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Introduction: The US Army Load Carriage Decision Aid (LCDA) metabolic model is used by militaries across the globe and is intended to predict physiological responses, specifically metabolic costs, in a wide range of dismounted warfighter operations. However, the LCDA has yet to be adapted for vest-borne load carriage, which is commonplace in tactical populations, and differs in energetic costs to backpacking and other forms of load carriage., Purpose: The purpose of this study is to develop and validate a metabolic model term that accurately estimates the effect of weighted vest loads on standing and walking metabolic rate for military mission-planning and general applications., Methods: Twenty healthy, physically active military-age adults (4 women, 16 men; age, 26 ± 8 yr old; height, 1.74 ± 0.09 m; body mass, 81 ± 16 kg) walked for 6 to 21 min with four levels of weighted vest loading (0 to 66% body mass) at up to 11 treadmill speeds (0.45 to 1.97 m·s -1 ). Using indirect calorimetry measurements, we derived a new model term for estimating metabolic rate when carrying vest-borne loads. Model estimates were evaluated internally by k -fold cross-validation and externally against 12 reference datasets (264 total participants). We tested if the 90% confidence interval of the mean paired difference was within equivalence limits equal to 10% of the measured walking metabolic rate. Estimation accuracy, precision, and level of agreement were also evaluated by the bias, standard deviation of paired differences, and concordance correlation coefficient (CCC), respectively., Results: Metabolic rate estimates using the new weighted vest term were statistically equivalent ( P < 0.01) to measured values in the current study (bias, -0.01 ± 0.54 W·kg -1 ; CCC, 0.973) as well as from the 12 reference datasets (bias, -0.16 ± 0.59 W·kg -1 ; CCC, 0.963)., Conclusions: The updated LCDA metabolic model calculates accurate predictions of metabolic rate when carrying heavy backpack and vest-borne loads. Tactical populations and recreational athletes that train with weighted vests can confidently use the simplified LCDA metabolic calculator provided as Supplemental Digital Content to estimate metabolic rates for work/rest guidance, training periodization, and nutritional interventions., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American College of Sports Medicine.)

Introduction: The load carriage decision aid (LCDA) walking equation was developed from literature-aggregated group mean data to calculate standing and level walking energy expenditures in healthy, military-age adults. The LCDA walking equation has not been validated for use in individuals or graded walking., Purpose: We aimed to validate the LCDA walking equation as a predictor of standing and level walking energy expenditure in individuals and expand to a new graded walking equation for uphill and downhill walking., Methods: We compiled standing, level walking, and graded walking energy expenditures measured in 95 participants from 11 studies. Walking speeds reached up to 1.96 m·s with grades ranging between -40% and 45%. The LCDA walking equation was validated against the aggregated standing and level walking data. The new LCDA graded walking equation was developed and cross-validated on the graded walking trials. We compared each equation against four reference predictive equations with the standard error of estimation (SEE) as the primary criterion., Results: The LCDA walking equation accurately estimated standing and level walking energy expenditure (bias, -0.02 ± 0.20 W·kg; SEE, 0.20 W·kg). Addition of the novel grade term resulted in precise estimates of uphill and downhill walking energy expenditure (bias, 0.09 ± 0.40 W·kg; SEE, 0.42 W·kg)., Conclusions: The LCDA walking equation is a valid predictor of standing and walking energy expenditure in healthy, military-age individuals. We developed a novel grade term for estimating both uphill and downhill walking energy expenditure with a single equation. Practitioners can use the new LCDA graded walking equation to calculate energy expenditure during standing as well as walking on level, uphill, and downhill slopes.

Introduction: The Load Carriage Decision Aid (LCDA) is a U.S. Army planning tool that predicts physiological responses of soldiers during different dismounted troop scenarios. We 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: We searched for studies that measured the energetic cost of standing and treadmill walking in healthy men and women via indirect calorimetry. We 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 minimized. 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., Conclusion: 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.

Background: Predicting the energy costs of human travel over snow can be of significant value to the military and other agencies planning work efforts when snow is present. The ability to quantify, and predict, those costs can help planners determine if snow will be a factor in the execution of dismounted tasks and operations. To adjust predictive models for the effect of terrain, and more specifically for surface conditions, on energy costs, terrain coefficients (ƞ) have been developed. The physiological demands of foot travel over snow have been studied previously, and there are well established methods of predicting metabolic costs of locomotion. By applying knowledge gained from prior studies of the effects of terrain and snow, and by leveraging those existing dismounted locomotion models, this paper seeks to outline the steps in developing an improved terrain coefficient (ƞ) for snow to be used in predictive modeling., Methods: Using published data, methods, and a well-informed understanding of the physical elements of terrain, e.g., characterization of snow sinkage (z), this study made adjustments to ƞ-values specific to snow., Results: This review of published metabolic cost methods suggest that an improved ƞ-value could be developed for use with the Pandolf equation, where z = depth (h)*(1 - (snow density (ρ 0 )/1.186)) and ƞ = 0.0005z 3  + 0.0001z 2  + 0.1072z + 1.2604., Conclusion: While the complexity of variables related to characteristics of snow, speed of movement, and individuals confound efforts to develop a simple, predictive model, this paper provides data-driven improvements to models that are used to predict the energy costs of dismounted movements over snow., (Copyright © 2018. Published by Elsevier Ltd.)

