Validation of heat strain risk assessment (HSRA) indices compared to wet bulb globe temperature (WBGT) and predicted heat strain (PHS) indices

Abstract

Introduction

Heat strain risk assessment (HSRA) indices have recently been developed for more precise prediction of heat strain. This study aimed to evaluate the validity of these indices compared to the wet bulb globe temperature (WBGT) and predicted heat strain (PHS) indices under different thermal and occupational conditions.

Methods

This cross-sectional study was performed in environemntal settings of warm-dry and warm-humid in 2024. In the study, 200 Iranian male workers from two warm industries participated. Initially, their demographic data were gathered. Subsequently, they were instructed to perform their usual tasks after a 30-minute rest period. After 90 min of working, physiological and environmental parameters were measured. Additionally, the metabolic rate and clothing thermal insulation of participants were assessed, and they filled out the observational-perceptual heat stress risk assessment (OPHSRA) questionnaire. After that, indices were calculated. The Spearman correlation coefficient was employed to analyze the relationships among them. Additionally, a linear regression analysis was conducted to plot the curve and compute the regression coefficients. Receiver operating characteristic (ROC) analysis was also used to determine the accuracy of these indices.

Results

The highest regression coefficients were observed between tympanic temperature and PHSRA (R² = 0.77), EHSRA (R² = 0.75), and PHS (R² = 0.72) indices. Similarly, the greatest regression coefficients were found between heart rate and PHSRA (R² = 0.71), EHSRA (R² = 0.68), and PHS (R² = 0.65) indices. The OPHSRA and WBGT indices had the lower regression coefficients with tympanic temperature (R² = 0.69 and R² = 0.67) and heart rate (R² = 0.61 and R² = 0.42) compared to the aforementioned indices, respectively. Moreover, EHSRA (AUC = 0.950 and AUC = 0.907) and PHSRA (AUC = 0.947 and AUC = 0.900) indices had the highest diagnostic accuracies of ROC curves related to tympanic temperature and heart rate, respectively.

Conclusion

The results showed that the HSRA indices have acceptable validity in predicting thermal strain.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-25655-z.

Introduction

Various harmful agents in the workplace, such as heat, can affect human health [1, 2]. Heat exposure can cause illnesses and diseases, such as heat syncope, heat cramps, heat exhaustion, heat shock, and cardiovascular diseases, among workers [3, 4]. For prevention of these effects, it is necessary that work environment is evaluated, and dangerous climatic conditions are determined. For this purpose, various indices have been developed. Ioannou et al. identified 340 indices to evaluate thermal stress [5]. Those can be categorized into three groups, physiological indices, environmental indices, and subjective indices. Each group of these indices has several limitations. Some valid physiological indices, such as core temperature, despite the high accuracy require invasive measurements or expensive equipment for accurate evaluation [6]. For this reason, the researchers tried to develop environmental and subjective indices. So far, many environmental indices have designed that two of the most well-known indices are wet-bulb globe temperature (WBGT) and predicted heat strain (PHS) indices [7]. However, the studies mention some limitations for these indices [8]. Alfano et al. stated that the WBGT index is not suitable for environments with high humidity and low air velocity and the WBGT index cannot accurately reflect this heat strain due to limited evaporation [6]. As another limitation, only three parameters, including dry bulb temperature, wet bulb temperature and globe temperature are directly entered in the equation of this index, and other parameters such as metabolism and thermal resistance of clothing are evaluated indirectly in the interpretation Table [9]. So that after 60 years of use, it needs a replacement index [6]. The predicted heat strain (PHS) index also has complexity in the steps of calculating and evaluating the index [10]. Furthermore, this index has been defined for specific conditions of the thermal strain risk factors. For example, the PHS index is applicable when individuals wear clothing with a thermal insulation of 1 Clo [8], and it cannot evaluate the role of personal protective equipment in occurrence of heat strain [11]. Moreover, equipment is required for measuring the parameters of environmental indices. Also, those don’t consider the personal differences in evaluation of heat strain among the workers [9]. These disadvantages limit their use. Subjective indices like the perceptual strain index (PeSI) have several positive properties, such as use ease, cheapness, and desirability, among users. However, the developed subjective indices do have not enough accuracy in predicting heat strain, which can be because of a limited number of evaluated factors [12]. Therefore, it is required that more accurate and comprehensive indices are developed in addition to available heat stress indices.

