Search

BACKGROUND The prevalence of obesity is rising. Most previous studies that examined the relations between BMI and physical activity (PA) measured BMI at a single timepoint. The association between BMI trajectories and habitual PA remains unclear. OBJECTIVE This study assesses the relations between BMI trajectories and habitual step-based PA among participants enrolled in the electronic cohort of the Framingham Heart Study (eFHS). METHODS We used a semiparametric group-based modeling to identify BMI trajectories from eFHS participants who attended research examinations at the Framingham Research Center over 14 years. Daily steps were recorded from the smartwatch provided at examination 3. We excluded participants with RESULTS We identified 3 trajectory groups for the 837 eFHS participants (mean age 53 years; 57.8% [484/837] female). Group 1 included 292 participants whose BMI was stable (slope 0.005; P=.75), group 2 included 468 participants whose BMI increased slightly (slope 0.123; PP CONCLUSIONS In this community-based eFHS, participants whose BMI trajectory increased greatly over time took significantly fewer steps, compared with participants with stable BMI trajectories. Our findings suggest that greater weight gain may correlate with lower levels of step-based physical activity.

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: 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.

BACKGROUND Daily routines (eg, physical activity and sleep patterns) are important for diabetes self-management. Traditional research methods are not optimal for documenting long-term daily routine patterns in participants with glycemic conditions. Mobile health offers an effective approach for collecting users’ long-term daily activities and analyzing their daily routine patterns in relation to diabetes status. OBJECTIVE This study aims to understand how routines function in diabetes self-management. We evaluate the associations of daily routine variables derived from a smartwatch with diabetes status in the electronic Framingham Heart Study (eFHS). METHODS The eFHS enrolled the Framingham Heart Study participants at health examination 3 between 2016 and 2019. At baseline, diabetes was defined as fasting blood glucose level ≥126 mg/dL or as a self-report of taking a glucose-lowering medication; prediabetes was defined as fasting blood glucose level of 100-125 mg/dL. Using smartwatch data, we calculated the average daily step counts and estimated the wake-up times and bedtimes for the eFHS participants on a given day. We compared the average daily step counts and the intraindividual variability of the wake-up times and bedtimes of the participants with diabetes and prediabetes with those of the referents who were neither diabetic nor prediabetic, adjusting for age, sex, and race or ethnicity. RESULTS We included 796 participants (494/796, 62.1% women; mean age 52.8, SD 8.7 years) who wore a smartwatch for at least 10 hours/day and remained in the study for at least 30 days after enrollment. On average, participants with diabetes (41/796, 5.2%) took 1611 fewer daily steps (95% CI 863-2360; PPP=.005) in the variation of their estimated bedtimes compared with the referents (546/796, 68.6%) without diabetes or prediabetes. Participants with prediabetes (209/796, 26.2%) also walked fewer daily steps (P=.04) and had a larger variation in their estimated wake-up times (P=.04) compared with the referents. CONCLUSIONS On average, participants with diabetes at baseline walked significantly fewer daily steps and had larger variations in their wake-up times and bedtimes than the referent group. These findings suggest that modifying the routines of participants with poor glycemic health may be an important approach to the self-management of diabetes. Future studies should be designed to improve the remote monitoring and self-management of diabetes. CLINICALTRIAL

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.

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.

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 eCohort 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. OBJECTIVE The 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. METHODS We 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). RESULTS Among 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). CONCLUSIONS We 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.

BACKGROUND New 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. OBJECTIVE The 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. METHODS We 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. RESULTS Compared 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. CONCLUSIONS Our 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.

