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.

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.

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.

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.

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.

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.

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.

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.

Impact of Reduced Enoxaparin Prophylactic Dosing on Bleeding and Thrombotic Risk for Low Body Weight Post-Operative Colorectal Surgery Patients.

Introduction: Colorectal surgery (CORS) patients are at the highest risk of any general surgery population for the development of post-operative venous thromboembolism (VTE) and are also at an increased risk for post-operative bleeding, especially with procedures related to early cancer stages. Low molecular weight heparin (LMWH) is recommended for VTE prophylaxis in this surgical population. Current guidelines and primary literature along with institutional protocols support dosage adjustments for obesity and impaired renal function, but literature providing optimal VTE prophylactic LMWH dosing is extremely limited for underweight, surgical patients. Objectives: The aim of this study was to evaluate both bleeding and VTE rates for hospitalized underweight colorectal surgery patients receiving post-operative VTE prophylaxis with standard enoxaparin 40 mg subcutaneously (SQ) every 24 hours versus reduced dose enoxaparin 30 mg SQ every 24 hours. Methods: This trial was a multicenter, retrospective chart review with data collected on post-operative CORS patients over a 10-year period. Patients were included if aged 18 years and older, weighed less than 50 kg, and received at least 1 dose of enoxaparin 40 mg or 30 mg post-operatively. The primary endpoint was a descriptive comparison of post-operative bleeding with use of standard enoxaparin 40 mg SQ every 24 hours compared to reduced dose enoxaparin 30 mg SQ every 24 hours for post-operative VTE prophylaxis. The secondary endpoint was the descriptive comparison of post-operative thrombotic event rates in the same population. Results: The total population investigated included 146 patients. One hundred twenty-nine (88.4%) received standard enoxaparin and 17 (11.6%) received reduced enoxaparin. Overall, 9 bleeding events (7.0%) and 2 major VTE events (1.6%) occurred, all within the standard enoxaparin group. Conclusion: Standard dose enoxaparin for post-operative VTE prophylaxis demonstrated increased bleeding and thrombotic events requiring medical and surgical intervention in comparison to reduced dose enoxaparin for low body weight CORS patients. The results of this study are hypothesis generating and continued research involving more robust systematic reviews are needed to evaluate both safety and efficacy of reduced VTE prophylaxis with LMWH in the underweight, surgical patient population.

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.

Multimorbidity is associated with lower total 24-hour movement activity among US adults.

Objective: Having chronic conditions may result in reduced physical and cognitive function but less is known about multimorbidity with daily movement. We examined the association of multimorbidity and device-measured total daily movement in a nationally representative sample of US adults aged ≥ 30 years from the 2011-2014 National Health and Nutrition Examination Surveys. Methods: Any multimorbidity (≥2 conditions) and complex multimorbidity (≥3 conditions across ≥ 3 body systems) were quantified using 16 chronic conditions via self-report and/or clinical thresholds. Total movement over 24-hours (Monitor-Independent Movement Summary units [MIMS-units]) was measured using a wrist-worn device (ActiGraph GT3X). Multivariable linear regression examined the association of 1) each chronic condition, 2) number of conditions, 3) any multimorbidity, and 4) complex multimorbidity with total movement. Covariates included age, gender, race/ethnicity, educational attainment, and smoking status. Results: Among US adults (N = 7304, mean age: 53.2 ± 0.34 years, 53.2% female, 69.4% Non-Hispanic White), 62.2% had any multimorbidity with 34.2% having complex multimorbidity. After adjustment, a higher number of chronic conditions was associated with incrementally lower total movement (ß MIMS-units [95% CI] compared to those with no chronic conditions; one: -419 [-772, -66], two: -605 [-933, -278], three: -1201 [-1506, -895], four: -1908 [-2351, -1465], 5+: -2972 [-3384, -2560]). Complex multimorbidity presence was associated with -1709 (95% CI: -2062, -1357) and -1269 (-1620, -918) lower total movement compared to those without multimorbidity and multimorbidity but not complex, respectively. Conclusions: Multimorbidity was associated with lower 24-h movement among US adults and may be helpful for identifying adults at risk for low movement.

