A Comprehensive Model for Estimating Heat Vulnerability of Young Athletes

Current methods for estimating heat vulnerability of young athletes use a heat index (HI) or a wet bulb globe thermometer (WBGT), neither of which fully include the environmental or physiological characteristics that can a ect a person's heat budget, particularly where activity occurs on a synthetic surface. This study analyzed and compared the standard methods, HI and WBGT, with a novel and more comprehensive method termed COMFA-Kid (CK) which is based on an energy budget model explicitly designed for youth. The COMFA model was presented at the same time to demonstrate the di erence between a child and an adult during activity. Micrometeorological measurements were taken at a synthetic-surfaced football eld during mid-day in hot environmental conditions. Standard methods (HI and WBGT) indicated that conditions on the eld were relatively safe for youth to engage in activities related to football practice or games, whereas the CK method indicated that conditions were dangerously hot and could lead to exertional heat illness. Estimates using the CK method also indicated that coaches and sta standing on the sidelines, and parents sitting in the stands, would not only be safe from heat but would be thermally comfortable. The di erence in thermal comfort experienced by coaches and sta o the eld, versus that experienced by young players on the eld, could a ect decision making regarding the duration and intensity of practices and time in the game. The CK method, which is easy to use and available for modi cation for speci c conditions, would lead to more accurate estimates of heat safety

experienced by coaches and sta o the eld, versus that experienced by young players on the eld, could a ect decision making regarding the duration and intensity of practices and time in the game. The CK method, which is easy to use and available for modi cation for speci c conditions, would lead to more accurate estimates of heat safety on outdoor synthetic surfaces in particular, and in sports with a high prevalence of heat illness such as football, and should be considered as a complementary or alternative preventive measure against heat. Keywords: heat stress index; WBGT; young athletes thermal health; energy budget thermal model; ball eld design 1. Introduction Exertional heat illness (EHI) is a major cause of death and disability among young athletes in the U.S. [1]. EHI is associated with symptoms such as persistent muscular cramps, anorexia, diarrhea, and a body-core temperature above 40 C which will cause coordination di culties, cognitive function reduction, and reduced endurance performance [2], and is the result of exercising in environmental conditions that are too hot. The highest incidence of EHI among participants of organized sport among all ages is in American football [3]. Additionally, as reported in the literature, there has been a considerable increase over the past three decades in EHI fatalities in football [3]. Among collegiate athletes, an analysis of NCAA Injury Surveillance Program data found that football comprised most (75%) EHI events and occurred at the highest rate during preseason practices [4]. In youth sport, the incidence of injury and death due to heat is also greatest in football. An analysis of data from Int. J. Environ. Res. Public Health2020,17, 6156; doi:10.3390 /ijerph17176156 /journal/ijerph

Int. J. Environ. Res. Public Health2020,17, 6156 2 of 11 the National High School Sports-Related Injury Surveillance System (2005/2006–2010/2011) found the exertional heat illness rate in football (4.42 per 100,000 athlete exposures) to be 11.4 times greater than that in all other sports in the high school dataset combined [2]. Further, it is estimated that between 2005 to 2009, more than 9000 high school athletes were treated for exertional heat illness annually [1]. The most time lost from athletics due to heat illness was among football players, and occurred most frequently in August during early season football practices [1]. In addition to illness, most heat-related deaths related to youth sport participation in the U.S. occur during participation in football [1,2]. In an e ort to prevent heat-related illness and death in the broader context of outdoor activities and environments, and among young athletes in particular, a variety of metrics and indices have been developed and used in estimating environmental heat risks. The heat index (HI), as described by the National Oceanic and Atmospheric Administration's National Weather Service [5], was initially created by Steadman [6] using ambient dry bulb temperature and relative humidity as input data: HI= 42.379+2.049Ta+10.143RH 0.225Ta RH 6.838 10 3 Ta 2 5.482 10 2 R2+1.229 10 3 Ta 2R +8.528 10 4 Ta R 2 1.990 10 6 Ta 2R2 (1) where Ta is the ambient dry bulb temperature ( F), and RH is relative humidity (%). This model derived a de nitive scale of apparent temperature, which takes account of the e ects of temperature and humidity on the reaction of humans. It has been widely used in studies to estimate urban environment heat exposure and to assess the risk of acquiring heat stress [7–9]. However, there are some limitations to the HI's original development in several aspects when used for predicting young athletes' heat stress during practice and games. For example, the microclimatic environmental variables such as sky view factor, ground surface temperature, surrounding surface temperature, and porosity of windbreaks were not considered in the model. Moreover, the mean radiant temperature that factors in short

