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.

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.

Television Viewing from Young Adulthood to Middle Age and Premature Cardiovascular Disease Events: A Prospective Cohort Study.

BACKGROUND: Previous literature has explored the relationship between television viewing and cardiovascular disease (CVD) in adults; however, there remains a paucity of longitudinal data describing how young adult television viewing relates to premature CVD events. OBJECTIVE: To ascertain the relationship between level and annualized changes in television viewing from young adulthood to middle age and the incidence of premature CVD events before age 60. DESIGN: The Coronary Artery Risk Development in Young Adults (CARDIA) study, a prospective community-based cohort with over 30 years of follow-up (1985-present). PARTICIPANTS: Black and White men and women who were 18-30 years old at baseline (1985-1986). MAIN MEASURES: Independent variables: Individualized television viewing trajectories were developed using linear mixed models. DEPENDENT VARIABLES: Fatal and nonfatal coronary heart disease (CHD), heart failure, and stroke outcomes were analyzed separately and as a combined CVD event outcome. KEY RESULTS: Among 4318 included participants, every 1-h increase in daily hours of television viewing at age 23 was associated with higher odds of incident CHD (adjusted odds ratio [AOR] 1.26, 95% confidence interval [CI] 1.06-1.49) and incident CVD events (AOR 1.16, 95% CI 1.03-1.32). Each additional hour of daily television viewing annually was associated with higher annual odds of CHD incidence (AOR 1.55, 95% CI 1.06-2.25), stroke incidence (AOR 1.58, 95% CI 1.02-2.46), and CVD incidence (AOR 1.32, 95% CI 1.03-1.69). Race and sex modified the association between television viewing level at age 23 and CHD, heart failure, and stroke, with White men most consistently having significant associations. CONCLUSIONS: In this prospective cohort study, greater television viewing in young adulthood and annual increases in television viewing across midlife were associated with incident premature CVD events, particularly CHD. Young adulthood as well as behaviors across midlife may be important periods to promote healthy television viewing behavior patterns.

Association of physical activity and screen time with cardiovascular disease risk in the Adolescent Brain Cognitive Development Study.

BACKGROUND: According to the Physical Activity Guidelines Advisory Committee Scientific Report, limited evidence is available on sedentary behaviors (screen time) and their joint associations with physical activity (steps) for cardiovascular health in adolescence. The objective of this study was to identify joint associations of screen time and physical activity categories with cardiovascular disease (CVD) risk factors (blood pressure, hemoglobin A1c, cholesterol) in adolescence. METHODS: This study analyzed data from the Adolescent Brain Cognitive Development (ABCD) Study, comprising a diverse sample of 4,718 U.S. adolescents aged 10-15 years between 2018 and 2021. Steps were measured by a Fitbit wearable device and levels were categorized as low (1,000-6,000), medium (> 6,000-12,000), and high (> 12,000) averaged daily step counts. Self-reported recreational screen time hours per day were classified as low (0-4), medium (> 4-8), and high (> 8) hours per day. CVD risk factors including blood pressure, hemoglobin A1c, and cholesterol (total and HDL) were measured. RESULTS: The analytical sample averaged 6.6 h of screen time per day and 9,722 steps per day. In models including both screen time and steps, the high screen time category was associated with a 4.27 higher diastolic blood pressure percentile (95% CI 1.83-6.73) and lower HDL cholesterol (B= -2.85, 95% CI -4.77 to -0.94 mg/dL) compared to the low screen time category. Medium (B = 3.68, 95% CI 1.24-6.11) and low (B = 7.64, 95% CI 4.07-11.20) step categories were associated with higher diastolic blood pressure percentile compared to the high step category. The medium step category was associated with lower HDL cholesterol (B= -1.99, 95% CI -3.80 to -0.19 mg/dL) compared to the high step category. Findings were similar when screen time and step counts were analyzed as continuous variables; higher continuous step count was additionally associated with lower total cholesterol (mg/dL). CONCLUSIONS: Combinations of low screen time and high steps were generally associated with favorable cardiovascular health markers including lower diastolic blood pressure and higher HDL cholesterol, which can inform future adolescent health guidelines.

