Magee, Meghan K.

Purpose: The purpose of the current study was to cross-validate previously developed RMR prediction equations using laboratory measurements of RMR as a criterion. Methods: National Collegiate Athletic Association (NCAA) Division I (n = 60) and III (n = 113) male wrestlers participated in the current study (n = 173; Age: 19.6 ± 1.5 yrs.; Height: 176.3 ± 6.3 cm; Weight: 76.6 ± 10.9 kg; Body Fat: 11.4 ± 3.0%). Indirect calorimetry was used to measure RMR and Skinfold thickness was used to estimate body fat percentage (BF%) during the preseason period under standardized dress (underwear only), fasting, and resting conditions. Results: Mean measured RMR was 2102±341 kcal·d −1 . All body mass-based RMR prediction equations significantly underestimated the measured RMR, with the exception of the Jagim and Schofield equations. The Tinsley fat-free mass (FFM) equation and the Cunningham equation had the lowest constant error (CE) values when compared to measured RMR (mean bias ± standard error: 80 ± 20 kcal and 126 ± 20 kcal·d −1 , respectively). Linear regression indicated the Tinsley FFM (R 2 = 0.422, RMSE = 279 kcals, %RMSE = 13.3%), and Cunningham FFM equations had the closest agreement to measured RMR (R 2 = 0.422, RMSE = 301 kcals, %RMSE = 14.3%), along with the Jagim equation for a body mass-based prediction equation (R 2 = 0.377, RMSE = 338 kcals, %RMSE = 16.1%). The standard error of the estimate (SEE) ranged from 260 to 272 kcal·day −1 across equations. Coefficients of determination (R 2 ) ranged from 37% to 42%. The new body mass and FFM-based equations exhibited good agreement based on the CE (26 and -13 kcal·day −1 ), RMSE (274 and 252 kcal·day −1 ), respectively. Conclusions: The newly developed equations demonstrated mean bias <2% of measured RMR.

Abstract The purpose of the current study was to identify the influence of individual characteristics on differences in body fat percentage between air displacement plethysmography and dual-energy X-ray absorptiometry (change in body fat percentage) in 360 collegiate student-athletes. Body fat percentage was obtained from air displacement plethysmography and dual-energy X-ray absorptiometry. A 2 (sex)×3 (Brozek body fat percentage, Siri body fat percentage, and dual-energy x-ray absorptiometry body fat percentage) analysis of variance was used to evaluate the difference between the body fat percentages obtained from air displacement plethysmography and dual-energy x-ray absorptiometry in men and women. The agreement between air displacement plethysmography and dual-energy X-ray absorptiometry was evaluated via Bland–Altman plot and linear regression. Multiple linear regression was used to identify the influence of sex, race, height, and body mass on the change in body fat percentages. Dual-energy X-ray absorptiometry yielded greater body fat percentages in both men (4.8±2.8; p < 0.001) and women (2.2±2.9; p < 0.001) compared to air displacement plethysmography measurements. Proportional and systematic biases were observed between air displacement plethysmography and dual-energy X-ray absorptiometry. Race, height, and mass were significant predictors of the body fat percentage difference between air displacement plethysmography and dual-energy X-ray absorptiometry (R 2=0.445, p<0.001, and standard error of the estimate=2.527). The present study suggests that air displacement plethysmography underestimates body fat percentages when compared to dual-energy X-ray absorptiometry and this underestimation may not be the same for every athlete due to their individual characteristics.

Abstract Korpinen, M, Magee, M, Dulak-Sigler, C, Fields, JB, Jagim, AR, King, E, Burke, A, and Jones, MT. Seasonal changes in neuromuscular performance across a training year in women collegiate lacrosse athletes. J Strength Cond Res 39(11): 1186–1193, 2025—Body composition and athlete load management are vital to adequate training stimuli and to mitigate neuromuscular (NM) fatigue. Research is limited regarding NM performance (NMP) in women lacrosse athletes. Therefore, the purpose of this study was to investigate changes in NMP in women lacrosse athletes across a training year. Collegiate women lacrosse athletes ( n = 9, mean ± SD ; age = 19.1 ± 0.9 years, height = 168.4 ± 6.8 cm, body mass = 68.0 ± 8.1 kg) completed countermovement jump (CMJ) and isokinetic strength (IS) testing of dominant and nondominant legs 4 times throughout the season: preseason start, preseason end, in-season end, and off-season end to assess NMP. Measures included (a) CMJ output variables of reactive strength index modified (RSImod), jump height, relative values of braking force, peak power, peak propulsive force, impulse; (b) CMJ strategy variables of time-to-takeoff, eccentric duration, concentric duration, relative braking time, CMJ depth; and (c) IS relative peak torques of quadriceps and hamstrings musculature. Secondary measures including body composition variables of body mass, % body fat (%BF), and fat-free mass. Repeated measures ANOVA with Bonferroni post hoc analysis assessed changes in CMJ and IS parameters to determine patterns in NMP ( p < 0.05). Changes observed from preseason start to offseason end included increased CMJ depth ( p = 0.019), relative braking time ( p = 0.009), and %BF ( p = 0.01). The IS variables increased throughout the training year, except for a decrease during preseason, perhaps indicative of overreaching. Negative changes in alternative CMJ variables and body composition reduced NMP, which highlights the importance of off-season program design and implementation to elicit desired training adaptations.

