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

Harmonising data on the correlates of physical activity and sedentary behaviour in young people: Methods and lessons learnt from the International Children's Accelerometry database (ICAD)

Background: Large, heterogeneous datasets are required to enhance understanding of the multi-level influences on children's physical activity and sedentary behaviour. One route to achieving this is through the pooling and co-analysis of data from multiple studies. Where this approach is used, transparency of the methodology for data collation and harmonisation is essential to enable appropriate analysis and interpretation of the derived data. In this paper, we describe the acquisition, management and harmonisation of non-accelerometer data in a project to expand the International Children's Accelerometry Database (ICAD). Method: Following a consultation process, ICAD partners were requested to share accelerometer data and information on selected behavioural, social, environmental and health-related constructs. All data were collated into a single repository for cataloguing and harmonisation. Harmonised variables were derived iteratively, with input from the ICAD investigators and a panel of invited experts. Extensive documentation, describing the source data and harmonisation procedure, was prepared and made available through the ICAD website. Results: Work to expand ICAD has increased the number of studies with longitudinal accelerometer data, and expanded the breadth of behavioural, social and environmental characteristics that can be used as exposure variables. A set of core harmonised variables, including parent education, ethnicity, school travel mode/duration and car ownership, were derived for use by the research community. Guidance documents and facilities to enable the creation of new harmonised variables were also devised and made available to ICAD users. An expanded ICAD database was made available in May 2017. Conclusion: The project to expand ICAD further demonstrates the feasibility of pooling data on physical activity, sedentary behaviour and potential determinants from multiple studies. Key to this process is the rigorous conduct and reporting of retrospective data harmonisation, which is essential to the appropriate analysis and interpretation of derived data. These documents, made available through the ICAD website, may also serve as a guide to others undertaking similar projects

Cross-Sectional Associations of Reallocating Time Between Sedentary and Active Behaviours on Cardiometabolic Risk Factors in Young People:An International Children’s Accelerometry Database (ICAD) Analysis

Introduction: Sedentary time and time spent in various intensity-specific physical activity are co-dependent, and increasing time spent in one behaviour requires decreased time in another. Objective: The aim of the present study was to examine the theoretical associations with reallocating time between categories of intensities and cardiometabolic risk factors in a large and heterogeneous sample of children and adolescents. Methods: We analysed pooled data from 13 studies comprising 18,200 children and adolescents aged 4–18 years from the International Children’s Accelerometry Database (ICAD). Waist-mounted accelerometers measured sedentary time, light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA). Cardiometabolic risk factors included waist circumference (WC), systolic blood pressure (SBP), fasting high- and low-density lipoprotein cholesterol (HDL-C and LDL-C), triglycerides, insulin, and glucose. Associations of reallocating time between the various intensity categories with cardiometabolic risk factors were explored using isotemporal substitution modelling. Results: Replacing 10 min of sedentary time with 10 min of MVPA showed favourable associations with WC, SBP, LDL-C, insulin, triglycerides, and glucose; the greatest magnitude was observed for insulin (reduction of 2–4%), WC (reduction of 0.5–1%), and triglycerides (1–2%). In addition, replacing 10 min of sedentary time with an equal amount of LPA showed beneficial associations with WC, although only in adolescents. Conclusions: Replacing sedentary time and/or LPA with MVPA in children and adolescents is favourably associated with most markers of cardiometabolic risk. Efforts aimed at replacing sedentary time with active behaviours, particularly those of at least moderate intensity, appear to be an effective strategy to reduce cardiometabolic risk in young people

Cross-sectional associations of reallocating time between sedentary and active behaviours on cardiometabolic risk factors in young people: An International Children’s Accelerometry Database (ICAD) analysis

Introduction: Sedentary time and time spent in various intensity-specific physical activity are co-dependent, and increasing time spent in one behaviour requires decreased time in another. Objective: The aim of the present study was to examine the theoretical associations with reallocating time between categories of intensities and cardiometabolic risk factors in a large and heterogeneous sample of children and adolescents. Methods: We analysed pooled data from 13 studies comprising 18,200 children and adolescents aged 4–18 years from the International Children’s Accelerometry Database (ICAD). Waist-mounted accelerometers measured sedentary time, light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA). Cardiometabolic risk factors included waist circumference (WC), systolic blood pressure (SBP), fasting high- and low-density lipoprotein cholesterol (HDL-C and LDL-C), triglycerides, insulin, and glucose. Associations of reallocating time between the various intensity categories with cardiometabolic risk factors were explored using isotemporal substitution modelling. Results: Replacing 10 min of sedentary time with 10 min of MVPA showed favourable associations with WC, SBP, LDL-C, insulin, triglycerides, and glucose; the greatest magnitude was observed for insulin (reduction of 2–4%), WC (reduction of 0.5–1%), and triglycerides (1–2%). In addition, replacing 10 min of sedentary time with an equal amount of LPA showed beneficial associations with WC, although only in adolescents. Conclusions: Replacing sedentary time and/or LPA with MVPA in children and adolescents is favourably associated with most markers of cardiometabolic risk. Efforts aimed at replacing sedentary time with active behaviours, particularly those of at least moderate intensity, appear to be an effective strategy to reduce cardiometabolic risk in young people

Additional file 1: of A primary school active break programme (ACTI-BREAK): study protocol for a pilot cluster randomised controlled trial

Completed SPIRIT Checklist. (DOCX 64 kb

Information concerning variables used in the analyses (mean (SD) unless stated).

1<p>Percentage of adolescents reporting ‘yes’.</p

Physical activity during school break time at T2 and T3 (raw data).

<p>Physical activity during school break time at T2 and T3 (raw data).</p

Cross-sectional and longitudinal associations between individual, behavioural, social and organisational/policy factors and break time%MVPA.

*<p><i>p</i><0.1, ^**<i>p</i><0.05, ^***<i>p</i><0.01.</p><p>–  =  Not entered in fully-adjusted model.</p>1<p>Males are the referent group.</p>a<p>Separate models for each dependent variable.</p>b<p>Adjusted for all significant variables from the crude model.</p>c<p>Adjusted for T2 MVPA and all significant variables from the crude model.</p

Cross-sectional and longitudinal associations between individual, behavioural, social and organisational/policy factors and break time%LPA.

*<p><i>p</i><0.1, ^**<i>p</i><0.05, ^***<i>p</i><0.01.</p><p>–  =  Not entered in fully-adjusted model.</p>1<p>Males are the referent group.</p>a<p>Separate models for each dependent variable.</p>b<p>Adjusted for all significant variables from the crude model.</p>c<p>Adjusted for T2 LPA and all significant variables from the crude model.</p

Cross-sectional and longitudinal associations between individual, behavioural, social and organisational/policy factors and break time%sedentary time.

*<p><i>p</i><0.1, ^**<i>p</i><0.05, ^***<i>p</i><0.01.</p><p>–  =  Not entered in fully-adjusted model.</p>1<p>Males are the referent group.</p>a<p>Separate models for each dependent variable.</p>b<p>Adjusted for all significant variables from the crude model.</p>c<p>Adjusted for T2 sedentary time and all significant variables from the crude model.</p