A 19-year-old female collegiate rower with a history of multiple food allergies presented with full-body urticaria after two separate practices, 4 days apart. Patient has a history of anaphylaxis every 2 months and takes allergy medications. No known allergens were identified after food recall. Referred to a medical doctor and allergist, patient underwent a 3-week wheat elimination diet and had no further reactions. Diagnosed with food-dependent exercise-induced anaphylaxis, patient was instructed to avoid gluten and other trigger foods pre-and postexercise, and to carry an epinephrine auto-injector while rowing. Patient made a full return to sport.

OBJECTIVE:Determine differences in running biomechanics in female endurance runners between days when they did and did not report menstrual cycle-related symptoms. METHODS:Observational study. Subjects were provided RunScribe sensors to attach to their shoes to collect biomechanical data when running. Daily during the study period, subjects were sent a text message to complete a survey asking about their wellness, menstrual status, and training status. Descriptive measures (mean ± SD) were generated for whether runners reported being asymptomatic or symptomatic during runs and run workout details. Paired sample t-tests were executed to identify differences in impact Gs, braking Gs, pronation excursion, maximum pronation velocity, foot strike type, and gait speed between runs on days participants reported having menstrual-related symptoms (symptomatic) or not (asymptomatic). Participants needed to have recorded runs spanning the entire data collection window to be included for comparative analyses. RESULTS:Twenty-seven university club runners (age 20.5 ± 1.5) participated in the study. All runners (n = 27) experienced at least one menstrual cycle-related symptom during data collection. The average number of asymptomatic runs was 22.3 ± 17.1 and symptomatic runs was 9.1 ± 7.5. Daily mileage averaged 4.3 ± 1.9 miles and total mileage was 154.2 ± 115.4 miles. Fourteen runners had run data viable for pairwise sampling. There was no significant difference in biomechanical measures between symptomatic or asymptomatic days (p > .05). CONCLUSION:This study prospectively monitored distance runners' activity while simultaneously recording symptoms related to the menstrual cycle. While runners reported fewer days running when symptomatic, we did not identify a difference in objective biomechanical measures between asymptomatic or symptomatic runs. Perceived symptom burden was present in this sport population and may warrant further exploration of perceived expectations of the menstrual cycle to athletic performance.

Background:Mental health significantly impacts athletes' daily functioning and performance. Some coping techniques, such as substance abuse, can lead to addictive behaviors detrimental to sport participation. Purpose:This study aimed to identify the prevalence of anxiety, depression, and substance use in varsity student-athletes, examine their associations, and determine if academic and athletic factors (e.g., competition level, current sport season) are linked to these issues. Methods:An anonymous survey assessed mental health (anxiety and depression) and substance use in varsity athletes aged 18-25 participating in organized sports (high school, Junior College, NAIA, and NCAA Divisions I, II, III) and enrolled in academic classes. Four validated surveys were used: GAD-7 for anxiety, PHQ-9 for depression, AUDIT for alcohol use, and TAPS for substance use. Results:Sixty-two participants (19.87 ± 1.47 years; males: 30.6%, females: 67.7%) completed the study. Most participants (45.2%) were in-season, and 54.8% competed at the NCAA Division III level. Elevated levels of anxiety (64.5%), depression (62.9%), and substance use (alcohol: 59.7%; other substances: 49.18%) were reported. Only two participants reported illicit drug use (mushrooms). Significant associations were found between mental health issues, substance use, and athletic factors (competition level, sport season, academic year, sex). Conclusion:This study highlights the high prevalence of anxiety, depression, and substance use among student-athletes, particularly at the Division III level. These issues affect both academic and athletic performance. Clinicians should focus on early screening and be competent in recognizing and addressing mental health problems in student-athletes to make effective referrals.

