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Purpose: To compare the effects of 7 weeks of training with different hang-power-clean (HPC) intensities on the maximum dynamic strength, vertical jump, and sprint performance of female handball players. Methods: Professional handball athletes with at least 1 year of HPC experience volunteered to participate. The athletes were balanced by position and randomly assigned to a group with HPC at 90% (HPC90%) or 50% (HPC50%) of HPC 1-repetition maximum (1RM). The training volume was equalized between groups. Pretraining and posttraining assessments included 1RM HPC, jump height in the squat jump (SJ) and countermovement jump (CMJ), and sprint speeds at 5, 20, and 30 m. Results: From pretraining to posttraining, both groups significantly increased (P ≤ .05) 1RM HPC, although a small effect size was observed in favor of HPC90%. HPC90% and HPC50% did not induce significant changes (P > .05) in the SJ and CMJ height, although for the SJ, a small effect size was observed in favor of HPC90%. Both groups induced a significant improvement (P ≤ .05) in 5-, 20-, and 30-m sprint speeds, although for all speeds, small to moderate effect sizes were observed in favor of HPC90%. Conclusions: Both training groups induced significant improvements in 1RM HPC and sprint speeds, whereas no significant changes were observed in vertical jumps. In addition, based mainly on the effect sizes, the HPC90% group was more effective for increasing 1RM HPC, SJ, and sprint speeds than the HPC50% group.

Purpose: To quantify and compare loads encountered in individual training sessions and games during noncongested and congested weeks in semiprofessional women basketball players. Methods: Using an observational, longitudinal design, 12 players from the same team had their external (PlayerLoad, relative PlayerLoad, and total and high-intensity inertial movement analysis variables) and internal load (session rating of perceived exertion [sRPE], sRPE-load, percentage of heart rate peak, and modified summated-heart-rate-zones load) monitored across a regular season. Training and game data were categorized into noncongested (0–1 game) and congested weeks (2–3 games). Linear mixed models and Cohen d effect sizes were used for analyses. Results: Comparisons between training sessions revealed higher (P < .05, d = 1.35–5.33) PlayerLoad, total inertial movement analysis, sRPE, and sRPE-load during training session 1 than training session 2 in congested weeks. Comparisons between training sessions and games revealed higher (P ≤ .001, d = 1.10–1.66) sRPE and sRPE-load during games than training sessions 1 and 2 in noncongested weeks, alongside higher (P ≤ .001, d = 1.87–3.55) sRPE during game 1 than training sessions 1 and 2 in congested weeks. Comparisons between games revealed higher (P < .05, d = 0.57–2.82) loads in game 3 during congested weeks compared with all other games. Conclusions: Training appeared to be tapered in congested weeks, likely to account for upcoming increases in game loading, but remained relatively consistent across sessions during noncongested weeks. Individual game loads remained relatively consistent but were noticeably increased when a third game was played in the week.

Purpose: To examine the test–retest reliability of countermovement jump (CMJ) and isometric strength testing measures in elite-level under-18 and under-23 academy football players. Methods: A total of 36 players performed 3 maximal CMJs and isometric abductor (IABS), adductor (IADS), and posterior chain (IPCS) strength tests on 2 separate test days using dual force plates (CMJ and IPCS) and a portable strength testing device (IABS and IADS). Relative (intraclass correlation coefficient) and absolute (coefficient of variation, standard error of the measurement, and minimal detectable change [MDC%]) reliabilities for 34 CMJ, 10 IABS, 10 IADS, and 11 IPCS measures were analyzed using between-sessions best, mean, and within-session methods. Results: For all methods, relative reliability was good to excellent for all CMJ and all IADS measures and poor to good for all IABS and IPCS measures. Absolute reliability was good (ie, coefficient of variation < 10%) for 27 (best) and 28 (mean) CMJ variables and for 6 (IABS and IADS) and 2 (IPCS) isometric measures. Commonly used CMJ measures (jump height, eccentric duration, and flight-time:contraction-time ratio) had good to excellent relative reliability and an MDC% range of 14.6% to 23.7%. Likewise, commonly used isometric peak force measures for IABS, IADS, and IPCS had good to excellent relative reliability and an MDC% range of 22.2% to 26.4%. Conclusions: Commonly used CMJ and isometric strength measures had good test–retest reliability but might be limited by their MDC%. Rate-of-force-development measures (for all isometric tests) and impulse measures (IPCS) are limited by poor relative and absolute reliability and high MDC%. MDC% statistics should be considered in the context of typical responsiveness.

