To compare basal metabolic rate (BMR) predicted by different equations with measured BMR of the Brazilian paralympic track & field team aiming to verify which of these equations is best suited for use in this group. Method: 19 male and 11 female athletes grouped according to functional classification (vision impairment-VI, limb deficiency-LD, and cerebral palsy-CP) had their BMR measured by indirect calorimetry and compared with values predicted by different equations: Cunningham, Owen, Harris-Benedict, FAO/OMS, Dietary Reference Intakes, and Mifflin. Body composition data were obtained by skinfold measurements. Results were reported as mean and standard deviation and analyzed using the Wilcoxon test and Pearson´s Correlation Coefficient. The Root Mean Squared Prediction Error (RMSPE) was calculated to identify the similarity between the estimated and predicted BMR. Results: Mean measured BMR was 25 ± 4.2, 26 ± 2.4, and 26 ± 2.7 kcal/kg of fat free mass/day for VI, LD, and CP, respectively. Owen´s equation had the best predictive performance in comparison with measured BMR for LD and CP athletes, within 104 and 125 kcal/day, while Mifflin’s equation predicted within 146 kcal/day for VI athletes. Conclusion: for this specific group of athletes the Owen and Mifflin equations provided the best predictions of BMR.
Claudia Ridel Juzwiak, Ciro Winckler, Daniel Paduan Joaquim, Andressa Silva and Marco Tulio de Mello
Peter G. Breithaupt, Rachel C. Colley and Kristi B. Adamo
The aim of the current study was to investigate the relationship between the Oxygen Uptake Efficiency Slope (OUES) and traditional measures of cardiorespiratory function in an overweight/obese pediatric sample. Maximal treadmill exercise testing with indirect calorimetry was completed on 56 obese children aged 7–18 years. Maximal OUES, submaximal OUES, VO2peak, VEpeak, and ventilatory threshold (VT) were determined. In line with comparable research in healthy-weight samples, maximal and submaximal OUES were both correlated with VO2peak, VEpeak, and VT (r2= 0.44−0.91) in the obese pediatric sample. Correlations were also found with anthropometric variables, including height (cm), body surface area (m2), body mass (kg), and fat free mass (kg). In comparing our data to a published sample of healthy weight children, maximal and submaximal exercise OUES were both higher in our obese sample. However, when we adjusted for any of body mass (kg), BSA (m2), or FFM (kg) the obese children were found to be less efficient. The results of this study suggest the use of OUES to be an appropriate measure of efficiency of ventilation and cardiorespiratory function in obese children, while also showing that our sample of obese children were less efficient on a per kilogram basis when compared with their healthy weight peers.
Gerda Jimmy, Roland Seiler and Urs Maeder
Accelerometry has been established as an objective method that can be used to assess physical activity behavior in large groups. The purpose of the current study was to provide a validated equation to translate accelerometer counts of the triaxial GT3X into energy expenditure in young children.
Thirty-two children aged 5–9 years performed locomotor and play activities that are typical for their age group. Children wore a GT3X accelerometer and their energy expenditure was measured with indirect calorimetry. Twenty-one children were randomly selected to serve as development group. A cubic 2-regression model involving separate equations for locomotor and play activities was developed on the basis of model fit. It was then validated using data of the remaining children and compared with a linear 2-regression model and a linear 1-regression model.
All 3 regression models produced strong correlations between predicted and measured MET values. Agreement was acceptable for the cubic model and good for both linear regression approaches.
The current linear 1-regression model provides valid estimates of energy expenditure for ActiGraph GT3X data for 5- to 9-year-old children and shows equal or better predictive validity than a cubic or a linear 2-regression model.
Barbara E. Ainsworth, Robert G. McMurray and Susan K. Veazey
The purpose of this study was to determine the accuracy of two submaximal exercise tests, the Sitting-Chair Step Test (Smith & Gilligan. 1983) and the Modified Step Test (Amundsen, DeVahl, & Ellingham, 1989) to predict peak oxygen uptake (VO2 peak) in 28 adults ages 60 to 85 years. VO2 peak was measured by indirect calorimetry during a treadmill maximal graded exercise test (VO2 peak, range 11.6–31.1 ml · kg −l · min−1). In each of the submaximal tests, VO2 was predicted by plotting stage-by-stage submaximal heart rate (HR) and perceived exertion (RPE) data against VO2 for each stage and extrapolating the data to respective age-predicted maximal HR or RPE values. In the Sitting-Chair Step Test (n = 23), no significant differences were observed between measured and predicted VO2 peak values (p > .05). However, predicted VO2 peak values from the HR were 4.3 ml · kg−1 · min−1 higher than VO2 peak values predicted from the RPE data (p < .05). In the Modified Step Test (n = 22), no significant differences were observed between measured and predicted VO2 peak values (p > .05). Predictive accuracy was modest, explaining 49–78% of the variance in VO2 peak. These data suggest that the Sitting-Chair Step Test and the Modified Step Test have moderate validity in predicting VO2 peak in older men and women.
