EFFECT OF SHORT-TERM EQUAL-VOLUME RESISTANCE TRAINING WITH DIFFERENT WORKOUT FREQUENCY ON MUSCLE MASS AND STRENGTH IN UNTRAINED MEN AND WOMEN
Bibliographic record
Abstract
Changes in muscle mass and strength will vary, depending on the volume and frequency of training. The purpose of this study was to determine the effect of short-term equal-volume resistance training with different workout frequency on lean tissue mass and muscle strength. Twenty-nine untrained volunteers (27-58 years; 23 women, 6 men) were assigned randomly to 1 of 2 groups: group 1 (n = 15; 12 women, 3 men) trained 2 times per week and performed 3 sets of 10 repetitions to fatigue for 9 exercises, group 2 (n = 14; 11 women, 3 men) trained 3 times per week and performed 2 sets of 10 repetitions to fatigue for 9 exercises. Prior to and following training, whole-body lean tissue mass (dual energy x-ray absorptiometry) and strength (1 repetition maximum squat and bench press) were measured. Both groups increased lean tissue mass (2.2%), squat strength (28%), and bench press strength (22-30%) with training (p < 0.05), with no other differences. These results suggest that the volume of resistance training may be more important than frequency in developing muscle mass and strength in men and women initiating a resistance training program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".