A New Approach to Improve Cognition, Muscle Strength, and Postural Balance in Community-Dwelling Elderly with a 3-D Virtual Reality Kayak Program
Bibliographic record
Abstract
Aging is usually accompanied with deterioration of physical abilities, such as muscular strength, sensory sensitivity, and functional capacity. Recently, intervention methods with virtual reality have been introduced, providing an enjoyable therapy for elderly. The aim of this study was to investigate whether a 3-D virtual reality kayak program could improve the cognitive function, muscle strength, and balance of community-dwelling elderly. Importantly, kayaking involves most of the upper body musculature and needs the balance control. Seventy-two participants were randomly allocated into the kayak program group (n = 36) and the control group (n = 36). The two groups were well matched with respect to general characteristics at baseline. The participants in both groups performed a conventional exercise program for 30 min, and then the 3-D virtual reality kayak program was performed in the kayak program group for 20 min, two times a week for 6 weeks. Cognitive function was measured using the Montreal Cognitive Assessment. Muscle strength was measured using the arm curl and handgrip strength tests. Standing and sitting balance was measured using the Good Balance system. The post-test was performed in the same manner as the pre-test; the overall outcomes such as cognitive function (p < 0.05), muscle strength (p < 0.05), and balance (standing and sitting balance, p < 0.05) were significantly improved in kayak program group compared to the control group. We propose that the 3-D virtual reality kayak program is a promising intervention method for improving the cognitive function, muscle strength, and balance of elderly.
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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 | 0.001 |
| 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) | 0.002 | 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".