Context To our knowledge, no researchers have investigated thermoregulatory responses and exertional heat illness (EHI) risk factors in marching band (MB) artists performing physical activity in high environmental temperatures. Objective To examine core temperature (T c ) and EHI risk factors in MB artists. Design Descriptive epidemiology study. Setting Three rehearsals and 2 football games for 2 National Collegiate Athletic Association Division I institution's MBs. Patients or Other Participants Nineteen volunteers (females = 13, males = 6; age = 20.5 ± 0.9 years, height = 165.1 ± 7.1 cm, mass = 75.0 ± 19.1 kg) completed the study. Main Outcome Measure(s) We measured T c , wet bulb globe temperature, and relative humidity preactivity, during activity, and postactivity. Other variables were activity time and intensity, body surface area, hydration characteristics (fluid volume, sweat rate, urine specific gravity, percentage of body mass loss), and medical history (eg, previous EHI, medications). The statistical analysis consisted of descriptive information (mean ± standard deviation), comparative analyses that determined differences within days, and correlations that identified variables significantly associated with T c . Results The mean time for rehearsals was 102.8 ± 19.8 minutes and for games was 260.5 ± 47.7 minutes. Mean maximum T c was 39.1 ± 1.1°C for games and 38.4 ± 0.7°C for rehearsals; the highest T c (41.2°C) occurred during a game. Fluid consumption did not match sweat rates ( P < .001). Participants reported to games in a hypohydrated state 63.6% of the time. The maximum T c correlated with the maximum wet bulb globe temperature ( r = 0.618, P < .001) and was higher in individuals using mental health medications ( r pb = −0.254, P = .022) and females ( r pb = 0.330, P = .002). Body surface area ( r = −0.449, P < .001) and instrument mass ( r = −0.479, P < .001) were negatively correlated with T c . Conclusions Marching band artists experienced high T c during activity and should have access to athletic trainers who can implement EHI-prevention and -management strategies.

Context Research exists on energy balances (EBs) and eating disorder (ED) risks in physically active populations and occupations by settings, but the EB and ED risk in athletic trainers (ATs) have not been investigated. Objective To assess ATs' energy needs, including the macronutrient profile, and examine ED risk and pathogenic behavioral differences between sexes (men, women) and job statuses (part time or full time) and among settings (college or university, high school, nontraditional). Design Cross-sectional study. Setting Free living in job settings. Patients or Other Participants Athletic trainers (n = 46; male part-time graduate assistant ATs = 12, male full-time ATs = 11, female part-time graduate assistant ATs = 11, female full-time ATs = 12) in the southeastern United States. Main Outcome Measure(s) Anthropometric measures (sex, age, height, weight, body composition), demographic characteristics (job status [full- or part-time AT], job setting [college/university, high school, nontraditional], years of AT experience, exercise background, alcohol use), resting metabolic rate, energy intake (EI), total daily energy expenditure (TDEE), EB, exercise energy expenditure, macronutrients (carbohydrates, protein, fats), the Eating Disorder Inventory-3, and the Eating Disorder Inventory-3 Symptom Checklist. Results The majority of participants (84.8%, n = 39) had an ED risk, with 26.1% (n = 12) engaging in at least 1 pathogenic behavior, 50% (n = 23) in 2 pathogenic behaviors, and 10.8% (n = 5) in >2 pathogenic behaviors. Also, 82.6% of ATs (n = 38) presented in negative EB (EI < TDEE). Differences were found in resting metabolic rate for sex and job status ( F 1,45 = 16.48, P = .001), EI ( F 1,45 = 12.01, P = .001), TDEE ( F 1,45 = 40.36, P < .001), and exercise energy expenditure ( F 1,38 = 5.353, P = .026). No differences were present in EB for sex and job status ( F 1,45 = 1.751, P = .193); χ 2 analysis revealed no significant relationship between ATs' sex and EB ( \(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\bf{\alpha}}\)\(\def\bupbeta{\bf{\beta}}\)\(\def\bupgamma{\bf{\gamma}}\)\(\def\bupdelta{\bf{\delta}}\)\(\def\bupvarepsilon{\bf{\varepsilon}}\)\(\def\bupzeta{\bf{\zeta}}\)\(\def\bupeta{\bf{\eta}}\)\(\def\buptheta{\bf{\theta}}\)\(\def\bupiota{\bf{\iota}}\)\(\def\bupkappa{\bf{\kappa}}\)\(\def\buplambda{\bf{\lambda}}\)\(\def\bupmu{\bf{\mu}}\)\(\def\bupnu{\bf{\nu}}\)\(\def\bupxi{\bf{\xi}}\)\(\def\bupomicron{\bf{\micron}}\)\(\def\buppi{\bf{\pi}}\)\(\def\buprho{\bf{\rho}}\)\(\def\bupsigma{\bf{\sigma}}\)\(\def\buptau{\bf{\tau}}\)\(\def\bupupsilon{\bf{\upsilon}}\)\(\def\bupphi{\bf{\phi}}\)\(\def\bupchi{\bf{\chi}}\)\(\def\buppsy{\bf{\psy}}\)\(\def\bupomega{\bf{\omega}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\({\rm{\chi }}_{1,46}^2\) = 0.0, P = 1.00) and job status and EB ( \({\rm{\chi }}_{1,46}^2\) = 2.42, P = .120). No significant relationship existed between Daily Reference Intakes recommendations for all macronutrients and sex or job status. Conclusions These athletic trainers experienced negative EB, similar to other professionals in high-demand occupations. Regardless of sex or job status, ATs had a high ED risk and participated in unhealthy pathogenic behaviors. The physical and mental concerns associated with these findings indicate a need for interventions targeted at ATs' health behaviors.

