Defining continuous glucose monitor time in range in a large community-based cohort without diabetes.

CONTEXT: Continuous glucose monitor (CGM) companies are beginning to market these sensors to populations without diabetes, but the range of CGM values clinicians should expect to see for this population is unclear because there have been no large studies reporting these ranges. OBJECTIVE: To report the physiological range of continuous glucose monitor (CGM) time in range values observed across glycemic status, including individuals without diabetes, to serve as a reference for clinicians. DESIGN: The Framingham Heart Study, a prospective cohort study. SETTING: Community-dwelling individuals. PATIENTS OR PARTICIPANTS: Adults with normoglycemia (n=560), prediabetes (n=463), and diabetes (n=152). INTERVENTION: We conducted a cross-sectional investigation in participants who wore a Dexcom G6 Pro CGM for ≥7 complete days. MAIN OUTCOME MEASURES: CGM metrics including mean glucose and time spent in glucose ranges. RESULTS: Normoglycemic participants (mean age 58.5y, 64.5% women, 93.3% non-Hispanic white) spent 87.0% time in the 70-140mg/dL CGM range, and, on average, >15min/day (1.2% time) >180mg/dL. Furthermore, normoglycemic participants spent ∼3 hours/day (12.1% time) with CGM glucose >140mg/dL. On average, participants with prediabetes and diabetes spent 77.1% and 46.2% time in 70-140mg/dL, respectively. CONCLUSIONS: Our results contribute to the understanding of the physiological CGM range in >1000 participants without diabetes. These results are also important for clinicians to reference as CGM sensors become more widely accessible to people without known diabetes.

Associations of Physical Activity Engagement with Cerebral Amyloid-ß and Tau from Midlife.

Background: Higher midlife physical activity engagement has been associated with lower dementia risk in late life. However, the underlying mechanisms contributing to the protective effect remain unclear. Objective: The goal of the current study was to evaluate the associations of physical activity with cerebral amyloid-ß (Aß) and tau in a predominately middle-aged community-based cohort, as well as to explore whether the associations differ by sex or age. Methods: Participants from the Framingham Heart Study underwent 11C-Pittsburgh Compound B amyloid and 18F-Flortaucipir tau positron emission tomography (PET) imaging. Total physical activity levels were evaluated by self-report using the Physical Activity Index (PAI). Cross-sectional associations between total PAI with regional Aß and tau PET retention were evaluated using linear regression models adjusted for demographic and cardiovascular risk factors. Interactions with sex and age group were examined and stratified analyses were performed when significant. FDR-correction for multiple comparisons was applied. Results: The sample included 354 participants (mean age 53±8 years, 51% female). Higher total PAI scores were associated with lower entorhinal cortex tau PET binding (ß (SE) = -0.021(0.008), p = 0.049). There were significant interactions with sex. In men alone, total PAI inversely associated with entorhinal cortex (ß (SE) = -0.035(0.009), p = 0.001), inferior temporal (ß (SE) = -0.029(0.010), p = 0.012), and rhinal cortex tau(ß (SE) = -0.033(0.010), p = 0.002). Conclusions: The results suggest that higher midlife physical activity engagement may confer resistance to tau pathology. However, the effects may vary based on sex, highlighting the importance of better understanding and tailoring lifestyle interventions to address sex disparities.

Agreement Between Apple Watch and Actical Step Counts in a Community Setting: Cross-Sectional Investigation From the Framingham Heart Study.

