The relationship between physical fitness and clustered risk, and tracking of clustered risk from adolescence to young adulthood: eight years follow-up in the Danish Youth and Sport Study

INTRODUCTION: Cardiovascular disease (CVD) is usually caused by high levels of many risk factors simultaneously over many years. Therefore, it is of great interest to study if subjects stay within rank order over time in both the biological risk factors and the behaviour that influences these risk factors. Many studies have described stability (tracking) in single risk factors, especially in children where hard endpoints are lacking, but few have analysed tracking in clustered risk. METHODS: Two examinations were conducted 8 years apart. The first time, 133 males and 172 females were 16–19 years of age. Eight years later, 98 males and 137 females participated. They were each time ranked into quartiles by sex in four CVD risk factors all related to the metabolic syndrome. Risk factors were the ratio between total cholesterol and HDL, triglyceride, systolic BP and body fat. The upper quartile was defined as being at risk, and if a subject had two or more risk factors, he/she was defined as a case (15–20 % of the subjects). Odds ratios (OR) for being a case was calculated between quartiles of fitness in both cross-sectional studies. The stability of combined risk was calculated as the OR between cases and non-cases at the first examination to be a case at the second examination. RESULTS: ORs for having two or more risk factors between quartiles of fitness were 3.1, 3.8 and 4.9 for quartiles two to four, respectively. At the second examination, OR were 0.7, 3.5 and 4.9, respectively. The probability for "a case" at the first examination to be "a case" at the second was 6.0. CONCLUSIONS: The relationship between an exposure like physical fitness and CVD risk factors is much stronger when clustering of risk factors are analysed compared to the relationship to single risk factors. The stability over time in multiple risk factors analysed together is strong. This relationship should be seen in the light of moderate or weak tracking of single risk factors, and is strong evidence for early intervention in children where risk factors cluster

Changes in children’s television and computer time according to parental education, parental income and ethnicity: A 6-year longitudinal EYHS study

Objectives : To investigate changes in children's television and computer time according to three socioeconomic status (SES) indicators. Design : Prospective cohort study. Methods : Data were drawn from the European Youth Heart Study and included longitudinal data collected in 1997 and 2003 in Denmark. Television and computer time were self-reported by children. Parental education, income and ethnicity were parent-reported. Baseline data were available for 549 children (47.0% boys, 9.6 years). Generalized linear mixed models analyzed whether changes in television and computer time from baseline to follow-up differed according to the SES-indicators. Result : TV viewing time increased with 25% over time (ExpB = 1.25, 95% CI = 1.04-1.50). At both time points, children with two higher educated parents viewed 25% less hours of television than children with no higher educated parents (ExpB = 0.75, 95% CI = 0.60-0.94) and one higher educated parent (ExpB = 0.75, 95% CI = 0.59-0.97). Among children with no higher educated parents the odds of being in a higher category of computer time increased with 80% over time (OR = 1.80, 95% CI = 1.24-2.60). Among children with two higher educated parents the odds of being in a higher category of computer time decreased with 45% over time (OR = 0.55, 95% CI = 0.32-0.94). The association with ethnicity showed that white children had 42% lower odds (OR = 0.58; 95% CI = 0.34-1.00) of being in a higher category of computer time than non-white children. No significant associations were found for parental income. Conclusions : The most important SES measure of screen-based behaviors in children was parental education. Ethnicity was only associated with computer time. Financial resources were less relevant for changes in television viewing and computer use

Sex Differences in the Association between Level of Childhood Interleukin-6 and Insulin Resistance in Adolescence

The purpose of this study was to determine whether levels of interleukin-6 (IL-6) in childhood are related to insulin resistance in adolescence. Further, to explore how fatness and cardiorespiratory fitness (VO2peak) moderate this relationship. Methods. 292 nine-year-old children (n = 292) were followed for 4 years. Anthropometrics and VO2peak were measured. Fasting blood samples were analyzed for IL-6, insulin, and glucose. Homeostasis model assessment (HOMA-IR) was used as a measure of insulin resistance. Results. For girls but not boys, levels of IL-6 at age 9 yrs correlated with HOMA-IR at age 13 yrs: r = 0.223, P = 0.008. Girls with IL-6 levels within the highest quartile at age 9 yrs had an odds ratio of 3.68 (CI = 1.58–8.57) being in the highest quartile of HOMA-IR four years later. Conclusion. In this cohort, IL-6 levels in childhood were related to insulin resistance in adolescence, but only for girls

