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Few US Marines earn perfect 300 scores on both their Physical Fitness Test (PFT) and Combat Fitness Test (CFT). The number 300 invokes the legendary 300 Spartans that fought at the Battle of Thermopylae, which inspired high physical fitness capabilities for elite ground forces ever since. Purpose: Determine distinguishing characteristics of the "300 Marines" (perfect PFT and CFT scores) that may provide insights into the physical and physiological requirements associated with this capability. These tests have been refined over time to reflect physical capabilities associated with Marine Corps basic rifleman performance. Materials and methods: Data were analyzed from US Marines, including 497 women (age, 29 ± 7 years; height 1.63 ± 0.07 m; body mass, 67.4 ± 8.4 kg) and 1,224 men (30 ± 8 years; 1.77 ± 0.07 m; 86.1 ± 11.1 kg). Marines were grouped by whether they earned perfect 300 scores on both the PFT and CFT (300 Marines) or not. We analyzed group differences in individual fitness test events and body composition (dual-energy x-ray absorptiometry). Results: Only 2.5% (n = 43) of this sample earned perfect PFT and CFT scores (n = 21 women; n = 22 men). Compared to sex-matched peers, 300 Marines performed more pull-ups, with faster three-mile run, maneuver-under-fire, and movement-to-contact times (each p < 0.001); 300 Marines of both sexes had lower fat mass, body mass index, and percent body fat (each p < 0.001). The lower percent body fat was explained by greater lean mass (p = 0.041) but similar body mass (p = 0.085) in women, whereas men had similar lean mass (p = 0.618), but lower total body mass (p = 0.025). Conclusion: Marines earning perfect PFT and CFT scores are most distinguished from their peers by their maneuverability, suggesting speed and agility capabilities. While both sexes had considerably lower percent body fat than their peers, 300 Marine women were relatively more muscular while men were lighter. [ABSTRACT FROM AUTHOR]

Purpose: Body composition assessment methods are dependent on their underlying principles, and assumptions of each method may be affected by age and sex. This study compared an abdominal circumference-focused method of percent body fat estimation (AC %BF) to a criterion method of dual-energy x-ray absorptiometry (DXA), and a comparative assessment with bioelectrical impedance (BIA), in younger (≤30 years) and older (>age 30 years) physically fit (meeting/exceeding annual US Marine Corps fitness testing requirements) men and women. Methods: Fit healthy US Marines (430 men, 179 women; 18–57 years) were assessed for body composition by DXA (iDXA, GE Lunar), anthropometry, and BIA (Quantum IV, RJL Systems). Results: Compared to DXA %BF, male AC %BF underestimated for both ≤30 and >30 years age groups (bias, -2.6 ± 3.7 and -2.5 ± 3.7%); while female AC %BF overestimated for both ≤30 and >30 years age groups (2.3 ± 4.3 and 1.3 ± 4.8%). On an individual basis, lean men and women were overestimated and higher %BF individuals were underestimated. Predictions from BIA were more accurate and reflected less relationship to adiposity for each age and sex group (males: ≤30, 0.4 ± 3.2, >30 years, -0.5 ± 3.5; women: ≤30, 1.4 ± 3.1, >30 years, 0.0 ± 3.3). Total body water (hydration) and bone mineral content (BMC) as a proportion of fat-free mass (FFM) remained consistent across the age range; however, women had a higher proportion of %BMC/FFM than men. Older men and women (>age 30 years) were larger and carried more fat but had similar FFM compared to younger men and women. Conclusion: The AC %BF provides a field expedient method for the US Marine Corps to classify individuals for obesity prevention, but does not provide research-grade quantitative body composition data. [ABSTRACT FROM AUTHOR]

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 (ρ0)/1.186)) and ƞ = 0.0005z3 + 0.0001z2 + 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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−1 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−1; SEE, 0.20 W·kg−1). Addition of the novel grade term resulted in precise estimates of uphill and downhill walking energy expenditure (bias, 0.09 ± 0.40 W·kg−1; SEE, 0.42 W·kg−1). 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. [ABSTRACT FROM AUTHOR]

This study examined complex terrain march performance and cardiorespiratory responses when carrying different Soldier loads. Nine active duty military personnel (age, 21 ± 3 yr; height, 1.72 ± 0.07 m; body mass (BM), 83.4 ± 12.9 kg) attended two test visits during which they completed consecutive laps around a 2.5-km mixed terrain course with either a fighting load (30% BM) or an approach load (45% BM). Respiratory rate and heart rate data were collected using physiological status monitors. Training impulse (TRIMP) scores were calculated using Banister's formula to provide an integrated measure of both time and cardiorespiratory demands. Completion times were not significantly different between the fighting and approach loads for either Lap 1 (p = 0.38) or Lap 2 (p = 0.09). Respiration rate was not significantly higher with the approach load than the fighting load during Lap 1 (p = 0.17) but was significantly higher for Lap 2 (p = 0.04). However, heart rate was significantly higher with the approach load versus the fighting load during both Lap 1 (p = 0.03) and Lap 2 (p = 0.04). Furthermore, TRIMP was significantly greater with the approach load versus the fighting load during both Lap 1 (p = 0.02) and Lap 2 (p = 0.02). Trained military personnel can maintain similar pacing while carrying either fighting or approach loads during short mixed terrain marches. However, cardiorespiratory demands are greatly elevated with the approach load and will likely continue to rise during longer distance marches. [ABSTRACT FROM AUTHOR]

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]

Introduction: Dismounted military operations often involve prolonged load carriage over complex terrain, which can result in excessive metabolic costs that can directly impair soldiers' performance. Although estimating these demands is a critical interest for mission planning purposes, it is unclear whether existing estimation equations developed from controlled laboratory- and field-based studies accurately account for energy costs of traveling over complex terrain. This study investigated the accuracy of the following equations for military populations when applied to data collected over complex terrain with two different levels of load carriage: American College of Sports Medicine (2002), Givoni and Goldman (1971), Jobe and White (2009), Minetti et al (2002), Pandolf et al (1977), and Santee et al (2003).Materials and Methods: Nine active duty military personnel (age 21 ± 3 yr; height 1.72 ± 0.07 m; body mass 83.4 ± 12.9 kg; VO2 max 47.8 ± 3.9 mL/kg/min) were monitored during load carriage (with loads equal to 30% and 45% of body mass) over a 10-km mixed terrain course on two separate test days. The course was divided into four 2.5-km laps of 40 segments based on distance, grade, and/or surface factors. Timing gates and radio-frequency identification cards (SportIdent; Scarborough Orienteering, Huntington Beach, CA) were used to record completion times for each course segment. Breath-by-breath measures of energy expenditure were collected using portable oxygen exchange devices (COSMED Sri., Rome, Italy) and compared model estimates.Results: The Santee et al equation performed best, demonstrating the smallest estimation bias (-13 ± 87 W) and lowest root mean square error (99 W).Conclusion: Current predictive equations underestimate the metabolic cost of load carriage by military personnel over complex terrain. Applying the Santee et al correction factor to the Pandolf et al equation may be the most suitable approach for estimating metabolic demands in these circumstances. However, this work also outlines the need for improvements to these methods, new method development and validation, or the use of a multi-model approach to account for mixed terrain. [ABSTRACT FROM AUTHOR]