A Comparison Between Brain Vitalization Gymnastics and Elderly Gymnastics to Improving Cognitive Function Among Elderly
Bibliographic record
Abstract
Objective: Two of the most frequently elderly's health-associated problems are balanced disorder and cognitive decline. One of the solutions to prevent cognitive decline in the elderly is via performing brain vitalization gymnastics. According to these facts and arguments, the authors were interested in studying the effects of brain vitalization gymnastics performed twice every week for four weeks upon cognitive function in the elderly. Methods: This was a randomized pretest-posttest control group design study involving 38 elderly subjects who were registered in the geriatric subgroup of West Denpasar primary health care clinic. The subjects were equally divided into two groups, i.e., those who performed brain vitalization gymnastics and elderly gymnastics. Montreal Cognitive Assessment Indonesian Version(MoCA-Ina) score as tested with paired t-test among brain vitalization gymnastics and elderly gymnastics groups. Results: both groups before and after the exercise increased by 1.53 and 0.11 points, respectively. Furthermore, the brain vitalization gymnastics group had a statistically significant higher MoCA-Ina score as opposed to the elderly gymnastics group (p=0.047). Conclusion: This study had shown that brain vitalization gymnastics was more effective in increasing elderly's MoCA-Ina score as opposed to elderly gymnastics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.002 | 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".