Researchers have tried to develop novel indices to eliminate the disadvantages of the mentioned indices. For example, Liang et al. represented a heat stress index using parameters of dry temperature and wet temperature based on Cox regression [13]. Golbabaei et al. developed the outdoor environmental heat index (OEHI) by the parameters of air temperature and air enthalpy [14]. Dehghan et al. introduced the heat strain score index (HSSI), which is calculated by 12 perceptual items and five observational items [15]. These indices also only consider some main parameters affecting heat strain, which decreases their accuracy. However, all limitations of the previous indices cannot be resolved by an index. For this reason, Yazdanirad et al. developed a set of indices, named as heat strain risk assessment (HSRA) indices, to overcome these limitations. Those include the objective indices of the environmental heat strain risk assessment (EHSRA) index [16] and the personal heat strain risk assessment (PHSRA) index [17] and the subjective index of the observational perceptual heat strain risk assessment (OPHSRA) index [18]. The EHSRA index evaluates six main parameters, including air temperature, globe temperature, wet temperature, airflow speed, metabolism, and clothing thermal resistance [16]. The PHSRA index assesses personal parameters of age, maximal aerobic capacity, body mass index, and body surface area in addition to the mentioned six parameters [17]. The OPHSRA index subjectively evaluates 16 effective items in the four groups of environment, job, management, and clothing factors [18]. Unlike mentioned indices, these indices are useable in a variety of environmental and occupational conditions. Those can be easily measured, calculated and interpreted [16–18]. Main parameters of heat stress are evaluated in these indices. Moreover, PHSRA index considers personal differences in evaluation of heat stress, it can be used for screening in the warm workplaces [17]. OPHSRA index also subjectively assesses a variety of heat stress risk factors without need to equipment. So, it has sufficient accuracy for subjective assessment of heat stress [18].

Each of these indices can be used for a desired goal in evaluating the thermal strain of workers. Those can be computed based on data obtained by inexpensive environmental readings and estimations or subjective evaluation. Given that these indices have been recently developed, their validity must be investigated in a variety of conditions. Therefore, the present study aimed to evaluate the validity of these indices compared to the WBGT and PHS indices under different thermal and occupational conditions. If the validity of the HSRA indices is confirmed in different environmental and occupational conditions, those can be used to evaluate heat stress with sufficient accuracy, use easiness, interpretation easiness, and comprehensiveness in occupational settings. Also, these individuals can be used to screen employees in hot work environments and identify important risk factors for heat stress in the desired workplace.

Materials and methods

This cross-sectional study was performed in 2024. In the study, 200 Iranian male workers participated from a steel factory with warm and dry climatic conditions and a petrochemical factory with warm and humid climatic conditions. Inclusion criteria for the study included having a career length of more than 1 year, not having infectious, pulmonary, cardiovascular, hypertension, renal, hyperthyroidism, and digestive diseases, and not taking medications affecting heart rate and blood pressure. Participants were also asked to abstain from coffee, caffeine, and alcohol consumption and smoking for at least 12 h prior to the study. Furthermore, examinations were conducted to ensure the health of the tympanic membrane and auditory canal among participants. Various equations were used in the present study, as represented in Table 1. The exclusion criteria included non-cooperation in physiological measurements, excessive fatigue sensation, a heart rate exceeding the maximum allowable limit (Eq. 1), and a tympanic temperature exceeding 39 °C [19]. Figure 1 shows the steps of the present study.

Fig. 1.

The steps of the present study

Sample size

Regarding the minimum probable correlation between the novel index with tympanic temperature and heart rate was considered to be 0.2, the sample size with a confidence level of 95% and a test power of 80% was estimated by 194 persons (Eq. 2) [20].

