Effect of training cessation on muscular performance: A meta‐analysis
Bibliographic record
Abstract
The purpose of this study was to assess the effect of resistance training cessation on strength performance through a meta-analysis. Seven databases were searched from which 103 of 284 potential studies met inclusion criteria. Training status, sex, age, and the duration of training cessation were used as moderators. Standardized mean difference (SMD) in muscular performance was calculated and weighted by the inverse of variance to calculate an overall effect and its 95% confidence interval (CI). Results indicated a detrimental effect of resistance training cessation on all components of muscular performance: [submaximal strength; SMD (95% CI) = -0.62 (-0.80 to -0.45), P < 0.01], [maximal force; SMD (95% CI) = -0.46 (-0.54 to -0.37), P < 0.01], [maximal power; SMD (95% CI) = -0.20 (-0.28 to -0.13), P < 0.01]. A dose-response relationship between the amplitude of SMD and the duration of training cessation was identified. The effect of resistance training cessation was found to be larger in older people (> 65 years old). The effect was also larger in inactive people for maximal force and maximal power when compared with recreational athletes. Resistance training cessation decreases all components of muscular strength. The magnitude of the effect differs according to training status, age or the duration of training cessation.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.043 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".