Body surface area equations for physically active men and women.

OBJECTIVES: To improve predictive formulae for estimating body surface area (BSA) in healthy men and women using a modern three-dimensional scanner technology. METHODS: Body surface areas were obtained from a convenience sample of 1267 US Marines (464 women and 803 men) using a whole body surface scanner (Size Stream SS20). The reliability of SS20 measures of total and regional BSA within participants was compared across triplicate scans. We then derived a series of formulae to estimate SS20-measured BSA using various combinations of sex, height, and mass. We also assessed relationships between percent body fat measured by dual-energy x-ray absorptiometry and sex-specific formulae errors in Marines. RESULTS: Body surface areas recorded by the SS20 were highly reliable whether measured for the total body or by region (ICC ≥ .962). Formulae estimates of BSA from sex, height, and mass were precise (root-mean-square deviation, 0.031 m2 ). Errors from the Marine Corps formulae were positively associated with percent body fat for men (p = .001) but not women (p = .843). CONCLUSIONS: Clinicians, military leaders, and researchers can use the newly developed BSA formulae for precise estimates in healthy physically active men and women. Users should be aware that height- and mass-based BSA estimates are less accurate for individuals with extremely low or high percent body fat.

Verification of Maximal Oxygen Uptake in Active Military Personnel During Treadmill Running.

ABSTRACT: Figueiredo, PS, Looney, DP, Pryor, JL, Doughty, EM, McClung, HL, Vangala, SV, Santee, WR, Beidleman, BA, and Potter, AW. Verification of maximal oxygen uptake in active military personnel during treadmill running. J Strength Cond Res 36(4): 1053-1058, 2022-It is unclear whether verification tests are required to confirm "true" maximal oxygen uptake (V̇o2max) in modern warfighter populations. Our study investigated the prevalence of V̇o2max attainment in U.S. Army soldiers performing a traditional incremental running test. In addition, we examined the utility of supramaximal verification testing as well as repeated trials for familiarization for accurate V̇o2max assessment. Sixteen U.S. Army soldiers (1 woman, 15 men; age, 21 ± 2 years; height, 1.73 ± 0.06 m; body mass, 71.6 ± 10.1 kg) completed 2 laboratory visits, each with an incremental running test (modified Astrand protocol) and a verification test (110% maximal incremental test speed) on a motorized treadmill. We evaluated V̇o2max attainment during incremental testing by testing for the definitive V̇O2 plateau using a linear least-squares regression approach. Peak oxygen uptake (V̇o2peak) was considered statistically equivalent between tests if the 90% confidence interval around the mean difference was within ±2.1 ml·kg-1·min-1. Oxygen uptake plateaus were identified in 14 of 16 volunteers for visit 1 (87.5%) and all 16 volunteers for visit 2 (100%). Peak oxygen uptake was not statistically equivalent, apparent from the mean difference in V̇o2peak measures between the incremental test and verification test on visit 1 (2.3 ml·kg-1·min-1, [1.3-3.2]) or visit 2 (1.1 ml·kg-1·min-1 [0.2-2.1]). Interestingly, V̇o2peak was equivalent, apparent from the mean difference in V̇o2peak measures between visits for the incremental tests (0.0 ml·kg-1·min-1 [-0.8 to 0.9]) but not the verification tests (-1.2 ml·kg-1·min-1 [-2.2 to -0.2]). Modern U.S. Army soldiers can attain V̇o2max by performing a modified Astrand treadmill running test. Additional familiarization and verification tests for confirming V̇o2max in healthy active military personnel may be unnecessary.

A digital tool for prevention and management of cold weather injuries-Cold Weather Ensemble Decision Aid (CoWEDA).

