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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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Scott A. Conger, Stacy N. Scott, Eugene C. Fitzhugh, Dixie L. Thompson and David R. Bassett

Background:

It is unknown if activity monitors can detect the increased energy expenditure (EE) of wheelchair propulsion at different speeds or on different surfaces.

Methods:

Individuals who used manual wheelchairs (n = 14) performed 5 wheeling activities: on a level surface at 3 speeds, on a rubberized track at 1 fixed speed and on a sidewalk course at a self-selected speed. EE was measured using a portable indirect calorimetry system and estimated by an Actical (AC) worn on the wrist and a SenseWear (SW) activity monitor worn on the upper arm. Repeated-measures ANOVA was used to compare measured EE to the estimates from the standard AC prediction equation and SW using 2 different equations.

Results:

Repeated-measures ANOVA demonstrated a significant main effect between measured EE and estimated EE. There were no differences between the criterion method and the AC across the 5 activities. The SW overestimated EE when wheeling at 3 speeds on a level surface, and during sidewalk wheeling. The wheelchair-specific SW equation improved the EE prediction during low intensity activities, but error progressively increased during higher intensity activities.

Conclusions:

During manual wheelchair propulsion, the wrist-mounted AC provided valid estimates of EE, whereas the SW tended to overestimate EE.

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Orjan Ekblom, Gisela Nyberg, Elin Ekblom Bak, Ulf Ekelund and Claude Marcus

Background:

Wrist-worn accelerometers may provide an alternative to hip-worn monitors for assessing physical activity as they are easier to wear and may thus facilitate long-term recordings. The current study aimed at a) assessing the validity of the Actiwatch (wrist-worn) for estimating energy expenditure, b) determining cut-off values for light, moderate, and vigorous activities, c) studying the comparability between the Actiwatch and the Actigraph (hip-worn), and d) assessing reliability.

Methods:

For validity, indirect calorimetry was used as criterion measure. ROC-analyses were applied to identify cut-off values. Comparability was tested by simultaneously wearing of the 2 accelerometers during free-living condition. Reliability was tested in a mechanical shaker.

Results:

All-over correlation between accelerometer output and energy expenditure were found to be 0.80 (P < .001).Based on ROC-analysis, cut-off values for 1.5, 3, and 6 METs were found to be 80, 262, and 406 counts per 15 s, respectively. Energy expenditure estimates differed between the Actiwatch and the Actigraph (P < .05). The intra- and interinstrument coefficient of variation of the Actiwatch ranged between 0.72% and 8.4%.

Conclusion:

The wrist-worn Actiwatch appears to be valid and reliable for estimating energy expenditure and physical activity intensity in children aged 8 to 10 years.

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Sally A. Sherman, Renee J. Rogers, Kelliann K. Davis, Ryan L. Minster, Seth A. Creasy, Nicole C. Mullarkey, Matthew O’Dell, Patrick Donahue and John M. Jakicic

Background:

Whether the energy cost of vinyasa yoga meets the criteria for moderate-to-vigorous physical activity has not been established.

Purpose:

To compare energy expenditure during acute bouts of vinyasa yoga and 2 walking protocols.

Methods:

Participants (20 males, 18 females) performed 60-minute sessions of vinyasa yoga (YOGA), treadmill walking at a self-selected brisk pace (SELF), and treadmill walking at a pace that matched the heart rate of the YOGA session (HR-Match). Energy expenditure was assessed via indirect calorimetry.

Results:

Energy expenditure was significantly lower in YOGA compared with HR-Match (difference = 79.5 ± 44.3 kcal; P < .001) and SELF (difference = 51.7 ± 62.6 kcal; P < .001), but not in SELF compared with HR-Match (difference = 27.8 ± 72.6 kcal; P = .054). A similar pattern was observed for metabolic equivalents (HR-Match = 4.7 ± 0.8, SELF = 4.4 ± 0.7, YOGA = 3.6 ± 0.6; P < .001). Analyses using only the initial 45 minutes from each of the sessions, which excluded the restorative component of YOGA, showed energy expenditure was significantly lower in YOGA compared with HR-Match (difference = 68.0 ± 40.1 kcal; P < .001) but not compared with SELF (difference = 15.1 ± 48.7 kcal; P = .189).

Conclusions:

YOGA meets the criteria for moderate-intensity physical activity. Thus, YOGA may be a viable form of physical activity to achieve public health guidelines and to elicit health benefits.

