The research focused on post-activation performance enhancement, or PAPE, an acute phenomenon in which brief, high-intensity muscle contractions transiently improve subsequent strength, speed, or power. Physiologists believe the effect arises from heightened neuromuscular excitability, including more efficient motor unit recruitment and increased sensitivity of muscle fibers to calcium ions. But PAPE is a double-edged sword: the same conditioning activity that primes the neuromuscular system also generates fatigue, and performance only improves when potentiation outweighs that residual fatigue. The central challenge, therefore, is inducing sufficient activation while minimizing the fatigue that masks it.

To probe that balance, a team led by Penglin Diao of Wuhan Sports University and Ziyu Wang of the University of Canterbury recruited eighteen male boxers from the Shandong Provincial Boxing Team, aged 17.6 years on average. All held national second-class athlete certificates, had placed in the top three at provincial-level competitions, and trained roughly 27.8 hours per week. Each athlete completed four experimental conditions in a randomized, counterbalanced crossover design, separated by 72-hour washout periods: an active control involving two sets of ten light bench press repetitions below 30 percent of one-repetition maximum, and three heavy conditions at 85 percent 1RM in which each set was terminated when bar velocity dropped by 10, 20, or 30 percent relative to the fastest repetition in that set.

Velocity loss, the decline in bar speed within a set, served as the study’s regulatory dial. A linear position transducer monitored each repetition’s concentric velocity in real time, and the set ended the instant the prescribed threshold was reached. Because no fixed repetition number was prescribed, the threshold indirectly determined how much work each athlete performed: participants averaged about 4.6 total repetitions and a volume-load of roughly 339 kilograms in the 10 percent condition, climbing to about 5.9 repetitions and 433 kilograms at 20 percent, and 6.9 repetitions and 509 kilograms at 30 percent. Straight-punch performance—peak punch velocity measured with a wearable wrist sensor, peak punch force recorded by a force-sensing target at 1000 Hz, and an estimated punch-power index combining the two—was assessed at baseline and at 4, 8, 12, and 16 minutes after conditioning.

The results painted a strikingly time-dependent picture. At 8 and 12 minutes post-conditioning, both the 10 and 20 percent velocity-loss conditions produced significantly higher rear-hand punch velocity, punch force, and punch-power index, along with higher lead-hand punch-power index, than the active control, with standardized effect sizes ranging from 0.835 to 1.267—large by conventional criteria. In sharp contrast, the 30 percent condition backfired: it produced lower rear-hand punch velocity, lower lead- and rear-hand punch force, and lower rear-hand punch-power index than control at 4 minutes, with deficits persisting for selected outcomes through 8 to 12 minutes and effect sizes plunging as far as −2.439.

Direct comparisons among the three velocity-loss conditions added nuance. The 10 and 20 percent thresholds never differed significantly from each other at any time point for either hand or any outcome, suggesting that once the conditioning dose stays modest, the precise cutoff matters less than staying below the fatigue-inducing zone. Both lower thresholds, however, produced significantly higher values than the 30 percent condition for several outcomes at selected time points, with rear-hand punch-power index differences reaching an estimated 1281 watts. By 16 minutes, most between-condition differences had dissolved, indicating that the enhancement window is genuinely transient.

A curious asymmetry emerged between the hands. Favorable differences relative to control were descriptively more apparent for the rear-hand straight punch, which in boxing typically involves a longer acceleration path and generates greater peak force and velocity than the lead-hand jab. The authors speculate that when conditioning enhances neuromuscular activation, the potentiation effect may be more readily expressed during the rear-hand punch, whose extended force-production demands give the primed musculature more opportunity to demonstrate its enhanced output. The lead-hand punch, with its shorter trajectory and lower velocity outputs, showed less consistent enhancement, and lead-hand punch velocity in particular failed to exhibit a significant condition-by-time interaction.

The findings align with a broader literature on velocity-based training. Previous work has shown that greater velocity loss within a set is associated with higher blood lactate, greater perceived exertion, larger reductions in power output, and more neuromuscular fatigue that can impair subsequent explosive performance. Meta-analytic evidence suggests that recovery periods of roughly 7 to 10 minutes after high-intensity conditioning typically produce favorable enhancement, and that highly trained athletes may benefit from slightly longer intervals that allow fatigue to dissipate more fully before potentiation is expressed. The present study’s 8-to-12-minute sweet spot fits comfortably within that framework.

