Estimating Metabolic Energy Expenditure During Level Running in Healthy, Military-Age Women and Men

Author:

Looney David P.1,Hoogkamer Wouter2,Kram Rodger3,Arellano Christopher J.45,Spiering Barry A.6

Affiliation:

1. Military Performance Division (MPD), United States Army Research Institute of Environmental Medicine (USARIEM), Natick, Massachusetts;

2. Department of Kinesiology, University of Massachusetts, Amherst, Massachusetts;

3. Department of Integrative Physiology, University of Colorado Boulder, Boulder, Colorado;

4. Center for Neuromotor and Biomechanics Research, University of Houston, Houston, Texas;

5. Department of Health and Human Performance, University of Houston, Houston, Texas; and

6. New Balance Sports Research Lab, Boston, Massachusetts

Abstract

Abstract Looney, DP, Hoogkamer, W, Kram, R, Arellano, CJ, and Spiering, BA. Estimating metabolic energy expenditure during level running in healthy, military-age women and men. J Strength Cond Res 37(12): 2496–2503, 2023—Quantifying the rate of metabolic energy expenditure (Ṁ) of varied aerobic exercise modalities is important for optimizing fueling and performance and maintaining safety in military personnel operating in extreme conditions. However, although equations exist for estimating oxygen uptake during running, surprisingly, there are no general equations that estimate Ṁ. Our purpose was to generate a general equation for estimating Ṁ during level running in healthy, military-age (18–44 years) women and men. We compiled indirect calorimetry data collected during treadmill running from 3 types of sources: original individual subject data (n = 45), published individual subject data (30 studies; n = 421), and published group mean data (20 studies, n = 619). Linear and quadratic equations were fit on the aggregated data set using a mixed-effects modeling approach. A chi-squared (χ 2) difference test was conducted to determine whether the more complex quadratic equation was justified (p < 0.05). Our primary indicator of model goodness-of-fit was the root-mean-square deviation (RMSD). We also examined whether individual characteristics (age, height, body mass, and maximal oxygen uptake [V̇O2max]) could minimize prediction errors. The compiled data set exhibited considerable variability in Ṁ (14.54 ± 3.52 W·kg−1), respiratory exchange ratios (0.89 ± 0.06), and running speeds (3.50 ± 0.86 m·s−1). The quadratic regression equation had reduced residual sum of squares compared with the linear fit (χ 2, 3,484; p < 0.001), with higher combined accuracy and precision (RMSD, 1.31 vs. 1.33 W·kg−1). Age (p = 0.034), height (p = 0.026), and body mass (p = 0.019) were associated with the magnitude of under and overestimation, which was not the case for V̇O2max (p = 0.898). The newly derived running energy expenditure estimation (RE3) model accurately predicts level running Ṁ at speeds from 1.78 to 5.70 m·s−1 in healthy, military-age women and men. Users can rely on the following equations for improved predictions of running Ṁ as a function of running speed (S, m·s−1) in either watts (W·kg−1 = 4.43 + 1.51·S + 0.37·S2) or kilocalories per minute (kcal·kg−1·min−1 = 308.8 + 105.2·S + 25.58·S2).

Funder

U.S. Army Medical Research and Development Command

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Physical Therapy, Sports Therapy and Rehabilitation,Orthopedics and Sports Medicine,General Medicine

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