Accurate prediction of the metabolic energy that walking requires can inform numerous health, bodily status, and fitness outcomes. We adopted a two-step approach to identifying a concise, generalized equation for predicting level human walking metabolism. Using literature-aggregated values we compared 1) the predictive accuracy of three literature equations: American College of Sports Medicine (ACSM), Pandolf et al., and Height-Weight-Speed (HWS); and 2) the goodness-of-fit possible from one- vs. two-component descriptions of walking metabolism. Literature metabolic rate values (n = 127; speed range = 0.4 to 1.9 m/s) were aggregated from 25 subject populations (n= 5-42) whose means spanned a 1.8-fold range of heights and a 4.2-fold range of weights. Population-specific resting metabolic rates (V̇o_2rest) were determined using standardized equations. Our first finding was that the ACSM and Pandolf et al. equations underpredicted nearly all 127 literature-aggregated values. Consequently, their standard errors of estimate (SEE) were nearly four times greater than those of the HWS equation (4.51 and 4.39 vs. 1.13 ml O₂·kg⁻¹·min⁻¹, respectively). For our second comparison, empirical best-fit relationships for walking metabolism were derived from the data set in one- and two-component forms for three V̇o₂-speed model types: linear (∝V^1.0), exponential (∝V^2.0), and exponential/height (∝V^2.0/Ht). We found that the proportion of variance (R²) accounted for, when averaged across the three model types, was substantially lower for one- vs. two-component versions (0.63 ± 0.1 vs. 0.90 ± 0.03) and the predictive errors were nearly twice as great (SEE = 2.22 vs. 1.21 ml O₂·kg⁻¹·min⁻¹). Our final analysis identified the following concise, generalized equation for predicting level human walking metabolism: V̇o_2total = V̇o_2rest + 3.85 + 5.97·V²/Ht (where V is measured in m/s, Ht in meters, and V̇o₂ in ml O₂·kg⁻¹·min⁻¹).

Lindsay W. Ludlow, Peter G. Weyand. Energy Expenditure during level human walking: seeking a simple and accurate predictive solution. Journal of Applied Physiology, 2016; 120 (5): 481 DOI:10.1152/japplphysiol.00864.2015