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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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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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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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Kristin L. Osterberg and Christopher L. Melby

This study determined the effect of an intense bout of resistive exercise on postexercise oxygen consumption, resting metabolic rate, and resting fat oxidation in young women (N = 7, ages 22-35). On the morning of Day 1, resting metabolic rate (RMR) was measured by indirect calorimetry. At 13:00 hr, preexercise resting oxygen consumption was measured followed by 100 min of resistive exercise. Postexercise oxygen consumption was then measured for a 3-hr recovery period. On the following morning (Day 2), RMR was once again measured in a fasted state at 07:00. Postexercise oxygen consumption remained elevated during the entire 3-hr postexercise recovery period compared to the pre-exercise baseline. Resting metabolic rate was increased by 4.2% (p < .05) from Day 1 (morning prior to exercise: 1,419 ± 58 kcal/24 hr) compared to Day 2 (16 hr following exercise: 1,479 ± 65 kcal/24 hr). Resting fat oxidation as determined by the respiratory exchange ratio was also significantly elevated on Day 2 compared to Day 1. These results indicate that among young women, acute strenuous resistance exercise of the nature used in this study is capable of producing modest but prolonged elevations of postexercise metabolic rate and possibly fat oxidation.

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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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Akiko Sato, Yoshimitsu Shimoyama, Tomoji Ishikawa and Nobuko Murayama

The purpose of this study was to examine the effect of high-intensity physical activity during training on the biochemical status of thiamin and riboflavin in athletes. Thiamin and riboflavin concentrations in whole blood of a group of 19 athletes (6 men and 13 women) were measured during a low-intensity preparatory period and compared with measurements taken during a high-intensity training period. Additional variables measured included anthropometric characteristics, estimated energy expenditure during swim training, distance covered, resting energy expenditure obtained by indirect calorimetry, estimated energy requirement per day, and dietary intake of energy, thiamin, and riboflavin estimated from 3-day food records. For both male and female subjects, no major changes were observed in anthropometric characteristics or dietary intake, but energy expenditure during swim training per day significantly increased in the intensive-training period (496 ± 0 kcal in the preparation period compared with 995 ± 96 kcal in the intensive-training period for male subjects [p < .001] and 361 ± 27 kcal vs. 819 ± 48 kcal, respectively, for female subjects [p < .001]). Blood thiamin concentration decreased significantly during the intensive-training period compared with the preparation period (41 ± 6 ng/ml decreased to 36 ± 3 ng/ml for male subjects [p = .048], and 38 ± 10 ng/ml decreased to 31 ± 5 ng/ml for female subjects [p = .004]); however, the concentration of riboflavin was unchanged. These results suggest that intense training affects thiamin concentration, but not riboflavin concentration, in the whole blood of college swimmers.

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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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Meredith C. Peddie, Claire Cameron, Nancy Rehrer and Tracy Perry

Background:

Interrupting sedentary time induces improvements in glucose metabolism; however, it is unclear how much activity is required to reduce the negative effects of prolonged sitting.

Methods:

Sixty-six participants sat continuously for 9 hours except for required bathroom breaks. Participants were fed meal replacement beverages at 60, 240 and 420 min. Blood samples were obtained hourly for 9 hours, with additional samples collected 30 and 45 min after each feeding. Responses were calculated as incremental area under the curve (iAUC) for plasma glucose, insulin and triglyceride. Participants wore a triaxial accelerometer and a heart rate monitor. Energy expenditure was estimated using indirect calorimetry.

Results:

After controlling for age, sex and BMI, every 100 count increase in accelerometer derived total movement was associated with a 0.06 mmol·L-1·9 hours decrease in glucose iAUC (95% CI 0.004–0.1; P = .035), but not associated with changes in insulin or triglyceride iAUC. Every 1 bpm increase in mean heart rate was associated with a 0.76 mmol·L-1·9 hours increase in triglyceride iAUC (95% CI 0.13–1.38).

Conclusion:

Accelerometer measured movement during periods of prolonged sitting can result in minor improvements in postprandial glucose metabolism, but not lipid metabolism.

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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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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.