Effect of Equal Volume, High-Repetition Resistance Training to Volitional Fatigue, With Different Workout Frequencies, on Muscle Mass and Neuromuscular Performance in Postmenopausal Women
Bibliographic record
Abstract
ABSTRACT: Grzyb, K, Candow, DG, Schoenfeld, BJ, Bernat, P, Butchart, S, and Neary, JP. Effect of equal volume, high-repetition resistance training to volitional fatigue, with different workout frequencies, on muscle mass and neuromuscular performance in postmenopausal women. J Strength Cond Res 36(1): 31-36, 2022-This study examined the effects of equal volume, high-repetition resistance training (HRRT) performed to volitional fatigue, with different workout frequencies, on muscle mass and neuromuscular performance (strength, endurance) in untrained postmenopausal women. Subjects were randomized to perform HRRT 2 d·wk-1 (HRRT-2; 3 sets of 20-30 repetitions/set for elbow and knee flexion and extension) or 3 d·wk-1 (HRRT-3; 2 sets of 20-30 repetitions/set per exercise) for 8 weeks. Baseline and post-training assessments were made for muscle thickness, strength (1 repetition maximum [1RM]) and endurance (number of repetitions performed at 50% baseline 1RM) for elbow and knee flexor and extensor muscle groups. Significance was set at p < 0.05. There was a significant increase over time for all measures of muscle thickness, strength, and endurance (p < 0.005), with no differences between groups. Untrained postmenopausal women can expect to achieve similar improvements in muscle size, strength, and endurance when training 2 or 3 days per week, provided total weekly training volume is equal.
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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 | 0.001 |
| 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".