BMI change, fitness change and cardiometabolic risk factors among 8th grade youth

Effectiveness of Physical-Literacy-Based Online Education on Indices of Physical Fitness in High-School Adolescents: Intervention Study during the COVID-19 Pandemic Period

There is a global consensus that physical literacy (PL) is an important determinant of physical fitness (PF), but studies have rarely examined the effects of PL-based interventions on PF. The aim of this research was to evaluate the effectiveness of specific online video-based PL intervention on PF indices in high-school students from Croatia. Participants were 423 high-school adolescents (295 girls, 128 boys; 14-to-18 years of age), divided into an intervention group (n = 230) and a control group (n = 193). The intervention lasted 12 weeks. Educational video materials were disseminated to the intervention group by the closed social network during the pandemic period. Variables included height, mass, BMI, cardiorespiratory fitness (CRF), and power, strength, and flexibility indices. Pre- to post-testing design was applied, with two-way analysis of variance for repeated measurement (Time × Group). Applied intervention induced positive effects in CRF (the intervention group improved their capacities, while no changes occurred in the control group) and BMI (the intervention group retained their BMI levels at the pre-testing level, while BMI of the control group slightly increased over the course of the study), with better effectiveness in girls than in boys. No effects were evidenced for other variables. The positive effects of applied educational intervention on BMI and CRF are encouraging knowing that both indices are related to health status.

The Physical Behaviour Intensity Spectrum and Body Mass Index in School-Aged Youth: A Compositional Analysis of Pooled Individual Participant Data

We examined the compositional associations between the intensity spectrum derived from incremental acceleration intensity bands and the body mass index (BMI) z-score in youth, and investigated the estimated differences in BMI z-score following time reallocations between intensity bands. School-aged youth from 63 schools wore wrist accelerometers, and data of 1453 participants (57.5% girls) were analysed. Nine acceleration intensity bands (range: 0−50 mg to ≥700 mg) were used to generate time-use compositions. Multivariate regression assessed the associations between intensity band compositions and BMI z-scores. Compositional isotemporal substitution estimated the differences in BMI z-score following time reallocations between intensity bands. The ≥700 mg intensity bandwas strongly and inversely associated with BMI z-score (p < 0.001). The estimated differences in BMI z-score when 5 min were reallocated to and from the ≥700 mg band and reallocated equally among the remaining bands were −0.28 and 0.44, respectively (boys), and −0.39 and 1.06, respectively (girls). The time in the ≥700 mg intensity band was significantly associated with BMI z-score, irrespective of sex. When even modest durations of time in this band were reallocated, the asymmetrical estimated differences in BMI z-score were clinically meaningful. The findings highlight the utility of the full physical activity intensity spectrum over a priori-determined absolute intensity cut-point approaches.

Speed of Movement, Fatness, and the Change in Cardiometabolic Risk Factors in Children

We aimed to examine speed of movement and its interactive association with fatness to changes in cardiometabolic risk factors over one year in children. The analysis included 8345 children aged 6-13 years. Cardiometabolic risk score was computed by summing Z-scores of waist circumference, the average of systolic and diastolic blood pressure, fasting glucose, high-density lipoprotein cholesterol (multiplied by -1), and triglycerides. Both high baseline and improvement in speed of movement were associated with favourable changes in percent body fat, lipids, and cardiometabolic risk score. Percentages of the association between baseline speed of movement and changes in cardiometabolic risk score, triglycerides, and high-density lipoprotein cholesterol explained by baseline BMI were 24.6% (19.6-29.1%), 26.2% (19.7-31.1%), and 12.5% (9.6-15.4%), respectively. The corresponding number for percent body fat was 47.0% (40.4-54.1%), 43.3% (36.7-51.7%), and 29.8% (25.0-34.6%), respectively. Speed of movement mediated the association between fatness and cardiometabolic risk factors. Improved speed of movement was associated with a lower increase in blood pressure in obese children only. Speed of movement is a strong predictor of changes in cardiometabolic risk factors. Fatness and speed of movement are interactively associated with cardiometabolic risk factors. Speed of movement may attenuate the positive association between fatness and blood pressure.