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)

Background: New models of scalable population-based data collection that integrate digital and mobile health (mHealth) data are necessary. Objective: The aim of this study was to describe a cardiovascular digital and mHealth electronic cohort (e-cohort) embedded in a traditional longitudinal cohort study, the Framingham Heart Study (FHS). Methods: We invited eligible and consenting FHS Generation 3 and Omni participants to download the electronic Framingham Heart Study (eFHS) app onto their mobile phones and co-deployed a digital blood pressure (BP) cuff. Thereafter, participants were also offered a smartwatch (Apple Watch). Participants are invited to complete surveys through the eFHS app, to perform weekly BP measurements, and to wear the smartwatch daily. Results: Up to July 2017, we enrolled 790 eFHS participants, representing 76% (790/1044) of potentially eligible FHS participants. eFHS participants were, on average, 53±8 years of age and 57% were women. A total of 85% (675/790) of eFHS participants completed all of the baseline survey and 59% (470/790) completed the 3-month survey. A total of 42% (241/573) and 76% (306/405) of eFHS participants adhered to weekly digital BP and heart rate (HR) uploads, respectively, over 12 weeks. Conclusions: We have designed an e-cohort focused on identifying novel cardiovascular disease risk factors using a new smartphone app, a digital BP cuff, and a smartwatch. Despite minimal training and support, preliminary findings over a 3-month follow-up period show that uptake is high and adherence to periodic app-based surveys, weekly digital BP assessments, and smartwatch HR measures is acceptable.

BACKGROUND New models of scalable population-based data collection that integrate digital and mobile health (mHealth) data are necessary. OBJECTIVE The aim of this study was to describe a cardiovascular digital and mHealth electronic cohort (e-cohort) embedded in a traditional longitudinal cohort study, the Framingham Heart Study (FHS). METHODS We invited eligible and consenting FHS Generation 3 and Omni participants to download the electronic Framingham Heart Study (eFHS) app onto their mobile phones and co-deployed a digital blood pressure (BP) cuff. Thereafter, participants were also offered a smartwatch (Apple Watch). Participants are invited to complete surveys through the eFHS app, to perform weekly BP measurements, and to wear the smartwatch daily. RESULTS Up to July 2017, we enrolled 790 eFHS participants, representing 76% (790/1044) of potentially eligible FHS participants. eFHS participants were, on average, 53±8 years of age and 57% were women. A total of 85% (675/790) of eFHS participants completed all of the baseline survey and 59% (470/790) completed the 3-month survey. A total of 42% (241/573) and 76% (306/405) of eFHS participants adhered to weekly digital BP and heart rate (HR) uploads, respectively, over 12 weeks. CONCLUSIONS We have designed an e-cohort focused on identifying novel cardiovascular disease risk factors using a new smartphone app, a digital BP cuff, and a smartwatch. Despite minimal training and support, preliminary findings over a 3-month follow-up period show that uptake is high and adherence to periodic app-based surveys, weekly digital BP assessments, and smartwatch HR measures is acceptable.

Background: New “e-Cohort” study designs provide resource-effective methods for collecting participant data. It is unclear if implementing an e-cohort without direct, in-person participant contact can achieve successful participation rates. The FHS-HeH randomized pilot study compared two distinct implementation strategies for co-enrolling participants from the Framingham Heart Study (FHS) into the Health eHeart Study, a digital cohort with infrastructure for collecting mHealth data. Methods: FHS participants who had an email address and smartphone were randomized to one of two approaches: remote vs. on-site support. In the remote arm, participants received an email containing an enrollment URL, and, upon enrollment, were sent four Bluetooth sensor devices. Participants in the on-site arm were invited to visit FHS and were provided in-person support for enrollment and connecting the devices. Results: Compared to participants that declined, individuals that accepted an invitation to participate in our pilot study (n=101 remote , n=101 on-site ) were more often women, highly educated, and younger (Figure 1). All on-site participants completed the consent, compared to 93% of the remote arm. Of participants who consented to participate, connection and initial use of devices was also higher in the on-site arm (100% connected the activity monitor, 94% the blood pressure cuff and scale, and 84% the electrocardiogram) compared to the remote arm (74%, 75%, 80%, and 42%). Roughly 75-78% of those that initially connected in both arms were still using the devices by the 3 rd month and 58-60% were still participating by the 6 th month. Conclusions: Our pilot study demonstrated that 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 to offering only remote support. Once connected, drop-off rates were similar in both groups.

