Auto‐regulatory progressive training compared to linear programming on muscular strength, endurance, and body composition in recreationally active males
Bibliographic record
Abstract
ABSTRACT We compared eight weeks of auto‐regulatory progressive resistance exercise (APRE) to linear programming resistance exercise (LPRE) on changes in muscular strength and endurance, anaerobic power, and body composition in recreationally active males. Twenty‐four recreationally active males (age: 24 ± 3 y; body mass: 78.3 ± 10.3 kg) were randomly assigned to one of two groups: APRE (n = 12) and LPRE (n = 12). Both groups performed supervised training 3x/week for eight weeks Upper and lower body muscular strength and endurance, anaerobic power, and body composition were assessed at baseline, week 4, and 48 h after the final training session. Repeated measures ANOVA and hedge's g effect sizes (ES) were used to interpret the data. After training, there was a significant increase in absolute leg press (APRE: ES = 2.23; LPRE: ES = 1.35) and chest press strength (APRE: ES = 2.19; LPRE: ES = 0.98), upper (APRE: ES = 2.50; LPRE: ES = 1.074), and lower body peak power (APRE: ES = 0.78; LPRE: ES = 0.39), and upper (APRE: ES = 2.50; LPRE: ES = 1.60) and lower mean power (APRE: ES = 0.99; LPRE: ES = 0.54) over time in both groups compared to baseline. Following APRE, absolute leg press strength was significantly greater compared to LPRE (p = 0.04; ES = 2.41, ES = 1.36), while absolute chest press strength gains were similar between groups (p = 0.08; ES = 2.21, ES = 0.98). Skeletal muscle mass significantly increased similarly in both groups over time (APRE: ES = 0.46; LPRE: ES = 0.21), while there was no change over time or between groups for body fat %. APRE and LPRE were both effective at improving anaerobic power and skeletal muscle mass; however, APRE was more effective at improving lower body muscular strength in recreationally active males.
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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 | 0.000 |
| 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".