Data collection

At first, the persons working in the mentioned industries were selected based on the characteristics of their jobs and workplaces. Then, their medical records were reviewed and the subjects with inclusion criteria were invited to the study. All participants gave their signed written informed consent letters. After that, the aims and steps of the study were explained to individuals who consented to participate in the study. The study protocol was approved by the Ethics Committee of Isfahan University of Medical Sciences (approval no IR.MUI.RESEARCH.REC.1401.308). All the methods included in this study are in accordance with the declaration of Helsinki. Subsequently, demographic information, such as age, height, weight, occupation, level of physical activity, and work experience of the participants, was gathered. After that, participants rested in a room with moderate air temperature for 30 min. During the rest period, their heart rate and tympanic temperature were recorded at the times of 20, 25, and 30 min, adhering to Iso 9886 standards. After that, the persons were asked to conduct the routine work. Their heart rate and tympanic temperature were measured at times of 30, 60, and 90 min after starting the work. Also, the participants are asked to complete the observational-perceptual heat stress risk assessment (OPHSRA) questionnaire. This questionnaire is available in Appendix A, respectively. Additionally, environmental parameters such as dry temperature (Ta), wet temperature (Tw), globe temperature (Tg), and wind speed (Va) were concurrently measured based on the Iso 7243 and Iso 7726 standards. The metabolic rate was assessed based on the Iso 8996 standard. The insulation value of clothing (Ic) was also determined based on the Iso 9920 standard. Then, values of body mass index (BMI) (kg/m²) [21], maximum aerobic capacity (VO2max) (ml/(kg·min)) [22], and body surface area (BSA) (cm²) [23] were computed using Eq. 3 to 5, respectively.

Instruments

The tympanic temperature was measured using a Braun IRT 6530 thermometer with an accuracy of 0.1 degrees Celsius. This instrument has been patented with features of pre-heated tips and a precise alignment system. The results of a study performed by Fenemor et al. showed that tympanic temperature had an acceptable agreement with core temperature during exercise in a heat chamber [24]. Heart rate measurements were also performed with a Beurer PM70 pulse monitor with an accuracy of one beat per minute. Additionally, environmental conditions such as dry temperature, wet temperature, and globe temperature were assessed using a TES 1369B WBGT meter with an accuracy of 0.1 degrees Celsius. Wind velocity was also measured using a TES 1340 device with an accuracy of 0.01 m per second. Body height and weight were obtained using a tape and a Hamilton digital scale with accuracies of 0.01 m and 0.1 kg, respectively. To measure the subjective indices, the OPHSRA questionnaire (16 questions) was used. The validity and reliability of this questionnaire have been evaluated and confirmed by Yazdanirad et al. [18]. In this study, the content validity ratio (CVR), content validity index (CVI), and Cronbach’s alpha coefficient for the OPHSRA questionnaire were reported as 0.793, 0.913, and 0.910, respectively [18].

Calculation of indices

• WBGT index

This index is calculated by Eq. 6 [25].

• PHS index

The calculation of this index was performed using the software recommended by the Iso 7933 standard [26]. Within this software, the required sweating rate () is computed in grams per hour by Eq. 7.

• EHSRA index

This index is calculated by Eq. 8 [16]. When dry temperature is lower than normal skin temperature (35 ⁰ C), the coefficient sign of air velocity changes from positive to negative because it decreases heat strain in these conditions. In the EHSRA index, a score less than 12.02 indicates a low-risk level, a score between 12.02 and 15.87 shows a moderate risk level, a score between 15.88 and 17.56 indicates a high-risk level and a score greater than 17.56 shows a very high-risk level.

• PHSRA index

This index is calculated using Eq. 9 [17]. In the formula, the coefficients’ sign of body surface area and wind speed change from positive (+ 0.106 and + 0.245) to negative (−0.106 and − 0.245) when the dry temperature is less than 35 °C as normal skin temperature. Because thermal strain is reduced under these conditions. In the PHSRA index, a score less than 12.93 indicates a low-risk level, a score between 12.93 and 16.47 shows an average risk level, a score between 16.48 and 18.87 indicates a high-risk level, and a score greater than 18.87 shows a very high-risk level.

• OPHSRA index

This index is calculated by Eq. 10 [27]. It should be noted that participants have the option to choose multiple answers for questions Q1 and Q6. Additionally, the score of Q15, which evaluates air movement, changes from a positive value to a negative value if the score of Q11, which evaluates the perception of air temperature, is less than two. Because thermal strain is reduced under these circumstances. In the OPHSRA index, a score less than 17.04 indicates a low-risk level, a score between 17.04 and 20.05 shows an average risk level, a score from 20.05 to 22.10 indicates a high-risk level and a score greater than 22.10 shows a very high-risk level.

Table 1.

