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
Mathieu L. Maltais, Karine Perreault, Alexandre Courchesne-Loyer, Jean-Christophe Lagacé, Razieh Barsalani and Isabelle J. Dionne
The decrease in resting energy expenditure (REE) and fat oxidation with aging is associated with an increase in fat mass (FM), and both could be prevented by exercise such as resistance training. Dairy consumption has also been shown to promote FM loss in different subpopulations and to be positively associated with fat oxidation. Therefore, we sought to determine whether resistance exercise combined with dairy supplementation could have an additive impact on FM and energy metabolism, especially in individuals with a deficit in muscle mass. Twenty-six older overweight sarcopenic men (65 ± 5 years old) were recruited for the study. They participated in 4 months of resistance exercise and were randomized into three groups for postexercise shakes (control, dairy, and nondairy isocaloric and isoprotein supplement with 375 ml and ~280 calories per shake). Body composition was measured by dual X-ray absorptiometry and REE by indirect calorimetry. Fasting glucose, insulin, leptin, inflammatory profile, and blood lipid profile were also measured. Significant decreases were observed with FM only in the dairy supplement group; no changes were observed for any other variables. To conclude, FM may decrease without changes in metabolic parameters during resistance training and dairy supplementation with no caloric restriction without having any impact on metabolic properties. More studies are warranted to explain this significant decrease in FM.
Kristin L. Osterberg and Christopher L. Melby
This study determined the effect of an intense bout of resistive exercise on postexercise oxygen consumption, resting metabolic rate, and resting fat oxidation in young women (N = 7, ages 22-35). On the morning of Day 1, resting metabolic rate (RMR) was measured by indirect calorimetry. At 13:00 hr, preexercise resting oxygen consumption was measured followed by 100 min of resistive exercise. Postexercise oxygen consumption was then measured for a 3-hr recovery period. On the following morning (Day 2), RMR was once again measured in a fasted state at 07:00. Postexercise oxygen consumption remained elevated during the entire 3-hr postexercise recovery period compared to the pre-exercise baseline. Resting metabolic rate was increased by 4.2% (p < .05) from Day 1 (morning prior to exercise: 1,419 ± 58 kcal/24 hr) compared to Day 2 (16 hr following exercise: 1,479 ± 65 kcal/24 hr). Resting fat oxidation as determined by the respiratory exchange ratio was also significantly elevated on Day 2 compared to Day 1. These results indicate that among young women, acute strenuous resistance exercise of the nature used in this study is capable of producing modest but prolonged elevations of postexercise metabolic rate and possibly fat oxidation.
Nirjhar Dutta and Mark A. Pereira
The objective of this study was to estimate the mean difference in energy expenditure (EE) in healthy adults between playing active video games (AVGs) compared with traditional video games (TVGs) or rest.
A systematic search was conducted on Ovid MEDLINE, Web of Knowledge, and Academic Search Premier between 1998 and April 2012 for relevant keywords, yielding 15 studies. EE and heart rate (HR) data were extracted, and random effects meta-analysis was performed.
EE during AVG play was 1.81 (95% CI, 1.29–2.34; I 2 = 94.2%) kcal/kg/hr higher, or about 108 kcal higher per hour for a 60-kg person, compared with TVG play. Mean HR was 21 (95% CI, 13.7–28.3; I 2 = 93.4%) beats higher per minute during AVG play compared with TVG play. There was wide variation in the EE and HR estimates across studies because different games were evaluated. Overall metabolic equivalent associated with AVG play was 2.62 (95% CI, 2.25–3.00; I 2 = 99.2%), equivalent to a light activity level. Most studies had low risk of bias due to proper study design and use of indirect calorimetry to measure EE.
AVGs may be used to replace sedentary screen time (eg, television watching or TVG play) with light activity in healthy adults.
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.
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.
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.
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.
Jeanne F. Nichols, Hilary Aralis, Sonia Garcia Merino, Michelle T. Barrack, Lindsay Stalker-Fader and Mitchell J. Rauh
There is a growing need to accurately assess exercise energy expenditure (EEE) in athletic populations that may be at risk for health disorders because of an imbalance between energy intake and energy expenditure. The Actiheart combines heart rate and uniaxial accelerometry to estimate energy expenditure above rest. The authors’ purpose was to determine the utility of the Actiheart for predicting EEE in female adolescent runners (N = 39, age 15.7 ± 1.1 yr). EEE was measured by indirect calorimetry and predicted by the Actiheart during three 8-min stages of treadmill running at individualized velocities corresponding to each runner’s training, including recovery, tempo, and 5-km-race pace. Repeated-measures ANOVA with Bonferroni post hoc comparisons across the 3 running stages indicated that the Actiheart was sensitive to changes in intensity (p < .01), but accelerometer output tended to plateau at race pace. Pairwise comparisons of the mean difference between Actiheart- and criterion-measured EEE yielded values of 0.0436, 0.0539, and 0.0753 kcal · kg−1 · min−1 during recovery, tempo, and race pace, respectively (p < .0001). Bland–Altman plots indicated that the Actiheart consistently underestimated EEE except in 1 runner’s recovery bout. A linear mixed-model regression analysis with height as a covariate provided an improved EEE prediction model, with the overall standard error of the estimate for the 3 speeds reduced to 0.0101 kcal · kg−1 · min−1. Using the manufacturer’s equation that combines heart rate and uniaxial motion, the Actiheart may have limited use in accurately assessing EEE, and therefore energy availability, in young, female competitive runners.
Steven Gastinger, Guillaume Nicolas, Anthony Sorel, Hamid Sefati and Jacques Prioux
The aim of this article was to compare 2 portable devices (a heart-rate monitor and an electromagnetic-coil system) that evaluate 2 different physiological parameters—heart rate (HR) and ventilation (VE)—with the objective of estimating energy expenditure (EE). The authors set out to prove that VE is a more pertinent setting than HR to estimate EE during light to moderate activities (sitting and standing at rest and walking at 4, 5, and 6 km/hr). Eleven healthy men were recruited to take part in this study (27.6 ± 5.4 yr, 73.7 ± 9.7 kg). The authors determined the relationships between HR and EE and between VE and EE during light to moderate activities. They compared EE measured by indirect calorimetry (EEREF) with EE estimated by HR monitor (EEHR) and EE estimated by electromagnetic coils (EEMAG) in upright sitting and standing positions and during walking exercises. They compared EEREF with EEHR and EEMAG. The results showed no significant difference between the values of EEREF and EEMAG. However, they showed several significant differences between the values of EEREF and EEHR (for standing at rest and walking at 5 and 6 km/hr). These results showed that the electromagnetic-coil system seems to be more accurate than the HR monitor to estimate EE at rest and during exercise. Taking into consideration these results, it would be interesting to associate the parameters VE and HR to estimate EE. Furthermore, a new version of the electromagnetic-coil device was recently developed and provides the possibility to perform measurement under daily life conditions.