Background: This study investigated the effects of decreased energy availability (EA) and carbohydrate availability (CA) on reproductive and metabolic hormones in male endurance-trained athletes. Methods: Thirteen athletes (age: 26.08 ± 4.3 years; weight: 70.9 ± 6.5 kg; height: 179.9 ± 4.2 cm) participated in two training weeks with varying training volumes (low [LV] and high [HV]). The participants logged their diet and exercise for seven days and provided blood samples to measure hormone levels (Testosterone [T], insulin, leptin, cortisol, and interleukin-6 [IL-6]). Results: Results showed that 46.2% (HV) and 38.5% (LV) of participants were at risk for low EA (≤25 kcal/kg FFM·d-1), while 53.8% (HV) and 69.2% (LV) had low CA (<6 g/kg). Strong positive correlations were found between leptin and body fat percentage (DXABFP) in both weeks (HV: r(11) = 0.88, p < 0.001; LV: r(11) = 0.93, p < 0.001). Moderate correlations were observed between T and DXABFP (r(11) = 0.56, p = 0.05) and negative correlations between leptin and fat intake (r(11) = −0.60, p = 0.03). Regression analyses indicated significant relationships between DXABFP and T (F(1,11) = 4.91, p = 0.049), leptin (HV: F(1,11) = 40.56, p < 0.001; LV: F(1,11) = 74.67, p < 0.001), and cortisol (F(1,11) = 6.69, p = 0.025). Conclusions: These findings suggest that monitoring body composition and macronutrients can be clinically useful for male athletes, especially those without access to blood testing. Ultimately, a greater understanding of health and performance outcomes for male athletes is needed.

Context 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). Objective 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). Design Cross-sectional study. Setting Free living in occupational settings. Patients or Other Participants 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. Main Outcome Measure(s) 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. Results 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. Conclusions 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.

Clinical Scenario: Athletic trainers should be aware of how physical activity may influence the risk of nocturnal hypoglycemia in adolescents with type 1 diabetes (T1D). Clinical Question: Does the timing and intensity of physical activity affect the occurrence and risk of nocturnal hypoglycemia in adolescents with T1D? Results of the Search: Four studies meeting the inclusion criteria were found and included in the appraisal. Three studies were prospective observational cohorts, and one was a randomized case-crossover study. Clinical Bottom Line: Adolescents with T1D may be at greater risk of nocturnal hypoglycemia after performing 30–60 minutes of moderate-to-vigorous physical activity during the late afternoon or evening. Implications: Athletic trainers should educate adolescents with T1D and their parents about the risk of nocturnal hypoglycemia after MVPA, and work in collaboration with the diabetes care physician to ensure prevention techniques are included in the patient'’s diabetes care plan. Level of Evidence: Strength of Recommendation Taxonomy Grade B.

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), exercise energy expenditure, EB, 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 (F1,45 = 16.48, P = .001), EI (F1,45 = 12.01, P = .001), TDEE (F1,45 = 40.36, P < .001), and exercise energy expenditure (F1,38 = 5.353, P = .026). No differences were present in EB for sex and job status (F1,45 = 1.751, P = .193); χ2 analysis revealed no significant relationship between ATs' sex and EB ({\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 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.OBJECTIVE: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.DESIGN:Cross-sectional study.SETTING:Free living in sport-specific settings.PATIENTS OR OTHER PARTICIPANTS: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).MAIN OUTCOME MEASURE(S):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.RESULTS: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).CONCLUSIONS: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.

Clinical Scenario: Due to the Female Athlete Triad (Triad) being a 3-pronged syndrome, treatments can vary depending on the symptoms that clinicians focus on. With reproductive and bone health compromised, assessment and recovery methods include monitoring menstrual regularity and dual-energy X-ray absorptiometry scans. Low levels of estrogen have demonstrated negative effects on bone mineral density (BMD). Clinical Question: Does supplemental estrogen improve BMD in athletes with Female Athlete Triad symptoms? Summary of Key Findings: Supplemental estrogen does improve BMD with estrogen patches demonstrating increased improvement compared with oral contraceptive pills. Clinical Bottom Line: Restoration of regular menstruation, improvement of BMD, and ensuring optimal energy levels is the best approach for treating Triad symptoms. Transdermal patches are a new treatment option that address both menstrual function and BMD but still require further research. Strength of Recommendation: Available studies demonstrated a level 2 evidence for supplemental estrogen (oral contraceptive pills and estrogen patches) providing improvements for bone health related to the Triad.

METHODS: All athletes (BBALL: n=10; 19.8±1.3 yrs, 173.9±13.6 cm, 74.6±9.1 kg, 27.1±3.2 % fat; LAX: n=20; 20.4±1.8 yrs, 168.4±6.6 cm, 68.8±8.9 kg, 27.9±3% fat) were outfitted with heart rate and activity monitors during four consecutive days on five different occasions (20 days total) across their competitive seasons to assess differences in activity energy expenditure (AEE), total daily energy expenditure (TDEE) and physical activity level (PAL). Data collected was categorized by type of scheduled daily activities: Practice, Game, Conditioning or Off. All dependent variables were analyzed using a mixed factorial ANOVA with paired sample T-Tests as post-hocs when necessary. RESULTS: All results are outlined below in Table 1. Independent of day type, TDEE, AEE, and PAL levels were greater (p<0.05) in LAX athletes. Changes between day types for each sport were significantly different (p<0.05) for TDEE, AEE, and PAL. CONCLUSION: Calculated levels for TDEE, AEE, and PAL in female collegiate BBALL and LAX athletes were determined to all be different, irrespective of the scheduled daily activity. LAX athletes, regardless of scheduled activities, had greater TDEE, AEE, and PAL compared to BBALL athletes. Caloric expenditure in female collegiate athletes varies significantly depending on scheduled team activities with energy needs progressively increasing between Off, Conditioning, Practice, and Games.

