Associations Between School Transport and Obesity by Gender, Grade, Physical Activity, Race/Ethnicity, and Economic Disadvantage

Declining rates of active transportation to school have coincided with the childhood obesity epidemic. The contribution of school transport modes to obesity among children may vary by sociodemographic characteristics. PURPOSE: To examine the prevalence of school transport modes and obesity by gender, grade, physical activity, race/ethnicity, and economic disadvantage in a representative sample of Texas school children. METHODS: Cross-sectional data on reported sociodemographic characteristics, school transport mode, and physical activity behavior were collected from the Texas School Physical Activity and Nutrition (SPAN) Survey, 2015-2016. Measured height and weight were used to calculate BMI and classify 4th, 8th, and 11th grade students by obesity status. The sampling frame had 14,976 students from 359 schools to provide weighted state-level estimates by grade. Associations were conducted between school transport modes and obesity. Interaction terms were included to test if school transport mode-obesity associations differed by gender, grade, physical activity, race/ethnicity, or economic disadvantage. RESULTS: Active and passive school transport modes were not significantly associated with obesity (p\u3e0.05). Gender, grade, physical activity, race/ethnicity, and economic disadvantage were significantly associated with obesity (p\u3c0.05). Bike to school by race/ethnicity and walk to school by grade were significantly associated with obesity (p\u3c0.05), after controlling for all other sociodemographic characteristics. Hispanic/African American students who biked to school were significantly more likely to have obesity compared to White/Other students who did not bike to school (OR=5.48, p\u3c0.05, 95% CI: 1.25, 24.00). Students in 8th grade who walked to school were significantly less likely to have obesity than 4th/11th grade students who did not walk to school (OR=0.42, p\u3c0.05, 95% CI: 0.19, 0.91). CONCLUSION: These findings suggest that associations between active school transport modes and obesity differ by sociodemographic characteristics, including race/ethnicity and grade. Population-based approaches to childhood obesity prevention may benefit from understanding disparities in opportunities for school transport modes

Issues in accelerometer methodology: the role of epoch length on estimates of physical activity and relationships with health outcomes in overweight, post-menopausal women

<p>Abstract</p> <p>Background</p> <p>Current accelerometer technology allows for data collection using brief time sampling intervals (i.e., epochs). The study aims were to examine the role of epoch length on physical activity estimates and subsequent relationships with clinically-meaningful health outcomes in post-menopausal women.</p> <p>Methods</p> <p>Data was obtained from the Woman On the Move through Activity and Nutrition Study (n = 102). Differences in activity estimates presented as 60s and 10s epochs were evaluated using paired t-tests. Relationships with health outcomes were examined using correlational and regression analyses to evaluate differences by epoch length.</p> <p>Results</p> <p>Inactivity, moderate- and vigorous-intensity activity (MVPA) were significantly higher and light-intensity activity was significantly lower (all <it>P </it>< 0.001) when presented as 10s epochs. The correlation between inactivity and self-reported physical activity was stronger with 10s estimates (<it>P </it>< 0.03); however, the regression slopes were not significantly different. Conversely, relationships between MVPA and body weight, BMI, whole body and trunk lean and fat mass, and femoral neck bone mineral density was stronger with 60s estimates (all <it>P </it>< 0.05); however, regression slopes were similar.</p> <p>Conclusion</p> <p>These findings suggest that although the use of a shorter time sampling interval may suggestively reduce misclassification error of physical activity estimates, associations with health outcomes did not yield strikingly different results. Additional studies are needed to further our understanding of the ways in which epoch length contributes to the ascertainment of physical activity in research studies.</p> <p>Trial Registration</p> <p>Clinical Trials Identifier: NCT00023543</p

Contemporary screen time usage among children 9–10‐years‐old is associated with higher body mass index percentile at 1‐year follow‐up: A prospective cohort study

ObjectiveThere is a paucity of prospective research exploring the relationship among contemporary screen time modalities (e.g., video streaming, video chatting, texting and social networking) and body mass index (BMI) percentile. The objective of this study was to determine the prospective associations between screen time behaviours in a large and demographically diverse population-based cohort of 9-10-year-old children and BMI percentile at 1-year follow-up.MethodsWe analyzed prospective cohort data from the Adolescent Brain Cognitive Development (ABCD) Study (N&nbsp;=&nbsp;11 066). Multiple linear regression analyses were conducted to estimate associations between baseline screen time behaviours (exposure) and BMI percentile at 1-year follow-up, adjusting for race/ethnicity, sex, household income, parent education, depression, binge-eating disorder and baseline BMI percentile.ResultsEach additional hour of total screen time per day was prospectively associated with a 0.22 higher BMI percentile at 1-year follow-up (95% CI 0.10-0.34) after adjusting for covariates. When examining specific screen time behaviours, each additional hour of texting (B&nbsp;=&nbsp;0.92, 95% CI 0.29-1.55), video chat (B&nbsp;=&nbsp;0.72, 95% CI 0.09-1.36) and video games (B&nbsp;=&nbsp;0.42, 95% CI 0.06-0.78) was significantly prospectively associated with higher BMI percentile.ConclusionsScreen time is prospectively associated with a higher BMI percentile 1 year later among children 9-10 years old

