Wildland Firefighter Critical Training Elicits Positive Adaptations to Markers of Cardiovascular and Metabolic Health

Abstract

Introduction

The purpose of this study was to identify physiologic changes in body composition and resting metabolic markers of health across 2 wk of critical training (CT) in wildland firefighters (WLFFs).

Methods

Twenty-two male and 3 female participants were recruited from 2 hotshot crews across the western United States prior to the 2022 fire season and monitored over their 80-h CT. Body weight (BW) and skinfolds were recorded before and after CT to estimate body fat (BF) and lean body weight (LBW). Blood was analyzed for changes in hematocrit, hemoglobin, plasma volume, and resting values of a lipid and metabolic panel.

Results

The high physical demands of CT resulted in improvements in total cholesterol (−19.3 mg/dL, P<0.001), triglycerides (−34.4 mg/dL, P<0.001), low-density lipoprotein cholesterol (−18.1 mg/dL, P<0.001), very-low-density lipoprotein cholesterol (−5.2 mg/dL, P<0.001), high-density lipoprotein cholesterol (+4.0 mg/dL, P=0.002), non-high-density lipoprotein cholesterol (−19.3 mg/dL, P<0.001), and fasting glucose (−4.3 mg/dL, P=0.008) from before CT to after CT. Significant decreases in hemoglobin and hematocrit were also seen (P<0.001) with corresponding increases in estimated plasma volume (+6.1%, P<0.001). These alterations were seen despite maintenance of BW, LBW, and BF. Lower pretraining BF was associated with a greater magnitude of improvements in fasting glucose and cholesterol markers.

Conclusions

The observed improvements in baseline metabolic and cardiovascular markers along with plasma volume expansion suggest a positive response to the physical stress of WLFF CT. It appears that higher preseason fitness was associated with greater adaptations to the CT stressor.

Keywords

lipid profile cholesterol blood glucose occupational physiology

Introduction

Wildland firefighting is a demanding occupation involving strenuous activity for extended periods of time. Collectively, it is well documented that during the fire season, wildland firefighters (WLFFs) display high daily energy expenditures during prolonged shifts¹^-3 while mitigating added physiologic stress from high environmental temperatures,⁴ personal protective equipment (PPE),⁵ smoke inhalation,⁶ nutritional challenges,^7,8 and sleep deprivation.⁹ Unsurprisingly, WLFFs must maintain appropriate cardiovascular and musculoskeletal fitness to manage these high physical demands and ensure health and safety during field operations.^3,10

Cumulatively, high occupational demands can have deleterious influences on the short-term and long-term health of WLFFs. High physiologic stress during a fire season can elevate the risk of stress and overexertion injuries, especially during the early season.¹¹ Chronic exposure to these stressors increases the risk of development of cardiovascular and metabolic diseases, often determined using body composition and a comprehensive blood metabolic panel to illustrate a cardiovascular risk profile.¹²^-14 In fact, recent investigations have illustrated maladaptive alterations in body weight (BW) and cholesterol across multiple WLFF crews in a single fire season despite apparent maintenance of aerobic fitness.^15,16

To prepare for upcoming stressors and meet type I certification, interagency hotshot crews (IHCs) complete an 80-h preseason training period, referred to as critical training (CT), prior to being sent on national fire assignments.¹⁷ Crews independently undergo CT using a wide variety of classroom and field exercises to meet operational training requirements under the discretion of the crew supervisor. Despite the importance of this training, little research has been conducted to examine the physiologic response to CT. Previous work from our laboratory identified beneficial alterations in lipid and glucose markers across 11 d of CT in a single IHC.¹⁸ However, since training is not standardized across IHCs, additional sampling is necessary to determine the representative cardiovascular and metabolic stress due to CT. Thus, the purpose of this study was to examine the impact of the 80-h CT period on markers of cardiovascular and metabolic health in male and female IHC WLFFs across 2 crews.

