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For gender-diverse (GD) college marching band (MB) artists, the risks for anxiety and depression may be higher as they navigate the demands and stressors associated with MB, college, and their gender identity. To examine the risks of anxiety and depression across GD MB artists and to explore their barriers and attitudes toward seeking mental health (MH) care. Cross-sectional study. Online survey. Seventy-eight GD individuals (transgender = 12, nonbinary = 66, age = 19 ± 1 years). A survey was used to assess demographics, anxiety risk using the State-Trait Anxiety Inventory, depression risk using the Center for Epidemiologic Studies Depression Scale, and barriers and attitudes using the Barriers Towards Seeking Help Checklist, the Attitudes Toward Seeking Professional Psychological Help Scale-Short Form, and the Mental Help Seeking Attitudes Scale. We calculated descriptive statistics and univariate analyses to evaluate scores, risks, and differences between MH and receiving assistance. Participants had high state anxiety (mean = 52.0 ± 112.1), trait anxiety (mean = 55.2 ± 10.0), and symptoms of depression (mean = 30.4 ± 12.0) based on the State-Trait Anxiety Inventory and the Center for Epidemiologic Studies Depression Scale. Overall, 78.2% (n = 61 of 78) of GD MB artists were considered at risk for both state and trait anxiety and depression, and 18% (n = 11 of 61) did not seek help from an MH professional. These GD MB artists cited a lack of time (82.1%; n = 64 of 78) as the primary barrier to seeking professional help. The mean score on the Attitudes Toward Seeking Professional Psychological Help Scale-Short Form for all GD artists was 19.5 ± 5.0, and the total score for the Mental Help Seeking Attitudes Scale was 47.8 ± 9.2, which indicated more favorable attitudes toward seeking professional help. We identified high rates of clinical symptoms for depression and anxiety among GD MB artists. The data are consistent with those from other minority populations and above the normative values for cisgender students. The lack of help-seeking behaviors in nearly 15% of at-risk participants highlights the need for specialized resources for GD patients and those participating in MB. [ABSTRACT FROM AUTHOR]

Marching band (MB) artists experience stressors influencing their physical, mental, and emotional health warranting medical support, and they face challenges similar to those of other college students and athletes. Mental health illnesses exist in collegiate and MB settings, but barriers affect access to treatment. To examine MB artists' perceived barriers to and attitudes toward seeking care from mental health professionals. The secondary aim was to explore barriers to and attitudes about seeking mental health counseling between genders and history of pursuing mental health counseling. Cross-sectional study. Online survey. A total of 534 MB artists (women = 312, men = 222; age = 19.7 ± 1.4 years). Participants completed surveys on demographics and past medical history along with the Barriers to Help Seeking Checklist, the Attitudes Toward Seeking Professional Psychological Help-Short Form Scale (ATSPPH-SF), and the Mental Help Seeking Attitudes Scale (MHSAS). Descriptive statistics were calculated to assess demographic data. Cross-tabulations and χ2 statistics were used to evaluate individual barriers (Barriers to Help Seeking Checklist) between genders. Scales were scored 1 to 7 and 10 to 30 on the MHSAS and ATSPPH-SF, respectively. A 1-way analysis of variance measured differences in the total mean score on the ATSPPH-SF between genders. The highest barrier reported was lack of time to seek services (69.1%; n = 369), followed by 47.6% (n = 254) for services not available during my free time. Average scores were 4.0 ± 0.4 on the MHSAS (indicating neutral attitudes toward seeking help) and 17.97 ± 5.48 on the ATSPPH-SF (indicating slightly positive attitudes to seeking help). No differences were seen for the total mean scores on the MHSAS and ATSPPH-SF between genders. Marching band artists' barriers to and attitudes toward mental health care influenced their ability to seek care in times of need and demonstrated some similarities to those of collegiate athletes. Awareness of the obstacles MB artists face in receiving mental health care will assist health care providers in advocating for improved care in this setting. [ABSTRACT FROM AUTHOR]

