Prediction of energy expenditure in a whole body indirect calorimeter at both low and high levels of physical activity.
OBJECTIVE
In studies that involve the use of a room calorimeter, 24 h energy intake is often larger than 24 h energy expenditure (24 h EE) because of a decrease in activity energy expenditure due to the confined space. This positive energy balance can have large consequences for the interpretation of substrate balances. The objective of this study was to develop a method for predicting an individual's 24 h EE in a room calorimeter at both low (1.4xRMR) and high (1.8xRMR) levels of physical activity.


METHODS
Two methods are presented that predict an individual's 24 h EE in a metabolic chamber. The first method was based on three components: (1) a 30 min measurement of resting metabolic rate (RMR) using a ventilated hood system; (2) measurement of exercise energy expenditure during 10 min of treadmill walking; and (3) estimation of free-living energy expenditure using a tri-axial motion sensor. Using these measurements we calculated the amount of treadmill time needed for each individual in order to obtain a total 24 h EE at either a low (1.4xRMR) or a high (1.8xRMR) level of physical activity. We also developed a method to predict total 24 h EE during the chamber stay by using the energy expenditure values for the different levels of activity as measured during the hours already spent in the chamber. This would provide us with a tool to adjust the exercise time and/or energy intake during the chamber stay.


RESULTS
Method 1: there was no significant difference in expected and measured 24 h EE under either low (9.35+/-0.56 vs 9.51+/-0.47 MJ/day; measured vs predicted) or high activity conditions (13.41+/-0.74 vs 13.97+/-0.78 MJ/day; measured vs predicted). Method 2: the developed algorithm predicted 24 h EE for 97.6+/-4.0% of the final value at 3 h into the test day, and for 98.6+/-3.7% at 7 h into the test day.


CONCLUSIONS
Both methods provide accurate prediction of energy expenditure in a room calorimeter at both high and low levels of physical activity. It equally shows that it is possible to accurately predict total 24 h EE from energy expenditure values obtained at 3 and 7 h into the study.
de Jonge
L
L
Pennington Biomedical Research Center, Baton Rouge, Louisiana 70808-4124, USA. dejongeh@pbrc.edu
Nguyen
T
T
Smith
S R
SR
Zachwieja
J J
JJ
Roy
H J
HJ
Bray
G A
GA
D000222:Adaptation, Physiological / Q000502:physiology*; D000328:Adult; D001481:Basal Metabolism / Q000502:physiology; D002153:Calorimetry, Indirect / Q000379:methods; D002149:Energy Intake / Q000502:physiology; D004734:Energy Metabolism / Q000502:physiology*; D015444:Exercise / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D010101:Oxygen Consumption