Development of a Core Set of Quality Criteria for Virtual Reality Applications Designed for Older Adults: Multistep Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) applications are gaining growing significance, particularly among older adults. These applications can provide valuable support to older adults by offering immersive VR content that positively influences various aspects of their daily lives, including activities of daily living. Furthermore, VR applications can contribute to the enhancement of cognitive and motor skills, ultimately leading to an improved quality of life for older individuals. Nevertheless, to ensure a positive impact, it is crucial to develop VR experiences that are tailored to the needs and preferences of the users. OBJECTIVE: This study aims to develop a core set of quality criteria and guidelines for the development of user-centered VR applications specifically designed for older adults (target group). METHODS: The multistep qualitative study design comprised several key stages, beginning with a systematic literature search. This was followed by a framework analysis aimed at identifying a core set of criteria. Subsequently, these criteria underwent validation through expert workshops. The outcomes achieved through this iterative process were organized and categorized into criteria, accompanied by explanations detailing the underlying categories or codes. RESULTS: The quality criteria core set for older adults-friendly VR applications has been developed through an iterative process. It is divided into 2 distinct parts, each containing criteria categorized into specific areas. The first part includes the following categories: (1) quality assurance of medical/health content, (2) data protection provisions, (3) quality requirements, (4) consumer protection, and (5) interoperability. The second part includes the following categories: (1) graphic/quality, (2) 3D character/avatar, (3) providing in-game instructions and prompts, (4) interaction, (5) navigation, and (6) promotion of user motivation and loyalty to use. The results imply a differentiated scope as well as a differentiated granularity of the criteria. CONCLUSIONS: Considering the ongoing advancement of VR technology and the diverse needs within the older adult demographic, it is essential to assess the quality criteria core set results on an individual basis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".