Engaging in exercise and appropriate nutritional intake improves mental health by reducing anxiety, depression, and sleep disturbances. However, few researchers have examined energy availability (EA), mental health, and sleep patterns in athletic trainers (ATs). To examine ATs' EA, mental health risk (ie, depression, anxiety), and sleep disturbances by sex (male, female), job status (part time [PT AT], full time [FT AT]), and occupational setting (college or university, high school, or nontraditional). Cross-sectional study. Free living in occupational settings. A total of 47 ATs (male PT ATs = 12, male FT ATs = 12; female PT ATs = 11, female FT ATs = 12) in the southeastern United States. Anthropometric measurements consisted of age, height, weight, and body composition. Energy availability was measured through energy intake and exercise energy expenditure. We used surveys to assess the depression risk, anxiety (state or trait) risk, and sleep quality. Thirty-nine ATs engaged in exercise, and 8 did not exercise. Overall, 61.5% (n = 24/39) reported low EA (LEA); 14.9% (n = 7/47) displayed a risk for depression; 25.5% (n = 12/47) indicated a high risk for state anxiety; 25.5% (n = 12/47) were at high risk for trait anxiety, and 89.4% (n = 42/47) described sleep disturbances. No differences were found by sex and job status for LEA, depression risk, state or trait anxiety, or sleep disturbances. Those ATs not engaged in exercise had a greater risk for depression (risk ratio [RR] = 1.950), state anxiety (RR = 2.438), trait anxiety (RR = 1.625), and sleep disturbances (RR = 1.147), whereas ATs with LEA had an RR of 0.156 for depression, 0.375 for state anxiety, 0.500 for trait anxiety, and 1.146 for sleep disturbances. Although most ATs engaged in exercise, their dietary intake was inadequate, they were at increased risk for depression and anxiety, and they experienced sleep disturbances. Those who did not exercise were at an increased risk for depression and anxiety. Energy availability, mental health, and sleep affect overall quality of life and can affect ATs' ability to provide optimal health care. [ABSTRACT FROM AUTHOR]

Female athletes and performing artists can present with low energy availability (LEA) from either unintentional (eg, inadvertent undereating) or intentional (eg, eating disorder [ED]) methods. Whereas LEA and ED risk have been examined independently, few researchers have examined them simultaneously. Awareness of LEA with or without ED risk may provide clinicians with innovative prevention and intervention strategies. To examine LEA with or without ED risk (eg, eating attitudes, pathogenic behaviors) in female collegiate athletes and performing artists and compare sport type and LEA with the overall ED risk. Cross-sectional study. Free living in sport-specific settings. A total of 121 collegiate female athletes and performing artists (age = 19.8 ± 2.0 years, height = 168.9 ± 7.7 cm, mass = 63.6 ± 9.3 kg) participating in equestrian (n = 28), soccer (n = 20), beach volleyball (n = 18), softball (n = 17), volleyball (n = 12), and ballet (n = 26). Anthropometric measurements (height, mass, 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 was used to examine differences between LEA and sport type, LEA and ED risk, ED risk and sport type, and pathogenic behaviors and sport type. Most (81%, n = 98) female athletes and performing artists displayed LEA and differences between LEA and sport type (\(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\bf{\alpha}}\)\(\def\bupbeta{\bf{\beta}}\)\(\def\bupgamma{\bf{\gamma}}\)\(\def\bupdelta{\bf{\delta}}\)\(\def\bupvarepsilon{\bf{\varepsilon}}\)\(\def\bupzeta{\bf{\zeta}}\)\(\def\bupeta{\bf{\eta}}\)\(\def\buptheta{\bf{\theta}}\)\(\def\bupiota{\bf{\iota}}\)\(\def\bupkappa{\bf{\kappa}}\)\(\def\buplambda{\bf{\lambda}}\)\(\def\bupmu{\bf{\mu}}\)\(\def\bupnu{\bf{\nu}}\)\(\def\bupxi{\bf{\xi}}\)\(\def\bupomicron{\bf{\micron}}\)\(\def\buppi{\bf{\pi}}\)\(\def\buprho{\bf{\rho}}\)\(\def\bupsigma{\bf{\sigma}}\)\(\def\buptau{\bf{\tau}}\)\(\def\bupupsilon{\bf{\upsilon}}\)\(\def\bupphi{\bf{\phi}}\)\(\def\bupchi{\bf{\chi}}\)\(\def\buppsy{\bf{\psy}}\)\(\def\bupomega{\bf{\omega}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\({\rm{\chi }}_5^2\) = 43.8, P <.001). The majority (76.0%, n = 92) presented with an ED risk, but the ED risk did not differ by sport type (P =.94). The EDI-3 Symptom Checklist revealed that 61.2% (n = 74) engaged in pathogenic behaviors, with dieting being the most common (51.2%, n = 62). Most (76.0%, n = 92) displayed LEA with an ED risk. No differences were found in LEA by ED risk and sport type. Softball players reported the most LEA with an ED risk (82.4%, n = 14), followed by ballet dancers (76%, n = 19). Our results suggested that a large proportion of collegiate female athletes and performing artists were at risk for LEA with an ED risk, thus warranting education, identification, prevention, and intervention strategies relative to fueling for performance. [ABSTRACT FROM AUTHOR]

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. To investigate ATs' use of weight charts in athletic settings and describe their subsequent clinical decisions. Cross-sectional survey. High schools and National Collegiate Athletic Association Divisions I, II, III and National Association Intercollegiate Athletics colleges. 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%]). 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. 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. 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. [ABSTRACT FROM AUTHOR]