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Modeling the Metabolic Costs of Heavy Military Backpacking

INTRODUCTION: Existing predictive equations underestimate the metabolic costs of heavy military load carriage. Metabolic costs are specific to each type of military equipment, and backpack loads often impose the most sustained burden on the dismounted warfighter. PURPOSE: This study aimed to develop...

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Detalles Bibliográficos
Autores principales: LOONEY, DAVID P., LAVOIE, ELIZABETH M., VANGALA, SAI V., HOLDEN, LUCAS D., FIGUEIREDO, PETER S., FRIEDL, KARL E., FRYKMAN, PETER N., HANCOCK, JASON W., MONTAIN, SCOTT J., PRYOR, J. LUKE, SANTEE, WILLIAM R., POTTER, ADAM W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919998/
https://www.ncbi.nlm.nih.gov/pubmed/34856578
http://dx.doi.org/10.1249/MSS.0000000000002833
Descripción
Sumario:INTRODUCTION: Existing predictive equations underestimate the metabolic costs of heavy military load carriage. Metabolic costs are specific to each type of military equipment, and backpack loads often impose the most sustained burden on the dismounted warfighter. PURPOSE: This study aimed to develop and validate an equation for estimating metabolic rates during heavy backpacking for the US Army Load Carriage Decision Aid (LCDA), an integrated software mission planning tool. METHODS: Thirty healthy, active military-age adults (3 women, 27 men; age, 25 ± 7 yr; height, 1.74 ± 0.07 m; body mass, 77 ± 15 kg) walked for 6–21 min while carrying backpacks loaded up to 66% body mass at speeds between 0.45 and 1.97 m·s(−1). A new predictive model, the LCDA backpacking equation, was developed on metabolic rate data calculated from indirect calorimetry. Model estimation performance was evaluated internally by k-fold cross-validation and externally against seven historical reference data sets. We tested if the 90% confidence interval of the mean paired difference was within equivalence limits equal to 10% of the measured metabolic rate. Estimation accuracy and level of agreement were also evaluated by the bias and concordance correlation coefficient (CCC), respectively. RESULTS: Estimates from the LCDA backpacking equation were statistically equivalent (P < 0.01) to metabolic rates measured in the current study (bias, −0.01 ± 0.62 W·kg(−1); CCC, 0.965) and from the seven independent data sets (bias, −0.08 ± 0.59 W·kg(−1); CCC, 0.926). CONCLUSIONS: The newly derived LCDA backpacking equation provides close estimates of steady-state metabolic energy expenditure during heavy load carriage. These advances enable further optimization of thermal-work strain monitoring, sports nutrition, and hydration strategies.