A Comparison of Increases in Volume Load Over 8 Weeks of Low-Versus High-Load Resistance Training
Bibliographic record
Abstract
BACKGROUND: It has been hypothesized that the ability to increase volume load (VL) via a progressive increase in the magnitude of load for a given exercise within a given repetition range could enhance the adaptive response to resistance training. OBJECTIVES: The purpose of this study was to compare changes in volume load (VL) over eight weeks of resistance training (RT) in high-versus low-load protocols. MATERIALS AND METHODS: Eighteen well-trained men were matched according to baseline strength were randomly assigned to either a low-load RT (LOW, n = 9) where 25 - 35 repetitions were performed per exercise, or a high-load RT (HIGH, n = 9) where 8 - 12 repetitions were performed per exercise. Both groups performed three sets of seven exercises for all major muscles three times per week on non-consecutive days. RESULTS: After adjusting for the pre-test scores, there was a significant difference between the two intervention groups on post-intervention total VL with a very large effect size (F (1, 15) = 16.598, P = .001, ηp(2) = .525). There was a significant relationship between pre-intervention and post-intervention total VL (F (1, 15) = 32.048, P < .0001, ηp(2) = .681) in which the pre-test scores explained 68% of the variance in the post-test scores. CONCLUSIONS: This study indicates that low-load RT results in greater accumulations in VL compared to high-load RT over the course of 8 weeks of training.
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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) | 0.000 | 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".