For coaches working with similarly trained male adolescent boxers, the practical message is concrete: allowing approximately 8 to 12 minutes of recovery between heavy bench press conditioning at 85 percent 1RM and subsequent explosive punching tasks may be a practically relevant window, and keeping velocity loss to 10 or 20 percent appears preferable to pushing sets to 30 percent. But the researchers are careful about how far the conclusions stretch. Because repetition volume was not matched across conditions—the higher thresholds simply permitted more repetitions—the findings cannot isolate the independent effect of the velocity-loss threshold from the accompanying differences in conditioning dose and fatigue exposure, and no optimal threshold can be established from these data.

Further caveats temper the excitement. The control condition was an active bench press protocol rather than a passive rest, and its exact loads were not retained, so the observed differences reflect comparisons between active pressing protocols rather than effects relative to a demonstrably non-fatiguing baseline. Biological maturation was not assessed, the sample was modest, no direct neuromuscular or metabolic measures of fatigue were collected, and the punch-power index was derived from force and velocity recorded by separate devices without waveform-level synchronization, making it an estimated force-velocity product rather than directly measured instantaneous power. The study examined only acute responses, and whether the same patterns hold for female boxers, older athletes, or chronic training adaptations remains unknown. Still, for a sport decided by milliseconds and newtons, the demonstration that a boxer’s warm-up can be tuned by watching a bar slow down offers an intriguing glimpse of performance science applied at the edge of the ring.

Subject of Research: Acute effects of velocity-loss-regulated bench press conditioning on straight-punch performance in adolescent boxers

Article Title: Acute effects of bench press conditioning performed with different velocity-loss thresholds on punch performance in elite adolescent boxers

Article References: Diao, P., Bao, J., Liu, X., Li, Z., Liu, Y., Liu, X., Jiang, Y., Zhang, Y., & Wang, Z. (2026). Acute effects of bench press conditioning performed with different velocity-loss thresholds on punch performance in elite adolescent boxers. Frontiers in Physiology, 17, Article 1973551. https://doi.org/10.3389/fphys.2026.1973551

Image Credits: AI Generated

DOI: 10.3389/fphys.2026.1973551

Keywords: boxing, adolescent athletes, post-activation performance enhancement, velocity loss, velocity-based training, bench press, punch performance, exercise physiology, combat sports, warm-up design, neuromuscular fatigue, strength and conditioning

The research team, led by Tsuyoshi Nagatani of Edith Cowan University alongside colleagues including Christopher Latella, Paul Comfort, Eric Drinkwater and G. Gregory Haff, compared three alternatives to the traditional set, in which repetitions are performed consecutively with rest only between sets. Cluster sets insert brief rest periods between individual repetitions or small groups of repetitions within a set, adding total rest and lengthening the session. Rest-redistribution strategies take a different route: instead of adding rest, they rearrange the rest already in the program. Intra-set rest-redistribution carves a slice out of the between-set rest and moves it inside the set, while inter-set rest-redistribution splits a large set into many smaller ones, for example turning three sets of ten into six sets of five, keeping total rest constant.

Because only a handful of studies have directly pitted cluster sets against rest-redistribution, the authors turned to a network meta-analysis, a statistical framework that combines direct comparisons with indirect ones linked through a common comparator, in this case the traditional set. They searched PubMed and Web of Science, screened 1,121 records, and ultimately included 37 studies encompassing 611 unique participants, 557 of them male and 54 female. The outcomes of interest were mean velocity, mean propulsive velocity and peak velocity, the kinematic signatures that velocity-based training practitioners use to gauge fatigue and readiness. Analyses were run in a Bayesian framework using Markov Chain Monte Carlo simulation, with posterior standardized mean differences and 95 percent credible intervals, and treatments were ranked with SUCRA scores, which express the probability that each set structure is the most effective.

