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Apply It!: • Adult caregivers should limit screen time in preschoolers to provide more opportunities for physical activity. • Schools should hire specialized PE teachers and aim to meet PE standards: daily, 225 minutes per week, of which 50% of time is spent in MVPA. • Organized sports should be redesigned so they are fun and enjoyable for children to enhance their active playtime. • Families should set physical activity goals and record progress to increase physical activity levels. Summary Statement: Preventing physical inactivity in youth starts in the preschool years and requires strategies targeting schools, caregivers, and families that limit excessive screen time and improve participation in PE, organized sports, and active play. Pulled Text: • Excessive screen time can displace time spent in structured and unstructured play, thereby directly lowering physical activity levels and could even indirectly lowering physical activity by impeding the development of motor skills and physical literacy. • There is growing evidence that physical activity improves cognition, which could enhance academic achievement. • The Project Play framework provides a roadmap to help children enter into organized sports, centered on the developmentally appropriate needs of children, and remain engaged throughout childhood. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to determine if replacing time spent in high- and low-impact physical activity (PA) predicts changes in pediatric bone mineral density (BMD) and content (BMC). Methods: We analyzed data from the longitudinal Bone Mineral Density in Childhood Study (N = 2337 with up to seven visits). The participants were age 5-19 yr at baseline, 51.2% were female, and 80.6% were nonblack. Spine, total hip, and femoral neck areal BMD and total body less head (TBLH) BMC Z-scores were calculated. Hours per day spent in high- and low-impact PA were self-reported. Standard covariate-adjusted (partition model) and time allocation-sensitive isotemporal substitution modeling frameworks were applied to linear mixed models. Statistical interactions with sex, self-reported ancestry, age, and bone fragility genetic scores (percentage of areal BMD-lowering alleles carried) were tested. Results: In standard models, high-impact PA was positively associated with bone Z-score at all four skeletal sites (e.g., TBLH-BMC Z-score: beta = 0.05, P = 2.0 x 10 -22), whereas low-impact PA was not associated with any of the bone Z-scores. In isotemporal substitution models, replacing 1 h⋅d-1 of low- for high-impact PA was associated with higher bone Z-scores (e.g., TBLH-BMC Z-score: beta = 0.06, P = 2.9 x 10-15). Conversely, replacing 1 h⋅d 1 of high-for low-impact PA was associated with lower bone Z-scores (e.g., TBLH-BMC Z-score: beta = - 0.06, P = 2.9 x 10 -15). The substitution associations were similar for each sex and ancestry group, and for those with higher and lower genetic scores for bone fragility (P-interactions > 0.05), but increased in strength among the older adolescents (P-age interactions < 0.05). Conclusions: Time-sensitive models suggest that replacing low-impact PA for high-impact PA would be beneficial for the growing skeleton in the majority of children. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to determine whether moderate to vigorous physical activity (MVPA) and sedentary behavior (SB) were independently associated with body mass index (BMI) and waist circumference (WC) in children and adolescents. Methods: Data from the International Children's Accelerometry Database were used to address our objectives (N = 11,115; 6-18 yr; 51% female). We calculated age- and gender-specific BMI and WC z-scores and used accelerometry to estimate MVPA and total SB. Self-reported television viewing was used as a measure of leisure time SB. Quantile regression was used to analyze the data. Results: MVPA and total SB were associated with lower and higher BMI and WC z-scores, respectively. These associations were strongest at the higher percentiles of the z-score distributions. After including MVPA and total SB in the same model, the MVPA associations remained, but the SB associations were no longer present. For example, each additional hour per day of MVPA was not associated with BMI z-score at the 10th percentile (b = -0.02, P = 0.170) but was associated with lower BMI z-score at the 50th (b = -0.19, P < 0.001) and 90th percentiles (b = -0.41, P < 0.001). More television viewing was associated with higher BMI and WC, and the associations were strongest at the higher percentiles of the z-score distributions, with adjustment for MVPA and total SB. Conclusions: Our observation of stronger associations at the higher percentiles indicates that increasing MVPA and decreasing television viewing at the population-level could shift the upper tails of the BMI and WC frequency distributions to lower values, thereby lowering the number of children and adolescents classified as obese. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to determine whether physical activity patterns are associated with sleep later at night and if nighttime sleep is associated with physical activity patterns the next day among adult women. Methods: Women (N = 353) living throughout the United States wore a wrist and a hip accelerometer for 7 d. Total sleep time (TST, hours per night) and sleep efficiency (SE, %) were estimated from the wrist accelerometer, and moderate to vigorous physical activity (MVPA, >1040 counts per minute, h x d-1) and sedentary behavior (SB, <100 counts per minute, h x d-1) were estimated from the hip accelerometer. Mixed-effects models adjusted for age, race, body mass index, education, employment, marital status, health status, and hip accelerometer wear time were used to analyze the data. Follow-up analyses using quantile regression were used to investigate associations among women with below average TST and MVPA and above average SB. Results: The average age of our sample was 55.5 yr (SD = 10.2 yr). The majority of participants were White (79%) and married (72%), and half were employed full time (49%). The participants spent on average 8.9 and 1.1 h x d-1 in SB and MVPA, respectively, and 6.8 h per night asleep. No associations were observed between MVPA and SB with nighttime TST or SE. There were no associations between nighttime TST and SE with MVPA or SB the next day. The findings were the same in the quantile regression analyses. Conclusion: In free-living adult women, accelerometry-estimated nighttime sleep and physical activity patterns were not associated with one another. On the basis of our observational study involving a sample of adult women, higher physical activity will not necessarily improve sleep at night on a day-to-day basis (and vice versa). [ABSTRACT FROM AUTHOR]

