Active ascent accelerates the time course but not the overall incidence and severity of acute mountain sickness at 3,600 m.

Acute mountain sickness (AMS) typically peaks following the first night at high altitude (HA) and resolves over the next 2-3 days, but the impact of active ascent on AMS is debated. To determine the impact of ascent conditions on AMS, 78 healthy Soldiers (means ± SD; age = 26 ± 5 yr) were tested at baseline residence, transported to Taos, NM (2,845 m), hiked (n = 39) or were driven (n = 39) to HA (3,600 m), and stayed for 4 days. AMS-cerebral (AMS-C) factor score was assessed at HA twice on day 1 (HA1), five times on days 2 and 3 (HA2 and HA3), and once on day 4 (HA4). If AMS-C was ≥0.7 at any assessment, individuals were AMS susceptible (AMS+; n = 33); others were nonsusceptible (AMS-; n = 45). Daily peak AMS-C scores were analyzed. Ascent conditions (active vs. passive) did not impact the overall incidence and severity of AMS at HA1-HA4. The AMS+ group, however, demonstrated a higher (P < 0.05) AMS incidence in the active vs. passive ascent cohort on HA1 (93% vs. 56%), similar incidence on HA2 (60% vs. 78%), lower incidence (P < 0.05) on HA3 (33% vs. 67%), and similar incidence on HA4 (13% vs. 28%). The AMS+ group also demonstrated a higher (P < 0.05) AMS severity in the active vs. passive ascent cohort on HA1 (1.35 ± 0.97 vs. 0.90 ± 0.70), similar score on HA2 (1.00 ± 0.97 vs. 1.34 ± 0.70), and lower (P < 0.05) score on HA3 (0.56 ± 0.55 vs. 1.02 ± 0.75) and HA4 (0.32 ± 0.41 vs. 0.60 ± 0.72). Active compared with passive ascent accelerated the time course of AMS with more individuals sick on HA1 and less individuals sick on HA3 and HA4.NEW & NOTEWORTHY This research demonstrated that active ascent accelerated the time course but not overall incidence and severity of acute mountain sickness (AMS) following rapid ascent to 3,600 m in unacclimatized lowlanders. Active ascenders became sicker faster and recovered quicker than passive ascenders, which may be due to differences in body fluid regulation. Findings from this well-controlled large sample-size study suggest that previously reported discrepancies in the literature regarding the impact of exercise on AMS may be related to differences in the timing of AMS measurements between studies.

Using a Contemporary Portable Metabolic Gas Exchange System for Assessing Energy Expenditure: A Validity and Reliability Study.

There are several methods available to assess energy expenditure, all associated with inherent pros and cons that must be adequately considered for use in specific environments and populations. A requirement of all methods is that they must be valid and reliable in their capability to accurately measure oxygen consumption (VO2) and carbon dioxide production (VCO2). The purpose of this study was to evaluate the reliability and validity of the mobile CO2/O2 Breath and Respiration Analyzer (COBRA) relative to a criterion system (Parvomedics TrueOne 2400®, PARVO) with additional measurements to compare the COBRA to a portable system (Vyaire Medical, Oxycon Mobile®, OXY). Fourteen volunteers with a mean of 24 years old, body weight of 76 kg, and a VO2peak of 3.8 L∙min-1 performed four repeated trials of progressive exercises. Simultaneous steady-state measurements of VO2, VCO2, and minute ventilation (VE) by the COBRA/PARVO and OXY systems were conducted at rest, while walking (23-36% VO2peak), jogging (49-67% VO2peak), and running (60-76% VO2peak). Data collection was randomized by the order of system tested (COBRA/PARVO and OXY) and was standardized to maintain work intensity (rest to run) progression across study trials and days (two trials/day over two days). Systematic bias was examined to assess the accuracy of the COBRA to PARVO and OXY to PARVO across work intensities. Intra- and inter-unit variability were assessed with interclass correlation coefficients (ICC) and a 95% limit of agreement intervals. The COBRA and PARVO produced similar measures for VO2 (Bias ± SD, 0.01 ± 0.13 L·min-1; 95% LoA, (-0.24, 0.27 L·min-1); R2 = 0.982), VCO2 (0.06 ± 0.13 L·min-1; (-0.19, 0.31 L·min-1); R2 = 0.982), VE (2.07 ± 2.76 L·min-1; (-3.35, 7.49 L·min-1); R2 = 0.991) across work intensities. There was a linear bias across both the COBRA and OXY with increased work intensity. The coefficient of variation for the COBRA ranged from 7 to 9% across measures for VO2, VCO2, and VE. COBRA was reliable across measurements for VO2 (ICC = 0.825; 0.951), VCO2 (ICC = 0.785; 0.876), and VE (ICC = 0.857; 0.945) for intra-unit reliability, respectively. The COBRA is an accurate and reliable mobile system for measuring gas exchange at rest and across a range of work intensities.

