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Purpose: To compare the effects of 6-week barbell weighted-jump-squat (WJS) training with and without eccentric load reduction on explosive performance. Methods: Twenty well-trained male athletes were randomly assigned to either an experimental group (n = 10) or a control group (n = 10). Participants completed 12 WJS training sessions (6 sets of 5 repetitions of barbell back squat at 30% of 1-repetition maximum [1RM]) twice a week over a 6-week period. While the control group used 0% eccentric loading (ie, traditional WJS), the experimental group utilized a 50% eccentric loading reduction with a mechanical braking unit (ie, eccentric load set at 15% of 1RM). Performance assessments, including countermovement jump, 20-m sprint, standing long jump, and 1RM barbell back squat, were conducted both before (pretests) and after (posttests) the intervention. Results: Both the experimental group and the control group demonstrated a significant increase in countermovement-jump height (+6.4% [4.0%] vs +4.9% [5.7%]; P < .001) and peak power output (+2.3% [2.7%] vs +1.9% [5.1%]; P = .017), faster 20-m sprint times (+9.4% [4.8%] vs +9.2% [5.5%]; P < .001), longer standing long jump (+3.1% [2.5%] vs +3.0% [3.3%]; P < .001), and higher 1RM back squat (+6.4% [4.0%] vs +4.9% [5.7%]; P < .001) from pretests to posttests. However, there was no significant condition × time interaction for any variable (all P ≥ .294). Conclusions: Both WJS training methods, with and without load reduction in the eccentric phase, effectively enhance explosive performance. Nevertheless, athletes in later stages of injury rehabilitation or intense training may find reducing eccentric load a more tolerable strategy for achieving similar performance gains compared with traditional isoinertial loading.

Purpose: This study explored the effects of 4 bench-press (BP) training programs with different velocity-loss (VL) thresholds (0%, 15%, 25%, and 50%) on strength gains and neuromuscular adaptations. Methods: Forty-six resistance-trained men (22.8 [4.4] y) were randomly assigned into 4 groups that differed in the VL allowed within the set: 0% (VL0), 15% (VL15), 25% (VL25), and 50% (VL50). Training loads (40%–55% 1-repetition maximum), frequency (2 sessions/wk), number of sets (3), and interset recovery (4 min) were identical for all groups. Participants completed the following tests before and after an 8-week (16-session) BP training program: (1) maximal isometric test, (2) progressive loading test, and (3) fatigue test in the BP exercise. During all tests, triceps brachii muscle electromyography was assessed. Results: After completing the resistance-training program, no significant group × time interactions were noticed for isometric and dynamic BP strength variables. The dose–response relationship exhibited an inverted U-shaped relationship pattern, with VL25 showing the greatest effect sizes for almost all strength variables analyzed. The total number of repetitions performed during the training program increased as the VL magnitude increased. Conclusions: The group that trained with high VL threshold (50%), which performed a total of 876 repetitions, did not experience additional strength gains compared with those experienced by the 0%, 15%, and 25% of VL groups, which performed significantly fewer repetitions (48, 357, and 547, respectively). These findings suggest that when light loads (40%–55% 1-repetition maximum) are used, low and moderate VL thresholds (0%–25%) provide a higher training efficiency.

Purpose: To compare designs of training sessions applied by world-class cross-country skiers during their most successful junior and senior season. Methods: Retrospective analysis of self-reported training characteristics (ie, training form, intensity, and exercise mode) among 8 male and 7 female world-class cross-country skiers was conducted. Results: Total number of sessions (441 [71] vs 519 [34], P < .001, large effect) and mean duration (1.5 [0.1] h vs 1.7 [0.1] h, P < .001, moderate effect) increased from junior to senior age. More double-session days were performed at senior age (124 [50] vs 197 [29] d, P < .001, large). The number (310 [64] vs 393 [64], P < .001, large effect) and duration (1.3 [0.1] h vs 1.5 [0.1] h, P < .001, moderate effect) of low-intensity training sessions increased from junior to senior age. Regarding intensive training, most emphasis was put on high-intensity training sessions lasting 20 to 39 minutes with <5-minute intervals at junior age, while 40 to 59 minutes of moderate-intensity training with 5- to 9-minute intervals was predominant at senior age. More MIXED (combined moderate- and high-intensity) sessions (9 [7] vs 14 [7], P = .023, moderate effect) and longer races (0.5 [0.1] h vs 0.6 [0.1] h, P = 0.29, moderate effect) compensated for fewer high-intensity training sessions at senior age (36 [17] vs 25 [10], P = .027, moderate effect). Duration of strength-training sessions increased significantly (0.6 [0.1] vs 0.8 [0.2] h, P = 0.30, moderate effect), while other training forms remained unchanged. Conclusions: World-class cross-country skiers increased their training volume from junior to senior age primarily by more and longer low-intensity training sessions and more often training twice per day. Concurrently, the most frequent intensive sessions were modified from high- to moderate-intensity training, lasted longer, and contained longer intervals.

