Is the Load Carriage Decision Aid (LCDA) an Improvement on other Aids?

Research Paper Title

Metabolic Costs of Standing and Walking in Healthy Military-Age Adults: A Meta-regression.

Background

The Load Carriage Decision Aid (LCDA) is a US Army planning tool that predicts physiological responses of soldiers during different dismounted troop scenarios.

The researchers aimed to develop an equation that calculates standing and walking metabolic rates in healthy military-age adults for the LCDA using a meta-regression.

Methods

The researchers searched for studies that measured the energetic cost of standing and treadmill walking in healthy men and women via indirect calorimetry.

They used mixed effects meta-regression to determine an optimal equation to calculate standing and walking metabolic rates as a function of walking speed (S, m·s).

The optimal equation was used to determine the economical speed at which the metabolic cost per distance walked is minimised.

The estimation precision of the new LCDA walking equation was compared with that of seven reference predictive equations.

Results

The meta-regression included 48 studies. The optimal equation for calculating normal standing and walking metabolic rates (W·kg) was 1.44 + 1.94S + 0.24S. The economical speed for level walking was 1.39 m·s (~ 3.1 mph).

The LCDA walking equation was more precise across all walking speeds (bias ± SD, 0.01 ± 0.33 W·kg) than the reference predictive equations.

Conclusions

Practitioners can use the new LCDA walking equation to calculate energy expenditure during standing and walking at speeds <2 m·s in healthy, military-age adults.

The LCDA walking equation avoids the errors estimated by other equations at lower and higher walking speeds.

Reference

Looney, D.P., Potter, A.W., Pryor, J.L., Bremner, P.E., Chalmers, C.R., McClung, H.L., Welles, A.P. & Santee, W.R. (2019) Metabolic Costs of Standing and Walking in Healthy Military-Age Adults: A Meta-regression. Medicine and Science in Sport and Exercise. 51(2), pp.346-351. doi: 10.1249/MSS.0000000000001779.

Advertisements

This site uses Akismet to reduce spam. Learn how your comment data is processed.