Gamification, the application of game design elements such as points and levels in nongame contexts, is often used in digital health interventions, but evidence on its effectiveness is limited.To test the effectiveness of a gamification intervention designed using insights from behavioral economics to enhance social incentives within families to increase physical activity.The Behavioral Economics Framingham Incentive Trial (BE FIT) was a randomized clinical trial with a 12-week intervention period and a 12-week follow-up period. The investigation was a community-based study between December 7, 2015, and August 14, 2016. Participants in the modified intent-to-treat analysis were adults enrolled in the Framingham Heart Study, a long-standing cohort of families.All participants tracked daily step counts using a wearable device or a smartphone, established a baseline, selected a step goal increase, and received daily individual feedback on goal performance by text message or email for 24 weeks. Families in the gamification arm could earn points and progress through levels based on physical activity goal achievement during the 12-week intervention. The game design was meant to enhance collaboration, accountability, and peer support.The primary outcome was the proportion of participant-days that step goals were achieved during the intervention period. Secondary outcomes included the proportion of participant-days that step goals were achieved during the follow-up period and the change in the mean daily steps during the intervention and follow-up periods.Among 200 adults comprising 94 families, the mean age was 55.4 years, and 56.0% (n = 112) were female. During the intervention period, participants in the gamification arm achieved step goals on a significantly greater proportion of participant-days (0.53 vs 0.32; adjusted difference, 0.27; 95% CI, 0.20-0.33; P .001) and had a significantly greater increase in the mean daily steps compared with baseline (1661 vs 636; adjusted difference, 953; 95% CI, 505-1401; P .001) than the control arm. During the follow-up period, physical activity in the gamification arm declined but remained significantly greater than that in the control arm for the proportion of participant-days achieving step goals (0.44 vs 0.33; adjusted difference, 0.12; 95% CI, 0.05-0.19; P .001) and the mean daily steps compared with baseline (1385 vs 798; adjusted difference, 494; 95% CI, 170-818; P .01).Gamification designed to leverage insights from behavioral economics to enhance social incentives significantly increased physical activity among families in the community.clinicaltrials.gov Identifier: NCT02531763.

Background: Social networks can influence individual health behaviors, but interventions that leverage social incentives within these networks to change health behaviors have not been well examined. The objective of this study was to test the effectiveness of a social incentive-based gamification intervention to increase physical activity in the community. Methods: The Behavioral Economics Framingham Incentive Trial (BE FIT) was a randomized clinical trial that recruited 206 adults comprising 97 groups of two or three family members in the Framingham Heart Study and occurred between December 2015 and August 2016. Participants used a wearable device or smartphone application to establish a baseline step count and selected a step goal increase for a 12-week primary intervention period and a 12-week follow-up period. Participants in both the control and intervention arms received daily feedback on their performance for 24 weeks. During the first 12 weeks, participants in the intervention arm played a game (including points, levels, and lifelines) with their family members that was designed using insights from behavioral economics to enhance social incentives such as peer support, accountability, and collaboration. The primary outcome was the mean proportion of participant-days the step goal was achieved during the primary intervention period. Secondary outcomes included the mean proportion of participant-days the step goal was achieved during the follow-up period and mean daily steps during the intervention and follow-up periods. Results: Participants in the intervention arm achieved step goals on a greater proportion of participant-days and had greater mean daily steps than the control arm during the intervention and follow-up periods (TABLE). Conclusions: Among groups of family members in a community, a social incentive-based gamification intervention was effective at increasing physical activity during the 12-week intervention period and effects were sustained during the 12-week follow-up period.

Background New 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. Objective The 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. Methods We 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. Results Compared 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. Conclusions Our 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.