Purpose: Agility in young soccer players has long been associated with physical attributes like strength, speed, and power. The aims of this study were to investigate the relationship between cognitive factors and agility performance in this unique population. Methods: 39 young soccer players age 13.56 (SD 0.58) years were assessed for leg-muscle function (jump tests) and maturation status (peak height velocity). The Y-Agility Test, which included decision making in players, was used to measure performance. Cognitive factors such as perceptual skills and decision making were evaluated with the Y-Agility Test. Results: The linear regression showed an absence of significance between the Y-Agility Test and drop-jump contact (P = .283), Y-Agility Test and drop-jump flight (P = .185), Y-Agility Test and squat jump (P = .868), and Y-Agility Test and countermovement jump (P = .310). The linear mixed-model analyses suggested a difference between early-average maturers (P = .009) and early-late maturers (P = .005) but did not show a difference between average-late subjects (P = 1.000). Drop-jump flight did not show a difference in maturation (early-average P = 1.000; early-late P = 1.000; average P = 1.000). Squat-jump performance did not demonstrate any significance (early-average P = .618; early-late P = 1.000; P = 1.000). Countermovement-jump performance did not show any significance (early-average P = 1.000; early-late P = 1.000; average-late P = .492). Finally, agility performance does not show any significance between maturation levels (early-average maturer P = .450; early-late P = 1.000; average-late P = .830). Conclusion: Agility in young soccer players appears to follow a nonlinear trajectory, with cognitive factors possibly playing a more significant role than previously thought.

Purpose: To compare power output and pacing between maximal 1500- and 2000-m on-water rowing performances. Methods: Twenty-six (female n = 4, male n = 22) international rowers, across 6 boat classes, completed maximal 1500- and 2000-m on-water races, separated by 24 to 48 hours. Crew combinations and seat orders remained consistent between races. Peach PowerLine instrumentation measured power output and stroke rate. Differences in completion time, mean power output (MPO), percentage prognostic velocity (PPV; percentage of world record velocity in each boat class), stroke rate, and pacing variance were assessed using linear mixed modeling. Results: Compared with 2000-m, completion times were 90.4 (6.1) seconds shorter over 1500 m (−24.7% [0.7%]). Both MPO (P = .255, ${\eta }_{\mathrm{p}}^2=.06$ ) and PPV (P = .340, ${\eta }_{\mathrm{p}}^2=.18$ ) were not different between distances. Broadly, crews adopted a reverse-J-shaped pacing across both distances, demonstrating a reduced variance over 1500 m (P = .035, ${\eta }_{\mathrm{p}}^2=.62$ ). Percentage change in MPO from 2000 to 1500 m demonstrated a strong negative association with pacing variance over 1500 m (R ² = .74, P = .027). Conclusions: International rowing crews did not increase MPO or PPV when racing maximally over 1500 m compared to 2000 m. Comparable strategies were adopted over both distances, with less variance in pacing observed over 1500 m. The crews that demonstrated greater increases in MPO over the shorter race employed a flatter pacing strategy. To improve 1500-m on-water performance, rowers may need to adopt a more even pacing approach.

Objective: To analyze the influence of congested and noncongested fixture periods during 2 seasons in a professional male football team on soft-tissue injury incidence and external load. Methods: Thirty-three professional football players from a Portuguese Liga I team participated in this study. Weekly external load and soft-tissue injury rate and burden of 2 consecutive seasons (2021–22 and 2022–23) were analyzed. Results: Total soft-tissue injury rate and burden for the 2 seasons were 3.9 and 3.2 injuries per 1000 hours and 71.8 and 60.5 days per 1000 hours for congested and noncongested periods, respectively. No significant differences were observed between congested and noncongested periods. Total high-speed running, sprint distance, distance above 80% and 90% of maximal velocity, and meters accelerating and decelerating above 2 m/s² were significantly higher for noncongested weeks. Match accelerations and decelerations above 3 m/s² were higher during congested periods and training during noncongested periods. No differences between the 2 periods were observed for the total number of accelerations and decelerations above 3 m/s². Overall, physical outputs per week were higher for training during noncongested weeks, whereas matches during congested periods registered higher external load. Conclusions: No effect of a congested schedule was observed on soft-tissue injury rates and injury burden. Higher match exposure during congested periods increased external load performed per week, and during noncongested periods, training load was superior to congested weeks.

