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Katie J. Thralls, Jeanne F. Nichols, Michelle T. Barrack, Mark Kern and Mitchell J. Rauh

Early detection of the female athlete triad is essential for the long-term health of adolescent female athletes. The purpose of this study was to assess relationships between common anthropometric markers (ideal body weight [IBW] via the Hamwi formula, youth-percentile body mass index [BMI], adult BMI categories, and body fat percentage [BF%]) and triad components, (low energy availability [EA], measured by dietary restraint [DR], menstrual dysfunction [MD], low bone mineral density [BMD]). In the sample (n = 320) of adolescent female athletes (age 15.9± 1.2 y), Spearman’s rho correlations and multiple logistic regression analyses evaluated associations between anthropometric clinical cutoffs and triad components. All underweight categories for the anthropometric measures predicted greater likelihood of MD and low BMD. Athletes with an IBW ≤85% were nearly 4 times more likely to report MD (OR = 3.7, 95% CI [1.8, 7.9]) and had low BMD (OR = 4.1, 95% CI [1.2, 14.2]). Those in <5th percentile for their age-specific BMI were 9 times more likely to report MD (OR 9.1, 95% CI [1.8, 46.9]) and had low BMD than those in the 50th to 85th percentile. Athletes with a high BF% were almost 3 times more likely to report DR (OR = 2.8, 95% CI [1.4, 6.1]). Our study indicates that low age-adjusted BMI and low IBW may serve as evidence-based clinical indicators that may be practically evaluated in the field, predicting MD and low BMD in adolescents. These measures should be tested for their ability as tools to minimize the risk for the triad.

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Jeanne F. Nichols, Mitchell J. Rauh, Michelle T. Barrack, Hava-Shoshana Barkai and Yael Pernick

The authors’ purpose was to determine the prevalence and compare associations of disordered eating (DE) and menstrual irregularity (MI) among high school athletes. The Eating Disorder Examination Questionnaire (EDE-Q) and a menstrual-history questionnaire were administered to 423 athletes (15.7 ± 1.2 y, 61.2 ± 10.2 kg) categorized as lean build (LB; n = 146) or nonlean build (NLB; n = 277). Among all athletes, 20.0% met the criteria for DE and 20.1% for MI. Although the prevalence of MI was higher in LB (26.7%) than NLB (16.6%) athletes (P = 0.01), no differences were found for DE. For both sport types, oligo/amenorrheic athletes consistently reported higher EDE-Q scores than eumenorrheic athletes (P < 0.05). Athletes with DE were over 2 times as likely (OR = 2.3, 95%CI: 1.3, 4.2) to report oligo/amenorrhea than athletes without DE. These data establish an association between DE and MI among high school athletes and indicate that LB athletes have more MI but not DE than NLB athletes.

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Jason Brumitt, Bryan C. Heiderscheit, Robert C. Manske, Paul Niemuth, Alma Mattocks and Mitchell J. Rauh

Context:

The Lower-Extremity Functional Test (LEFT) has been used to assess readiness to return to sport after a lowerextremity injury. Current recommendations suggest that women should complete the LEFT in 135 s (average; range 120–150 s) and men should complete the test in 100 s (average; range 90–125 s). However, these estimates are based on limited data and may not be reflective of college athletes. Thus, additional assessment, including normative data, of the LEFT in sport populations is warranted.

Objective:

To examine LEFT times based on descriptive information and off-season training habits in NCAA Division III (DIII) athletes. In addition, this study prospectively examined the LEFT’s ability to discriminate sport-related injury occurrence.

Design:

Descriptive epidemiology.

Setting:

DIII university.

Subjects:

189 DIII college athletes (106 women, 83 men) from 15 teams.

Main Outcome Measures:

LEFT times, preseason questionnaire, and time-loss injuries during the sport season.

Results:

Men completed the LEFT (105 ± 9 s) significantly faster than their female counterparts (117 ± 10 s) (P < .0001). Female athletes who reported >3–5 h/wk of plyometric training during the off-season had significantly slower LEFT scores than those who performed ≤3 h/wk of plyometric training (P = .03). The overall incidence of a lower-quadrant (LQ) time-loss injury for female athletes was 4.5/1000 athletic exposures (AEs) and 3.7/1000 AEs for male athletes. Female athletes with slower LEFT scores (≥118 s) experienced a higher rate of LQ time-loss injuries than those with faster LEFT scores (≤117 s) (P = .03).

Conclusion:

Only off-season plyometric training practices seem to affect LEFT score times among female athletes. Women with slower LEFT scores are more likely to be injured than those with faster LEFT scores. Injury rates in men were not influenced by performance on the LEFT.

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Jeanne F. Nichols, Hilary Aralis, Sonia Garcia Merino, Michelle T. Barrack, Lindsay Stalker-Fader and Mitchell J. Rauh

There is a growing need to accurately assess exercise energy expenditure (EEE) in athletic populations that may be at risk for health disorders because of an imbalance between energy intake and energy expenditure. The Actiheart combines heart rate and uniaxial accelerometry to estimate energy expenditure above rest. The authors’ purpose was to determine the utility of the Actiheart for predicting EEE in female adolescent runners (N = 39, age 15.7 ± 1.1 yr). EEE was measured by indirect calorimetry and predicted by the Actiheart during three 8-min stages of treadmill running at individualized velocities corresponding to each runner’s training, including recovery, tempo, and 5-km-race pace. Repeated-measures ANOVA with Bonferroni post hoc comparisons across the 3 running stages indicated that the Actiheart was sensitive to changes in intensity (p < .01), but accelerometer output tended to plateau at race pace. Pairwise comparisons of the mean difference between Actiheart- and criterion-measured EEE yielded values of 0.0436, 0.0539, and 0.0753 kcal · kg−1 · min−1 during recovery, tempo, and race pace, respectively (p < .0001). Bland–Altman plots indicated that the Actiheart consistently underestimated EEE except in 1 runner’s recovery bout. A linear mixed-model regression analysis with height as a covariate provided an improved EEE prediction model, with the overall standard error of the estimate for the 3 speeds reduced to 0.0101 kcal · kg−1 · min−1. Using the manufacturer’s equation that combines heart rate and uniaxial motion, the Actiheart may have limited use in accurately assessing EEE, and therefore energy availability, in young, female competitive runners.