Sofiya Alhassan, Kate Lyden, Cheryl Howe, Sarah Kozey Keadle, Ogechi Nwaokelemeh and Patty S. Freedson
This study examined the validity of commonly used regression equations for the Actigraph and Actical accelerometers in predicting energy expenditure (EE) in children and adolescents. Sixty healthy (8–16 yrs) participants completed four treadmill (TM) and five self-paced activities of daily living (ADL). Four Actigraph (AG) and three Actical (AC) regression equations were used to estimate EE. Bias (±95% CI) and root mean squared errors were used to assess the validity of the regression equations compared with indirect calorimetry. For children, the Freedson (AG) model accurately predicted EE for all activities combined and the Treuth (AG) model accurately predicted EE for TM activities. For adolescents, the Freedson model accurately predicted EE for TM activities and the Treuth model accurately predicted EE for all activities and for TM activities. No other equation accurately estimated EE. The percent agreement for the AG and AC equations were better for light and vigorous compared with moderate intensity activities. The Trost (AG) equation most accurately classified all activity intensity categories. Overall, equations yield inconsistent point estimates of EE.
Samantha Stephens, Tim Takken, Dale W. Esliger, Eleanor Pullenayegum, Joseph Beyene, Mark Tremblay, Jane Schneiderman, Doug Biggar, Pat Longmuir, Brian McCrindle, Audrey Abad, Dan Ignas, Janjaap Van Der Net and Brian Feldman
The purpose of this study was to assess the criterion validity of existing accelerometer-based energy expenditure (EE) prediction equations among children with chronic conditions, and to develop new prediction equations. Children with congenital heart disease (CHD), cystic fibrosis (CF), dermatomyositis (JDM), juvenile arthritis (JA), inherited muscle disease (IMD), and hemophilia (HE) completed 7 tasks while EE was measured using indirect calorimetry with counts determined by accelerometer. Agreement between predicted EE and measured EE was assessed. Disease-specific equations and cut points were developed and cross-validated. In total, 196 subjects participated. One participant dropped out before testing due to time constraints, while 15 CHD, 32 CF, 31 JDM, 31 JA, 30 IMD, 28 HE, and 29 healthy controls completed the study. Agreement between predicted and measured EE varied across disease group and ranged from (ICC) .13–.46. Disease-specific prediction equations exhibited a range of results (ICC .62–.88) (SE 0.45–0.78). In conclusion, poor agreement was demonstrated using current prediction equations in children with chronic conditions. Disease-specific equations and cut points were developed.
Sarah Kozey, Kate Lyden, John Staudenmayer and Patty Freedson
To compare intensity misclassification and activity MET values using measured RMR (measMET) compared with 3.5 ml·kg−1·min−1 (standMET) and corrected METs [corrMET = mean standMET × (3.5 ÷ Harris-Benedict RMR)] in subgroups.
RMR was measured for 252 subjects following a 4-hr fast and before completion of 11 activities. VO2 was measured during activity using indirect calorimetry (n = 2555 activities). Subjects were classified by BMI category (normal-weight or overweight/obese), sex, age (decade 20, 30, 40, or 50 y), and fitness quintiles (low to high). Activities were classified into low, moderate, and vigorous intensity categories.
The (mean ± SD) measMET was 6.1 ± 2.64 METs. StandMET [mean (95% CI)] was (0.51(0.42, 0.59) METs) less than measMET. CorrMET was not statistically different from measMET (−0.02 (−0.11, 0.06) METs). 12.2% of the activities were misclassified using standMETs compared with an 8.6% misclassification rate for METs based on predicted RMR (P < .0001). StandMET differences and misclassification rates were highest for low fit, overweight, and older individuals while there were no differences when corrMETs were used.
