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Thermal Indices and Thermophysiological Modeling for Heat Stress

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ABSTRACT

The assessment of the risk of human exposure to heat is a topic as relevant today as a century ago. The introduction and use of heat stress indices and models to predict and quantify heat stress and heat strain has helped to reduce morbidity and mortality in industrial, military, sports, and leisure activities dramatically. Models used range from simple instruments that attempt to mimic the human‐environment heat exchange to complex thermophysiological models that simulate both internal and external heat and mass transfer, including related processes through (protective) clothing. This article discusses the most commonly used indices and models and looks at how these are deployed in the different contexts of industrial, military, and biometeorological applications, with focus on use to predict related thermal sensations, acute risk of heat illness, and epidemiological analysis of morbidity and mortality. A critical assessment is made of tendencies to use simple indices such as WBGT in more complex conditions (e.g., while wearing protective clothing), or when employed in conjunction with inappropriate sensors. Regarding the more complex thermophysiological models, the article discusses more recent developments including model individualization approaches and advanced systems that combine simulation models with (body worn) sensors to provide real‐time risk assessment. The models discussed in the article range from historical indices to recent developments in using thermophysiological models in (bio) meteorological applications as an indicator of the combined effect of outdoor weather settings on humans. © 2016 American Physiological Society. Compr Physiol 6:255‐302, 2016.

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Figure 1. Figure 1. Official WBGT instrument according to ISO 7726 and ISO 7243, showing (left to right) shielded dry bulb, black globe (15 cm diameter), and natural wet bulb (wetted cotton wick over sensor with water reservoir below) sensors.
Figure 2. Figure 2. Cross‐section of cylindrical model containing five concentric annular tissue compartments; dimensions shown are for reference individual (70 kg, 1.8 m2). Kraning and Gonzalez (), redrawn with permission J. Therm. Biology.
Figure 3. Figure 3. Schematic representation of the Lotens clothing model, showing the four clothing/air layers and the related network of heat and vapor resistances for conduction, radiation, and convection (). Reproduced with permission from author (copyright holder).
Figure 4. Figure 4. Graphical representation of the classic Wissler model that terminates at the wrists and ankles. Each of the 25 elements consists of 21 radial layers and 12 angular segments. The new model has four additional elements for two hands and two feet that are highlighted in red. Hensley et al. (), reproduced with permission Journal of Biomechanical Engineering.
Figure 5. Figure 5. Schematic representation of feedback control system of thermoregulation using a two compartment (core, skin) representation of the body. In this “set point” concept, an individual unified controller is assumed to be present.
Figure 6. Figure 6. Schematic representation of feedback control system of thermoregulation using a two compartment (core, skin) representation of the body. This model contains thresholds for individual effector systems, which in combination lead to an overall body “balance point” (), rather than being controlled by an individual set point value.
Figure 7. Figure 7. Schematic diagram of the FPC human body model with segmental, spatial, tissue, and nodal subdivisions. Reproduced from () with approval of the copyright holder.
Figure 8. Figure 8. Schematic diagram of the FPC Model of human thermoregulation: feedback system with local skin and tissue temperatures, head core (hypothalamus) temperature, and the rate of change of skin temperature as punitive signals. Set point temperatures T sk,i,0, T i,0, and T hy,0 refer to the body's thermoneutral state at 30°C room temperature. Reproduced from () with approval of the copyright holder.
Figure 9. Figure 9. Control equation coefficients as nonlinear functions of temperature input signals from the body core and the skin. Reproduced from () with kind permission from Springer Science + Business Media.
Figure 10. Figure 10. 3D geometry models for detailed human heat‐transfer analysis using a combination of a thermophysiological model with computational fluid dynamics simulations.
Figure 11. Figure 11. Schematic diagram of the calculation process constituting the scalable FPC Human Anthropometry model. Reproduced from () with approval of the copyright holder.
Figure 12. Figure 12. Comparison of predicted body part lengths forming the stature with data obtained for male subjects from the CEASAR Project () for 10 body height categories (). Reproduced from () with approval of the copyright holder.
Figure 13. Figure 13. Relative weight of body parts: comparison of the Reference Model with measured data (). Reproduced from () with approval of the copyright holder.
Figure 14. Figure 14. The model's body fat content (left) and total skin surface area (right) as functions of the body height and weight obtained for (35 years old) male subjects compared with experimentally based data (). Reproduced from () with approval of the copyright holder.
Figure 15. Figure 15. Representation of the core to skin heat resistance network to represent individual differences in anthropometrics and blood circulation in a two radial node model (). Reproduced with permission of the copyright holder (G. Havenith).
Figure 16. Figure 16. Input parameters into the thermoregulatory system model to simulate responses of individuals: direct model input and variations of body configuration as indirect input (). Reproduced with permission from Journal of Applied Physiology.
Figure 17. Figure 17. Schematic representation of control function for skin blood flow and sweat rate with effect of fitness and acclimatization causing shifts in thresholds and gain values (). Reproduced with permission from Journal of Applied Physiology.
Figure 18. Figure 18. Simulation results for subjects with different anthropometric characteristics using the Yokota individualized model (). Reproduced with permission from Journal of Thermal Biology.
Figure 19. Figure 19. Comparison of predicted and measured group‐average (left) and individual (right) rectal temperature responses. Left: group of athletes (n = 6) exercising at 11.7 met; right: single athlete exercising at 13.2 met. Fit.M: fit male (VO2max =60 mL·kg−1·min−1), s.fit.M: standard‐fit male (40 mL kg−1·min−1), s.fit.F: standard‐fit female (40 mL kg−1·min−1), ref.M: Reference Model (40 mL kg−1·min−1).
Figure 20. Figure 20. Physiology‐based Dynamic Bayesian Network for thermoregulation. Formula temperature, formula gain, formula loss, formula rate, formula from accelerometry, and formula flux. White nodes represent latent variables and gray nodes are observed variables (23). Reproduced with permission from IEEE.
Figure 21. Figure 21. Schematic diagram of the “individualized” FPC model adapted for use with different types of peripheral sensors and boundary conditions at the body/skin surface to predict the body core temperature and other physiological responses. Reproduced from () with approval of the copyright holder.
Figure 22. Figure 22. Measured and predicted responses of subjects (n = 20) wearing a permeable suit and exposed for 110 min to hot environmental conditions of 40.4°C, 23.4% RH, 0.4 m/s. The group‐average rectal temperature (T re) and total body weight loss (M sw) were predicted from different configurations of skin temperature sensors (T sk,m and T sk,c) and environmental conditions (Ehx) as input boundary conditions.
Figure 23. Figure 23. Concept of UTCI derived as equivalent temperature from the dynamic multivariate response of the thermophysiological UTCI‐Fiala model (), which was coupled with a clothing model (). Reproduced from () with permission from Journal of Industrial Health.
Figure 24. Figure 24. Validation of model prediction using the quality of prediction at high risk core temperatures, using concept of False Positives and False Negatives (), assuming 38.7°C is used as cut off criterion for the predicted value of core temperature and 39.0°C for actual core temperature as the limit value for risk. Modified and reproduced with permission from copyright holder ().


