Adiposity, fitness, health-related quality of life and the reallocation of time between children’s school day activity behaviours: a compositional data analysis

Sedentary time (ST), light (LPA), and moderate-to-vigorous physical activity (MVPA) constitute the range of school day activity behaviours. This study investigated whether the composition of school activity behaviours was associated with health indicators, and the predicted changes in health when time was reallocated between activity behaviours. Accelerometers were worn for 7-days between October and December 2010 by 318 UK children aged 10-11, to provide estimates of school day ST, LPA, and MVPA. BMI z-scores and percent waist-to-height ratio were calculated as indicators of adiposity. Cardiorespiratory fitness (CRF) was assessed using the 20-m Shuttle Run Test. The PedsQLTM questionnaire was completed to assess psychosocial and physical health-related quality of life (HRQL). Log-ratio multiple linear regression models predicted health indicators for the mean school day activity composition, and for new compositions where fixed durations of time were reallocated from one activity behaviour to another, while the remaining behaviours were unchanged. The school day activity composition significantly predicted adiposity and CRF (p=0.04-0.002), but not HRQL. Replacing MVPA with ST or LPA around the mean activity composition predicted higher adiposity and lower CRF. When ST or LPA were substituted with MVPA, the relationships with adiposity and CRF were asymmetrical with favourable, but smaller predicted changes in adiposity and CRF than when MVPA was replaced. Predicted changes in HRQL were negligible. The school day activity composition significantly predicted adiposity and CRF but not HRQL. Reallocating time from ST and LPA to MVPA is advocated through comprehensive school physical activity promotion approaches

Promoting healthy weight in primary school children through physical activity and nutrition education: a pragmatic evaluation of the CHANGE! randomised intervention study

Background: This pragmatic evaluation investigated the effectiveness of the Children’s Health, Activity and Nutrition: Get Educated! (CHANGE!) Project, a cluster randomised intervention to promote healthy weight using an educational focus on physical activity and healthy eating. Methods: Participants (n = 318, aged 10–11 years) from 6 Intervention and 6 Comparison schools took part in the 20 weeks intervention between November 2010 and March/April 2011. This consisted of a teacher-led curriculum, learning resources, and homework tasks. Primary outcome measures were waist circumference, body mass index (BMI), and BMI z-scores. Secondary outcomes were objectively-assessed physical activity and sedentary time, and food intake. Outcomes were assessed at baseline, at post-intervention (20 weeks), and at follow-up (30 weeks). Data were analysed using 2-level multi-level modelling (levels: school, student) and adjusted for baseline values of the outcomes and potential confounders. Differences in intervention effect by subgroup (sex, weight status, socio-economic status) were explored using statistical interaction. Results: Significant between-group effects were observed for waist circumference at post-intervention (β for intervention effect =−1.63 (95% CI = −2.20, -1.07) cm, p<0.001) and for BMI z-score at follow-up (β=−0.24 (95% CI = −0.48, -0.003), p=0.04). At follow-up there was also a significant intervention effect for light intensity physical activity (β=25.97 (95% CI = 8.04, 43.89) min, p=0.01). Interaction analyses revealed that the intervention was most effective for overweight/obese participants (waist circumference: β=−2.82 (95% CI = −4.06, -1.58) cm, p<0.001), girls (BMI: β=−0.39 (95% CI = −0.81, 0.03) kg/m2, p=0.07), and participants with higher family socioeconomic status (breakfast consumption: β=8.82 (95% CI = 6.47, 11.16), p=0.07). Conclusions: The CHANGE! intervention positively influenced body size outcomes and light physical activity, and most effectively influenced body size outcomes among overweight and obese children and girls. The findings add support for the effectiveness of combined school-based physical activity and nutrition interventions. Additional work is required to test intervention fidelity and the sustained effectiveness of this intervention in the medium and long term

Sustained Effects of Incredible Years as a Preventive Intervention in Preschool Children with Conduct Problems

