Effects of immersive leisure-based virtual reality cognitive training on cognitive and physical function in community-based older adults: A randomized controlled trial
Bibliographic record
Abstract
Background: Older adults are at risk of developing cognitive impairments, and cognitive training is commonly used to enhance cognitive function in this population. The effectiveness of cognitive training is further optimized with the integration of leisure-based activities, such as horticultural therapy. However, to the best of our knowledge, there is a lack of studies examining the effect of integrating virtual reality (VR) with leisure-based activities to provide real-world experiences and enhance cognitive outcomes in older adults. Furthermore, while immersive VR cognitive training has demonstrated effectiveness in enhancing multiple cognitive domains, methodological limitations-such as the absence of control groups or the use of passive controls-hinder the ability to draw conclusive conclusions regarding its comparative effectiveness. Objective: This study conducted immersive leisure-based VR cognitive training in community-dwelling older adults to investigate its effectiveness on cognitive and physical functions. We employed an active control group in which participants received well-arranged leisure activities without focusing on cognitive components. Methods: This cluster randomized controlled trial was conducted in the community facilities in northern Taiwan between 2022 and 2023. The VR cognitive training group received simulated gardening activities, such as planting, fertilizing, and harvesting, and tasks involving cognitive challenges, such as producing plant essential oils, for 60 min daily, 2 days per week, for 8 weeks. The control group received non-cognitive training. The outcomes evaluated were cognitive function assessed by Montreal Cognitive Assessment (MoCA), immediate memory assessed by Wechsler Memory Scale (WMS)-Word List, working memory and mental flexibility assessed by WMS-Digit Span Forward, WMS-Digit Span Backward, and WMG-Digit Span Sequencing (DSS), and physical function assessed by the Timed Up and Go (TUG) test. Results: = 0.015), and TUG (0.008*) compared with the control group. Conclusions: This study is the first to examine the effects of fully immersive, leisure-based VR cognitive training on cognitive and physical function in community-dwelling older adults, highlighting its potential as a promising tool for promoting health compared to the non-cognitive training commonly used in community facilities.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".