BACKGROUND: Step counting is comparable among many research-grade and consumer-grade accelerometers in laboratory settings. OBJECTIVE: The purpose of this study was to compare the agreement between Actical and Apple Watch step-counting in a community setting. METHODS: Among Third Generation Framingham Heart Study participants (N=3486), we examined the agreement of step-counting between those who wore a consumer-grade accelerometer (Apple Watch Series 0) and a research-grade accelerometer (Actical) on the same days. Secondarily, we examined the agreement during each hour when both devices were worn to account for differences in wear time between devices. RESULTS: We studied 523 participants (n=3223 person-days, mean age 51.7, SD 8.9 years; women: n=298, 57.0%). Between devices, we observed modest correlation (intraclass correlation [ICC] 0.56, 95% CI 0.54-0.59), poor continuous agreement (29.7%, n=957 of days having steps counts with ≤15% difference), a mean difference of 499 steps per day higher count by Actical, and wide limits of agreement, roughly ±9000 steps per day. However, devices showed stronger agreement in identifying who meets various steps per day thresholds (eg, at 8000 steps per day, kappa coefficient=0.49), for which devices were concordant for 74.8% (n=391) of participants. In secondary analyses, in the hours during which both devices were worn (n=456 participants, n=18,760 person-hours), the correlation was much stronger (ICC 0.86, 95% CI 0.85-0.86), but continuous agreement remained poor (27.3%, n=5115 of hours having step counts with ≤15% difference) between devices and was slightly worse for those with mobility limitations or obesity. CONCLUSIONS: Our investigation suggests poor overall agreement between steps counted by the Actical device and those counted by the Apple Watch device, with stronger agreement in discriminating who meets certain step thresholds. The impact of these challenges may be minimized if accelerometers are used by individuals to determine whether they are meeting physical activity guidelines or tracking step counts. It is also possible that some of the limitations of these older accelerometers may be improved in newer devices.

Cardiorespiratory Fitness Is Associated with Decreased Platelet Reactivity.

PURPOSE: Platelets are key mediators in cardiovascular disease (CVD). Low cardiorespiratory fitness (CRF) is a risk factor for CVD. The purpose of our study was to assess if CRF associates with platelet function. METHODS: Platelet assays and cardiopulmonary exercise testing were conducted in the Framingham Heart Study ( N = 3014). Linear mixed effects models estimated associations between CRF (assessed by peak oxygen uptake [V̇O 2 ]) and multiple platelet reactivity assays. Models were adjusted for multiple medications, risk factors, relatedness, and prevalent CVD. RESULTS: Nineteen associations passed the significance threshold in the fully adjusted models, all indicating higher CRF associated with decreased platelet reactivity. Significant traits spanned multiple platelet agonists. Strongest associations were observed in multiplate whole blood testing after TRAP-6 (e.g., velocity, beta = -0.563, 95% CI = -0.735 to -0.391, P = 1.38E-10), ADP (e.g., velocity, beta = -0.514, 95% CI = -0.681 to -0348, P = 1.41E-09), collagen (e.g., velocity, beta = -0.387, 95% CI = -0.549 to -0.224, P = 3.01E-06), ristocetin (e.g., AUC, beta = -0.365, 95% CI = -0.522 to -0.208, P = 5.17E-06) and arachidonic acid stimulation of platelets (e.g., velocity, beta = -0.298, 95% CI = -0.435 to -0.162, P = 3.39E-04), and light transmission aggregometry (LTA) after ristocetin stimulation (e.g., max aggregation, beta = -0.362, 95% CI = -0.540 to -0.184, P = 6.64E-05). One trait passed significance threshold in the aspirin subsample (LTA ristocetin primary slope, beta = -0.733, 95% CI = -1.134 to -0.333, P = 3.30E-04) and another in a model including von Willebrand Factor levels as a covariate (U46619, a thromboxane receptor mimetic, AUC in the Optimul assay, beta = -0.36, 95% CI = -0.551 to -0.168, P = 2.35E-04). No strong interactions were observed between the associations and sex, age, or body mass index in formal interaction analyses. CONCLUSIONS: Our findings build on past work that shows CRF to be associated with reduced CVD by suggesting decreased platelet reactivity may play a mechanistic role. We found significant associations with multiple platelet agonists, indicating higher CRF may globally inhibit platelets; however, given multiple strong associations after TRAP-6 and ADP stimulation, PAR-1 and purinergic signaling may be most heavily involved. This is notable because each of these receptor pathways are tied to anticoagulant (DOAC/thrombin inhibitors) and antiplatelet therapies (P2Y12/PAR1/PAR4 inhibitors) for CVD prevention.