Screen Time from Adolescence to Adulthood and Cardiometabolic Disease: a Prospective Cohort Study.

BACKGROUND: Previous studies have analyzed the relationship between screen time and cardiometabolic disease risk factors among adolescents, but few have examined the longitudinal effects of screen time on cardiometabolic health into adulthood using nationally representative data. OBJECTIVE: To determine prospective associations between screen time and later cardiometabolic disease over a 24-year period using a nationally representative adolescent cohort. DESIGN: Longitudinal prospective cohort data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) collected from 1994 to 2018. PARTICIPANTS: Adolescents aged 11-18 years old at baseline (1994-1995) followed for 24 years. MAIN MEASURES: Predictors: screen time (five repeated measures of self-reported television and video watching from adolescence to adulthood). OUTCOMES: Five repeated measures of body mass index (BMI); two repeated measures of waist circumference, hypertension, hyperlipidemia, and diabetes collected at 15- and 24-year follow-up exams. KEY RESULTS: For the 7105 adolescents in the sample (49.7% female, 35.0% non-white), the baseline adolescent average screen time per day was 2.86 ± 0.08 hours per day, which generally declined through 24-year follow-up. Average BMI at baseline was 22.57 ± 0.13 kg/m2, which increased to 30.27 ± 0.18 kg/m2 through follow-up. By 24-year follow-up, 43.4% of participants had obesity, 8.4% had diabetes, 31.8% had hypertension, and 14.9% had hyperlipidemia. In mixed-effects generalized linear models, each additional hour of screen time per day was associated with 0.06 (95% CI 0.04-0.09) within-person increase in BMI. Each additional hour of screen time per day was associated with higher within-person odds of high waist circumference (AOR 1.17, 95% CI 1.09-1.26), obesity (AOR 1.09, 95% CI 1.03-1.15), and diabetes (AOR 1.17, 95% CI 1.07-1.28). Screen time was not significantly associated with hypertension or hyperlipidemia. CONCLUSIONS: In this prospective cohort study, higher screen time in adolescence was associated with higher odds of select indicators of cardiometabolic disease in adulthood.

Social epidemiology of Fitbit daily steps in early adolescence.

BACKGROUND: Sociodemographic disparities in adolescent physical activity have been documented but mostly rely on self-reported data. Our objective was to examine differences in device-based step metrics, including daily step count (steps d-1), by sociodemographic factors among a diverse sample of 10-to-14-year-old adolescents in the US. METHODS: We analyzed prospective cohort data from Year 2 (2018-2020) of the Adolescent Brain Cognitive Development (ABCD) Study (N = 6460). Mixed-effects models were conducted to estimate associations of sociodemographic factors (sex, sexual orientation, race/ethnicity, household income, parental education, and parental marital status) with repeated measures of steps d-1 over the course of 21 days. RESULTS: Participants (49.6% female, 39.0% racial/ethnic minority) accumulated an average of 9095.8 steps d-1. In mixed-effects models, 1543.6 more steps d-1 were recorded for male versus female sex, Black versus White race (328.8 more steps d-1), heterosexual versus sexual minority sexual orientation (676.4 more steps d-1), >$200,000 versus <$25,000 household income (1003.3 more steps d-1), and having married/partnered parents versus unmarried/unpartnered parents (326.3 more steps d-1). We found effect modification by household income for Black adolescents and by sex for Asian adolescents. CONCLUSIONS: Given sociodemographic differences in adolescent steps d-1, physical activity guidelines should focus on key populations and adopt strategies optimized for adolescents from diverse backgrounds. IMPACT: Sociodemographic disparities in physical activity have been documented but mostly rely on self-reported data, which can be limited by reporting and prevarication bias. In this demographically diverse sample of 10-14-year-old early adolescents in the U.S., we found notable and nuanced sociodemographic disparities in Fitbit steps per day. More daily steps were recorded for male versus female sex, Black versus White race, heterosexual versus sexual minority, >$100,000 versus <$25,000 household income, and having married/partnered versus unmarried/unpartnered parents. We found effect modification by household income for Black adolescents and by sex for Asian adolescents.

Parent-adolescent agreement in reported moderate-to-vigorous intensity physical activity during the COVID-19 pandemic.

