Comparison of On-Site Versus Remote Mobile Device Support in the Framingham Heart Study Using the Health eHeart Study for Digital Follow-up: Randomized Pilot Study Set Within an Observational Study Design

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 \u3e /=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 \u3e /=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

Design and Preliminary Findings From a New Electronic Cohort Embedded in the Framingham Heart Study

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

Relations Between BMI Trajectories and Habitual Physical Activity Measured by a Smartwatch in the Electronic Cohort of the Framingham Heart Study: Cohort Study

BackgroundThe 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. ObjectiveThis 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). MethodsWe 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 <30 days or <5 hours of smartwatch wear data. We used generalized linear models to examine the association between BMI trajectories and daily step counts. ResultsWe 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; P<.001), and group 3 included 77 participants whose BMI increased greatly (slope 0.318; P<.001). The median follow-up period for step count was 516 days. Adjusting for age, sex, wear time, and cohort, participants in groups 2 and 3 took 422 (95% CI –823 to –21) and 1437 (95% CI –2084 to –790) fewer average daily steps, compared with participants in group 1. After adjusting for metabolic and social risk factors, group 2 took 382 (95% CI –773 to 10) and group 3 took 1120 (95% CI –1766 to –475) fewer steps, compared with group 1. ConclusionsIn 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

Association of Habitual Physical Activity With Home Blood Pressure in the Electronic Framingham Heart Study (eFHS):Cross-sectional Study

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

Comparison of Daily Routines Between Middle-aged and Older Participants With and Those Without Diabetes in the Electronic Framingham Heart Study:Cohort Study

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; P<.001) and had 12 more minutes (95% CI 6-18; P<.001) in the variation of their estimated wake-up times, 6 more minutes (95% CI 2-9; P=.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