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.

Real-time core body temperature estimation from heart rate for first responders wearing different levels of personal protective equipment.

First responders often wear personal protective equipment (PPE) for protection from on-the-job hazards. While PPE ensembles offer individuals protection, they limit one's ability to thermoregulate, and can place the wearer in danger of heat exhaustion and higher cardiac stress. Automatically monitoring thermal-work strain is one means to manage these risks, but measuring core body temperature (Tc) has proved problematic. An algorithm that estimates Tc from sequential measures of heart rate (HR) was compared to the observed Tc from 27 US soldiers participating in three different chemical/biological training events (45-90 min duration) while wearing PPE. Hotter participants (higher Tc) averaged (HRs) of 140 bpm and reached Tc around 39 °C. Overall the algorithm had a small bias (0.02 °C) and root mean square error (0.21 °C). Limits of agreement (LoA ± 0.48 °C) were similar to comparisons of Tc measured by oesophageal and rectal probes. The algorithm shows promise for use in real-time monitoring of encapsulated first responders. PRACTITIONER SUMMARY: An algorithm to estimate core temperature (Tc) from non-invasive measures of HR was validated. Three independent studies (n = 27) compared the estimated Tc to the observed Tc in humans participating in chemical/ biological hazard training. The algorithm's bias and variance to observed data were similar to that found from comparisons of oesophageal and rectal measurements.

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.

Individualized monitoring of heat illness risk: novel adaptive physiological strain index to assess exercise-heat strain from athletes to fully encapsulated workers.

Objective. Exercise-heat strain estimation approaches often involve combinations of body core temperature (Tcore), skin temperature (Tsk) and heart rate (HR). A successful existing measure is the 'Physiological Strain Index' (PSI), which combines HR and Tcore values to estimate strain. However, depending on variables such as aerobic fitness and clothing, the equation's 'maximal/critical' Tcore must be changed to accurately represent the strain, in part because high Tsk (small Tcore-Tsk) can increase cardiovascular strain and thereby negatively affect performance. Here, an 'adaptive PSI' (aPSI) is presented where the original PSI Tcorecriticalvalue is 'adapted' dynamically by the delta between Tcore and Tsk.Approach. PSI and aPSI were computed for athletes (ELITE,N= 11 male and 8 female, 8 km time-trial) and soldiers in fully encapsulating personal protective equipment (PPE,N= 8 male, 2 km approach-march). While these were dissimilar events, it was anticipated given that the clothing and work rates would elicit similar very-high exercise-heat strain values.Main results. Mean end HR values were similar (∼180 beats min-1) with higher Tcore = 40.1 ± 0.4 °C for ELITE versus PPE 38.4 ± 0.6 °C (P< 0.05). PSI end values were different between groups (P< 0.01) and appeared 'too-high' for ELITE (11.4 ± 0.8) and 'too-low' for PPE (7.6 ± 2.0). However, aPSI values were not different (9.9 ± 1.4 versus 9.0 ± 2.5 versus;p> 0.05) indicating a 'very high' level of exercise-heat strain for both conditions.Significance. A simple adaptation of the PSI equation, which accounts for differences in Tcore-to-Tsk gradients, provides a physiological approach to dynamically adapt PSI to provide a more accurate index of exercise-heat strain under very different working conditions.

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.

Early Prediction of Impending Exertional Heat Stroke With Wearable Multimodal Sensing and Anomaly Detection.

We employed wearable multimodal sensing (heart rate and triaxial accelerometry) with machine learning to enable early prediction of impending exertional heat stroke (EHS). US Army Rangers and Combat Engineers (N = 2,102) were instrumented while participating in rigorous 7-mile and 12-mile loaded rucksack timed marches. There were three EHS cases, and data from 478 Rangers were analyzed for model building and controls. The data-driven machine learning approach incorporated estimates of physiological strain (heart rate) and physical stress (estimated metabolic rate) trajectories, followed by reconstruction to obtain compressed representations which then fed into anomaly detection for EHS prediction. Impending EHS was predicted from 33 to 69 min before collapse. These findings demonstrate that low dimensional physiological stress to strain patterns with machine learning anomaly detection enables early prediction of impending EHS which will allow interventions that minimize or avoid pathophysiological sequelae. We describe how our approach can be expanded to other physical activities and enhanced with novel sensors.

Hydration assessment using the cardiovascular response to standing.

