Predicting Metabolic Cost of Running with and without Backpack Loads

Abstract

In the past, a mathematical equation to predict the metabolic cost of standing or walking (Mw) was developed. However, this equation was limited to speeds <2.2m.s-1 and overestimated the metabolic cost of walking or running at higher speeds. The purpose of this study was, therefore, to develop a mathematical model for the metabolic cost of running (M sub r), in order to be able to predict the metabolic cost under a wide range of speeds, external loads and grades. Twelve male subjects were tested on a level treadmill under different combinations of speed and external load. Speed varied between 2.2 to 3.2 M sub w using 0.2 m.s-1 intervals and external loads between 0-30kg with 10kg intervals. Four of the subjects were also tested at 2 and 4% incline while speed and load remained constant (2.4m.s-1, 20kg). The model developed is based on M sub w and is proportionately linear with external load (L) carried as follows: M sub r = M sub w-0.5(1-0.01L)M sub w-15L-850)(watt). The correlation coefficient between predicted and observed values was 0.99 (P<0.01) with SER of 7.7%. The accuracy of the model was validated by its ability to predict the metabolic cost of running under different conditions extracted from the literature. A highly significant correlation (r = 0.95, P<0.02, SER=6.5%) was found between our predicted and the reported values. In conclusion, the new equation permits accurate calculation of energy cost of running under a large range of speeds, external loads and inclines.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1987
Accession Number
ADA185585

Entities

People

  • K. B. Pandolf
  • L. A . Stroschein
  • Y. Epstein

Organizations

  • United States Army Research Institute of Environmental Medicine

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Backpacks
  • Coefficients
  • Equations
  • Intervals
  • Literature
  • Mathematical Models
  • Models
  • Treadmills

Readers

  • Exercise and Sports Science.
  • Regression Analysis.