Methods

Participants

The sample size was estimated by assuming the effect size to be 0.8 for a difference in total cholesterol. Based on previous work by us in a similar population,¹⁸ we assumed a 12% decrease in total cholesterol, and a significance of 0.05; thus, a sample size of 6 was required. Based on a convenience sample, 25 male and female WLFFs were recruited from two IHCs in the western region of the United States and monitored for the duration of their CT period. Institutional review board approval was granted by the University of Montana (#44-20). Participants provided written informed consent and completed a participation readiness questionnaire (PAR-Q+). Moreover, participants were allowed to withdraw from participation at any time.

Experimental Design

Participants were followed during an 80-h CT period prior to the 2022 fire season. Upon arrival on the first day of the training period, participants completed paperwork, including informed consent, PAR-Q+, and a training questionnaire. BW was measured each morning of training. Body composition was measured on the first day and last day of training. Morning antecubital blood draws were also taken before and after training, with the participants having fasted and being uncaffeinated for >12 h.

Body Mass and Body Composition

Body weight was recorded each morning in a T-shirt and shorts (∼0.25 and 0.5 kg, respectively) to the nearest tenth of a kilogram. If participants could not wear that ensemble because of work assignments, they were weighed in occupationally required clothing, consisting of a cotton T-shirt (0.25 kg), wildland fire boots and socks (4.5 kg), and FS 5100-92 specified Nomex pants (1.2 kg).¹⁹ Estimated nude BW was calculated by accounting for the weight of the items worn by each individual. Furthermore, BW and height were used to calculate body mass index (BMI). Body composition was estimated using a 3-site skinfold method with a calibrated Lange skinfold caliper (Beta Technology, Santa Cruz, CA). The same trained technician took skinfolds in a rotational order by measuring the chest, abdomen, and thigh for men and tricep, suprailium, and thigh for women. Measurements were repeated until the results were within 2 mm. Body density was then calculated using established sex-specific formulas and converted to body fat (BF) percentages using the Siri equation.²⁰^-22 Lean body weight was calculated using BW and BF.

Blood Collection and Analysis

Blood was collected following a >12-h fast using an antecubital venous draw into an EDTA-coated vacutainer for whole blood and a gel-barrier vacutainer to isolate serum. These vacutainers were centrifuged at the LabCorp manufacturer’s settings without adjustment for 10 min. Following centrifugation, the tubes were placed on ice in a cooler for <90 min before being taken to the nearest LabCorp facility (Laboratory Corporation of America, Burlington, NC) for analysis. Whole blood was analyzed for estimated plasma volume (PV), estimated cell volume (CV), estimated blood volume (BV), hemoglobin (Hb), and hematocrit (Ht). Changes in PV were calculated from Ht and Hb and corrected for hemoconcentration according to the methods of Dill and Costill.²³ Serum was analyzed for lipid markers, including total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), very-low-density lipoprotein cholesterol (VLDL-C), total triglycerides, and glucose. From HDL-C and total cholesterol, non-high-density lipoprotein cholesterol (non-HDL-C) was calculated. From LDL-C and HDL-C, the LDL-to-HDL ratio (LDL:HDL) was calculated. Additionally, serum was analyzed for metabolic markers, including total protein, albumin, globulin, sodium, potassium, chloride, calcium, and carbon dioxide (CO₂). From albumin and globulin, the albumin-to-globulin ratio was calculated (albumin:globulin).

Statistical Analysis

Body composition, blood constituents, serum lipid panel, and serum metabolic panel were all analyzed using the two-tailed paired samples t-test. Two-tailed Pearson correlations were used to analyze relationships between physiologic markers. All data are represented as mean±SEM. Analyses were conducted using SPSS data analysis software, version 27 (SPSS Inc, Chicago, IL), with significance set at P <0.05.