The present study evaluated preventive effects of the Incredible Years program for parents of preschool children who were at risk for a chronic pattern of conduct problems, in the Netherlands. In a matched control design, 72 parents of children with conduct problems received the Incredible Years program. These families (intervention group) were compared with 72 families who received care as usual (control group). Two years after termination of the intervention, it appeared that observed and selfrated parenting skills were significantly improved in the intervention group. Likewise, in this group, observed child conduct problems showed sustained intervention effects. The decrease in observed critical parenting mediated the decrease in observed child conduct problems over time. In addition, it appeared that parental influence increased over time

A Data Science and Machine Learning Approach to Measure and Monitor Physical Activity in Children

Physical Activity is a fundamental component for the maintenance of a healthy lifestyle. Recommendations for physical activity levels are issued by most governments as part of public health measures. Therefore, it is vital for regulatory purposes, that there are reliable measurements of physical activity. However, the techniques and protocols used in existing physical activity research, including laboratory-based measurement, have received increasingly critical scrutiny in recent times. Consequently, physical activity researchers have begun to explore the use of wearable sensing technology to capture large amounts of data and the use of machine learning techniques, specifically artificial neural networks, to produce classifications for specific physical activity events. This paper explores this idea further and presents a supervised machine learning approach that utilises data obtained from accelerometer sensors worn by children in free-living environments. The paper posits a rigorous data science approach that presents a set of activities and features suitable for measuring physical activity in children in free-living environments. A Multilayer Perceptron neural network is used to classify physical activities by activity type, using ecologically valid data from body worn accelerometer sensors. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 92% using the initial data set, and 99.8% using interpolated cases

Lifestyle Behaviors Associated With Body Fat Percent in 9- to 11-Year-Old Children

Purpose:To examine (1) associations between body fat percent (BF) and lifestyle behaviors in children aged 9–11 years and (2) the consistency of these associations over a 10-year period. Methods: In this repeat, cross-sectional study, 15,977 children aged 9–11 years completed an anthropometric assessment and the SportsLinx Lifestyle survey between 2004 and 2013. Body fat was estimated according to the sum of the triceps and subscapular skinfold measurements. Multilevel models were utilized to examine associations between BF and responses to the lifestyle survey while controlling for known covariates. Results: Lifestyle behaviors explained 8.6% of the total variance in body fat. Specifically, negative associations were found between BF and active transport to school ( β = −0.99 [0.19], P < .001), full-fat milk (−0.07 [0.15], P < .001), and sweetened beverage consumption (−0.40 [0.15], P = .007). Relative to the reference group of ≤8:00 PM, later bedtime was positively associated with BF: 8:00 to 8:59 PM ( β = 1.60 [0.26], P < .001); 9:00 to 10:00 PM ( β = 1.04 [0.24], P < .001); ≥10:00 PM ( β = 1.18 [0.30], P < .001). Two-way interactions revealed opposing associations between BF and the consumption of low-calorie beverages for boys ( β = 0.95 [0.25], P < .001) and girls ( β = −0.85 [0.37], P = .021). There was no significant change in these associations over a 10-year period. Conclusions: In this population-level study covering a decade of data collection, lifestyle behaviors were associated with BF. Policies and interventions targeting population-level behavior change, such as active transport to school, sleep time, and consumption of full-fat milk, may offer an opportunity for improvements in BF

Cardiorespiratory fitness predicts clustered cardiometabolic risk in 10-11.9 year olds

The aim of this study was to investigate levels of clustered cardiometabolic risk and the odds of being ‘at risk’ according to cardiorespiratory fitness status in children. Data from 88 10–11.9-year-old children (mean age 11.05 ± 0.51 years), who participated in either the REACH Year 6 or the Benefits of Fitness Circuits for Primary School Populations studies were combined. Waist circumference, systolic blood pressure, diastolic blood pressure, glucose, triglycerides, high-density lipoprotein cholesterol, adiponectin and C-reactive protein were assessed and used to estimate clustered cardiometabolic risk. Participants were classified as ‘fit’ or ‘unfit’ using recently published definitions (46.6 and 41.9 mL/kg/min for boys and girls, respectively), and continuous clustered risk scores between fitness groups were assessed. Participants were subsequently assigned to a ‘normal’ or ‘high’ clustered cardiometabolic risk group based on risk scores, and logistic regression analysis assessed the odds of belonging to the increased cardiometabolic risk group according to fitness. The unfit group exhibited significantly higher clustered cardiometabolic risk scores (p < 0.001) than the fit group. A clear association between fitness group and being at increased cardiometabolic risk (B = 2.509, p = 0.001) was also identified, and participants classed as being unfit were found to have odds of being classified as ‘at risk’ of 12.30 (95 % CI = 2.64–57.33).\ud \ud Conclusion Assessing cardiorespiratory fitness is a valid method of identifying children most at risk of cardiometabolic pathologies. The ROC thresholds could be used to identify populations of children most at risk and may therefore be used to effectively target a cardiometabolic risk-reducing public health intervention