PURPOSE: To describe the agreement between parent- and adolescent- reports of adolescent moderate-to-vigorous intensity physical activity (MVPA) and to determine sociodemographic factors associated with MVPA reporting differences during the COVID-19 pandemic. METHODS: We analyzed data collected in May 2020 from the Adolescent Brain Cognitive Development Study (ABCD, N = 4841), a U.S. prospective cohort study. We quantified past weekly adolescent MVPA levels as reported by the parent and adolescent (referent). Intra-class correlation coefficients (ICC) and Bland-Altman plots were used to examine the degree of agreement between parent- and adolescent- reports. RESULTS: When quantifying adolescent MVPA during the same recall period, median (p25, p75) MVPA (h∙wk.- 1) was 2.17 (0.00, 6.00) as reported by adolescents and 1.52 (0.29, 4.75) by parents with a mean difference of 4.89. Statistically significant differences in reports of MVPA were found in households with income > $75,000: on average, adolescents reported higher MVPA levels than their parents. Bland-Altman plots illustrated that, among adolescents reporting no or little MVPA, there was higher parent-adolescent agreement. However, among adolescents reporting high levels of MVPA, there was less agreement between the parent- and adolescent- reports. CONCLUSIONS: Despite more time spent together at home during the pandemic, there was generally low agreement between parent- and adolescent- reports of adolescent MVPA. Future research could examine parent-adolescent agreement of MVPA within the context of device-based measures (e.g., accelerometers), determine reasons for differences in parent-adolescent reporting of MVPA, and inform interventions for improved parental involvement and monitoring of MVPA.

Implementation Quality Impacts Fourth Grade Students' Participation in Physically Active Academic Lessons.

It is important to assess implementation of active learning interventions to maximize their impact. Implementation quality, or how well one engages program participants, has been less studied than other implementation components (e.g., dose, fidelity). This cross-sectional, exploratory study examined associations between teacher engagement behaviors, quality of teacher engagement (i.e., teacher feedback), and student physical activity outcomes during active classroom lessons. This study used data from the Texas Initiatives for Children's Activity and Nutrition (I-CAN!) randomized controlled trial. Fixed effects regressions investigated the impact of teacher engagement behaviors on student physical activity outcomes. Bivariate correlations examined associations between teacher feedback and student physical activity outcomes. A latent profile analysis explored whether there were subsets of teachers with similar feedback profiles. The final analytic sample included 82 teachers. Teacher-directed changes and teacher participation in physical activity were each associated with higher ratings for how many and how often children were active during lessons. Teacher participation in physical activity was also significantly related to higher ratings for student physical activity intensity (all p < .05). Physical Activity Reinforcement and Technical Instruction feedback were positively associated with activity intensity (r = - .20, p < .05 and r = .34, p < .01, respectively). Technical Instruction feedback was positively associated with how many (r = .25, p < .01) and how often (r = .41, p < .01) students were active during lessons. Negative feedback was negatively associated with how often (r = - .25, p < .05) students were active and activity intensity (r = - .25, p < .05). Game Instruction was negatively related to how often students were active (r = -.23, p < .05). All teachers were represented by high levels of Game Instruction and Classroom Management feedback, moderate levels of Content Reinforcement and Content Instruction feedback, and low levels of Negative, Technical Instruction, and Physical Activity Reinforcement feedback. These data did not indicate the existence of multiple feedback profiles. Findings suggest that teacher engagement and feedback to students during active lessons can promote student physical activity. Teachers are primarily responsible for implementing school-based interventions, so it is critical to develop strategies that increase their ability to implement them successfully. Opportunities to maximize intervention delivery, such as co-designing with teachers, should be utilized when designing school-based, physical activity interventions.

Physical activity, screen time, and outdoor learning environment practices and policy implementation: a cross sectional study of Texas child care centers.