Associations Between Adverse Childhood Experiences and Early Adolescent Physical Activity in the United States.

OBJECTIVE: To determine the associations between the number of adverse childhood experiences (ACEs) and objectively-measured physical activity (PA) in a population-based, demographically diverse cohort of 9-14-year-olds and to determine which subtypes of ACEs were associated with physical activity levels. METHODS: We analyzed data (n = 7046) from the Adolescent Brain Cognitive Development (ABCD) Study 4.0 release at baseline and year 2 follow-up. ACE (cumulative score and subtypes) and physical activity (average Fitbit daily steps assessed at Year 2) were analyzed using linear regression analyses. Covariates included race and ethnicity, sex, household income, parent education, body mass index, study site, twins/siblings, and data collection period. RESULTS: Adjusted models suggest an inverse association between number of ACEs and Fitbit daily steps, with ≥4 (compared to 0) ACEs associated with 567 fewer daily steps (95% CI -902.2, -232.2). Of the ACEs subtypes, emotional abuse (B = -719.3, 95% CI -1430.8, -7.9), physical neglect (B = -423.7, 95% CI -752.8, -94.6), household mental illness (B = -317.1, 95% CI -488.3, -145.9), and household divorce or separation (B = -275.4, 95% CI -521.5, -29.2) were inversely and statistically significant associated with Fitbit daily steps after adjusting for confounders. CONCLUSIONS: Our results suggest that there is an inverse, dose-dependent relationship between cumulative number of ACEs and physical activity as measured by daily steps. This work highlights the importance of screening for ACEs among young people at an early age to help identify those who could benefit from interventions or community programs that support increased physical activity.

Method for Activity Sleep Harmonization (MASH): a novel method for harmonizing data from two wearable devices to estimate 24-h sleep-wake cycles.

Background: Daily 24-h sleep-wake cycles have important implications for health, however researcher preferences in choice and location of wearable devices for behavior measurement can make 24-h cycles difficult to estimate. Further, missing data due to device malfunction, improper initialization, and/or the participant forgetting to wear one or both devices can complicate construction of daily behavioral compositions. The Method for Activity Sleep Harmonization (MASH) is a process that harmonizes data from two different devices using data from women who concurrently wore hip (waking) and wrist (sleep) devices for ≥ 4 days. Methods: MASH was developed using data from 1285 older community-dwelling women (ages: 60-72 years) who concurrently wore a hip-worn ActiGraph GT3X + accelerometer (waking activity) and a wrist-worn Actiwatch 2 device (sleep) for ≥ 4 days (N = 10,123 days) at the same time. MASH is a two-tiered process using (1) scored sleep data (from Actiwatch) or (2) one-dimensional convolutional neural networks (1D CNN) to create predicted wake intervals, reconcile sleep and activity data disagreement, and create day-level night-day-night pairings. MASH chooses between two different 1D CNN models based on data availability (ActiGraph + Actiwatch or ActiGraph-only). MASH was evaluated using Receiver Operating Characteristic (ROC) and Precision-Recall curves and sleep-wake intervals are compared before (pre-harmonization) and after MASH application. Results: MASH 1D CNNs had excellent performance (ActiGraph + Actiwatch ROC-AUC = 0.991 and ActiGraph-only ROC-AUC = 0.983). After exclusions (partial wear [n = 1285], missing sleep data proceeding activity data [n = 269], and < 60 min sleep [n = 9]), 8560 days were used to show the utility of MASH. Of the 8560 days, 46.0% had ≥ 1-min disagreement between the devices or used the 1D CNN for sleep estimates. The MASH waking intervals were corrected (median minutes [IQR]: -27.0 [-115.0, 8.0]) relative to their pre-harmonization estimates. Most correction (-18.0 [-93.0, 2.0] minutes) was due to reducing sedentary behavior. The other waking behaviors were reduced a median (IQR) of -1.0 (-4.0, 1.0) minutes. Conclusions: Implementing MASH to harmonize concurrently worn hip and wrist devices can minimizes data loss and correct for disagreement between devices, ultimately improving accuracy of 24-h compositions necessary for time-use epidemiology.