Using 3.5 ml·kg−1·min−1 to calculate activity METs causes higher misclassification of activities and inaccurate point estimates of METs than a corrected baseline which considers individual height, weight, and age. These errors disproportionally impact subgroups of the population with the lowest activity levels.
Steven K. Malin, Brooke R. Stephens, Carrie G. Sharoff, Todd A. Hagobian, Stuart R. Chipkin and Barry Braun
Exercise and metformin may prevent or delay Type 2 diabetes by, in part, raising the capacity for fat oxidation. Whether the addition of metformin has additive effects on fat oxidation during and after exercise is unknown. Therefore, the purpose of this study was to evaluate the effect of metformin on substrate oxidation during and after exercise. Using a double-blind, counter-balanced crossover design, substrate oxidation was assessed by indirect calorimetry in 15 individuals taking metformin (2,000 mg/d) and placebo for 8–10 d. Measurements were made during cycle exercise at 5 submaximal cycle workloads, starting at 30% peak work (Wpeak) and increasing by 10% every 8 min to 70% Wpeak. Substrate oxidation was also measured for 50 min postexercise. Differences between conditions were assessed using analysis of variance with repeated measures, and values are reported as M ± SE. During exercise, fat oxidation (0.19 ± 0.03 vs. 0.15 ± 0.01 g/min, p < .01) and percentage of energy from fat (32% ± 3% vs. 28% ± 3%, p < .01) were higher with metformin than with placebo. Postexercise, metformin slightly lowered fat oxidation (0.12 ± 0.02 to 0.10 ± 0.02 g/min, p < .01) compared with placebo. There was an inverse relationship between postexercise fat oxidation and the rate of fat oxidation during exercise (r = –.68, p < .05). In healthy individuals, metformin has opposing actions on fat oxidation during and after exercise. Whether the same effects are evident in insulin-resistant individuals remains to be determined.
Joel D. Reece, Vaughn Barry, Dana K. Fuller and Jennifer Caputo
This study determined the validity and sensitivity of the SenseWear armband (SWA) during sedentary and light office duties compared with indirect calorimetry (IC).
Participants (N = 22), 30 to 64 years of age, randomly performed 6 conditions for 5 minutes each (ie, supine, sitting no movement, standing no movement, sitting office work, standing office work, walking at 1.0 mph). Steady state for each activity (ie, average for minutes 4 and 5) was analyzed.
Energy expenditure (EE) for the SWA (1.58 kcal/min) and the IC (1.64 kcal/min) were significantly correlated, r(20) = 0.90, P < .001 and ICC = 0.90, 95% CI (0.699, 0.966). Correlation results for each condition varied in strength, r(20) = 0.53 to 0.83 and ICC = 0.49 to 0.81, but were all significant (P < .05). A significant interaction between measurement method and condition existed (P < .001). The SWA under predicted EE during standing with no movement, sitting office work, and standing office work.
The SWA and IC EE rates were strongly correlated during sedentary and light activity office behaviors. However, the SWA may under predict EE during office work (standing or sitting) and when standing motionless, making it slightly less sensitive than IC.
Rebecca J. Toone and James A. Betts
This study was designed to compare the effects of energy-matched carbohydrate (CHO) and carbohydrate-protein (CHO-PRO) supplements on cycling time-trial performance. Twelve competitive male cyclists and triathletes each completed 2 trials in a randomized and counterbalanced order that were separated by 5–10 d and applied in a double-blind manner. Participants performed a 45-min variable-intensity exercise protocol on a cycle ergometer while ingesting either a 9% CHO solution or a mixture of 6.8% CHO plus 2.2% protein in volumes providing 22 kJ/kg body mass. Participants were then asked to cycle 6 km in the shortest time possible. Blood glucose and lactate concentrations were measured every 15 min during exercise, along with measures of substrate oxidation via indirect calorimetry, heart rate, and ratings of perceived exertion. Mean time to complete the 6-km time trial was 433 ± 21 s in CHO trials and 438 ± 22 s in CHO-PRO trials, which represents a 0.94% (CI: 0.01, 1.86) decrement in performance with the inclusion of protein (p = .048). However, no other variable measured in this study was significantly different between trials. Reducing the quantity of CHO included in a supplement and replacing it with protein may not represent an effective nutritional strategy when the supplement is ingested during exercise. This may reflect the central ergogenic influence of exogenous CHO during such activity.