GRIP STRENGTH DOES NOT DIFFER BETWEEN FREE WEIGHTS OR MACHINES IN OLDER ADULTS: META-ANALYSIS
Bibliographic record
Abstract
Introduction: The aging can lead to low levels of muscle strength, related to the emergence of diseases, physical limitations and an increase in premature death in the elderly. The handgrip strength test is a parameter to measure muscle strength, and strength training with free weights or machines are efficient to increase this outcome. However, these equipments differ in appearance, biomechanics and selection strategy within a periodization, which ends up generating questions in the scientific community regarding which one is more efficient for the outcomes that want to meansure. Objective: This study aimed to compare whether there is a significant difference in handgrip strength in strength training that used free weights or machines in elderly individuals through a systematic review and meta-analysis. Methods: This research followed the protocols and fulfilled all the requirements of Cochrane and The PRISMA Statement. The literature search was performed in the electronic databases Cochrane Library, EMBASE, MEDLINE/PubMed, SCOPUS, Web of Science and Science Direct, and searches were performed in the references of studies already published on the subject. Eligibility criteria included: full articles of clinical trials in English with elderly individuals able to practice physical exercises; interventions with strength training that compared free weights with machines; and assessment of pre- and post-training handgrip strength. Results: A total of 436 studies retrieved from the databases, two studies met the inclusion criteria for performing the systematic review and meta-analysis. Statistical analysis showed no significant difference between groups for handgrip strength (P = 0,56). There was no heterogeneity between the studies (P = 1,00). Conclusion: The conclusion of this study was that there was no significant difference in handgrip strength between strength training using free weights or machines in elderly. Level of Evidence I; Systematic Review and Meta-analysis.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.066 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".