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  • Author: Lars Bo Andersen x
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Søren Brage, Niels Wedderkopp, Lars Bo Andersen and Karsten Froberg

Four Computer Science and Applications (CSA, Model 7164) accelerometers were validated against speed and heart rate in a field trial, consisting of two walking and two preset running speeds, and 3 min of running at freely chosen speeds. Fifteen children (9–11 years) were recruited from a suburban school in Denmark. Mean CSA output was calculated and converted to acceleration by calibration to sinusoidal accelerations in a mechanical setup, the latter variable being independent of frequency-based filtering. Mean CSA output and estimated acceleration both correlated significantly with speed (r 2 = 0.55 and r 2 = 0.76, respectively) and heart rate (r 2 = 0.60 and r 2 = 0.81, respectively), controlled for gender. ANOVA post hoc test failed to show significant differences in accelerometer output between running speeds. Inter-individual variability of CSA output and acceleration could not be explained by differences in step frequency in walking but running values correlated significantly with step frequency (r = −0.86 and r = −0.47 for CSA output and acceleration, respectively). Conversion of CSA output to average acceleration provides more precise estimates of intensity with less inter-individual variability than raw CSA output. Different running intensities, however, are generally not well differentiated with vertical accelerometry.

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Line Anita Bjørkelund Børrestad, Lars Østergaard, Lars Bo Andersen and Elling Bere

Background:

To provide more accurate assessment of commuting behavior and potential health effect, it is important to have accurate methods. Therefore, the current study aimed to a) compare questionnaire reported mode of commuting with objectively measured data from accelerometer and cycle computer, b) compare moderate vigorous physical activity (MVPA) among children cycling vs. walking to school, and c) thus calculate possible underestimated MVPA, when using accelerometers to measure commuter cycling.

Methods:

A total of 78 children, average age 11.4 (SD = 0.5), participated in the study. Physical activity was measured with cycle computers and accelerometers for 4 days. Mode of commuting and demographic information was self-reported in a questionnaire.

Results:

Children who reported to cycle to school spent significantly more time cycling than those who walked to school, 53.6 (SD = ± 33.9) minutes per day vs. 25.5 (SD = ± 24.6) minutes per day (P = .002) (ie, showing that MVPA, measured by accelerometers, underestimated 28.1 minutes per day among children cycling to school vs. those not cycling to school).

Conclusion:

To provide more accurate assessment of active commuting in children and adolescents future studies should incorporate multiple methodologies such as global position systems (GPS), accelerometers, cycle computers, and self-reported measurements.

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Magnus Dencker, Tina Tanha, Per Wollmer, Magnus K. Karlsson, Lars Bo Andersen and Ola Thorsson

Background:

Limited data exist of tracking and changes in accelerometer-measured physical activity in children.

Methods:

Physical activity was assessed by accelerometers for 4 days in 167 children (boys, n = 90; girls n = 77) age 9.8 ± 0.6 years. Follow-up measurement was made 2.0 ± 0.1 yrs later (range 1.9−2.1 yrs). General physical activity (GPA) was defined as mean count/minute. Minutes of inactivity; light, moderate, and vigorous physical activity (LMVPA); moderate and vigorous physical activity (MVPA); and vigorous physical activity (VPA) per day were calculated both as absolute values and as percentage of total registration time.

Results:

Spearman rank order correlation indicated low tracking of MVPA and VPA in girls (r = .25−0.33, P < .05), and low-moderate tracking of GPA, inactivity, LMVPA, MVPA and VPA in boys (r = .23−0.40, P < .05). Time in inactivity increased at follow-up by 5%−14%. Most of the assessed physical activity variables were decreased at follow-up by 6% to 30%.

Conclusions:

Physical activity tracks at a low-moderate degree from age 10−12 years, which confirm previous investigations that have used self-report assessments. The low-moderate tracking of physical activity variables indicate that those who were most active initially remained most active. Increasing inactive behavior was observed and that several other physical activity variables were decreased at follow-up.

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Stig Eiberg, Henriette Hasselstrom, Vivian Grønfeldt, Karsten Froberg, Ashley Cooper and Lars Bo Andersen

The aim of this study was to investigate whether risk factors for cardiovascular disease cluster in 6- to 7-year-old children and whether low physical fitness is a predictor of risk factor clustering. The study included 369 boys (6.8 ± 0.4 years) and 327 girls (6.7 ± 0.4 years). VO2max was directly measured during a treadmill test. The ratio of total cholesterol to high-density cholesterol, triglyceride levels, the ratio of insulin to glucose, systolic blood pressure, and the sum of four skinfolds were selected as risk factors. A child was considered at risk for individual factors if he or she had values in the least favorable quartile. The number of children with more than three cardiovascular disease cluster risk factors was not significantly different from a binominal distribution. This lack of clustering could be a result of the fact that these young children have not yet developed insulin resistance. Children in the lowest quartile of fitness had an odds ratio of 2.1 (CI: 1.0–4.4) for having three or more risk factors compared with the most fit. This is interesting with regard to prevention because it indicates that an intervention involving increased physical activity might postpone or even prevent the development of risk factors.

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Russell Jago, Karsten Froberg, Ashley R. Cooper, Stig Eiberg and Lars Bo Andersen

Background:

It is not clear the extent to which change in adiposity and cardiovascular fitness (CRF) during early childhood are associated with change in cardiovascular risk factors or if associations are independent or interactive.

