A digital tool for prevention and management of cold weather injuries-Cold Weather Ensemble Decision Aid (CoWEDA)

Cold operational readiness in the military: from science to practice

Cold weather operations are logistically difficult to orchestrate and extremely challenging for soldiers. Decades of research and empirical evidence indicate that humans are extremely vulnerable to cold and that individual responses are highly variable. In this context, it may be necessary to develop personalised strategies to sustain soldiers' performance and ensure overall mission success in the cold. Systematic cold weather training is essential for soldiers to best prepare to operate during, and recover from, cold weather operations. The purpose of this review is to highlight key aspects of cold weather training, including (1) human responses to cold, (2) nutrition, (3) sleep and (4) protective equipment requirements. Bringing science to practice to improve training principles can facilitate soldiers performing safely and effectively in the cold. Cold weather training prepares soldiers for operations in cold, harsh environments. However, decreases in physical, psychological and thermoregulatory performance have been reported following such training, which influences operational ability and increases the overall risk of injuries. When optimising the planning of field training exercises or operational missions, it is important to understand the soldiers' physical and cognitive performance capacity, as well as their capacity to cope and recover during and after the exercise or mission. Even though the body is fully recovered in terms of body composition or hormonal concentrations, physical or cognitive performance can still be unrecovered. When overlooked, symptoms of overtraining and risk of injury may increase, decreasing operational readiness.

Modeling thermoregulatory responses during high-intensity exercise in warm environments

The six cylinder thermoregulatory model (SCTM) has been validated thoroughly for resting humans. This type of modeling is helpful to predict and develop guidance for safe performance of work and recreational activities. In the context of a warming global climate, updating the accuracy of the model for intense exercise in warm environments will help a wide range of individuals in athletic, recreational, and military settings. Three sets of previously collected data were used to determine SCTM accuracy. Dataset 1: two groups [large (LG) 91.5 kg and small (SM) 67.7 kg] of individuals performed 60 min of semirecumbent cycling in temperate conditions (25.1°C) at metabolic rates of 570-700 W. Dataset 2: two LG (100 kg) and SM (65.8 kg) groups performed 60 min of semirecumbent cycling in warm/hot environmental conditions (36.2°C) at metabolic rates of 590-680 W. Dataset 3: seven volunteers completed 8-km track trials (∼30 min) in cool (17°C) and warm (30°C) environments. The volunteers' metabolic rates were estimated to be 1,268 W and 1,166 W, respectively. For all datasets, SCTM-predicted core temperatures were found to be similar to the observed core temperatures. The root mean square deviations (RMSDs) ranged from 0.06 to 0.46°C with an average of 0.2°C deviation, which is less than the acceptance threshold of 0.5°C. Thus, the present validation shows that SCTM predicts core temperatures with acceptable accuracy during intense exercise in warm environments and successfully captures core temperature differences between large and small individuals.NEW & NOTEWORTHY The SCTM has been validated thoroughly for resting humans in warm and cold environments and during water immersion. The present study further demonstrated that SCTM predicts core temperatures with acceptable accuracy during intense exercise up to 1,300 W in temperate and warm environments and captures core temperature differences between large and small individuals. SCTM is potentially useful to develop guidance for safe operation in athletic, military, and occupational settings during exposure to warm or hot environments.

Human body numerical simulation: An accurate model for a thigh subjected to a cold treatment

This article presents the development of a digital twin model of a thigh portion subjected to various thermal treatments. Two scenarios are investigated: cold water immersion (CWI) and whole body cryotherapy (WBC), for which the comparison of numerical results with experimental measurements validates the consistency of the developed model. The use of real geometry on a first subject demonstrates the high heterogeneity of the temperature field and the need for accurate geometry. A second subject with thicker adipose tissue highlights the impact of the subject's actual morphology on the validity of the treatment and the necessity to work with real geometry in order to optimize cold modalities and develop personalized treatments.