Figure 1. Official WBGT instrument according to ISO 7726 and ISO 7243, showing (left to right) shielded dry bulb, black globe (15 cm diameter), and natural wet bulb (wetted cotton wick over sensor with water reservoir below) sensors.


Figure 2. Cross‐section of cylindrical model containing five concentric annular tissue compartments; dimensions shown are for reference individual (70 kg, 1.8 m2). Kraning and Gonzalez (), redrawn with permission J. Therm. Biology.


Figure 3. Schematic representation of the Lotens clothing model, showing the four clothing/air layers and the related network of heat and vapor resistances for conduction, radiation, and convection (). Reproduced with permission from author (copyright holder).


Figure 4. Graphical representation of the classic Wissler model that terminates at the wrists and ankles. Each of the 25 elements consists of 21 radial layers and 12 angular segments. The new model has four additional elements for two hands and two feet that are highlighted in red. Hensley et al. (), reproduced with permission Journal of Biomechanical Engineering.


Figure 5. Schematic representation of feedback control system of thermoregulation using a two compartment (core, skin) representation of the body. In this “set point” concept, an individual unified controller is assumed to be present.


Figure 6. Schematic representation of feedback control system of thermoregulation using a two compartment (core, skin) representation of the body. This model contains thresholds for individual effector systems, which in combination lead to an overall body “balance point” (), rather than being controlled by an individual set point value.


Figure 7. Schematic diagram of the FPC human body model with segmental, spatial, tissue, and nodal subdivisions. Reproduced from () with approval of the copyright holder.


Figure 8. Schematic diagram of the FPC Model of human thermoregulation: feedback system with local skin and tissue temperatures, head core (hypothalamus) temperature, and the rate of change of skin temperature as punitive signals. Set point temperatures T sk,i,0, T i,0, and T hy,0 refer to the body's thermoneutral state at 30°C room temperature. Reproduced from () with approval of the copyright holder.


