Brain Gym Exercise Give Benefit to Improve Cognitive Function among Elderly: A Systematic Review
Bibliographic record
Abstract
Being elderly is potentially risk for the emerging of progressive neurodegenerative syndrome, including cognitive impairment. Various efforts have been made to minimize the negative impact. Brain gym is an alternative intervention that is widely used. A series of motion between the legs and hands employed to stimulate the brain to remain optimal. This study aims to determine whether brain gym exercise is beneficial for cognitive improvement among elderly in Indonesia. The systematic literature review was carried out from 28 May 2020 – 05 June 2020 at Semantic Scholar, Google Scholar, Garba Rujukan Digital (Garuda). Searching process employed keywords compiled using PICOS (Population, Intervention, Comparison and Outcomes) method and applied a filtering of articles using clinical trial or randomized controlled trial design, published in the last five years, and free/open access literature. There are 5 articles that meet the inclusion criteria: sample age 60 years and over, type of brain gym intervention, and focus on assessing cognitive function using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment Indonesian Version (MoCA-Ina. Overall, the result shows in participants (n=211) experienced a significant increase in cognitive function (p<0.05). Indeed, additional outcomes were found, namely a decrease in stress levels and an increase in physical activity function.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".