The headline finding is that all three alternative structures beat the traditional set at preserving mean velocity. Cluster sets showed a standardized mean difference of 0.60 (95 percent credible interval 0.42 to 0.80), intra-set rest-redistribution 0.41 (0.08 to 0.75) and inter-set rest-redistribution 0.42 (0.26 to 0.61). For peak velocity, cluster sets (0.48, 0.20 to 0.76) and intra-set rest-redistribution (0.39, 0.04 to 0.73) again outperformed traditional sets, but the credible interval for inter-set rest-redistribution crossed zero, leaving its effect on peak velocity uncertain. In the SUCRA rankings, cluster sets held the highest probability of being the best structure for both mean velocity (99.7 percent) and peak velocity (95.6 percent), followed by intra-set rest-redistribution and then inter-set rest-redistribution.

Yet the more consequential result is what the analysis did not find. When the alternative structures were compared directly with one another, the credible intervals for mean and peak velocity crossed zero in every case. Cluster sets versus intra-set rest-redistribution produced a credible interval of −0.19 to 0.58 for mean velocity, and cluster sets versus inter-set rest-redistribution ranged from −0.05 to 0.41. In plain terms, although cluster sets carry the highest probability of ranking first, the evidence does not support a practically meaningful advantage over either redistribution strategy. The physiological logic behind the cluster advantage is straightforward: brief intra-set pauses allow partial resynthesis of phosphocreatine, blunting the metabolic disruption, lactate accumulation and neural drive decrements that cause velocity to crater in uninterrupted sets. But rest-redistribution achieves a similar effect without adding a single second to the session, which is precisely why the question of whether cluster sets are meaningfully better matters so much to practitioners.

The authors also tested whether the benefits depend on context, using network meta-regressions. Exercise type, comparing lower-body movements such as squats and clean pulls with the bench press, did not significantly moderate the effects, with a regression coefficient of 0.10 and a credible interval spanning zero, although there was a tendency toward larger benefits in lower-body exercise, plausibly because larger muscle masses and longer ranges of motion generate greater metabolic stress. Relative training intensity, split between moderate loads of 60 to 79 percent of one-repetition maximum and high loads of 80 percent or more, likewise failed to moderate the effects (coefficient 0.15, credible interval −0.12 to 0.43). The practical implication is that alternative set structures appear robust across exercise selection and loading zones, rather than being niche tools for specific lifts or intensity zones.

Peak velocity told a more nuanced story. The uncertain effect of inter-set rest-redistribution was driven partly by the small number of studies and by the exercises examined. The one study reporting a clear peak-velocity benefit examined the clean pull from the floor, a full-range movement with large barbell displacement and high metabolic demand, whereas the two studies showing no benefit used partial weightlifting derivatives, the hang pull and countermovement shrug, which involve shorter displacement and less time under tension and may simply not generate enough fatigue for redistributed rest to matter. The authors caution that the apparent superiority of cluster sets and intra-set redistribution for peak velocity may therefore be inflated by these study characteristics, and they call for more research on full-range, multi-joint movements.

For coaches, the practical takeaway may be the most valuable part of the paper. Because intra-set rest-redistribution produced velocity outcomes statistically comparable to cluster sets without extending session duration, it emerges as a time-efficient alternative for athletes training under tight schedules. Inter-set rest-redistribution, while effective for mean velocity, carries logistical friction: some implementations redistribute rest between every single repetition, forcing athletes to rack and un-rack a barbell dozens of times, and in team environments one athlete must finish all their scattered repetitions before partners can rotate through. The authors suggest that cluster sets and intra-set redistribution are the more practical choices in real-world settings, with the final decision guided by session time, equipment and training context rather than by any large performance differential.

The review is not without limitations, which the authors acknowledge candidly. Several studies comparing intra-set rest-redistribution with traditional sets failed to equalize total rest duration, inadvertently giving the intervention more recovery and potentially inflating its benefit. Evidence for peak velocity rested on far fewer studies than mean velocity, some consistency checks could not be performed for certain comparisons, and the participant pool was overwhelmingly male, preventing any analysis of sex as a moderator. Publication bias was detected for the mean velocity model, though not for peak velocity. Registered prospectively on the Open Science Framework, the review nevertheless represents the first analysis to treat the two rest-redistribution variants as distinct constructs, and its central message is likely to reshape how velocity-focused programming is discussed: the way rest is arranged matters far more than the label attached to it, and almost any thoughtful departure from the traditional set will keep the bar moving faster.