Association of Smartwatch-Based Heart Rate and Physical Activity With Cardiorespiratory Fitness Measures in the Community: Cohort Study.

BACKGROUND: Resting heart rate (HR) and routine physical activity are associated with cardiorespiratory fitness levels. Commercial smartwatches permit remote HR monitoring and step count recording in real-world settings over long periods of time, but the relationship between smartwatch-measured HR and daily steps to cardiorespiratory fitness remains incompletely characterized in the community. OBJECTIVE: This study aimed to examine the association of nonactive HR and daily steps measured by a smartwatch with a multidimensional fitness assessment via cardiopulmonary exercise testing (CPET) among participants in the electronic Framingham Heart Study. METHODS: Electronic Framingham Heart Study participants were enrolled in a research examination (2016-2019) and provided with a study smartwatch that collected longitudinal HR and physical activity data for up to 3 years. At the same examination, the participants underwent CPET on a cycle ergometer. Multivariable linear models were used to test the association of CPET indices with nonactive HR and daily steps from the smartwatch. RESULTS: We included 662 participants (mean age 53, SD 9 years; n=391, 59% women, n=599, 91% White; mean nonactive HR 73, SD 6 beats per minute) with a median of 1836 (IQR 889-3559) HR records and a median of 128 (IQR 65-227) watch-wearing days for each individual. In multivariable-adjusted models, lower nonactive HR and higher daily steps were associated with higher peak oxygen uptake (VO2), % predicted peak VO2, and VO2 at the ventilatory anaerobic threshold, with false discovery rate (FDR)-adjusted P values <.001 for all. Reductions of 2.4 beats per minute in nonactive HR, or increases of nearly 1000 daily steps, corresponded to a 1.3 mL/kg/min higher peak VO2. In addition, ventilatory efficiency (VE/VCO2; FDR-adjusted P=.009), % predicted maximum HR (FDR-adjusted P<.001), and systolic blood pressure-to-workload slope (FDR-adjusted P=.01) were associated with nonactive HR but not associated with daily steps. CONCLUSIONS: Our findings suggest that smartwatch-based assessments are associated with a broad array of cardiorespiratory fitness responses in the community, including measures of global fitness (peak VO2), ventilatory efficiency, and blood pressure response to exercise. Metrics captured by wearable devices offer a valuable opportunity to use extensive data on health factors and behaviors to provide a window into individual cardiovascular fitness levels.

Characterizing longitudinal change in accelerometry-based moderate-to-vigorous physical activity in the Hispanic Community Health Study/Study of Latinos and the Framingham Heart Study.

BACKGROUND: Physical activity promotes health and is particularly important during middle and older age for decreasing morbidity and mortality. We assessed the correlates of changes over time in moderate-to-vigorous physical activity (MVPA) in Hispanic/Latino adults from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL: mean [SD] age 49.2 y [11.5]) and compared them to a cohort of primarily White adults from the Framingham Heart Study (FHS: mean [SD] 46.9 y [9.2]). METHODS: Between 2008 and 2019, we assessed accelerometry-based MVPA at two time points with an average follow-up of: 7.6 y, SD 1.3 for HCHS/SOL, and 7.8 y, SD 0.7 for FHS. We used multinomial logistic regression to relate socio-demographic and health behaviors with changes in compliance with 2018 US recommendations for MVPA from time 1 to time 2 (remained active or inactive; became active or inactive) across the two cohorts. RESULTS: In HCHS/SOL mean MVPA was 22.6 (SD, 23.8) minutes at time 1 and dropped to 16.7 (19.0) minutes at time 2. In FHS Mean MVPA was 21.7 min (SD, 17.7) at time 1 and dropped to 21.3 min (SD, 19.2) at time 2. Across both cohorts, odds of meeting MVPA guidelines over time were about 6% lower in individuals who had lower quality diets vs. higher, about half in older vs. younger adults, about three times lower in women vs. men, and 9% lower in individuals who had a higher vs. lower BMI at baseline. Cohorts differed in how age, gender, income, education, depressive symptoms, marital status and perception of general health and pain associated with changes in physical activity. High income older Hispanics/Latino adults were more likely to become inactive at the follow-up visit as were HCHS/SOL women who were retired and FHS participants who had lower levels of education and income. Higher depressive symptomology was associated with becoming active only in HCHS/SOL women. Being male and married was associated with becoming inactive in both cohorts. Higher perception of general health and lower perception of pain were associated with remaining active only in FHS adults. CONCLUSIONS: These findings highlight potentially high-risk groups for targeted MVPA intervention.