This paper describes a Cold Weather Ensemble Decision Aid (CoWEDA) that provides guidance for cold weather injury prevention, mission planning, and clothing selection. CoWEDA incorporates current science from the disciplines of physiology, meteorology, clothing, and computer modeling. The thermal performance of a cold weather ensemble is defined by endurance times, which are the time intervals from initial exposure until the safety limits are reached. These safety limits correspond to conservative temperature thresholds that provide a warning of the approaching onset of frostbite and/or hypothermia. A validated six-cylinder thermoregulatory model is used to predict human thermal responses to cold while wearing different ensembles. The performance metrics, model, and a database of clothing properties were integrated into a user-friendly software application. CoWEDA is the first tool that allows users to build their own ensembles from the clothing menu (i.e., jackets, footwear, and accessories) for each body region (i.e., head, torso, lower body, hands, feet) and view their selections in the context of physiological strain and the operational consequences. Comparison of predicted values to skin and core temperatures, measured during 17 cold exposures ranging from 0 to -40°C, indicated that the accuracy of CoWEDA prediction is acceptable, and most predictions are within measured mean ± SD. CoWEDA predicts the risk of frostbite and hypothermia and ensures that a selected clothing ensemble is appropriate for expected weather conditions and activities. CoWEDA represents a significant enhancement of required clothing insulation (IREQ, ISO 11079) and wind chill index-based guidance for cold weather safety and survival.

Formulae for calculating body surface area in modern U.S. Army Soldiers.

PURPOSE: Body surface area (BSA) is an important measurement for many thermophysiological, pharmaceutical, toxicological, environmental, and military applications. Unfortunately, BSA is difficult to quantify, and existing prediction methods are not optimized for contemporary populations. METHODS: The present study analyzed data body measurements from 5603 male and female participants of a US Army Anthropometric Survey to determine optimal methods for estimating BSA in modern US Army Soldiers. This data included 94 individual body measurements as well as three dimensional (3D) whole body scans for each participant. We used this data to assess and compared 15 existing equations to the measured data. We also derived best fitting nonlinear regression models for estimating BSA from different combinations of sex, height, and weight and iteratively included the remaining 91 measurements to determine which combinations resulted in the highest goodness-of-fit. RESULTS: We found that inclusion of armspan measurements as a third body dimension maximized the model goodness-of-fit. CONCLUSION: Some of the existing formulae provide reasonable estimates of 3D-scanner derived BSA; while our new formulae derived from this study allows for more accurate estimates of BSA using one or more common input variables.

Heat strain imposed by personal protective ensembles: quantitative analysis using a thermoregulation model.

The objective of this paper is to study the effects of personal protective equipment (PPE) and specific PPE layers, defined as thermal/evaporative resistances and the mass, on heat strain during physical activity. A stepwise thermal manikin testing and modeling approach was used to analyze a PPE ensemble with four layers: uniform, ballistic protection, chemical protective clothing, and mask and gloves. The PPE was tested on a thermal manikin, starting with the uniform, then adding an additional layer in each step. Wearing PPE increases the metabolic rates [Formula: see text], thus [Formula: see text] were adjusted according to the mass of each of four configurations. A human thermoregulatory model was used to predict endurance time for each configuration at fixed [Formula: see text] and at its mass adjusted [Formula: see text]. Reductions in endurance time due to resistances, and due to mass, were separately determined using predicted results. Fractional contributions of PPE's thermal/evaporative resistances by layer show that the ballistic protection and the chemical protective clothing layers contribute about 20 %, respectively. Wearing the ballistic protection over the uniform reduced endurance time from 146 to 75 min, with 31 min of the decrement due to the additional resistances of the ballistic protection, and 40 min due to increased [Formula: see text] associated with the additional mass. Effects of mass on heat strain are of a similar magnitude relative to effects of increased resistances. Reducing resistances and mass can both significantly alleviate heat strain.

Metabolic Costs of Walking with Weighted Vests.

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.

Relationship between core temperature, skin temperature, and heat flux during exercise in heat.