BACKGROUND: Early care and education (ECE) centers are important for combating childhood obesity. Understanding policies and practices of ECE centers is necessary for promotion of healthy behaviors. The purpose of this study is to describe self-reported practices, outdoor environment aspects, and center policies for physical activity and screen time in a statewide convenience sample of non-Head Start Texas ECE centers. METHODS: Licensed home and child care centers in Texas with email addresses publicly available on the Department of Family and Protective Services website (N = 6568) were invited to participate in an online survey. Descriptive statistics of self-reported practices, policies, and outdoor learning environment are described. RESULTS: 827 surveys were collected (response rate = 12.6%). Exclusion criteria yielded a cross-sectional sample of 481 center-only respondents. > 80% of centers meet best practice recommendations for screen time practices for infants and toddlers, although written policies were low (M = 1.4 policies, SD = 1.65, range = 0-6). For physical activity, < 30% meet best practice recommendations with M = 3.9 policies (SD = 3.0, range = 0-10) policies reported. Outdoor learning environment indicators (M = 5.7 policies, SD = 2.5, range = 0-12) and adequate play settings, storage (< 40%), and greenery (< 20%) were reported. CONCLUSIONS: This statewide convenience sample of non-Head Start Texas ECE centers shows numerous opportunities for improvement in practices and policies surrounding outdoor environments, physical activity, and screen time. With less than half of centers meeting the recommendations for physical activity and outdoor learning environments, dedicating resources to help centers enact and modify written policies and to implement programs to improve their outdoor learning environments could promote physical activity and reduce sedentary time of children.

Best Practices and Barriers to Obesity Prevention in Head Start: Differences Between Director and Teacher Perceptions.

INTRODUCTION: Practices and barriers to promoting healthy eating and physical activity at Head Start centers may influence children's energy balance behaviors. We examined differences between directors' and teachers' perspectives on best practices and barriers to promoting healthy eating and physical activity in Head Start centers. METHODS: We conducted a cross-sectional study of directors (n = 23) and teachers (n = 113) at 23 Head Start centers participating in the baseline assessment of the Texas Childhood Obesity Research Demonstration study. Participants completed surveys about practices and barriers to promoting healthy eating and physical activity. Multilevel regression models examined differences between director and teacher responses. RESULTS: More than half of directors and teachers reported meeting most best practices related to nutrition and physical activity; few directors or teachers (<25%) reported conducting physical activity for more than 60 minutes a day, and less than 40% of teachers helped children attend to satiety cues. Significantly more directors than teachers reported meeting 2 nutrition-related best practices: "Teachers rarely eat less healthy foods (especially sweets, salty snacks, and sugary drinks) in front of children" and "Teachers talk to children about trying/enjoying new foods" (P < .05). No barrier to healthy eating or physical activity was reported by more than 25% of directors or teachers. Significantly more teachers than directors reported barriers to healthy eating, citing lack of food service staff support, limited time, and insufficient funds (P < .05). CONCLUSION: More barriers to healthy eating were reported than were barriers to physical activity indicating that more support may be needed for healthy eating. Differences between responses of directors and teachers may have implications for future assessments of implementation of best practices and barriers to implementation related to nutrition and physical activity in early care and education centers.

Physical Activity Fragmentation and Falls in Older Adults: Findings From the National Health and Aging Trends Study.

BACKGROUND: Physical activity (PA) may be an important fall prevention strategy. Current PA guidelines emphasize total PA dose, but daily patterning of PA is underappreciated. With aging, PA bouts become less frequent and shorter in duration (ie, more fragmented). PA fragmentation may be an indicator of fall risk, but the relationship is not well understood. This study examined daily PA accumulation and patterns with fall risk in older adults. METHODS: Participants (n = 685, 54.3% women, 61.5% aged 70-79 years) from the National Health and Aging Trends Study with wrist-worn accelerometry PA data from Round 11 (baseline) and sample person interviews with fall data from Round 12 (follow-up) were included. PA variables were categorized into tertiles and incident falls were defined as ≥1 self-reported fall in the year following the PA assessment between baseline and follow-up. A modified Poisson approach was used to estimate the relative risk of both PA accumulation and fragmentation with falls. RESULTS: Overall, 40.0% reported an incident fall. After adjustment for sociodemographic and health characteristics, those in the highest tertile of total PA accumulation had lower fall risk (aRR = 0.74, 95% CI: 0.57, 0.95) and those in the highest tertile of PA fragmentation had increased fall risk (aRR = 1.33, 95% CI: 1.03, 1.73). Models were attenuated after adjustment for physical functioning. CONCLUSIONS: PA fragmentation may identify fall risk in older adults. Longitudinal studies are needed to disentangle the temporal sequencing of the complex relationship between PA and physical functioning across the life course.

Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity.

Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period during the life course. Analyzing data from 2971 participants (M = 11.94, SD = 0.64 years) wearing Fitbit Charge HR 2 devices in the Adolescent Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUCMean = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.

TV Viewing From Young Adulthood to Middle Age and Cardiovascular Disease Risk.

INTRODUCTION: Few studies have longitudinally examined TV viewing trajectories and cardiovascular disease risk factors. The objective of this study was to determine the association between level and annualized changes in young adult TV viewing and the incidence of cardiovascular disease risk factors from young adulthood to middle age. METHODS: In 2023, prospective community-based cohort data of 4,318 Coronary Artery Risk Development in Young Adults study participants (1990-1991 to 2015-2016) were analyzed. Individualized daily TV viewing trajectories for each participant were developed using linear mixed models. RESULTS: Every additional hour of TV viewing at age 23 years was associated with higher odds of incident hypertension (AOR=1.16; 95% CI=1.11, 1.22), diabetes (AOR=1.19; 95% CI=1.11, 1.28), high triglycerides (AOR=1.17; 95% CI=1.08, 1.26), dyslipidemia (AOR=1.10; 95% CI=1.03, 1.16), and obesity (AOR=1.12; 95% CI=1.06, 1.17). In addition, each hourly increase in daily TV viewing was associated with higher annual odds of incident hypertension (AOR=1.26; 95% CI=1.16, 1.37), low high-density lipoprotein cholesterol (AOR=1.15; 95% CI=1.03, 1.30), high triglycerides (AOR=1.32; 95% CI=1.15, 1.51), dyslipidemia (AOR=1.22; 95% CI=1.11, 1.34), and obesity (AOR=1.17; 95% CI=1.07, 1.27) over the follow-up period. CONCLUSIONS: In this prospective cohort study, higher TV viewing in young adulthood and annual increases in TV viewing were associated with incident hypertension, high triglycerides, and obesity. Young adulthood as well as behaviors across midlife may be important time periods to promote healthful TV viewing behavior patterns.

Behavior reallocations within 24-h movement profiles and change in cardiometabolic biomarkers during pregnancy.

OBJECTIVE: The goal of this study was to investigate associations of reallocations within 24-h movement profiles and changes in cardiometabolic biomarkers from early to late pregnancy. METHODS: In 137 individuals with prepregnancy overweight/obesity, waking movement was measured using wrist-worn accelerometers, sleep was self-reported, and biomarkers were measured in fasting serum samples at 12 and 32 weeks' gestation. We used compositional isotemporal substitution models. RESULTS: On average, biomarkers increased 21%-83% across pregnancy. For those with guideline-recommended moderate/vigorous-intensity physical activity (MVPA) in early pregnancy, reallocating 30 min from MVPA to sleep, sedentary behavior, or light-intensity physical activity (LPA) was associated with a 0.6 mmol/L greater increase in total cholesterol (95% CI: -0.1 to 1.2) and a 0.7 mmol/L greater increase in low-density lipoprotein (LDL) cholesterol (95% CI: 0.1 to 1.3) from early to late pregnancy. For those with low MVPA in early pregnancy, reallocating 30 min from sleep, sedentary behavior, or LPA to MVPA was associated with a 0.6 mmol/L lower increase in total cholesterol (95% CI: -1.3 to 0.1) and a 0.8 mmol/L lower increase in LDL cholesterol (95% CI: -1.4 to -0.1) from early to late pregnancy. There were no associations with change in glucose, insulin, homeostatic model assessment for insulin resistance, very low-density lipoprotein, or high-density lipoprotein cholesterol, triglycerides, or free fatty acids. CONCLUSIONS: Maintaining or achieving a 24-h movement profile with guideline-recommended amounts of MVPA may be beneficial for reducing pregnancy-induced increases in total and LDL cholesterol.

Theoretical Behavior Substitutions during the 24-Hour Day in Pregnancy and Infant Growth Outcomes.