Single fibre enables acoustic fabrics via nanometre-scale vibrations.

Fabrics, by virtue of their composition and structure, have traditionally been used as acoustic absorbers1,2. Here, inspired by the auditory system3, we introduce a fabric that operates as a sensitive audible microphone while retaining the traditional qualities of fabrics, such as machine washability and draping. The fabric medium is composed of high-Young's modulus textile yarns in the weft of a cotton warp, converting tenuous 10-7-atmosphere pressure waves at audible frequencies into lower-order mechanical vibration modes. Woven into the fabric is a thermally drawn composite piezoelectric fibre that conforms to the fabric and converts the mechanical vibrations into electrical signals. Key to the fibre sensitivity is an elastomeric cladding that concentrates the mechanical stress in a piezocomposite layer with a high piezoelectric charge coefficient of approximately 46 picocoulombs per newton, a result of the thermal drawing process. Concurrent measurements of electric output and spatial vibration patterns in response to audible acoustic excitation reveal that fabric vibrational modes with nanometre amplitude displacement are the source of the electrical output of the fibre. With the fibre subsuming less than 0.1% of the fabric by volume, a single fibre draw enables tens of square metres of fabric microphone. Three different applications exemplify the usefulness of this study: a woven shirt with dual acoustic fibres measures the precise direction of an acoustic impulse, bidirectional communications are established between two fabrics working as sound emitters and receivers, and a shirt auscultates cardiac sound signals.

Estimated and measured core temperature responses to high-intensity warm weather military training: implications for exertional heat illness risk assessment.

OBJECTIVE: Humans avoid overheating through physiological and behavioral mechanisms. However, elite athletes, industrial workers, and military personnel, driven by the tasks at hand, may choose to continue working and face an increased risk of exertional heat illness (EHI). We wanted to examine the efficacy of a new core temperature (Tcr) estimation algorithm in assessing EHI risk. APPROACH: Physiological responses of 21 male Royal Marines recruits (age 21 ± 2 y, height 1.79 ± 0.05 m, weight 80.5 ± 7.2 kg) were collected during a physically-demanding criterion road march (14.5 km in 90 min with a 9.6 kg load; air temperature 16 °C, relative humidity ≥ 84%). Measured Tcr (thermometer pill) and estimated Tcr (ECTempTM Tcr-est) were compared. MAIN RESULTS: Measured Tcr either increased to an asymptote Tcr < 39.5 °C (WARM; n= 11), or progressively increased to Tcr > 40.0 °C (HOT; n= 10). In the HOT group, Tcr-est reflected measured Tcr up to Tcr = 40.0 °C (Bias = - 0.10 ± 0.37 °C, root mean square error = 0.37 ± 0.13 °C). In the WARM group, Tcr-est overestimated Tcr (Bias = 0.34 ± 0.40 °C) and was higher from mid-point to end. A logistic regression (Skin temperature approximate entropy and mean heart rate) was able to predict group membership (95% accuracy) at 20 min, allowing a WARM group ECTempTM correction factor (corrected Bias = 0.00 ± 0.29 °C). SIGNIFICANCE: The Tcr-est successfully tracked Tcr in the HOT group with high risk of exertional heat illness (EHI) (40% incidence). Skin temperature complexity shows promise as a non-invasive means of insight into the state of thermoregulatory control mechanisms.

Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?

Goal: The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed non-infected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

Divers risk accelerated fatigue and core temperature rise during fully-immersed exercise in warmer water temperature extremes.

