Modeling thermoregulatory responses during high-intensity exercise in warm environments.

The six cylinder thermoregulatory model (SCTM) has been validated thoroughly for resting humans. This type of modeling is helpful to predict and develop guidance for safe performance of work and recreational activities. In the context of a warming global climate, updating the accuracy of the model for intense exercise in warm environments will help a wide range of individuals in athletic, recreational, and military settings. Three sets of previously collected data were used to determine SCTM accuracy. Dataset 1: two groups [large (LG) 91.5 kg and small (SM) 67.7 kg] of individuals performed 60 min of semirecumbent cycling in temperate conditions (25.1°C) at metabolic rates of 570-700 W. Dataset 2: two LG (100 kg) and SM (65.8 kg) groups performed 60 min of semirecumbent cycling in warm/hot environmental conditions (36.2°C) at metabolic rates of 590-680 W. Dataset 3: seven volunteers completed 8-km track trials (∼30 min) in cool (17°C) and warm (30°C) environments. The volunteers' metabolic rates were estimated to be 1,268 W and 1,166 W, respectively. For all datasets, SCTM-predicted core temperatures were found to be similar to the observed core temperatures. The root mean square deviations (RMSDs) ranged from 0.06 to 0.46°C with an average of 0.2°C deviation, which is less than the acceptance threshold of 0.5°C. Thus, the present validation shows that SCTM predicts core temperatures with acceptable accuracy during intense exercise in warm environments and successfully captures core temperature differences between large and small individuals.NEW & NOTEWORTHY The SCTM has been validated thoroughly for resting humans in warm and cold environments and during water immersion. The present study further demonstrated that SCTM predicts core temperatures with acceptable accuracy during intense exercise up to 1,300 W in temperate and warm environments and captures core temperature differences between large and small individuals. SCTM is potentially useful to develop guidance for safe operation in athletic, military, and occupational settings during exposure to warm or hot environments.

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.

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.

Estimation of metabolic energy expenditure from core temperature using a human thermoregulatory model.

Human metabolic energy expenditure is critical to many scientific disciplines but can only be measured using expensive and/or restrictive equipment. The aim of this work is to determine whether the SCENARIO thermoregulatory model can be adapted to estimate metabolic rate (M) from core body temperature (TC). To validate this method of M estimation, data were collected from fifteen test volunteers (age = 23 ± 3yr, height = 1.73 ± 0.07m, mass = 68.6 ± 8.7kg, body fat = 16.7 ± 7.3%; mean ± SD) who wore long sleeved nylon jackets and pants (Itot,clo = 1.22, Im = 0.41) during treadmill exercise tasks (32 trials; 7.8 ± 0.5km in 1h; air temp. = 22°C, 50% RH, wind speed = 0.35ms-1). Core body temperatures were recorded by ingested thermometer pill and M data were measured via whole room indirect calorimetry. Metabolic rate was estimated for 5min epochs in a two-step process. First, for a given epoch, a range of M values were input to the SCENARIO model and a corresponding range of TC values were output. Second, the output TC range value with the lowest absolute error relative to the observed TC for the given epoch was identified and its corresponding M range input was selected as the estimated M for that epoch. This process was then repeated for each subsequent remaining epoch. Root mean square error (RMSE), mean absolute error (MAE), and bias between observed and estimated M were 186W, 130 ± 174W, and 33 ± 183W, respectively. The RMSE for total energy expenditure by exercise period was 0.30 MJ. These results indicate that the SCENARIO model is useful for estimating M from TC when measurement is otherwise impractical.

Wearable physiological monitoring for human thermal-work strain optimization.