The equations used in the present study

Variable	Equation
Maximum heart rate	Equation 1
	Explanation	-
Sample size	Equation 2
	Explanation	is equal to 1.96 based on a confidence level of 95%, is equal to 0.84 based on a test power of 80%, and w is equal to 0.203 based on a minimum correlation coefficient of 0.2.
Body mass index	Equation 3
	Explanation	is the human weight (Kg) and H is the human height (m).
Maximum aerobic capacity	Equation 4
	Explanation	is the maximum permissible heart rate estimated by Eq. 1 (beat/min) and is heart rate during resting (beat/min).
Body surface area	Equation 5
	Explanation	Where H is body height (cm) and is body weight (kg).
WBGT index	Equation 6	outdoor environment indoor environment
	Explanation	Tw, Tg, and Ta are wet temperature (°C), globe temperature (°C), and dry temperature (°C), respectively.
PHS index	Equation 7
	Explanation	is the required evaporative heat, is the evaporative heat on the skin surface, is the metabolism heat, is the convective heat exchange in the respiratory tracts, is the evaporative heat exchange in the respiratory tracts, C is the convective heat exchange on the skin surface, R is the radiation heat exchange on the skin surface, and the is the heat storage in the body. The unit of the stated parameters is watts per square meter. is the required sweat efficiency and is the required sweat for skin wetness.
EHSRA index	Equation 8
	Explanation	, , , , , and are dry temperature (degree of centigrade), wet temperature (degree of centigrade), globe temperature (degree of centigrade), air velocity (meter per second), total metabolism (Watts), and clothing thermal insulation (Clo), respectively.
PHSRA index	Equation 9
	Explanation	, , , , , , , , , and are age (year), body mass index (kg/m2), maximum aerobic capacity (ml/(kg·min)), body surface area (m2), dry temperature (0 C), wet temperature (0 C), globe temperature (0 C), wind speed (m/s), total metabolism (watts), and insulation of clothes (clo), respectively.
OPHSRA index	Equation 10
	Explanation	The responses related to the questions of Q1 to Q16 are represented by scores (see Appendix A).

Data analysis

The SPSS software was used to analyze data. Firstly, the values of indices were computed using specific equations. To assess the normality of the data, the Kolmogorov-Smirnov test was utilized. Because the variables exhibit a non-normal distribution, the Spearman correlation coefficient was employed to analyze the relationships among them [28]. Additionally, a linear regression analysis was conducted to plot the curve and compute the regression coefficients. Receiver operating characteristic (ROC) analysis was also used to determine the accuracy of these indices in diagnosing cut points of thermal strain (a tympanic temperature of 38 degrees Celsius and a heart rate of 130 beats per minute). ROC analysis was chosen because it can show that an index discriminates well between values lower and higher than the gold standard cutoff point as a health hazard thereshod. Indeed, it can reveal overall diagnostic power of the index [29]. Moreover, Kappa coefficient was computed between the studied indices and physiological indices as gold standard. Kappa statistics determine agreement level between predicted values by heat stress index and observed values by physiological parameters [30]. Therefore, these analyses can be useful for evaluating the validity of the novel indices. Because those can examine the diagnostic power and agreement of new indices to replace physiological indices as gold standard.

Results

Table 2 presents the statistical distribution of demographic variables among the participants. The findings indicate that individuals with a variety of demographic characteristics were included in the study. Additionally, Table 3 presents the statistical distribution of key variables affecting heat strain in two different thermal conditions. As a result, the study was performed in a variety of environmental conditions, metabolism, and clothing to investigate the validity of the indices. Table 4 reports the statistical distribution of the studied indices. Based on the results, the range of the studied indices was wide.

Table 2.

The statistical distribution of demographic variables among the participants

variable		Range		Mean		Standard deviation
Age (year)	21.00–55.00		36.62		8.23
Career length (Year)	1.00–40.00		12.74		7.84
Body mass index (kg/m2)	19.24–34.93		26.07		4.08
Activity rate (hours/week)	0.00–11.00		2.46		2.15
Smoking rate (cigarette/day)	0.00–20.00		3.39		1.56

Table 3.

The statistical distribution of key variables affecting heat strain in two different thermal conditions

	Warm and dry environment (n = 110)			Warm and humid environment (n = 90)
Range	Range	Mean	Standard deviation	Range	Mean	Standard deviation
Dry temperature (0C)	22.00–44.20	33.94	5.29	24.10–49.10	36.78	6.84
Wet temperature (0C)	12.10–25.50	17.77	2.15	14.00–37.50	27.42	6.24
Relative humidity (percent)	7.66–51.90	19.15	9.92	13.75–75.93	49.98	16.90
Globe temperature (0C)	23.40–64.60	39.54	9.99	24.10–57.90	41.56	10.10
Mean radiant temperature (C)	23.55–64.51	39.70	10.33	24.10–59.39	41.71	10.40
Wind speed (m/s)	0.00–4.20	0.70	0.37	0.01–2.17	0.42	0.39
Air pressure (mmHg)	749.76–756.62	754.39	1.82	751.66–754.53	753.15	0.90
Total metabolism (watts)	125.00–465.00	229.80	96.17	125.00–415.00	235.67	92.25
Clothing thermal resistance (clo)	0.50–1.35	0.86	0.16	0.65–1.04	0.80	0.10

Table 4.

