Proprioceptive Training to Improve Static and Dynamic Balance in Elderly
Bibliographic record
Abstract
Background: The risk of falls is a major cause of disability in older adults. A single fall, for the elderly, increases the risk of frequent falls and often causes an increased fear of falling again, which can become debilitating. Objectives: The purpose of the present study was to test the effects of 12 weeks of proprioceptive training on the static and dynamic balance of older adults who have experienced at least one fall without compromising consequences. Method: The sample consisted of older adults, aged 60 to 80 years, randomly divided into two groups: an experimental group, which followed a proprioceptive training protocol at a physiotherapy studio, and a control group, which did not observe any treatment. Static and dynamic balance assessment was performed pre-and post-intervention. The tests administered were the Berg Balance Test for the evaluation of static balance, and the Four-Square Step Test, for dynamic balance. A questionnaire to assess confidence and fear of falling was administered at the end of the protocol. Independent sample t-test was performed to analyze differences between groups and two-way ANOVA to test the null hypothesis of no change different over time between groups (interaction intervention × time). A Chi-Square was performed to analyze perceptions. Conclusions: The results showed that 12 weeks of proprioceptive training effectively improved dynamic and static balance in older adults. The perceptions of the experimental group were more positive than the other one, in terms of the importance of physical activity to prevent the risk of falls, fear of falling again, and experience of falls during the last 12 weeks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".