Fields, Jennifer B.

Abstract Askow, AT, Jennings, W, Jagim, AR, Fields, JB, Beaudoin, RG, Sanchez, GM, Weeks, JE, Oliver, JM, and Jones, MT. Athlete external load measures across a competitive season in high school basketball. J Strength Cond Res 37(11): 2206–2212, 2023—The purpose of this retrospective analysis was to quantify in-season external load and to determine if relationships existed between load metrics and basketball performance. Eleven male high school varsity basketball athletes (n = 11; mass 80.5 ± 9.6 kg, height 190.2 ± 9.4 cm, age 17.6 ± 0.7 years) were monitored across a season. PlayerLoad (PL), PL per minute (PL·min −1 ), total jumps, and explosive movements (EMs) were quantified using a commercially available local positioning unit. Basketball-specific performance metrics, including points scored, points allowed, point differentials, and shooting percentages for each quarter and game, were compiled. Data were analyzed using repeated-measure analysis of variance to evaluate differences in load by starting status, session type, game outcome, and game type. Pearson's correlation coefficients were used to assess relationships between load metrics and basketball performance. Statistical significance was set at p < 0.05. The mean values across 23 games for PL, PL·min −1 , total jumps, and EMs were 457 ± 104 AU, 10.9 ± 1.6 AU, 42.6 ± 9.6, and 46.7 ± 7.2, respectively. Relationships were observed ( p < 0.05) between PL and points scored ( r = 0.38) and free throw percentage ( r = 0.21). Further relationships were observed between PL·min −1 and free throw shooting percentage ( r = −0.27), and between points scored and total jumps ( r = 0.28), and EMs ( r = 0.26). Notable differences in game demands were observed for playing status. Meaningful differences in measures of external load were observed between each quarter of play, with the highest measures evident in quarters 1 and 3. Guards and forwards experienced minimal differences in external load during gameplay, and game outcome did not result in differences. Higher point totals corresponded with higher PL, total jumps, and EM.

The purpose of this study was to compare external workloads between collegiate men’s (MLAX) and women’s lacrosse (WLAX) matches and examine positional differences across the season. Athletes (MLAX: n = 10; WLAX: n = 13) wore a global positional system device during all matches. External load metrics included in the analysis were total distance (TD), sprint distance (SD), accelerations (>3 m/s2), sprint efforts, player load per minute (PL/min), top speed, and distances spent in various speed zones. WLAX had higher TD (p = 0.001), SD (p < 0.001), distances in SZs 2–5 (p < 0.001), PL (p < 0.001), and sprint efforts (p < 0.001) compared to MLAX. However, MLAX performed more acceleration (p < 0.001) and deceleration (p < 0.001) efforts. WLAX midfielders (M) and defenders (D) reached higher top speeds and performed more accelerations than attackers (p < 0.001). Midfielders covered the greatest distance at high speeds (p = 0.011) and the smallest distance at low speeds (<0.001) for WLAX. For MLAX, midfielders performed the highest SDs, top speeds, accelerations, decelerations, and distances in higher speed zones (p < 0.001) compared to attackers and defenders. Results indicate that there are significant gender and positional differences in external workload demands during match play, specifically for volume- and intensity-derived workload parameters, between men’s and women’s lacrosse. Therefore, sports performance coaches should create gender- and position-specific conditioning programs to prepare athletes for match demands.

The purpose of this study was to examine sex differences in resting metabolic rate (RMR) and associations between measured RMR and body composition parameters in athletes. One-hundred and ninety collegiate men (n = 98; age: 20.1 ± 1.6 yr.; body mass: 92.7 ± 17.5 kg; height: 181.6 ± 6.2 cm, body mass index: 28.0 ± 4.7 kg/m2) and women (n = 92; age: 19.4 ± 1.1 yr.; body mass: 65.2 ± 11.0 kg; height: 168.0 ± 6.6 cm, body mass index: 23.0 ± 3.6 kg/m2) athletes volunteered to participate in this study. Athletes completed a body composition assessment using air displacement plethysmography and RMR using indirect calorimetry. Assessments were completed in a fasted state and after refraining from intense physical activity > 24 h prior to testing. Data were collected during the 2016–2019 seasons. Men had a higher RMR compared to women (2595 ± 433 vs. 1709 ± 308 kcals; p < 0.001); however, when adjusted for body mass (p = 0.064) and fat-free mass (p = 0.084), the observed differences were not significant. Height, body mass, body mass index, fat-free mass, and fat mass were positively associated with RMR in both men and women athletes (r = 0.4–0.8; p < 0.001). Body mass (men: β = 0.784; women: β = 0.832)) was the strongest predictor of RMR. Men athletes have a higher absolute RMR compared to their women counterparts, which is influenced by greater body mass and fat-free mass. Body mass is the strongest predictor of RMR in both men and women 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 (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.