Association of Physical Inactivity with MRI Markers of Brain Aging: Assessing Mediation by Cardiometabolic and Epigenetic Factors.

INTRODUCTION: Cardiometabolic risk factors and epigenetic patterns, increased in physically inactive individuals, are associated with an accelerated brain aging process. OBJECTIVE: To determine whether cardiometabolic risk factors and epigenetic patterns mediate the association of physical inactivity with unfavorable brain morphology. METHODS: We included dementia and stroke free participants from the Framingham Heart Study Third Generation and Offspring cohorts who had accelerometery and brain MRI data (n = 2,507, 53.9% women, mean age 53.9 years). We examined mediation by the 2017-revised Framingham Stroke Risk Profile (FSRP, using weights for age, cardiovascular disease, atrial fibrillation, diabetes and smoking status, antihypertension medications, and systolic blood pressure) and the homeostatic model of insulin resistance (HOMA-IR) in models of the association of physical inactivity with brain aging, adjusting for age, age-squared, sex, accelerometer wear time, cohort, time from exam-to-MRI, and season. We similarly assessed mediation by an epigenetic age-prediction algorithm, GrimAge, in a smaller sample of participants who had DNA methylation data (n = 1,418). RESULTS: FSRP and HOMA-IR explained 8.3-20.5% of associations of higher moderate-to-vigorous physical activity (MVPA), higher steps, and lower sedentary time with higher brain volume. Additionally, FSRP and GrimAge explained 10.3-22.0% of associations of physical inactivity with lower white matter diffusivity and FSRP explained 19.7% of the association of MVPA with lower free water accumulation. CONCLUSION: Our results suggest that cardiometabolic risk factors and epigenetic patterns partially mediate the associations of physical inactivity with lower brain volume, higher white matter diffusivity, and aggregation of free water in the extracellular compartments of the brain.

Association of Depressive Symptom Trajectory With Physical Activity Collected by mHealth Devices in the Electronic Framingham Heart Study: Cohort Study.

BACKGROUND: Few studies have examined the association between depressive symptom trajectories and physical activity collected by mobile health (mHealth) devices. OBJECTIVE: We aimed to investigate if antecedent depressive symptom trajectories predict subsequent physical activity among participants in the electronic Framingham Heart Study (eFHS). METHODS: We performed group-based multi-trajectory modeling to construct depressive symptom trajectory groups using both depressive symptoms (Center for Epidemiological Studies-Depression [CES-D] scores) and antidepressant medication use in eFHS participants who attended 3 Framingham Heart Study research exams over 14 years. At the third exam, eFHS participants were instructed to use a smartphone app for submitting physical activity index (PAI) surveys. In addition, they were provided with a study smartwatch to track their daily step counts. We performed linear mixed models to examine the association between depressive symptom trajectories and physical activity including app-based PAI and smartwatch-collected step counts over a 1-year follow-up adjusting for age, sex, wear hour, BMI, smoking status, and other health variables. RESULTS: We identified 3 depressive symptom trajectory groups from 722 eFHS participants (mean age 53, SD 8.5 years; n=432, 60% women). The low symptom group (n=570; mean follow-up 287, SD 109 days) consisted of participants with consistently low CES-D scores, and a small proportion reported antidepressant use. The moderate symptom group (n=71; mean follow-up 280, SD 118 days) included participants with intermediate CES-D scores, who showed the highest and increasing likelihood of reporting antidepressant use across 3 exams. The high symptom group (n=81; mean follow-up 252, SD 116 days) comprised participants with the highest CES-D scores, and the proportion of antidepressant use fell between the other 2 groups. Compared to the low symptom group, the high symptom group had decreased PAI (mean difference -1.09, 95% CI -2.16 to -0.01) and the moderate symptom group walked fewer daily steps (823 fewer, 95% CI -1421 to -226) during the 1-year follow-up. CONCLUSIONS: Antecedent depressive symptoms or antidepressant medication use was associated with lower subsequent physical activity collected by mHealth devices in eFHS. Future investigation of interventions to improve mood including via mHealth technologies to help promote people's daily physical activity is needed.

