Little is known about how the types and patterns of physical activity vary between children of different activity levels. Knowing how more active children achieve their higher activity levels can inform the design of interventions to promote physical activity. PURPOSE: The aim of this study was to assess how children of different activity levels differ in the types of physical activities they participate in, and the time of day they do this on school and non school days. METHODS: A cross-sectional analysis using 3800 children (1794 males, 2006 females) participating in the Avon Longitudinal Study of Parents and Children (ALSPAC). The mean (SD) age of the children was 13.8 (0.19) years. Physical Activity was measured using an accelerometer, worn over a 7-day period. Children were categorised into gender-specific tertiles of activity (T1 = less active, T2 = moderately active, T3 = highly active), using both counts/min and min/d of moderate to vigorous activity (MVPA), using a cut-point of 3600 counts/min. Time of day and type of activity were assessed using an adapted version of the Previous Day Physical Activity Recall questionnaire (PDPAR). Activities were grouped as; 'housework‘, 'outside activities‘, 'TV‘, 'active job‘, 'sports‘ and 'travel‘. The proportion of activity occasions within the tertiles, were analysed using the Chi squared test. Results for counts/min and MVPA were similar. RESULTS: Based on counts/min, differences were seen for sports participation; T1 (26%) T2 (33.4%) T3 (40.5%) (X2 = 185.79, p <0.001). These differences were observed on school days T1 (26.4%) T2 (33.7%) T3 (39.9%) (X2 = 117.49, p <0.001), and non school days T1 (25.6%) T2 (32.5%) T3 (41.9%) (X2 = 54.66, p <0.001). Overall, the least active children engaged in TV activities more often than moderately and highly active children, T1 (35.0%) T2 (33.1%) T3 (31.9%) (X2 = 17.32, p <0.001). These differences were not observed on school days, T1 (34.5%) T2 (32.8%) T3 (32.7%) (X2 = 3.96, p >0.05) or non school days alone, T1 (34.3%) T2 (33.2%) T3 (32.4%) (X2 = 2.46, p >0.05). The remaining activity behaviours showed no differences. CONCLUSIONS: Sports participation and TV activities vary according to differences in children‘s total physical activity levels. These results have implications for the design of physical activity interventions for children.
Accurate estimates of habitual physical activity (PA) are essential when examining the associations between PA and health outcomes. Evidence suggests that a minimum of 4 days of monitoring is required for assessing PA by accelerometry. However, intra-individual variation throughout the year may lead to inaccurate assessments of habitual PA. PURPOSE To estimate intra-individual variation of children's PA over the course of a year using accelerometry. METHODS Children aged 11 to 12 were recruited from a large birth cohort- the Avon Longitudinal Study of Parents and Children (ALSPAC). Children were asked to wear an accelerometer for 7 days and then again on a further 2 occasions in the next two seasons of the year. Accelerometer counts per minute (cpm) were used as the main PA outcome measure. Multilevel modelling was used to estimate the inter- and intra-individual variance in PA. RESULTS 208 children had data for three separate occasions throughout the year; mean (SD) counts per minute were 601 (179), 611 (189) and 600 (191) for the first occasion (Sep 2003 to Jan 2004), second occasion (Jan to July 2004) and third occasion (March to Sep 2004) respectively. Defining seasons as December to February, March to May, June to August, and September to November, the mean (SD) counts per minute were 524 (148), 641 (203), 583 (178) and 614 (196) respectively. A random intercepts model was fitted to separate the inter- and intra-individual variance components of PA, with adjustment for gender. The intra-individual SD was 131 counts per minute, and the coefficient of variation (SD as % of mean counts per minute) was 22%. The intra-class correlation was 0.50. Two sine and cosine functions of the month of data collection were then added to the model as fixed effects, allowing two peaks and troughs throughout the year. The highest counts were in May and October, and the lowest in January and July. The intraindividual variance component was reduced by 4% by inclusion of these terms in the model, and the intra-class correlation increased slightly to 0.52. CONCLUSION This study suggests that intra-individual variance should be considered when accelerometry is used to estimate habitual PA in children. Further work will consider whether this variation is driven by differences in PA between school holidays and term-time. Supported by NIH Grant R01 HL071248-01A1