Firefighters often complete air management courses (AMC) to assess the ability to tolerate personal protective equipment, appropriately manage the breathing system and assess occupational performance. Little information is known relative to the physiological demands of AMCs, nor how to assess work efficiency in order to characterize occupational performance and evaluate progress. Purpose: To assess the physiological demands of an AMC and examine differences across BMI categories. A secondary aim was to develop an equation to assess work efficiency in firefighters. Methods: Fifty-seven firefighters (Women, n = 4; age: 37.2 ± 8.4 yr.; height: 182.0 ± 6.9 cm; body mass: 90.8 ± 13.1 kg; BMI: 27.8 ± 3.6 kg·m−2) completed an AMC per routine evaluation while wearing a department issued self-contained breathing apparatus and full protective gear. Course completion time, starting pounds per square inch (PSI) on the air cylinder, changes in PSI, and distance traveled were recorded. All firefighters were equipped with a wearable sensor integrated with a triaxial accelerometer and telemetry to assess movement kinematics, heart rate, energy expenditure, and training impulse. The AMC consisted of an initial section involving a hose line advance, rescue (body drag), stair climb, ladder raise, and forcible entry. This section was followed by a repeating loop, which consisted of a stair climb, search, hoist, and recovery walk. Firefighters repeated the course loop until the self-contained breathing apparatus air supply pressure reached 200 PSI, at which time they were instructed to lay down until the PSI reached zero. Results: Average completion time was 22.8 ± 1.4 min, with a mean distance of 1.4 ± 0.3 km and an average velocity of 2.4 ± 1.2 m·s−1. Throughout the AMC, the mean heart rate was 158.7 ± 11.5 bpm equating to 86.8 ± 6.3% of the age-predicted max heart rate and a training impulse of 55 ± 3 AU. Mean energy expenditure was 464 ± 86 kcals and work efficiency was 49.8 ± 14.9 km·PSI−1·s. Regression analysis determined that fat-free mass index (R2 = 0.315; β = −5.069), body fat percentage (R2 = 0.139; β = −0.853), fat-free mass (R2 = 0.176; β = −0.744), weight (R2 = 0.329; β = −0.681), and age (R2 = 0.096; β = −0.571) were significant predictors of work efficiency. Conclusions: The AMC is a highly aerobic task with near-maximal heart rates reached throughout the course. Smaller and leaner individuals achieved a higher degree of work efficiency during the AMC.

Abstract Fields, JB, Jones, MT, Feit, MK, and Jagim, AR. Athlete external loads across a collegiate men’s lacrosse season. J Strength Cond Res 37(8): e455–e461, 2023—Tracking and quantifying athlete workload may provide insight into best practice periodization strategies and workload management. Limited information exists detailing the external demands associated with collegiate men's lacrosse. Therefore, the purpose of this study was to investigate and compare practice, game, and positional differences across a lacrosse season. Male collegiate lacrosse athletes ( n = 17; body mass: 79.9 kg ± 9.7 kg; body fat %: 14.7 ± 3.8%) wore a global positional system device during practices ( n = 66) and games ( n = 19). External load metrics were total distance (TD), sprint distance (SD), accelerations (>3 m·s −2 ), sprint efforts, player load (PL), PL/min, and top speed. Multiple analysis of variances assessed differences in external loads across session (practice versus game) and sport position (attackers, midfielders, and defenders). A 3 × 4 analysis of variance was used to determine a position-by-time (first quarter [Q1], second quarter [Q2], third quarter [Q3], and fourth quarter [Q4]) interaction ( p < 0.05). Total distance ( p < 0.001, η 2 : 0.047), SD ( p < 0.001, η 2 : 0.093), sprint efforts ( p < 0.001, η 2 : 0.077), PL ( p < 0.001, η 2 : 0.022), and top speeds ( p < 0.001, η 2 : 0.086) were all higher in games compared with practices. Attackers and defenders covered more TD ( p < 0.001, η 2 = 0.0257), accelerations ( p < 0.001, η 2 = 0.126), PL ( p < 0.001, η 2 = 0.233), and PL/min ( p < 0.001, η 2 = 0.193) than midfielders. Attackers and midfielders covered more SD ( p = 0.002 η 2 = 0.043), sprint efforts ( p < 0.001, η 2 = 0.053), and achieved higher top speed ( p < 0.001, η 2 = 0.0063) than defenders. Volume and intensity metrics declined by Q4 in attackers and defenders ( p < 0.05), whereas external load measures did not differ across quarters for midfielders. Monitoring in-game external loads can assist coaches with individualizing training programs and preparing athletes for game demands.