PURPOSE: To assess associations of theoretically reallocating time from sleep, sedentary behavior, or light intensity physical activity (LPA) to moderate/vigorous intensity physical activity (MVPA) during pregnancy with infant growth outcomes. METHODS: We used data from a cohort of pregnant individuals with overweight or obesity (n = 116). At 9-15 and 30-36 weeks gestation, waking movement was measured using wrist-worn accelerometers and sleep duration was self-reported. Outcomes were obtained from delivery electronic health records (birth) and study visits (12 months). We used compositional isotemporal substitution models. RESULTS: In early pregnancy, reallocating 10 minutes of sleep, sedentary behavior, or LPA to MVPA was associated with 20% (RR = 0.80; 95%CI: 0.75,0.85), 21% (RR = 0.79; 95%CI: 0.75,0.84), and 25% (RR = 0.75; 95%CI: 0.70,0.81) lower risk of large-for-gestational age (LGA) birthweight, respectively, and 17% (RR = 0.83; 95%CI: 0.75,0.91), 18% (RR = 0.82; 95%CI: 0.75,0.91), and 22% (RR = 0.78; 95%CI: 0.70,0.88) lower risk of rapid infant growth (birth to 12 months), respectively. In late pregnancy, reallocating 10 minutes to MVPA was associated with 18% to 22% lower risk of LGA birthweight, but was not associated with rapid infant growth. Reallocating time to MVPA in early or late pregnancy was not associated with high newborn body fat percent. CONCLUSIONS: Our findings suggest beneficial associations of theoretically reallocating time from sleep, sedentary behavior, or LPA to MVPA, especially during early pregnancy, for reducing LGA birthweight and rapid infant growth.

Multimorbidity trajectories in early adulthood and middle age: Findings from the CARDIA prospective cohort study.

Background: Multimorbidity research has focused on the prevalence and consequences of multimorbidity in older populations. Less is known about the accumulation of chronic conditions earlier in the life course. Methods: We identified patterns of longitudinal multimorbidity accumulation using 30 years of data from in-person exams, annual follow-ups, and adjudicated end-points among 4,945 participants of the Coronary Artery Risk Development in Young Adults (CARDIA) study. Chronic conditions included arthritis, asthma, atrial fibrillation, cancer, end stage renal disease, chronic obstructive pulmonary disease, coronary heart disease, diabetes, heart failure, hyperlipidemia, hypertension, and stroke. Trajectory patterns were identified using latent class growth curve models. Results: Mean age (SD) at baseline (1985-6) was 24.9 (3.6), 55% were female, and 51% were Black. The median follow-up was 30 years (interquartile range 25-30). We identified six trajectory classes characterized by when conditions began to accumulate and the rapidity of accumulation: (1) early-fifties, slow, (2) mid-forties, fast, (3) mid-thirties, fast, (4) late-twenties, slow, (5) mid-twenties, slow, and (6) mid-twenties, fast. Compared with participants in the early-fifties, slow trajectory class, participants in mid-twenties, fast were more likely to be female, Black, and currently smoking and had a higher baseline mean waist circumference (83.6 vs. 75.6 cm) and BMI (27.0 vs. 23.4 kg/m2) and lower baseline physical activity (414.1 vs. 442.4 exercise units). Conclusions: A life course approach that recognizes the heterogeneity in patterns of accumulation of chronic conditions from early adulthood into middle age could be helpful for identifying high risk subgroups and developing approaches to delay multimorbidity progression.

Sociodemographic Associations With Blood Pressure in 10-14-Year-Old Adolescents.