CONTEXT: Female athletes/performing artists can present with low energy availability (LEA) either through unintentional (e.g., inadvertent undereating) or intentional methods (e.g., eating disorder [ED]). While LEA and ED risk have been examined independently, little research has examined these simultaneously. Awareness of LEA with or without ED risk may provide clinicians with innovative prevention and intervention strategies. OBJECTIVE: To examine LEA with or without ED risk (e.g., eating attitudes, pathogenic behaviors) in female collegiate athletes/performing artists. DESIGN: Cross-sectional and descriptive. SETTING: Free-living in sport-specific settings. PARTICIPANTS: Collegiate female athletes/performing artist (n=121; age: 19.8±2.0 years; height: 168.9±7.7 cm; weight: 63.6±9.26 kg) in equestrian (n=28), volleyball (n=12), softball (n=17), beach volleyball (n=18), ballet (n=26) and soccer (n=20) participated in this study. MAIN OUTCOME MEASURES: Anthropometric measurements (height, weight, body composition), resting metabolic rate, energy intake, total daily energy expenditure, exercise energy expenditure, Eating Disorder Inventory-3 (EDI-3), and EDI-3 Symptom Checklist were assessed. Chi-square analysis examined differences between LEA and sport type, LEA and ED risk, ED risk and sport type, and pathogenic behaviors and sport type. RESULTS: Female athletes/performing artists (81%; n=98) displayed LEA and significant differences were found between LEA and sport type (χ 2 5 =43.8, P <.01). Female athletes/performing artists (76.0%; n=92) presented with ED risk with no significant difference between ED risk and sport. EDI-3 Symptom Checklist revealed 61.2% (n=74) engaged in pathogenic behaviors, with dieting the most common (51.2%; n=62). Athletes/performing artist displayed LEA with ED risk (76.0%; n=92). No significant differences were found between LEA with ED risk and sport. Softball was the highest with 82.4% (n=14) reporting LEA with ED risk followed by ballet (76%; n=19). CONCLUSION: Our results suggest a large proportion of collegiate female athletes/performing artists are at risk for LEA with ED risk, thus warranting education, identification, prevention, and intervention strategies relative to fueling for performance.

Context: No research has investigated thermoregulatory responses and exertional heat illness (EHI) risk factors in marching band (MB) artists performing physical activity in high environmental temperatures. Objective: Examine core temperature (Tc) and EHI risk factors in MB artists. Design: Descriptive epidemiology study. Setting: Three rehearsals and 2 football games for 2 NCAA Division I MBs. Participants: Nineteen volunteers completed the study (female = 13, males = 6; age = 20.5 ± 0.9 years; weight = 75.0 ± 19.1 kg; height = 165.1 ± 7.1 cm). Main Outcome Measures: We measured Tc pre-, post-, and every 15 minutes during activity and recorded wet-bulb globe temperature (WBGT) and relative humidity (RH) every 15 minutes. Other variables included activity time and intensity, ground surface, hydration characteristics (fluid volume, sweat rate, urine specific gravity, percent body mass loss [%BM]), and medical history (eg, previous EHI, medications). Statistical analysis included descriptives (mean ± standard deviation), comparative analyses determined differences within and between days, and linear regression identified variables that significantly explained Tc. Results: Mean rehearsal time = 102.8 ± 19.8 minutes and game time = 260.5 ± 47.7 minutes. Max game Tc (39.1 ± 1.1°C) was significantly higher than rehearsal (38.4 ± 0.7°C, P = .003). The highest max game Tc = 41.2°C. Participants consumed significantly more fluid than their sweat rates ( P < .003), which minimized %BM loss, particularly during rehearsals (−0.4 ± 0.6%). Mean game %BM loss = −0.9 ± 2.0%; however, 63.6% of the time, participants reported hypohydrated to game day. Max Tc was significantly predicted by max WBGT, max RH, ground surface, using mental health medications, and hours of sleep (adjusted R 2 = 0.542, P < .001). Conclusions: Marching band artists experience high Tc during activity and should have access to athletic trainers who can implement EHI prevention and management strategies.