there are some limitations to the HI's original development in several aspects when used for predicting young athletes' heat stress during practice and games. For example, the microclimatic environmental variables such as sky view factor, ground surface temperature, surrounding surface temperature, and porosity of windbreaks were not considered in the model. Moreover, the mean radiant temperature that factors in short and longwave radiation, a key factor a ecting human's thermal comfort in hot conditions, was not considered. Further, the human parameters were designed and assumed for an adult with regular clothing and a moderate working load (the basic human body dimensions were designed for a typical adult human with a height of 1.7 m and a weight of 67 kg; the heat production is for a person walking outdoors at a normal speed, 1.4 m/s, the clothing resistance was for a shirt and long pants). These missing considerations limit the applicability of HI for predicting heat stress levels under a microclimatic environment for a young person engaged in physically demanding activities. The e ects of radiation heat obtained from the sun and surrounding environment, for example, are the most important factor in determining the outdoor human energy budget in hot weather. Additionally, human body dimensions, especially body surface area to mass ratio, are much higher for young people than adults, which results in additional heat exchange with the environment. Moreover, heat production for intensive activity is much higher than walking at a moderate speed. For example, the heat production for a 10-year-old boy playing football can be almost double the heat production of an adult man walking at a speed of 2.5–3.0 mph (408 to 210 W/m 2 ). In addition, clothing resistance would greatly a ect the thermal comfort level, especially for athletes [10]. The wet bulb globe temperature (WBGT) is another climatic index used worldwide that was rst developed to prevent heat illness in military training camps and then used as a standard by ISO 7243[11]. It is also often used as a preventive measure against heat in sanctioned high school athletic events. The WBGT is calculated using

level, especially for athletes [10]. The wet bulb globe temperature (WBGT) is another climatic index used worldwide that was rst developed to prevent heat illness in military training camps and then used as a standard by ISO 7243[11]. It is also often used as a preventive measure against heat in sanctioned high school athletic events. The WBGT is calculated using the following formula: WBGT=0.7Tw+ 0.2Tg+0.1Ta (2) where Ta is the air temperature ( C), Tg is black globe temperature measured by a globe 15 cm in diameter ( C), and Tw is the natural wet bulb temperature ( C). If the WBGT index is above 28 C (82 F), consideration should be given to canceling or rescheduling ongoing competitive athletic events until conditions giving rise to heat stress are lessened [11]. However,

Int. J. Environ. Res. Public Health2020,17, 6156 3 of 11 some studies have concluded several limitations of using the WBGT index to predict young athletes' thermal stress conditions. Limitations include a failure of the index to include microclimatic factors such as sky view factor, ground surface temperature, surrounding surface temperature, or windbreak porosity [12]. For human factors, the interpretation of the thermal sensation of the model was originally interpreted by workers and managers working in the eld, which cannot represent the athletes' thermal sensations [13]. In addition, the WBGT values do not vary linearly as a function of the metabolic rate (M) [14]. Using the average value of di erent M values is not appropriate and can lead to detrimental health outcomes, especially for athletes involved in intensive athletic activities such as football. Moreover, although di erent clothing conditions other than “normal working clothes” (with a clothing resistance of 0.6) were proposed to be added into the model, the clothing factor is independent of the climate and the WBGT values [14]. However, the clothing factor can be strongly a ected by climate according to a previous study, especially for athletes [10]. The most important limitation of the WBGT application is its shape. People, and young athletes in particular, are not spherical in shape, but rather are more cylindrical. Results obtained from a spherical instrument are not appropriate to be applied to a cylindrical human form. A recent study [15] identi ed the magnitude of this error and suggested the use of a cylindrical instrument (or cylindrical model) to eliminate that error. When applying the WBGT, the Occupational Safety and Health Administration provides two options for the input of climate data: using the WBGT meter; or calculating a value using local weather station data. However, calculation of WBGT from weather station data does not include any terrestrial radiation, which could introduce a large error when compared to eld measurements. A recently developed model, however, has the potential to mitigate the errors and limitations of HI and WBGT. The COMFA-Kids (CK) model was developed based on the COMFA model [16]. It is