Rationale and Objectives Abdominal aortic calcification (AAC) can be quantified using computed tomography (CT), but imaging planes are prescribed based on bony landmarks, so that individual variation between the landmark and the aortoiliac junction can result in variable aortic coverage. In the Framingham CT substudy, we scanned a 15-cm (Z-direction) abdominal segment cranial to the S1 vertebral body. We sought to determine the range and distribution of length of aorta scanned and the distribution of AAC within the abdominal aorta and to compare burden of AAC measured from fixed-length segments versus AAC from all slices cranial to the aortoiliac bifurcation. Materials and Methods AAC was quantified by modified Agatston score (AS) in 100 Framingham Heart Study participants (60 ± 13 years old, 51 men). We compared the AS measured from 5-cm and 8-cm segments with the ASALL (total visualized aorta). Results Of 100, 73 participants had AAC >0. The total length of aorta imaged was ≥8 cm in 84% of participants. Qualitatively, 5-cm and 8-cm segments correctly identified 96% and 99%, respectively, of participants as having or not having AAC. Quantitatively, AS8cm was within 20% of ASALL in four-fifths and within 30% of ASALL in nine-tenths of participants. AS5cm more severely underestimated ASALL. Conclusion The use of S1 as the caudal imaging landmark in a 15-cm slab yields ≥8 cm aortic coverage in most adults. Both 5-cm and 8-cm analysis strategies are comparable to analyzing the total visualized abdominal aorta for prevalent AAC, but only 8-cm segment analysis yields quantitatively similar measures of AAC.

Arterial calcification is associated with cardiovascular morbidity and mortality. To improve the understanding of the pathogenesis involved with iliac artery calcium (IAC), we sought to examine the associations between the burden of IAC with adiposity measures and peripheral artery disease (PAD). Participants (n = 1,236, 52% women, mean age 60 years) were drawn from the Framingham Heart Study Offspring cohort who underwent multidetector computed tomography. The extent of IAC was quantified based on calcified atherosclerotic plaques detected in the iliac arteries. High IAC was defined based on gender-specific 90th percentile cut-off points from a healthy referent subsample. PAD is defined as an ankle-brachial index0.9, intermittent claudication, and/or history of lower extremity revascularization. The association between PAD and IAC was assessed using multivariable-adjusted logistic regression models. The burden of high IAC was 20.5% in women and 25.5% in men. High IAC was not associated with generalized (body mass index) or area-specific (waist circumference, and volumes of thoracic periaortic, abdominal subcutaneous, and visceral adipose tissue) adiposity measures (all p ≥0.22). High IAC was associated with increased odds of PAD (odds ratio 10.36, 95% confidence interval 4.28 to 25.09). This association persisted even after additionally adjusting for coronary artery calcium (odds ratio 11.25, 95% confidence interval 4.29 to 29.53). Burden of IAC was associated with an increased risk of PAD.

Abdominal aortic calcium (AAC) is associated with incident cardiovascular disease. However, the age- and gender-related distribution of AAC in a community-dwelling population free of standard cardiovascular disease risk factors has not been described. A total of 3,285 participants (aged 50.2 ± 9.9 years) in the Framingham Heart Study Offspring and Third Generation cohorts underwent abdominal multidetector computed tomography from 1998 to 2005. The presence and amount of AAC was quantified (Agatston score) by an experienced reader using standardized criteria. A healthy referent subsample (n = 1,656, 803 men) free of hypertension, hyperlipidemia, diabetes, obesity, and smoking was identified, and participants were stratified by gender and age (45, 45 to 54, 55 to 64, 65 to 74, and ≥75 years). The prevalence and burden of AAC increased monotonically and supra-linearly with age in both genders but was greater in men than in women in each age group. For those45 years old,16% of the referent subsample participants had any quantifiable AAC. However, for those65 years old, nearly 90% of the referent participants had0 AAC. Across the entire study sample, AAC prevalence and burden similarly increased with greater age. Defining the 90th percentile of the referent group AAC as "high," the prevalence of high AAC was 19% for each gender in the overall study sample. The AAC also increased across categories of 10-year coronary heart disease risk, as calculated using the Framingham Risk Score, in the entire study sample. We found AAC to be widely prevalent, with the burden of AAC associated with 10-year coronary risk, in a white, free-living adult cohort.