Physical activity is difficult to assess accurately, especially in children. Various equations have been derived to estimate physical activity energy expenditure (PAEE) from body movement measured by accelerometry or heart rate (HR) data. However, few studies have utilised combined HR and movement sensing (HR+M). PURPOSE: The primary purpose of this study was to compare the accuracy of uniaxial accelerometry and HR+M to predict PAEE during six common activities in children. As a secondary aim we assess the validity of three sets of treadmill-derived equations (Corder et al, MSSE 2005) to predict PAEE in this sample. METHODS: PAEE was measured by indirect calorimetry during six activities (lying, sitting, slow walking, walking, jogging and hopscotch) in 181 children (12.4 ± 0.2y). Associations between measured and predicted PAEE (accelerometry output and HR+M) were assessed by linear regression analysis. The validity of these equations was cross-validated in a sub sample of participants. The validity of previously derived PAEE equations from treadmill walking and running was assessed. RESULTS: Data from the Actigraph and the HR+M were significantly associated with measured PAEE values (r2 = 0.91 and 0.90, P < 0.01). In cross-validation analyses, significant correlations were observed between the estimation errors of both predictions (Actigraph r=0.46, P < 0.01; Actiheart r=0.27, P < 0.01), both manifesting as under estimations at high-energy expenditures, increasing with PAEE. Systematic errors (i.e. significant correlations between estimation errors) were observed for all treadmill-derived equations. Uniaxial accelerometry over estimated PAEE significantly (r = −0.74, P < 0.01). The branched equation model over estimated PAEE at low intensities (r = 0.23, P < 0.01), whereas the HR+M prediction equation showed less systematic error (r = −0.09, P < 0.01). CONCLUSIONS: Both accelerometry and HR+M are valid to predict PAEE during selected physical activities in children. However, both models seem to underestimate PAEE at high intensity physical activity. Accelerometry derived PAEE during a progressive treadmill test was not suitable for predicting PAEE during the six activities. Both the HR+M model and the branched equation model derived from combined HR and movement sensing during treadmill locomotion showed less systematic error and were valid for PAEE prediction. Our results suggest that it may be possible to derive accurate PAEE prediction models using HR+M data that would not be possible using movement data alone due to the mechanical limitations of accelerometers.
Factors in early life may increase subsequent risk of obesity. It is not known if this increased risk is mediated through physical activity (PA) or through poor control of appetite. Identifying early life factors that determine PA may improve strategies to prevent the development of obesity. PURPOSE: To identify early life factors that determine objectively measured PA in children. METHODS: Children from the Avon Longitudinal Study of Parents and Children were asked to wear a uni-axial accelerometer for 7 days at age 11. Data on parental health and lifestyle and child behaviour and development were collected from pregnancy onwards. Multivariable regression was used to examine the associations between early life factors and PA. The physical activity outcome measure was minutes of moderate to vigorous physical activity (MVPA) per day. RESULTS: Valid accelerometer data were collected from 5595 children. Associations between early life factors and MVPA are shown in the Table.TableCONCLUSIONS: Parental PA when the child was 21 months old predicted children's PA at age 11 years. Few early life factors predicted PA and in those that did, the associations were small. Similar associations with maternal and partner's smoking suggest that social factors determine PA rather than intrauterine programming. The lack of association with factors in pregnancy and early growth (data not shown) suggest that associations between early life factors and obesity are not mediated through PA. Physical activity at age 11 may be more strongly influenced by proximal factors. Supported by NIH GrantR01 HL071248-01A1
The increasing prevalence of obesity among the world's children and youth was the impetus for an international conference convened in Toronto, Canada, to examine issues related to physical activity and obesity in children (24-27 June 2007). The goal of the conference was to assimilate, interpret, and share scientific evidence with key stakeholders to develop recommendations concerning effective physical activity policies and programs to address obesity in children. The conference was attended by approximately 1,000 delegates from 33 countries who gathered to listen to the invited speakers and to share information on promising practices related to the promotion of physical activity with the aim of reducing the burden of obesity in children. The major topics addressed at the conference included the biological and behavioural causes of obesity, current and past levels of physical activity and sedentarism in children, the role of the social, family, and built environments in addressing the physical activity deficit, and the role of legislation and industry in promoting physical activity. Promising physical activity interventions among children were presented, and important research, policy, and practice recommendations to address the issue of physical inactivity and obesity were provided.
