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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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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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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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Lance Ratcliff, Sareen S. Gropper, B. Douglas White, David M. Shannon and Kevin W. Huggins

This study compared type of habitual exercise and meal form on diet-induced thermogenesis (DIT) in 29 men age 19–28 yr. Resting metabolic rate (RMR) and DIT response to solid-meal (bar) vs. liquid-meal (shake) ingestion were measured via indirect calorimetry; classifications were sedentary (n = 9), endurance trained (n = 11), or resistance trained (n = 9). Height, weight, and body composition (using bioelectrical impedance) were measured for each subject. Energy expenditure was determined before and every 30 min after meal consumption for 210 min. RMR was significantly (p = .045) higher in the endurance- and resistance-trained groups. However, when expressed per kilogram fat-free mass (FFM; relative RMR), differences were not significant. Both DIT (kcal/min) and relative DIT (kcal · min−1 · kg FFM−1) significantly increased with time (p < .0001) from RMR for each meal form. There was no significant exercise-group effect on DIT or relative DIT. There was a significant (p = .012) effect of meal form on DIT; shakes elicited a higher DIT. This significant difference was not found for relative DIT. There was a significant interaction between group and meal form for DIT (p = .008) and relative DIT (p < .0001). Shakes elicited a significantly greater DIT (p = .0002) and relative DIT (p = .0001) in the resistance-trained group. In the sedentary group, relative DIT from shakes was significantly lower than from bars (p = .019). In conclusion, habitual exercise appears to increase RMR, and meal form may impart changes in relative DIT depending on exercise status.

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John S. Cuddy, Dustin R. Slivka, Walter S. Hailes, Charles L. Dumke and Brent C. Ruby

Purpose:

The purpose of this study was to determine the metabolic profile during the 2006 Ironman World Championship in Kailua-Kona, Hawaii.

Methods:

One recreational male triathlete completed the race in 10:40:16. Before the race, linear regression models were established from both laboratory and feld measures to estimate energy expenditure and substrate utilization. The subject was provided with an oral dose of 2H2 18O approximately 64 h before the race to calculate total energy expenditure (TEE) and water turnover with the doubly labeled water (DLW) technique. Body weight, blood sodium and hematocrit, and muscle glycogen (via muscle biopsy) were analyzed pre- and postrace.

Results:

The TEE from DLW and indirect calorimetry was similar: 37.3 MJ (8,926 kcal) and 37.8 MJ (9,029 kcal), respectively. Total body water turnover was 16.6 L, and body weight decreased 5.9 kg. Hematocrit increased from 46 to 51% PCV. Muscle glycogen decreased from 152 to 48 mmoL/kg wet weight pre- to postrace.

Conclusion:

These data demonstrate the unique physiological demands of the Ironman World Championship and should be considered by athletes and coaches to prepare sufficient nutritional and hydration plans.

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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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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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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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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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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.