Abstract Magee, MK, Fields, JB, Jagim, A, Lockard, B, Miller, A, Stroiney, D, and Jones, MT. Estimation of whole-body bone mineral density through air displacement plethysmography in a large sample of elite athletes. J Strength Cond Res 39(4): 427–432, 2025—Dual-energy x-ray absorptiometry (DXA) is used to determine bone mineral density (BMD) and body composition. Regulations and cost may limit DXA's utility in some athletic settings. Air displacement plethysmography (ADP), while able to measure body composition, does not measure BMD. The purpose was to evaluate the influence of body composition parameters obtained from ADP on BMD. Collegiate athletes (males, n = 153; females, n = 208) underwent ADP and DXA assessments. For ADP testing, the Brozek equation was used to estimate body fat percent (BF%) and fat-free mass (FFM) from body density. Dual-energy x-ray absorptiometry tests were completed to obtain BMD using the whole-body scan mode. Stepwise regression was conducted using age, body mass, height, sex, race, FFM (ADP), BF% (ADP), and sport type as independent variables. Total BMD (DXA) was the dependent variable. Accuracy and precision of the final equation were evaluated through Bland–Altman and linear regression. The final regression equation yielded an R 2 of 0.691 with race, mass, BF%, age, and sport type as significant predictors ( p < 0.05) of BMD. Bland–Altman and linear regression showed systematic {95% confidence interval [CI] (−0.619 to −0.360)} and proportional bias (95% CI [0.173–0.374]). Although an ADP-derived regression equation may not provide a precise prediction of BMD, it may be useful to establish risk for low BMD if DXA is unavailable. Dual-energy x-ray absorptiometry may not be feasible for some athletic programs; therefore, an estimation equation for BMD derived from ADP values is advantageous for practitioners. This equation may assist in screening athletes for risk of low BMD.

Abstract Jagim, AR, Harty, PS, Jones, MT, Fields, JB, Magee, M, Smith-Ryan, AE, Luedke, J, and Kerksick, CM. Fat-free mass index in sport: normative profiles and applications for collegiate athletes. J Strength Cond Res 38(9): 1687–1693, 2024—Recent concerns have been raised regarding the ethical considerations of conducting body composition assessments in sports. Specific apprehensions pertain to the inappropriate use of percent body fat and the limited application of the results to performance and recovery. Fat-free mass index (FFMI), a height-adjusted assessment of FFM, can serve as an alternative body composition metric to focus on in sports. Fat-free mass index provides valuable context regarding an optimal amount of FFM, accounting for skeletal frame and height while helping to qualify an athlete's FFM as low, moderate, or high. This review posits that shifting the focus of body composition measures to FFM can support more ideal targets across athletic seasons, careers, and the return to play after injury. In addition, a FFM focus may help change the perception of body composition assessment and how athletes perceive their current body because of the increased focus on the FFM compartment and the goal of maximizing tissue accrual, rather than focusing on fat loss. Fat-free mass index is calculated by dividing FFM (kg) by height (m 2 ) and can serve to normalize FFM, relative to height, and enable comparisons across athletes, sport types, and sex. Previous research has identified differences in FFMI across sex and among sport categories. Still, there is a need for more published data to develop optimal ranges for FFMI across sex, sports, and positions. As more data become available, FFMI has the potential to provide normative guidelines for optimal FFM development, performance, and injury risk reduction. The purpose of the current review was to summarize FFMI values across collegiate sport categories and competitive status to provide normative profiles, according to sex and sport type.

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.

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 (β = −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.