Comparison of informational and experiential interventions for VR acceptance in rural older adults: a feasibility study
Bibliographic record
Abstract
Background. Virtual reality (VR) offers a promising solution to deliver portable, engaging rehabilitation services, particularly for rural populations with limited access to care. However, its adoption among older adults may be hindered by key barriers, including technological unfamiliarity or concerns about visually induced motion sickness (VIMS). Overcoming these initial acceptance barriers is a critical step for implementing this gerontechnology which could deliver physical and mental health support directly to the homes of older adults. Research aim. We aimed to determine the feasibility of using brief, low-resource interventions, either an informational handout or a short immersive VR experience, to improve attitudes toward VR in rural older adults. Methods. In this comparative feasibility study, sixty-four rural adults aged 60 and older received either an informational handout describing VR or completed four immersive VR activities using hand tracking on a consumer headset. Attitudes toward VR and VIMS symptoms were assessed using pre-post intervention surveys.Results. The immersive VR experience did not result in a significant increase in VIMS symptoms, demonstrating high tolerability. Both the informational handout group and the VR experience group showed significant positive improvements in attitudes toward VR.Conclusions. Brief, low-resource interventions can increase technology acceptance for VR among rural older adults. The finding that a simple informational handout can be as effective as a hands-on immersive experience has significant, practical implications for the scalable and cost-effective deployment of VR-based technologies in clinical and home-based gerontological settings.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".