Methods:

383 Danish children were examined at ages 6 and 9. CRF, sum of skinfolds (SSF), and blood pressure were assessed. Fasting blood samples were used to calculate total cholesterol (TC), high and low density lipoprotein cholesterol (HDL-C & LDL-C), triglycerides, insulin, glucose, and HOMA-IR. Regression models examined whether CRF change or SSF change were independently or interactively associated with risk variables.

Results:

Change in SSF was independently associated with change in TC (z =4.83, P < .0o1), LDL-C (z =4.38, P < .001), systolic (z = 3.45, P < .001), and diastolic (z = 2.45, P = .014) blood pressure. CRF change was independently associated with change in TC (z =-3.86, P < .001), HDL-C (z =3.85, P < .001), and systolic blood pressure (z = 2.06, P = .040).

Conclusions:

Change in fitness and adiposity were independently associated with the development of cardiovascular risk factors among young children suggesting a need to increase CRF and prevent weight gain early during development to improve cardiovascular health.

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Anders Grøntved, Grete Skøtt Pedersen, Lars Bo Andersen, Peter Lund Kristensen, Niels Christian Møller and Karsten Froberg

Independent associations between personal- and demographic characteristics and physical activity in 3–6 year old children attending preschool were identified in this study. Boys spent a larger proportion of the time on moderate-and-vigorous physical activity (MVPA; p < .001) and had a higher total physical activity level compared with girls (p < .001). The 3–4 year old children spent less time on MVPA and had a lower total physical activity level compared with both 4–5 (p < .01) and 5–6 year old children (p < .001). The individual preschool, gender and age of preschool children were strong predictors of physical activity (R2-total model=(0.36−0.39)) during preschool attendance.

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Glen Nielsen, Anna Bugge, Bianca Hermansen, Jesper Svensson and Lars Bo Andersen

Background:

This study investigates the influence of school playground facilities on children’s daily physical activity.

Methods:

Participants were 594 school children measured at preschool (age 6 to 7 years) and 3 years later in third grade (518 children age 9 to 10 years) from 18 schools in 2 suburban municipalities in Denmark. Physical activity data were obtained using accelerometers. These were related to the number of permanent play facilities in school grounds and the school playground area (m2).

Results:

The number of play facilities in the school grounds was positively associated with all measures of children’s activity. In preschool every 10 additional play facilities the children had access to was associated with an increase in the average accelerometer counts of 14% (r = .273, P < .001) in school time and 6.9% (r = .195, P < .001) overall. For the children in third grade, access to 10 additional play facilities was associated with an increase in school time activity level of 26% (r = .364, P < .001) and an increase in overall activity level of 9.4% (r = .211, P < .001). School playground area did not affect activity levels independently of the number of permanent play facilities.

Conclusion:

Increasing the number of play facilities in primary school playgrounds may increase the level of children’s daily physical activity.

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Lars Bo Andersen, Niels Wedderkopp, Peter Kristensen, Niels Christian Moller, Karsten Froberg and Ashley R. Cooper

Background:

Cycling to school may potentially increase physical activity level in sedentary children. Transport to school occur twice a day and could improve cardiovascular health in children. Commuter cycling is associated with lower mortality and cardiovascular disease rate in adults, but limited evidence exists in children.

Methods:

Participants were 334 children (age 9.7 ± 0.5 years) who were followed up 6 years later. Mode of travel to school was investigated by questionnaire. Cardiovascular (CVD) risk factors were compared by mode of travel to school both at baseline and at follow up and for subjects who changed mode of transportation. No difference was found between walkers and passive travelers, and these groups were merged in the analysis.

Results:

A consistent pattern of better CVD risk factor profile in commuter cyclists compared with children using other means of transport was found. Participants, who did not cycle to school at baseline, and who had changed to cycling at follow up, were fitter, had better cholesterol/HDL ratio, better glucose metabolism, and a lower composite CVD risk factor score than those who did not cycle at either time point.

Conclusion:

Cycling to school may contribute to a better cardiovascular risk factor profile in young people.

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Daniel Arvidsson, Elias Johannesson, Lars Bo Andersen, Magnus Karlsson, Per Wollmer, Ola Thorsson and Magnus Dencker

Background: Nerve growth factor (NGF) and brain-derived neural factor (BDNF) are important for brain function and detectable in the blood. This study explored the longitudinal associations of physical activity and body fat with serum NGF and BDNF in children. Methods: Two waves of measurements were performed 2 years apart in 8- to 11-year-old children, including physical activity using the ActiGraph model 7164, body composition by dual-energy X-ray absorptiometry, and serum NGF and BDNF determined by multiplex immunoassay. The first wave included 248 children. Full information maximum likelihood estimation with robust standard errors was applied in structural equation modeling. Results: Vigorous physical activity showed a direct positive longitudinal relationship with NGF (standardized coefficient β = 0.30, P = .01) but not with BDNF (β = 0.04, P = .84). At the same time, body fat percentage was positively related to both NGF (β = 0.59, P < .001) and BDNF (β = 0.17, P = .04). There was an indication of an indirect relationship of vigorous physical activity with NGF (product of unstandardized coefficient β = −0.18, P = .02) and BDNF (β = −0.07, P = .05) through the negative relationship with body fat percentage (β = −0.36, P < .001). Conclusions: Vigorous physical activity is directly related to serum NGF and indirectly through the level of body fat. The relationships with serum BDNF are more complex.