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OBJECTIVES:: Decreased physical activity levels in children may partly explain the rising prevalence of functional constipation in childhood. The aim of the present study, therefore, was to examine the association between physical activity and functional constipation during the preschool period. METHODS:: This study was embedded in the Generation R study, a large prospective birth-cohort study in Rotterdam, The Netherlands. Physical activity was measured by an Actigraph accelerometer in 347 children (182 boys, 165 girls; mean age 25.1 months) and data were expressed as counts per minute. Data were categorized into light activity (302-614 counts/15 seconds), moderate activity (615-1230 counts/15 seconds), and vigorous activity (≥1231 counts/15 seconds). Functional constipation in the third and fourth year of life was defined according to the Rome II criteria. RESULTS:: Children spending time in the highest tertile of light (adjusted odds ratio [OR] 0.34; 95% confidence interval [CI] 0.13-0.87), moderate (adjusted OR 0.37; 95% CI 0.14-0.97), and total activity (adjusted OR 0.37; 95% CI 0.15-0.92) at the age of 2 years had significantly less functional constipation in the fourth year of life. For functional constipation in the third year of life, the results were in similar direction but not statistically significant. Additionally, children with physical activity of more than the WHO recommendation of 60 min/day had significantly less functional constipation in the fourth year of life (adjusted OR 0.48; 95% CI 0.24-0.97). CONCLUSIONS:: Physical activity is associated with a decreased risk of functional constipation in the preschool period, but this may be time dependent. Copyright © 2013 by European Society for Pediatric Gastroenterology.

PURPOSE: This study aimed to examine the reliability and validity of the steep ramp test (SRT), a feasible, maximal exercise test on a cycle ergometer that does not require the use of respiratory gas analysis, in healthy children and adolescents.METHODS: Seventy-five children were randomly divided in a reliability group (n = 37, 17 boys and 20 girls; mean ± SD age = 13.86 ± 3.22 yr), which performed two SRTs within 2 wk, and a validity group (n = 38, 17 boys and 21 girls; mean ± SD age = 13.85 ± 3.20 yr), which performed both an SRT and a regular cardiopulmonary exercise test (CPET) with respiratory gas analysis within 2 wk. Peak work rate (WRpeak) was the main outcome of the SRT. Peak oxygen uptake (V˙O2peak) was the main outcome of the CPET. Reliability was examined with the intraclass correlation coefficient and a Bland and Altman plot, whereas validity was assessed using Pearson correlation coefficients and stepwise linear regression analysis.RESULTS: Reliability statistics for the WRpeak values attained at the two SRTs showed an intraclass correlation coefficient of 0.986 (P < 0.001). The average difference between the two SRTs was -6.4 W, with limits of agreement between +24.5 and -37.5 W. A high correlation between WRpeak attained at the SRT and the V˙O2peak achieved during the CPET was found (r = 0.958; P < 0.001). Stepwise linear regression analysis provided the following prediction equation: V˙O2peak (mL·min) = (8.262WRpeak SRT) + 177.096 (R = 0.917, SEE = 237.4).CONCLUSION: The results suggest that the SRT is a reliable and valid exercise test in healthy children and adolescents, which can be used to predict V˙O2peak.

Purpose: To develop and evaluate two artificial neural network (ANN) models based on single-sensor accelerometer data and an ANN model based on the data of two accelerometers for the identification of types of physical activity in adults. Methods: Forty-nine subjects (21 men and 28 women; age range = 22-62 yr) performed a controlled sequence of activities: sitting, standing, using the stairs, and walking and cycling at two self-paced speeds. All subjects wore an ActiGraph accelerometer on the hip and the ankle. In the ANN models, the following accelerometer signal characteristics were used: 10th, 25th, 75th, and 90th percentiles, absolute deviation, coefficient of variability, and lag-one autocorrelation. Results: The model based on the hip accelerometer data and the model based on the ankle accelerometer data correctly classified the five activities 80.4% and 77.7% of the time, respectively, whereas the model based on the data from both sensors achieved a percentage of 83.0%. The hip model produced a better classification of the activities cycling, using the stairs, and sitting, whereas the ankle model was better able to correctly classify the activities walking and standing still. All three models often misclassified using the stairs and standing still. The accuracy of the models significantly decreased when a distinction was made between regular versus brisk walking or cycling and between going up and going down the stairs. Conclusions: Relatively simple ANN models perform well in identifying the type but not the speed of the activity of adults from accelerometer data. © 2010 by the American College of Sports Medicine.