Validating an advanced smartphone application for thermal advising in cold environments

The ClimApp smartphone application was developed to merge meteorological forecast data with personal information for individualized and improved thermal warning during heat and cold stress and for indoor comfort in buildings. For cold environments, ClimApp predicts the personal thermal stress and strain by the use of the Insulation REQuired model that combines weather and personal physiological data with additional consideration of the Wind Chill index based on the local weather forecast. In this study, we validated the individualized ClimApp index relative to measurements and compared it with the Universal Temperature Climate Index (UTCI). To this aim, 55 participants (27 females) were exposed to at least 1 h in an outdoor environment of 10 °C or below (average 1.4 °C air temperature, 74.9% relative humidity, and 4.7 m/s air velocity) inputting their activity level and clothing insulation as instructed by ClimApp. The UTCI and ClimApp indices were calculated and compared to the participants' perceived thermal sensation. The ClimApp index root mean square deviation (RMSD) was below the standard deviation of the perceived thermal sensation which indicates a valid prediction and the UTCI RMSD was higher than the standard deviation which indicates an invalid prediction. The correlation of ClimApp and UTCI to the perceived thermal sensation was statistically significant for both models.

Validation of a human thermoregulatory model during prolonged immersion in warm water

This study validates the Six Cylinder Thermoregulatory Model (SCTM) during prolonged warm water immersion, which underpins the Probability of Survival Decision Aid (PSDA) currently in use by the United States Coast Guard (USCG). PSDA predicts survival time for hypothermia and dehydration. USCG has been using PSDA for search and rescue operation since 2010. In 2019, USCG organized a workshop to review PSDA performance and concluded that PSDA is an essential tool for operation, although it occasionally overestimates survival times in warm waters above 16 °C. Forty-six human subjects were immersed from the neck down in 18, 22, and 26 °C water for 45 min up to 10 h. Rectal temperature (Tcore), 10-site mean skin temperature (Tsk), and water loss were measured. At the end of immersion, Tcore ranged from 35.2 to 38.0 °C, and Tsk ranged from 19.7 to 27.4 °C. The SCTM-predicted Tcore, Tsk and water loss were compared to the measured values. Root mean squared deviation (RMSD) was used to test for acceptable predictions. Tcore RMSDs were 0.2, 0.14, and 0.3 °C in 18, 22, and 26 °C water respectively. Tsk RMSDs were 1.44, 0.76, and 1.1 °C in 18, 22, and 26 °C water respectively. SCTM underpredicted water loss by 84%. Overall, SCTM predicted Tcore and Tsk with acceptable accuracy in 18 and 22 °C water for up to 10 h, but overpredicted in 26 °C water. Future studies and algorithm development are required to improve water loss prediction as well as Tcore and Tsk prediction in 26 °C water.

Development of interactive guidance for cold exposure using a thermoregulatory model

For decades, the Wind Chill Temperature Index (WCT) and its various iterations have been used to assess the risk of frostbite on unclothed body parts. This paper presents an innovative knowledge-based Cold Weather Ensemble Decision Aid (CoWEDA) that can be used to guide the selection of the most appropriate cold weather ensemble(s) relative to anticipated mission physical activities and environmental conditions. CoWEDA consists of a validated six-cylinder thermoregulatory model, a database of clothing properties, algorithms for calculating the whole ensemble properties from individual garments and a graphical user interface. The user-friendly CoWEDA allows users to select from an inventory of clothing items to build an ensemble suitable for their needs. CoWEDA predicts the risks of both frostbite and hypothermia and ensures that a selected clothing ensemble will provide adequate protection to prevent cold injury. CoWEDA predictions provide not only estimates of frostbite risk similar to WCT tables but also hypothermia times and clothing required to prevent cold injuries. In addition, a CoWEDA model variant can predict survivability and clothing requirements during cold water immersion. Thus, CoWEDA represents a significant enhancement of the WCT-based guidance for cold weather safety and survival by providing greater individual fidelity in cold injury predictions.

Use case for predictive physiological models: tactical insights about frozen Russian soldiers in Ukraine

Biomathematical models quantitatively describe human physiological responses to environmental and operational stressors and have been used for planning and real-time prevention of cold injury. These same models can be applied from a military tactical perspective to gain valuable insights into the health status of opponent soldiers. This paper describes a use case for predicting physiological status of Russian soldiers invading Ukraine using open-source information. In March 2022, media outlets reported Russian soldiers in a stalled convoy invading Ukraine were at serious risk of hypothermia and predicted these soldiers would be "freezing to death" within days because of declining temperatures (down to -20°C). Using existing Army models, clothing data and open-source intelligence, modelling and analyses were conducted within hours to quantitatively assess the conditions and provide science-based predictions. These predictions projected a significant increase in risks of frostbite for exposed skin and toes and feet, with a very low (negligible) risk of hypothermia. Several days later, media outlets confirmed these predictions, reporting a steep rise in evacuations for foot frostbite injuries in these Russian forces. This demonstrated what can be done today with the existing mathematical physiology and how models traditionally focused on health risk can be used for tactical intelligence.