Safe performance limits of soldiers and athletes have typically relied on predictive work-rest models of ambient conditions, average work intensity, and characteristics of the population. Bioengineering advances in noninvasive sensor technologies, including miniaturization, reduced cost, power requirements, and comfort, now make it possible to produce individual predictions of safe thermal-work limits. These precision medicine assessments depend on the development of thoughtful algorithms based on physics and physiology. Both physiological telemetry and thermal-strain indexes have been available for >50 years, but greater computing power and better wearable sensors now make it possible to provide actionable information at the individual level. Core temperature can be practically estimated from time series heart rate data and, using an adaptive physiological strain index, provides meaningful predictions of safe work limits that cannot be predicted from only core temperature or heart rate measurements. Early adopters of this technology include specialized occupations where individuals operate in complete encapsulation such as chemical protective suits. Emerging technologies that focus on heat flux measurements at the skin show even greater potential for estimating thermal-work strain using a parsimonious sensor set. Applications of these wearable technologies include many sports and military training venues where inexperienced individuals can learn effective work pacing strategies and train to safe personal limits. The same strategies can also provide a technologically based performance edge for experienced workers and athletes faced with novel and nonintuitive physiological challenges, such as health care providers in full protective clothing treating Ebola patients in West Africa in 2014. NEW & NOTEWORTHY This mini-review details how the application of computational techniques borrowed from signal processing and control theory can provide meaningful advances for the applied physiological problem of real-time thermal-work strain monitoring. The work examines the development of practical core body temperature estimation techniques and how these can be used in combination with current and updated thermal-work strain indexes to provide objective state assessments and to optimize work rest schedules for a given task.

Is normobaric hypoxia an effective treatment for sustaining previously acquired altitude acclimatization?

This study examined whether normobaric hypoxia (NH) treatment is more efficacious for sustaining high-altitude (HA) acclimatization-induced improvements in ventilatory and hematologic responses, acute mountain sickness (AMS), and cognitive function during reintroduction to altitude (RA) than no treatment at all. Seventeen sea-level (SL) residents (age = 23 ± 6 yr; means ± SE) completed in the following order: 1) 4 days of SL testing; 2) 12 days of HA acclimatization at 4,300 m; 3) 12 days at SL post-HA acclimatization (Post) where each received either NH (n = 9, [Formula: see text] = 0.122) or Sham (n = 8; [Formula: see text] = 0.207) treatment; and 4) 24-h reintroduction to 4,300-m altitude (RA) in a hypobaric chamber (460 Torr). End-tidal carbon dioxide pressure ([Formula: see text]), hematocrit (Hct), and AMS cerebral factor score were assessed at SL, on HA2 and HA11, and after 20 h of RA. Cognitive function was assessed using the SynWin multitask performance test at SL, on HA1 and HA11, and after 4 h of RA. There was no difference between NH and Sham treatment, so data were combined. [Formula: see text] (mmHg) decreased from SL (37.2 ± 0.5) to HA2 (32.2 ± 0.6), decreased further by HA11 (27.1 ± 0.4), and then increased from HA11 during RA (29.3 ± 0.6). Hct (%) increased from SL (42.3 ± 1.1) to HA2 (45.9 ± 1.0), increased again from HA2 to HA11 (48.5 ± 0.8), and then decreased from HA11 during RA (46.4 ± 1.2). AMS prevalence (%) increased from SL (0 ± 0) to HA2 (76 ± 11) and then decreased at HA11 (0 ± 0) and remained depressed during RA (17 ± 10). SynWin scores decreased from SL (1,615 ± 62) to HA1 (1,306 ± 94), improved from HA1 to HA11 (1,770 ± 82), and remained increased during RA (1,707 ± 75). These results demonstrate that HA acclimatization-induced improvements in ventilatory and hematologic responses, AMS, and cognitive function are partially retained during RA after 12 days at SL whether or not NH treatment is utilized.NEW & NOTEWORTHY This study demonstrates that normobaric hypoxia treatment over a 12-day period at sea level was not more effective for sustaining high-altitude (HA) acclimatization during reintroduction to HA than no treatment at all. The noteworthy aspect is that athletes, mountaineers, and military personnel do not have to go to extraordinary means to retain HA acclimatization to an easily accessible and relevant altitude if reexposure occurs within a 2-wk time period.

Thermal-work strain during Marine rifle squad operations in Afghanistan.