Measurement of the cross-section and charge asymmetry of WW bosons produced in proton-proton collisions at s=8\sqrt{s}=8 TeV with the ATLAS detector

This paper presents measurements of the $W^+ \rightarrow \mu^+\nu$ and $W^- \rightarrow \mu^-\nu$ cross-sections and the associated charge asymmetry as a function of the absolute pseudorapidity of the decay muon. The data were collected in proton--proton collisions at a centre-of-mass energy of 8 TeV with the ATLAS experiment at the LHC and correspond to a total integrated luminosity of 20.2~\mbox{fb^{-1}}. The precision of the cross-section measurements varies between 0.8% to 1.5% as a function of the pseudorapidity, excluding the 1.9% uncertainty on the integrated luminosity. The charge asymmetry is measured with an uncertainty between 0.002 and 0.003. The results are compared with predictions based on next-to-next-to-leading-order calculations with various parton distribution functions and have the sensitivity to discriminate between them.Comment: 38 pages in total, author list starting page 22, 5 figures, 4 tables, submitted to EPJC. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/STDM-2017-13

Search for chargino-neutralino production with mass splittings near the electroweak scale in three-lepton final states in √s=13 TeV pp collisions with the ATLAS detector

A search for supersymmetry through the pair production of electroweakinos with mass splittings near the electroweak scale and decaying via on-shell W and Z bosons is presented for a three-lepton final state. The analyzed proton-proton collision data taken at a center-of-mass energy of √s=13  TeV were collected between 2015 and 2018 by the ATLAS experiment at the Large Hadron Collider, corresponding to an integrated luminosity of 139  fb−1. A search, emulating the recursive jigsaw reconstruction technique with easily reproducible laboratory-frame variables, is performed. The two excesses observed in the 2015–2016 data recursive jigsaw analysis in the low-mass three-lepton phase space are reproduced. Results with the full data set are in agreement with the Standard Model expectations. They are interpreted to set exclusion limits at the 95% confidence level on simplified models of chargino-neutralino pair production for masses up to 345 GeV

Taking the next step: a systematic review and meta-analysis of physical activity and behavior change interventions in recent post-treatment breast cancer survivors

Research has shown that recent post-treatment breast cancer survivors face significant challenges around physical activity as they transition to recovery. This review examined randomized controlled trials targeting physical activity behavior change in breast cancer survivors <5 years post-treatment and describes 1) characteristics of interventions for breast cancer survivors as well as 2) effect size estimates for these studies

Search for direct stau production in events with two hadronic tau-leptons in root s=13 TeV pp collisions with the ATLAS detector

A search for the direct production of the supersymmetric partners ofτ-leptons (staus) in final stateswith two hadronically decayingτ-leptons is presented. The analysis uses a dataset of pp collisions corresponding to an integrated luminosity of139fb−1, recorded with the ATLAS detector at the LargeHadron Collider at a center-of-mass energy of 13 TeV. No significant deviation from the expected StandardModel background is observed. Limits are derived in scenarios of direct production of stau pairs with eachstau decaying into the stable lightest neutralino and oneτ-lepton in simplified models where the two staumass eigenstates are degenerate. Stau masses from 120 GeV to 390 GeV are excluded at 95% confidencelevel for a massless lightest neutralino

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

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

Social epidemiology of cardiometabolic risk factors in early adolescents

BackgroundTo estimate associations between sociodemographic factors and cardiometabolic risk factors among a demographically diverse sample of U.S. adolescents aged 10-14 years.MethodsThis study analyzed data from the Adolescent Brain Cognitive Development (ABCD) Study (N&nbsp;=&nbsp;1412), Years 2 and 3 (2018-2021). Cardiometabolic risk factors including hemoglobin A1c and cholesterol (total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and non-HDL-C) were assessed. Multivariable linear regression models were conducted to estimate the associations between sociodemographic factors (age, sex, race and ethnicity, household income, and parental education) and cardiometabolic risk factors (hemoglobin A1c, TC, HDL-C, and non-HDL-C).ResultsThe average hemoglobin A1c level was 5.2 % (±0.4 %), the average TC level was 156.6 (±28.9) mg/dL, and the average HDL-C level was 56.0 (±12.9) mg/dL. Out of our sample, 0.5&nbsp;% had diabetes (hemoglobin A1c&nbsp;≥&nbsp;6.5&nbsp;%), 7.6&nbsp;% had high TC (≥200&nbsp;mg/dL), and 7.4&nbsp;% had low HDL-C (&lt;40&nbsp;mg/dL). Older age was associated with lower TC, HDL-C, and non-HDL-C levels. Male sex was associated with higher hemoglobin A1c (beta coefficient [B] 0.04; 95&nbsp;% confidence interval [CI], 0.00, 0.08; p&nbsp;=&nbsp;0.037) and lower TC (B -3.14; 95&nbsp;% CI, -6.17, -0.11; p&nbsp;=&nbsp;0.042) compared to female sex. Black and Native American race and ethnicity were associated with higher hemoglobin A1c compared to White race. Higher household income was associated with higher TC and HDL-C.ConclusionThis study of a diverse population of early adolescents identified sociodemographic differences in hemoglobin A1c and cholesterol levels that can inform clinical and public health interventions