Purpose: This study aimed to examine the impact of different rest periods between short sprint interval training (SSIT) trials on the physiological and performance adaptations of female volleyball players. Methods: Twenty-four trained college athletes volunteered to participate in this study and were randomly assigned to 3 SSIT groups with different work-to-rest ratios (1:2 [5-s run:10-s rest], 1:4 [5-s run:20-s rest], and 1:6 [5-s work:30-s rest]). Before and after 6-week training, physiological parameters (maximum oxygen uptake, first and second ventilatory thresholds, and peak and mean power output) and physical performance measures (ie, countermovement vertical jump, 10-m sprint, and T-test change-of-direction speed) were evaluated. Results: After the training period, all groups improved (P = .001) their sport-related performance and physiological parameters, ranging from moderate to very large effect sizes. Comparative analysis of the magnitude of training effects indicated that the 1:6 SSIT group had in a significantly greater change in countermovement vertical jump (P = .007), 10-m sprint (P = .014), peak power output (P = .019), and mean power output (P = .05) compared with 1:2 SSIT group. By contrast, the 1:2 SSIT group demonstrated significantly (P = .022) greater changes in maximum oxygen uptake than the 1:6 SSIT group. However, the change-of-direction speed and changes in first and second ventilatory thresholds were the same among the groups (P > .05). Conclusions: When performing SSIT, longer rest intervals are suitable for physical and anaerobic performance, and shorter rest periods are appropriate for enhancing the cardiorespiratory fitness of female volleyball players’ performance.

Purpose: To profile the training characteristics of an elite team pursuit cycling squad and assess variations in training intensity and load accumulation across the 36-week period prior to a world-record performance at the 2018 Commonwealth Games. Methods: Training data of 5 male track endurance cyclists (mean [SD]; age 21.9 [3.52] y; 4.4 [0.16] W·kg⁻¹ at anaerobic threshold; 6.2 [0.28] W·kg⁻¹ maximal oxygen uptake 68.7 [2.99] mL kg·min⁻¹) were analyzed with weekly total training volume and heart rate, power output, and torque intensity distributions calculated with reference to their 3:49.804 min:s.ms performance requirements for a 4-km team pursuit. Results: Athletes completed 543 (37) h⁻¹ of training across 436 (16) sessions. On-bike activities accounted for 69.9% of all training sessions, with participants cycling 11,246 (1139) km⁻¹ in the training period of interest, whereas 12.7% of sessions involved gym/strength training. A pyramidal intensity distribution was evident with over 65% and 70% of training, respectively, performed at low-intensity zone heart rate and power output, whereas 5.3% and 7.7% of training was performed above anaerobic threshold. The athletes accumulated 4.4% of total training volume at, or above, their world-record team pursuit lead position torque (55 N·m). Conclusions: These data provide updated and novel insight to the power and torque demands and load accumulation contributing to world-record team pursuit performance. Although the observed pyramidal intensity distribution is common in endurance sports, the lack of shift toward a polarized intensity distribution during taper and competition peaking differs from previous research.

Purpose: To compare the training characteristics of an elite team pursuit cycling squad in the 3-month preparation phases prior to 2 successive world-record (WR) performances. Methods: Training data of 5 male track endurance cyclists (mean [SD]; age 23.4 [3.46] y; body mass 80.2 [2.74] kg; 4.5 [0.17] W·kg⁻¹ at LT₂; maximal aerobic power 6.2 [0.27] W·kg⁻¹; maximal oxygen uptake 65.9 [2.89] mL·kg⁻¹·min⁻¹) were analyzed with weekly total training volume by training type and heart rate, power output, and torque intensity distributions calculated with reference to the respective WRs’ performance requirements. Results: Athletes completed 805 (82.81) and 725 (68.40) min·wk^–1 of training, respectively, in each season. In the second season, there was a 32% increase in total track volume, although track sessions were shorter (ie, greater frequency) in the second season. A pyramidal intensity distribution was consistent across both seasons, with 81% of training, on average, performed below LT₁ power output each week, whereas 6% of training was performed above LT₂. Athletes accumulated greater volume above WR team pursuit lead power (2.4% vs 0.9%) and torque (6.2% vs 3.2%) in 2019. In one athlete, mean single-leg-press peak rate of force development was 71% and 46% higher at mid- and late-phases, respectively, during the preparation period. Conclusions: These findings provide novel insights into the common and contrasting methods contributing to successive WR team pursuit performances. Greater accumulation of volume above race-specific power and torque (eg, team pursuit lead), as well as improved neuromuscular force-generating capacities, may be worthy of investigation for implementation in training programs.