ImportanceThe 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.ObjectiveTo 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 ParticipantsThis 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.ExposuresCategories 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 MeasuresParticipant 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.ResultsAmong 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 RelevanceIn 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.

This work aimed to identify clinical practice guidelines (CPGs) that include recommendations for the prevention, diagnosis, and treatment of women’s malnutrition during pregnancy and to evaluate the quality of these guidelines using the Appraisal of Guidelines for Research and Evaluation (AGREE II) instrument. We conducted a literature review using PubMed and different websites from January 2009 to February 2021. The quality of the CPGs was independently assessed by reviewers using the AGREE II instrument, which defines guidelines scoring >70% in the overall assessment as “high quality”. The analysis included 43 guidelines. Among the main findings, we identified that only half of the CPGs (51.1%) obtained a final “high quality” evaluation. AGREE II results varied widely across domains and categories. The two domains that obtained the highest scores were scope and purpose with 88.3% (range 39 to 100%) and clarity of presentation with 87.2% (range 25 to 100%). Among the “high quality” CPGs, the best scores were achieved by the three guidelines published by the National Institute of Health and Care Excellence (NICE) and the World Health Organization (WHO). Due to the importance of maternal nutrition in pregnancy, it is essential to join forces to improve the quality of the guidelines, especially in CPGs that do not meet the reference standards for quality.

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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.

Background Alcohol expectancies are beliefs regarding positive (e.g., tension reduction) or negative (e.g., loss of motor coordination) effects of alcohol. Based on Social Learning Theory, social media can influence alcohol expectancies in adolescents. In particular, problematic social media use – which can reflect elements of addiction, including mood modification, tolerance, withdrawal, conflict, and relapse – could be linked to alcohol expectancies. We aimed to determine the associations between problematic social media use and alcohol expectancies in a national (U.S.) cohort of 10-14-year-old early adolescents. Methods We analyzed cross-sectional data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 9,008) at the Year 2 assessment (2018–2020). Unadjusted and adjusted linear regression analyses were conducted to examine the associations between problematic social media use and alcohol expectancies (positive and negative), adjusting for race/ethnicity, sex, household income, parent education, sexual orientation, parental marital status, and study site. Furthermore, we computed marginal predicted probabilities to aid in interpreting findings. Results The sample was 48.7% female and racially and ethnically diverse (43.0% non-White), with a mean age of 12.02 ± 0.66 years old. In models adjusted for confounders including both time spent on social media and problematic social media use, time spent on social media was not associated with positive or negative alcohol expectancies, but higher problematic social media use score was associated with higher positive (B = 0.045, 95% confidence interval [CI] 0.020–0.069) and negative (B = 0.072, 95% CI 0.043–0.101) alcohol expectancies scores. Conclusion Problematic social media use was associated with both positive and negative alcohol expectancies in a demographically diverse national sample of early adolescents in the U.S. Given the small effect sizes of the current study, future studies should further examine these relationships prospectively, as well as the mechanisms linking problematic social media use to alcohol expectancies and alcohol consumption. Because alcohol expectancies are modifiable and linked with alcohol initiation, they could be a target for future prevention efforts.