ABSTRACTPredictive 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.

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.

Sport nutrition knowledge has been shown to influence dietary habits of athletes. The purpose of the current study was to examine relationships between sport nutrition knowledge and body composition and examine potential predictors of body weight goals in collegiate athletes. Participants included National Collegiate Athletic Association Division III women (n = 42, height: 169.9 ± 6.9 cm; body mass: 67.1 ± 8.6 kg; fat-free mass: 51.3 ± 6.6 kg; body fat percent: 24.2 ± 5.3%) and men (n = 25, height: 180.8 ± 7.2 cm; body mass: 89.2 ± 20.5 kg; fat-free mass: 75.9 ± 12.2 kg; body fat percent: 13.5 ± 8.9%) athletes. Body composition was assessed via air displacement plethysmography. Athletes completed a validated questionnaire designed to assess sport nutrition knowledge and were asked questions about their perceived dietary energy and macronutrient requirements, as well as their body weight goal (i.e., lose, maintain, gain weight). Athletes answered 47.98 ± 11.29% of questions correctly on the nutrition questionnaire with no differences observed between sexes (men: 49.52 ± 11.76% vs. women: 47.03 ± 11.04%; p = 0.40). An inverse relationship between sport nutrition knowledge scores and body fat percentage (BF%) (r = −0.330; p = 0.008), and fat mass (r = −0.268; p = 0.032) was observed for all athletes. Fat mass (β = 0.224), BF% (β = 0.217), and body mass index (BMI) (β = 0.421) were all significant (p < 0.05) predictors of body weight goal in women. All athletes significantly (p < 0.001) underestimated daily energy (−1360 ± 610.2 kcal/day), carbohydrate (−301.6 ± 149.2 grams/day [g/day]), and fat (−41.4 ± 34.5 g/day) requirements. Division III collegiate athletes have a low level of sport nutrition knowledge, which was associated with a higher BF%. Women athletes with a higher body weight, BF% and BMI were more likely to select weight loss as a body weight goal. Athletes also significantly underestimated their energy and carbohydrate requirements based upon the demands of their sport, independent of sex.

Vitamin D may play a role in performance and injury risk, yet the required supplementation dosage for collegiate athletes is unclear. The objective of this study was to define the dosage of vitamin D3 supplementation required to beneficially affect serum 25-hydroxyvitamin D (25(OH)D) among a sample of collegiate basketball athletes. This was a quasi-experimental trial, participants were allocated to one of three groups of vitamin D3 daily at the beginning of pre-season training and dependent upon their baseline vitamin D status as follows: insufficient (<75 nmol/L) to 10,000 IU, sufficient (75–125 nmol/L) to 5000 IU and optimal (>125 nmol/L) to no supplementation. Follow-up assessments were completed ~ 5 months later in post season. The majority (n = 13) were allocated to 10,000 IU vs. n = 5 to 5000 IU and n = 2 to no supplementation. The 10,000 IU group showed the greatest change (35.0 ± 27.0 nmol/L) vs. the 5000 IU group (−9.3 ± 9.6 nmol/L) and no supplementation group (−41.6 ± 11.7 nmol/L, p < 0.01). Only 1 participant reached optimal status in the 10,000 IU group. In conclusion, a daily dosage of 10,000 IU vitamin D3 supplementation mitigated the high prevalence of vitamin D deficiency among collegiate basketball players but was insufficient for all to reach sufficient levels.