Objective— The objective of this study was to determine the prevalence and risk factor (RF) correlates of aortic plaque (AP) detected by cardiovascular magnetic resonance (CMR), which mainly shows noncalcified plaques, and by noncontrast computed tomography (CT), which best depicts calcified plaques, in community-dwelling adults. Approach and Results— A total of 1016 Framingham Heart Study Offspring cohort members (64±9 years; 474 men) underwent CMR and CT of the aorta. Potential RFs for AP (age; sex; body mass index; blood pressure; low-density lipoprotein and high-density lipoprotein cholesterol; fasting glucose; C-reactive protein; prevalent hypertension, diabetes mellitus, smoking; use of antihypertensive, diabetes mellitus, or lipid-lowering drugs) were compared between participants, with zero versus nonzero AP by CMR and by CT. Candidate RFs attaining P r =0.28, P Conclusions— AP by CMR and CT are both associated with smoking and increasing age, but other RFs differ between calcified and noncalcified AP. The relative predictive value of AP detected by CMR versus by CT for incident cardiovascular events remains to be determined.

Vascular calcium is well studied in the coronary and peripheral arteries, although there are limited data focusing on calcium deposits specific to renal arteries. The associations among renal artery calcium (RAC), cardiovascular disease risk factors, and indexes of renal function are unknown. We examined 2,699 Framingham Heart Study participants who were part of a multidetector computed tomography substudy from 2008 to 2011. RAC was measured as a calcified plaque of130 HU and an area of3 contiguous pixels. Detectable RAC was defined as an Agatston score0. Chronic kidney disease was defined as an estimated glomerular filtration rate of60 ml/min/1.73 m(2). Microalbuminuria was defined as an albumin/creatinine ratio of ≥17 mg/g for men and ≥25 mg/g for women. Multivariable adjusted logistic regression models were used to evaluate the associations between RAC, cardiovascular disease risk factors, and renal function. The associations were secondarily adjusted for coronary artery calcium (CAC) that was used as a marker of nonrenal systemic vascular calcium. The prevalence of RAC was 28.2%; this was similar in women (28.8%) and men (27.5%). Patients with RAC had a higher odds of microalbuminuria (odds ratio [OR] 1.79, 95% confidence interval [CI] 1.22 to 2.61, p = 0.003), hypertension (OR 2.11, 95% CI 1.69 to 2.64, p0.001), and diabetes (OR 1.60, 95% CI 1.14 to 2.24, p = 0.01) but not chronic kidney disease (OR 0.87, 95% CI 0.58 to 1.32). After adjustment for CAC, the association with microalbuminuria and hypertension persisted, but the association with diabetes became nonsignificant. In conclusion, RAC is common and independently associated with microalbuminuria and hypertension after adjustment for nonrenal vascular calcium. RAC may be uniquely associated with these markers of renal end-organ damage.

Coronary artery calcium (CAC) may improve risk stratification for coronary heart disease (CHD) beyond traditional risk factors. Participants from the Framingham Heart Study Offspring and Third Generation cohorts (48% women, mean age 53 years), underwent non-contrast electrocardiographically triggered cardiac multidetector computed tomography (MDCT). We determined the prevalence of absolute CAC (Agatston Score >0, >100, >400) and relative age and sex specific strata (25th, 50th, 75th, 90th, 95th percentile) in a healthy subset free of clinically apparent cardiovascular disease (CLINCVD) or CHD risk factors (n=1586), the overall sample at risk (n=3238), and participants at intermediate Framingham risk score (FRS, 6-20% 10 year CHD event risk) (n=1177). Absolute Agatston Score and relative cutpoints of CAC increased with age and FRS, was higher in men as compared to women in each of the three cohorts, and increased from the healthy subset to the overall cohort to subjects at intermediate risk. However, among subjects with CAC, there was substantial disagreement between absolute and relative cutpoints for labeling subjects as having elevated CAC. In general, more subjects were considered having elevated CAC using relative cutpoints, especially in women and younger participants. Fewer subjects at intermediate FRS have elevated CAC using comparable absolute vs. relative cutpoints (men: 32% Agatston Score >100 vs. 36% >75th percentile; women: 24% Agatston Score >100 vs. 34% >75th percentile). In conclusion, we provided the distribution of CAC in a healthy subset, the overall cohort, and subjects at intermediate risk from the Framingham Heart Study for both absolute and relative cutpoints of CAC. Absolute cutpoints underestimate the proportion of subjects with elevated CAC, specifically in women, younger persons, and persons at intermediate CHD risk.