Results

Twenty-two men and 3 women completed the CT period. The male and female participants’ data are pooled for the remainder of the analysis. Across the CT period, the WLFFs showed no changes in BW, BF, LBM, or BMI (Table 1). Participants exhibited a decrease in Hb (P<0.001) and Ht (P<0.001) (Table 2). A significant increase in the change in BV (6.21±0.2%, P<0.001) and PV (6.16±0.2%, P<0.001) was observed from before CT to after CT, with no change in CV (Table 2). The lipid blood panel (Table 3) showed significant decreases in total cholesterol (P<0.001), triglycerides (P<0.001), LDL-C (P<0.001), VLDL-C (P<0.001), non-HDL-C (P<0.001), LDL:HDL (P<0.001), and fasting blood glucose (P=0.008). Additionally, a significant increase was observed in HDL-C (P=0.002). Additional metabolic panel markers (Table 3) showed small but statistically significant decreases in total protein (P=0.005), globulin (P=0.003), and CO₂ (P=0.001), with increases in the albumin:globulin (P=0.039) and chloride (P=0.03). No differences were seen in albumin, sodium, potassium, or calcium (P>0.05).

Table 1

Body weight and composition before and after wildland firefighter critical training

	Before	After	P
Body weight (kg)	81.2±2.3	80.9±2.6	0.29
Body mass index (kg/m²)	25.6±0.6	25.5±0.5	0.32
Body fat (%)	14.4±0.9	14.4±0.9	0.86
Lean body weight (kg)	69.3±2.2	69.1±2.2	0.35

Data are presented as mean±SEM.

Table 2

Whole blood constituents before and after wildland firefighter critical training

	Before	After	P
Hemoglobin (g/dL)	15.8±0.2	14.9±0.2^a	<0.001
Hematocrit (%)	47.0±0.6	44.3±0.6^a	<0.001
Est. plasma volume (%)	53.1±0.6	59.2±1.0^a	<0.001
Est. cell volume (%)	46.9±0.6	47.0±0.6	0.865
Est. blood volume (%)	100.0±0.0	106.2±0.8^a	<0.001

Est., estimated.

Data are presented as mean±SEM.

Indicates significant difference from the prewildland firefighter critical training value (P<0.05).

Table 3

Serum lipid and metabolic markers before and after wildland firefighter critical training

	Before	After	P
Total cholesterol (mg/dL)	176.3±6.7	157.0±5.3^a	<0.001
Triglycerides (mg/dL)	93.2±8.0	58.8±4.4^a	<0.001
LDL-C (mg/dL)	103.2±6.0	85.1±5.2^a	<0.001
HDL-C (mg/dL)	55.8±2.0	59.8±1.6^a	0.002
VLDL-C (mg/dL)	17.3±1.3	12.1±0.6^a	<0.001
Non-HDL-C (mg/dL)	120.5±6.7	97.2±5.6^a	<0.001
LDL:HDL	1.9±0.1	1.5±0.1^a	<0.001
Glucose (mg/dL)	93.7±1.3	89.2±1.4^a	0.008
Total protein (mg/dL)	7.3±0.07	7.1±0.07^a	0.005
Albumin (g/dL)	4.8±0.05	4.7±0.05	0.17
Globulin (g/dL)	2.5±0.06	2.3±0.05^a	0.002
Albumin:globulin	1.9±0.05	2.05±0.06^a	0.039
Sodium (mmol/L)	139.8±0.3	140.0±0.38	0.648
Potassium (mmol/L)	4.6±0.08	4.5±0.07	0.19
Chloride (mmol/L)	101.6±0.35	103.0±0.4^a	0.03
Calcium (mg/dL)	9.8±0.07	9.6±0.07	0.056
CO₂ (mmol/L)	22.7±0.5	21.4±0.4^a	0.001

CO₂, carbon dioxide; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LDL/HDL, LDL to HDL cholesterol ratio; Non-HDL-C, non-high-density lipoprotein cholesterol; VLDL-C, very-low-density lipoprotein cholesterol.

Data are presented as mean±SEM.

Indicates significant difference from the prewildland firefighter critical training value (P<0.05).