We are all exposed, but some are more exposed than others

This paper defines functional cold exposure zones that illustrate whether a person is at risk of developing physical performance loss or cold weather injuries. Individual variation in body characteristics, activity level, clothing and protective equipment all contribute to variation in the effective exposure. Nevertheless, with the right education, training, and cold-adapted behaviours the exposure differences might not necessarily lead to increased risk for cold injury. To support the preparation process for cold weather operations, this paper presents a biophysical analysis explaining how much cold exposure risk can vary between individuals in the same environment. The results suggest that smaller persons are prone to be underdressed for moderate activity levels and larger persons are prone to be overdressed. The consequences of these discrepancies place people at different risks for performance loss or cold weather injuries. Nonetheless, even if all are well-dressed at the whole-body level, variation in hand morphology is also expected to influence hand skin temperatures that can be maintained; with smaller hands being more prone to reach skin temperatures associated with dexterity loss or cold weather injuries. In conclusion, this work focusses on bringing cold science to the Arctic warrior, establishing that combating cold stress is not a one size fits all approach.

Associations between temperature variations and tourist arrivals: analysis based on Baidu Index of hot-spring tourism in 44 cities in China

Hot-spring tourism refers to entertainment, health preservation, commercial conferences, and other leisure activities at hot-spring locations. This tourism often shows periodic variability, which may be due to seasonal temperature variations. However, studies on the effects of temperature variations on tourist arrivals at hot springs are limited. Therefore, this study aimed to evaluate this relationship in 31 provincial capital cities and 13 s-tier cities in China. Using the Baidu Index, we obtained data for tourist arrivals to hot springs in each city and constructed a generalised additive model to explore the associations between temperature variations and tourist arrivals. We also analysed the statistical significance of the estimated effects during different seasons to explore potential effect modification. A 1 °C increase in temperature was associated with a 1.81% (95% confidence interval (CI): 1.69-1.93) decrease in daily tourist arrivals for hot-spring tourism. Significant positive associations between the abovementioned factors were observed in summer (2.18% change, 95% CI: 1.32-3.04). The effect of temperature on the volume of tourist arrivals may last for approximately 2 months. Robustness analysis confirmed the data reliability. The results indicate that significant relationships exist between temperature variations and hot-spring tourism arrivals, which vary seasonally. This study has significant implications for travel agencies to effectively manage tourist visits to hot spring locations.

A numerical tool for assessing human thermal safety and thermal comfort in cold-weather activities

This paper describes a newly developed software tool to evaluate human thermal safety and thermal comfort in cold-weather activities aimed at guiding users to arrange activity plans and select appropriate clothing ensembles. The software inputs include conditions of activity, environment, human body, and clothing ensemble. It outputs physiological temperatures, cold injury risks, thermal sensations, and thermal comforts in intuitive ways like cloud maps and curves. The software tool is characterized by (1) integration of a thermoregulatory model that predicts human thermophysiological responses under exercise conditions in cold environments, (2) the functions of clothing ensemble database and individual parameter database, (3) the human centric outputs that directly reflect human physiological and mental status, and (4) the user-friendly operation interface and output interface, as well as a wide applicability. The software is validated with human test studies covering ambient temperatures from - 30.6 to 5 °C, clothing ensembles from 1.34 to 3.20 clo, and activity intensities from 2 to 9 Mets. The average prediction RMSEs of core temperature, mean skin temperature, thermal sensation, and thermal comfort are 0.16 °C, 0.45 °C, 0.58, and 1.41, respectively. The software is an advanced expansion to current standards and guidance of cold exposure assessment and a meaningful tool for the fields of occupational health care, cold protection, and environmental ergonomics.

Three dimensional models of human thermoregulation: A review

Numerous human thermoregulatory models have been developed and widely used in various applications such as aerospace, medicine, public health, and physiology research. This paper is a review of three dimensional (3D) models for human thermoregulation. This review begins with a short introduction of thermoregulatory model development followed by key principles for mathematical description of human thermoregulation systems. Different representations of 3D human bodies are discussed with respect to their detail and prediction capability. The human body was divided into fifteen layered cylinders in early 3D models (cylinder model). Recent 3D models have utilized medical image datasets to develop geometrically correct human models (realistic geometry model). The finite element method is mostly used to solve the governing equations and get numerical solutions. The realistic geometry models provide a high degree of anatomical realism and predict whole-body thermoregulatory responses at high resolution and at organ and tissue levels. Thus, 3D models extend to a wide range of applications where temperature distribution is critical, such as hypothermia/hyperthermia therapy and physiology research. The development of thermoregulatory models will continue with the growth in computational power, advancement in numerical methods and simulation software, advances in modern imaging techniques, and progress in the basic science of thermal physiology.

