Effects of group-based virtual reality training on activities of daily living and functional outcomes in older adults: a randomised control trial
Bibliographic record
Abstract
Virtual reality training (VRT), a fun, inexpensive and accessible technology, has the potential to improve activities of daily living (ADL) and functional status in older adults. The potential impact of VRT can be increased through group-based training. The aim of this study was to investigate the effect of group- based VRT on ADL and functional outcomes in older adults over 65 years of age. Forty-three older adults included in the study were randomized into three groups (group- based VRT, individual VRT and control group). VRT was performed with Xbox 360 Kinect twice a week for 8 weeks. Each session lasted 45 min. Physical activity level, satisfaction level with physical activity, mood, mobility and balance performance, functional exercise capacity and ADL were evaluated. 36 people completed the study. A significant group × time interaction was found in Timed Up and Go test (TUG) (F [2, 57] = 8.60; η2= 0.004, P= <.001) and in Single Leg Stance Test (SLST)) (F [2, 57] = 5.69; η2= 8.509 × 10−4, P= <.007). After 8 weeks group- based VRT showed better scores in overall TUG (p < .001) and SLST (p= .015), whereas individual VRT and control group did not exhibit significant changes. Our results suggested that 8 weeks group- based VRT could improve mobility and balance performance in older adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".