Pretraining total cholesterol was strongly correlated with corresponding pretraining concentrations of LDL-C (R=0.96, P<0.001), non-HDL-C (R=0.96, P<0.001), LDL: ratio (R=0.73, P<0.001), VLDL-C (R=0.52, P=0.007), and total triglycerides (R=0.58, P=0.002). Lower pretraining BF was associated with a greater magnitude of improvements in fasting glucose (R=0.54, P=0.006), HDL-C (R=−0.46, P=0.022), and LDL-C (R=0.41, P=0.042 across CT). The magnitude of change (delta, Δ) in total cholesterol was directly associated with ΔLDL-C (R=0.95, P<0.001) and Δtriglycerides (R=0.46, P=0.022).

Discussion

This study examined the effect of an 80-h WLFF CT period on markers of cardiovascular and metabolic health across 2 IHCs in the western United States. We found improvements in total, HDL, and LDL cholesterol; triglycerides; and blood glucose from before CT to after CT. Notably, these alterations are despite normal pretraining levels of these markers in an already fit population. These results are similar to those of a previous report from our laboratory¹⁸ but expand the generalizability since each IHC undergoes a unique training stimulus. This study also demonstrated a significant PV expansion over the course of 2 wk. Whether this is the result of direct heat exposure or the need to wear WLFF PPE is not clear. Regardless, this could provide an important acclimation for the subsequent fire season.²⁴

In occupational settings with high environmental temperatures, PV expansion can have protective effects against thermal strain.²⁵ This is crucial for WLFFs as they complete prolonged intensive exercise in the heat, increasing their core temperatures and placing them at risk of potentially life-threatening heat-related illnesses.^26,27 Throughout CT, we observed ∼6% increases in estimated BV and PV with concomitant decreases in Hb and Ht. Quantitatively, these alterations are consistent with previous research examining short-term heat acclimation during WLFF simulation in full PPE, showing a 7% increase in PV.²⁴ Further, these results are consistent with those from previous work from our laboratory examining CT, which showed similar elevations in plasma and BV (+10.5%).¹⁸ Thus, it appears that CT acts as a vehicle for acclimation and prepares WLFFs to manage their thermoregulation as they enter the fire season.

Lipid and metabolic panel parameters are often used as indirect indications of cardiovascular and metabolic health as dyslipidemia is directly associated with coronary heart disease.²⁸ It is well established that repeated bouts of aerobic and resistance exercise can improve resting cholesterol and fasting glucose values.²⁹ In the current study, 2 wk of CT were sufficient to elicit significant improvements in total cholesterol (−11%), total triglycerides (−37%), LDL-C (−18%), VLDL-C (−30%), non-HDL-C (−19%), LDL:HDL (−21%), fasting glucose (−5%), and HDL-C (+7%). These results are consistent with and even surpass the response seen in previous work examining CT, in which improvements were only seen in total cholesterol and LDL-C.¹⁸ Importantly, it appears that lower preseason BF was associated with greater improvements in changes in LDL-C, HDL-C, and fasting glucose, further emphasizing the need for satisfactory preseason fitness. Activity monitor data from this study published elsewhere³⁰ indicated that higher activity levels during CT were related to greater improvements in HDL-C (R=0.31, P=0.049), while greater sedentary time showed maladaptations in BF (R=0.45, P=0.028) and LDL-C (R=−0.41, P=0.045). Thus, it appears that physical activity levels during CT indicate the magnitude of change in these markers.

