Load Rather Than Contraction Type Influences Rate of Perceived Exertion and Pain
Bibliographic record
Abstract
The purpose of the study was to determine whether muscle contraction type (concentric [CON] or eccentric [ECC]) or loading (relative or absolute) has a greater impact on the perceptual and metabolic responses to conventional resistance exercise. Additionally, overall effort, pain sensations, and specific pain descriptors were compared with physiological responses. Seven healthy men (mean +/- SE, 25.71 +/- 2.17 years) with resistance training experience completed 2 one-repetition maximum (1-RM) trials. Subsequently, 2 randomized, counterbalanced, experimental sessions were completed consisting of 4 sets of 10 repetitions for 6 exercises. These sessions were performed at 65% CON 1-RM for CON only contractions or 65% CON 1-RM + 20% for ECC contractions. Blood samples were taken pre, post, and 15 minutes postexercise. OMNI-RPE (OMNI-Res), CR-10 pain rating, McGill pain ratings, and heart rate (HR) were recorded after each set. A significant time effect occurred for OMNI-Res, pain, lactate, and HR (p < 0.05). No significant pattern emerged for the contraction type, except for higher HR and lactate immediately postexercise for the CON contractions. Physiological measures were not significantly related to perceptual measures. When considered with previous data, muscle loading rather than contraction type plays the primary role in perceptual alterations of effort sense and pain. Practical applications of the investigation are that strength and conditioning professionals may be able to load CON and ECC contractions in a relative fashion by increasing loads in the ECC portion by 20% above the CON load that would result in comparable perceptual experiences.
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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 | 0.003 |
| 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".