Fat-Free Mass Index in a Large Sample of Collegiate American Football Athletes.

High levels of fat-free mass (FFM) are favorable for athletes and are related to sport performance. However, fat-free mass index (FFMI), which includes adjustments for height, may offer a better way to characterize FFM beyond raw values. As FFMI is understudied relative to sport, the purpose of the current study was to assess position and age group differences in FFMI among collegiate American football players. National Collegiate Athletic Association DIII (n=111) football players underwent body composition assessment via bioelectrical impedance analysis. FFMI was calculated by dividing FFM by height squared. One-way analyses of variance with Bonferroni post-hoc tests were conducted to evaluate differences in FFMI by position and age groups (α<0.05). The overall mean FFMI was 23.50 ± 2.04 kg · m-2, with values ranging from 18.1-27.7 kg · m-2. FFMI was highest in linemen (24.8 ± 1.5 kg · m-2) and lowest in specialty players (20.6 ± 1.4 kg · m-2) (p<0.05). No differences in FFMI were apparent across age groups (p>0.05). Current findings demonstrate that an athlete's upper limit for FFMI may exceed 25 kg · m-2, and differences exist across positions, likely due to position-specific demands. These measurements serve as a foundation for tailoring nutritional and exercise plans, forecasting athletic performance, and supplying coaches with standardized data about the potential for additional FFM accretion in collegiate American football players.

Fat-Free Mass Index in a Large Sample of National Collegiate Athletic Association Men and Women Athletes From a Variety of Sports.

ABSTRACT: Magee, MK, Fields, JB, Jagim, AR, and Jones, MT. Fat-free mass index in a large sample of National Collegiate Athletic Association men and women athletes from a variety of sports. J Strength Cond Res 38(2): 311-317, 2024-Fat-free mass index (FFMI) can be used to categorize fat-free mass (FFM) relative to height. Normative values have been established in a variety of sports, as has suggested lower and upper thresholds of FFMI. However, FFMI has not been reported in a large sample of athletes, representing both sexes and multiple sport types. The purpose of this study was to evaluate differences in FFMI and establish normative values across a large sample of collegiate sports. A total of 1,961 athletes (men: n = 596, 10 sports; women: n = 1,365, 8 sports) participated. Height and mass were measured using a stadiometer and calibrated digital scale, respectively. Fat-free mass was assessed with air displacement plethysmography and used to calculate FFMI. Kruskal-Wallis test and one-way analysis of variance evaluated differences in FFMI in women's and men's sports, respectively. When collapsed across sport type and stratified by sex, men had a higher FFMI (21.5 ± 1.9 kg·m -2 vs. 17.9 ± 1.8 kg·m -2 ; p < 0.001). Differences occurred in FFMI across sport. In women, basketball athletes had the highest FFMI (18.9 kg·m -2 ) and highest 99th percentile for FFMI, whereas rowers had the lowest (16.9 kg·m -2 ). In men, throwers had the highest FFMI (25.7 kg·m -2 ) and highest 99th percentile, whereas volleyball athletes had the lowest (19.9 kg·m -2 ). Differences in FFMI may be attributable to dietary habits and physiological demands of respective sports. These results can provide information relative to differences in FFMI values across sports. In addition, sport-specific normative values can be used as benchmarks and targets for training, nutrition, and goal setting.

Body Composition, Energy Availability, Risk of Eating Disorder, and Sport Nutrition Knowledge in Young Athletes.

Young athletes may be at risk for low energy availability (LEA) or dietary habits that are indicative of eating disorders. Thus, the purpose of the current study was to investigate the prevalence of LEA among high school athletes and examine those at risk for eating disorders. A secondary aim was to examine relationships between sport nutrition knowledge, body composition, and LEA. METHODS: 94 male (n = 42) and female (n = 52) mean ± SD age: 18.09 ± 2.44 y; height: 172.6 ± 9.8 cm; body mass: 68.7 ± 14.5 kg; BMI: 22.91 ± 3.3 kg·m-2) athletes completed a body composition assessment and electronic versions of the abridged sports nutrition knowledge questionnaire (ASNK-Q), brief eating disorder in athletes questionnaire (BEDA-Q), and the low energy availability for females questionnaire (LEAF-Q; females only). RESULTS: 52.1% of female athletes were classified as being at risk for LEA. Moderate inverse relationships existed for computed LEAF-Q scores and BMI (r = -0.394; p < 0.01). A total of 42.9% of males (n = 18) and 68.6% of females (n = 35) were at risk for eating disorders, with females being at greater risk (p < 0.01). Body fat percentage was a predictor (ß = -0.095; p = -0.01) for eating disorder risk status. For every 1 unit increase in body fat percentage, athletes were 0.909 (95% CI: 0.845-0.977) times less likely to be classified as at risk for an eating disorder. Male (46.5 ± 13.9) and female (46.9 ± 11.4) athletes scored poorly on the ASNK-Q, with no differences between sex (p = 0.895). CONCLUSIONS: Female athletes were at a greater risk for eating disorders. No relationships existed between sport nutrition knowledge and %BF. Female athletes with a higher %BF had a lower risk for an eating disorder and risk for LEA.

The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes.

