Participatory design and evaluation of virtual reality games to promote engagement in physical activity for people living with dementia
Bibliographic record
Abstract
BACKGROUND: Exercise is a key component of physical health and quality of life for people living with dementia; however, challenges related to dementia symptoms and aging can make it difficult for people living with dementia to engage in exercise. While immersive virtual reality is showing increasing promise for exercise and rehabilitation applications, there is a lack of research regarding its use with people living with dementia. METHODS: Through participatory design with exercise therapists, kinesiologists, and people living with dementia, we designed two virtual reality environments (a farm and a gym) that were implemented on head-mounted displays to support five different upper-body exercises. Virtual reality and comparable human-guided exercises were tested with six people living with dementia. Both qualitative and quantitative measures were used, including reaching distance, distance traversed, and speed as well as feelings of enjoyment, engagement, interest, easiness, comfort, and level of effort. RESULTS: Participants' subjective responses, motion, and fitness parameters all demonstrated comparable results between virtual reality and human-guided exercises. Therapists' feedback also supported virtual reality exercise as an appropriate and engaging method for people living with dementia. CONCLUSIONS: Collaborating with experts and people living with dementia throughout the design process resulted in an intuitive and engaging design. The results suggest that head-mounted virtual reality has promising potential to support physical activity for people living with dementia.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".