PURPOSE: This paper investigates the relationship between core temperature (T c), skin temperature (T s) and heat flux (HF) during exercise in hot conditions. METHOD: Nine test volunteers, wearing an Army Combat Uniform and body armor, participated in three sessions at 25 °C/50 % relative humidity (RH); 35 °C/70 % RH; and 42 °C/20 % RH. Each session consisted of two 1-h treadmill walks at ~350 W and ~540 W intensity. T s and HF from six sites on the forehead, sternum, pectoralis, left rib cage, left scapula, and left thigh, and T c (i.e., core temperature pill used as a suppository) were measured. Multiple linear regressions were conducted to derive algorithms that estimate T c from T s and HF at each site. A simple model was developed to simulate influences of thermal conductivity and thickness of the local body tissues on the relationship between T c, T s, and HF. RESULTS: Coefficient of determination (R (2)) ranged from 0.30 to 0.88, varying with locations and conditions. Good sites for T c measurement at surface were the sternum, and a combination of the sternum, scapula, and rib sites. The combination of T s and HF measured at the sternum explained ~75 % or more of variance in observed T c in hot environments. The forehead was found unsuitable for exercise in heat due to sweating and evaporative heat loss. The derived algorithms are likely applicable only for the same ensemble or ensembles with similar thermal and vapor resistances. CONCLUSION: Algorithms for T c measurement are location-specific and their accuracy is dependent, to a large degree, on sensor placement.

Peak performance and cardiometabolic responses of modern US army soldiers during heavy, fatiguing vest-borne load carriage.

INTRODUCTION: Physiological limits imposed by vest-borne loads must be defined for optimal performance monitoring of the modern dismounted warfighter. PURPOSE: To evaluate how weighted vests affect locomotion economy and relative cardiometabolic strain during military load carriage while identifying key physiological predictors of exhaustion limits. METHODS: Fifteen US Army soldiers (4 women, 11 men; age, 26 ± 8 years; height, 173 ± 10 cm; body mass (BM), 79 ± 16 kg) performed four incremental walking tests with different vest loads (0, 22, 44, or 66% BM). We examined the effects of vest-borne loading on peak walking speed, the physiological costs of transport, and relative work intensity. We then sought to determine which of the cardiometabolic indicators (oxygen uptake, heart rate, respiration rate) was most predictive of task failure. RESULTS: Peak walking speed significantly decreased with successively heavier vest loads (p < 0.01). Physiological costs per kilometer walked were significantly higher with added vest loads for each measure (p < 0.05). Relative oxygen uptake and heart rate were significantly higher during the loaded trials than the 0% BM trial (p < 0.01) yet not different from one another (p > 0.07). Conversely, respiration rate was significantly higher with the heavier load in every comparison (p < 0.01). Probability modeling revealed heart rate as the best predictor of task failure (marginal R2, 0.587, conditional R2, 0.791). CONCLUSION: Heavy vest-borne loads cause exceptional losses in performance capabilities and increased physiological strain during walking. Heart rate provides a useful non-invasive indicator of relative intensity and task failure during military load carriage.

Applications of real-time thermoregulatory models to occupational heat stress: validation with military and civilian field studies.

A real-time thermoregulatory model using noninvasive measurements as inputs was developed for predicting physiological responses of individuals working long hours. The purpose of the model is to reduce heat-related injuries and illness by predicting the physiological effects of thermal stress on individuals while working. The model was originally validated mainly by using data from controlled laboratory studies. This study expands the validation of the model with field data from 26 test volunteers, including US Marines, Australian soldiers, and US wildland fire fighters (WLFF). These data encompass a range of environmental conditions (air temperature: 19-30° C; relative humidity: 25-63%) and clothing (i.e., battle dress uniform, chemical-biological protective garment, WLFF protective gear), while performing diverse activities (e.g., marksmanship, marching, extinguishing fires, and digging). The predicted core temperatures (Tc), calculated using environmental, anthropometric, clothing, and heart rate measures collected in the field as model inputs, were compared with subjects' Tc collected with ingested telemetry temperature pills. Root mean standard deviation (RMSD) values, used for goodness of fit comparisons, indicated that overall, the model predictions were in close agreement with the measured values (grand mean of RMSD: 0.15-0.38° C). Although the field data showed more individual variability in the physiological data relative to more controlled laboratory studies, this study showed that the performance of the model was adequate.