Increases in physical activity without proper nutritional knowledge may expose recreational athletes to compromised energy needs and macronutrient profiles. PURPOSE: To examine the energy needs across a 2-week high intense functional exercise program in female and male recreational athletes. METHODS: Thirty adults (age: 31.2 ± 8.1; females: 164.7 ± 7.1 cm, 69.9 ± 11.1 kg; body fat%: 29.2 ± 5.5%; males: n=12, 176.9 ± 6.2 cm, 89.5 ± 15.1 kg, body fat%: 22.3 ± 8.8%) participated in a larger cross-sectional study. Participants completed a demographic survey, a 7 day online dietary and exercise log across 2 weeks. Measurements included; height, weight, and DXA scan (body fat%) at the beginning of the study. Exercise energy expenditure (EEE) was calculated using Ainsworth/Heyward equations, energy availability (EA) was calculated by EA = ((EI - EEE)/FFM.kg-1) and energy balance (EB) was calculated by EB = (EI – TDEE = 0). Macronutrients (CHO, PRO, and fats) were assessed using ACSM recommendations (recs.). Low EA (LEA) was defined at >30 kcals/FFM.kg-1 and EB was defined as negative, balanced, or positive, and Macros were defined as low, within or above recs. RESULTS: Results yielded LEA (week 1: 73.3%, n = 22, week 2: 80% n=24) and negative EB (week 1: 94.4%, n=17; 75.0%, n=9) across the two weeks. No significant differences were found between gender or training weeks for energy needs and Macros. Over the 2 weeks, participants demonstrated similar energy needs including: EI (week 1: 1752.3 ± 599.8 kcals, week 2: 1831.2 ± 634.4 kcals), EEE (week 1: 310.7 ± 63.7 kcals, week 2:302.7 ± 62.1 kcals), EA: (week 1: 25 ± 10.1 kcals/FFM.kg-1, week 2: 25.1 ± 9.8 kcals/FFM.kg-1), TDEE (week 1: 2518.5 ± 266.1 kcals, week 2: 2543.1 ± 286.1 kcals), and EB (week 1: -766.2 ± 627.4 kcals, week 2: -712 ± 652.3 kcals). Macronutrients were also similar between weeks; with PRO intake within recommendations (week 1: 50%, n=15; week 2: 63%, n=17, n=6), CHO intake was extremely low (week 1: 96.7%, n=29; week 2: 93.3%, n=28,) and fats were within recs. (week 1: 62.1%, n=18; week 2: 46.7%, n=9). CONCLUSION: Participants demonstrated consistent EI and EEE habits over the 2 weeks, however, the recreational athletes under consumed CHO and presented at risk for LEA and negative EB. This leads to compromised fueling for the EEE utilized during training. Funded by Avadim Technology

Hydration assessment is an important measure to help reduce the risk of exertional heat illness. Maintaining adequate hydration can be problematic for football players practicing in the heat on consecutive days. PURPOSE: To examine day to day differences and the relationships between urinary markers of urine color (Ucol) and urine specific gravity (USG), and percent body mass loss (%BML) during football practices in the heat. METHODS: Thirty-one male high school football players (16 ± 1 years; 181.2 ± 12.0 cm; 68.1 ± 5.4 kg; BMI: 20.8 ± 1.8 km/m2) volunteered to participate in this study. Before and after each practice, players were weighed (in shorts) and provided a urine sample. Urine was assessed for Ucol and USG and was assessed by the same person. Correlations assessed relationships, while t-tests assessed differences between both pre-post differences and subsequent days. P value was significant at P<.05. RESULTS: Mean wet-bulb globe temperature across 8 practices was 30.6 ± 2.5oC. There were significant correlations between pre-Ucol and pre-USG (r = 0.73, p=0.00, n = 209) and post-Ucol and post-USG (r = 0.66, p = 0.00, n = 209). Post-Ucol (5 ± 1) was significantly greater than pre-Ucol (4 ± 2; p = 0.00). Post-USG (1.022 ± 0.008) was significantly greater than pre-USG (1.020 ± 0.008; p = 0.00). Post body mass measures were significantly lower than pre-body mass resulting in 0.9 ± 1.1%BML. Post-practice body mass and USG were not significantly different from the next day’s pre-practice measures (p > 0.05); however, post-Ucol was significantly higher (5 ± 2) compared to the next day’s pre-practice Ucol (4 ± 1; p = 0.000). CONCLUSION: Although the football players’ body mass measures were similar on subsequent days, their Ucol was lighter before the next day’s practice. We also found a strong relationship between Ucol and USG, suggesting Ucol is an acceptable hydration measure where USG is not feasible in field settings. Due to individual variability in these hydration measures, clinicians should provide individualized recommendations to ensure adequate hydration during practices in the heat as well as from one day to the next. This study was fully funded by the National Athletic Trainers’ Association Research & Education Foundation.