Background— Parental premature cardiovascular disease (CVD) is a risk factor for coronary heart disease (CHD). We related validated parental premature CVD with the subclinical measures of coronary artery (CAC) and abdominal aortic (AAC) calcification in the community. Methods and Results— We studied 2 generations of Framingham Heart Study subjects who underwent multidetector computed tomography measurements of CAC and AAC and who had 2 parents in the study. Subjects included 797 Framingham Offspring (mean age, 63 years; 56% women) and 1238 Third Generation (Gen3) (mean age, 46 years; 47% women) participants free of CVD. Generalized estimating equations adjusted for major CVD risk factors were used to relate validated parental premature CVD and CHD to CAC and AAC, defined by >90th percentile age- and sex-specific cut points from a healthy subsample. Parental premature CVD was associated with CAC among Gen3 (odds ratio=2.17 [1.41 to 3.33]; P P =0.12). Parental premature CHD was associated with CAC among Gen3 (odds ratio=2.22 [1.22 to 4.01]) but not Offspring. Parental premature CVD was not associated with AAC in either cohort. Parental premature CHD was associated with AAC among Gen3 (odds ratio=1.65 [0.99 to 2.75]; P =0.05) but not among Offspring. The magnitude of risk conferred was greater for paternal than maternal premature CVD. Conclusions— Parental premature CVD is associated with CAC, and premature CHD is associated with AAC, after adjustment for risk factors, particularly in younger middle-aged adults. Risk conferred by parental premature CVD on vascular calcification may be mediated through novel mechanisms not accounted for by classic CVD risk factors known to cause atherosclerosis.

Background Although heart rate variability (HRV) is altered in a variety of pathological conditions, the association of reduced HRV with risk for new cardiac events has not been studied in a large community-based population. Methods and Results The first 2 hours of ambulatory ECG recordings obtained on subjects of the Framingham Heart Study who were free of clinically apparent coronary heart disease or congestive heart failure were reprocessed to assess HRV. Five frequency-domain measures and three time-domain measures were obtained. The associations between HRV measures and the incidence of new cardiac events (angina pectoris, myocardial infarction, coronary heart disease death, or congestive heart failure) were assessed with proportional hazards regression analyses. There were 2501 eligible subjects with a mean age of 53 years. During a mean follow-up of 3.5 years, cardiac events occurred in 58 subjects. After adjustment for age, sex, cigarette smoking, diabetes, left ventricular hypertrophy, and other relevant risk factors, all HRV measures except the ratio of low-frequency to high-frequency power were significantly associated with risk for a cardiac event ( P =.0016 to .0496). A one–standard deviation decrement in the standard deviation of total normal RR intervals (natural log transformed) was associated with a hazard ratio of 1.47 for new cardiac events (95% confidence interval of 1.16 to 1.86). Conclusions The estimation of HRV by ambulatory monitoring offers prognostic information beyond that provided by the evaluation of traditional cardiovascular disease risk factors.