Purpose: To investigate race-management strategies over a longitudinal case study of one of the world’s best female swimmers of the 200-m freestyle to understand if only 1 race-management strategy allowed her to succeed or whether several profiles have been used over the 8 years of analysis. Methods: Different race-management strategies within and between 50-m laps emerged from cluster analysis. To better explain race management, additional characteristics described the level of adversity, the level of competition, the performance outcome, and the type of race. Results: Two strategies of race management between laps have been used, and both allowed her to succeed in her career. The first was characterized by a fast start and a greater decrease of the speed between laps, whereas the second exhibited a more stable speed management. When those strategies were examined in relation to the level of competition and the level of adversity, it appeared that the first strategy was used more in international competitions and associated with higher time intervals between the studied swimmer and the direct rivals, while the second one was used more in national competitions and associated with lower time intervals. Conclusions: Findings suggested that this top elite swimmer did not adhere to a single “ideal” race-management strategy. Instead, she demonstrated flexibility and the ability to adapt her race management to contextual factors throughout her career, effectively controlling adversity. This highlights the importance of including adversity analysis in race-management studies.

Purpose: This investigation sought to determine industry perceptions of postgraduate strength and conditioning (S&C) degrees to understand whether graduates are equipped for the demands of sport performance service roles. Methods: Survey data were collected from 111 participants employed as performance staff or in a role that recruits and supervises performance staff. The survey consisted of 3 main sections: (1) perceptions of career-development opportunities in S&C, (2) perception of S&C postgraduate programs, and (3) perceptions of employability in S&C. Exploratory factor analysis was conducted to identify the key factors considered to be of greatest relevance to career progression in S&C. Results: A 2-factor solution was achieved for each of the 3 sections, resulting in 6 total factors. These factors are Academic and Professional Development, Mentorship and Sport Diversity, Student Preparation, Require Greater Emphasis, Testing and Training, and Personal and Professional Growth. Conclusions: Postgraduate S&C programs require a broad range of placement/internship opportunities to (1) provide diverse experiences, (2) allow students to build contacts and develop professional networks, (3) gain exposure to working in high-performance environments and multidisciplinary teams, and (4) access high-quality mentors. Alongside the ability to deliver training and testing, graduates should be equipped with strong organizational and relationship-building skills. Improved graduate capabilities can raise the standards of the profession and result in enhanced service provision to athletes.

Purpose: The first heart-rate (HR) -variability (HRV) -derived threshold based on detrended fluctuation analysis alpha 1 (DFA a1) has shown inconsistent agreement with the gas-exchange threshold (GET). This study examined whether a custom method of computing the first HRV threshold (HRVT₁) based on individual HRV characteristics would improve agreement. Methods: Fourteen participants underwent ramp incremental testing measuring gas-exchange variables and RR intervals. Comparisons were made between the oxygen consumption ${\displaystyle {(\dot{\mathrm{V}}\mathrm{O}}_2)/\mathrm{HR}}$ at the GET versus the ${\dot{\mathrm{V}}\mathrm{O}}_2/\mathrm{HR}$ at the standard DFA a1 = 0.75 (HRVT_1s) or a custom value (HRVT_1c) based on the DFA a1 midway between the maximum seen during the early ramp incremental and 0.5. Results: Mean values for GET ${\dot{\mathrm{V}}\mathrm{O}}_2$ versus HRVT_1s ${\dot{\mathrm{V}}\mathrm{O}}_2$ and GET_HR versus HRVT_1sHR were statistically different (25.4 [3.3] vs 29.8 [6.8] mL·kg⁻¹·min⁻¹, P = .01, d = 0.80; 131 [11] vs 146 [22] beats·min⁻¹, P = .005, d = 0.91). There were no statistical differences when using the HRVT_1c (25.4 [3.3] vs 25.1 [5.7] mL·kg⁻¹·min⁻¹, P = .77, d = 0.08; 131 [11] vs 132 [17] beats·min⁻¹, P = .65, d = 0.12). Equivalence between GET and HRVT_1c ${\dot{\mathrm{V}}\mathrm{O}}_2/\mathrm{HR}$ was also verified. Mean maximal DFA a1 during the early ramp incremental was 1.52 (0.22) with mean HRVT_1c of 1.01 (0.11). Pearson r correlation coefficients were between .67 and .70 for all GET to HRVT₁ comparisons. The second HRV threshold and respiratory compensation point parameters showed agreement and correlations in line with prior studies. Conclusions: The HRVT_1c showed stronger agreement to GET parameters than seen using the HRVT_1s. It is recommended that evaluations of the HRVT₁ consider this approach in determining the HR and ${\dot{\mathrm{V}}\mathrm{O}}_2$ at this threshold.

