4.5 Article

Predicting metabolic rate across walking speed: one fit for all body sizes?

期刊

JOURNAL OF APPLIED PHYSIOLOGY
卷 115, 期 9, 页码 1332-1342

出版社

AMER PHYSIOLOGICAL SOC
DOI: 10.1152/japplphysiol.01333.2012

关键词

energy expenditure; height; weight; gait; biomechanics; generalized equation; algorithm

资金

  1. Medical and Materiel command of the United States DOD [DAMD17-03-2-005]
  2. Southern Methodist University

向作者/读者索取更多资源

We formulated a one-size-fits-all model that predicts the energy requirements of level human walking from height, weight, and walking speed. Our three-component model theorizes that the energy expended per kilogram per stride is independent of stature at mechanically equivalent walking speeds. We measured steady-state rates of oxygen uptake of 78 subjects who spanned a nearly twofold range of statures (1.07-2.11 m) and sevenfold range of body masses (16-112 kg) at treadmill speeds from 0.4 to 1.9 m/s. We tested the size independence of the model by deriving best-fit equations in the form of the model on four stature groups (n >= 15): short, moderately short, moderately tall, and tall. The mean walking metabolic rates predicted by these four independently derived equations for the same set of reference subjects (n = 16; stature range: 1.30-1.90 m) agreed with one another to within an average of 5.2 +/- 3.7% at the four intermediate speeds in our protocol. We next evaluated the model's gross predictive accuracy by dividing our 78 subjects into 39 stature-matched pairs of experimental and validation group subjects. The model best-fit equation derived on the experimental group subjects predicted the walking metabolic rates of the validation group subjects to within an average of 8.1 +/- 6.7% (R-2 = 0.90; standard error of estimate = 1.34 ml O-2.kg(-1).min(-1)). The predictive error of the American College of Sports Medicine equation (18.0 +/- 13.1%), which does not include stature as a predictor, was more than twice as large for the same subject group. We conclude that the energy cost of level human walking can be accurately predicted from height, weight, and walking speed.

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