The Effects of Percentage-Based, Rating of Perceived Exertion, Repetitions in Reserve, and Velocity-Based Training on Performance and Fatigue Responses
Bibliographic record
Abstract
ABSTRACT: Cowley, N, Nicholson, V, Timmins, R, Munteanu, G, Wood, T, García-Ramos, A, Owen, C, and Weakley, J. The effects of percentage-based, rating of perceived exertion, repetitions in reserve, and velocity-based training on performance and fatigue responses. J Strength Cond Res 39(4): e516-e529, 2025-This study assessed the effects of percentage-based training (%1RM), rating of perceived exertion (RPE), repetitions in reserve (RIR), and velocity-based training (VBT) on (a) acute kinematic outputs, perceptions of effort, and changes in neuromuscular function during resistance training; and (b) neuromuscular fatigue and perceptions of soreness 24 hours after exercise. In a randomized crossover design, 15 subjects completed a %1RM, RPE, RIR, and VBT training condition involving a fatiguing protocol followed by 5 sets of the free-weight back squat and bench press at 70% of 1 repetition maximum. Subjects returned at 24 hours to assess neuromuscular fatigue and perceived soreness. Percentage-based training and RPE allowed the smallest volume loads, with %1RM prescription causing sets to be regularly taken to failure. Alternatively, RIR and VBT allowed greater maintenance of training volume. Velocity-based training had the most accurate training prescription, with all sets being within 5% of the intended starting velocity, while the RPE and %1RM prescriptive methods caused subjects to train with loads that were increasingly inaccurate. The RPE prescriptive method had the lowest reported values for differential RPE while the %1RM had the greatest change across the session. At no point were there between-group differences in measures of neuromuscular fatigue or perceived soreness. These findings demonstrate that autoregulatory prescriptive methods can be used to mitigate the risk of training to failure, ensure accurate training prescription that can maintain training volume, and enhance within-training kinematic outputs without altering neuromuscular fatigue or perceptions of soreness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".