The cardiovascular response to standing (sit-to-stand change in heart rate; SSΔHR) is commonly employed as a screening tool to detect hypohydration (body water deficit). No study has systematically evaluated SSΔHR cut points using different magnitudes or different types of controlled hypohydration. The objective of this study was to determine the diagnostic accuracy of the often proposed 20 b/min SSΔHR cut point using both hypertonic and isotonic models of hypohydration. Thirteen healthy young adults (8M, 5F) underwent three bouts of controlled hypohydration. The first bout used sweating to elicit large losses of body water (mass) (>3 % sweat). The second two bouts were matched to elicit 3 % body mass losses (3 % diuretic; 3 % sweat). A euhydration control trial (EUH) was paired with each hypohydration trial for a total of six trials. Heart rate was assessed after 3-min sitting and after 1-min standing during all trials. SSΔHR was compared among trials, and receiver operator characteristic curve analysis was used to determine diagnostic accuracy of the 20 b/min SSΔHR cut point. Volunteers lost 4.5 ± 1.1, 3.0 ± 0.6, and 3.2 ± 0.6 % body mass during >3 % sweat, 3 % diuretic, and 3 % sweat trials, respectively. SSΔHR (b/min) was 9 ± 8 (EUH), 20 ± 12 (>3 % sweat; P < 0.05 vs. EUH), 17 ± 7 (3 % diuretic; P < 0.05 vs. EUH), and 13 ± 11 (3 % sweat). The 20 beats/min cut point had high specificity (90 %) but low sensitivity (44 %) and overall diagnostic accuracy of 67 %. SSΔHR increased significantly in response to severe hypertonic hypohydration and moderate isotonic hypohydration, but not moderate hypertonic hypohydration. However, the 20 beats/min cut point afforded only marginal diagnostic accuracy.

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.

Influence of Soldiers' Cardiorespiratory Fitness on Physiological Responses and Dropouts During a Loaded Long-distance March.

INTRODUCTION: In military service, marching is an important, common, and physically demanding task. Minimizing dropouts, maintaining operational readiness during the march, and achieving a fast recovery are desirable because the soldiers have to be ready for duty, sometimes shortly after an exhausting task. The present field study investigated the influence of the soldiers' cardiorespiratory fitness on physiological responses during a long-lasting and challenging 34 km march. MATERIALS AND METHODS: Heart rate (HR), body core temperature (BCT), total energy expenditure (TEE), energy intake, motivation, and pain sensation were investigated in 44 soldiers (20.3 ± 1.3 years, 178.5 ± 7.0 cm, 74.8 ± 9.8 kg, body mass index: 23.4 ± 2.7 kg × m-2, peak oxygen uptake ($\dot{\rm{V}}$O2peak): 54.2 ± 7.9 mL × kg-1 × min-1) during almost 8 hours of marching. All soldiers were equipped with a portable electrocardiogram to record HR and an accelerometer on the hip, all swallowed a telemetry pill to record BCT, and all filled out a pre- and post-march questionnaire. The influence of aerobic capacity on the physiological responses during the march was examined by dividing the soldiers into three fitness groups according to their $\dot{\rm{V}}$O2peak. RESULTS: The group with the lowest aerobic capacity ($\dot{\rm{V}}$O2peak: 44.9 ± 4.8 mL × kg-1 × min-1) compared to the group with the highest aerobic capacity ($\dot{\rm{V}}$O2peak: 61.7 ± 2.2 mL × kg-1 × min-1) showed a significantly higher (P < .05) mean HR (133 ± 9 bpm and 125 ± 8 bpm, respectively) as well as peak BCT (38.6 ± 0.3 and 38.4 ± 0.2 °C, respectively) during the march. In terms of recovery ability during the break, no significant differences could be identified between the three groups in either HR or BCT. The energy deficit during the march was remarkably high, as the soldiers could only replace 22%, 26%, and 36% of the total energy expenditure in the lower, middle, and higher fitness group, respectively. The cardiorespiratory fittest soldiers showed a significantly higher motivation to perform when compared to the least cardiorespiratory fit soldiers (P = .002; scale from 1 [not at all] to 10 [extremely]; scale difference of 2.3). A total of nine soldiers (16%) had to end marching early: four soldiers (21%) in the group with the lowest aerobic capacity, five (28%) in the middle group, and none in the highest group. CONCLUSION: Soldiers with a high $\dot{\rm{V}}$O2peak showed a lower mean HR and peak BCT throughout the long-distance march, as well as higher performance motivation, no dropouts, and lower energy deficit. All soldiers showed an enormous energy deficit; therefore, corresponding nutritional strategies are recommended.

Real-world walking economy: can laboratory equations predict field energy expenditure?