The statistical distribution of the studied indices

Index		Range	Mean	Standard deviation
Heat strain indices	EHSRA	0.56–24.32	12.97	5.82
	PHSRA	0.60- 25.64	13.70	5.86
	OPHSRA	1.14–28.60	16.23	7.31
	WBGT	15.55–41.51	27.30	6.40
	PHS	34.00–499.00	210.28	114.52
Physiological indices	Tympanic temperature	36.70–39.10	37.69	0.56
	Heart rate	70.00–188.00	120.92	28.44

Figure 2 indicates regression curves between physiological parameters and the indices. The analysis revealed that the highest regression coefficients were observed between tympanic temperature and the PHSRA (0.77), EHSRA (0.75), and PHS (0.72) indices. Similarly, the greatest regression coefficients were found between heart rate and PHSRA (0.71), EHSRA (0.68), and PHS (0.65) indices. The OPHSRA and WBGT indices had the lower regression coefficients with tympanic temperature (0.69 and 0.67) and heart rate (0.61 and 0.42) compared to the aforementioned indices, respectively.

Fig. 2.

Regression curves between physiological parameters and the indices under study

Table 5 Presents the correlation between various indices and physiological parameters in different Climatic conditions. The analysis indicates that in warm and dry settings, the highest associations were found between tympanic temperature with the indices of PHSRA (0.869) and PHS (0.866). Additionally, heart rate had the highest correlation with the indices of PHSRA (0.856) and EHSRA (0.848) in these conditions. In warm and humid settings, the results revealed the highest correlations were between tympanic temperature with the indices of PHSRA (0.866) and EHSRA (0.848). Moreover, the highest correlations were between heart rate with the indices of PHSRA (0.810) and PHS (0.809). Overall, in all examined conditions, tympanic temperature had the highest correlations with the indices of PHSRA (0.878) and PHS (0.872), while heart rate showed the highest correlations with the indices of PHSRA (0.840) and EHSRA (0.827).

Table 5.

The correlation between various indices and physiological metrics in different climatic conditions

Developed indices	Heat strain indices	Warm and dry environment			Warm and humid environment			All environment
		Convective (N = 45)	Radiative (N = 65)	Both (N = 110)	Convective (N = 41)	Radiative (N = 49)	Both (N = 90)
EHSRA	Tympanic temperature	0.643**	0.834**	0.862**	0.892**	0.679**	0.848**	0.866**
	Heart rate	0.710**	0.805**	0.848**	0.769**	0.697**	0.788**	0.827**
PHSRA	Tympanic temperature	0.685**	0.843**	0.869**	0.907**	0.716**	0.866**	0.878**
	Heart rate	0.721**	0.819**	0.856**	0.787**	0.746**	0.810**	0.840**
OPHSRA	Tympanic temperature	0.675**	0.843**	0.867**	0.854**	0.523**	0.794**	0.829**
	Heart rate	0.680**	0.759**	0.821**	0.711**	0.526**	0.719**	0.781**
WBGT	Tympanic temperature	0.480**	0.789**	0.806**	0.850**	0.587**	0.809**	0.818**
	Heart rate	0.216	0.566**	0.633**	0.625**	0.494**	0.683**	0.647**
PHS	Tympanic temperature	0.632**	0.837**	0.866**	0.883**	0.595**	0.829**	0.872**
	Heart rate	0.690**	0.751**	0.839**	0.856**	0.703**	0.809**	0.822**

^*P < 0.05

^**P < 0.05

Table 6 presents the correlation between various indices and physiological parameters in different activity levels. In light activity, the indices of PHSRA (0.871) and PHS (0.862) showed the highest correlations with tympanic temperature. In moderate activity, the highest correlations were observed between tympanic temperature with the indices of PHSRA (0.871) and WBGT (0.868). In high activity, the highest correlations were between tympanic temperature with the indices of WBGT (0.969) and EHSRA (0.897). Additionally, in light activity, there were the highest correlations between heart rate with the indices of PHSRA (0.874) and OPHSRA (0.859). In moderate activity, the indices of PHSRA (0.889) and WBGT (0.868) showed the highest correlations with heart rate, and in high activity, the indices of PHSRA (0.789) and OPHSRA (0.759) indicated the highest correlations with this physiological parameter.