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 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.

Sweat loss prediction using a multi-model approach.

A new multi-model approach (MMA) for sweat loss prediction is proposed to improve prediction accuracy. MMA was computed as the average of sweat loss predicted by two existing thermoregulation models: i.e., the rational model SCENARIO and the empirical model Heat Strain Decision Aid (HSDA). Three independent physiological datasets, a total of 44 trials, were used to compare predictions by MMA, SCENARIO, and HSDA. The observed sweat losses were collected under different combinations of uniform ensembles, environmental conditions (15-40°C, RH 25-75%), and exercise intensities (250-600 W). Root mean square deviation (RMSD), residual plots, and paired t tests were used to compare predictions with observations. Overall, MMA reduced RMSD by 30-39% in comparison with either SCENARIO or HSDA, and increased the prediction accuracy to 66% from 34% or 55%. Of the MMA predictions, 70% fell within the range of mean observed value ± SD, while only 43% of SCENARIO and 50% of HSDA predictions fell within the same range. Paired t tests showed that differences between observations and MMA predictions were not significant, but differences between observations and SCENARIO or HSDA predictions were significantly different for two datasets. Thus, MMA predicted sweat loss more accurately than either of the two single models for the three datasets used. Future work will be to evaluate MMA using additional physiological data to expand the scope of populations and conditions.

Effects of modern military backpack loads on walking speed and cardiometabolic responses of US Army Soldiers.

INTRODUCTION: Military leaders must understand how modern military equipment loads affect trade-offs between movement speed and physiological strain to optimize pacing strategies. PURPOSE: To evaluate the effects of load carried in a recently developed military backpack on the walking speed and cardiometabolic responses of dismounted warfighters. METHODS: Fifteen soldiers (1 woman, 14 men; age, 22 ± 2 years; height, 173 ± 7 cm; body mass (BM), 73 ± 10 kg) completed incremental walking tests with four external load conditions (0, 22, 44, or 66% BM) using the US Army's newest backpack: the Modular Lightweight Load-Carrying Equipment 4000 (MOLLE 4000). Oxygen uptake (V̇O2) and heart rate (HR) were evaluated relative to maximal values (V̇O2max and HRmax respectively). Testing ceased when participants completed the highest tested speed (1.97 m s-1), exceeded a respiratory exchange ratio (RER) of 1.00, or reached volitional exhaustion. RESULTS: Peak speed significantly decreased (p < 0.03) with successively heavier loads (0% BM, 1.95 ± 0.06 m s-1; 22% BM, 1.87 ± 0.10 m s-1; 44% BM, 1.69 ± 0.13 m s-1; 66% BM, 1.48 ± 0.13 m s-1). Peak V̇O2 was significantly lower (p < 0.01) with 0% BM (47 ± 5% V̇O2max) than each load (22% BM, 58 ± 8% V̇O2max; 44% BM, 63 ± 10% V̇O2max; 66% BM, 61 ± 11% V̇O2max). Peak HR was significantly lower (p < 0.01) with 0% BM (71 ± 5% HRmax) versus each load (22% BM, 83 ± 6% HRmax; 44% BM, 87 ± 6% HRmax; 66% BM, 88 ± 6% HRmax). CONCLUSION: Overburdened warfighters suffer severe impairments in walking speed even when carrying recently developed military load carriage equipment. Our results suggest that the relative work intensity of heavy load carriage may be better described when expressed relative to HRmax versus V̇O2max.

Terrain coefficients for predicting energy costs of walking over snow.

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.

Estimating Energy Expenditure during Level, Uphill, and Downhill Walking.

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.

Heat Strain Decision Aid (HSDA) accurately predicts individual-based core body temperature rise while wearing chemical protective clothing.

