King, Erica L.

Abstract Burke, AA, Saine, ND, King, EL, Fields, JB, Jagim, AR, Miller, AD, Green, BC, and Jones, MT. Longitudinal modeling of pitching workload and countermovement jump performance as determinants of fastball velocity and consistency in collegiate baseball pitchers. J Strength Cond Res XX(X): 000–000, 2026—Maintenance of four-seam fastball (FSFB) velocity across the baseball season is critical, yet the extent to which workload and neuromuscular status relate to velocity is unclear. Thus, aims of the current study were to assess the relationship between external pitching workload metrics and FSFB velocity; quantify within-season FSFB velocity; and examine how countermovement jump (CMJ) performance predicts FSFB velocity. Collegiate pitchers ( n = 8) completed weekly CMJ testing during a 15-week, 53-game season. Countermovement jump metrics included jump height (JH), reactive strength index–modified (RSImod), peak braking force, and peak propulsive force normalized to body mass. Workload variables (innings, total pitches, FSFB pitches) and FSFB velocity were evaluated via repeated measures correlation (RMCORR). A longitudinal heat map visualized velocity trends, and a hierarchical location–scale model assessed predictors of mean velocity and variance. Repeated measures correlation showed weak negative associations between FSFB velocity and innings ( r = −0.15, p = 0.11), total pitches ( r = −0.18, p = 0.05), and FSFB pitches ( r = −0.18, p = 0.05). Heat map analysis identified 3 distinct velocity clusters. Location model results indicated no predictors of mean velocity. The scale model showed JH ( γ = −0.060, p = 0.001) predicted reduced velocity variance, and RSImod ( γ = 3.800, p < 0.001) predicted increased variance. Findings suggest that velocity patterns varied across athletes, where greater workloads were negatively associated with FSFB pitch velocity. Further, CMJ performance related to velocity stability rather than mean velocity. Routine longitudinal monitoring and continued strength-power training are recommended to support FSFB velocity.

Abstract Fields, JB, Jones, MT, Kitts, SE, King, EL, and Jagim, AR. Relationships between disordered eating, body dissatisfaction, and low energy availability in women collegiate athletes. J Strength Cond Res 40(2): e164–e171, 2026—Women collegiate student-athletes face unique challenges regarding nutrition, body image, and athletic performance, increasing their risk for low energy availability (LEA), disordered eating (DE), and body dissatisfaction (BID). However, limited data exist exploring LEA, DE, and BID across a variety of women's sports. Therefore, the purpose of this study was to evaluate the prevalence of and relationships among LEA risk, DE behaviors, and BID in women collegiate athletes from multiple sports. Division I, II, and III women student-athletes ( n = 115) participated in this cross-sectional, descriptive study. Athletes completed the low energy availability for females questionnaire (LEAF-Q), eating attitudes test (EAT-26), muscularity-oriented eating test (MOET), and body dissatisfaction items from the eating disorder examination questionnaire (EDE-Q) to assess LEA risk, DE behaviors, and BID, respectively. Sixty-four percentages of athletes were at risk for LEA, with the highest prevalence in volleyball (100%), wrestling (68%), lacrosse (67%), and cross country (57%). However, only 8% of athletes were classified at risk for eating disorders. Notably, 81 and 84% of athletes reported body weight and shape dissatisfaction, respectively, which was strongly associated with disordered eating behaviors (EAT-26: r = 0.707; MOET: r = 0.723, p < 0.001). These results show a high prevalence of LEA risk and BID across athletes, with DE behaviors strongly linked to body image concerns. Despite a high LEA risk among athletes, clinical eating disorder risk remained low, suggesting that undereating may often be unintentional. Targeted interventions focused on nutritional education, body image, and psychological support are critical for improving health and performance outcomes among collegiate women student-athletes.

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.

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.

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.