4-meter walking velocity at baseline, and returned yearly for repeat measurement of walking performance and assessment of mobility. Participants were categorized into tertiles based on their 1-year change in walking performance (Tertile 1: greatest decline). Mobility loss was defined as becoming newly unable to walk one-quarter mile or walk up and down 1 flight of stairs without assistance after the 1-year follow-up. We used Cox proportional hazards models to examine the association between 1-year change in walking performance and mobility loss, adjusting for potential confounders. Results: 907 participants with PAD (mean age 71 +/- 9 years, 40% female, 23% black) were included. Median follow-up time was 38 months. Participants in Tertile 1 were older (mean 73 ± 9 years compared to 71 ± 9 in Tertile 2, and 70 ± 9 in Tertile 3; p=0.0004), had lower ABI (p=0.001), and included a higher prevalence of female (42% vs. 40% in Tertile 2, and 37% in Tertile 3; p=0.36). Participants with greater decline in 6-minute walk at 1-year follow-up had higher rates of mobility loss than participants with less decline. No significant associations of change in 4-meter walking speed and mobility loss were observed (Table). Conclusion: Among people with PAD, greater 1-year declines in six-minute walk distance are associated with higher rates of mobility loss. Further study is needed to determine whether interventions that prevent decline in six-minute walk can also prevent mobility loss.

Questionnaires that assess dietary habits, eating behaviors, and relevant psychosocial constructs are routinely used in obesity research and clinical practice. The 6 factor questionnaire (6FQ) was previously developed as an assessment tool for psycho-behavioral phenotyping. The primary purpose of this study was to confirm and validate the original findings in a large diverse adult population.A total of 5399 self-selected participants (mean age of 48 ± 13 years and body mass index of 32 ± 8 kg/mMean factor score and odds ratio analyses consistently demonstrated a statistically significant relationship between factors and body weight even after adjusting for age, sex, and race/ethnicity.Although the study was correlational in design, the results demonstrate that the 6FQ, an instrument that represents multidimensional unhealthful lifestyle patterns associated with diet, physical activity, cognition, and self-perception worsen with increasing body weight. Psycho-behavioral phenotyping may be a useful approach when assessing and treating patients with obesity.

BackgroundeCohort studies offer an efficient approach for data collection. However, eCohort studies are challenged by volunteer bias and low adherence. We designed an eCohort embedded in the Framingham Heart Study (eFHS) to address these challenges and to compare the digital data to traditional data collection.ObjectiveThe aim of this study was to evaluate adherence of the eFHS app-based surveys deployed at baseline (time of enrollment in the eCohort) and every 3 months up to 1 year, and to compare baseline digital surveys with surveys collected at the research center.MethodsWe defined adherence rates as the proportion of participants who completed at least one survey at a given 3-month period and computed adherence rates for each 3-month period. To evaluate agreement, we compared several baseline measures obtained in the eFHS app survey to those obtained at the in-person research center exam using the concordance correlation coefficient (CCC).ResultsAmong the 1948 eFHS participants (mean age 53, SD 9 years; 57% women), we found high adherence to baseline surveys (89%) and a decrease in adherence over time (58% at 3 months, 52% at 6 months, 41% at 9 months, and 40% at 12 months). eFHS participants who returned surveys were more likely to be women (adjusted odds ratio [aOR] 1.58, 95% CI 1.18-2.11) and less likely to be smokers (aOR 0.53, 95% CI 0.32-0.90). Compared to in-person exam data, we observed moderate agreement for baseline app-based surveys of the Physical Activity Index (mean difference 2.27, CCC=0.56), and high agreement for average drinks per week (mean difference 0.54, CCC=0.82) and depressive symptoms scores (mean difference 0.03, CCC=0.77).ConclusionsWe observed that eFHS participants had a high survey return at baseline and each 3-month survey period over the 12 months of follow up. We observed moderate to high agreement between digital and research center measures for several types of surveys, including physical activity, depressive symptoms, and alcohol use. Thus, this digital data collection mechanism is a promising tool to collect data related to cardiovascular disease and its risk factors.

