Age in Place, Move in VR: Investigating the Feasibility and Usability of a Custom‐made Virtual Reality Exergame to Promote the Well‐being of Community‐Dwelling Older Adults
Bibliographic record
Abstract
Abstract Background Interactive virtual reality (VR) games combined with exercise (exergames) are a plausible strategy to encourage physical activity (PA) among older adults. However, there is little systematic evaluation of the feasibility, usability, and potential benefits of deploying at‐home VR exergames among this population. Method Fifteen community‐dwelling older adults (M = 67.33 ± 5.3, age range 60‐77) (Table1) were recruited to play 18 sessions (each 15‐20 min) of a custom‐made VR exergame at home over six weeks. Recruitment, retention, and adherence rates were calculated to determine feasibility. The usability of the exergame was assessed through i) a game user experience questionnaire, ii) a self‐reported physical/emotional discomfort questionnaire, and iii) a perceived enjoyment scale. The exploratory outcome measures included changes in i) PA, ii) exercise self‐efficacy, iii) affect, iv) various cognitive and perceptual tasks and were assessed i) before and after acute data, and ii) before and after the intervention. Result Thirteen participants completed the study (13% attrition rate) without missing any exergaming session (100% adherence rate). The completion rate of cognitive assessments, perceptual tasks, and self‐reported questionnaires was = 97%. Low levels of cybersickness were reported (M = 0.9 ±1.0 out of 10). The average perceived enjoyment and perceived rate of exertion were 3.2. ±1.1 (out of 5) and 2.4±1.9 (out of 10), respectively. Most of the participants (92.3%) found the exergame’s instructions easy to follow and had an overall positive experience (84.6%). Whereas the exergame was perceived as useful for increasing PA levels (61.6%), mood enhancement was reported among less than half of participants (46.2%) and only 38% indicated that they would choose VR exergaming for at‐home PA. The likelihood of playing VR exergames in the future was also low (38.5%). The exploratory outcome measures are presented in Table 2. Conclusion Our study provides insights into the feasibility and usability of a custom‐made VR exergaming intervention to promote PA in community‐dwelling older adults which may potentially benefit their well‐being. Our findings inform the future adoption of VR at home. Our results can also be used to inform large‐scale trials and support the design and deployment of other remote exergames and assessments.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".