Citations

Abstract Kuhlman, NM, Jagim, AR, Jones, MT, Feit, MK, and Fields, JB. A comparison of match external load demands across women’s collegiate field sports. J Strength Cond Res 39(2): 234–241, 2025—Historically, soccer (SOC), lacrosse (LAX), and field hockey (FH) have been grouped together as “field sports” due to their intermittent nature of match play and similar physiological demands. However, questions have emerged regarding potential variations in demands between sports. The purpose of this study was to compare external match workloads between collegiate women's SOC, LAX, and FH, and examine within-sport positional differences. Athletes (SOC: n = 15; LAX: n = 15; FH: n = 14) wore a global positioning system device during all matches throughout a competitive season. External load metrics included in the analysis were total distance (TD), sprint distance (SD), accelerations (>3 m·s −2 ), sprint efforts (SE), player load (PL), PL per minute (PL/min), top speed (m·s −1 ), and distances covered in select speed zones. Soccer performed higher external load volumes (TD, PL) ( p ≤ 0.025) but incurred lower intensities (SD, SE, accelerations) ( p < 0.001) compared with LAX and FH. Within-sport positional differences were apparent in SOC ( p < 0.001–0.018), LAX ( p < 0.001–0.045), and FH ( p < 0.001–0.004). However, FH exhibited more positional variance in external load metrics compared with SOC or LAX. Field hockey midfielders covered greater volumes (TD, PL) compared with attackers and defenders ( p < 0.001), while attackers sustained greater intensities (SD, SE, top speed) compared with midfielders and defenders ( p < 0.004). Results indicate that there are significant differences in external workload parameters between sports and across positions. Practitioners should tailor sport-specific and position-specific programs to match demands, and future research should explore the proposed categorizations of LAX as a “fast collision invasion sport,” FH as a “fast contact invasion sport,” and SOC as a “slow contact invasion sport.”

Sleep is critical to athlete health and sport performance. Purpose To evaluate the relationship between objective sleep data and next-day basketball game performance. Methods Collegiate men basketball athletes (n = 4) wore smart ring sleep trackers the night before games (n = 22). Sleep variables included hours of total sleep (TS), rapid eye movement sleep duration (RM), deep sleep duration (DS), and heart rate variability (HRV). Game performance was evaluated using field goal percentage (FG%) and player efficiency (EFF). Results TS, RM, DS, and HRV demonstrated very weak relationships with FG% ( p > 0.05) and EFF ( p > 0.05). Individual player analysis revealed one athlete exhibited strong to moderate correlations for TS-EFF (r = 0.67), RS-EFF (r = 0.41), TS-FG% (r = 0.51), and RM-FG% (r = 0.67) ( p < 0.05). Sleep metrics had no effect on FG% (R 2 = 0.025) and EFF (R 2 = 0.053) ( p > 0.05). Conclusions Results suggest a lack of group-level associations between smart ring-measured sleep variables and next-day game performance. This highlights the need for individualized sleep monitoring rather than relying solely on objective metrics.

Energy drinks are a commonly consumed beverage, and studies suggest a possible performance-enhancing effect. A Google Scholar search using the keywords “energy drinks” and “exercise” yields numerous results, underscoring the voluminous research on this topic. However, there are questions regarding the effectiveness and safety of energy drinks. These questions include, but are not limited to: (1) What are the main active ingredients in energy drinks? (2) Do energy drinks assist in weight management? (3) Do energy drinks enhance aerobic performance? (4) Do energy drinks enhance athletic speed? (5) Do energy drinks improve reaction time? (6) Do energy drinks enhance lean tissue mass? (7) Can energy drinks improve cognitive performance? (8) Does the acute consumption of energy drinks elevate resting energy expenditure? (9) Is there any evidence to suggest that energy drinks are more effective than an identical serving of caffeine alone? (10) Are there sex differences in the response to energy drink consumption? (11) Do energy drinks affect sleep or sleepiness? (12) Should pregnant women avoid energy drinks? (13) Do energy drinks adversely affect cardiovascular function? (14) Does consuming energy drinks cause brain damage? (15) What are other safety considerations regarding energy drinks? (16) Is there any evidence to suggest that energy drinks are more effective than an identical serving of caffeine alone? (17) If caffeine is the main active ingredient in energy drinks and coffee, why is there a discrepancy in the adverse events reported for each? To address these questions, we performed an evidence-based scientific evaluation of the literature on energy drink supplementation.