Physiological responses to work in cold water have been well studied but little is known about the effects of exercise in warm water; an overlooked but critical issue for certain military, scientific, recreational, and professional diving operations. This investigation examined core temperature responses to fatiguing, fully-immersed exercise in extremely warm waters. Twenty-one male U.S. Navy divers (body mass, 87.3 ± 12.3 kg) were monitored during rest and fatiguing exercise while fully-immersed in four different water temperatures (Tw): 34.4, 35.8, 37.2, and 38.6°C (Tw34.4, Tw35.8, Tw37.2, and Tw38.6 respectively). Participants exercised on an underwater cycle ergometer until volitional fatigue or core temperature limits were reached. Core body temperature and heart rate were monitored continuously. Trial performance time decreased significantly as water temperature increased (Tw34.4, 174 ± 12 min; Tw35.8, 115 ± 13 min; Tw37.2, 50 ± 13 min; Tw38.6, 34 ± 14 min). Peak core body temperature during work was significantly lower in Tw34.4 water (38.31 ± 0.49°C) than in warmer temperatures (Tw35.8, 38.60 ± 0.55°C; Tw37.2, 38.82 ± 0.76°C; Tw38.6, 38.97 ± 0.65°C). Core body temperature rate of change increased significantly with warmer water temperature (Tw34.4, 0.39 ± 0.28°C·h-1; Tw35.8, 0.80 ± 0.19°C·h-1; Tw37.2, 2.02 ± 0.31°C·h-1; Tw38.6, 3.54 ± 0.41°C·h-1). Physically active divers risk severe hyperthermia in warmer waters. Increases in water temperature drastically increase the rate of core body temperature rise during work in warm water. New predictive models for core temperature based on workload and duration of warm water exposure are needed to ensure warm water diving safety.

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.

Core temperature responses of military working dogs during training activities and exercise walks.

Heat strain is common in military working dogs (MWDs), but can be mitigated by limiting duration of activity to avoid overheating and allowing sufficient time for recovery. To determine work/rest times for MWDs, temperature responses during training must be characterized. This study measured body core temperature of 48 MWDs at Lackland Air Force Base, San Antonio, TX. Twenty-four MWDs in training for patrol and detection activities participated under a range of ambient temperatures in August (27°C-32°C), October (22°C-26°C) and March (approximately 13°C). These MWDs swallowed a telemetric thermometer pill to measure continuous gastrointestinal tract temperature (Tgi). Twenty-four kennel MWDs participated in July (25°C-29°C). In these dogs rectal temperature (Tre) was measured manually during a standard exercise walk. For the MWDs in training, Tgi before the first activity was 38.5±0.5°C (mean±SD) and final Tgi was 39.8±0.6°C after sessions that lasted 13.1±4.9 minutes (5.4 to 26.3 minutes). Peak Tgi, 0.4±0.4°C above final Tgi, occurred 8 to 12 minutes into recovery. Before beginning a second activity 40 to 165 minutes later, Tgi was within 0.5°C of initial values for 80% of dogs. For the kennel MWDs, Tre was 39.0±0.8°C (37.7°C to 40.7°C) at the start and 40.1±0.6°C at the end of the 21.3±2.8 minute walk. The continuous increase in core temperature during activity of both groups of MWDs indicates that limiting exercise duration is important for minimizing risk of overheating in MWDs. The observation of continued increase in Tgi to a peak after exercise ends suggests that for MWDs suspected of overheating temperature should be monitored for at least 15 minutes postexercise to ensure recovery.

Mathematical prediction of core body temperature from environment, activity, and clothing: The heat strain decision aid (HSDA).

Physiological models provide useful summaries of complex interrelated regulatory functions. These can often be reduced to simple input requirements and simple predictions for pragmatic applications. This paper demonstrates this modeling efficiency by tracing the development of one such simple model, the Heat Strain Decision Aid (HSDA), originally developed to address Army needs. The HSDA, which derives from the Givoni-Goldman equilibrium body core temperature prediction model, uses 16 inputs from four elements: individual characteristics, physical activity, clothing biophysics, and environmental conditions. These inputs are used to mathematically predict core temperature (Tc) rise over time and can estimate water turnover from sweat loss. Based on a history of military applications such as derivation of training and mission planning tools, we conclude that the HSDA model is a robust integration of physiological rules that can guide a variety of useful predictions. The HSDA model is limited to generalized predictions of thermal strain and does not provide individualized predictions that could be obtained from physiological sensor data-driven predictive models. This fully transparent physiological model should be improved and extended with new findings and new challenging scenarios.

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.

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.

Estimation of human core temperature from sequential heart rate observations.