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John M. Schuna Jr., Daniel S. Hsia, Catrine Tudor-Locke and Neil M. Johannsen

Background: Active workstations offer the potential for augmenting energy expenditure (EE) in sedentary occupations. However, comparisons of EE during pedal and treadmill desk usage at self-selected intensities are lacking. Methods: A sample of 16 adult participants (8 men and 8 women; 33.9 [7.1] y, 22.5 [2.7] kg/m2) employed in sedentary occupations had their EE measured using indirect calorimetry during 4 conditions: (1) seated rest, (2) seated typing in a traditional office chair, (3) self-paced pedaling on a pedal desk while typing, and (4) self-paced walking on a treadmill desk while typing. Results: For men and women, self-paced pedal and treadmill desk typing significantly increased EE above seated typing (pedal desk: +1.20 to 1.28 kcal/min and treadmill desk: +1.43 to 1.93 kcal/min, P < .001). In men, treadmill desk typing (3.46 [0.19] kcal/min) elicited a significantly higher mean EE than pedal desk typing (2.73 [0.21] kcal/min, P < .001). No significant difference in EE was observed between treadmill desk typing (2.68 [0.19] kcal/min) and pedal desk typing among women (2.52 [0.21] kcal/min). Conclusions: Self-paced treadmill desk usage elicited significantly higher EE than self-paced pedal desk usage in men but not in women. Both pedal and treadmill desk usage at self-selected intensities elicited approximate 2-fold increases in EE above what would typically be expected during traditional seated office work.

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Claudia Ridel Juzwiak, Ciro Winckler, Daniel Paduan Joaquim, Andressa Silva and Marco Tulio de Mello

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.

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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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Seth A. Creasy, Renee J. Rogers, Thomas D. Byard, Robert J. Kowalsky and John M. Jakicic

Background:

Identifying strategies to increase energy expenditure (EE) may help combat the harmful effects of sedentary behavior. This study examined EE during sitting, standing, and walking.

Methods:

Participants (N = 74) were randomized to 2 of the following activities: sitting using a laptop computer (SIT-C), sitting watching television (SIT-T), standing watching television (STAND), and walking at a self-selected pace ≤3.0 (mph) (WALK). Each activity lasted 15 minutes with a 3-minute transition period between activities. The experimental conditions were: SIT-C to STAND (N = 18), SIT-T to WALK (N = 18), STAND to SIT-C (N = 20), and WALK to SIT-T (N = 18). EE was measured using indirect calorimetry.

Results:

Based on the first activity performed, EE during WALK (55.92 ± 14.19 kcal) was significantly greater than SIT-C (19.63 ± 6.90 kcal), SIT-T (18.66 ± 4.01 kcal), and STAND (21.92 ± 5.08 kcal) (P < .001). Cumulative EE in SIT-T to WALK (74.50 ± 17.88 kcal) and WALK to SIT-T (82.72 ± 21.70 kcal) was significantly greater than EE in SIT-C to STAND (45.38 ± 14.78 kcal) and STAND to SIT-C (45.64 ± 9.69 kcal) (P < .001).

Conclusions:

Conclusion: Substituting periods of sitting or standing with walking significantly increases EE, but substituting periods of sitting with standing may not affect EE. Thus, the potential benefits of standing as opposed to sitting need further investigation beyond the role of EE.

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Brian R. Hunt, James D. George, Pat R. Vehrs, A. Garth Fisher and Gilbert W. Fellingham

The purpose of this study was to validate the ability of the 1-mile jog test to predict VO2max in fit teenagers. Forty-one males and 42 females performed the steady-state, submaximal jogging test on an indoor track, along with a maximal graded exercise test (GXT) on a treadmill. Open circuit calorimetry was used during the GXT to measure maximal oxygen consumption (VO2max). We generated the following age-specific prediction equation applicable to boys and girls 13–17 years old (n = 83, Radj = .88, SEE = 3.26 ml · kg−1 · min−1): VO2max = 92.91 + 6.50 × gender (0 = female, 1 = male) − 0.141 × body mass (kg) − 1.562 × jog time (min) − 0.125 × heart rate (bpm). Cross-validation results were acceptable (SEEpress = 3.44 ml · kg−1 · min−1). As a field test, the submaximal 1-mile jogging test may alleviate problems associated with pacing, motivation, discouragement, injury, and fatigue that are sometimes associated with maximal effort timed or distance run tests.

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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.