Physical Activity Over the Lifecourse and Cardiovascular Disease.

Despite improvements in cardiovascular care in recent decades, cardiovascular disease (CVD) remains a leading cause of death worldwide. At its core, CVD is a largely preventable disease with diligent risk factor management and early detection. As highlighted in the American Heart Association's Life's Essential 8, physical activity plays a central role in CVD prevention at an individual and population level. Despite pervasive knowledge of the numerous cardiovascular and noncardiovascular health benefits of physical activity, physical activity has steadily decreased over time and unfavorable changes in physical activity occur throughout people's lives. Here, we use a lifecourse framework to examine the evidence reporting on the association of physical activity with CVD. From in utero to older adults, we review and discuss the evidence detailing how physical activity may prevent incident CVD and mitigate CVD-related morbidity and death across all life stages.

Step Count, Self-reported Physical Activity, and Predicted 5-Year Risk of Atrial Fibrillation: Cross-sectional Analysis.

BACKGROUND: Physical inactivity is a known risk factor for atrial fibrillation (AF). Wearable devices, such as smartwatches, present an opportunity to investigate the relation between daily step count and AF risk. OBJECTIVE: The objective of this study was to investigate the association between daily step count and the predicted 5-year risk of AF. METHODS: Participants from the electronic Framingham Heart Study used an Apple smartwatch. Individuals with diagnosed AF were excluded. Daily step count, watch wear time (hours and days), and self-reported physical activity data were collected. Individuals' 5-year risk of AF was estimated, using the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)-AF score. The relation between daily step count and predicted 5-year AF risk was examined via linear regression, adjusting for age, sex, and wear time. Secondary analyses examined effect modification by sex and obesity (BMI≥30 kg/m2), as well as the relation between self-reported physical activity and predicted 5-year AF risk. RESULTS: We examined 923 electronic Framingham Heart Study participants (age: mean 53, SD 9 years; female: n=563, 61%) who had a median daily step count of 7227 (IQR 5699-8970). Most participants (n=823, 89.2%) had a <2.5% CHARGE-AF risk. Every 1000 steps were associated with a 0.08% lower CHARGE-AF risk (P<.001). A stronger association was observed in men and individuals with obesity. In contrast, self-reported physical activity was not associated with CHARGE-AF risk. CONCLUSIONS: Higher daily step counts were associated with a lower predicted 5-year risk of AF, and this relation was stronger in men and participants with obesity. The utility of a wearable daily step counter for AF risk reduction merits further investigation.

Increasing Engagement in the Electronic Framingham Heart Study: Factorial Randomized Controlled Trial.

