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.
Robert G. McMurray, Christopher D. Baggett, Joanne S. Harrell, Michael L. Pennell, and Shrikant I. Bangdiwala
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.
Peter A. Hosick, Robert G. McMurray, A.C. Hackney, Claudio L. Battaglini, Terry P. Combs, and Joanne S. Harrell
Reports suggest children with high aerobic fitness (VO2max; mL/kg/min) have healthier profiles of TNF-α and IL-6; however, research has not accounted for differences in adiposity between high-fit and low-fit individuals. Thus, this study examined differences in inflammatory markers of obese and normal weight children of different fitness levels, using two different VO2max units: per unit of fat free mass (VO2FFM) or total body mass (VO2kg). Children (n = 124; ages 8–12) were divided into four matched groups; normal weight high-fit (NH), normal weight low-fit (NL), obese high-fit (OH), and obese low-fit (OL). Height, weight, skinfolds, body mass index (BMI), and predicted VO2max were measured and a morning, fasting blood sample taken. IL-6 was elevated in the NL and OL groups compared with the NH group, as well as the OL group compared with the OH group. No differences were found in TNF-α. The relationship between IL-6 or TNF-α and the two units of predicted VO2max did not differ suggesting that either VO2FFM or VO2kg can be used to describe aerobic power when studying inflammation and exercise in youth. The relationship between IL-6 or TNF-α and predicted VO2max, whether expressed per mass or per fat-free mass was similar, suggesting that both can be used to describe aerobic power when studying inflammation and exercise in youth. Given the polar design of this study, this relationship should be confirmed including overweight subjects.
Russell Jago, Kimberly L. Drews, Robert G. McMurray, Tom Baranowski, Pietro Galassetti, Gary D. Foster, Ester Moe, and John B. Buse
This paper examined whether a two-year change in fitness, body mass index (BMI) or the additive effect of change in fitness and BMI were associated with change in cardiometabolic risk factors among youth. Cardiometabolic risk factors, BMI group (normal weight, overweight or obese) were obtained from participants at the start of 6th grade and end of 8th grade. Shuttle run laps were assessed and categorized in quintiles at both time points. Regression models were used to examine whether changes in obesity, fitness or the additive effect of change in BMI and fitness were associated with change in risk factors. There was strong evidence (p < .001) that change in BMI was associated with change in cardiometabolic risk factors. There was weaker evidence of a fitness effect, with some evidence that change in fitness was associated with change in total cholesterol, HDL-C, LDL-C and clustered risk score among boys, as well as HDL-C among girls. Male HDL-C was the only model for which there was some evidence of a BMI, fitness and additive BMI*fitness effect. Changing body mass is central to the reduction of youth cardiometabolic risk. Fitness effects were negligible once change in body mass had been taken into account.
Russell Jago, Robert G. McMurray, Stanley Bassin, Laura Pyle, Steve Bruecker, John M. Jakicic, Esther Moe, Tinker Murray, and Stella L. Volpe
Two pilot studies were conducted to examine whether 6th grade students can achieve moderate to vigorous physical activity (MVPA) from 1) activity-based physical education (AB-PE) with 585 participants and 2) a curricular-based (CB-PE) program with 1,544 participants and randomly sampled heart rates during lessons. AB-PE participants spent between 54–66% with a heart rate >140 bpm. CB-PE participants spent between 49–58% with a heart rate >140 bpm. Girls’ mean heart rate was 3.7 bpm lower than the boys. PE can be readily modified so that students spend more than 50% of time in MVPA.
Karin A. Pfeiffer, Kathleen B. Watson, Robert G. McMurray, David R. Bassett, Nancy F. Butte, Scott E. Crouter, Stephen D. Herrmann, Stewart G. Trost, Barbara E. Ainsworth, Janet E. Fulton, David Berrigan, and For the CDC/NCI/NCCOR Research Group
Purpose: This study compared the accuracy of physical activity energy expenditure (PAEE) prediction using 2 methods of accounting for age dependency versus 1 standard (single) value across all ages. Methods: PAEE estimates were derived by pooling data from 5 studies. Participants, 6–18 years (n = 929), engaged in 14 activities while in a room calorimeter or wearing a portable metabolic analyzer. Linear regression was used to estimate the measurement error in PAEE (expressed as youth metabolic equivalent) associated with using age groups (6–9, 10–12, 13–15, and 16–18 y) and age-in-years [each year of chronological age (eg, 12 = 12.0–12.99 y)] versus the standard (a single value across all ages). Results: Age groups and age-in-years showed similar error, and both showed less error than the standard method for cycling, skilled, and moderate- to vigorous-intensity activities. For sedentary and light activities, the standard had similar error to the other 2 methods. Mean values for root mean square error ranged from 0.2 to 1.7 youth metabolic equivalent across all activities. Error reduction ranged from −0.2% to 21.7% for age groups and −0.23% to 18.2% for age-in-years compared with the standard. Conclusions: Accounting for age showed lower errors than a standard (single) value; using an age-dependent model in the Youth Compendium is recommended.