Reciprocal Longitudinal Relationship Between Fitness, Fatness, and Metabolic Syndrome in Brazilian Children and Adolescents: A 3-Year Longitudinal Study

PURPOSE

To verify the reciprocal longitudinal relationships between cardiorespiratory fitness (CRF), percentage body fat (%body fat), and metabolic syndrome in Brazilian primary school students.

METHOD

This longitudinal study involved 420 children and adolescents followed for 3 years (2011-2014). The continuous Metabolic Syndrome (cMetSyn) score was calculated by summing adjusted z scores of glucose, systolic blood pressure, total cholesterol/high-density lipoprotein cholesterol ratio, triglycerides, and waist circumference. The CRF was assessed using running/walking tests, and %body fat was assessed through sex-specific 2-site skinfold thickness. Cross-lagged panel models were used to analyze longitudinal reciprocal relationships between CRF and %body fat with cMetSyn.

RESULTS

Results indicated that 2011 %body fat significantly predicted both 2014 CRF scores and 2014 cMetSyn scores (P < .001); however, 2011 CRF only predicted 2014 %body fat (P < .001) but not 2014 cMetSyn (P = .103). Furthermore, 2011 cMetSyn predicted 2014 %body fat (P = .002). The model explained 36%, 48%, and 37% of the variance in 2014 CRF, %body fat, and cMetSyn, respectively.

CONCLUSION

The results suggest a reciprocal inverse relationship between %body fat and metabolic syndrome risk and that %body fat may play a more important role in the risk of developing metabolic syndrome compared with CRF.

The mediating role of adiposity in the longitudinal association between cardiorespiratory fitness and blood pressure in adolescents: LabMed cohort study

BACKGROUND

The aim of this prospective cohort study was to examine whether the association between the cardiorespiratory fitness (CRF) at baseline and blood pressure (BP) at follow-up is mediated by adiposity in adolescents.

MATERIALS AND METHODS

The sample comprised 734 adolescents (349 girls) aged 12-18 years from the LabMed Physical Activity Cohort Study. The variables of interest were measured in 2011 (baseline) and in 2013 (2-year follow-up). CRF was assessed by the 20-m shuttle run test. Body mass index, waist circumference, body fat percentage, pubertal status and resting BP were assessed according to standard procedures. Boot-strapped mediation procedures were performed, and indirect effects (IE) with confidence intervals (CI) not including zero were considered statistically significant.

RESULTS

After adjusting for potential confounders, body mass index acted as a mediator of the relationship between CRF and systolic BP (IE = -0. 023; CI = -0.039; -0.009), pulse pressure (IE = -0.023; CI = -0.034; -0.012) and rate product pressure (IE = -2.839; CI = -5.329; -0.340). Similar results were obtained for waist circumference as mediator for systolic BP (IE = -0.019; CI = -0.033; -0.005), pulse pressure (IE = -0.017; CI = -0.028; -0.007) and rate product pressure (IE = -3.793; CI = -6.097; -1.689). Likewise, body fat percentage mediated the association for: systolic BP (IE = -0.029; CI = -0.048; -0.010), pulse pressure (IE = -0.027; CI = -0.041; -0.013) and rate product pressure (IE = -4.280; CI = -7.488; -1.264).

CONCLUSIONS

Adiposity mediated the association between CRF and BP in adolescents. Therefore, both optimal CRF and adiposity levels are important to maintain normal BP ranges throughout adolescence.

Association of Cardiorespiratory Fitness Levels During Youth With Health Risk Later in Life: A Systematic Review and Meta-analysis

IMPORTANCE

Although the associations between cardiorespiratory fitness (CRF) and health in adults are well understood, to date, no systematic review has quantitatively examined the association between CRF during youth and health parameters later in life.

OBJECTIVES

To examine the prospective association between CRF in childhood and adolescence and future health status and to assess whether changes in CRF are associated with future health status at least 1 year later.

DATA SOURCES

For this systematic review and meta-analysis, MEDLINE, Embase, and SPORTDiscus electronic databases were searched for relevant articles published from database inception to January 30, 2020.

STUDY SELECTION

The following inclusion criteria were used: CRF measured using a validated test and assessed at baseline and/or its change from baseline to the end of follow-up, healthy population with a mean age of 3 to 18 years at baseline, and prospective cohort design with a follow-up period of at least 1 year.

