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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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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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Brett R. Ely, Matthew R. Ely and Samuel N. Cheuvront

The use of caffeine supplements in athletic and military populations has increased in recent years. Excessive caffeine consumption in conjunction with exercise in a hot environment may predispose individuals to heat illness.

Purpose:

To examine heat balance induced by a large dose of caffeine during exercise in a hot environment.

Methods:

Ten men, not heat acclimated and not habitual caffeine users, consumed either caffeine (CAF; 9 mg/kg) or placebo (PLA) before performing cycle-ergometer exercise for 30 min at 50% VO2peak in a 40 °C, 25% relative humidity environment while body temperature (core and skin) and ratings of thermal comfort (TC) were monitored. Heat-exchange variables were calculated using partitional calorimetry and thermometry.

Results:

Mean body temperature (Tb) was higher (p < .05) with CAF (37.18 ± 0.15 °C) than with PLA (36.93 ± 0.15 °C) at the start of exercise. Heat production was slightly higher (~8 W, p < .05) with CAF. There were no differences in heat storage, dry heat gains, TC, or Tb during exercise.

Conclusions:

A caffeine dose of 9 mg/kg does not appreciably alter heat balance during work in a hot environment. The small increase in Tb observed with CAF was undetected by the participants and is unlikely to increase physiological strain sufficiently to affect endurance performance or risk of heat illness.

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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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Alison L. Innerd and Liane B. Azevedo

Background:

The aim of this study is to establish the energy expenditure (EE) of a range of child-relevant activities and to compare different methods of estimating activity MET.

Methods:

27 children (17 boys) aged 9 to 11 years participated. Participants were randomly assigned to 1 of 2 routines of 6 activities ranging from sedentary to vigorous intensity. Indirect calorimetry was used to estimate resting and physical activity EE. Activity metabolic equivalent (MET) was determined using individual resting metabolic rate (RMR), the Harrell-MET and the Schofield equation.

Results:

Activity EE ranges from 123.7± 35.7 J/min/Kg (playing cards) to 823.1 ± 177.8 J/min/kg (basketball). Individual RMR, the Harrell-MET and the Schofield equation MET prediction were relatively similar at light and moderate but not at vigorous intensity. Schofield equation provided a better comparison with the Compendium of Energy Expenditure for Youth.

Conclusion:

This information might be advantageous to support the development of a new Compendium of Energy Expenditure for Youth.

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Nirjhar Dutta and Mark A. Pereira

Background:

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.

Methods:

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.

Results:

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.

Conclusion:

AVGs may be used to replace sedentary screen time (eg, television watching or TVG play) with light activity in healthy adults.

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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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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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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.