Table 6.

The correlation between various indices and physiological parameters in different activity levels

Developed indices	Heat strain indices	Light (N = 114)	Moderate (N = 65)	High (N = 22)
EHSRA	Tympanic temperature	0.857**	0.855**	0.897**
	Heart rate	0.855**	0.854**	0.752**
PHSRA	Tympanic temperature	0.871**	0.871**	0.891**
	Heart rate	0.874**	0.889**	0.789**
OPHSRA	Tympanic temperature	0.836**	0.843**	0.780**
	Heart rate	0.859**	0.825**	0.759**
WBGT	Tympanic temperature	0.831**	0.868**	0.969**
	Heart rate	0.794**	0.830**	0.759**
PHS	Tympanic temperature	0.862**	0.836**	0.892**
	Heart rate	0.849**	0.795**	0.699**

^*P < 0.05

^**p < 0.01

Table 7 reports the outcomes of the receiver operating characteristic (ROC) analysis. The results showed that the area under the curve (AUC) values for HSRA indices exceeded 0.80, demonstrating satisfactory diagnostic accuracy of the curves [31]. The findings indicated that EHSRA (0.950 and 0.907) and PHSRA (0.947 and 0.900) indices had the highest diagnostic accuracies of ROC curves related to tympanic temperature and heart rate, respectively.

Table 7.

The outcomes of the receiver operating characteristic (ROC) analysis

Index	Tympanic temperature				Heart rate
	AUC	95% CI	Sensitivity	Specificity	AUC	95% CI	Sensitivity	Specificity
EHSRA	0.950	0.922–0.978	0.945	0.884	0.900	0.856–0.944	0.851	0.819
PHSRA	0.947	0.919–0.976	0.927	0.863	0.907	0.864–0.950	0.851	0.819
OPHSRA	0.915	0.876–0.953	0.829	0.885	0.860	0.809–0.912	0.784	0.890
WBGT	0.913	0.875–0.951	0.873	0.849	0.793	0.731–0.855	0.838	0.638
PHS	0.931	0.895–0.966	0.909	0.863	0.895	0.866–0.937	0.811	0.856

Figure 3 depicts the statistical distribution of the risk levels related to physiological and studied indices. Moreover, Table 8 represents the Kappa coefficient between the indices and physiological parameters in different climatic conditions. The results revealed that there was good agreement between the risk levels of EHSRA, PHSRA, and OPHSRA indices and those of physiological parameters. So that these indices could predict the different levels of heat strain risks. However, the results indicated that the WBGT index overestimates the heat strain risk in the workers. As a result, the highest kappa coefficients were observed between the PHSRA index and tympanic temperature (0.615) and between the EHSRA index and heart rate (0.421). Table 9 reports summary of comparative performance related to the studied indices.

Fig. 3.

The statistical distribution of the risk levels related to physiological and studied indices

Table 8.

The kappa coefficient between the studied indices and physiological parameters in different Climatic conditions

Index	Heat strain indices	Warm and dry environment			Warm and humid environment			All environment
		Convective (N = 45)	Radiative (N = 65)	Both (N = 110)	Convective (N = 41)	Radiative (N = 49)	Both (N = 90)
EHSRA	Tympanic temperature	0.403*	0.506**	0.591**	0.661**	0.336**	0.544**	0.588**
	Heart rate	0.069	0.442**	0.445**	0.338**	0.224*	0.351**	0.421**
PHSRA	Tympanic temperature	0.403*	0.552**	0.623**	0.686**	0.367**	0.572**	0.615**
	Heart rate	0.013	0.402**	0.435**	0.315*	0.228*	0.350**	0.414**
OPHSRA	Tympanic temperature	0.403*	0.520**	0.596**	0.526**	0.185*	0.438**	0.536**
	Heart rate	0.069	0.427**	0.434**	0.337**	0.085	0.307**	0.390**
WBGT	Tympanic temperature	0.297*	0.438**	0.523**	0.412**	0.000	0.250**	0.427**
	Heart rate	0.060	0.296**	0.335**	0.158	0.000	0.129*	0.269**
PHS	Tympanic temperature	387**	412**	0.463**	384**	277**	0.422**	0.470**
	Heart rate	157**	289**	0.323**	0.187*	0.114*	214**	0.297**

^*P < 0.05

^**p < 0.01

Table 9.