Rationale: A sedentary lifestyle is associated with increased risk for cardiovascular disease (CVD). Smartwatches enable accurate daily activity monitoring for physical activity measurement and intervention. Few studies, however, have examined physical activity measures from smartwatches in relation to traditional risk factors associated with future risk for CVD. Objective: To investigate the association of habitual physical activity measured by smartwatch with predicted CVD risk in adults. Methods and Results: We enrolled consenting FHS (Framingham Heart Study) participants in an ongoing eFHS (electronic Framingham Heart Study) at the time of their FHS research center examination. We provided participants with a smartwatch (Apple Watch Series 0) and instructed them to wear it daily, which measured their habitual physical activity as the average daily step count. We estimated the 10-year predicted risk of CVD using the American College of Cardiology/American Heart Association 2013 pooled cohort risk equation. We estimated the association between physical activity and predicted risk of CVD using linear mixed effects models adjusting for age, sex, wear time, and familial structure. Our study included 903 eFHS participants (mean age 53±9 years, 61% women, 9% non-White) who wore the smartwatch ≥5 hours per day for ≥30 days. Median daily step count was similar among men (7202 with interquartile range 3619) and women (7260 with interquartile range 3068; P =0.52). Average 10-year predicted CVD risk was 4.5% (interquartile range, 6.1%) for men and 1.2% (interquartile range, 2.2%) for women ( P =1.3×10 −26 ). Every 1000 steps higher habitual physical activity was associated with 0.18% lower predicted CVD risk ( P =3.2×10 −4 ). The association was attenuated but remained significant after further adjustment for body mass index ( P =0.01). Conclusions: In this community-based sample of adults, higher daily physical activity measured by a study smartwatch was associated with lower predicted risk of CVD. Future research should examine the longitudinal association of prospectively measured daily activity and incident CVD.

Background Limited data exist on the differential ability of variables on transthoracic echocardiogram (TTE) to predict heart failure (HF) readmission across the spectrum of left ventricular (LV) systolic function. Methods We linked 15 years of TTE report data (1/6/2003-5/3/2018) at Beth Israel Deaconess Medical Center to complete Medicare claims. In those with recent HF, we evaluated the relationship between variables on baseline TTE and HF readmission, stratified by LVEF. Results After excluding TTEs with uninterpretable diastology, 5,900 individuals (mean age: 76.9 years; 49.1% female) were included, of which 2545 individuals (41.6%) were admitted for HF. Diastolic variables augmented prediction compared to demographics, comorbidities, and echocardiographic structural variables (p < 0.001), though discrimination was modest (c-statistic = 0.63). LV dimensions and eccentric hypertrophy predicted HF in HF with reduced (HFrEF) but not preserved (HFpEF) systolic function, whereas LV wall thickness, NT-proBNP, pulmonary vein D- and Ar-wave velocities, and atrial dimensions predicted HF in HFpEF but not HFrEF (all interaction p < 0.10). Prediction of HF readmission was not different in HFpEF and HFrEF (p = 0.93). Conclusions In this single-center echocardiographic study linked to Medicare claims, left ventricular dimensions and eccentric hypertrophy predicted HF readmission in HFrEF but not HFpEF and left ventricular wall thickness predicted HF readmission in HFpEF but not HFrEF. Regardless of LVEF, diastolic variables augmented prediction of HF readmission compared to echocardiographic structural variables, demographics, and comorbidities alone. The additional role of medication adherence, readmission history, and functional status in differential prediction of HF readmission by LVEF category should be considered for future study.

A sedentary lifestyle is associated with increased risk for hypertension. Smartwatches enable accurate measurement of habitual physical activity. We hypothesize that higher habitual physical activity is associated with lower home blood pressure (BP). Electronic Framingham Heart Study (eFHS)