Exertional heat stroke (EHS) remains a persistent threat for individuals working or playing in the heat, including athletes and military and emergency service personnel. However, influence of biological sex and/or body mass index (BMI) on the risk of EHS remain poorly understood. The purpose of this study was to retrospectively assess the influence of sex and BMI on risk of EHS in the active-duty US Army. We analyzed data from 2016 to 2021, using a matched case-control approach, where each individual with a diagnosis of EHS was matched to five controls based on calendar time, unit ID, and job category, to capture control individuals who were matched to EHS events by location, time, and activity. We used a multivariate logistic regression model mutually adjusted for sex, BMI, and age to compare 745 ( n = 61 F) individuals (26 ± 7 yr) with a diagnosed EHS to 4,290 ( n = 384 F) case controls (25 ± 5 yr). Group average BMI were similar: 26.6 ± 3.1 (EHS) and 26.5 ± 3.6 kg/m² (CON). BMI was significantly ( P < 0.0001) associated with higher risk of EHS with a 3% increase in risk of EHS for every unit increase in BMI. Notably, sex was not associated with any difference in risk for EHS ( P = 0.54). These data suggest that young healthy people with higher BMI have significantly higher risk of EHS, but, contrary to what some have proposed, this risk was not higher in young women.

Risk of exertional heat stroke (EHS) is an ongoing challenge for United States military personnel, for athletes and for individuals with occupational stressors that involve prolonged activity in hot environments. Higher body mass index (BMI) is significantly associated with increased risk for EHS in activity duty U.S. Soldiers. During exercise, heat is generated primarily by contracting skeletal muscle (and other metabolically active body mass) and dissipated based on body surface area (BSA). Thus, in compensable environments, a higher BSA·mass⁻¹ may be a benefit to heat dissipation and decrease the risk of EHS. The purpose of the present analysis was to test the hypothesis that BSA·mass⁻¹ ratio is an important biophysical characteristic contributing to the risk of EHS. We employed a matched case-control approach, where each individual with a diagnosis of EHS was matched to five controls who were never diagnosed with EHS but were in the same unit and had the same job title. We used a multivariate conditional logistic regression model including variables of BSA·mass⁻¹, sex, age, military rank, and race. BSA·mass⁻¹ significantly predicted EHS risk (P = 0.006), such that people with higher BSA·mass⁻¹ were at lower risk of developing EHS when controlling for other potential factors such as age and race. This relationship persisted after adjustment for other anthropometric measures of body size including weight, BMI, and BSA. These data suggest that biophysical factors play an important role in EHS risk, particularly in a healthy military-aged cohort of men and women. NEW & NOTEWORTHY With the impacts of climate change yielding higher average ambient temperatures over time, the incidence of EHS for individuals participating in outdoor activities may consequently increase. With the larger sample size in this study compared with prior research in this field, we were able to use various methods that had not been applied before. For example, we were able to mutually adjust for different measurements of body size to understand which metric had the highest association with EHS risk. Understanding factors that may be modifiable may be important for developing interventions to counteract the increased risk of EHS associated with climate change.

The feasibility of detecting mild dehydration by using autonomic responses to cognitive stress was studied. To induce cognitive stress, subjects (n = 17) performed the Stroop task, which comprised four minutes of rest and four minutes of test. Nine indices of autonomic control based on electrodermal activity (EDA) and pulse rate variability (PRV) were obtained during both the rest and test stages of the Stroop task. Measurements were taken on three consecutive days in which subjects were “wet” (not dehydrated) and “dry” (experiencing mild dehydration caused by fluid restriction). Nine approaches were tested for classification of “wet” and “dry” conditions: (1) linear (LDA) and (2) quadratic discriminant analysis (QDA), (3) logistic regression, (4) support vector machines (SVM) with cubic, (5) fine Gaussian kernel, (6) medium Gaussian kernel, (7) a k-nearest neighbor (KNN) classifier, (8) decision trees, and (9) subspace ensemble of KNN classifiers (SE-KNN). The classification models were tested for all possible combinations of the nine indices of autonomic nervous system control, and their performance was assessed by using leave-one-subject-out cross-validation. An overall accuracy of mild dehydration detection was 91.2% when using the cubic SE-KNN and indices obtained only at rest, and the accuracy was 91.2% when using the cubic SVM classifiers and indices obtained only at test. Accuracy was 86.8% when rest-to-test increments in the autonomic indices were used along with the KNN and QDA classifiers. In summary, measures of autonomic function based on EDA and PRV are suitable for detecting mild dehydration and could potentially be used for the noninvasive testing of dehydration.