Bernhard Kampmann

UTCI-Fiala multi-node model of human heat transfer and temperature regulation.

The UTCI-Fiala mathematical model of human temperature regulation forms the basis of the new Universal Thermal Climate Index (UTC). Following extensive validation tests, adaptations and extensions, such as the inclusion of an adaptive clothing model, the model was used to predict human temperature and regulatory responses for combinations of the prevailing outdoor climate conditions. This paper provides an overview of the underlying algorithms and methods that constitute the multi-node dynamic UTCI-Fiala model of human thermal physiology and comfort. Treated topics include modelling heat and mass transfer within the body, numerical techniques, modelling environmental heat exchanges, thermoregulatory reactions of the central nervous system, and perceptual responses. Other contributions of this special issue describe the validation of the UTCI-Fiala model against measured data and the development of the adaptive clothing model for outdoor climates.

Estimation of metabolic rate from cardiac frequency for field studies: correcting for “thermal pulses”

Abstract (1) Metabolic rate in field studies is usually estimated during short measurements, but may be estimated for complete shifts by linking these measurements to cardiac frequency. (2) An increase of body temperature leads to a rise of cardiac frequency; this increase of cardiac frequency from thermal origin (“ thermal pulses ”) should be taken into account when metabolic rate is to be calculated out of cardiac frequency. (3) By improving a heuristic methodology proposed by Vogt et al. (Le Travail Humaine 33 (1970) 125), it is possible to determine the contribution of thermal pulses to cardiac frequency for complete shifts in field studies, if body core temperature is measured continuously. (4) This methodology was applied in a study where strain of coal miners at hot working places underground was determined during 112 shifts; the thermal pulses constitute 31% of the increase of cardiac frequency above resting values in the mean of all shifts—if the rise of metabolic rates above resting values is determined from cardiac frequency, then the consideration of thermal pulses reduces the overestimation of metabolic rates by 25%.

Metabolic costs of physiological heat stress responses - Q10 coefficients relating oxygen consumption to body temperature

Q10 describes the influence of temperature on physiological processes as the ratio of the rate of a physiological process at a particular temperature to the rate at a temperature 10 °C lower [1]. In terms of rates of oxygen consumption (VO2) related to rectal temperatures (tre), this can be written as [2]: Q10=(VO2/VO2,ref)10/tre-tre,ref (1a) or equivalently, VO2=VO2,ref.Q10tre-tre,ref/10 (1b) Q10 varies between 2 and 3 in biological systems [2], and Q10 = 2 is applied in modelling the rate of metabolic heat production in relation to body temperature [3,4]. This paper aims to determine Q10 for the influence of body temperature on oxygen consumption for light work in warm environments.

Intra- and inter-individual variability of strain during uncompensable heat stress determined from a longitudinal study.

Four mine rescue brigadesmen performed three different standardized trainings in uncompensable heat stress with different equipment, clothing and climatic stress. The strain during these trainings may be considered as typical for training and missions of firemen, mine rescue brigadesmen and subjects working under protective clothing. - During ten years the diverse trainings were repeated. Heart rates and body temperatures were recorded throughout the exposures. A significant linear trend over time only was found for body mass (increase in three of the subjects). Specific physical fitness (fitness per body mass) as well as heart rate or body temperature showed no significant trend over time for initial or final values. The variability of the physiological strain is described in good approximation by normal distributions and shows quite a high magnitude. On base of the whole data set inter-individual components of variance are estimated by a 2-factorial ANOVA (person, time of measurement) with the factor time of measurement nested under the factor person. Confidence intervals for the estimated mean values and respectively, the calculation of the required number of measurements for a given confidence interval are determined by performing a two factorial ANOVA with both factors fully crossed.

Heat acclimation and its relation to resting core temperature and heart rate

Acclimation as an adaptive response of the human body to repeatedly occurring heat stress causes a reduction of core temperature (Tco) and heart rate (HR) at the end of heat exposure. The analysis of three acclimation series (WBGT = 33.5 ◦ C) showed that the lowering of Tco and HR occurred already in the resting period preceding heat stress. The lowered resting values accounted for a substantial part of the beneficial effects of acclimation and may be mainly induced by the physical exercise, as a similar reduction of resting values was also observed under thermally neutral conditions. Expanding the database with short-term acclimation series revealed that the resting values were less reduced for females compared to males, but that the same relations between resting and final Tco and HR existed. The results further suggest that the reduction of resting Tco reflects long term effects of adaptation whereby the resting HR also depends on unspecific situational influences. The lowering of the initial values might be a suitable instrument when considering the effects of acclimation in thermoregulatory models for the assessment of heat stress at the workplace.