Balance as an Additional Effect of Strength and Flexibility Aquatic Training in Sedentary Lifestyle Elderly Women
Bibliographic record
Abstract
Context. Physiological changes due to the aging process cause balance deficit and increased risk of falls, more frequently in women. Exercises improve balance and prevent falls; and aquatic exercises are recommended as an alternative strategy to improve muscle strength, flexibility, and balance, in a safer environment for older persons. Objective. To evaluate the additional effects of on balance an aquatic muscle strengthening and flexibility training program in healthy sedentary lifestyle elderly women. Method. This controlled clinical trial included 56 healthy sedentary women, aged from 65 to 70 years, divided into two groups. The aquatic group (AG) underwent aquatic training (45 minutes/session, 32 sessions), and the control group (CG) received no intervention. Data were collected pre- and post-intervention, during a one-week period. Lower limb muscle strength was measured by a force sensor (myometer). Flexibility was measured by biophotogrammetry. Functional balance was evaluated by the Performance Oriented Mobility Assessment (POMA) and the Berg Balance Scale (BBS). Results. Muscle strength, flexibility, and balance improved in AG ( p<0.001 ), but not in CG. Conclusion. The aquatic exercises program, which was originally developed to promote muscle strength and flexibility, also improved functional balance. Aquatic training is an option for physical health promotion for sedentary lifestyle elderly women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".