We addressed a practical question that remains largely unanswered after more than a century of active investigation: can equations developed in the laboratory accurately predict the energy expended under free-walking conditions in the field? Seven subjects walked a field course of 6,415 m that varied in gradient (-3.0 to +5.0%) and terrain (asphalt, grass) under unloaded (body weight only, Wb) and balanced, torso-loaded (1.30 × Wb) conditions at self-selected speeds while wearing portable calorimeter and GPS units. Portable calorimeter measures were corrected for a consistent measurement-range offset (+13.8 ± 1.8%, means ± SD) versus a well-validated laboratory system (Parvomedics TrueOne). Predicted energy expenditure totals (mL O2/kg) from four literature equations: ACSM, Looney, Minimum Mechanics, and Pandolf, were generated using the speeds and gradients measured throughout each trial in conjunction with empirically determined terrain/treadmill factors (asphalt = 1.0, grass = 1.08). The mean energy expenditure total measured for the unloaded field trials (981 ± 91 mL O2/kg) was overpredicted by +4%, +13%, +17%, and +20% by the Minimum Mechanics, ACSM, Pandolf, and Looney equations, respectively (corresponding predicted totals: 1,018 ± 19, 1,108 ± 26, 1,145 ± 37, and 1,176 ± 24 mL O2/kg). The measured loaded-trial total (1,310 ± 153 mL O2/kg) was slightly underpredicted by the Minimum Mechanics equation (-2%, 1,289 ± 22 mL O2/kg) and overpredicted by the Pandolf equation (+13%, 1,463 ± 32 mL O2/kg). Computational comparisons for hypothetical trials at different constant speeds (range: 0.6-1.8 m/s) on variable-gradient loop courses revealed between-equation prediction differences from 0% to 37%. We conclude that treadmill-based predictions of free-walking field energy expenditure are equation-dependent but can be highly accurate with rigorous implementation.NEW & NOTEWORTHY Here, we investigated the accuracy with which four laboratory-based equations can predict field-walking energy expenditure at freely selected speeds across varying gradients and terrain. Empirical tests involving 6,415-m trials under two load conditions indicated that predictions are significantly equation dependent but can be highly accurate (i.e., ±4%). Computations inputting identical weight, speed, and gradient values for different theoretical constant-speed trials (0.6-1.8 m/s) identified between-equation prediction differences as large as 37%.

Validation of ambulatory monitoring devices to measure energy expenditure and heart rate in a military setting.

Objectives.To investigate the validity of different devices and algorithms used in military organizations worldwide to assess physical activity energy expenditure (PAEE) and heart rate (HR) among soldiers.Design.Device validation study.Methods. Twenty-three male participants serving their mandatory military service accomplished, firstly, nine different military specific activities indoors, and secondly, a normal military routine outdoors. Participants wore simultaneously an ActiHeart, Everion, MetaMax 3B, Garmin Fenix 3, Hidalgo EQ02, and PADIS 2.0 system. The PAEE and HR data of each system were compared to the criterion measures MetaMax 3B and Hidalgo EQ02, respectively.Results. Overall, the recorded systematic errors in PAEE estimation ranged from 0.1 (±1.8) kcal.min-1to -1.7 (±1.8) kcal.min-1for the systems PADIS 2.0 and Hidalgo EQ02 running the Royal Dutch Army algorithm, respectively, and in the HR assessment ranged from -0.1 (±2.1) b.min-1to 0.8 (±3.0) b.min-1for the PADIS 2.0 and ActiHeart systems, respectively. The mean absolute percentage error (MAPE) in PAEE estimation ranged from 29.9% to 75.1%, with only the Everion system showing an overall MAPE <30%, but all investigated devices reported overall MAPE <1.4% in the HR assessment.Conclusions. The present study demonstrated poor to moderate validity in terms of PAEE estimation, but excellent validity in all investigated devices in terms of HR assessment. Overall, the Everion performed among the best in both parameters and with a device placement on the upper arm, the Everion system is particularly useful during military service, as it does not interfere with other relevant equipment.

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.

New metric of hypoxic dose predicts altitude acclimatization status following various ascent profiles.

Medical personnel need practical guidelines on how to construct high altitude ascents to induce altitude acclimatization and avoid acute mountain sickness (AMS) following the first night of sleep at high altitude. Using multiple logistic regression and a comprehensive database, we developed a quantitative prediction model using ascent profile as the independent variable and altitude acclimatization status as the dependent variable from 188 volunteers (147 men, 41 women) who underwent various ascent profiles to 4 km. The accumulated altitude exposure (AAE), a new metric of hypoxic dose, was defined as the ascent profile and was calculated by multiplying the altitude elevation (km) by the number of days (d) at that altitude prior to ascent to 4 km. Altitude acclimatization status was defined as the likely presence or absence of AMS after ~24 h of exposure at 4 km. AMS was assessed using the Cerebral Factor Score (AMS-C) from the Environmental Symptoms Questionnaire and deemed present if AMS-C was ≥0.7. Other predictor variables included in the model were age and body mass index (BMI). Sex, race, and smoking status were considered in model development but eliminated due to inadequate numbers in each of the ascent profiles. The AAE (km·d) significantly (P < 0.0001) predicted AMS in the model. For every 1 km·d increase in AAE, the odds of getting sick decreased by 41.3%. Equivalently, for every 1 km·d decrease in AAE, the odds of getting sick increased by 70.4%. Age and BMI were not significant predictors. The model demonstrated excellent discrimination (AUC = 0.83 (95% CI = 0.79-0.91) and calibration (Hosmer-Lemeshow = 0.11). The model provides a priori estimates of altitude acclimatization status resulting from the use of various rapid, staged, and graded ascent profiles.