BACKGROUND: Smartphone apps and mobile health devices offer innovative ways to collect longitudinal cardiovascular data. Randomized evidence regarding effective strategies to maintain longitudinal engagement is limited. OBJECTIVE: This study aimed to evaluate smartphone messaging interventions on remote transmission of blood pressure (BP) and heart rate (HR) data. METHODS: We conducted a 2 × 2 × 2 factorial blinded randomized trial with randomization implemented centrally to ensure allocation concealment. We invited participants from the Electronic Framingham Heart Study (eFHS), an e-cohort embedded in the FHS, and asked participants to measure their BP (Withings digital cuff) weekly and wear their smartwatch daily. We assessed 3 weekly notification strategies to promote adherence: personalized versus standard; weekend versus weekday; and morning versus evening. Personalized notifications included the participant's name and were tailored to whether or not data from the prior week were transmitted to the research team. Intervention notification messages were delivered weekly automatically via the eFHS app. We assessed if participants transmitted at least one BP or HR measurement within 7 days of each notification after randomization. Outcomes were adherence to BP and HR transmission at 3 months (primary) and 6 months (secondary). RESULTS: Of the 791 FHS participants, 655 (82.8%) were eligible and randomized (mean age 53, SD 9 years; 392/655, 59.8% women; 596/655, 91% White). For the personalized versus standard notifications, 38.9% (126/324) versus 28.8% (94/327) participants sent BP data at 3 months (difference=10.1%, 95% CI 2.9%-17.4%; P=.006), but no significant differences were observed for HR data transmission (212/324, 65.4% vs 209/327, 63.9%; P=.69). Personalized notifications were associated with increased BP and HR data transmission versus standard at 6 months (BP: 107/291, 36.8% vs 66/295, 22.4%; difference=14.4%, 95% CI 7.1- 21.7%; P<.001; HR: 186/281, 66.2% vs 158/281, 56.2%; difference=10%, 95% CI 2%-18%; P=.02). For BP and HR primary or secondary outcomes, there was no evidence of differences in data transmission for notifications sent on weekend versus weekday or morning versus evening. CONCLUSIONS: Personalized notifications increased longitudinal adherence to BP and HR transmission from mobile and digital devices among eFHS participants. Our results suggest that personalized messaging is a powerful tool to promote adherence to mobile health systems in cardiovascular research. TRIAL REGISTRATION: ClinicalTrials.gov NCT03516019; https://clinicaltrials.gov/ct2/show/NCT03516019.

Prospective Association of Daily Steps With Cardiovascular Disease: A Harmonized Meta-Analysis.

BACKGROUND: Taking fewer than the widely promoted "10 000 steps per day" has recently been associated with lower risk of all-cause mortality. The relationship of steps and cardiovascular disease (CVD) risk remains poorly described. A meta-analysis examining the dose-response relationship between steps per day and CVD can help inform clinical and public health guidelines. METHODS: Eight prospective studies (20 152 adults [ie, ≥18 years of age]) were included with device-measured steps and participants followed for CVD events. Studies quantified steps per day and CVD events were defined as fatal and nonfatal coronary heart disease, stroke, and heart failure. Cox proportional hazards regression analyses were completed using study-specific quartiles and hazard ratios (HR) and 95% CI were meta-analyzed with inverse-variance-weighted random effects models. RESULTS: The mean age of participants was 63.2±12.4 years and 52% were women. The mean follow-up was 6.2 years (123 209 person-years), with a total of 1523 CVD events (12.4 per 1000 participant-years) reported. There was a significant difference in the association of steps per day and CVD between older (ie, ≥60 years of age) and younger adults (ie, <60 years of age). For older adults, the HR for quartile 2 was 0.80 (95% CI, 0.69 to 0.93), 0.62 for quartile 3 (95% CI, 0.52 to 0.74), and 0.51 for quartile 4 (95% CI, 0.41 to 0.63) compared with the lowest quartile. For younger adults, the HR for quartile 2 was 0.79 (95% CI, 0.46 to 1.35), 0.90 for quartile 3 (95% CI, 0.64 to 1.25), and 0.95 for quartile 4 (95% CI, 0.61 to 1.48) compared with the lowest quartile. Restricted cubic splines demonstrated a nonlinear association whereby more steps were associated with decreased risk of CVD among older adults. CONCLUSIONS: For older adults, taking more daily steps was associated with a progressively decreased risk of CVD. Monitoring and promoting steps per day is a simple metric for clinician-patient communication and population health to reduce the risk of CVD.

Association of Accelerometer-Measured Physical Activity and Sedentary Time with Epigenetic Markers of Aging.