Moderate-to-vigorous intensity physical activity among U.S. adolescents before and during the COVID-19 pandemic: Findings from the Adolescent Brain Cognitive Development Study.

Only 16.1% percent of U.S. adolescents meet the recommendation of at least 60 minutes of moderate-to-vigorous intensity physical activity (MVPA) per day. Studies report declined levels of adolescent MVPA in early stages of the pandemic, but gaps remain in understanding changes beyond the initial three months of the pandemic. This study aims to describe and compare self-reported adolescent MVPA levels at multiple timepoints before and during the COVID-19 pandemic among 11,865 9-11-year-old U.S. adolescents from the Adolescent Brain Cognitive Development (ABCD) Study, including pre-pandemic (September 2016-October 2018), early (May, June, and August 2020), and later (October and December 2020, March 2021) stages of the pandemic. Poisson regression models with robust error variance were used to estimate crude and adjusted prevalence ratios (APRs) of the proportion of adolescents meeting national MVPA guidelines during early and later stages of the pandemic compared to pre-pandemic. The proportion of adolescents meeting MVPA guidelines decreased from pre (16.4%), early (11.0%), and later (4.7%) COVID-19 pandemic timepoints. Adolescent MVPA guideline adherence at early- and later-pandemic stages was 24% lower (APR 0.76, 95% CI 0.62, 0.93) and 68% lower (APR 0.32, 95% CI 0.24, 0.43) than pre-pandemic adherence, respectively. Weekly MVPA duration decreased throughout May 2020 to March 2021 (χ2 = 488.9, p < 0.0001). Study findings build upon existing evidence that the low achievement of national MVPA guidelines before the pandemic became even lower during the pandemic, demonstrating the need to support and improve access to adolescent MVPA opportunities during COVID-19 pandemic recovery efforts and in future pandemics.

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.

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.

Association of leisure-time physical activity and sedentary behavior with carotid atherosclerosis morphology: The ARIC carotid-MRI study.

Objective: We evaluated the prospective association of midlife leisure-time physical activity (LTPA) and sedentary behavior (SB), and their temporal patterns, with MRI-measured carotid atherosclerotic morphology. Methods: Participants enrolled in the Carotid MRI substudy (2004-2006) of the Atherosclerosis Risk in Communities (ARIC) Study and with self-reported assessments of LTPA and SB at visits 1 (1987-1989) and 3 (1993-1995) were included in this study. LTPA was ascertained using the ARIC/Baecke physical activity questionnaire and categorized according to the American Heart Association's metric of poor, intermediate, or ideal physical activity. SB, measured as TV viewing frequency, was categorized as high, medium, and low. We used multivariable adjusted linear and logistic regression models to examine the associations between midlife (visit 3 only) and persistent (visit 1 to 3) LTPA and TV viewing with carotid artery plaque burden and components. Results: Among the 1,582 (mean age: 59 years, 43% male, 18% Black) participants, 45.7%, 21.7%, and 32.6% reported ideal, intermediate, or poor LTPA, respectively. High TV viewing was reported in 33.8% of participants, with 46.4% and 19.8% reporting medium or low TV viewing, respectively. Compared to poor LTPA, ideal LTPA in midlife was not associated with total wall volume (ß=0.01, 95% CI: -0.01, 0.03), maximum carotid wall thickness (ß=0.06, 95% CI: -0.08, 0.21), normalized wall index (ß=-0.01, 95% CI: -0.03, 0.01), or maximum stenosis (ß=-0.11, 95% CI: -1.98, 1.76). Low or middle, compared to high, TV viewing was also not associated with carotid artery measures of plaque burden. Compared to poor LTPA or high TV viewing, ideal LTPA (odds ratio (OR): 0.82, 95% CI: 0.55, 1.23) and low TV viewing (OR=0.90, 95% CI: 0.56, 1.44) was not associated with odds of lipid core presence, respectively. Conclusion: Overall, this study does not provide strong evidence for an association between LTPA and SB with carotid plaque measures.