PURPOSE: We examined the accuracy of the Heat Strain Decision Aid (HSDA) as a predictor of core body temperature in healthy individuals wearing chemical protective clothing during laboratory and field exercises in hot and humid conditions. METHODS: The laboratory experiment examined three chemical protective clothing ensembles in eight male volunteers (age 24 ±â€¯6 years; height 178 ±â€¯5 cm; body mass 76.6 ±â€¯8.4 kg) during intermittent treadmill marching in an environmental chamber (air temperature 29.3 ±â€¯0.1 °C; relative humidity 56 ±â€¯1%; wind speed 0.4 ±â€¯0.1 m s-1). The field experiment examined four different chemical protective clothing ensembles in twenty activity military volunteers (26 ±â€¯5 years; 175 ±â€¯8 cm; 80.2 ±â€¯12.1 kg) during a prolonged road march (26.0 ±â€¯0.5 °C; 55 ±â€¯3%; 4.3 ±â€¯0.7 m s-1). Predictive accuracy and precision were evaluated by the bias, mean absolute error (MAE), and root mean square error (RMSE). Additionally, accuracy was evaluated using a prediction bias of ±0.27 °C as an acceptable limit and by comparing predictions to observations within the standard deviation (SD) of the observed data. RESULTS: Core body temperature predictions were accurate for each chemical protective clothing ensemble in laboratory (Bias -0.10 ±â€¯0.36 °C; MAE 0.28 ±â€¯0.24 °C; RMSE 0.37 ±â€¯0.24 °C) and field experiments (Bias 0.23 ±â€¯0.32 °C; MAE 0.30 ±â€¯0.25 °C; RMSE 0.40 ±â€¯0.25 °C). From all modeled data, 72% of all predictions were within one standard deviation of the observed data including 92% of predictions for the laboratory experiment (SD ±â€¯0.64 °C) and 67% for the field experiment (SD ±â€¯0.38 °C). Individual-based predictions showed modest errors outside the SD range with 98% of predictions falling <1 °C; while, 81% of all errors were within 0.5 °C of observed data. CONCLUSION: The HSDA acceptably predicts core body temperature when wearing chemical protective clothing during laboratory and field exercises in hot and humid conditions.

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

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.

Cardiorespiratory responses to heavy military load carriage over complex terrain.

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.

Complex Terrain Load Carriage Energy Expenditure Estimation Using Global Positioning System Devices.

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.

Metabolic Costs of Military Load Carriage over Complex Terrain.

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.

Estimation of core body temperature from skin temperature, heat flux, and heart rate using a Kalman filter.

Core body temperature (TC) is a key physiological metric of thermal heat-strain yet it remains difficult to measure non-invasively in the field. This work used combinations of observations of skin temperature (TS), heat flux (HF), and heart rate (HR) to accurately estimate TC using a Kalman Filter (KF). Data were collected from eight volunteers (age 22 ±â€¯4 yr, height 1.75 ±â€¯0.10 m, body mass 76.4 ±â€¯10.7 kg, and body fat 23.4 ±â€¯5.8%, mean ±â€¯standard deviation) while walking at two different metabolic rates (∼350 and ∼550 W) under three conditions (warm: 25 °C, 50% relative humidity (RH); hot-humid: 35 °C, 70% RH; and hot-dry: 40 °C, 20% RH). Skin temperature and HF data were collected from six locations: pectoralis, inner thigh, scapula, sternum, rib cage, and forehead. Kalman filter variables were learned via linear regression and covariance calculations between TC and TS, HF, and HR. Root mean square error (RMSE) and bias were calculated to identify the best performing models. The pectoralis (RMSE 0.18 ±â€¯0.04 °C; bias -0.01 ±â€¯0.09 °C), rib (RMSE 0.18 ±â€¯0.09 °C; bias -0.03 ±â€¯0.09 °C), and sternum (RMSE 0.20 ±â€¯0.10 °C; bias -0.04 ±â€¯0.13 °C) were found to have the lowest error values when using TS, HF, and HR but, using only two of these measures provided similar accuracy.