Sleeping for One Week on a Temperature-Controlled Mattress Cover Improves Sleep and Cardiovascular Recovery.

Body temperature should be tightly regulated for optimal sleep. However, various extrinsic and intrinsic factors can alter body temperature during sleep. In a free-living study, we examined how sleep and cardiovascular health metrics were affected by sleeping for one week with (Pod ON) vs. without (Pod OFF), an active temperature-controlled mattress cover (the Eight Sleep Pod). A total of 54 subjects wore a home sleep test device (HST) for eight nights: four nights each with Pod ON and OFF (>300 total HST nights). Nightly sleeping heart rate (HR) and heart rate variability (HRV) were collected. Compared to Pod OFF, men and women sleeping at cooler temperatures in the first half of the night significantly improved deep (+14 min; +22% mean change; p = 0.003) and REM (+9 min; +25% mean change; p = 0.033) sleep, respectively. Men sleeping at warm temperatures in the second half of the night significantly improved light sleep (+23 min; +19% mean change; p = 0.023). Overall, sleeping HR (-2% mean change) and HRV (+7% mean change) significantly improved with Pod ON (p < 0.01). To our knowledge, this is the first study to show a continuously temperature-regulated bed surface can (1) significantly modify time spent in specific sleep stages in certain parts of the night, and (2) enhance cardiovascular recovery during sleep.

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.

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.

Circumference-Based Predictions of Body Fat Revisited: Preliminary Results From a US Marine Corps Body Composition Survey.

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.

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.