DATA EXTRACTION AND SYNTHESIS

Data were processed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Random-effects models were used to estimate the pooled effect size.

MAIN OUTCOMES AND MEASURES

Anthropometric and adiposity measurements and cardiometabolic health parameters.

RESULTS

Fifty-five studies were included with a total of 37 563 youths (46% female). Weak-moderate associations were found between CRF at baseline and body mass index (r = -0.11; 95% CI, -0.18 to -0.04; I2 = 59.03), waist circumference (r = -0.29; 95% CI, -0.42 to -0.14; I2 = 69.42), skinfold thickness (r = -0.34; 95% CI, -0.41 to -0.26; I2 = 83.87), obesity (r = -0.15; 95% CI, -0.23 to -0.06; I2 = 86.75), total cholesterol level (r = -0.12; 95% CI, -0.19 to -0.05; I2 = 75.81), high-density lipoprotein cholesterol (HDL-C) level (r = 0.11; 95% CI, 0.05-0.18; I2 = 69.06), total cholesterol to HDL-C ratio (r = -0.19; 95% CI, -0.26 to -0.13; I2 = 67.07), triglyceride levels (r = -0.10; 95% CI, -0.18 to -0.02; I2 = 73.43), homeostasis model assessment for insulin resistance (r = -0.12; 95% CI, -0.18 to -0.06; I2 = 68.26), fasting insulin level (r = -0.07; 95% CI, -0.11 to -0.03; I2 = 0), and cardiometabolic risk (r = -0.18; 95% CI, -0.29 to -0.07; I2 = 90.61) at follow-up. Meta-regression analyses found that early associations in waist circumference (β = 0.014; 95% CI, 0.002-0.026), skinfold thickness (β = 0.006; 95% CI, 0.002-0.011), HDL-C level (β = -0.006; 95% CI, -0.011 to -0.001), triglyceride levels (β = 0.009; 95% CI, 0.004-0.014), and cardiometabolic risk (β = 0.007; 95% CI, 0.003-0.011) from baseline to follow-up dissipated over time. Weak-moderate associations were found between change in CRF and body mass index (r = -0.17; 95% CI, -0.24 to -0.11; I2 = 39.65), skinfold thickness (r = -0.36; 95% CI, -0.58 to -0.09; I2 = 96.84), obesity (r = -0.21; 95% CI, -0.35 to -0.06; I2 = 91.08), HDL-C level (r = 0.05; 95% CI, 0.02-0.08; I2 = 0), low-density lipoprotein cholesterol level (r = -0.06; 95% CI, -0.11 to -0.01; I2 = 58.94), and cardiometabolic risk (r = -0.08; 95% CI, -0.15 to -0.02; I2 = 69.53) later in life.

CONCLUSIONS AND RELEVANCE

This study suggests that early intervention and prevention strategies that target youth CRF may be associated with maintaining health parameters in later life.

Youth sports participation and health status in early adulthood: A 12-year follow-up

Examining association between serial participation in youth organized sports (OS) and concurrent cardiometabolic risk factors, with long-term health status, will aid understanding the role of OS participation. Combining data from a prospective study and a follow-up survey, we aimed to determine association between youth OS participation and cardiometabolic risk factors with health and physical activity (PA) in young adulthood. Cardiometabolic risk factors were monitored yearly, and OS involvement through middle school, high school, and college, together with current status was recorded 12 years after the initial study began; 462 participants completed follow-up. Cardiometabolic risk factors included: body mass index (BMI), waist circumference, blood pressure, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL) cholesterol and triglycerides, fasting glucose and insulin. Participants continuing OS participation from middle to high school and/or college had significantly lower BMI/BMI z-scores [24.7 vs 27.4 (p < 0.05) and 0.51 vs 0.82 (p < 0.05), respectively] and significantly higher HDL [48.7 vs 45.4 (p ≤ 0.05)] than those without OS participation after middle school. Waist circumference of females was significantly smaller in those who participated in OS from middle to high school and/or college (84.9 cm) compared to those who had no OS (92.1 cm), (p < 0.05). Participants continuing OS from middle to high school and/or college reported significantly higher follow-up PA levels than those who had no OS after middle school (p < 0.001). This study provides novel data showing serial participation in OS from middle to high school and/or college was associated with a superior cardiometabolic health profile in school and increased PA levels at 12-year follow-up.