An Assessment of State-Level Planning for Physical Activity Promotion in the United States.

The prevalence and attributable risk of disease due to physical inactivity require it to be made a public health priority. Public health planning allows for prioritization and resource allocation, particularly at the state and local levels. The extent to which state planning efforts for physical activity exist in the United States is unknown. The purpose of this paper is to describe the scope in which physical activity is incorporated in state-level public health plans in the United States, with an emphasis on alignment with the national guidelines and the National Physical Activity Plan. A standardized internet search audit was developed and conducted for each of 50 US states and the District of Columbia between May 2017 and January 2018 to determine the prevalence and characteristics of health planning documents that include physical activity. Data abstracted for analysis used a standardized search protocol that included the components of the Physical Activity Guidelines for Americans and the US National Physical Activity Plan. Results found that most states had between 4 and 6 chronic disease prevention and control plans that mention physical activity; however, it was inconsistently aligned with recommendations from both the Guidelines and the National Plan. Only 2 states had stand-alone public health planning documents explicitly dedicated to physical activity promotion. No state planning documents addressed children and adolescents, adults, and older adults simultaneously. To be maximally effective, state public health planning for physical activity should be made a priority and these efforts should align as much as possible with current guidance from the Physical Activity Guidelines for Americans and the US National Physical Activity Plan.

Analysis of Cardiorespiratory Fitness in Early Adulthood and Midlife With All-Cause Mortality and Fatal or Nonfatal Cardiovascular Disease.

Importance: Optimizing cardiovascular fitness is a prevention strategy against premature death and cardiovascular disease (CVD) events. Since this evidence has largely been established in older populations, the importance of cardiorespiratory fitness during earlier periods of adulthood remains unclear. Objective: To examine the association of early-adulthood cardiorespiratory fitness and percentage of early-adulthood cardiorespiratory fitness retained during midlife with subsequent risk of all-cause mortality and CVD-related morbidity and mortality overall as well as by sex and race. Design, Setting, and Participants: This retrospective population-based cohort study analyzed data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, an ongoing prospective cohort study conducted at field center clinics in Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; and Oakland, California. Participants in the CARDIA study were aged 18 to 30 years when they completed the baseline graded exercise test protocol in 1985 to 1986 and have since undergone follow-up examinations biannually and every 2 to 5 years. Data were collected through August 31, 2020, and were analyzed in October 2022. Exposures: Cardiorespiratory fitness was estimated from a symptom-limited, maximal graded exercise test protocol conducted at baseline and at year 7 and year 20 follow-up examinations. Main Outcomes and Measures: All-cause mortality and combined fatal and nonfatal CVD events were obtained since year 20 of follow-up examinations through August 31, 2020. Cox proportional hazards regression models were used to estimate hazard ratios (HRs) for each primary exposure with each outcome. Results: A total of 4808 participants (mean [SD] age at baseline, 24.8 [3.7] years; 2670 females [56%]; 2438 Black individuals [51%]) were included in the sample. During 68 751 person-years of follow-up, there were 302 deaths (6.3%) and 274 CVD events (5.7%) since year 20. Every 1-minute increment in cardiorespiratory fitness at baseline was associated with a lower risk of all-cause mortality in females (HR, 0.73; 95% CI, 0.64-0.82) and males (HR, 0.87; 95% CI, 0.80-0.96). Every 5% increment in cardiorespiratory fitness retained through year 20 was associated with a lower risk of all-cause mortality (HR, 0.89; 95% CI, 0.79-0.99), but no evidence of effect modification by race or sex was found. Every 1-minute increment in cardiorespiratory fitness at baseline was associated with a lower risk of fatal or nonfatal CVD (HR, 0.89; 95% CI, 0.82-0.96), and the estimated HR per 5% increment in cardiorespiratory fitness retained throughout midlife was 0.89 (95% CI, 0.78-1.00), with no evidence for interaction by race or sex. Conclusions and Relevance: This cohort study found that higher early-adulthood cardiorespiratory fitness and greater retention of early-adulthood cardiorespiratory fitness throughout midlife were associated with a lower risk of premature death and CVD events. Additional research is needed to clarify the association of cardiorespiratory fitness timing across the life course with risk of clinical outcomes.

