Periodized Resistance Training for Enhancing Skeletal Muscle Hypertrophy and Strength: A Mini-Review
Bibliographic record
Abstract
Prescribing the proper resistance training (RT) program is critical to optimize skeletal muscle hypertrophy and strength. Periodization is a strategy that entails planned manipulations of training variables to maximize fitness adaptations while minimizing the risk of overtraining. Multiple meta-analyses have shown periodized RT to be superior to non-periodized RT for enhancing muscular strength. These findings are consistent irrespective of training status or training volume. Both the linear model and the undulating model are effective for enhancing strength, although a greater benefit might be achieved through the undulating model. Despite the suggested superiority of periodized RT for strength development, some authors suggest that this might be a consequence of the study designs employed rather than the nature of periodized training. In addition, several limitations exist in the periodization literature, making it difficult to accurately assess the efficacy of periodized RT. With regard to enhancing skeletal muscle hypertrophy, both the undulating model and the linear model appear equally effective; however, this conclusion can only be generalized to untrained populations. When comparing periodized RT to non-periodized RT programs, the research is unclear on whether periodized RT is necessary to maximize skeletal muscle hypertrophy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".