Comparison of 2 weekly-equalized volume resistance-training routines using different frequencies on body composition and performance in trained males
Bibliographic record
Abstract
The present study compared the effects of 2 weekly-equalized volume and relative load interventions on body composition, strength, and power. Based on individual baseline maximal strength values, 18 recreationally trained men were pair-matched and consequently randomly assigned to one of the following experimental groups: a low volume per session with a high frequency (LV-HF, n = 9) group who trained for 4 days (Mondays, Tuesdays, Thursdays, and Fridays) or a high volume per session and low frequency (HV-LF, n = 9) group who trained for 2 days (Mondays and Thursdays). Both groups performed 2 different routines over 6 weeks. Participants were tested pre- and post- intervention for maximal strength, upper body power, fat-free mass, limb circumferences, and muscle thickness. Compared with baseline values, both groups increased their fat-free mass (HV-LF: +1.19 ± 1.94; LV-HF: +1.36 ± 1.06 kg, p < 0.05) and vastus medialis thickness (HV-LF: +2.18 ± 1.88, p < 0.01; LV-HF: +1.82 ± 2.43 mm, p < 0.05), but only the HV-LF group enhanced arm circumference (1.08 ± 1.47 cm, p < 0.05) and elbow flexors thickness (2.21 ± 2.81 mm, P < 0.01) values and decreased their fat mass (–2.41 ± 1.10, P < 0.01). Both groups improved (p < 0.01) the maximal loads lifted in the bench press (LV-HF: +0.14 ± 0.01; HV-LF: +0.14 ± 0.01 kg·body mass −1 ) and the squat (LV-HF: +0.14 ± 0.06; HV-LF: 0.17 ± 0.01 kg·body mass −1 ) exercises as well as in upper body power (LV-HF: +0.22 ± 0.25; HV-LF: +0.27 ± 0.22 W·body mass −1 ) Although both training strategies improved performance and lower body muscle mass, only the HV-LF protocol increased upper body hypertrophy and improved body composition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".