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Jung-Min Lee, Pedro F. Saint-Maurice, Youngwon Kim, Glenn A. Gaesser and Gregory Welk

Background:

The assessment of physical activity (PA) and energy expenditure (EE) in youth is complicated by inherent variability in growth and maturation during childhood and adolescence. This study provides descriptive summaries of the EE of a diverse range of activities in children ages 7 to 13.

Methods:

A sample of 105 7- to 13-year-old children (boys: 57%, girls: 43%, and Age: 9.9 ± 1.9) performed a series of 12 activities from a pool of 24 activities while being monitored with an indirect calorimetry system.

Results:

Across physical activities, averages of VO2 ml·kg·min-1, VO2 L·min-1, EE, and METs ranged from 3.3 to 53.7 ml·kg·min-1, from 0.15 to 3.2 L·min-1, from 0.7 to 15.9 kcal·min-1, 1.5 MET to 7.8 MET, respectively.

Conclusions:

The energy costs of the activities varied by age, sex, and BMI status reinforcing the need to consider adjustments when examining the relative intensity of PA in youth.

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Dac Minh Tuan Nguyen, Virgile Lecoultre, Yoshiyuki Sunami and Yves Schutz

Background:

Physical activity (PA) and related energy expenditure (EE) is often assessed by means of a single technique. Because of inherent limitations, single techniques may not allow for an accurate assessment both PA and related EE. The aim of this study was to develop a model to accurately assess common PA types and durations and thus EE in free-living conditions, combining data from global positioning system (GPS) and 2 accelerometers.

Methods:

Forty-one volunteers participated in the study. First, a model was developed and adjusted to measured EE with a first group of subjects (Protocol I, n = 12) who performed 6 structured and supervised PA. Then, the model was validated over 2 experimental phases with 2 groups (n = 12 and n = 17) performing scheduled (Protocol I) and spontaneous common activities in real-life condition (Protocol II). Predicted EE was compared with actual EE as measured by portable indirect calorimetry.

Results:

In protocol I, performed PA types could be recognized with little error. The duration of each PA type could be predicted with an accuracy below 1 minute. Measured and predicted EE were strongly associated (r = .97, P < .001).

Conclusion:

Combining GPS and 2 accelerometers allows for an accurate assessment of PA and EE in free-living situations.

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Kent W. Goben, Gary A. Sforzo and Patricia A. Frye

This study investigated the effect of varying exercise intensity on the thermic effect of food (TEF). Sixteen lean male subjects were matched for VO2max and randomly assigned to either a high or low intensity group for 30 min of treadmill exercise. Caloric expenditure was measured using indirect calorimetry at rest and at 30-min intervals OYer 3 hrs following each of three conditions: a 750-kcal liquid meal, high or low intensity exercise, and a 750-kcal liquid meal followed by high or low intensity exercise. Low intensity exercise enhanced the TEF during recovery at 60 and 90 min while high intensity enhanced it only at 180 min but depressed it at 30 min. Total metabolic expense for a 3-hr postmeal period was not differently affected by the two exercise intensities. Exercise following a meal had a synergistic effect on metabolism; however, this effect was delayed until 180 min postmeal when exercise intensity was high. The circulatory demands of high intensity exercise may have initially blunted the TEF, but ultimately the TEF measured over the 3-hr period was at least equal to that experienced following low intensity exercise.

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Kate Lyden, Natalia Petruski, Stephanie Mix, John Staudenmayer and Patty Freedson

Background:

Physical activity and sedentary behavior measurement tools need to be validated in free-living settings. Direct observation (DO) may be an appropriate criterion for these studies. However, it is not known if trained observers can correctly judge the absolute intensity of free-living activities.

Purpose:

To compare DO estimates of total MET-hours and time in activity intensity categories to a criterion measure from indirect calorimetry (IC).

Methods:

Fifteen participants were directly observed on three separate days for two hours each day. During this time participants wore an Oxycon Mobile indirect calorimeter and performed any activity of their choice within the reception area of the wireless metabolic equipment. Participants were provided with a desk for sedentary activities (writing, reading, computer use) and had access to exercise equipment (treadmill, bike).

Results:

DO accurately and precisely estimated MET-hours [% bias (95% CI) = –12.7% (–16.4, –7.3), ICC = 0.98], time in low intensity activity [% bias (95% CI) = 2.1% (1.1, 3.2), ICC = 1.00] and time in moderate to vigorous intensity activity [% bias (95% CI) –4.9% (–7.4, –2.5), ICC = 1.00].

Conclusion:

This study provides evidence that DO can be used as a criterion measure of absolute intensity in free-living validation studies.

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Sarah Kozey, Kate Lyden, John Staudenmayer and Patty Freedson

Purpose:

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.

Methods:

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.

Results:

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.

Conclusion:

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.

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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.

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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.