Independent and Interactive Associations of Fitness and Fatness With Changes in Cardiometabolic Risk in Children: A Longitudinal Analysis

Background: Findings for associations between cardiorespiratory fitness (CRF) and cardiometabolic risk (CMR) factors are inconsistent, and the interactive association between CRF and fatness with CMR factors is unclear in children. Our study aimed to examine whether CRF and fatness are independently and interactively associated with CMR factors. Methods: We included 5,869 children aged 6-13 years in the analysis. Physical examinations, blood tests, and CRF were measured at baseline and 1 year later. Cardiometabolic risk score (CMRS) was computed by summing Z scores of waist circumference (WC), averaged systolic and diastolic blood pressure, glucose, high-density lipoprotein cholesterol (HDL-C, multiplied by -1), and triglycerides. Results: There was a high correlation between fatness and CRF in both boys and girls. High baseline CRF was independently associated with favorable changes in CMRS, BMI, WC, percent body fat (PBF), total cholesterol, LDL-C, and HDL-C (all P < 0.025). Improved CRF was independently associated with favorable changes in CMRS, BMI, WC, PBF, total cholesterol, LDL-C, HDL-C, triglycerides, and fasting glucose (all P < 0.0321). Baseline BMI was positively associated with changes in CMRS, WC, blood pressure, triglycerides, insulin, and HOMA-IR (all P < 0.0462). Low PBF at baseline was associated with favorable changes in CMRS, BMI, WC, blood pressure, HDL-C, triglycerides, insulin, and HOMA-IR (all P < 0.0423). The percentage of the total effect of baseline CRF on changes in CMRS, triglycerides, HDL-C, PBF, and WC mediated by baseline BMI was 66.0, 61.6, 40.3, 20.7, and 9.2%, respectively. Baseline CRF was a significant mediator for the association between baseline BMI and changes in CMRS (mediated by 4.3%), triglycerides (5.1%), and HDL-C (12.0%). An inverse association was found between baseline CRF and CMRS in children with high baseline BMI/PBF only. Improved CRF was associated with decreased BMI and WC in children with low baseline CRF. Conclusions: Fatness and CRF are each independently associated with changes in CMR factors. Fatness is a major mediator for the association between CRF and CMR factors, whereas the association between fatness and CMR factors is also mediated by CRF. The beneficial effect of high CRF on CMR factors was more evident in obese or unfit children.

Cardiometabolic Risk Factors and Physical Activity Patterns Maximizing Fitness and Minimizing Fatness Variation in Malaysian Adolescents: A Novel Application of Reduced Rank Regression

Patterns of physical activity (PA) that optimize both fitness and fatness may better predict cardiometabolic health. Reduced rank regression (RRR) was applied to identify combinations of the type (e.g., football vs. skipping), location and timing of activity, explaining variation in cardiorespiratory fitness (CRF) and Body Mass Index (BMI). Multivariable regressions estimated longitudinal associations of PA pattern scores with cardiometabolic health in n = 579 adolescents aged 13–17 years from the Malaysian Health and Adolescent Longitudinal Research Team study. PA pattern scores in boys were associated with higher fitness (r = 0.3) and lower fatness (r = −0.3); however, in girls, pattern scores were only associated with higher fitness (r = 0.4) (fatness, r = −0.1). Pattern scores changed by β = −0.01 (95% confidence interval (CI) −0.04, 0.03) and β = −0.08 (95% CI −0.1, −0.06) per year from 13 to 17 years in boys and girls respectively. Higher CRF and lower BMI were associated with better cardiometabolic health at 17 years, but PA pattern scores were not in either cross-sectional or longitudinal models. RRR identified sex-specific PA patterns associated with fitness and fatness but the total variation they explained was small. PA pattern scores changed little through adolescence, which may explain the limited evidence on health associations. Objective PA measurement may improve RRR for identifying optimal PA patterns for cardiometabolic health.

ANTHROPOMETRY AND CLUSTERED CARDIOMETABOLIC RISK FACTORS IN YOUNG PEOPLE: A SYSTEMATIC REVIEW

OBJECTIVE

To conduct a systematic review of the literature on the ability of anthropometric indicators to predict clustered cardiometabolic risk factors (CMRF) in children and adolescents.