Engaging in pathogenic behaviors (PB; e.g., dieting, purging, etc.) to control weight (WT) is often seen in athletics. Female athletes, especially those in aesthetic sports, have a higher risk of disordered eating, eating disorders and body image dissatisfaction. PURPOSE: To examine PB and WT perceptions [current: CWT, ideal: IWT, mental weight: MWT (perceived WT if they didn’t control their WT)] in collegiate athletes/dancers. METHODS: A convenient sample of female athletes/dancers (n=125; age: 19.8 ± 2.0; height: 163.9 ± 28.8 cm; WT: 63.6 ± 9.2 kg) across 6 sports and dance (i.e., equestrian (EQ), volleyball, beach volleyball, softball, soccer, ballet) from an NCAA Division I institution participated in a larger cross-sectional study. Participants were measured for height, WT, and body composition and completed a demographic survey (included self-reporting IWT and MWT) and the Eating Disorder Inventory-Symptoms Checklist (for PB). Basic descriptive statistics assessed demographic information. Cross-tabulations assessed the proportion of participants classified as “at risk” for PB across sport. A repeated measures ANOVA examined perceptions of WT (CWT vs. IWT vs. MWT) across sport. RESULTS: Significant differences were found for use of PB across sport [61.4%: Χ2(5, N=125) = 16.5, P=0.006]. EQ (8.9%) and ballet had the highest risk (13.4%). Significant differences were found between dieting and sport type [Χ2(5, N=125) = 12.2, P=0.033] for an overall risk of 52.8% with highest risk for EQ (13.6%) and ballet (16%). Significant differences were found between excessive exercise and sport type [Χ2(5, N=125) = 32.7, P≤0.01] for an overall risk of 13.6% with highest risk for EQ (10.4%). No significant differences were found for binge eating, purging, laxatives, diet pills, and diuretics. A significant main effect was revealed for WT perceptions across sport (F1,115=1625, P≤0.01, η2=.988), with significant interactions for WT type (F2,115=40.3, P≤0.01, η2=.260) and WT type and sport (F10,115=3.3, P=0.001, η2=.124). CONCLUSION: Overall athletes report engaging in PB, especially dieting and excessive exercise to control their WT, with aesthetic sports at higher percentages. Athletes WT perceptions are of concern, as all sports want to be smaller and assume their WT would be higher if they didn’t control their WT.

A large portion of the adult population suffers from night leg cramps (NLC); but there are few safe and effective treatment options. Magnesium oxide supplementation has been found ineffective; however, low pH topical foam with magnesium sulfate has not been examined. PURPOSE: Examine the effectiveness of a topical low pH foam with and without magnesium sulfate on NLC spasm frequency, severity/pain and the effects on activities of daily life. METHODS: A double-blind randomized trial of 36 (females = 27, males = 9) adult participants (age: 52.0 ± 11.9 yrs.; weight: 94.8 ± 24.3 kg; height: 167.9 ± 9.0 cm; body fat%: 39.8%±12.3%) who experienced a minimum of 3-NLCs per week were recruited from local medical facilities in southeast region. Participants were randomized into 2 groups (Control [C] or Intervention [INT]) and completed a 14 consecutive day home-based treatment (Theraworx Relief®). Participants were given 5 bottles of foam (C or INT) to rub on their lower limbs twice a day and in the event of a cramp for the 14 days and completed surveys to assess frequency of NLC, pain levels, restless leg syndrome quality of life questionnaire (RLSQL) and the multi-dimensional fatigue inventory to assess social and daily function, sleep quality, and emotional well-being which were turned in at the end of each week. RESULTS: The INT group had significant improvements in post-intervention: total social function (P=0.02), total daily function (P=0.003), total emotional well-being (P=0.03), and total RLSQL (P=0.01). Regression models also demonstrated significant improvement within the INT group in emotional well-being (-13.3; P=.03), total number of NLCs (-1.9; P=.02), and severity x frequency (-12.8; P=.02). The C group had significant improvements in daily (-11.5; P=.03) and social function (-10.9; P=.04). CONCLUSION: Theraworx Relief® with magnesium significantly improved quality of life as measured by domains on the total RLSQL. Although there was no difference in frequency and severity of NLCs between groups, we did see a significant reduction in NLCs within the intervention group. Few evidence-based treatments options are available for NLCs. Given the high prevalence of this condition and potential impact on health and well-being this treatment has the potential to improve health outcomes in patients who suffer with NLCs.