Introduction: Military load carriage can cause extreme energy expenditure (EE) that is difficult to estimate due to complex terrain grades and surfaces. Global Positioning System (GPS) devices capture rapid changes in walking speed and terrain but the delayed respiratory response to movement is problematic. We investigated the accuracy using GPS data in three different equations to estimate EE during complex terrain load carriage., Methods: Twelve active duty military personnel (age, 20 ± 3 yr; height, 174 ± 8 cm; body mass, 85 ± 13 kg) hiked a complex terrain trail on multiple visits under different external load conditions. Energy expenditure was estimated by inputting GPS data into three different equations: the Pandolf-Santee equation, a recent GPS-based equation from de Müllenheim et al.; and the Minimum Mechanics model. Minute-by-minute EE estimates were exponentially smoothed using smoothing factors between 0.05 and 0.95 and compared with mobile metabolic sensor EE measurements., Results: The Pandolf-Santee equation had no significant estimation bias (-2 ± 12 W; P = 0.89). Significant biases were detected for the de Müllenheim equation (38 ± 13 W; P = 0.004) and the Minimum Mechanics model (-101 ± 7 W; P < 0.001)., Conclusions: Energy expenditure can be accurately estimated from GPS data using the Pandolf-Santee equation. Applying a basic exponential smoothing factor of 0.5 to GPS data enables more precise tracking of EE during non-steady-state exercise.

Introduction: Military load carriage can cause extreme energy expenditure (EE) that is difficult to estimate due to complex terrain grades and surfaces. Global Positioning System (GPS) devices capture rapid changes in walking speed and terrain but the delayed respiratory response to movement is problematic. We investigated the accuracy using GPS data in three different equations to estimate EE during complex terrain load carriage. Methods: Twelve active duty military personnel (age, 20 ± 3 yr; height, 174 ± 8 cm; body mass, 85 ± 13 kg) hiked a complex terrain trail on multiple visits under different external load conditions. Energy expenditure was estimated by inputting GPS data into three different equations: the Pandolf–Santee equation, a recent GPS-based equation from de Müllenheim et al.; and the Minimum Mechanics model. Minute-by-minute EE estimates were exponentially smoothed using smoothing factors between 0.05 and 0.95 and compared with mobile metabolic sensor EE measurements. Results: The Pandolf–Santee equation had no significant estimation bias (−2 ± 12 W; P = 0.89). Significant biases were detected for the de Müllenheim equation (38 ± 13 W; P = 0.004) and the Minimum Mechanics model (−101 ± 7 W; P < 0.001). Conclusions: Energy expenditure can be accurately estimated from GPS data using the Pandolf–Santee equation. Applying a basic exponential smoothing factor of 0.5 to GPS data enables more precise tracking of EE during non–steady-state exercise. [ABSTRACT FROM AUTHOR]

Quantifying the metabolic costs of carrying or wearing various military uniform components is essential for accurate assessment of the metabolic cost of warfighters performing military tasks under scenarios and environmental conditions. PURPOSE: Evaluate the metabolic demand of walking at incremental speeds in a controlled laboratory environment while wearing military-issued combat boots compared to athletic sneakers. METHODS: In this randomized, cross-over study, nineteen active military-age men (age, 24 ± 5 years; height, 177 ± 6 cm; body mass, 80 ± 13 kg) performed an incremental treadmill walking test in combat boots and athletic sneakers on separate visits. Treadmill speed started at 4.18 km/h for 3 min, then increased by 0.23 km/h every 2 min. Tests were terminated when volunteers either completed the highest treadmill speed (7.08 km/h), reached volitional fatigue, or exceeded a respiratory exchange ratio of 1.0. Volunteers exhaled into a laboratory metabolic cart that measured expired gases. Metabolic demand was quantified as the average oxygen uptake (...O2) over the final minute of each walking speed. RESULTS: Across all speeds, walking in combat boots resulted in significantly higher ...O2 than walking in athletic sneakers (0.63 ± 1.05 mL⋅kg-1⋅min-1; p = 0.01). Regression analysis determined a V O2 difference between footwear of 0.31 mL⋅kg-1⋅min-1 at 4.18 km/h that increased by 0.22 mL⋅kg-1⋅min-1 with each 1 km/h increase (R², 0.68). Regressing walking ...O2 when wearing combat boots on walking ...O2 while wearing athletic sneakers revealed both a positive offset and slope (Trendline, ...O2boots = 0.04 + 1.04- ...O2sneakers; R², 0.97). CONCLUSIONS: The metabolic demand of walking in military combat boots is greater and more pronounced at increased walking speeds than when wearing common athletic sneakers. The views expressed in this abstract are those of the authors and do not reflect the official policy of the Department of Army, Department of Defense, or the U.S. Government. [ABSTRACT FROM AUTHOR]