Core temperature (CT) in combination with heart rate (HR) can be a good indicator of impending heat exhaustion for occupations involving exposure to heat, heavy workloads, and wearing protective clothing. However, continuously measuring CT in an ambulatory environment is difficult. To address this problem we developed a model to estimate the time course of CT using a series of HR measurements as a leading indicator using a Kalman filter. The model was trained using data from 17 volunteers engaged in a 24 h military field exercise (air temperatures 24-36 °C, and 42%-97% relative humidity and CTs ranging from 36.0-40.0 °C). Validation data from laboratory and field studies (N = 83) encompassing various combinations of temperature, hydration, clothing, and acclimation state were examined using the Bland-Altman limits of agreement (LoA) method. We found our model had an overall bias of -0.03 ± 0.32 °C and that 95% of all CT estimates fall within ±0.63 °C (>52 000 total observations). While the model for estimating CT is not a replacement for direct measurement of CT (literature comparisons of esophageal and rectal methods average LoAs of ±0.58 °C) our results suggest it is accurate enough to provide practical indication of thermal work strain for use in the work place.

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.

United States Army Research Institute of Environmental Medicine: Warfighter research focusing on the past 25 years.

The United States Army Research Institute of Environmental Medicine (USARIEM) celebrated its 50th anniversary on July 1, 2011. This article reviews its history, evolution, and transition of its research programs as well as its scientific and military accomplishments, emphasizing the past 25 yr. During the 1990s, USARIEM published a series of pocket guides providing guidance for sustaining Warfighter health and performance in Southwest Asia, Somalia, the former Republic of Yugoslavia, Rwanda, and Haiti. Issues identified during Operation Desert Storm elicited research that improved nutritional guidelines for protracted desert operations; safer use of nuclear, chemical, and biological protective clothing; equipment, development, and fielding of efficient microclimate cooling systems; and effective evaluation of pharmaceuticals to protect soldiers from chemical and biological threats. During the first decade of the 21st century, USARIEM and the Department of the Army published official medical/performance doctrines for operations in the heat and cold and at high altitude. The current Global War on Terrorism focused research to improve doctrines for hot, cold, and high-altitude operations, reduce musculoskeletal training injuries, provide improved field nutrition, more efficient planning for operational water requirements, and improve both military clothing and materiel. This article also describes the critically important interactions and communications between USARIEM and deployed units and the benefits to Warfighters from this association. This report presents USARIEM's unique and world-class facilities, organizational changes, scientific and support personnel, and major research accomplishments, including the publication of 2,200 scientific papers over the past 25 yr.

Methods of evaluating protective clothing relative to heat and cold stress: thermal manikin, biomedical modeling, and human testing.

Personal protective equipment (PPE) refers to clothing and equipment designed to protect individuals from chemical, biological, radiological, nuclear, and explosive hazards. The materials used to provide this protection may exacerbate thermal strain by limiting heat and water vapor transfer. Any new PPE must therefore be evaluated to ensure that it poses no greater thermal strain than the current standard for the same level of hazard protection. This review describes how such evaluations are typically conducted. Comprehensive evaluation of PPE begins with a biophysical assessment of materials using a guarded hot plate to determine the thermal characteristics (thermal resistance and water vapor permeability). These characteristics are then evaluated on a thermal manikin wearing the PPE, since thermal properties may change once the materials have been constructed into a garment. These data may be used in biomedical models to predict thermal strain under a variety of environmental and work conditions. When the biophysical data indicate that the evaporative resistance (ratio of permeability to insulation) is significantly better than the current standard, the PPE is evaluated through human testing in controlled laboratory conditions appropriate for the conditions under which the PPE would be used if fielded. Data from each phase of PPE evaluation are used in predictive models to determine user guidelines, such as maximal work time, work/rest cycles, and fluid intake requirements. By considering thermal stress early in the development process, health hazards related to temperature extremes can be mitigated while maintaining or improving the effectiveness of the PPE for protection from external hazards.

Human thermoregulatory system state estimation using non-invasive physiological sensors.