DATA SOURCE

Studies published from June 1st, 2011 to May 31st, 2016 in the PubMed, SciELO and LILACS databases were analyzed. The research was based on keywords derived from the terms "anthropometric indicators" AND "cardiometabolic risk factors". Observational studies on the ability of anthropometric indicators as predictors of clustered CMRF in children and adolescents in Portuguese, English and Spanish languages were included. Studies with a specific group of obese patients or with other diseases were not included.

DATA SYNTHESIS

Of the 2,755 articles retrieved, 31 were selected for systematic review. Twenty-eight studies analyzed body mass index (BMI) as a predictor of clustered CMRF. Only 3 of the 25 cross-sectional studies found no association between anthropometric indicators and clustered CMRF. The results of six studies that compared the predictive ability of different anthropometric measures for clustered CMRF were divergent, and it was not possible to define a single indicator as the best predictor of clustered CMRF. Only six articles were cohort studies, and the findings suggested that changes in adiposity during childhood predict alterations in the clustered CMRF in adolescence.

CONCLUSIONS

BMI, waist circumference and waist-to-height ratio were predictors of clustered CMRF in childhood and adolescence and exhibited a similar predictive ability for these outcomes. These findings suggest anthropometric indicators as an interesting screening tool of clustered CMRF at early ages.

Diet, physical activity and behavioural interventions for the treatment of overweight or obese children from the age of 6 to 11 years

BACKGROUND

Child and adolescent overweight and obesity has increased globally, and can be associated with significant short- and long-term health consequences. This is an update of a Cochrane review published first in 2003, and updated previously in 2009. However, the update has now been split into six reviews addressing different childhood obesity treatments at different ages.

OBJECTIVES

To assess the effects of diet, physical activity and behavioural interventions (behaviour-changing interventions) for the treatment of overweight or obese children aged 6 to 11 years.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, PsycINFO, CINAHL, LILACS as well as trial registers ClinicalTrials.gov and ICTRP Search Portal. We checked references of studies and systematic reviews. We did not apply any language restrictions. The date of the last search was July 2016 for all databases.

SELECTION CRITERIA

We selected randomised controlled trials (RCTs) of diet, physical activity, and behavioural interventions (behaviour-changing interventions) for treating overweight or obese children aged 6 to 11 years, with a minimum of six months' follow-up. We excluded interventions that specifically dealt with the treatment of eating disorders or type 2 diabetes, or included participants with a secondary or syndromic cause of obesity.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened references, extracted data, assessed risk of bias, and evaluated the quality of the evidence using the GRADE instrument. We contacted study authors for additional information. We carried out meta-analyses according to the statistical guidelines in the Cochrane Handbook for Systematic Reviews of Interventions.

MAIN RESULTS

We included 70 RCTs with a total of 8461 participants randomised to either the intervention or control groups. The number of participants per trial ranged from 16 to 686. Fifty-five trials compared a behaviour-changing intervention with no treatment/usual care control and 15 evaluated the effectiveness of adding an additional component to a behaviour-changing intervention. Sixty-four trials were parallel RCTs, and four were cluster RCTs. Sixty-four trials were multicomponent, two were diet only and four were physical activity only interventions. Ten trials had more than two arms. The overall quality of the evidence was low or very low and 62 trials had a high risk of bias for at least one criterion. Total duration of trials ranged from six months to three years. The median age of participants was 10 years old and the median BMI z score was 2.2.Primary analyses demonstrated that behaviour-changing interventions compared to no treatment/usual care control at longest follow-up reduced BMI, BMI z score and weight. Mean difference (MD) in BMI was -0.53 kg/m2 (95% confidence interval (CI) -0.82 to -0.24); P < 0.00001; 24 trials; 2785 participants; low-quality evidence. MD in BMI z score was -0.06 units (95% CI -0.10 to -0.02); P = 0.001; 37 trials; 4019 participants; low-quality evidence and MD in weight was -1.45 kg (95% CI -1.88 to -1.02); P < 0.00001; 17 trials; 1774 participants; low-quality evidence.Thirty-one trials reported on serious adverse events, with 29 trials reporting zero occurrences RR 0.57 (95% CI 0.17 to 1.93); P = 0.37; 4/2105 participants in the behaviour-changing intervention groups compared with 7/1991 participants in the comparator groups). Few trials reported health-related quality of life or behaviour change outcomes, and none of the analyses demonstrated a substantial difference in these outcomes between intervention and control. In two trials reporting on minutes per day of TV viewing, a small reduction of 6.6 minutes per day (95% CI -12.88 to -0.31), P = 0.04; 2 trials; 55 participants) was found in favour of the intervention. No trials reported on all-cause mortality, morbidity or socioeconomic effects, and few trials reported on participant views; none of which could be meta-analysed.As the meta-analyses revealed substantial heterogeneity, we conducted subgroup analyses to examine the impact of type of comparator, type of intervention, risk of attrition bias, setting, duration of post-intervention follow-up period, parental involvement and baseline BMI z score. No subgroup effects were shown for any of the subgroups on any of the outcomes. Some data indicated that a reduction in BMI immediately post-intervention was no longer evident at follow-up at less than six months, which has to be investigated in further trials.