Despite these acute improvements in cardiovascular and metabolic health, previous research has established detrimental alterations across a single fire season.^15,16 In 2019, Coker et al¹⁵ showed seasonal increases in total cholesterol and LDL-C in a single crew. These results were confirmed across multiple crews in 2022, showing negative modifications in total cholesterol, LDL-C, VLDL-C, HDL-C, and total triglycerides.¹⁶ Importantly, the postseason values across these crews were quantitatively similar to those seen in the current study before CT. This suggests that despite the acute improvements seen in CT, it does not provide long-term cardiovascular and metabolic protection for WLFFs. Somewhat paradoxically, these chronic perturbations are still visible despite high physical activity across the season, previously established as beneficial for lipid panel markers.^3,29 Thus, it is likely that there are additional confounding factors (sleep, diet, etc) at play across a fire season that are not yet well understood.^7,8

Regardless, the significance of these data still provides an important context. Notably, significant depressions in fasting glucose across CT, evident both in the current study and previous research from our laboratory, indicate training-induced improvements in glycemic control.¹⁸ Compromised glucose regulation can result in type 2 diabetes mellitus, a growing concern in the United States that is closely linked with obesity, hypertension, and coronary heart disease.³¹ Thus, these short-term adaptations provide promising evidence that CT is a positive stressor that can offer protection from these deleterious effects. Further, it appears that this maintenance extends beyond acute alterations. Despite elevations in cholesterol, fasting blood glucose remains consistent across the season.¹⁵ Together, these data infer that CT provides lasting adaptations to WLFFs’ glucose metabolism despite changes in diet and lifestyle outside of subject control.⁷

Managing BW and composition throughout a fire season can be a challenge for WLFFs as high daily energy expenditures and altered dietary habits can challenge appropriate energy balance.^2,3,8 However, throughout the season, WLFFs’ BW, BMI, and fat mass have been shown to increase.¹⁵ Conversely, during the 80-h CT period, previous data from our group demonstrated an improvement in lean body mass and BF percentage.¹⁸ These data were not reproduced within the current sample, in which no significant changes were seen in BW, BMI, BF percentage, or lean body mass. However, variations were seen across the crews. The BW increased by 0.35 kg in 1 crew and decreased by 0.91 kg in the other crew. Though subtle, this variability highlights the differing demands and responses between the 2 IHC CT periods.

Limitations

The participants in this study were a convenience sample of IHC WLFFs without a control group. Activities performed by these 2 crews likely do not represent those carried out by all crews during CT. Our sample only contained 3 women (12% of the sample pool). While this number is similar to that from other WLFF-related studies and within the greater WLFF community, it prevents us from identifying any sex-specific differences.^2,3,32 The biggest limitation in this field study is the inability to control diet before and during the CT period. Thus, it is hard to isolate whether these alterations were primarily due to the exercise stimulus, alterations in diet, or some other confounding factor. In this study previously reported,³⁰ 48 h before CT started, 50% of participants reported abstaining from physical activity, 32% reported light physical activity, and 18% reported moderate-to-heavy physical activity. These variations could contribute to the inconsistencies seen across the CT period. Furthermore, the occupational stressors of WLFFs shift daily, depending on their assigned jobs, and could account for the large variability seen across individuals.

Conclusions

This study confirms that CT provides a sufficient physiologic stressor to elicit positive adaptations in markers of cardiovascular and metabolic health while maintaining BW and composition. Those with more favorable preseason body composition showed the greatest magnitude of improvement, perhaps allowing for more work to be done, highlighting the need to prioritize preseason fitness. Additionally, increased activity levels during CT influenced the magnitude of change, indicating that physical activity itself was partially responsible for these alterations.

Footnotes

Acknowledgements

Acknowledgments: The authors thank the participating fire crew members for their time and effort. Special gratitude is extended to Molly West and Skylar Brown for their data collection efforts.

Author Contributions: study concept and design (KSC, JAS, SCG, CLD); obtaining funding (CLD, JAS); acquisition of the data (JAS, SCG); analysis of the data (KSC, CLD); drafting of the manuscript (KSC, CLD); critical revision of the manuscript (KSC, JAS, SCG, CLD); approval of the final manuscript (KSC, JAS, SCG, CLD).

Financial/Material Support: This study was funded by the United States Forest Service (16-CR-11138200-005). The authors report no potential conflicts of interest.