Purpose: We aimed to determine whether baseline sedentary behavior was associated with changes in body mass index (BMI) over 9 yr. Methods: Participants were enrolled into the National Institutes of Health American Association of Retired Persons (NIH-AARP) Diet and Health study in 1995-1996 (median age, 63 yr), and BMI was reported at baseline and 9 yr later (n = 158,436). Sitting time (<3 (referent), 3-4, 5-6, 7-8, or ≥9 h•d-1), television viewing (none, <1, 1-2, 3-4, 5-6, 7-8, or ≥9 h•d-1), and the covariates (age, sex, race, education, smoking, moderate-to-vigorous physical activity, caloric intake, and sleep duration) were reported at baseline. We used longitudinal quantile regression to model changes at the 10th, 25th, 50th, 75th, and 90th BMI percentiles. Results: More sitting at baseline was associated with additional increases in BMI over time, and the association was stronger at the upper BMI percentiles (e.g., <3 (referent) vs 5-6 h•d-1 of sitting additional increases: 50th percentile = 0.41 kg•m-2 and 95% confidence interval (CI) = 0.34-0.48; 90th percentile = 0.85 kg•m-2 and 95% CI = 0.72-0.98). Similar associations were observed between more television viewing at baseline and additional increases in BMI over time (e.g., no television (referent) vs 3-4 h•d-1 of television: 50th percentile = 1.96 kg•m-2 and 95% CI = 1.77-2.15; 90th percentile = 2.11 kg•m-2 and 95% CI = 1.49-2.73). Conclusions: Reducing sedentary behavior could help prevent an increase in BMI in adulthood especially at the upper percentiles of the BMI distribution and thereby reduce the prevalence of obesity. [ABSTRACT FROM AUTHOR]

A clear and rapid understanding of a health care organization's Quality Work Life is highly important as it can inform decision makers of issues that could impact their workers' psychological and physical health. Research has shown there is a relationship between workers' psychosocial work environment and their health and well-being. The Worklife Pulse Survey is a 21-item questionnaire providing a snapshot of health care organizations' Quality Work Life. This tool is deployed as part of Accreditation Canada's Qmentum accreditation program and is available both in English and in French. The present research sought to validate the 2 versions of the survey as well as the comparability of the generated data. The results obtained from 9578 French-speaking and 16 398 English-speaking respondents support the validity of both versions of the Worklife Pulse Survey as well as the comparability of the responses for all participants, for each gender separately, and for each age group separately. [ABSTRACT FROM AUTHOR]

