Is power training effective to produce muscle hypertrophy in older adults? A systematic review and meta-analysis
Bibliographic record
Abstract
Power training has been suggested to be effective in improving strength, power, and functional capacity in older adults. However, there is still a lack of systematic investigations reporting its effectiveness for muscle hypertrophy. Thus, this study investigated the effect of power training on muscle hypertrophy and compared its magnitude with traditional moderate-velocity resistance training in older adults. A systematic search was conducted to identify clinical trials investigating the effect of power training on muscle hypertrophy (power training vs. control) and/or comparing the effect of power training versus moderate-velocity resistance training for a meta-analytical approach. Ten studies comparing power training to control conditions and 9 studies comparing power training to moderate-velocity resistance training were selected. Three studies were classified as high quality and 2 were preregistered. The meta-analysis showed that power training was superior for muscle hypertrophy compared with control condition (n = 8 studies; standardised mean difference (SMD) = 0.31; 95% confidence interval (CI) = 0.04, 0.58; p = 0.029), and resulted in similar hypertrophy compared with moderate-velocity resistance training (n = 7 studies; SMD = 0.07; 95% CI = –0.18, 0.32; p = 0.50). No significant heterogeneity was observed (p = 0.46 and 0.54, and I2 = 0% and 0%, respectively). Our data suggest that power training is effective for muscle hypertrophy in older adults, with similar effectiveness as moderate-velocity resistance training. (PROSPERO registration no.: CRD42019128951.) Novelty It is known that power training might be superior to moderate-velocity resistance training for function improvements in older adults, but there was no meta-analysis investigating its effect on muscle hypertrophy. Power training is effective to induce muscle hypertrophy in older adults to a similar extent as moderate-velocity resistance training.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".