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Richard R. Suminski, Larry T. Wier, Walker Poston, Brian Arenare, Anthony Randles and Andrew S. Jackson

Background:

Nonexercise models were developed to predict maximal oxygen consumption (VO2max). While these models are accurate, they don’t consider smoking, which negatively impacts measured VO2max. The purpose of this study was to examine the effects of smoking on both measured and predicted VO2max.

Methods:

Indirect calorimetry was used to measure VO2max in 2,749 men and women. Physical activity using the NASA Physical Activity Status Scale (PASS), body mass index (BMI), and smoking (pack-y = packs·day * y of smoking) also were assessed. Pack-y groupings were Never (0 pack-y), Light (1–10), Moderate (11–20), and Heavy (>20). Multiple regression analysis was used to examine the effect of smoking on VO2max predicted by PASS, age, BMI, and gender.

Results:

Measured VO2max was significantly lower in the heavy smoking group compared with the other pack-y groups. The combined effects of PASS, age, BMI, and gender on measured VO2max were significant. With smoking in the model, the estimated effects on measured VO2max from Light, Moderate, and Heavy smoking were –0.83, –0.85, and –2.56 ml·kg−1·min−1, respectively (P < .05).

Conclusions:

Given that 21% of American adults smoke and 12% of them are heavy smokers, it is recommended that smoking be considered when using nonexercise models to predict VO2max.

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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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Gerda Jimmy, Roland Seiler and Urs Maeder

Background:

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.

Methods:

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.

Results:

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.

Conclusions:

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.

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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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Kathryn H. Myburgh, Claire Berman, Illana Novick, Timothy D. Noakes and Estelle V. Lambert

We studied 21 ballet dancers aged 19.4 ± 1.4 years, hypothesizing that undernu-trition was a major factor in menstrual irregularity in this population. Menstrual history was determined by questionnaire. Eight dancers had always been regular (R). Thirteen subjects had a history of menstrual irregularity (HI). Of these, 2 were currently regularly menstruating, 3 had short cycles, 6 were oligomenorrheic, and 2 were amenorrheic. Subjects completed a weighed dietary record and an Eating Attitudes Test (EAT). The following physiological parameters were measured: body composition by anthropometry, resting metabolic rate (RMR) by open-circuit indirect calorimetry, and serum thyroid hormone concentrations by radioimmunoassay. R subjects had significantly higher RMR than HI subjects. Also, HI subjects had lower RMR than predicted by fat-free mass, compared to the R subjects. Neitherreported energy intake nor serum thyroid hormone concentrations were different between R and HI subjects. EAT scores varied and were not different between groups. We concluded that in ballet dancers, low RMR is more strongly associated with menstrual irregularity than is currentreported energy intake or serum thyroid hormone concentrations.

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Hermann-J. Engels, John C. Wirth, Sueda Celik and Jodee L. Dorsey

This study assessed the influence of caffeine on metabolic and cardiovascular functions during sustained, light intensity cycling and at rest. Eight healthy, recreationally active adults participated in four randomly assigned, double-blind experimental trials of 60 min upright seated cycle exercise (30% VO2max) or equivalent rest with caffeine (5 mg ⋅ kg−1) or placebo consumed 60 min prior to data collection. Gas exchange was measured by open-circuit spirom-etry indirect calorimetry. Global blood flow was evaluated by thoracic impedance cardiography and arterial blood pressure by auscultation. A repeated measures ANOVA indicated that pretrial caffeine increased oxygen uptake and energy expenditure rate (p < 0.05) but did not change respiratory exchange ratio. Systolic, diastolic, and mean arterial blood pressure were elevated following caffeine intake (p < 0.05). Cardiac output, heart rate, stroke volume, and systemic vascular resistance were not significantly different between caffeine and placebo sessions. For each of the metabolic and hemodynamic variables examined, the effects of caffeine were similar during constant-load, light intensity cycling and at rest. These data illustrate that caffeine's mild thermogenic influence can be mediated without a major shift in substrate oxidation mixture. Caffeine at this dosage level alters cardiovascular dynamics by augmenting arterial blood pressure.