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Christopher L. Melby, Kristen L. Osterberg, Alyssa Resch, Brenda Davy, Susan Johnson and Kevin Davy

Thirteen physically active, eumenorrheic, normal-weight (BMI ≤ 25 kg/m2) females, aged 18–30 years, completed 4 experimental conditions, with the order based on a Latin Square Design: (a) CHO/Ex: moderate-intensity exer-· cise (65% V̇O2peak) with a net energy cost of ~500 kcals, during which time the subject consumed a carbohydrate beverage (45 g CHO) at specific time intervals; (b) CHO/NoEx: a period of time identical to (a) but with subjects consuming the carbohydrate while sitting quietly rather than exercising; (c) NoCHO/ Ex: same exercise protocol as condition (a) during which time subjects consumed a non-caloric placebo beverage; and (d) NoCHO/NoEx: same as the no-exercise condition (b) but with subjects consuming a non-caloric placebo beverage. Energy expenditure, and fat and carbohydrate oxidation rates for the entire exercise/sitting period plus a 90-min recovery period were determined by continuous indirect calorimetry. Following recovery, subjects ate ad libitum amounts of food from a buffet and were asked to record dietary intake during the remainder of the day. Total fat oxidation (exercise plus recovery) was attenuated by carbohydrate compared to placebo ingestion by only ~4.5 g. There was a trend (p = .08) for a carbohydrate effect on buffet energy intake such that the CHO/Ex and CHO/NoEx energy intakes were lower than the NoCHO/Ex and NoCHO/NoEx energy intakes, respectively (mean for CHO conditions: 683 kcal; NoCHO conditions: 777 kcal). Average total energy intake (buffet plus remainder of the day) was significantly lower (p < .05) following the conditions when carbohydrate was consumed (CHO/Ex = 1470 kcal; CHO/NoEx = 1285 kcal) compared to the noncaloric placebo (NoCHO/Ex =1767 kcal; NoCHO/ NoEx = 1660 kcal). In conclusion, in young women engaging in regular exercise, ingestion of 45 g of carbohydrate during exercise only modestly suppresses total fat oxidation during exercise. Furthermore, the ingestion of carbohydrate with or without exercise resulted in a lower energy intake for the remainder of the day

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James Cameron Morehen, Warren Jeremy Bradley, Jon Clarke, Craig Twist, Catherine Hambly, John Roger Speakman, James Peter Morton and Graeme Leonard Close

Rugby League is a high-intensity collision sport competed over 80 min. Training loads are monitored to maximize recovery and assist in the design of nutritional strategies although no data are available on the total energy expenditure (TEE) of players. We therefore assessed resting metabolic rate (RMR) and TEE in six Super League players over 2 consecutive weeks in-season including one game per week. Fasted RMR was assessed followed by a baseline urine sample before oral administration of a bolus dose of hydrogen (deuterium 2H) and oxygen (18O) stable isotopes in the form of water (2H2 18O). Every 24 hr thereafter, players provided urine for analysis of TEE via DLW method. Individual training load was quantified using session rating of perceived exertion (sRPE) and data were analyzed using magnitude-based inferences. There were unclear differences in RMR between forwards and backs (7.7 ± 0.5 cf. 8.0 ± 0.3 MJ, respectively). Indirect calorimetry produced RMR values most likely lower than predictive equations (7.9 ± 0.4 cf. 9.2 ± 0.4 MJ, respectively). A most likely increase in TEE from Week 1 to 2 was observed (17.9 ± 2.1 cf. 24.2 ± 3.4 MJ) explained by a most likelyincrease in weekly sRPE (432 ± 19 cf. 555 ± 22 AU), respectively. The difference in TEE between forward and backs was unclear (21.6 ± 4.2 cf. 20.5 ± 4.9 MJ, respectively). We report greater TEE than previously reported in rugby that could be explained by the ability of DLW to account for all match and training-related activities that contributes to TEE.

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Shelby L. Francis, Ajay Singhvi, Eva Tsalikian, Michael J. Tansey and Kathleen F. Janz

Purpose:

Determining fitness is important when assessing adolescents with type 1 diabetes mellitus (T1DM). Submaximal tests estimate fitness, but none have been validated in this population. This study cross-validates the Ebbeling and Nemeth equations to predict fitness (VO2max (ml/kg/min)) in adolescents with T1DM.

Methods:

Adolescents with T1DM (n = 20) completed a maximal treadmill test using indirect calorimetry. Participants completed one 4-min stage between 2.0 and 4.5 mph and 5% grade (Ebbeling/Nemeth protocol). Speed and grade were then increased until exhaustion. Predicted VO2max was calculated using the Ebbeling and Nemeth equations and compared with observed VO2max using paired t tests. Pearson correlation coefficients, 95% confidence intervals, coefficients of determination (R2), and total error (TE) were calculated.

Results:

The mean observed VO2max was 47.0 ml/kg/min (SD = 6.9); the Ebbeling and Nemeth mean predictions were 42.4 (SD = 9.4) and 43.5 ml/kg/min (SD = 6.9), respectively. Paired t tests resulted in statistically significant (p < .01) mean differences between observed and predicted VO2max for both predictions. The association between the Ebbeling prediction and observed VO2max was r = .90 (95% CI = 0.76, 0.96), R 2 = .81, and TE = 6.5 ml/kg/min. The association between the Nemeth prediction and observed VO2max was r = .81 (95% CI = 0.57, 0.92), R 2 = .66, and TE = 5.6 ml/kg/min.

Conclusion:

The Nemeth submaximal treadmill protocol provides a better estimate of fitness than the Ebbeling in adolescents with T1DM.