Back pain reporting in young girls appears to be puberty-related

BACKGROUND: There is a large increase in back pain reporting in the early teens. In no previous study has the prevalence of low back pain been investigated in relation to the onset of puberty. The objective of this study was to establish whether the onset of puberty is associated with back pain reporting in young girls. METHODS: A subsample of 254 girls aged 8–10 years and 165 girls aged 14–16 years from a cross-sectional survey of 481 children aged 8–10 years and 325 adolescents aged 14–16 years of both sexes. Main outcome measures were back pain defined as low back pain, mid back pain, and/or neck pain in the past month. Other variables of interest were Puberty (five different stages), age, body mass index, and smoking. Independent information on onset of puberty was obtained through a physical examination and on back pain through an individual structured interview. The association was studied between onset of puberty and the outcome variable (the one month period prevalence of back pain), controlling for overweight, and smoking. Odds ratios with 95% confidence intervals were used to describe bivariate associations, logistic regression with robust standard errors was used for multivariate analyses. RESULTS: There is a highly significant trend for increased back pain reporting with increasing level of puberty until maturity is reached. The biggest leap appears between the second level (beginning of puberty) and the third level (mid puberty) and the findings remain after controlling for the covariates. These results emanate from the low back, whereas pain in the mid back and neck do not seem to be linked with pubertal stage. CONCLUSION: In girls, the reporting of low back pain increases in frequency during puberty until maturity, regardless of age. Why some girls are susceptible to back pain in the early stage of puberty is unknown

TV Viewing and Physical Activity Are Independently Associated with Metabolic Risk in Children: The European Youth Heart Study

BACKGROUND: TV viewing has been linked to metabolic-risk factors in youth. However, it is unclear whether this association is independent of physical activity (PA) and obesity. METHODS AND FINDINGS: We did a population-based, cross-sectional study in 9- to 10-y-old and 15- to 16-y-old boys and girls from three regions in Europe (n = 1,921). We examined the independent associations between TV viewing, PA measured by accelerometry, and metabolic-risk factors (body fatness, blood pressure, fasting triglycerides, inverted high-density lipoprotein (HDL) cholesterol, glucose, and insulin levels). Clustered metabolic risk was expressed as a continuously distributed score calculated as the average of the standardized values of the six subcomponents. There was a positive association between TV viewing and adiposity (p = 0.021). However, after adjustment for PA, gender, age group, study location, sexual maturity, smoking status, birth weight, and parental socio-economic status, the association of TV viewing with clustered metabolic risk was no longer significant (p = 0.053). PA was independently and inversely associated with systolic and diastolic blood pressure, fasting glucose, insulin (all p < 0.01), and triglycerides (p = 0.02). PA was also significantly and inversely associated with the clustered risk score (p < 0.0001), independently of obesity and other confounding factors. CONCLUSIONS: TV viewing and PA may be separate entities and differently associated with adiposity and metabolic risk. The association between TV viewing and clustered metabolic risk is mediated by adiposity, whereas PA is associated with individual and clustered metabolic-risk indicators independently of obesity. Thus, preventive action against metabolic risk in children may need to target TV viewing and PA separately

Objectively measured physical activity and sedentary time in youth: the International children's accelerometry database (ICAD).

BACKGROUND: Physical activity and sedentary behaviour in youth have been reported to vary by sex, age, weight status and country. However, supporting data are often self-reported and/or do not encompass a wide range of ages or geographical locations. This study aimed to describe objectively-measured physical activity and sedentary time patterns in youth. METHODS: The International Children's Accelerometry Database (ICAD) consists of ActiGraph accelerometer data from 20 studies in ten countries, processed using common data reduction procedures. Analyses were conducted on 27,637 participants (2.8-18.4 years) who provided at least three days of valid accelerometer data. Linear regression was used to examine associations between age, sex, weight status, country and physical activity outcomes. RESULTS: Boys were less sedentary and more active than girls at all ages. After 5 years of age there was an average cross-sectional decrease of 4.2% in total physical activity with each additional year of age, due mainly to lower levels of light-intensity physical activity and greater time spent sedentary. Physical activity did not differ by weight status in the youngest children, but from age seven onwards, overweight/obese participants were less active than their normal weight counterparts. Physical activity varied between samples from different countries, with a 15-20% difference between the highest and lowest countries at age 9-10 and a 26-28% difference at age 12-13. CONCLUSIONS: Physical activity differed between samples from different countries, but the associations between demographic characteristics and physical activity were consistently observed. Further research is needed to explore environmental and sociocultural explanations for these differences

Equating accelerometer estimates among youth : the Rosetta Stone 2

Different accelerometer cutpoints used by different researchers often yields vastly different estimates of moderate-to-vigorous intensity physical activity (MVPA). This is recognized as cutpoint non-equivalence (CNE), which reduces the ability to accurately compare youth MVPA across studies. The objective of this research is to develop a cutpoint conversion system that standardizes minutes of MVPA for six different sets of published cutpoint

Association between maternal education and objectively measured physical activity and sedentary time in adolescents

