The Use and Impact of Virtual Reality Programs Supported by Aromatherapy for Older Adults: A Scoping Review
Bibliographic record
Abstract
Both virtual reality (VR) and aromatherapy have shown significant potential in enhancing the health and well-being of older adults. Aromatherapy has been noted to improve the immersive quality of VR experiences. However, the combined use of these interventions for older adults has not been systematically explored. This scoping review aims to identify existing VR programs supported by aromatherapy and evaluate their outcomes on older adults’ well-being. Following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, the review included both published and unpublished studies. A search across ten databases yielded 901 publications, from which six studies were analyzed, involving 94 participants with a mean age of 70 to 83 years. Results revealed positive impacts on well-being, cognition, and social engagement. Outcome measures included physical, psychological, emotional, and cognitive aspects like spatial orientation, stress, happiness, memory, and social interaction. Benefits included enhanced spatial awareness, memory, happiness, and reduced stress. Multisensory VR environments also fostered socialization through shared experiences and nostalgia. However, the individual differences in VR experiences indicate a need for personalized content. Despite promising findings, limited evidence supports clinical application in nursing practice. Further research is required to validate the health benefits of combining VR with aromatherapy.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".