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.

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.

Effectiveness of a paediatric weight management intervention for rural youth (iAmHealthy): Primary outcomes of a cluster randomised control trial.

BACKGROUND: Youth in rural areas are disproportionally affected by obesity. Given the unique barriers rural populations face, tailoring and increasing access to obesity interventions is necessary. OBJECTIVE: This paper evaluates the effectiveness of iAmHealthy, a family-based paediatric obesity intervention delivered to rural children, compared to a Newsletter Control. METHODS: Participating schools (n = 18) were randomly assigned to iAmHealthy or Newsletter Control. iAmHealthy consists of individual health coaching and group sessions delivered via televideo to a participant's home. The child and parent's body mass index (BMI), child physical activity and child dietary intake were assessed at baseline, post-treatment (8 months) and follow-up (20 months). Multilevel modeling estimated the effect of treatment at both time points. RESULTS: Parent and child dyads were recruited (n = 148) and randomised to iAmHealthy (n = 64) or the Control group (n = 84). The Control group had significant increases in child BMIz from baseline to follow-up. iAmHealthy youth had no significant changes in BMIz from baseline to post or follow-up. Child dietary intake, physical activity and parent BMI results are also discussed. CONCLUSIONS: This trial extends previous paediatric obesity work by simultaneously increasing convenience and dose of treatment. Results suggest iAmHealthy resulted in a change in BMIz trajectories and long-term health behaviour for youth.

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.

Epidemiologic Features of Recovery From SARS-CoV-2 Infection.

Importance: Persistent symptoms and disability following SARS-CoV-2 infection, known as post-COVID-19 condition or "long COVID," are frequently reported and pose a substantial personal and societal burden. Objective: To determine time to recovery following SARS-CoV-2 infection and identify factors associated with recovery by 90 days. Design, Setting, and Participants: For this prospective cohort study, standardized ascertainment of SARS-CoV-2 infection was conducted starting in April 1, 2020, across 14 ongoing National Institutes of Health-funded cohorts that have enrolled and followed participants since 1971. This report includes data collected through February 28, 2023, on adults aged 18 years or older with self-reported SARS-CoV-2 infection. Exposure: Preinfection health conditions and lifestyle factors assessed before and during the pandemic via prepandemic examinations and pandemic-era questionnaires. Main Outcomes and Measures: Probability of nonrecovery by 90 days and restricted mean recovery times were estimated using Kaplan-Meier curves, and Cox proportional hazards regression was performed to assess multivariable-adjusted associations with recovery by 90 days. Results: Of 4708 participants with self-reported SARS-CoV-2 infection (mean [SD] age, 61.3 [13.8] years; 2952 women [62.7%]), an estimated 22.5% (95% CI, 21.2%-23.7%) did not recover by 90 days post infection. Median (IQR) time to recovery was 20 (8-75) days. By 90 days post infection, there were significant differences in restricted mean recovery time according to sociodemographic, clinical, and lifestyle characteristics, particularly by acute infection severity (outpatient vs critical hospitalization, 32.9 days [95% CI, 31.9-33.9 days] vs 57.6 days [95% CI, 51.9-63.3 days]; log-rank P < .001). Recovery by 90 days post infection was associated with vaccination prior to infection (hazard ratio [HR], 1.30; 95% CI, 1.11-1.51) and infection during the sixth (Omicron variant) vs first wave (HR, 1.25; 95% CI, 1.06-1.49). These associations were mediated by reduced severity of acute infection (33.4% and 17.6%, respectively). Recovery was unfavorably associated with female sex (HR, 0.85; 95% CI, 0.79-0.92) and prepandemic clinical cardiovascular disease (HR, 0.84; 95% CI, 0.71-0.99). No significant multivariable-adjusted associations were observed for age, educational attainment, smoking history, obesity, diabetes, chronic kidney disease, asthma, chronic obstructive pulmonary disease, or elevated depressive symptoms. Results were similar for reinfections. Conclusions and Relevance: In this cohort study, more than 1 in 5 adults did not recover within 3 months of SARS-CoV-2 infection. Recovery within 3 months was less likely in women and those with preexisting cardiovascular disease and more likely in those with COVID-19 vaccination or infection during the Omicron variant wave.