Disclosures: The authors declare that they have no competing interests in access to these data or associations with companies involved with the products used in this research. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the US Department of Agriculture, Forest Service, the National Wildfire Coordination Group, or the Department of Interior.

References

Ruby

B.C.

Shriver

T.C.

Zderic

T.W.

Sharkey

B.J.

Burks

Tysk

Total energy expenditure during arduous wildfire suppression. Med Sci Sports Exerc 2002; 34(6), 1048–1054.

Cuddy

J.S.

Sol

J.A.

Hailes

W.S.

Ruby

B.C.

Work patterns dictate energy demands and thermal strain during wildland firefighting. Wilderness Environ Med 2015; 26(2), 221–226.

Sol

J.A.

Ruby

B.C.

Gaskill

S.E.

Dumke

C.L.

Domitrovich

J.W.

Metabolic demand of hiking in wildland firefighting. Wilderness Environ Med 2018; 29(3), 304–314.

Montain

S.J.

Sawka

M.N.

Cadarette

B.S.

Quigley

M.D.

McKay

J.M.

Physiological tolerance to uncompensable heat stress: effects of exercise intensity, protective clothing, and climate. J Appl Physiol 1994; 77(1), 216–222.

Carballo-Leyenda

Villa

J.G.

López-Satué

Collado

P.S.

Rodríguez-Marroyo

J.A.

Fractional contribution of wildland firefighters’ personal protective equipment on physiological strain. Front Physiol 2018; 9, 1139.

Navarro

K.M.

West

M.R.

O’Dell

Sen

Chen

I.C.

Fischer

E.V.

et al. Exposure to particulate matter and estimation of volatile organic compounds across wildland firefighter job tasks. Environ Sci Technol 2021; 55(17), 11795–11804.

Brooks

S.J.

West

M.R.

Domitrovich

J.W.

Sol

J.A.

Holubetz

Partridge

et al. Nutrient intake of wildland firefighters during arduous wildfire suppression: macronutrient and micronutrient consumption. J Occup Environ Med 2021; 63(12), e949–e956.

Marks

A.N.

Sol

J.A.

Domitrovich

J.W.

West

M.R.

Ruby

B.C.

Total energy intake and self-selected macronutrient distribution during wildland fire suppression. Wilderness Environ Med 2020; 31(2), 188–196.

Jeklin

A.T.

Davies

H.W.

Bredin

S.S.

Hives

B.A.

Meanwell

L.E.

Perrotta

A.S.

et al. Fatigue and sleep patterns among Canadian wildland firefighters during a 17-day fire line deployment. J Occup Environ Hyg 2020; 17(7–8), 364–371.

10.

Gaskill

S.E.

Dumke

C.L.

Palmer

C.G.

Ruby

B.C.

Domitrovich

J.W.

Sol

J.A.

Seasonal changes in wildland firefighter fitness and body composition. Int J Wildland Fire 2020; 29(3), 294–303.

11.

Britton

Lynch

C.F.

Ramirez

Torner

Buresh

Peek-Asa

Epidemiology of injuries to wildland firefighters. Am J Emerg Med 2013; 31(2), 339–345.

12.

Ferguson

M.D.

Semmens

E.O.

Dumke

Quindry

J.C.

Ward

T.J.

Measured pulmonary and systemic markers of inflammation and oxidative stress following wildland firefighter simulations. J Occup Environ Med 2016; 58(4), 407–413.

13.

Semmens

E.O.

Domitrovich

Conway

Noonan

C.W.

A cross-sectional survey of occupational history as a wildland firefighter and health. Am J Ind Med 2016; 59(4), 330–335.

14.

Navarro

K.M.

Kleinman

M.T.

Mackay

C.E.

Reinhardt

T.E.

Balmes

J.R.

Broyles

G.A.

et al. Wildland firefighter smoke exposure and risk of lung cancer and cardiovascular disease mortality. Environ Res 2019; 173, 462–468.

15.

Coker

R.H.

Murphy

C.J.