Finite element model of female thermoregulation with geometry based on medical images

INTRODUCTION

this study describes the development of a female finite element thermoregulatory model (FETM) METHOD: the female body model was developed from medical image datasets of a median U.S. female and was constructed to be anatomically correct. The body model preserves the geometric shapes of 13 organs and tissues, including skin, muscles, fat, bones, heart, lungs, brain, bladder, intestines, stomach, kidneys, liver, and eyes. Heat balance within the body is described by the bio-heat transfer equation. Heat exchange at the skin surface includes conduction, convection, radiation, and sweat evaporation. Vasodilation, vasoconstriction, sweating, and shivering are controlled by afferent and efferent signals to and from the skin and hypothalamus.

RESULTS

the model was validated with measured physiological data during exercise and rest in thermoneutral, hot, and cold conditions. Validations show the model predicted the core temperature (rectal and tympanic temperatures) and mean skin temperatures with acceptable accuracy (within 0.5 °C and 1.6 °C, respectively) CONCLUSION: this female FETM predicted high spatial resolution temperature distribution across the female body, which provides quantitative insights into human thermoregulatory responses in females to non-uniform and transient environmental exposure.

Biomarkers for warfighter safety and performance in hot and cold environments

Exposure to extreme environmental heat or cold during military activities can impose severe thermal strain, leading to impairments in task performance and increasing the risk of exertional heat (including heat stroke) and cold injuries that can be life-threatening. Substantial individual variability in physiological tolerance to thermal stress necessitates an individualized approach to mitigate the deleterious effects of thermal stress, such as physiological monitoring of individual thermal strain. During heat exposure, measurements of deep-body (T_c) and skin temperatures and heart rate can provide some indication of thermal strain. Combining these physiological variables with biomechanical markers of gait (in)stability may provide further insight on central nervous system dysfunction - the key criterion of exertional heat stroke (EHS). Thermal strain in cold environments can be monitored with skin temperature (peripheral and proximal), shivering thermogenesis and T_c. Non-invasive methods for real-time estimation of T_c have been developed and some appear to be promising but require further validation. Decision-support tools provide useful information for planning activities and biomarkers can be used to improve their predictions, thus maximizing safety and performance during hot- and cold-weather operations. With better understanding on the etiology and pathophysiology of EHS, the microbiome and markers of the inflammatory responses have been identified as novel biomarkers of heat intolerance. This review aims to (i) discuss selected physiological and biomechanical markers of heat or cold strain, (ii) how biomarkers may be used to ensure operational readiness in hot and cold environments, and (iii) present novel molecular biomarkers (e.g., microbiome, inflammatory cytokines) for preventing EHS.

A Database of Static Thermal Insulation and Evaporative Resistance Values of Dutch Firefighter Clothing Items and Ensembles

The rescue operations' environment can impair firefighters' performance and increase the risk of injuries, e.g., burns and hyperthermia. The bulk and carried weight of heavy protection contributes to lower physical performance, higher metabolic load and internal body heat production. For recommending optimal protection for the tasks and incident scenarios, knowledge of clothing thermal properties is needed. However, detailed data on firefighter protective clothing systems are not available. The aim of the study was to provide scientific background and a dataset that would allow for validation of thermo-physiological models for task-specific conditions of rescue work. Thermal insulation of 37 single items and their variations and 25 realistic protective clothing ensembles were measured on a thermal manikin. Twelve (12) ensembles that evenly covered the whole insulation range were selected for evaporative resistance testing. The equations for summing up individual item's insulation to ensemble insulation and calculating clothing area factor were derived from the dataset. The database of a firefighter clothing system was created. In addition, the local and regional thermal properties of the clothing ensembles were provided for use in future validation of advanced thermo-physiological models for rescue worker exposure predictions and for designing decision aid tools.