Subject of Research: A systematic review and Bayesian network meta-analysis comparing cluster set and rest-redistribution set configurations for maintaining movement velocity during resistance training.

Article Title: The Effectiveness of Cluster vs. Rest-Redistribution Set Configurations to Maintain Movement Velocity During Resistance Training: A Systematic Review and Bayesian Network Meta-analysis

Article References: Nagatani, T., Latella, C., Yang, J., Kendall, K. L., Vial, S., Comfort, P., Drinkwater, E. J., & Haff, G. G. (2026). The Effectiveness of Cluster vs. Rest-Redistribution Set Configurations to Maintain Movement Velocity During Resistance Training: A Systematic Review and Bayesian Network Meta-analysis. Sports Medicine – Open, 12(1), Article 141. https://doi.org/10.1186/s40798-026-01106-w

Image Credits: AI Generated

DOI: 10.1186/s40798-026-01106-w

Keywords: resistance training, cluster sets, rest redistribution, movement velocity, velocity loss, neuromuscular fatigue, network meta-analysis, strength and conditioning, velocity-based training, set structure, phosphocreatine, sports science

The research team, led by Ethan J. Grimmond of the University of New South Wales alongside colleagues from the club and the university’s School of Mathematics and Statistics, set out to answer a question that has long nagged sports scientists: do the neuromuscular assessments practitioners administer every day actually capture changes in muscle function that meaningfully alter subsequent physical output? The countermovement jump has long been the default measure of whole-body neuromuscular fatigue in team sports, but it has a known blind spot. Because jump performance relies on coordinated contribution from many muscle groups, fatigue isolated in one muscle can be masked by compensatory strategies elsewhere. Targeted isometric tests of individual muscle groups, by contrast, can expose fatigue that a global jump test would never detect.

The researchers focused on two assessments with particular relevance to Australian Rules football, a collision-heavy invasion sport with a high prevalence of hamstring and groin injuries. The first, labeled KNEEFLEX, measured isometric knee flexor strength using a NordBord dynamometer, with players in a kneeling plank position at 30 degrees of knee flexion, pulling their heels as hard as possible against the hook apparatus. This position was chosen deliberately because the lengthened hamstring and the hip and knee angles approximate the running gait during the foot-strike phase. The second, HIPADD, assessed long-lever isometric hip adductor strength on a ForceFrame, with players lying supine and squeezing their medial malleoli against force transducers. Before each test, players completed warm-up contractions at roughly 40, 60 and 80 percent of perceived maximum effort, followed by a set of three maximal three-to-five-second contractions, with the highest total force value retained for analysis.

Strength testing took place in the penultimate hour before each of three weekly on-field sessions across the 2023/2024 preseason, generating 746 individual player-session observations across 22 training days. On the training track, physical output was captured using Vector S7 global navigation satellite system devices with integrated triaxial accelerometers, each player wearing the same individually assigned unit throughout to minimize inter-device variability. The satellite units quantified running distances and intensities across absolute and relative velocity bands, while inertial movement analysis algorithms derived change-of-direction effort counts in both the mediolateral and anteroposterior planes at low, medium and high magnitude thresholds. The researchers then built linear mixed models that related each player’s percentage change in strength from his rolling seasonal average to subsequent training outputs, while statistically accounting for session, week, playing position and individual player identity.

The headline result concerned the knee flexors. Changes in pretraining knee flexor strength from baseline were significantly associated with subsequent running intensity measured in meters per minute at velocities exceeding 70 percent of each player’s relative maximum velocity. Notably, this was the only running variable that showed a significant association: neither absolute high-speed running thresholds nor very high-speed or sprint distances, nor acceleration and deceleration counts, tracked with pretraining hamstring strength. The authors had hypothesized that the strongest associations would appear at the very highest intensities, given that hamstring activation increases in proportion to running velocity, but the data told a different story. Average running intensity at velocities above 25.2 kilometers per hour was a mere 0.79 meters per minute, and above 80 percent relative maximum velocity just 0.46 meters per minute, so scores clustered near zero left little variance for fatigue to explain at those extremes.