AUTHORS' CONCLUSIONS

Multi-component behaviour-changing interventions that incorporate diet, physical activity and behaviour change may be beneficial in achieving small, short-term reductions in BMI, BMI z score and weight in children aged 6 to 11 years. The evidence suggests a very low occurrence of adverse events. The quality of the evidence was low or very low. The heterogeneity observed across all outcomes was not explained by subgrouping. Further research is required of behaviour-changing interventions in lower income countries and in children from different ethnic groups; also on the impact of behaviour-changing interventions on health-related quality of life and comorbidities. The sustainability of reduction in BMI/BMI z score and weight is a key consideration and there is a need for longer-term follow-up and further research on the most appropriate forms of post-intervention maintenance in order to ensure intervention benefits are sustained over the longer term.

Fitness, fatness and the reallocation of time between children's daily movement behaviours: an analysis of compositional data

BACKGROUND

Movement behaviours performed over a finite period such as a 24 h day are compositional data. Compositional data exist in a constrained simplex geometry that is incongruent with traditional multivariate analytical techniques. However, the expression of compositional data as log-ratio co-ordinate systems transfers them to the unconstrained real space, where standard multivariate statistics can be used. This study aimed to use a compositional data analysis approach to examine the adiposity and cardiorespiratory fitness predictions of time reallocations between children's daily movement behaviours.

METHODS

This study used cross-sectional data from the Active Schools: Skelmersdale study, which involved Year 5 children from a low-income community in northwest England (n = 169). Measures included accelerometer-derived 24 h activity (sedentary time [ST], light physical activity [LPA], moderate-to-vigorous physical activity [MVPA], and sleep), cardiorespiratory fitness determined by the 20 m shuttle run test, objectively measured height, weight and waist circumference (from which zBMI and percent waist circumference-to-height ratio (%WHtR) were derived) and sociodemographic covariates. Log-ratio multiple linear regression models were used to predict adiposity and fitness for the mean movement behaviour composition, and for new compositions where fixed durations of time had been reallocated from one behaviour to another, while the remaining behaviours were unchanged. Predictions were also made for reallocations of fixed durations of time using the mean composition of three different weight status categories (underweight, normal-weight, and overweight/obese) as the starting point.

RESULTS

Replacing MVPA with any other movement behaviour around the mean movement composition predicted higher adiposity and lower CRF. The log-ratio model predictions were asymmetrical: when time was reallocated to MVPA from sleep, ST, or LPA, the estimated detriments to fitness and adiposity were larger in magnitude than the estimated benefits of time reallocation from MVPA to sleep, ST or LPA. The greatest differences in fitness and fatness for reallocation of fixed duration of MVPA were predicted at the mean composition of overweight/obese children.

CONCLUSIONS

Findings reinforce the key role of MVPA for children's health. Reallocating time from ST and LPA to MVPA in children is advocated in school, home, and community settings.

Obesity as a Mediator between Cardiorespiratory Fitness and Blood Pressure in Preschoolers

OBJECTIVES

To analyze the relationships between body mass index (BMI), cardiorespiratory fitness (CRF), and blood pressure (BP), and to examine whether obesity acts as a mediator between fitness and BP in children.

