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  • Author: Shrikant Bangdiwala x
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Robert G. McMurray, Joanne S. Harrell, Shrikant I. Bangdiwala, Shibing Deng and Chris Baggett

This study evaluated factors that contribute to the increased energy cost of locomotion in youth. The subjects were 321 8-18-year-old youth, similar dispersed by age and sex. Oxygen uptake (VO2) was measured during rest (REE), running at 8 km · h−1 and cycling at 16 km · h−1, using a COSMED K4b2 metabolic system. Developmental stage was obtained via questionnaire. Stature, body mass, and skinfolds (triceps & subscapular) were measured. Both sexes had similar absolute VO2 (mL · min−1) at rest (p = 0.065) and running (p = 0.084), but the males had a higher VO2 during cycling (p = 0.046). There were no sex differences in relative VO2 (mL · kg−1 · min−1) at rest (p = 0.083); however, the males had a higher VO2 than the females during cycling and running (p £ 0.002). Multiple regression, tested for collinearity, found that absolute VO2 during cycling and running was mostly related to fat-free mass (p = 0.0001). Similar analyses for relative VO2 (mL · kg−1 · min−1) during cycling found that fat-free mass, sex, and skinfolds were significant contributors (p ‡ 0.003). During running the relative VO2 was related to skinfolds, fat-free mass, and resting energy expenditure (p < 0.05). Neither age nor developmental stage was a significant contributor. The results indicate that the VO2 of locomotion is most closely associated with fat-free mass. Thus, to compare youth of varying age or pubertal developmental status, fat-free mass should be taken into consideration.

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Robert G. McMurray, Christopher D. Baggett, Joanne S. Harrell, Michael L. Pennell and Shrikant I. Bangdiwala

The purpose of this study was to determine whether an equation could be developed to predict energy expenditure from Tritrac output, body mass, stature, and age in youth. The participants were 308 young people, 8–18 yrs of age, similarly dispersed across ages and genders. Stature (cm) and body mass (kg) were measured. Participants completed 10 min of nine activities, ranging from light to vigorous intensity. Simultaneous measures of Tritrac vector magnitude counts (VMAG) and oxygen uptake (Cosmed K4b2) were obtained. Mixed model analysis using VMAG, sex, age, stature, and body mass fit well, and all variables were significant (p ≤ .012). The model concordance correlation was R c = .812; standard deviation = 305ml/min. A slightly less complex model was also adequate: VO2 (ml/min) = (0.32·VMAG) + (6.97·cm) + (6.19·kg) − 858; standard deviation = 306 ml/min; R c =.809. These results indicate that the Tritrac R3D can be used with some success to assess energy expenditure in 8- to 18-year olds.

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Daniela A. Rubin, Robert G. McMurray, Joanne S. Harrell, Barbara W. Carlson and Shrikant Bangdiwala

The purpose of this project was to determine the accuracy in lipids measurement and risk factor classification using Reflotron, Cholestech, and Ektachem DT-60 dry-chemistry analyzers. Plasma and capillary venous blood from fasting subjects (n = 47) were analyzed for total cholesterol (TC), high density lipoprotein (HDL-C), and triglycerides (TG) using these analyzers and a CDC certified laboratory. Accuracy was evaluated by comparing the results of each portable analyzer against the CDC reference method. One-way ANOVAs were performed for TC, HDL-C, and TG between all portable analyzers and the reference method. Chi-square was used for risk classification (2001 NIH Guidelines). Compared to the reference method, the Ektachem and Reflotron provided significantly lower values for TC (p < .05). In addition, the Cholestech and Ektachem values for HDL-C were higher than the CDC (p < .05). The Reflotron and Cholestech provided higher values of TG than the CDC (p < .05). Chi-squares analyses for risk classification were not significant (p > .45) between analyzers. According to these results, the Ektachem and Cholestech analyzers met the current NCEP III guidelines for accuracy in measurement of TC, while only Ektachem met guidelines for TG. All 3 analyzers provided a good overall risk classification; however, values of HDL-C should be only used for screening purposes.