Background/Objectives: Adherence to sports-specific nutritional guidelines can help optimize athlete performance and health. However, adolescent athletes may not have adequate nutrition knowledge and understanding of specific nutritional requirements. The objective of the current study was to examine the nutrition knowledge and perceived dietary requirements of adolescent athletes. Methods: Male (n = 29, age: 15.7 ± 1.3 yrs.; height: 178.9 ± 8.3 cm; body mass: 74.7 ± 17.2 kg; body fat %: 13.9 ± 7.9%) and female (n = 15, age: 16.5 ± 1.4 yrs.; height: 169.9 ± 6.5 cm; body mass: 63.3 ± 4.5 kg; body fat %: 23.7 ± 3.8%) secondary school student-athletes completed body composition testing (hydrostatic weighing) and electronic surveys (Abridged Sports Nutrition Knowledge Questionnaire (ASNKQ); self-perception of dietary energy and macronutrient requirements). Results: Athletes demonstrated poor sports nutrition knowledge, with no differences observed between sexes for the number of questions answered correctly (males: 45.1 ± 9.8% vs. females: 43.1 ± 12.7%; p = 0.57). No relationships were observed between ASNKQ scores and body composition parameters or between ASNKQ scores and self-reported perception of dietary energy and macronutrient requirements (p > 0.05). Athletes self-reported a lower perceived energy (−560 ± 1272 kcal/d; p = 0.014) and carbohydrate intake (−73 ± 376 g/d; p = 0.014) requirement compared to calculated nutritional recommendations. Athletes self-reported a higher perceived protein intake (263 ± 586 g/d; p = 0.026) requirement compared to calculated nutritional requirements. Conclusions: The current sample of adolescent athletes from the secondary school level appears to have a poor level of sports nutrition knowledge and understanding of energy and macronutrient requirements.

Objective: To retrospectively analyze publicly available elbow ulnar collateral ligament reconstruction (UCLR) injury data for professional baseball players. Design: Descriptive epidemiology study. Setting: A retrospective analysis using an open-source database was performed. The database contained all known UCLR surgeries among Major League Baseball (MLB) and Minor League Baseball (MiLB) players. Participants: In total, 1801 professional male professional baseball pitchers (age: 24.5 ± 3.9 years) who have undergone UCLR between 1974 and 2024. Main Outcome Measures: The number of UCLR procedures each year. Independent Variables: Change-point analysis was used to identify changes in the number of UCLR surgeries over time among MLB and MiLB players. For MLB players, number of UCLR surgeries from 1974 to 2023 was analyzed, while for MiLB players, data starting from 1981 to 2024 were analyzed. Results: For MLB players, 3 change points were identified, occurring around 1989 (95% confidence interval [CI], 1988–1990), 2000 (95% CI, 2000–2000), and 2012 (95% CI, 2011–2017). For MiLB players, 3 change points were identified occurring around 2001 (95% CI, 2001–2001), 2009 (95% CI, 2008–2010), and 2013 (95% CI, 2013–2015). Conclusions: Overall, there has been a rise in the number of UCLR surgeries per year for the past 50 years in professional baseball, with distinct time points identified, representing increases in the number of UCLR surgeries each year. A higher number of UCLR surgeries are performed per year in the MiLB than in the MLB.

External (EL) and internal (IL) load are commonly used methods used to quantify training load in team sports. Playing time and playing position may influence the training loads for specific athletes throughout a season. The purpose of the current study was to evaluate the effect of athlete playing status and individual in-season practices on EL and IL across a collegiate women’s basketball season. Female basketball athletes were classified as high-minute (HMA; ≥15 min/game) or low-minute (LMA; <15 min/game) and wore microsensors during 53 practices for a total of 583 data points. EL was obtained via an inertial measurement unit (IMU) device that contained a triaxial accelerometer to obtain three-dimensional positioning data. IL and strength training (ST) load were determined via session rating of perceived exertion (sRPE) to create a daily summated value. Descriptive statistics indicate that athletes experienced individual differences in EL, ST, and IL throughout the season. A growth model showed that HMAs experienced higher EL than LMAs at the start of the season for practices (90.21 AU). Across all athletes, IL increased across the season (40.11 AU) and for each 1 unit of change in EL, IL increased by 1.04 AU. Repeated measures correlations identified a large relationship between IL and EL (r = 0.51, p < 0.001). A location-scale model indicated that the within-person variability of IL across all athletes was 3.29 AU but was not due to athlete playing status. It is recommended to base in-season training on individual loads and game demands to promote athlete readiness and improved sport performance.