PURPOSE: To determine the association between sociodemographic characteristics and blood pressure among a demographically diverse population-based sample of 10-14-year-old US adolescents. METHODS: We conducted cross-sectional analyses of data from the Adolescent Brain Cognitive Development Study (N = 4,466), year two (2018-2020). Logistic and linear regression models were used to determine the association between sociodemographic characteristics (sex, race/ethnicity, sexual orientation, household income, and parental education) with blood pressure among early adolescents. RESULTS: The sample was 49.3% female and 46.7% non-White. Overall, 4.1% had blood pressures in the hypertensive range. Male sex was associated with 48% higher odds of hypertensive-range blood pressures than female sex (95% confidence interval [CI], 1.02; 2.14), and Black race was associated with 85% higher odds of hypertensive-range blood pressures compared to White race (95% CI, 1.11; 3.08). Several annual household income categories less than $100,000 were associated with higher odds of hypertensive-range blood pressures compared to an annual household income greater than $200,000. We found effect modification by household income for Black adolescents; Black race (compared to White race) was more strongly associated with higher odds of hypertensive-range blood pressures in households with income greater than $75,000 (odds ratio 3.92; 95% CI, 1.95; 7.88) compared to those with income less than $75,000 (odds ratio 1.53; 95% CI, 0.80; 2.92). DISCUSSION: Sociodemographic characteristics are differentially associated with higher blood pressure in early adolescents. Future research could examine potential mediating factors (e.g., physical activity, nutrition, tobacco) linking sociodemographic characteristics and blood pressure to inform targeted interventions.

Transforming Big Data into AI-ready data for nutrition and obesity research.

OBJECTIVE: Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions. METHODS: We reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI- and ML-ready products were detailed. RESULTS: Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented. CONCLUSIONS: Big Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI- and ML-ready data need to be transparent to investigators and clinicians relying on the conclusions.

Trends in the Prevalence of Multiple Chronic Conditions Among US Adults With Hypertension From 1999-2000 Through 2017-2020.

BACKGROUND: The prevalence of many chronic conditions has increased among US adults. Many adults with hypertension have other chronic conditions. METHODS: We estimated changes in the age-adjusted prevalence of multiple (≥3) chronic conditions, not including hypertension, using data from the National Health and Nutrition Examination Survey, from 1999-2000 to 2017-2020, among US adults with (n = 24,851) and without (n = 24,337 hypertension. Hypertension included systolic blood pressure (BP) ≥130 mm Hg, diastolic BP ≥80 mm Hg, or antihypertensive medication use. We studied 14 chronic conditions: arthritis, asthma, cancer, coronary heart disease, chronic kidney disease, depression, diabetes, dyslipidemia, hepatitis B, hepatitis C, heart failure, lung disease, obesity, and stroke. RESULTS: From 1999-2000 to 2017-2020, the age-adjusted mean number of chronic conditions increased more among US adults with vs. without hypertension (2.2 to 2.8 vs. 1.7 to 2.0; P-interaction <0.001). Also, the age-adjusted prevalence of multiple chronic conditions increased from 39.0% to 52.0% among US adults with hypertension and from 26.0% to 30.0% among US adults without hypertension (P-interaction = 0.022). In 2017-2020, after age, gender, and race/ethnicity adjustment, US adults with hypertension were 1.94 (95% confidence interval: 1.72-2.18) times as likely to have multiple chronic conditions compared to those without hypertension. In 2017-2020, dyslipidemia, obesity, and arthritis were the most common 3 co-occurring chronic conditions among US adults with and without hypertension (age-adjusted prevalence 16.5% and 3.1%, respectively). CONCLUSIONS: In 2017-2020, more than half of US adults with hypertension had ≥3 additional chronic conditions, a substantial increase from 20 years ago.

Optimal Instruments for Measurement of Dietary Intake, Physical Activity, and Sleep Among Adults in Population-Based Studies: Report of a National Heart, Lung, and Blood Institute Workshop.

The National Heart, Lung, and Blood Institute convened a virtual workshop in September 2022 to discuss "Optimal Instruments for Measurement of Diet, Physical Activity, and Sleep." This report summarizes the proceedings, identifying current research gaps and future directions for measuring different lifestyle behaviors in adult population-based studies. Key discussions centered on integrating report-based methods, like questionnaires, with device-based assessments, including wearables and physiological measures such as biomarkers and omics to enhance self-reported metrics and better understand the underlying biologic mechanisms of chronic diseases. Emphasis was placed on the need for data harmonization, including the adoption of standard terminology, reproducible metrics, and accessible raw data, to enhance the analysis through artificial intelligence and machine learning techniques. The workshop highlighted the importance of standardizing procedures for integrated behavioral phenotypes using time-series data. These efforts aim to refine data accuracy and comparability across studies and populations, thereby advancing our understanding of lifestyle behaviors and their impact on chronic disease outcomes over the life course.