Association of Physical Activity and Screen Time With Body Mass Index Among US Adolescents.

Importance: The Physical Activity Guidelines Advisory Committee Scientific Report identified important research gaps to inform future guidance for adolescents, including limited evidence on the importance of sedentary behaviors (screen time) and their interactions with physical activity for adolescent health outcomes, including overweight and obesity. Objective: To identify the independent associations of physical activity and screen time categories, and the interactions between physical activity and screen time categories, with body mass index (BMI) and overweight and obesity in adolescents. Design, Setting, and Participants: This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) Study collected from September 10, 2018, to September 29, 2020. Data were analyzed from July 8 to December 20, 2022. A total of 5797 adolescents aged 10 to 14 years from 21 racially and ethnically diverse study sites across the US were included in the analysis. Exposures: Categories of total step count per day (with 1000 to 6000 steps per day indicating low, >6000 to 12 000 steps per day indicating medium, and >12 000 steps per day indicating high), as measured by a wearable digital device (Fitbit), and categories of self-reported screen time hours per day (with 0 to 4 hours per day indicating low, >4 to 8 hours per day indicating medium, and >8 hours per day indicating high). Main Outcomes and Measures: Participant BMI was calculated as weight in kilograms divided by height in meters squared and converted into sex- and age-specific percentiles in accordance with the Centers for Disease Control and Prevention growth curves and definitions. Individuals were classified as having overweight or obesity if their BMI was in the 85th percentile or higher for sex and age. Results: Among 5797 adolescents included in the analytic sample, 50.4% were male, 61.0% were White, 35.0% had overweight or obesity, and the mean (SD) age was 12.0 (0.6) years. Mean (SD) reported screen time use was 6.5 (5.4) hours per day, and mean (SD) overall step count was 9246.6 (3111.3) steps per day. In models including both screen time and step count, medium (risk ratio [RR], 1.24; 95% CI, 1.12-1.37) and high (RR, 1.29; 95% CI, 1.16-1.44) screen time categories were associated with higher overweight or obesity risk compared with the low screen time category. Medium (RR, 1.19; 95% CI, 1.06-1.35) and low (RR, 1.30; 95% CI, 1.11-1.51) step count categories were associated with higher overweight or obesity risk compared with the high step count category. Evidence of effect modification between screen time and step count was observed for BMI percentile. For instance, among adolescents with low screen use, medium step count was associated with a 1.55 higher BMI percentile, and low step count was associated with a 7.48 higher BMI percentile. However, among those with high screen use, step count categories did not significantly change the association with higher BMI percentile (low step count: 8.79 higher BMI percentile; medium step count: 8.76 higher BMI percentile; high step count: 8.26 higher BMI percentile). Conclusions and Relevance: In this cross-sectional study, a combination of low screen time and high step count was associated with lower BMI percentile in adolescents. These results suggest that high step count may not offset higher overweight or obesity risk for adolescents with high screen time, and low screen time may not offset higher overweight or obesity risk for adolescents with low step count. These findings addressed several research gaps identified by the Physical Activity Guidelines Advisory Committee Scientific Report and may be used to inform future screen time and physical activity guidance for adolescents.