BACKGROUND The prognostic implications of alterations in heart rate variability have not been studied in a large community-based population. METHODS AND RESULTS The first 2 hours of ambulatory ECG recordings obtained on original subjects of the Framingham Heart Study attending the 18th biennial examination were reprocessed to assess heart rate variability. Subjects with transient or persistent nonsinus rhythm, premature beats > 10% of total beats, < 1 hour of recording time, processed time < 50% of recorded time, and those taking antiarrhythmic medications were excluded. The associations between heart rate variability measures and all-cause mortality during 4 years of follow-up were assessed. There were 736 eligible subjects with a mean age (+/- SD) of 72 +/- 6 years. The following five frequency domain measures and three time domain measures were obtained: very-low-frequency power (0.01 to 0.04 Hz), low-frequency power (0.04 to 0.15 Hz), high-frequency power (0.15 to 0.40 Hz), total power (0.01 to 0.40 Hz), the ratio of low-frequency to high-frequency power, the standard deviation of total normal RR intervals, the percentage of differences between adjacent normal RR intervals that are > 50 milliseconds, and the square root of the mean of the squared differences between adjacent normal RR intervals. During follow-up, 74 subjects died. In separate proportional hazards regression analyses that adjusted for relevant risk factors, very-low-frequency power (P < .0001), low-frequency power (P < .0001), high-frequency power (P = .0014), total power (P < .0001), and the standard deviation of total normal RR intervals (P = .0019) were significantly associated with all-cause mortality. When all eight heart rate variability measures were assessed in a stepwise analysis that included other risk factors, low-frequency power entered the model first (P < .0001); thereafter, none of the other measures of heart rate variability significantly contributed to the prediction of all-cause mortality. A 1 SD decrement in low-frequency power (natural log transformed) was associated with 1.70 times greater hazard for all-cause mortality (95% confidence interval of 1.37 to 2.09). CONCLUSIONS The estimation of heart rate variability by ambulatory monitoring offers prognostic information beyond that provided by the evaluation of traditional risk factors.

Objectives. This study sought to examine clinical determinants of heart rate variability and to report normative reference values for eight heart rate variability measures. Background. Although the clinical implications of heart rate variability have been described, clinical determinants and normative values of heart rate variability measures have not been studied systematically in a large community-based population. Methods. The first 2 h of ambulatory electrocardiographic recordings obtained in Framingham Heart Study subjects attending a routine examination were reprocessed for heart rate variability. Recordings with transient or persistent nonsinus rhythm, premature beats >10% of total beats

Aortic and mitral stenosis are among the most common forms of valvular heart disease affecting the elderly. Valve calcification precedes clinical stenosis and may represent an important intermediate phenotype for valve disease.(1) Previously considered a degenerative consequence of aging, valve calcification and the resulting valvular stenosis are now recognized as “active” processes with marked histological similarities to atherosclerosis.(2–7) However, the failure of lipid-lowering strategies to prevent or slow the progression of valvular disease has raised questions about the role of atherosclerosis risk factors in valvular stenosis.(8–11) Improved understanding of the role of cardiovascular risk factors in valvular disease and the appropriate timing for their control could provide insights into the prevention of valvular disease. Atherosclerosis risk factors such as lipoproteins, cigarette smoking and metabolic syndrome have been associated with valvular calcium in several cross-sectional studies but in only few prospective studies. Prospective studies to date have been limited by short-term follow-up(2,12,13) and single assessments of risk factors(14) which may underestimate the long-term cumulative effects of cardiovascular risk factors on valve calcium. In addition, contemporary rates of treatment for cholesterol and other risk factors may attenuate the associations of valvular disease with risk factors. Prospective, longitudinal studies with repeated measurement of risk factors could overcome these limitations. Accordingly, using over 25 years of longitudinal data from the Framingham Offspring study, we sought to evaluate the association of long-term exposure to atherosclerosis risk factors and the prevalence of aortic valve and mitral valve calcium in a community-based sample. We also sought to establish the association between an adverse risk factor profile in early to mid-adulthood and valvular calcification measured nearly three decades later.