Designing Virtual Natural Environments for Older Adults: Think-Aloud Study
Bibliographic record
Abstract
BACKGROUND: Spending time in natural environments is beneficial for human health, but many older adults have limited or no access to natural environments. Virtual reality technology may be a means to facilitate nature experiences, and so, there is a need for knowledge on how to design virtual restorative natural environments for older adults. OBJECTIVE: The aim of this study was to identify, implement, and test older adults' preferences and ideas regarding virtual natural environments. METHODS: A total of 14 older adults (mean age 75, SD 5.9 years) participated in an iterative process to design such an environment. We used think-aloud protocols and qualitative content analysis and established questionnaires that targeted usability, affective aspects, and side effects. These data guided the design decisions for incremental implementations of a prototype. RESULTS: The participants' preferences included trueness to reality in terms of rendition and behavior; traces of human activity and natural processes that trigger the imagination and provide believability; the ability to roam, explore, and interact with the environment; and a familiar, relatable environment that evokes memories. The iterative design process resulted in a prototype featuring many of the participants' ideas and preferences, including a seated locomotion technique, animals, a boat ride, the discovery of a boat wreck, and apple picking. The questionnaire results indicated high perceived usability, interest, and enjoyment; low pressure and tension; moderate value and usefulness; and negligible side effects. CONCLUSIONS: We suggested 3 principles for virtual natural environments for older adults: realness, interactivity, and relatedness. Virtual natural environments should also provide a diversity of content and activities to accommodate the heterogeneity in older adults' preferences. These results can contribute to a framework for designing virtual natural environments for older adults. However, these findings need to be tested and potentially revised in future studies.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".