The physical activity habits of children are a matter of increasing public health concern, as physical activity is positively related to health. PURPOSE To measure physical activity levels and patterns in 2.258 children, ages 9 and 15 years from Denmark, Portugal, Estonia and Norway. The study is part of the European Youth Heart Study - a multi-national study investigating CHD risk factors in children. METHODS Physical activity was measured using the CSA 7164 accelerometer (Computer Science Applications, Shalimar, FL) for 4 consecutive days - 2 weekdays and 2 weekend days — during waking hours. The primary activity variable was total accelerometer counts per valid time of monitoring (counts/minute - cpm). The time children engaged in at least moderate intensity activity was calculated using age-specific cut-points. Energy expenditure estimates were calculated using published regression equations. Activity levels were compared with current health-related guidelines. RESULTS Boys were more active than girls at age 9 (784+282 v. 649+204 cpm, p < 0.001) and age 15 (615+229 v. 491+165, p < 0.001). Gender differences were more marked at higher intensities. Estimated energy expenditure for 9 year-olds was 2.86+0.33 METS for boys and 2.70+0.24 METS for girls. For 15 year-olds, figures were 1.99+0.21 METS and 1.87+0.15 METS respectively. Significant (p < 0.001) between-country differences in activity levels were observed (0–30%) within age/gender groups, with Norwegian children being the most active and Danish children the least active. For 9 year-olds, 98% boys and 99% girls were estimated to achieve current activity guidelines. For 15 year-olds, the figures were 86% and 72%. Boys spent a greater proportion of their time engaged in moderate activity compared to girls at age 9 (24.5+8.3% v. 20.4+6.7%, p < 0.001) and age 15 (12.4+5.8% v. 9.3+4.1%, p < 0.001). CONCLUSIONS Boys are more active than girls especially at higher activity intensities and the majority of boys and girls achieve current health-related activity guidelines.
The paper introduces a pervasive digital artwork which harnesses live heart-rate and GPS data to create a novel experience on a Pocket PC. The aims of the project, the technologies employed and the results of a preliminary trial are briefly described.
The aim of this study was to assess the association between physical fitness and clustering of cardiovascular disease (CVD) risk factors in boys and girls aged 9 years (children) and 15 years (adolescents). Subjects were 1020 randomly selected children and adolescents. Cardiorespiratory fitness was assessed by a maximal cycle ergometer test. A subject was defined as having a risk factor if he/she belonged to the upper quartile of risk within age and gender group for that risk factor. Clustering was analysed in relation to being at risk in a) three or more and b) four or more of five possible risk factors (TC:HDL ratio, insulin:glucose ratio, triglyceride, systolic BP and sum of four skinfolds. Physical fitness was weakly related to single CVD risk factors except sum of skinfolds where the relationship was strong. Low fitness increased the risk of having three or more CVD risk factors with odds ratios (OR) using the upper quartile of fitness as reference of 1.9 (95% CI: 0.8–4.1), 3.0 (95% CI: 1.4–6.3) and 11.4 (95% CI: 5.7–22.9), respectively. Using the criterion of four or more risk factors, an OR of 24.1 (95% CI 5.7–101.1) was found in the low fit group.
Previous studies have been unable to characterise the association between physical activity and obesity, possibly because most relied on inaccurate measures of physical activity and obesity. We carried out a cross sectional analysis on 5,500 12-year-old children enrolled in the Avon Longitudinal Study of Parents and Children. Total physical activity and minutes of moderate and vigorous physical activity (MVPA) were measured using the Actigraph accelerometer. Fat mass and obesity (defined as the top decile of fat mass) were measured using the Lunar Prodigy dual x-ray emission absorptiometry scanner. We found strong negative associations between MVPA and fat mass that were unaltered after adjustment for total physical activity. We found a strong negative dose-response association between MVPA and obesity. The odds ratio for obesity in adjusted models between top and the bottom quintiles of minutes of MVPA was 0.03 (95% confidence interval [CI] 0.01-0.13, p-value for trend <0.0001) in boys and 0.36 (95% CI 0.17-0.74, p-value for trend = 0.006) in girls. We demonstrated a strong graded inverse association between physical activity and obesity that was stronger in boys. Our data suggest that higher intensity physical activity may be more important than total activity.
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