INTRODUCTION/PURPOSE: Physical activity may influence chronic disease risk, in part, through epigenetic mechanisms. Previous studies have demonstrated that an acute bout of physical activity can influence DNA methylation status. Few studies have explored the relationship between habitual, accelerometer-measured physical activity or sedentary time with epigenetic markers of aging. METHODS: We used linear regression to examine cross-sectional associations of accelerometer-measured physical activity and sedentary time with extrinsic and intrinsic epigenetic age acceleration (EEAA and IEAA) models and GrimAge measured from blood samples from Framingham Heart Study participants with accelerometry and DNA methylation data ( n = 2435; mean age, 54.9 ± 14.3; 46.0% men). Residuals of Hannum-, Horvath-, and GrimAge-predicted epigenetic age were calculated by regressing epigenetic age on chronological age. We took into account blood cell composition for EEAA, IEAA, and AdjGrimAge. Moderate to vigorous physical activity was log-transformed to normalize its distribution. Adjustment models accounted for family structure, age, sex, smoking status, cohort-laboratory indicator, and accelerometer wear time. We additionally explored adjustment for body mass index (BMI). RESULTS: Walking 1500 more steps per day or spending 3 fewer hours sedentary was associated with >10 months lower GrimAge biological age (or ~1 month lower AdjGrimAge, after adjusting for blood cells, P < 0.05). Every 5 min·d -1 more moderate to vigorous physical activity was associated with 19-79 d of lower GrimAge (4-23 d lower using EEAA or AdjGrimAge, P < 0.01). Adjusting for BMI attenuated these results, but all statistically significant associations with AdjGrimAge remained. CONCLUSIONS: Greater habitual physical activity and lower sedentary time were associated with lower epigenetic age, which was partially explained by BMI. Further research should explore whether changes in physical activity influence methylation status and whether those modifications influence chronic disease risk.

Factors associated with long-term use of digital devices in the electronic Framingham Heart Study.

Long-term use of digital devices is critical for successful clinical or research use, but digital health studies are challenged by a rapid drop-off in participation. A nested e-cohort (eFHS) is embedded in the Framingham Heart Study and uses three system components: a new smartphone app, a digital blood pressure (BP) cuff, and a smartwatch. This study aims to identify factors associated with the use of individual eFHS system components over 1-year. Among 1948 eFHS enrollees, we examine participants who returned surveys within 90 days (n = 1918), and those who chose to use the smartwatch (n = 1243) and BP cuff (n = 1115). For each component, we investigate the same set of candidate predictors for usage and use generalized linear mixed models to select predictors (P < 0.1, P value from Z test statistic), adjusting for age, sex, and time (app use: 3-month period, device use: weekly). A multivariable model with the predictors selected from initial testing is used to identify factors associated with use of components (P < 0.05, P value from Z test statistic) adjusting for age, sex, and time. In multivariable models, older age is associated with higher use of all system components. Female sex and higher education levels are associated with higher completion of app-based surveys whereas higher scores for depressive symptoms, and lower than excellent self-rated health are associated with lower use of the smartwatch over the 12-month follow-up. Our findings show that sociodemographic and health related factors are significantly associated with long-term use of digital devices. Future research is needed to test interventional strategies focusing on these factors to evaluate improvement in long-term engagement.

Body mass index across adulthood and the development of airflow obstruction and emphysema.

BACKGROUND: Low body mass index (BMI) is associated with COPD, but temporal relationships between airflow obstruction (AO) development and emphysematous change are unclear. We investigated longitudinal changes in BMI, AO, and lung density throughout adulthood using data from the Framingham Offspring Cohort (FOC). METHODS: BMI trajectories were modelled throughout adulthood in 4587 FOC participants from Exam 2 (mean age = 44), through Exam 9 (mean age = 71), in AO participants and non-AO participants (AO n = 1036), determined by spirometry, using fractional polynomial growth curves. This process was repeated for low lung density (LLD) and non LLD participants (LLD n = 225) determined by Computed Tomography. Spirometry decline was compared separately between tertiles of BMI in those aged <40 years and associations between fat and lean mass (measured using Dual Energy X-ray Absorptiometry, DEXA) and development of AO and LLD were also assessed. Additional analyses were performed with adjustment for smoking volume. RESULTS: The BMI trajectory from 30 years of age was visually lower in the AO group than both non-AO smokers (non-

Predictors of incident diabetes in two populations: framingham heart study and hispanic community health study / study of latinos.