Investigating socioeconomic variation in physical activity (PA) and sedentary time is important as it may represent a pathway by which socioeconomic position (SEP) leads to ill health. Findings on the association between children's SEP and objectively assessed PA and/or sedentary time are mixed, and few studies have included international samples.Examine the associations between maternal education and adolescent's objectively assessed PA and sedentary time.This is an observational study of 12 770 adolescents (10-18 years) pooled from 10 studies from Europe, Australia, Brazil and the USA. Original PA data were collected between 1997 and 2009. The associations between maternal education and accelerometer variables were examined using robust multivariable regression, adjusted for a priori confounders (ie, body mass index, monitor wear time, season, age and sex) and regression coefficients combined across studies using random effects meta-analyses. Analyses were conducted in March 2014.Adolescents of university educated mothers spent more time sedentary (9.5 min/day, p=0.005) and less time in light activity (10 min/day, p<0.001) compared with adolescents of high school educated mothers. Pooled analysis across two studies from Brazil and Portugal (analysed separately because of the different coding of maternal education) showed that children of higher educated mothers (tertiary vs primary/secondary) spent less time in moderate to vigorous PA (MVPA) (6.6 min/day, p=0.001) and in light PA (39.2 min/day: p<0.001), and more time sedentary (45.9 min/day, p<0.001).Across a number of international samples, adolescents of mothers with lower education may not be at a disadvantage in terms of overall objectively measured PA

Weather and children's physical activity; how and why do relationships vary between countries?

Background: Globally most children do not engage in enough physical activity. Day length and weather conditions have been identified as determinants of physical activity, although how they may be overcome as barriers is not clear. We aim to examine if and how relationships between children’s physical activity and weather and day length vary between countries and identify settings in which children were better able to maintain activity levels given the weather conditions they experienced. Methods: In this repeated measures study, we used data from 23,451 participants in the International Children’s Accelerometry Database (ICAD). Daily accelerometer-measured physical activity (counts per minute; cpm) was matched to local weather conditions and the relationships assessed using multilevel regression models. Multilevel models accounted for clustering of days within occasions within children within study-cities, and allowed us to explore if and how the relationships between weather variables and physical activity differ by setting. Results: Increased precipitation and wind speed were associated with decreased cpm while better visibility and more hours of daylight were associated with increased cpm. Models indicated that increases in these variables resulted in average changes in mean cpm of 7.6/h of day length, −13.2/cm precipitation, 10.3/10 km visibility and −10.3/10kph wind speed (all p < 0.01). Temperature showed a cubic relationship with cpm, although between 0 and 20 degrees C the relationship was broadly linear. Age showed interactions with temperature and precipitation, with the associations larger among younger children. In terms of geographic trends, participants from Northern European countries and Melbourne, Australia were the most active, and also better maintained their activity levels given the weather conditions they experienced compared to those in the US and Western Europe. Conclusions: We found variation in the relationship between weather conditions and physical activity between ICAD studies and settings. Children in Northern Europe and Melbourne, Australia were not only more active on average, but also more active given the weather conditions they experienced. Future work should consider strategies to mitigate the impacts of weather conditions, especially among young children, and interventions involving changes to the physical environment should consider how they will operate in different weather conditions.The pooling of the data was funded through a grant from the National Prevention Research Initiative (Grant Number: G0701877) (http://www.mrc.ac.uk/research/initiatives/national-prevention-research-initiative-npri/). The funding partners relevant to this award are: British Heart Foundation; Cancer Research UK; Department of Health; Diabetes UK; Economic and Social Research Council; Medical Research Council; Research and Development Office for the Northern Ireland Health and Social Services; Chief Scientist Office; Scottish Executive Health Department; The Stroke Association; Welsh Assembly Government and World Cancer Research Fund. This work was additionally supported by the Medical Research Council [MC_UU_12015/3; MC_UU_12015/7], Bristol University, Loughborough University and Norwegian School of Sport Sciences. We also gratefully acknowledge the contribution of Professor Chris Riddoch, Professor Ken Judge and Dr. Pippa Griew to the development of ICAD. The UK Medical Research Council and the Wellcome Trust (Grant ref.: 102,215/2/13/2) and the University of Bristol provide core support for ALSPAC. The CLAN study was funded by Financial Markets Foundation for Children (baseline); follow-ups were funded by the National Health and Medical Research Council (274309). The HEAPS study was funded by VicHealth (baseline); follow-ups were funded by the Australian Research Council (DP0664206). The work of Flo Harrison and Esther M F van Sluijs was supported, wholly or in part, by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence (RES-590-28-0002). Funding from the British Heart Foundation, Department of Health, Economic and Social Research Council, Medical Research Council, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. The work of Esther MF van Sluijs was supported by the Medical Research Council (MC_UU_12015/7). Anna Goodman’s contribution was supported by an National Institute for Health Research (NIHR) post-doctoral fellowship (PDF-2010-03-130). Anna Timperio’s contribution was supported by a National Heart Foundation of Australia Future Leader Fellowship (Award 10,046). The views and opinions expressed herein are those of the authors and do not necessarily reflect those of any study funders