Performance and Perceptual Responses to Cluster Sets in Pneumatic-Resistance Exercises: Impact of Exercise Selection, Sex, and Strength
Bibliographic record
Abstract
PURPOSE: This study examined the effects of cluster sets (CS) versus traditional sets (TRAD) on performance and perceptual responses during pneumatic chest press (CP) and leg press (LP). Exercise-specific differences and the influence of sex and strength were also explored. METHODS: Forty-seven recreationally resistance-trained young adults (23 male and 24 female) performed CP and LP at 70% 1-repetition maximum in either CS (4 × [2 × 5], 30-s intraset rest, 150 s between sets) or TRAD (4 × 10, 180-s rest between sets) in randomized order. Mean concentric velocity (MCV), MCV loss, rating of perceived exertion (RPE), and estimated repetitions to failure were recorded. Repeated-measures analyses of variance were used for statistical comparisons, with sex and strength included as exploratory variables. RESULTS: MCV was higher (P < .001, partial η2 = .272), RPE was lower (P < .001, partial η2 = .246), and estimated repetitions to failure was higher (P < .001, partial η2 = .429) in CS than TRAD, with no exercise-specific differences. Although MCV loss was lower in CS (P < .001, partial η2 = .364), post hoc analyses revealed that this benefit was only significant during CP and among males. However, the sex-related effect did not remain significant after adjusting for strength. While sex- and strength-related interactions emerged for MCV, they were limited to higher-order interactions involving repetitions but did not alter the overall CS benefit. CONCLUSIONS: CS effectively maintained MCV, reduced RPE, and increased estimated repetitions to failure compared with TRAD across CP and LP using pneumatic-resistance devices. The benefit of CS in attenuating MCV loss differed by exercise and sex, with the sex effect moderated by strength.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".