Validity of a noninvasive estimation of deep body temperature when wearing personal protective equipment during exercise and recovery.

BACKGROUND: Deep body temperature is a critical indicator of heat strain. However, direct measures are often invasive, costly, and difficult to implement in the field. This study assessed the agreement between deep body temperature estimated from heart rate and that measured directly during repeated work bouts while wearing explosive ordnance disposal (EOD) protective clothing and during recovery. METHODS: Eight males completed three work and recovery periods across two separate days. Work consisted of treadmill walking on a 1% incline at 2.5, 4.0, or 5.5 km/h, in a random order, wearing EOD protective clothing. Ambient temperature and relative humidity were maintained at 24 °C and 50% [Wet bulb globe temperature (WBGT) (20.9 ± 1.2) °C] or 32 °C and 60% [WBGT (29.0 ± 0.2) °C] on the separate days, respectively. Heart rate and gastrointestinal temperature (TGI) were monitored continuously, and deep body temperature was also estimated from heart rate (ECTemp). RESULTS: The overall systematic bias between TGI and ECTemp was 0.01 °C with 95% limits of agreement (LoA) of ±0.64 °C and a root mean square error of 0.32 °C. The average error statistics among participants showed no significant differences in error between the exercise and recovery periods or the environmental conditions. At TGI levels of (37.0-37.5) °C, (37.5-38.0) °C, (38.0-38.5) °C, and > 38.5 °C, the systematic bias and ± 95% LoA were (0.08 ± 0.58) °C, (- 0.02 ± 0.69) °C, (- 0.07 ± 0.63) °C, and (- 0.32 ± 0.56) °C, respectively. CONCLUSIONS: The findings demonstrate acceptable validity of the ECTemp up to 38.5 °C. Conducting work within an ECTemp limit of 38.4 °C, in conditions similar to the present study, would protect the majority of personnel from an excessive elevation in deep body temperature (> 39.0 °C).

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.

Individualized short-term core temperature prediction in humans using biomathematical models.

This study compares and contrasts the ability of three different mathematical modeling techniques to predict individual-specific body core temperature variations during physical activity. The techniques include a first-principles, physiology-based (SCENARIO) model, a purely data-driven model, and a hybrid model that combines first-principles and data-driven components to provide an early, short-term (20-30 min ahead) warning of an impending heat injury. Their performance is investigated using two distinct datasets, a Field study and a Laboratory study. The results indicate that, for up to a 30 min prediction horizon, the purely data-driven model is the most accurate technique, followed by the hybrid. For this prediction horizon, the first-principles SCENARIO model produces root mean square prediction errors that are twice as large as those obtained with the other two techniques. Another important finding is that, if properly regularized and developed with representative data, data-driven and hybrid models can be made "portable" from individual to individual and across studies, thus significantly reducing the need for collecting developmental data and constructing and tuning individual-specific models.

A real-time heat strain risk classifier using heart rate and skin temperature.

Heat injury is a real concern to workers engaged in physically demanding tasks in high heat strain environments. Several real-time physiological monitoring systems exist that can provide indices of heat strain, e.g. physiological strain index (PSI), and provide alerts to medical personnel. However, these systems depend on core temperature measurement using expensive, ingestible thermometer pills. Seeking a better solution, we suggest the use of a model which can identify the probability that individuals are 'at risk' from heat injury using non-invasive measures. The intent is for the system to identify individuals who need monitoring more closely or who should apply heat strain mitigation strategies. We generated a model that can identify 'at risk' (PSI 7.5) workers from measures of heart rate and chest skin temperature. The model was built using data from six previously published exercise studies in which some subjects wore chemical protective equipment. The model has an overall classification error rate of 10% with one false negative error (2.7%), and outperforms an earlier model and a least squares regression model with classification errors of 21% and 14%, respectively. Additionally, the model allows the classification criteria to be adjusted based on the task and acceptable level of risk. We conclude that the model could be a valuable part of a multi-faceted heat strain management system.

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.

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.

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.