STUDY DESIGN

A cross-sectional analysis using a population-based sample of 1604 school children aged 4-7 years attending 21 schools from the provinces of Ciudad Real and Cuenca, Spain, was undertaken. Data on anthropometric variables, BP measurements, and CRF were collected. The relationships between body composition (BMI, percent body fat, and waist circumference), CRF, and mean arterial pressure was estimated using Pearson correlation coefficients. ANCOVA tested the differences in BP measurements by categories of BMI and CRF, controlling for different sets of confounders. The PROCESS macro developed by Preacher and Hayes was used for mediation analysis.

RESULTS

BP values were significantly higher in school children with excess weight and poorer CRF. In addition, BMI acts as a full mediator in the association between CRF and mean arterial pressure in boys at 62.28% (z = -5.433; P ≤ .001) and a partial mediator in girls at 35.24% (z = -5.246; P ≤ .001).

CONCLUSIONS

BMI mediates the relationship between CRF and mean arterial pressure. These findings highlight the importance of maintaining a healthy weight for the prevention of high BP levels in childhood.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT01971840.

A school-based intervention incorporating smartphone technology to improve health-related fitness among adolescents: rationale and study protocol for the NEAT and ATLAS 2.0 cluster randomised controlled trial and dissemination study

INTRODUCTION

Physical inactivity has been described as a global pandemic. Interventions aimed at developing skills in lifelong physical activities may provide the foundation for an active lifestyle into adulthood. In general, school-based physical activity interventions targeting adolescents have produced modest results and few have been designed to be 'scaled-up' and disseminated. This study aims to: (1) assess the effectiveness of two physical activity promotion programmes (ie, NEAT and ATLAS) that have been modified for scalability; and (2) evaluate the dissemination of these programmes throughout government funded secondary schools.

METHODS AND ANALYSIS

The study will be conducted in two phases. In the first phase (cluster randomised controlled trial), 16 schools will be randomly allocated to the intervention or a usual care control condition. In the second phase, the Reach, Effectiveness, Adoption, Implementation and Maintenance (Re-AIM) framework will be used to guide the design and evaluation of programme dissemination throughout New South Wales (NSW), Australia. In both phases, teachers will be trained to deliver the NEAT and ATLAS programmes, which will include: (1) interactive student seminars; (2) structured physical activity programmes; (3) lunch-time fitness sessions; and (4) web-based smartphone apps. In the cluster RCT, study outcomes will be assessed at baseline, 6 months (primary end point) and 12-months. Muscular fitness will be the primary outcome and secondary outcomes will include: objectively measured body composition, cardiorespiratory fitness, flexibility, resistance training skill competency, physical activity, self-reported recreational screen-time, sleep, sugar-sweetened beverage and junk food snack consumption, self-esteem and well-being.

ETHICS AND DISSEMINATION

This study has received approval from the University of Newcastle (H-2014-0312) and the NSW Department of Education (SERAP: 2012121) human research ethics committees. This study is funded by the Australian Research Council (FT140100399) and the NSW Department of Education.

TRIAL REGISTRATION NUMBER

ACTRN12615000360516; Pre-results.

Selected anthropometric variables and aerobic fitness as predictors of cardiovascular disease risk in children

The aim of this study was to assess the suitability of body mass index, waist circumference, waist-to-height ratio and aerobic fitness as predictors of cardiovascular risk factor clustering in children. A cross-sectional study was conducted with 290 school boys and girls from 6 to 10 years old, randomly selected. Blood was collected after a 12-hour fasting period. Blood pressure, waist circumference (WC), height and weight were evaluated according to international standards. Aerobic fitness (AF) was assessed by the 20-metre shuttle-run test. Clustering was considered when three of these factors were present: high systolic or diastolic blood pressure, high low-density lipoprotein (LDL) cholesterol, high triglycerides, high plasma glucose, high insulin concentrations and low high-density lipoprotein (HDL) cholesterol. A ROC curve identified the cut-off points of body mass index (BMI), WC, waist-to-height ratio (WHtR) and AF as predictors of risk factor clustering. BMI, WC and WHR resulted in significant areas under the ROC curves, which was not observed for AF. The anthropometric variables were good predictors of cardiovascular risk factor clustering in both sexes, whereas aerobic fitness should not be used to identify cardiovascular risk factor clustering in these children.