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

An international research team led by Nina Claassen and Konstantin Warneke systematically searched PubMed/MEDLINE, Web of Science, and Scopus, ultimately including 63 studies evaluating the validity, reliability, and device agreement of commercially available velocity sensors, and 38 studies assessing velocity-based one-repetition maximum prediction models. The review was preregistered in the PROSPERO database and followed PRISMA reporting guidelines. Methodological quality was assessed using an adapted version of the COSMIN risk of bias checklist, and the team pooled results using multilevel random-effects meta-analysis, examining metrics such as the intraclass correlation coefficient (ICC), Lin’s concordance correlation coefficient (CCC), and Pearson’s correlation coefficient.

The first major finding concerns the sensors themselves. Commercially available devices, which include linear position transducers such as GymAware, Tendo, and Vitruve, as well as inertial measurement units like PUSH and EnodePro, demonstrated good-to-excellent pooled validity and device agreement, with ICCs of 0.91 to 0.92 and confidence intervals ranging from 0.83 to 0.97. Intra- and inter-day reliability were similarly strong, with pooled ICCs of 0.90 to 0.91. In plain terms, when the same lifter performs the same movement on different days, or when two devices measure the same lift simultaneously, the readings generally track each other well.

However, the averages conceal considerable variability. The researchers found substantial heterogeneity across studies, and their moderator analyses revealed that sensor technology mattered significantly. Linear position transducers, which calculate bar speed directly from displacement over time, generally showed more consistent performance, with validity correlations ranging from 0.77 to 0.99. Inertial measurement units, which estimate velocity indirectly by integrating acceleration signals, displayed a much wider spread, with correlations ranging from 0.40 to 0.99. The authors speculate that this inconsistency stems from the sensitivity of acceleration integration to signal noise, drift, and calibration assumptions, making IMU-based readings more dependent on the specific device, exercise, and conditions.

Exercise type and training intensity also shaped the results. Device agreement was significantly higher at moderate and high training intensities compared with low intensities, and certain movement patterns, such as squat-based assessments, influenced agreement estimates depending on the statistical metric used. Peak velocity measurements tended to show lower concordance than mean velocity in some analyses. These patterns suggest that no single number can summarize sensor performance; instead, practitioners must consider the specific combination of device, exercise, load, and velocity variable when interpreting readings.

The second part of the review tackled the central promise of velocity-based training: predicting the one-repetition maximum without actually testing it. Here the pooled results were again favorable. Velocity-based prediction models showed good-to-excellent interday reliability, with a pooled ICC of 0.90, and high average validity, with an ICC of 0.91 and a pooled Pearson correlation of 0.96 between predicted and actual maximal strength. On the surface, this supports the practice of building a load–velocity profile from a handful of submaximal lifts, potentially even within a warm-up, and using it to estimate maximal strength.

Yet the devil, as the authors emphasize, is in the details. Large heterogeneity in lower-body exercises significantly biased the results. Squat-based and deadlift-based predictions showed systematically lower validity and reliability than upper-body movements such as the bench press, where two-point and polynomial models achieved ICCs of 0.89 to 0.96. Individual study estimates for squat predictions ranged dramatically, from ICCs as low as 0.24 to as high as 0.99, depending on the number of load points used and the modeling approach. Polynomial regression performed significantly worse than other methods in the validity analysis. This means that a prediction that works well for a bench press may be considerably less trustworthy for a heavy back squat.

Perhaps the most sobering finding concerns what is missing from the literature. The authors highlight a dearth of systematic measurement error and agreement analyses. Most studies reported only relative metrics such as correlation coefficients, which reflect the strength of association between devices but say nothing about the absolute magnitude of differences. High correlations do not guarantee that two sensors can be used interchangeably. Where agreement data existed, the reported limits of agreement indicated potentially meaningful device-to-device differences, in some cases spanning more than 0.3 meters per second around mean velocities near 1 meter per second. The authors also note that individual studies have reported overestimations of maximal strength of up to 30 kilograms and mean prediction errors of up to 20 percent, underscoring that favorable averages do not eliminate the risk of practically relevant errors for individual lifters.

The review also calls out methodological inconsistencies in how validity and agreement have been defined and reported. Some studies treated linear position transducers as a gold standard, blurring the distinction between validity against a true criterion and simple device-to-device agreement. Others presented Bland–Altman plots without numerical agreement indices or interpreted them incorrectly. The authors argue that future validation studies must complement relative validity analyses with standardized agreement and measurement error reporting, including limits of agreement expressed relative to mean velocities, to allow meaningful practical interpretation.

The bottom line for athletes and coaches is a nuanced one. Commercial velocity sensors generally provide high relative validity and reliability, and velocity-based one-repetition maximum prediction achieves impressive average accuracy. But the evidence is sensor- and exercise-specific, results vary with intensity and modeling approach, and the scarcity of absolute error analyses prohibits final conclusions. The authors conclude that velocity-based monitoring and one-repetition maximum prediction require cautious interpretation. For now, the technology appears sound enough to inform training decisions, particularly for upper-body exercises and when using linear position transducers at moderate to high loads, but practitioners should treat predicted maximal strength values as estimates with real uncertainty rather than precise measurements, and researchers should prioritize rigorous agreement analyses before the field can issue definitive practical recommendations.

Subject of Research: Validity, reliability, and device agreement of commercial velocity sensors and velocity-based one-repetition maximum prediction models in resistance training

Article Title: Reliability, Device Agreement and Validity of Load–Velocity Profiles: A Systematic Review with Meta-analysis

Article References: Claassen, N., Siegel, S. D., Sproll, M., Lebelt, N., Bargende, A. V., Fasold, A. M., & Warneke, K. (2026). Reliability, Device Agreement and Validity of Load–Velocity Profiles: A Systematic Review with Meta-analysis. Sports Medicine – Open, 12(1), Article 131. https://doi.org/10.1186/s40798-026-01102-0

Image Credits: AI Generated

DOI: 10.1186/s40798-026-01102-0

Keywords: velocity-based training, load-velocity profile, one-repetition maximum, linear position transducer, inertial measurement unit, systematic review, meta-analysis, resistance training, measurement validity, reliability, strength monitoring, sports science