Predictive resting metabolic rate (RMR) equations are widely used to determine total daily energy expenditure (TDEE). However, it remains unclear whether these predictive RMR equations accurately predict TDEE in the athletic populations. The purpose of this study was to examine the accuracy of 10 commonly used RMR prediction equations (Cunningham, De Lorenzo, Freire, Harris-Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) in collegiate men and women athletes. One-hundred eighty-seven National Collegiate Athletic Association Division III men (n = 97) and women (n = 90) athletes were recruited to participate in one day of metabolic testing. RMR was measured using indirect calorimetry and body composition was analyzed using air displacement plethysmography. A repeated measures ANOVA with Bonferroni post hoc analyses was selected to determine mean differences between measured and predicted RMR. Linear regression analysis was used to assess the accuracy of each RMR prediction method (p<0.05). All prediction equations significantly underestimated RMR (p<0.001), although there was no difference between the De Lorenzo and Watson equations and measured RMR (p = 1.00) for women, only. In men, the Tinsley and Freire equations were the most agreeable formulas with the lowest root-mean-square prediction error value of 404 and 412 kcals, respectively. In women, the De Lorenzo and Watson equations were the most agreeable equations with the lowest root-mean-squared error value of 171 and 211 kcals, respectively. The results demonstrate that such RMR equations may underestimate actual energy requirements of athletes and thus, practitioners should interpret such values with caution.Highlights All prediction equations significantly underestimated RMR in men athletes.All prediction equations, except for the De Lorenzo and Watson equations, significantly underestimated RMR in women athletes.Although a significant underestimation of RMR in men athletes, the Freire and Tinsley equations were the most agreeable prediction equations.In women athletes, the De Lorenzo and Watson equations were the most agreeable prediction equations.

Contributing Factors to Low Energy Availability in Female Athletes: A Narrative Review of Energy Availability, Training Demands, Nutrition Barriers, Body Image, and Disordered Eating.

Relative Energy Deficiency in sport is experiencing remarkable popularity of late, particularly among female athletes. This condition is underpinned by low energy availability, which is a byproduct of high energy expenditure, inadequate energy intake, or a combination of the two. Several contributing factors exist that may predispose an athlete to low energy availability, and therefore a holistic and comprehensive assessment may be required to identify the root causes. The focus of the current narrative review is to discuss the primary contributing factors as well as known risk factors for low energy availability among female athletes to help practitioners increase awareness on the topic and identify future areas of focus.

Does the Multistage 20-m Shuttle Run Test Accurately Predict VO2max in NCAA Division I Women Collegiate Field Hockey Athletes?

Laboratory assessments of maximal oxygen uptake (VO2max) are considered the "gold standard" for ascertaining cardiovascular fitness, but they are not always practical for use in team sport settings. Therefore, the purpose of the current study was to compare the criterion assessment of VO2max on a treadmill to the progressive, multistage 20-m shuttle run test (i.e., Beep test), and to determine the predictability of 6 previously established Beep test predictive equations (i.e., Chatterjee, Flouris, Leger, Leger and Gadoury, Ramsbottom, St. Clair-Gibson). Collegiate women field hockey athletes (n = 65, mean±SD: age 19.6 ± 1.2 years; weight 64.7 ± 6.1 kg) completed criterion VO2max (mean ± SD: 46.4 ± 4.6 mL·kg-1·min-1) and Beep tests to volitional fatigue. According to Bland-Altman and Ordinary Least Products Regressions, the Ramsbottom (46.5 ± 4.2 mL·kg-1·min-1) and Flouris (46.3 ± 3.8 mL·kg-1·min-1) equations were considered valid predictions of criterion measured VO2max (46.4 ± 4.6). The Chatterjee, Leger, Leger and Gadoury, and St. Clair-Gibson equations overestimated VO2max, and are not recommended for use with women collegiate field hockey athletes. The Ramsbottom and Flouris estimates of VO2max from 20-m shuttle performances may be used in this population. For accurate estimates of VO2max, the clientele's age, fitness level, and training history should be considered when selecting equations.

Prevalence of Low Energy Availability in Collegiate Women Soccer Athletes.

(1) Background: Limited information exists on the prevalence of low energy availability (LEA) in collegiate team sports. The purpose of this study was to examine the prevalence of LEA in collegiate women soccer players. (2) Methods: Collegiate women soccer athletes (n = 18, height: 1.67 ± 0.05 m; body mass: 65.3 ± 7.9 kg; body fat %: 24.9 ± 5.6%) had their body composition and sport nutrition knowledge assessed in the pre-season. Energy availability was assessed mid-season using a 4-day dietary log and activity energy expenditure values from a team-based monitoring system. A validated screening tool was used to screen for LEA. (3) Results: The screening tool classified 56.3% of athletes as at risk of LEA (<30 kcal/kg of FFM); however, the actual dietary intake identified 67% as LEA. Athletes identified as non-LEA consumed significantly more absolute (p = 0.040) and relative (p = 0.004) energy than LEA athletes. (4) Conclusions: There was a high prevalence of LEA among collegiate women soccer athletes. Although previously validated in women endurance athletes, the LEA screening tool was not effective in identifying those at risk of LEA in this sample of athletes.