Immersive virtual reality to assess and promote upper limb activity in older adults with and without major neurocognitive disorder – A qualitative study
Bibliographic record
Abstract
Background: This study explores how older adults with and without major neurocognitive disorder experienced the use of immersive VR applications aiming to promote and assess upper limb activity. Methods: A qualitative design within an interpretivist paradigm was employed. Thirty participants over 60 years were recruited from the Canadian population. After an upper limb immersive VR session, semi-structured interviews were conducted to gather insights from the participants. Thematic analysis using a six-phase framework was performed to identify and analyze key themes in the interview data. Results: Five main themes emerged from the analysis: Prior experience and apprehensions towards immersive VR, comfort and usability of the device, design of the virtual background, interaction in immersive VR, and clinical applicability of immersive VR. Overall, older adults seemed motivated by the playful, challenging and entertaining interactions offered by immersive VR. Conclusion: Immersive VR was described as beneficial for motricity and cognition. Participants found it to be an interesting tool for performing exercises and assessing performance more autonomously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".