Small teams of emergency workers/military can often find themselves engaged in critical, high exertion work conducted under challenging environmental conditions. These types of conditions present thermal work strain challenges which unmitigated can lead to collapse (heat exhaustion) or even death from heat stroke. Physiological measurement of these teams provides a mechanism that could be an effective tool in preventing thermal injury. While indices of thermal work strain have been proposed they suffer from ignoring thermoregulatory context and rely on measuring internal temperature (IT). Measurement of IT in free ranging ambulatory environments is problematic. In this paper we propose a physiology based Dynamic Bayesian Network (DBN) model that estimates internal temperature, heat production and heat transfer from observations of heart rate, accelerometry, and skin heat flux. We learn the model's conditional probability distributions from seven volunteers engaged in a 48 hour military field training exercise. We demonstrate that sum of our minute to minute heat production estimates correlate well with total daily energy expenditure (TDEE) measured using the doubly labeled water technique (r(2) = 0.73). We also demonstrate that the DBN is able to infer IT in new datasets to within ±0.5 °C over 85% of the time. Importantly, the additional thermoregulatory context allows critical high IT temperature to be estimated better than previous approaches. We conclude that the DBN approach shows promise in enabling practical real time thermal work strain monitoring applications from physiological monitoring systems that exist today.

A real-time algorithm for predicting core temperature in humans.

In this paper, we present a real-time implementation of a previously developed offline algorithm for predicting core temperature in humans. The real-time algorithm uses a zero-phase Butterworth digital filter to smooth the data and an autoregressive (AR) model to predict core temperature. The performance of the algorithm is assessed in terms of its prediction accuracy, quantified by the root mean squared error (RMSE), and in terms of prediction uncertainty, quantified by statistically based prediction intervals (PIs). To evaluate the performance of the algorithm, we simulated real-time implementation using core-temperature data collected during two different field studies, involving ten different individuals. One of the studies includes a case of heat illness suffered by one of the participants. The results indicate that although the real-time predictions yielded RMSEs that are larger than those of the offline algorithm, the real-time algorithm does produce sufficiently accurate predictions for practically meaningful prediction horizons (approximately 20 min). The algorithm reached alert (39 degrees C) and alarm (39.5 degrees C) thresholds for the heat-ill individual but did not even attain the alert threshold for the other individuals, demonstrating the algorithm's good sensitivity and specificity. The PIs reflected, in an intuitively expected manner, the uncertainty associated with real-time forecast as a function of prediction horizon and core-temperature variability. The results also corroborate the feasibility of "universal" AR models, where an offline-developed model based on one individual's data could be used to predict any other individual in real time. We conclude that the real-time implementation of the algorithm confirms the attributes observed in the offline version and, hence, could be considered as a warning tool for impending heat illnesses.

Providing statistical measures of reliability for body core temperature predictions.

This paper describes the use of a data-driven autoregressive integrated moving average model to predict body core temperature in humans during physical activity. We also propose a bootstrap technique to provide a measure of reliability of such predictions in the form of prediction intervals. We investigate the model's predictive capabilities and associated reliability using two distinct datasets, both obtained in the field under different environmental conditions. One dataset is used to develop the model, and the other one, containing an example of heat illness, is used to test the model. We demonstrate that accurate and reliable predictions of an extreme core temperature value of 39.5 degrees C, can be made 20 minutes ahead of time, even when the predictive model is developed on a different individual having core temperatures within healthy physiological limits. This result suggests that data-driven models can be made portable across different core temperature levels and across different individuals. Also, we show that the bootstrap prediction intervals cover the actual core temperature, and that they exhibit intuitively expected behavior as a function of the prediction horizon and the core temperature variability.

Field studies of exercise and food deprivation.

PURPOSE OF REVIEW: The increase in obesity in developed societies drives interest in the interplay of energy intake, metabolic energy expenditure, and body energy stores. A better understanding of energy management in physically active and undernourished humans should help guide strategies to manage obesity safely and effectively. This review focuses on field studies of men and women engaged in prolonged strenuous activities, ranging from ranger training to extreme expeditions. RECENT FINDINGS: Although scientifically unconventional and limited, field studies of exercise and food deprivation have yielded interesting findings: 4-5% body fat is the normal lower limit to fat reserves in physically active underfed young adult men, and in response to exercise and underfeeding, women used more fat mass and less fat-free mass to meet metabolic fuel requirements. SUMMARY: Field studies have shown that fat energy reserves in young adult men can be estimated as percentage body fat minus 5%, and initial body fat mass has a significant positive influence on fat oxidation rates per kilogram of fat-free mass during rapid weight loss associated with underfeeding and exercise. Data logging pedometers, activity monitors, global positioning systems, and wireless body and personal-area networks promise to make it easier to study and care for free-living humans.