Low energy availability (LEA: < 30kcal/kg/FFM) is one component of the Female Athlete Triad and is a catalyst for negative health consequence. Female athletes may be at increased risk for LEA due to a multitude of risks: individual judgments, body size expectations, uniforms, lack of nutrition knowledge or pathogenic behaviors. PURPOSE: Examine the prevalence of LEA and macronutrient intakes (protein [PRO], carbohydrate [CHO], and fats) and differences between sport type and academic status (e.g., freshman, sophomore, junior, senior) in female collegiate athletes. METHODS: Data from a larger cross-sectional study was used to examine 75 Female collegiate athletes (age: 19.5 ± 1.3 years; height: 170.4 ± 6.8 cm; weight: 65.6 ± 8.8 kg) across various sports [beach volleyball (n=18), softball (n=17), equestrian (n=28), and indoor volleyball (n=12)]. Data collection consisted of anthropometric data, surveys (e.g., demographics, health history, etc.), resting metabolic rate, a 7 day online dietary to measure energy intake (EI) and exercise logs to measure exercise energy expenditure (EEE). Basic descriptive stats and Chi-squares and cross-tabulations were used to examine the proportion of participants classified as “at risk” for LEA and across sport and academic status. RESULTS: Overall, 92% (n=69) of athletes demonstrated LEA (13.3±11.9 kcal/kg/FFM, EI: 1490.2±437.3 kcals, EEE: 874.4±490.8 kcals). Differences were found between LEA and PRO intake for both sport type (p<0.04) and academic status (p=0.04), with most equestrian athletes and freshman not meeting protein recommendations (<1.2 g/kg/day). Most athletes (98.7%, n=74) reported low CHO intake (<5 g/kg/day) with 90.7% (n=68) of athletes with LEA had inadequate CHO intake. Fat intake was adequately met by 64% (n=48) of athletes, however, 26.7% (n=20) of athletes with LEA consumed fats above the recommendation. CONCLUSIONS: Majority of female athletes demonstrated compromised LEA and macronutrient intake (CHO and PRO). Proper nutritional education, specifically EI and macronutrient intake, is essential for adequate health status and performance in athletes. Healthcare professionals should be aware of recommendations for proper dietary intake, be a resource for education, and implementation of proper nutritional fueling for female athletes.

Introduction: The shift of athletic training education from undergraduate degrees to professional master’s degrees and the prominence of computer-based credentialing may impact the hands-on experiences beneficial for developing confidence in athletic training competency domains. Health care provider confidence is critical for clinical skill development, performance and enhancing patient care. Purpose: To examine domain specific efficacy, its sources, learning contexts (i.e., classroom, laboratory, clinical settings) and clinical characteristics by program types. Method: Descriptive, cross-sectional design where 178 Athletic Trainers (AT; age 24.25 + 3.76, n = 72 male, n = 106 female) participated in the study (Master’s Program (MP) = 38; Undergraduate Program (UG) = 140). A questionnaire examining athletic training confidence was administered throughout multiple universities with accredited athletic training programs. Background characteristics, certification exam attempts, and programmatic characteristics were also ascertained. Results: Clinical settings were similar in both program types and there were few differences in domain-specific efficacy. Imaginal experiences, verbal persuasion and emotional states sources of efficacy differentiated master’s from undergraduate students. Conclusions: Sources of efficacy (e.g. vicarious experiences) occur naturally in athletic training educational settings; however, these sources need to be utilized. Educators should be informed about efficacy sources and devise strategies targeting each source for implementation across evolving learning contexts.