Individual calibration of accelerometers in children and their health-related implications

This study compared children’s physical activity (PA) levels, the prevalence of children meeting current guidelines of ≥60 minutes of daily moderate to vigorous PA (MVPA), and PA-health associations using individually calibrated (IC) and empirical accelerometer cutpoints. Data from 75 (n = 32 boys) 10-12 year old children were included in this study. Clustered cardiometabolic (CM) risk, directly measured cardiorespiratory fitness (CRF), anthropometric and 7 day accelerometer data were included within analysis. PA data were classified using Froude anchored IC, Evenson et al., 2008 (Ev) and Mackintosh et al., 2012 (Mack) cutpoints. The proportion of the cohort meeting ≥60mins MVPA/day ranged from 37%-56% depending on the cutpoints used. Reported PA differed significantly across the cutpoint sets. IC LPA and MPA were predictors of CRF (LPA: standardised β = 0.32, p = 0.002, MPA: standardised β = 0.27 p = 0.013). IC MPA also predicted BMI Z-score (standardised β = -0.35, p = 0.004). Ev VPA was a predictor of BMI Z-score (standardised β = -0.33, p = 0.012). Cutpoint choice has a substantial impact on reported PA levels though no significant associations with CM risk were observed. Froude IC cut points represent a promising approach towards classifying children’s PA data

Associations between cardiorespiratory fitness, physical activity and clustered cardiometabolic risk in children and adolescents: the HAPPY study

Clustering of cardiometabolic risk factors can occur during childhood and predisposes individuals to cardiometabolic disease. This study calculated clustered cardiometabolic risk in 100 children and adolescents aged 10-14 years (59 girls) and explored differences according to cardiorespiratory fitness (CRF) levels and time spent at different physical activity (PA) intensities. CRF was determined using a maximal cycle ergometer test, and PA was assessed using accelerometry. A cardiometabolic risk score was computed as the sum of the standardised scores for waist circumference, blood pressure, total cholesterol/high-density lipoprotein ratio, triglycerides and glucose. Differences in clustered cardiometabolic risk between fit and unfit participants, according to previously proposed health-related threshold values, and between tertiles for PA subcomponents were assessed using ANCOVA. Clustered risk was significantly lower (p < 0.001) in the fit group (mean 1.21 ± 3.42) compared to the unfit group (mean -0.74 ± 2.22), while no differences existed between tertiles for any subcomponent of PA. Conclusion These findings suggest that CRF may have an important cardioprotective role in children and adolescents and highlights the importance of promoting CRF in youth

High-order chromatin architecture determines the landscape of chromosomal alterations in cancer

The rapid growth of cancer genome structural information provides an opportunity for a better understanding of the mutational mechanisms of genomic alterations in cancer and the forces of selection that act upon them. Here we test the evidence for two major forces, spatial chromosome structure and purifying (or negative) selection, that shape the landscape of somatic copy-number alterations (SCNAs) in cancer1. Using a maximum likelihood framework we compare SCNA maps and three-dimensional genome architecture as determined by genome-wide chromosome conformation capture (HiC) and described by the proposed fractal-globule (FG) model2. This analysis provides evidence that the distribution of chromosomal alterations in cancer is spatially related to three-dimensional genomic architecture and additionally suggests that purifying selection as well as positive selection shapes the landscape of SCNAs during somatic evolution of cancer cells