Traditional sets versus rest-redistribution: a laboratory-controlled study of a specific cluster set configuration at fast and slow velocities
Bibliographic record
Abstract
This study investigated redistributing long inter-set rest intervals into shorter but more frequent intervals at 2 different concentric velocities. Resistance-trained men performed 4 randomised isokinetic unilateral knee extension protocols, 2 at 60°·s−1 and 2 at 360°·s−1. At each speed, subjects performed 40 repetitions with 285 s of rest using traditional sets (TS; 4 sets of 10 with 95 s of inter-set rest) and rest-redistribution (RR; 20 sets of 2 with 15 s inter-set rest). Before and at 2, 5, and 10 min after exercise, tensiomyography (TMG) and oxygenation (near-infrared spectroscopy; NIRS) were measured. NIRS was also measured during exercise, and rating of perceived exertion (RPE) was recorded after every 10 repetitions. At both speeds, RR displayed greater peak torque, total work, and power output during latter repetitions, but there were no differences between TS or RR when averaging all 40 repetitions. The RPE was less during RR at both speeds (p < 0.05). RR increased select muscle oxygen saturation and blood flow at both speeds. There were no effects of protocol on TMG, but effect sizes favoured a quicker recovery after RR. RR was likely beneficial in maintaining performance compared with the latter parts of TS sets and limiting perceived and peripheral fatigue. Novelty Although effective at slow velocities, rest-redistribution was likely more effective during high-velocity movements in this study. Rest-redistribution maintained the ability to produce force throughout an entire range of motion. Rest-redistribution reduced RPE during both high-velocity and high-force movements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".