weather station data. However, calculation of WBGT from weather station data does not include any terrestrial radiation, which could introduce a large error when compared to eld measurements. A recently developed model, however, has the potential to mitigate the errors and limitations of HI and WBGT. The COMFA-Kids (CK) model was developed based on the COMFA model [16]. It is the rst energy budget model to predict children's outdoor thermal comfort level considering both microclimatic and human parameters [17]. The basic equation of the CK model is Energy Budget=M+Rabs Conv Evap Tremitted (3) where M is the metabolic energy for heating up the body (W/m 2 ), Rabs is the absorbed solar and terrestrial radiation (W/m 2 ), Conv is the sensible convective heat exchange (W/m 2 ), Evap is the evaporative heat loss (W/m 2 ), and TRemitted is the emitted terrestrial radiation (W/m 2 ). The CK model has several characteristics that make it potentially a valuable method for estimating young athletes' heat stress levels: (a) It is a comprehensive model that includes all the climate, microclimatic, and human parameters a ecting human energy budgets; (b) It was developed based on children's thermal exchange characteristics, taking their higher metabolic rate, higher skin temperature, higher surface area-to-mass ratio, and lower sweating rate into consideration; (c) It is an open-architecture model that allows investigation of each stream of energy independently and can identify site characteristics that need to be modi ed to provide thermally safe conditions. For example, microclimatic environmental data such as sky view factor and albedo (re ected light or radiation) of the ground can be set based on the real conditions, while variables such as clothing resistance and metabolic heat production for di erent activities can be speci cally input and tested. This study used HI, WBGT, and the CK model to analyze and predict the level of heat stress that would be encountered by young athletes playing football during two hot days and compared the results. The COMFA model was used to demonstrate the energy budget value for an adult. 2. Materials and Methods 2.1. Microclimatic

speci cally input and tested. This study used HI, WBGT, and the CK model to analyze and predict the level of heat stress that would be encountered by young athletes playing football during two hot days and compared the results. The COMFA model was used to demonstrate the energy budget value for an adult. 2. Materials and Methods 2.1. Microclimatic Data Collection To calculate the HI, WBGT, and the energy budget values for a young athlete and an adult coach using the COMFA model and the CK model, microclimatic data were collected on an arti cial turf

Int. J. Environ. Res. Public Health2020,17, 6156 4 of 11 football eld at Veteran's Park, College Station, Texas on 10 October 2019 from 1:30–2:30 p.m., and 19 May 2020 from 1:30–3:30 p.m. A portable weather station (MaxiMet GMX501, Gill Instruments, Hampshire, UK) collected a full suite of microclimatic variables, including air temperature (Ta), direct solar radiation (SR), wind speed (Ws), and relative humidity (RH). This weather station was mounted 1.5 m o the ground. A Campbell Scienti c Black Globe Thermometer (BLACKGLOBE_L, Campbell Scienti c, Logan, UT, USA) was used to measure the black globe temperature. A thermal camera FLIR E5 (FLIR System, Inc., Wilsonville, OR, USA) measured the surface temperature of the eld and the environment. Data from MaxiMet GMX501 were collected with a CR310 data logger (Campbell Scienti c, Logan, UT, USA) at 10-s intervals. Data from all other instruments were collected with a CR3000 data logger (Campbell Scienti c, Logan, UT, USA) at the same time intervals. The Kestrel WBGT Heat Stress Tracker and Weather Meter was designed to detect microclimatic data and heat-related indices, including WBGT, Thermal Work Limit (TWL), and HI for outdoor workers and athletes. The Kestrel 5400 Heat Stress Tracker with LiNK (Kestrel Instruments, Boothwyn, PA, USA) was used in this study on 19 May 2020, to obtain the WBGT and HI values directly. Data were stored every 10 s. Kestrel LiNK (Kestrel Instruments, Boothwyn, PA, USA) for Windows was used to export the data. 2.2. Calculate Heat Stress Level Using HI, WBGT, the CK, and COMFA Model The HI and WBGT heat stress values were calculated using Ta and RH data from Maximet. Tg was measured by BLACKGLOBE_L. Tw was calculated using RH and Ta based on Stull's [18] equation that has been validated and widely used: Tw=Taatan 0.151977(RH%+8.313659) 1 2 +atan(Ta+RH%) atan(RH% 1.676331) +0.00391838(RH%) 3 2atan(0.023101RH%) 4.686035 (4) The HI and WBGT heat stress values can also be obtained from the Kestrel Heat Stress Tracker. Two energy budget models, the CK model, and the original COMFA model were used to determine the energy budget level for a 10-year-old boy playing football

been validated and widely used: Tw=Taatan 0.151977(RH%+8.313659) 1 2 +atan(Ta+RH%) atan(RH% 1.676331) +0.00391838(RH%) 3 2atan(0.023101RH%) 4.686035 (4) The HI and WBGT heat stress values can also be obtained from the Kestrel Heat Stress Tracker. Two energy budget models, the CK model, and the original COMFA model were used to determine the energy budget level for a 10-year-old boy playing football on the eld (RMR=52 W/m 2 ), and a 40-year-old man coaching on the sidelines (RMR=42 W/m 2 ). The MET rate is 7 for the boy who is “running moderately in a football game” and 4 for the man who is “coaching football” based on the 2011 Compendium of Physical Activities [19]. WHO standards for heights and weights were used to calculate other physiological values in the model. The CK model yields an energy budget value with the unit of W/m 2 , while HI and WBGT result in the units of C. To compare these, Harlan et al.'s [7] relationship between the energy budget values and HI values was used in this study (EB=60–120 W/m 2 , HI=26.7–31.7 C, Label=Caution; EB=121–200 W /m 2 , HI=32.2–40 C, Label=Extreme caution; EB=201–339 W/m 2 , HI=40.6–53.9 C, Label=Danger; EB=340 or higher W/m 2 , HI=54.4 C or higher, Label=Extreme danger). Table shows each heat stress category of the HI, WBGT, and the COMFA model for athletic activities (we use the same category for the CK model).

