How do older adults respond to head-mounted virtual reality?
Bibliographic record
Abstract
Objectives Head-mounted virtual reality (HMD-VR) is gaining popularity as a medium for health applications for older adults. However, this population is less technology experienced, raising questions on its usability. Here we investigated older adults’ attitudes towards HMD-VR and whether age, education, cognitive status and computer proficiency predicted attitudes towards HMD-VR. Furthermore we tested whether attitudes could change as the result of a first HMD-VR experience and whether HMD-VR induced cybersickness symptoms. Method We recruited 76 participants aged 57 to 94 years, and assigned them to an HMD-VR or control group. The groups (n=38) were matched on age, education, gender and assisted living status. After assessing the initial attitude towards HMD-VR, participants were either exposed to HMD-VR or to time-lapse videos and then the attitudes towards HMD-VR were re-measured. Materials The HMD-VR group used the Oculus Rift to interact in a VR environment, while the control group watched time-lapse videos. Cognitive status of participants was measured with the Montreal Cognitive Assessment and HMD-VR attitudes, computer proficiency, user experience, social desirability and cybersickness were evaluated through questionnaires. Results Attitudes towards HMD-VR significantly increased after a first HMD-VR experience, but remained neutral after exposure to time-lapse videos. There was a negative association of age and initial HMD-VR attitudes that was mediated by computer proficiency, but not by cognitive status or education. The HMD-VR and control group did not differ in self-reported cybersickness. Discussion Older adults had neutral attitudes towards HMD-VR, which improved by a first HMD-VR exposure. Moreover, we did not observe safety concerns when using HMD-VR with older adults. These results show that HMD-VR can be suitable for older adults. Conclusions Our results support the use of HMD-VR in the older population and suggest that the acceptability of HMD-VR should not be a concern.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".