Summary of comparative performance related to the studied indices

Index	Heat strain indices	R 2	AUC	Kappa coefficient
EHSRA	Tympanic temperature	0.750	0.950	0.588
	Heart rate	0.680	0.900	0.421
PHSRA	Tympanic temperature	0.770	0.947	0.615
	Heart rate	0.710	0.907	0.414
OPHSRA	Tympanic temperature	0.690	0.915	0.536
	Heart rate	0.610	0.860	0.390
WBGT	Tympanic temperature	0.670	0.913	0.427
	Heart rate	0.420	0.793	0.269
PHS	Tympanic temperature	0.720	0.931	0.470
	Heart rate	0.650	0.895	0.297

Discussion

Performance of indices

The results showed that the highest prediction power of heat strain was related to two subjective indices of PHSRA and EHSRA. Given that these indices need measurement equipment, it is logical that their validity is higher than subjective indices, such as OPHSRA. Moreover, it may be because the EHSRA index only appraises the main factors, including dry temperature, wet temperature, globe temperature, wind speed, total metabolism, and clothing thermal insulation. While the PHSRA index evaluates personal factors, including age, body surface area, maximal aerobic capacity, and body mass index, in addition to the mentioned main factors. Based on the results of other studies also, personal differences can affect the heat strain in humans [32–35]. Therefore, the use of these factors in the accurate evaluation of heat strain can be helpful.

Interpretation of results

The probable reason for the higher accuracy of PHSRA and EHSRA compared to the WBGT index can be the different coefficients of the factors in these indices. In the PHSRA and EHSRA indices, the coefficients of the globe temperature and dry temperature are greater than the wet temperature while in the WBGT index, the coefficient of wet temperature is greater than two other factors [9]. It can be understood that elevated global and air temperatures increase heat absorption via convection and radiation processes. Concurrently, increased relative humidity reduces the effectiveness of sweat evaporation from the skin’s surface [36]. Therefore, the effect of humidity on thermal strain is dependent on the activation of the sweating mechanism in a warm environment. For example, elevated humidity combined with low air and radiant temperatures can not lead to heat strain. This may explain why the WBGT index showed lower precision compared to HSRA indices. Moreover, WBGT only evaluates three variables of dry temperature, wet temperature, and globe temperature directly. HSRA indices consider other variables. EHSRA and PHSRA evaluate values of metabolism and clothing insulation, as two important factors affecting heat strain. Also, PHSRA assesses personal factors in addition to environmental factors. While these variables are not considered in the WBGT index. These explanations can justify better performance of EHSRA and PHSRA indices in predicting physiological parameters. Moreover, the PHSRA index had better validity compared to the PHS index in all conditions. However, the validity of the PHS index was higher than EHSRA in some conditions. It may be because the PHSRA index compared to the EHSRA index considers more factors to evaluate heat stress, including personal factors and environmental factors.

Comparison with previous studies

Based on the results of the present study, the PHSRA, EHSRA, PHS, and WBGT could explain 77%, 75%, 0.72%, and 67% of changes in tympanic temperature and 71%, 68%, 0.65%, and 42% of changes in heart rate among the studied workers, respectively. The results of a study performed by Falahati et al. showed that the regression coefficient between the WBGT index and tympanic temperature was equal to 0.57 [37]. Malchaire et al. also found that the predicted heat strain (PHS) index could predict 66% of changes in core temperature [38]. Also, in the research of Monazzam et al., the WBGT index explained 72% and 73% of changes in body temperature and heart rate, respectively [39]. These findings prove that the PHSRA and EHSRA indices are valid for predicting heat stress.

