Prediction of VO\u3csub\u3e2\u3c/sub\u3e Peak Using Sub-Maximum Bench Step Test in Children

The purpose of this study was to develop a valid prediction of maximal oxygen uptake from data collected during a submaximum bench stepping test among children ages 8-12 years. Twentyseven active subjects (16 male and 11 female), weight 36.1 kg, height 144.4 cm and VO2 47.4 ± 7.9 ml/kg/min participated. Subjects completed a maximal oxygen consumption test with analysis of expired air and a submaximal bench stepping test. A formula to predict VO2max was developed from height, resting heart rate and heart rate response during the submaximum bench stepping test. This formula accounted for 71% of the variability in maximal oxygen consumption and is the first step in verifying the validity of the submaximum bench stepping test to predict VO2max. VO2max = -2.354 + (Height in cm * 0.065) + (Resting Heart Rate * 0.008) + (Step Test Average Heart Rate as a Percentage of Resting Heart Rate * -0.870

Maturation level in adolescents: effects on body composition and physical activity changes

Background and aims: Longitudinal studies help move researchers closer to understanding determinants and mediators of maturation, physical activity (PA) and adiposity. The aim of this study was to longitudinally explore the influence of maturation on PA and adiposity changes in adolescents. Methods: Eighty healthy adolescents (42 girls and 38 boys) were followed over three academic years. A PA score was estimated using the Physical Activity Questionnaire (PAQ-A). Fat mass percentage (FMP) was assessed by anthropometric measurements. Sexual maturity was estimated by percentage of predicted adult stature and adolescents were classified into three changes groups: C0, change from on time to late maturation; C1, no change; C2, change from late/on time to on time/early maturation. A stepwise linear regression was conducted in order to estimate the predictors of PA and FMP changes. Results: An interaction between PA and maturation was statically significant (P<0.05). A non-significant trend was observed between three stages of change with a progressive reduction of FMP across the three stages of change in maturation level (C0 = 0.2752.70%; C1= -1.4901.10%; C2= -6.4172.57%; pairwise comparisons: C0 - C2 = 6.69%, P=0.081 and C1-C2 = 4.93%, P=0.080). Conclusions: Our results suggest that body composition changes observed during adolescence are not driven by changes in PA. PA alteration patterns were influenced by sex but not by maturation.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Supported by the Spanish Ministry of Education, Culture and Sport (AP2010-0583); the Spanish Ministry of Economy and Competitiveness (DEP2011-30565

Acquisition Challenge: The Importance of Incompressibility in Comparing Learning Curve Models

The Department of Defense (DoD) cost estimating methodology currently employs T. P. Wrights 75-plus-year-old learning curve formula. The goal of this research was to examine alternative learning curve models and determine if a more reliable and valid cost estimation method exists, which could be incorporated within the DoD acquisition environment. This study tested three alternative learning models (the Stanford-B model, DeJong\u27s learning formula, and the S-Curve model) to compare predicted against actual costs for the F-15 A-E jet fighter platform. The results indicate that the S-Curve and DeJong models offer improvement over current estimation techniques, but more importantly and unexpectedly highlight the importance of incompressibility (the amount of a process that is automated) in learning curve estimating

The association between the food environment and weight status among eastern North Carolina youth

Objective: To examine associations between various measures of the food environment and BMI percentile among youth. Design: Cross-sectional, observational. Setting: Pitt County, eastern North Carolina. Subjects:We extracted the electronic medical records for youth receiving well child check-ups from January 2007 to June 2008. We obtained addresses for food venues from two secondary sources and ground-truthing. A geographic information systems database was constructed by geocoding home addresses of 744 youth and food venues. We quantified participants\u27 accessibility to food venues by calculating \u27coverage\u27, number of food venues in buffers of 0●25, 0●5, 1 and 5 miles (0●4, 0●8, 1●6 and 8●0 km) and by calculating \u27proximity\u27 or distance to the closest food venue. We examined associations between BMI percentile and food venue accessibility using correlation and regression analyses. Results:There were negative associations between BMI percentile and coverage of farmers\u27 markets/produce markets in 0●25 and 0●5 mile Euclidean and 0●25, 0●5 and 1 mile road network buffers. There were positive associations between BMI percentile coverage of fast-food and pizza places in the 0●25 mile Euclidean and network buffers. In multivariate analyses adjusted for race, insurance status and rural/urban residence, proximity (network distance) to convenience stores was negatively associated with BMI percentile and proximity to farmers\u27 markets was positively associated with BMI percentile. Conclusions: Accessibility to various types of food venues is associated with BMI percentile in eastern North Carolina youth. Future longitudinal work should examine correlations between accessibility to and use of traditional and non-traditional food venues