Int. J. Environ. Res. Public Health2020,17, 6156 5 of 11 Table 1.HI, WBGT and the COMFA model heat stress category. Category HI COMFA Category * WBGT Safe <26.7 C <60 W/m 2 Green <27.8 C Caution 26.7–31.7 C 60–120 W/m 2 Yellow 27.9–30.5 C Extreme Caution 32.2–40 C 121–200 W/m 2 Orange 30.6–32.3 C Danger 40.6–53.9 C 201–339 W/m 2 Red 32.2–33.3 C Extreme Danger >54.4 C >340 W/m 2 Black >33.4 C * Value of WBGT for athletic activity is from Cat 3 where the measurements were taken. The heat safety categories for athletics are: Green: normal activities—provide at least three separate rest breaks each hour with a minimum duration of 3 min each during the workout. Yellow: use discretion for intense or prolonged exercise; provide at least three separate rest breaks each hour with a minimum duration of 4 min each. Orange: maximum practice time is 2 h. Red: maximum practice time is 1 h. Black: no outdoor workouts. Delay practice until a cooler WBGT is reached. See details at: //www.weather.gov/rah/WBGT20], and Grundstein et al. [21]. 3. Results 3.1. Microclimatic Conditions and Heat Stress Level Using HI, WBGT, the CK and COMFA Model of Testing Days Table on 10 October 2019 and 1:30–3:30 p.m. on 19 May 2020. Air temperature (Ta) was similar between the two test times (mean Ta is 34.0 C on 10 October and 33.3 C on 19 May). The date 19 May was more humid and less windy (mean RH was 48% and mean Ws was 2.4 m/s) than 10 October (mean RH was 42.9% and mean Ws was 4.04 m/s). Direct SR was much higher on 19 May (mean SR was 886.8 W/m 2 ) than 10 October (mean SR was 574.3 W/m 2 ). Mean HI (36.2 C) and WBGT (28.2 C and 28.8 C) of the two test periods were similar, while EB values for a 10-year-old young athlete and a 40-year-old coach on 19 May (376.5 W/m 2 and 228.3 W/m 2 ) were much higher than those of 10 October (254.6 W/m 2 and 108.2 W/m 2 ). Table

2 ). Mean HI (36.2 C) and WBGT (28.2 C and 28.8 C) of the two test periods were similar, while EB values for a 10-year-old young athlete and a 40-year-old coach on 19 May (376.5 W/m 2 and 228.3 W/m 2 ) were much higher than those of 10 October (254.6 W/m 2 and 108.2 W/m 2 ). Table 2. Meteorological data and heat stress/energy budget values from HI, WBGT, the COMFA model, and the CK model. Test Time Ta ( C) RH (%) Ws (m/s) SR (W/m 2 ) EB Athlete (W/m 2 ) EB Coach (W/m 2 ) HI ( C) WBGT ( C) October Max 35 46 7.14 950 383.7 232.1 37.6 28.9 Min 33.3 41 0.98 193 147.2 9.2 35.2 27.6 Mean 34.0 42.9 4.04 574.3 254.6 108.2 36.2 28.2 May Max 34.2 56 5.8 1005 468.1 308.4 38.3 29.8 Min 31 44 0.5 743 284.5 144.4 33.7 26.6 Mean 33.3 48 2.4 886.8 376.5 228.3 36.2 28.8 3.2. Comparison of Heat Stress Level Using HI, WBGT, the CK and COMFA Model Figurea,c show the HI and WBGT values calculated from microclimatic data using Maximet and direct from Kestrel on 19 May 2020; Figureb,d show the HI and WBGT values on 10 October 2019. All the calculated HI values were under the “Extreme Caution” level of the two days. Kestrel's HI values were higher than the calculated values. More than half of the Kestrel HI values were under “Danger”, and the rest were under “Extreme Caution”. For calculated WBGT values on the two days, based on the regional heat safety thresholds for athletes in the U.S. [18], the activity guidelines for the two periods were from “Green—normal activities” to “Yellow—use discretion for intense or prolonged exercise”. Kestrel's WBGT values were slightly higher than the calculated values while at the same activity guidance category. The calculated WBGT and HI values and Kestrel's values showed a strong relationship (R 2 =1). The higher Kestrel HI and WBGT values were signi cantly higher because of the higher Ta, RH, and Tw value measurement by Kestrel which was likely

This study compares standard heat vulnerability methods with a novel model for young athletes.