One of the remarkable results was the high strength of the OPHSRA as a subjective index. It was found that the OPHSRA index can explain 69% of changes in tympanic temperature and 61% of changes in heart rate. Moreover, the comparison of the results with other studies indicates that the OPHSRA index has a higher validity for predicting individual heat strain. In the study of Dehghan et al., the heat strain score index (HSSI) could predict 51% of changes in the tympanic temperature [40]. Goss et al. found that the OMNI thermal sensation scale explained 48% of the variation in core temperature [41]. In another study conducted by Dehghan and Ghanbari, the regression coefficient between the perceptual strain index (PeSI) with oral temperature and heart rate was reported by 61% and 82%, respectively [42]. It may be because a greater number of risk factors are evaluated in the OPHSRA index. However, the results revealed that the prediction power of this index is lower than those of the EHSRA and PHSRA indices. Objective indices are calculated based on data measured by equipment, while the OPHSRA index is calculated based on data obtained by people’s judgment. Therefore, it is logical that the error of human judgment is greater than the error of measurement equipment. However, the heat strain risk assessment by the OPHSRA index does not require any measurement device, and it is only determined by a questionnaire. Moreover, using this index, the main risk factors of heat strain in each workpalce can be idintified. It can be useful for planning preventive measures. This index can be particularly useful for community-based and informal work environments, as people in these environments do not have required measurement equipment and knowledge for determining the values of the index parameters. While the OPHSRA index is based on a questionnaire evaluation. So, ordinary members of the community can also specify their heat stress risk by completing the questionnaire and calculating the index score based on the guideline.

Strengths and limitations

In total, the results of this study show that HSRA indices have acceptable validity for predicting physiological parameters in different climatic conditions and different activity levels. Therefore, these indices are usable in various climatic regions and occupational environments. Therefore, the use of these indices is not only limited to industrial environments and can be used in non-industrial occupational settings like agriculture and construction.

As a limitation, the validity of these indices was only examined in the male population. It is suggested that the validity of these indices is investigated among females in the next studies. Moreover, the validity of the studied indices is investigated in a short observation window. While the long-term studies can better prove the prediction power of these indicators. Also, the prediction power of these indices was examined in two climatic zones. More comprehensive studies can show the power of the indices for use in diverse climatic zones. As another limitation, these induces cannot only determine the risk of dehydration, those can only specify the risk of increased tympanic temperature and heart rate among workers.

Conclusion

The results showed that PHSRA, EHSRA, and PHS indices had the greatest regression coefficients with tympanic temperature and heart rate, respectively. Also, in all climatic and occupational conditions, the PHSRA, EHSRA, and PHS indices had the highest correlation coefficients with tympanic temperature, respectively. It was found that the HSRA indices had acceptable accuracy in predicting heat strain parameters. The lowest accuracy was also related to the WBGT index. It was also observed that EHSRA, PHSRA, and OPHSRA indices had good agreement with physiological parameters. So, these indices could properly estimate the various levels of heat strain risks. However, each of the HSRA indices has specific features that the user can choose the index based on the desired goal and available facilities. The EHSRA index can be applied for environmental assessments using measurement equipment. PHSRA can be used to screen people in warm environments. The subjective index of OPHSRA can estimate the risk of heat strain without measurement equipment and only by a questionnaire. It must be mentioned that EHSRA, PHSRA, and OPHSRA indices can be used for purposes of diagnostic and ongoing surveillance. Moreover, PHSRA, and OPHSRA indices can apply for screening in the workplaces. Given the proper power of the HSRA indices in predicting heat stress in workplaces, it is suggested that those be added into occupational health guidelines or policies. So, it serves as a guide to determine whether employees are at risk of heat-related illnesses based on personal, environmental and occupational characteristics, and if they are exposed to dangerous conditions, it determines which parameters have created these conditions. So, control measures are focused on these parameters. Also, HSRA indices determine whether an individual is suitable for working in a hot environment based on individual characteristics. It is suggested that more investigations are carried out on investigating the validity of these indices in the various workplaces and diverse climatic zones in the next research. Also, it is proposed that the validity of these indices in predicting the risk of heat-related illnesses are investigated in a longitudinal follow-up. Moreover, the effectiveness of these indices in preventing these diseases in long-term times can be examined through longitudinal follow-up studies.

Authors’ contributions

Saeid Yazdanirad: Data collection, Methodology, Formal analysis, Investigation, Writing – original draft, Visualization. Milad Abbasi: Methodology, Investigation, Writing – review & editing. Amin Dehghan: Writing – original draft, Writing – review & editing, Visualization. Habibollah Dehghan: Conceptualization, Writing – review & editing, Supervision, Project administration.

Funding

This study was supported by the Isfahan University of Medical Sciences.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

All participants gave their signed written informed consent letters. The study protocol was approved by the Ethics Committee of Isfahan University of Medical Sciences “approval no IR.MUI.RESEARCH.REC.1401.308”. All the methods included in this study are in accordance with the declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.