The physiological burden created by heat strain and physical exercise, also called thermal-work strain, was quantified for 10 male Marines (age 21.9 ± 2.3 years, height 180.3 ± 5.2 cm, and weight 85.2 ± 10.8 kg) during three dismounted missions in Helmand Province, Afghanistan. Heart rate (HR) and core body temperature (T core) were recorded every 15 seconds (Equivital EQ-01; Hidalgo, Cambridge, United Kingdom) during periods of light, moderate, and heavy work and used to estimate metabolic rate. Meteorological measures, clothing characteristics, anthropometrics, and estimated metabolic rates were used to predict T core for the same missions during March (spring) and July (summer) conditions. Thermal-work strain was quantified from HR and T core values using the Physiological Strain Index (PSI) developed by Moran et al. July PSI and T core values were predicted and not observed due to lack of access to in-theater warfighters at that time. Our methods quantify and compare the predicted and observed thermal-work strain resulting from environment and worn or carried equipment and illustrate that a small increase in ambient temperature and solar load might result in increased thermal-work strain.

Real time gait pattern classification from chest worn accelerometry during a loaded road march.

Accelerometers, whether in smart phones or wearable physiological monitoring systems are becoming widely used to identify movement and activities of free living individuals. Although there has been much work in applying computationally intensive methods to this problem, this paper focuses on developing a real-time gait analysis approach that is intuitive, requires no individual calibration, can be extended to complex gait analysis, and can readily be adopted by ambulatory physiological monitors for use in real time. Chest-mounted tri-axial accelerometry data were collected from sixty-one male U.S. Army Ranger candidates engaged in an 8 or 12 mile loaded (35 Kg packs) timed road march. The pace of the road march was such that volunteers needed to both walk and run. To provide intuitive features we examined the periodic patterns generated from 4s periods of movement from the vertical and longitudinal accelerometer axes. Applying the "eigenfaces" face recognition approach we used Principal Components Analysis to find a single basis vector from 10% of the data (n=6) that could distinguish patterns of walk and run with a classification rate of 95% and 90% (n=55) respectively. Because these movement features are based on a gridded frequency count, the method is applicable for use by body-worn microprocessors.

Human liver dendritic cells promote T cell hyporesponsiveness.

The liver is believed to promote tolerance, which may be beneficial due to its constant exposure to foreign Ags from the portal circulation. Although dendritic cells (DCs) are critical mediators of immune responses, little is known about human liver DCs. We compared freshly purified liver DCs from surgical specimens with autologous blood DCs. Liver and blood DCs were equally immature, but had distinct subset compositions. BDCA-1(+) DCs represented the most prevalent liver DC subset, whereas the majority of peripheral blood DCs were CD16(+). Upon TLR4 ligation, blood DCs secreted multiple proinflammatory cytokines, whereas liver DCs produced substantial amounts of IL-10. Liver DCs induced less proliferation of allogeneic T cells both in a primary MLR and after restimulation. Similarly, Ag-specific CD4(+) T cells were less responsive to restimulation when initially stimulated by autologous liver DCs rather than blood DCs. In addition, liver DCs generated more suppressive CD4(+)CD25(+)FoxP3(+) T regulatory cells and IL-4-producing Th2 cells via an IL-10-dependent mechanism. Our findings are critical to understanding hepatic immunity and demonstrate that human liver DCs promote immunologic hyporesponsiveness that may contribute to hepatic tolerance.

Dendritic cells are required for effective cross-presentation in the murine liver.

UNLABELLED: The liver harbors a diversity of cell types that have been reported to stimulate T cells. Although most hepatic dendritic cells are immature, a small population of CD11c(high) conventional dendritic cells (cDCs) exists that expresses high levels of costimulatory molecules. We sought to determine the relative contribution of cDCs to cross-presentation by the liver. In vitro, liver nonparenchymal cells (NPCs) depleted of cDCs induced only minimal proliferation and activation of antigen-specific CD8(+) T cells when loaded with soluble protein antigen. Using a transgenic mouse with the CD11c promoter driving expression of the human diphtheria toxin receptor, we found that selective depletion of cDCs in vivo reduced the number and activation of antigen-specific CD8(+) T cells in the liver after intravenous administration of soluble protein antigen. Adoptive transfer of DCs, but not CD40 stimulation, restored the hepatic T-cell response. CONCLUSION: Our findings indicate that the ability of the liver to effectively cross-present soluble protein to antigen-specific CD8(+) T cells depends primarily on cDCs. Despite costimulation, other resident liver antigen-presenting cells cannot compensate for the absence of cDCs.