Jagim, Andrew R

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.

This study examined the effects of different types of masks (no mask, surgical mask (SM), and N95-mask) on physiological and perceptual responses during 30-min of self-paced cycle ergometer exercise. This study was a prospective randomly assigned experimental design. Outcomes included workload (Watts), oxygen saturation (SpO2), end-tidal carbon dioxide (PetCO2), heart rate (HR), respiratory rate (RR), rating of perceived exertion (RPE), and rating of perceived dyspnea (RPD). Volunteers (54–83 years (n = 19)) completed two familiarization sessions and three testing sessions on an air braked cycle ergometer. No significant difference was found for condition x time for any of the dependent variables. RPE, RPD, and PetCO2 were significantly higher with an N95-mask vs. no mask (NM) ((p = 0.012), (p = 0.002), (p < 0.001)). HR was significantly higher with the SM compared to the NM condition (p = 0.027) (NM 107.18 ± 9.96) (SM 112.34 ± 10.28), but no significant difference was found when comparing the SM to the N95 condition or when comparing the N95condition to the NM condition. Watts increased across time in each condition (p = 0.003). Initially RR increased during the first 3 min of exercise (p < 0.001) with an overall gradual increase noted across time regardless of mask condition (p < 0.001). SpO2 significantly decreased across time but remained within normal limits (>95%). No significant difference was found in Watts, RR, or SpO2 regardless of mask condition. Overall, the N95mask was associated with increased RPE, RPD, and PetCO2 levels. This suggests trapping of CO2 inside the mask leading to increased RPE and RPD.

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.

Aim: To compare the efficacy of two popular weight loss approaches on weight loss, body composition, and markers of health in sedentary obese women. Methods: In total, 51 sedentary women (age 34.5 ± 7.7 yrs.; weight 90.0 ± 14.5 kg; BMI 34.0 ± 5.1 kg/m2; 46.5 ± 7.0% fat) were matched and randomized to participate in the Weight Watchers® Momentum™ (WW) or Curves® (CV) Fitness and Weight Management program for 16 weeks. Participants in the WW group (n = 27) were provided a point-based diet program, received weekly progress checks and counseling, and were encouraged to exercise. Participants in the CV group (n = 24) followed a menu-based higher protein/low-fat diet (1200 kcal/d) for 1 week; 1500 kcal/d diet for 3 weeks; and 2000–2500 kcals/d for 2 weeks that was repeated three times (except the last segment) while participating in a supervised circuit-style resistance training program (3 d/wk). A general linear model (GLM) with repeated measures was used to analyze data and are presented as mean changes from baseline (mean [UL, LL]). Results: Supervised CV training resulted in greater amounts of vigorous and total physical activity. After 16 weeks, both groups lost weight (WW −6.1 [−7.8, −4.6], CV −4.9 [−6.2, −3.2] kg, p = 0.264). Participants in the CV group observed greater reductions in fat mass (WW −2.9 [−6.7, −0.2], CV −6.4 [−9.2, −3.6] kg, p = 0.081) and increases in lean mass (WW −2.5 [−4.3, −0.7], CV 1.3 [−0.6, 3.2] kg, p = 0.005) resulting in more favorable changes in percent body fat (WW −1.4 [−4.1, 1.2], CV −4.7 [−7.5, −1.8]%, p = 0.098). Both groups observed improvements in peak aerobic capacity and muscular endurance, although bench press lifting volume was greater in the CV group. Those in the CV group experienced a greater increase in HDLc and reduction in the CHL–HDLc ratio and triglycerides. Conclusion: Both interventions promoted weight loss and improvements in fitness and markers of health. The CV program, which included supervised resistance training and higher protein diet menus, promoted greater fat loss, increases in lean mass, and improvements in percent body fat and blood lipids. Trial Registration: clinicaltrials.gov, #NCT04372771, registered retrospectively 1 May 2020.

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.

Research quantifying the unique workload demands of starters and reserves in training and match settings throughout a season in collegiate soccer is limited. Purpose: The purpose of the current study is to compare accumulated workloads between starters and reserves in collegiate soccer. Methods: Twenty-two NCAA Division III female soccer athletes (height: 1.67 ± 0.05 m; body mass: 65.42 ± 6.33 kg; fat-free mass: 48.99 ± 3.81 kg; body fat %: 25.22 ± 4.78%) were equipped with wearable global positioning systems with on-board inertial sensors, which assessed a proprietary training load metric and distance covered for each practice and 22 matches throughout an entire season. Nine players were classified as starters (S), defined as those playing >50% of playing time throughout the entire season. The remaining 17 were reserves (R). Goalkeepers were excluded. A one-way ANOVA was used to determine the extent of differences in accumulated training load throughout the season by player status. Results: Accumulated training load and total distance covered for starters were greater than reserves ((S: 9431 ± 1471 vs. R: 6310 ± 2263 AU; p < 0.001) and (S: 401.7 ± 31.9 vs. R: 272.9 ± 51.4 km; p < 0.001), respectively) throughout the season. Conclusions: Starters covered a much greater distance throughout the season, resulting in almost double the training load compared to reserves. It is unknown if the high workloads experienced by starters or the low workloads of the reserves is more problematic. Managing player workloads in soccer may require attention to address potential imbalances that emerge between starters and reserves throughout a season.