Figure 9. Control equation coefficients as nonlinear functions of temperature input signals from the body core and the skin. Reproduced from () with kind permission from Springer Science + Business Media.


Figure 10. 3D geometry models for detailed human heat‐transfer analysis using a combination of a thermophysiological model with computational fluid dynamics simulations.


Figure 11. Schematic diagram of the calculation process constituting the scalable FPC Human Anthropometry model. Reproduced from () with approval of the copyright holder.


Figure 12. Comparison of predicted body part lengths forming the stature with data obtained for male subjects from the CEASAR Project () for 10 body height categories (). Reproduced from () with approval of the copyright holder.


Figure 13. Relative weight of body parts: comparison of the Reference Model with measured data (). Reproduced from () with approval of the copyright holder.


Figure 14. The model's body fat content (left) and total skin surface area (right) as functions of the body height and weight obtained for (35 years old) male subjects compared with experimentally based data (). Reproduced from () with approval of the copyright holder.


Figure 15. Representation of the core to skin heat resistance network to represent individual differences in anthropometrics and blood circulation in a two radial node model (). Reproduced with permission of the copyright holder (G. Havenith).


Figure 16. Input parameters into the thermoregulatory system model to simulate responses of individuals: direct model input and variations of body configuration as indirect input (). Reproduced with permission from Journal of Applied Physiology.


Figure 17. Schematic representation of control function for skin blood flow and sweat rate with effect of fitness and acclimatization causing shifts in thresholds and gain values (). Reproduced with permission from Journal of Applied Physiology.


Figure 18. Simulation results for subjects with different anthropometric characteristics using the Yokota individualized model (). Reproduced with permission from Journal of Thermal Biology.


Figure 19. Comparison of predicted and measured group‐average (left) and individual (right) rectal temperature responses. Left: group of athletes (n = 6) exercising at 11.7 met; right: single athlete exercising at 13.2 met. Fit.M: fit male (VO2max =60 mL·kg−1·min−1), s.fit.M: standard‐fit male (40 mL kg−1·min−1), s.fit.F: standard‐fit female (40 mL kg−1·min−1), ref.M: Reference Model (40 mL kg−1·min−1).


Figure 20. Physiology‐based Dynamic Bayesian Network for thermoregulation. Formula temperature, formula gain, formula loss, formula rate, formula from accelerometry, and formula flux. White nodes represent latent variables and gray nodes are observed variables (23). Reproduced with permission from IEEE.


Figure 21. Schematic diagram of the “individualized” FPC model adapted for use with different types of peripheral sensors and boundary conditions at the body/skin surface to predict the body core temperature and other physiological responses. Reproduced from () with approval of the copyright holder.


Figure 22. Measured and predicted responses of subjects (n = 20) wearing a permeable suit and exposed for 110 min to hot environmental conditions of 40.4°C, 23.4% RH, 0.4 m/s. The group‐average rectal temperature (T re) and total body weight loss (M sw) were predicted from different configurations of skin temperature sensors (T sk,m and T sk,c) and environmental conditions (Ehx) as input boundary conditions.


Figure 23. Concept of UTCI derived as equivalent temperature from the dynamic multivariate response of the thermophysiological UTCI‐Fiala model (), which was coupled with a clothing model (). Reproduced from () with permission from Journal of Industrial Health.


Figure 24. Validation of model prediction using the quality of prediction at high risk core temperatures, using concept of False Positives and False Negatives (), assuming 38.7°C is used as cut off criterion for the predicted value of core temperature and 39.0°C for actual core temperature as the limit value for risk. Modified and reproduced with permission from copyright holder ().
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Corrigendum

George Havenith, Dusan Fiala. Thermal Indices and Thermophysiological Modeling for Heat Stress. Compr Physiol 2015, 6: 255-302. doi: 10.1002/cphy.c140051

Corrections have been made to properly spell out the acronym PHS in two places in the article. The error was introduced during the copy editing process.

On page 264, “which led to a new version of the standard, the Public Health System (PHS),” was corrected to “which led to a new version of the standard, the predicted heat strain (PHS)”.

On page 288, “PHS. To mitigate the adverse health effects…” was corrected to “Public health system. To mitigate the adverse health effects…”


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George Havenith, Dusan Fiala. Thermal Indices and Thermophysiological Modeling for Heat Stress. Compr Physiol 2015, 6: 255-302. doi: 10.1002/cphy.c140051