Is functional training an efficient approach to improve body composition in older people? A systematic review
Bibliographic record
Abstract
Introduction: Increases in fat mass and reductions in lean mass are associated with the frailty and mortality of older people. In this context, Functional Training (FT) is an option to increase lean mass and reduce fat mass in older people. Thus, this systematic review aims to investigate the effects of FT on body fat and lean mass in older people. Methods: We included randomized controlled clinical trials, with at least one intervention group that employed FT, with the age of participants ≥60 years; and participants physically independent and healthy. We performed the systematic investigation in Pubmed MEDLINE, Scopus, Web of Science, Cochrane Library, and Google Scholar. We extracted the information and used the PEDro Scale to assess the methodological quality of each study. Results: Our research found 3,056 references with five appropriate studies. Of the five studies, three presented reductions in fat mass, all of them with interventions between three and 6 months, different training dose parameters, and 100% of the sample was composed of women. On the other hand, two studies with interventions between 10 and 12 weeks presented conflicting results. Conclusion: Despite the limited literature about lean mass, it appears that long-term FT interventions may reduce fat mass in older women. Clinical Trial Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=399257 , identifier CRD42023399257
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".