Balance, General Cognition and Lower Motoric Strength in Elderly: Tai Chi Versus Brisk Walking
Bibliographic record
Abstract
Background: The number of elderly in Indonesia continues to increase. A better quality of life can be achieved by preventing reduction of elderly’s cognition, balance and strength of the lower extremities. Tai Chi has been suggested as one of the sports to maintain cognition, balance and lower extremity strength for elder people. However, there are still few studies that compare Tai Chi to other exercises. The aim of this study was to compare the effects of practicing Tai Chi verrsus brisk walking.Methods: The study design was observational, cross-sectional. Elderly who practiced Tai Chi and Brisk-walk in Tegalega sport field were chosen as the target population. The respondents must be at least 60 years old, no history of cardiovascular disease in the last 3 months, no musculoskeletal complaints, no obvious signs and symptoms of chronic diseases. Afterwards, the respondents were examined using Timed Up and Go (TUG) Test for balance, Montreal Cognitive Assessment (MoCA) for cognition and leg dynamometer for the lower extrimities strength. The collected data was analyzed using Mann-Whitney statistical test and Kolmogorov-Smirnov test.Results: No significant difference was detected among the three parameters between Tai Chi group (TCG) and Brisk Walking group (BWG). Difference of 0.5 (p=0.314) and 0.6 (p=0.554) was found for TUG Test and MoCA Test between TCG and BWG using Mann Whitney. The leg dynamometer was tested using Kolmogorov-smirnov (p = 1.00).Conclusions: Similar result of balance, cognition, and lower motor strength are found between TCG and BWG. DOI: 10.15850/amj.v4n2.1063
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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) | 0.000 | 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) | 0.001 | 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".