A 7-segment numerical hand-glove/mitten model for predicting thermophysiological responses of the human hand in extremely cold conditions

A 7-segment and 29-node numerical hand-glove/mitten model was developed to simulate human hand physiological responses in various cold environments. To validate the model, simulated skin temperatures were compared to data from published literature and human trials conducted at -20, -40, and -60 °C. Results demonstrated that the model could reasonably predict cold-induced vasodilation (CIVD) responses at 0 °C temperature. At -20 °C, the model predicted skin temperature with the root mean square deviation (RMSD) falling within the measurement standard deviation (SD) for both the entire and local hand except for the posterior hand. At -40 and -60 °C, the model could predict the trend of the skin temperatures of the whole/local hand, but the RMSD was larger than the SD for the majority of predictions. A parametric analysis revealed that the palm and posterior hand had higher skin temperatures than the fingers, while the thumb had the lowest skin temperature of the fingers in all simulated cases except the case with a 3.5 clo mitten at -60 °C. The proposed numerical hand-glove/mitten model could reasonably predict local hand physiological responses in three extremely cold environments and provides fundamental knowledge for cold stress prediction and protective glove development, thereby improving the safety and health of industrial workers, firefighters, first responders, and troops.

A 3-D virtual human model for simulating heat and cold stress

In this study, we extended our previously developed anatomically detailed three-dimensional (3-D) thermoregulatory virtual human model for predicting heat stress to allow for predictions of heat and cold stress in one unified model. Starting with the modified Pennes bioheat transfer equation to estimate the spatiotemporal temperature distribution within the body as the underlying modeling structure, we developed a new formulation to characterize the spatial variation of blood temperature between body elements and within the limbs. We also implemented the means to represent heat generated from shivering and skin blood flow that apply to air exposure and water immersion. Then, we performed simulations and validated the model predictions with experimental data from nine studies, representing a wide range of heat- and cold-stress conditions in air and water and physical activities. We observed excellent agreement between model predictions and measured data, with average root mean squared errors of 0.2°C for core temperature, 0.9°C for mean skin temperature, and 27 W for heat from shivering. We found that a spatially varying blood temperature profile within the limbs was crucial to accurately predict core body temperature changes during very cold exposures. Our 3-D thermoregulatory virtual human model consistently predicted the body's thermal state accurately for each of the simulated hot and cold environmental conditions and exertional heat stress. As such, it serves as a reliable tool to assess whole body, localized tissue, and, potentially, organ-specific injury risks, helping develop injury prevention and mitigation strategies in a systematic and expeditious manner.NEW & NOTEWORTHY This work provides a new, unified modeling framework to accurately predict the human body's thermal response to both heat and cold stress caused by environmental conditions and exertional physical activity in one mathematical model. We show that this 3-D anatomically detailed model accurately predicts the spatiotemporal temperature distribution in the body under extreme conditions for exposures to air and water and could be used to help design medical interventions and countermeasures to prevent injuries.

ACSM Expert Consensus Statement: Injury Prevention and Exercise Performance during Cold-Weather Exercise

ABSTRACT

Cold injury can result from exercising at low temperatures and can impair exercise performance or cause lifelong debility or death. This consensus statement provides up-to-date information on the pathogenesis, nature, impacts, prevention, and treatment of the most common cold injuries.

ClimApp-Integrating Personal Factors with Weather Forecasts for Individualised Warning and Guidance on Thermal Stress

This paper describes the functional development of the ClimApp tool (available for free on iOS and Android devices), which combines current and 24 h weather forecasting with individual information to offer personalised guidance related to thermal exposure. Heat and cold stress assessments are based on ISO standards and thermal models where environmental settings and personal factors are integrated into the ClimApp index ranging from -4 (extremely cold) to +4 (extremely hot), while a range of -1 and +1 signifies low thermal stress. Advice for individuals or for groups is available, and the user can customise the model input according to their personal situation, including activity level, clothing, body characteristics, heat acclimatisation, indoor or outdoor situation, and geographical location. ClimApp output consists of a weather summary, a brief assessment of the thermal situation, and a thermal stress warning. Advice is provided via infographics and text depending on the user profile. ClimApp is available in 10 languages: English, Danish, Dutch, Swedish, Norwegian, Hellenic (Greek), Italian, German, Spanish and French. The tool also includes a research functionality providing a platform for worker and citizen science projects to collect individual data on physical thermal strain and the experienced thermal strain. The application may therefore improve the translation of heat and cold risk assessments and guidance for subpopulations. ClimApp provides the framework for personalising and downscaling weather reports, alerts and advice at the personal level, based on GPS location and adjustable input of individual factors.