Johannsen

Galvin

Ruby

B.C.

Wildland firefighting: adverse influence on indices of metabolic and cardiovascular health. J Occup Environ Med 2019; 61(3), e91–e94.

16.

Rosales

A.M.

Dodds

P.S.

Hailes

W.S.

Sol

J.A.

Coker

R.H.

Quindry

J.C.

et al. Deterioration of lipid metabolism despite fitness improvements in wildland firefighters. J Occup Environ Med 2022; 64(5), 385–389.

17.

US Department of the Interior, US Department of Agriculture

Standards for Interagency Hotshot Crew Operations - Report to the National Interagency Fire Center . US Department of the Interior, US Department of Agriculture, 2016 1–40.

18.

Gurney

S.C.

Christison

K.S.

Williamson-Reisdorph

C.M.

Sol

J.A.

Quindry

T.S.

Quindry

J.C.

et al. Alterations in metabolic and cardiovascular risk factors during critical training in wildland firefighters. J Occup Environ Med 2021; 63(7), 594–599.

19.

National Fire Protection Association

Standard on Protective Clothing and Equipment for Wildland Fire Fighting and Urban Interface Fire Fighting . National Fire Protection Association, 2022 1–116.

20.

Jackson

A.S.

Pollock

M.L.

Generalized equations for predicting body density of men. Br J Nutr 1978; 40(3), 497–504.

21.

Jackson

A.S.

Pollock

M.L.

Ward

Generalized equations for predicting body density of women. Med Sci Sports Exerc 1980; 12(3), 175–181.

22.

Siri

W.E.

Body composition from fluid spaces and density: analysis of methods. Nutrition 1993; 9(5), 480–491.

23.

Dill

D.B.

Costill

D.L.

Calculation of percentage changes in volumes of blood, plasma, and red cells in dehydration. J Appl Physiol 1974; 37(2), 247–248.

24.

Schleh

M.W.

Ruby

B.C.

Dumke

C.L.

Short term heat acclimation reduces heat stress, but is not augmented by dehydration. J Therm Biol 2018; 78, 227–234.

25.

Neal

R.A.

Corbett

Massey

H.C.

Tipton

M.J.

Effect of short-term heat acclimation with permissive dehydration on thermoregulation and temperate exercise performance. Scand J Med Sci Sports 2016; 26(8), 875–884.

26.

West

M.R.

Costello

Sol

J.A.

Domitrovich

J.W.

Risk for heat-related illness among wildland firefighters: job tasks and core body temperature change. Occup Environ Med 2020; 77(7), 433–438.

27.

Sol

J.A.

West

M.R.

Domitrovich

J.W.

Ruby

B.C.

Evaluation of environmental conditions on self-selected work and heat stress in wildland firefighting. Wilderness Environ Med 2021; 32(2), 149–159.

28.

Lloyd-Jones

D.M.

Wilson

P.W.

Larson

M.G.

Beiser

Leip

E.P.

D’Agostino

R.B.

et al. Framingham risk score and prediction of lifetime risk for coronary heart disease. Am J Cardiol 2004; 94(1), 20–24.

29.

Mann

Beedie

Jimenez

Differential effects of aerobic exercise, resistance training and combined exercise modalities on cholesterol and the lipid profile: review, synthesis and recommendations. Sports Med 2014; 44(2), 211–221.

30.

Christison

K.S.

Sol

J.A.

Gurney

S.C.

Dumke

C.L.

Nearly 25% of wild land firefighters reach critical levels of serum creatine kinase during 80-hour critical training. Wilderness Environ Med 2023; 34(3), 334–340.

31.

Leon

B.M.

Maddox

T.M.

Diabetes and cardiovascular disease: epidemiology, biological mechanisms, treatment recommendations and future research. World J Diabetes 2015; 6(13), 1246–1258.

32.

Fahy

Evarts

Stein

G.P.

US Fire Department Profile 2020. Report No. USS07 . National Fire Protection Association, 2022 1–6.