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Daniel Arvidsson, Mark Fitch, Mark L. Hudes and Sharon E. Fleming

Background:

Overweight children show different movement patterns during walking than normal-weight children, suggesting the accuracy of multisensory activity monitors may differ in these groups.

Methods:

Eleven normal and 15 high BMI African American children walked at 2, 4, 5, and 6 km/h on a treadmill wearing the Intelligent Device for Energy Expenditure and Activity (IDEEA) and SenseWear (SW). Accuracy was determined using indirect calorimetry and manually counted steps as references.

Results:

For IDEEA, no significant differences in accuracy were observed between BMI groups for energy expenditure (EE), but differences were significant by speed (+15% at 2 km/h to −10% at 6 km/h). For SW, EE accuracy was significantly different for high (+21%) versus normal BMI girls (−13%) at 2 km/h. For high BMI girls, EE was overestimated at low speed and underestimated at higher speeds. Underestimations in steps did not differ by BMI group at 4 to 6 km/h, but were significantly larger at 2 km/h than at the other speeds for all groups with IDEEA, and for normal BMI children with SW.

Conclusions:

Similar accuracies during walking may be expected in normal and overweight children using IDEEA and SW. Both monitors showed small errors for steps provided speed exceeded 2 km/h.

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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.

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Gianluca Vernillo, Aldo Savoldelli, Barbara Pellegrini and Federico Schena

Background:

Accurate assessments of physical activity and energy expenditure (EE) are needed to advance research on positive and negative graded walking.

Purpose:

To evaluate the validity of 2 SenseWear Armband monitors (Pro3 and the recently released Mini) during graded walking.

Methods:

Twenty healthy adults wore both monitors during randomized walking activities on a motorized treadmill at 7 grades (0%, ±5%, ±15%, and ±25%). Estimates of total EE from the monitors were computed using different algorithms and compared with values derived from indirect calorimetry methodology using a 2-way mixed model ANOVA (Device × Condition), correlation analyses and Bland-Altman plots.

Results:

There was no significant difference in EE between the 2 armbands in any of the conditions examined. Significant main effects for device and condition, as well as a consistent bias, were observed during positive and negative graded walking with a greater over- and under-estimation at higher slope.

Conclusions:

Both the armbands produced similar EE values and seem to be not accurate in estimation of EE during activities involving uphill and downhill walking. Additional work is needed to understand factors contributing to this discrepancy and to improve the ability of these monitors to accurately measure EE during graded walking.

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Dennis van Hamont, Christopher R. Harvey, Denis Massicotte, Russell Frew, François Peronnet and Nancy J. Rehrer

Effects of feeding glucose on substrate metabolism during cycling were studied. Trained (60.0 ± 1.9 mL · kg−1 · min−1) males (N = 5) completed two 75 min, 80% VO2max trials: 125 g 13C-glucose (CHO); 13C-glucose tracer, 10 g (C). During warm-up (30 min 30% VO2max) 2 ⋅ 2 g 13C-glucose was given as bicarbonate pool primer. Breath samples and blood glucose were analyzed for 13C/ 12C with IRMS. Protein oxidation was estimated from urine and sweat urea. Indirect calorimetry (protein corrected) and 13C/ 12C enrichment in expired CO2 and blood glucose allowed exogenous (Gexo), endogenous (Gendo), muscle (Gmuscle), and liver glucose oxidation calculations. During exercise (75 min) in CHO versus C (respectively): protein oxidation was lower (6.8 ± 2.7, 18.8 ± 5.9 g; P = 0.01); Gendo was reduced (71.2 ± 3.8, 80.7 ± 5.7%; P = 0.01); Gmuscle was reduced (55.3 ± 6.1, 65.9 ± 6.0%; P = 0.01) compensated by increased Gexo (58.3 ± 2.1, 3.87 ± 0.85 g; P = 0.000002). Glucose ingestion during exercise can spare endogenous protein and carbohydrate, in fed cyclists, without gly-cogen depletion.