BACKGROUND: Non-genetic factors contribute to differences in diabetes risk across race/ethnic and socioeconomic groups, which raises the question of whether effects of predictors of diabetes are similar across populations. We studied diabetes incidence in the primarily non-Hispanic White Framingham Heart Study (FHS, N = 4066) and the urban, largely immigrant Hispanic Community Health Study/Study of Latinos (HCHS/SOL, N = 6891) Please check if the affiliations are captured and presented correctly. METHODS: Clinical, behavioral, and socioeconomic characteristics were collected at in-person examinations followed by seven-day accelerometry. Among individuals without diabetes, Cox proportional hazards regression models (both age- and sex-adjusted, and then multivariable-adjusted for all candidate predictors) identified predictors of incident diabetes over a decade of follow-up, defined using clinical history or laboratory assessments. RESULTS: Four independent predictors were shared between FHS and HCHS/SOL. In each cohort, the multivariable-adjusted hazard of diabetes increased by approximately 50% for every ten-year increment of age and every five-unit increment of body mass index (BMI), and was 50-70% higher among hypertensive than among non-hypertensive individuals (all P < 0.01). Compared with full-time employment status, the multivariable-adjusted hazard ratio (HR) and 95% confidence interval (CI) for part-time employment was 0.61 (0.37,1.00) in FHS and 0.62 (0.41,0.95) in HCHS/SOL. Moderate-to-vigorous physical activity (MVPA) was an additional predictor in common observed in age- and sex-adjusted models, which did not persist after adjustment for other covariates (compared with MVPA ≤ 5 min/day, HR for MVPA level ≥ 30 min/day was 0.48 [0.31,0.74] in FHS and 0.74 [0.56,0.97] in HCHS/SOL). Additional predictors found in sex- and age-adjusted analyses among the FHS participants included male gender and lower education, but these predictors were not found to be independent of others in multivariable adjusted models, nor were they associated with diabetes risk among HCHS/SOL adults. CONCLUSIONS: The same four independent predictors - age, body mass index, hypertension and employment status - were associated with diabetes risk across two disparate US populations. While the reason for elevated diabetes risk in full-time workers is unclear, the findings suggest that diabetes may be part of the work-related burden of disease. Our findings also support prior evidence that differences by gender and socioeconomic position in diabetes risk are not universally present across populations.

The association of sedentary behaviour and physical activity with periodontal disease in NHANES 2011-2012.

AIM: Periodontal disease is one of the most prevalent oral pathologies and a major chronic disease worldwide. Lifestyle habits such as poor nutrition and smoking have been established to contribute to the development of periodontal disease, but limited research has investigated whether physical activity and sedentary lifestyle play a role. The purpose of this study is to evaluate the association between physical activity, sedentary behaviour, and periodontal disease. MATERIALS AND METHODS: We used a nationally representative data set from the National Health and Nutrition Examination Survey (NHANES) 2011-2012. We examined the association between physical activity and sedentary behaviour and periodontal disease using multivariable logistic regression models and reported odds ratios (ORs). RESULTS: Individuals with higher total physical activity, higher leisure time physical activity, and lower amount of total sedentary behaviour had lower periodontal disease prevalence. Adjusted multivariable regression models showed that higher sedentary behaviour (more than 7.5 h/day) was associated with higher odds of periodontal disease (OR = 1.17; 95% confidence interval = 1.00-1.36; p = .045). CONCLUSIONS: The findings showed that higher sedentary behaviour is associated with higher odds of periodontal disease. Future prospective longitudinal studies and strategies are needed to investigate implications further and define the magnitude of the association between physical activity and periodontal disease.

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

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 <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. 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; 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. 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.