Purpose: To analyze the evolution of the world ranking in swimming over the last 10 years, with particular attention to the effects of COVID-19 on the different levels of participating athletes. Methods: The top 200 world-ranked entries in all swimming events (50-m pool) were collected from 2013 to 2022. A mathematical model (Gaussian model) was proposed to evaluate the ranking progression for different performance levels (clusters) according to distance, stroke, and gender. The model was applied both with and without the COVID season data. Results: Overall results indicated a general progression in world rankings over the last 10 years, except for the COVID season and the post-Olympic year(s), with peak results in the 2021 postpandemic (Olympic) year. The gender gap in World Aquatics points scoring has shown an increasing gap in favor of males since 2017, reaching 1.5% in 2022. The top 200 positions of world rankings were grouped into 3 different clusters defined by the 23.3%, 66.5%, and 100% of ranked male swimmers (or 31.5%, 72.5%, and 100% for females) and with average World Aquatics scores of 910 (12), 858 (10) and 816 (11) points (907 [13], 847 [11], and 802 [12] for females). The Gaussian model showed a gap averaging ∼21 to ∼36 points between performance curves with or without COVID season data, with larger gaps for female rankings and cluster-3 swimmers. Conclusions: These results suggest that, given the lower relative performance of female swimmers in the different clusters of world rankings, female events may provide an opportunity to enter international-level swimming.

There is little evidence that body size alters exogenous glucose oxidation rates during exercise. This study assessed whether larger people oxidize more exogenous glucose during exercise than smaller people. Fifteen cyclists were allocated into two groups based on body mass (SMALL, <70 kg body mass, n = 9, two females) or (LARGE, >70 kg body mass, n = 6) matched for lactate threshold (SMALL: 2.3 ± 0.4 W/kg, LARGE: 2.3 ± 0.3 W/kg). SMALL completed 120 min of cycling at 95% of lactate threshold₁. LARGE completed two trials in a random order, one at 95% of lactate threshold₁ (thereby exercising at the same relative intensity [RELATIVE]) and one at an absolute intensity matched to SMALL (ABSOLUTE). In all trials, cyclists ingested 90 g/hr of ¹³C-enriched glucose. Total exogenous glucose oxidation was (mean ± SD) 33 ± 8 g/hr in SMALL versus 45 ± 13 g/hr in LARGE-RELATIVE (mean difference: 13 g/hr, 95% confidence interval [2, 24] g/hr, p = .03). Large positive correlations were observed for measures of exogenous carbohydrate oxidation versus body size (body mass, height, and body surface area; e.g., body surface area vs. peak exogenous glucose oxidation, r = .85, 95% confidence interval [.51, .95], p < .01). When larger athletes reduced the intensity from RELATIVE to ABSOLUTE, total exogenous glucose oxidation was 39 ± 7 g/hr (p = .43 vs. LARGE-RELATIVE). In conclusion, the capacity for exogenous glucose oxidation is, on average, higher in larger athletes than smaller athletes during exercise. The extent to which this is due to higher absolute exercise intensity requires further research, but body size may be a consideration in tailoring sports nutrition guidelines for carbohydrate intake during exercise.