The Effect of Different Exercise Training Types on Functionality in Older Fallers
Bibliographic record
Abstract
Background: Fall is one of the most common geriatric syndromes in the elderly population. It is important to determine the most effective exercise training in elderly individuals who are at risk of falling. Aim: To investigate the effects of different exercise trainings on functionality in older fallers. Method: A total of 30 older adults, 16 females and 14 males, were enrolled in this randomized controlled trial. The older adults were divided into 3 groups: vestibular exercise (VE), posturography balance exercise (PBE), and square step exercise (SSE) groups. All groups received VE training. Sensory Organization Test (SOT), Adaptation Test (ADT), Fall Efficacy Scale (FES), Montreal Cognitive Assessment (MoCA), Vestibular Disorders Activities of Daily Life Scale (VADL), and the World Health Organization Quality of Life Scale–Older Adults Module (WHOQOL-Old) were administered before and at the end of the 24 training sessions. Results: The MoCA and the composite balance score of the SOT were improved in the PBE and SSE groups and the FES in all groups. According to multiple comparison analyses, toes up in the VE and PBE groups, toes down of the ADT in the PBE group, VADL in the PBE and SSE groups, the WHOQOL-Old in all groups significantly improved (P < .010). Conclusion: VE training alone is not sufficient for older fallers. A combination of PBE and SSE training, which was applied 3 times a week for 8 weeks, was more effective in improving functionality in older fallers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".