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.

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.

Intensity and physiological strain of competitive ultra-endurance exercise in humans.

The aim of this study was to determine the magnitude and pattern of intensity, and physiological strain, of competitive exercise performed across several days, as in adventure racing. Data were obtained from three teams of four athletes (7 males, 5 females; mean age 36 years, s = 11; cycling .VO(2 peak) 53.9 ml . kg(-1) . min(-1), s = 6.3) in an international race (2003 Southern Traverse; 96 - 116 h). Heart rates (HR) averaged 64% (95% confidence interval: +/- 4%) of heart rate range [%HRR = (HR - HR(min))/(HR(max) - HR(min)) x 100] during the first 12 h of racing, fell to 41% (+/-4%) by 24 h, and remained so thereafter. The level and pattern of heart rate were similar across teams, despite one leading and one trailing all other teams. Core temperature remained between 36.0 and 39.2 degrees C despite widely varying thermal stress. Venous samples, obtained before, during, and after the race, revealed increased neutrophil, monocyte and lymphocyte concentrations (P < 0.01), and increased plasma volume (25 +/- 10%; P < 0.01) with a stable sodium concentration. Standardized exercise tests, performed pre and post race, showed little change in the heart rate-work rate relationship (P = 0.53), but a higher perception of effort post race (P < 0.01). These results provide the first comprehensive report of physiological strain associated with adventure racing.