ABSTRACT
INTRODUCTION: Military load carriage can cause extreme energy expenditure (EE) that is difficult to estimate due to complex terrain grades and surfaces. Global Positioning System (GPS) devices capture rapid changes in walking speed and terrain but the delayed respiratory response to movement is problematic. We investigated the accuracy using GPS data in three different equations to estimate EE during complex terrain load carriage. METHODS: Twelve active duty military personnel (age, 20 ± 3 yr; height, 174 ± 8 cm; body mass, 85 ± 13 kg) hiked a complex terrain trail on multiple visits under different external load conditions. Energy expenditure was estimated by inputting GPS data into three different equations: the Pandolf-Santee equation, a recent GPS-based equation from de Müllenheim et al.; and the Minimum Mechanics model. Minute-by-minute EE estimates were exponentially smoothed using smoothing factors between 0.05 and 0.95 and compared with mobile metabolic sensor EE measurements. RESULTS: The Pandolf-Santee equation had no significant estimation bias (-2 ± 12 W; P = 0.89). Significant biases were detected for the de Müllenheim equation (38 ± 13 W; P = 0.004) and the Minimum Mechanics model (-101 ± 7 W; P < 0.001). CONCLUSIONS: Energy expenditure can be accurately estimated from GPS data using the Pandolf-Santee equation. Applying a basic exponential smoothing factor of 0.5 to GPS data enables more precise tracking of EE during non-steady-state exercise.
Subject(s)
Energy Metabolism , Geographic Information Systems , Walking , Weight-Bearing , Adolescent , Female , Humans , Male , Military Personnel , Young AdultABSTRACT
Environmental heat illness and injuries are a serious concern for the Army and Marines. Currently, the Wet-Bulb Globe Temperature (WBGT) index is used to evaluate heat injury risk. The index is a weighted average of dry-bulb temperature (Tdb), black globe temperature (Tbg), and natural wet-bulb temperature (Tnwb). The WBGT index would be more widely used if it could be determined using standard weather instruments. This study compares models developed by Liljegren at Argonne National Laboratory and by Matthew at the U.S. Army Institute of Environmental Medicine that calculate WBGT using standard meteorological measurements. Both models use air temperature (Ta), relative humidity, wind speed, and global solar radiation (RG) to calculate Tnwb and Tbg. The WBGT and meteorological data used for model validation were collected at Griffin, Georgia and Yuma Proving Ground (YPG), Arizona. Liljegren (YPG: R(2) = 0.709, p < 0.01; Griffin: R(2) = 0.854, p < 0.01) showed closer agreement between calculated and actual WBGT than Matthew (YPG: R(2) = 0.630, p < 0.01; Griffin: R(2) = 0.677, p < 0.01). Compared to actual WBGT heat categorization, the Matthew model tended to underpredict compared to Liljegren's classification. Results indicate Liljegren is an acceptable alternative to direct WBGT measurement, but verification under other environmental conditions is needed.