Introduction: Epigenetic aging, a marker of biological aging measured by DNA methylation, may be affected by behaviors, including sleep and physical activity. However, investigations of physical activity and sleep with epigenetic aging among pediatric populations are scant and have not accounted for correlated behaviors., Methods: The study population included 472 Mexico City adolescents (52% female). Blood collection and 7-day wrist actigraphy (Actigraph GTX-BT) occurred during a follow-up visit when participants were 14.5 (2.09) years. Leukocyte DNA methylation was measured with the Infinium MethylationEPIC array after bisulfite conversion, and 9 epigenetic clocks were calculated. Sleep vs wake time was identified through a pruned dynamic programing algorithm, and physical activity was processed with Chandler cut-offs. Kmeans clustering was used to select actigraphy-assessed physical activity and sleep behavior clusters. Linear regression analyses were used to evaluate adjusted associations between the clusters and epigenetic aging., Results: There were 3 unique clusters: "Short sleep/high sedentary behavior", "Adequate sleep duration and late timing/low moderate or vigorous physical activity (MVPA)", and "Adequate sleep duration/high MVPA". Compared to the "Adequate duration/high MVPA", adolescents with "Adequate duration and late sleep timing/low MVPA" had more accelerated aging for the GrimAge clock (β = 0.63;95% CI 0.07, 1.19). In pubertal-stratified analyses, more mature adolescents in the "Adequate duration and late sleep timing/low MVPA group" had accelerated epigenetic aging. In contrast, females in the "Short sleep/high sedentary" group had decelerated epigenetic aging for the Wu pediatric clock., Conclusions: Associations between behavior clusters and epigenetic aging varied by pubertal status and sex. Contrary results in the Wu clock suggest the need for future research on pediatric-specific clocks., Competing Interests: Conflict of Interest and Funding Source: This work was supported by U.S. Environmental Protection Agency (US EPA) grants RD834800 and RD83543601 and National Institute for Environmental Health Sciences (NIEHS) grants P01 ES02284401, P30 ES017885 (Michigan Lifestage Environmental Exposures and Disease (M-LEEaD) NIEHS Core Center), R24 ES028502and R35 ES031686, and National Institute for Diabetes and Digestive and Kidney Disease P30 DK089503 (Michigan Nutrition Obesity Research Center). This study was also supported by National Heart, Lung, Blood Institute grant K01 HL151673., (Copyright © 2024 by the American College of Sports Medicine.)

Background: Solid organ transplant recipients (SOTRs) are less likely to mount an antibody response to SARS-CoV-2 mRNA vaccines. Understanding risk factors for impaired vaccine response can guide strategies for antibody testing and additional vaccine dose recommendations., Methods: Using a nationwide observational cohort of 1031 SOTRs, we created a machine learning model to explore, identify, rank, and quantify the association of 19 clinical factors with antibody responses to 2 doses of SARS-CoV-2 mRNA vaccines. External validation of the model was performed using a cohort of 512 SOTRs at Houston Methodist Hospital., Results: Mycophenolate mofetil use, a shorter time since transplant, and older age were the strongest predictors of a negative antibody response, collectively contributing to 76% of the model's prediction performance. Other clinical factors, including transplanted organ, vaccine type (mRNA-1273 versus BNT162b2), sex, race, and other immunosuppressants, showed comparatively weaker associations with an antibody response. This model showed moderate prediction performance, with an area under the receiver operating characteristic curve of 0.79 in our cohort and 0.67 in the external validation cohort. An online calculator based on our prediction model is available at http://transplantmodels.com/covidvaccine/ ., Conclusions: Our machine learning model helps understand which transplant patients need closer follow-up and additional doses of vaccine to achieve protective immunity. The online calculator based on this model can be incorporated into transplant providers' practice to facilitate patient-centric, precision risk stratification and inform vaccination strategies among SOTRs., Competing Interests: D.L.S. has the following financial disclosures: consulting and speaking honoraria from Sanofi, Novartis, CLS Behring, Jazz Pharmaceuticals, Veloxis, Mallinckrodt, Thermo Fisher Scientific, Regeneron, and AstraZeneca. R.K.A. has grant/research support from Aicuris, Astellas, Chimerix, Merck, Oxford Immunotec, Qiagen, and Takeda/Shire. M.L.L. is the Social Media Editor for Transplantation and is a consultant for Takeda/Shire and Patients Like Me. The other authors declare no conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Competing Interests: D.L.S. received consulting and speaking honoraria from Sanofi, Novartis, CLS Behring, Jazz Pharmaceuticals, Veloxis, Mallinckrodt, and Thermo Fisher Scientific. M.L.L. is the Social Media Editor for Transplantation. R.K.A. has grant/research support from Aicuris, Astellas, Chimerix, Merck, Oxford Immunotec, Qiagen, and Takeda/Shire. The other authors declare no conflicts of interest.

Competing Interests: D.L.S. has received consulting and speaking honoraria from Sanofi, Novartis, CLS Behring, Jazz Pharmaceuticals, Veloxis, Mallinckrodt, and Thermo Fisher Scientific. M.L.L. is the Social Media Editor for Transplantation. The other authors declare no conflicts of interest. J.L.A., J.M., T.P.-Y.C., A.T.A., B.J.B., R.K.A., A.A.R.T., M.L.L., A.B.M., J.M.G.-W., D.L.S., and W.A.W. participated in conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; drafting the work of revising it critically for important intellectual content; gave the final approval of the version to be published; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.