The second major finding concerned the hip adductors, and it came with a number attached. Declines in pretraining hip adduction strength were significantly associated with subsequent change-of-direction outputs at low and medium magnitude thresholds, but not at high magnitudes. The summated variables told an even stronger story, with the greatest association observed for combined low-to-medium efforts per minute. Translated into practical terms, decreases of 10, 15 and 20 percent in pretraining hip adductor strength corresponded to estimated reductions of roughly 2.49, 3.76 and 5.04 percent in change-of-direction efforts per minute, respectively. Such strength decrements were not rare curiosities: the club recorded drops below minus 15 percent on 38 occasions and below minus 20 percent on 20 occasions across the preseason, representing 5.2 and 2.8 percent of all hip adductor measurements.

The absence of associations at the highest thresholds carried its own important message about measurement technology. The manufacturer’s high-magnitude change-of-direction threshold of greater than 3.5 meters per second registered only 0.39 efforts per minute on average, a scarcity the authors argue likely reflects collision-based positional changes rather than the agile, preplanned and reactive cuts that define football movement. Elite soccer studies using lower high-intensity thresholds have found better agreement with video-coded explosive changes of direction, and tackle quantification research in Australian football shows collision-driven movements occur at far greater magnitudes than agility cuts. If the high threshold primarily captures collisions, then the low and medium bands are where agility genuinely lives, and those were precisely the bands most sensitive to adductor fatigue.

The findings also fuel a broader debate about absolute versus relative speed zones in team sport monitoring. Only the relative running threshold, expressed as a percentage of each player’s own maximum velocity, proved sensitive to neuromuscular state, while fixed absolute thresholds did not. This aligns with a growing body of criticism that arbitrary absolute cutoffs overestimate high-velocity running for faster players and underestimate it for slower ones, potentially misinforming decisions about readiness and load. For practitioners, the implication is that individualized velocity bands, paired with localized strength screening, provide a more physiologically faithful picture of the stress each athlete is actually absorbing.

The authors are careful to frame the study as observational, and they acknowledge its limitations. The data came from a single club during a single preseason, adherence to maximal effort in testing cannot be fully guaranteed despite athlete familiarization, and the use of a rolling seasonal average as the baseline means early-season scores may have been skewed by familiarization effects. Yet the practical implications are tangible. A strength drop exceeding 15 percent could serve as a triage flag, prompting medical staff to review a player before training or match play, and the estimated output reductions allow coaches to anticipate whether planned session demands can be tolerated or should be scaled to facilitate recovery. Rather than replacing the countermovement jump, the knee flexor and hip adductor tests appear to measure largely independent qualities, strengthening the case for a diverse neuromuscular battery. As accumulated neuromuscular fatigue is linked to elevated injury risk and degraded performance, a 90-second squeeze and a heel pull may become indispensable tools for deciding, before the first whistle of the day, exactly how hard a footballer’s body is ready to work.

Subject of Research: Pretraining lower-limb isometric strength changes and subsequent physical training outputs in elite Australian Rules football players

Article Title: Examining the Relationship Between Changes in Lower-Limb Isometric Strength and Subsequent Physical Training Outputs in Elite Australian Rules Football Players

Article References: Grimmond, E. J., Stindl, T. J., Engel, A. C., Maros, T. M., Lehane, S., & Borges, N. R. (2026). Examining the Relationship Between Changes in Lower-Limb Isometric Strength and Subsequent Physical Training Outputs in Elite Australian Rules Football Players. Sports Medicine – Open, 12(1), Article 132. https://doi.org/10.1186/s40798-026-01105-x

Image Credits: AI Generated

DOI: 10.1186/s40798-026-01105-x

Keywords: neuromuscular fatigue, isometric strength, Australian Rules football, player readiness, knee flexor strength, hip adductor strength, change of direction, GNSS tracking, training load monitoring, relative maximum velocity, dynamometry, preseason training