Context Determining an athlete's hydration status allows hydration-related concerns to be identified before significant medical or performance concerns arise. Weight charts are an accurate measure of hydration status changes, yet their clinical use by athletic trainers (ATs) is unknown. Objective To investigate ATs' use of weight charts in athletic settings and describe their subsequent clinical decisions. Design Cross-sectional survey. Setting High schools and National Collegiate Athletic Association Divisions I, II, III and National Association Intercollegiate Athletics colleges. Patients or Other Participants A total of 354 ATs (men = 162, women = 175; 17 respondents did not answer the demographic questions) responded across athletic settings (Division I [45.7%]; Division II, Division III, National Association Intercollegiate Athletics combined [n = 19.9%]; and high school [34.4%]). Main Outcome Measure(s) The 26-question online survey was developed by content experts and pilot tested before data collection. Participants answered questions focused on weight-chart use (implementation, timing, and calculations) and clinical decision processes (policies, interventions, and referral). Frequency statistics were calculated. Results The majority of ATs (57.2%) did not use weight charts. Of those who did, most (76.0%) used charts with football, soccer (28%), and wrestling (6%) athletes. They calculated changes as either an absolute (42.2%) or percentage (36.7%) change from prepractice to postpractice; only 11.7% used a baseline weight for calculations. Of those who used the percentage change in body mass, 66.0% selected a threshold of −3% to −4% for an intervention. Most ATs (97.0%) intervened with verbal education, whereas only one-third (37.0%) provided specific fluid amounts based on body mass changes. Conclusions Typically, ATs in athletic settings did not use weight charts. They considered a body mass change of –3% the indication for intervention but did not specify rehydration amounts for hypohydrated athletes. Educational workshops or technology applications could be developed to encourage ATs to use weight charts and calculate appropriate individual fluid interventions for their athletes.

CONTEXT: Research exists on energy balance (EB) and eating disorder (ED) risk in physically active populations and occupations by settings, but EB and ED in athletic trainers (ATs) has not been investigated. OBJECTIVE: To assess ATs' energy needs, including macronutrient profile, and to examine ED risk and pathogenic behavior between sex (males, females), job status (part-time=PT-AT; full-time=FT-AT) and setting (college/university, high school, non-traditional). DESIGN: Cross-sectional and descriptive. SETTING: Free-living in job settings. PARTICIPANT: ATs (n=46; males PT-AT n=12, males FT-AT n=11; females PT-AT n=11, female FT-AT n=12) in Southeastern United States. MAIN OUTCOME MEASURES: Anthropometric measurements (age, height, weight, body composition), resting metabolic rate (RMR), energy intake (EI), total daily energy expenditure (TDEE), exercise energy expenditure (EEE), EB, macronutrients (carbohydrates, protein, fats), Eating Disorder Inventory-3, and the Eating Disorder Inventory-3 Symptom Checklist. RESULTS: Majority (84.8%, n=39) had ED risk, with 26.1% (n=12) engaging in at least 1 pathogenic behavior, 50% (n=23) in 2 pathogenic behaviors, and 10.8% (n=5) in more than 2 pathogenic behaviors. 82.6% of ATs (n=38) presented in negative EB (EI<TDEE). Significant differences were found for sex and job status for RMR ( F (1,45)=16.48, P =.001), EI ( F (1,45)=12.01, P =.001), TDEE ( F (1,45)=40.36, P <.001) and EEE ( F (1,38)=5.353, P =.026). No significant differences were found in EB, sex and job status (F(1.45)=1.751, P=.193); Chi-squared analysis revealed no significant differences between ATs' sex and EB [χ 2 (1,46)=0.0, P=1.00] and job status and EB χ 2 (1,46) = 2.42, P= 0.120]. No significant difference found between Daily Reference Intakes recommendations for all macronutrients and sex or job status. CONCLUSIONS: Athletic trainers experience negative EB, similar to other high-demand occupational professions. Regardless of sex or job status, ATs have a high ED risk and participate in unhealthy pathogenic behaviors. The physical and mental concerns associated with these findings indicates a need for interventions targeted toward ATs' health behaviors.