Acceptance of physical activity virtual reality games by residents of long-term care facilities: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Little is known about the experience and the social and contextual factors influencing the acceptance of virtual reality (VR) physical activity games among long-term care (LTC) residents. Our study aims to address this research gap by investigating the unique experience of older adults with VR games. The findings will provide valuable insights into the factors influencing VR acceptance among LTC residents and help design inclusive VR technology that meets their needs and improves physical activity (PA) and well-being. OBJECTIVE: We aimed to: (1) investigate how participants experience VR exergames and the meaning they associate with their participation; and (2) examine the factors that influence the participant's experience in VR exergames and explore how these factors affect the overall experience. METHODS: We used a qualitative approach that follows the principles of the Interpretive Description methodology. Selective Optimization and Compensation (SOC) theory, Socioemotional Selectivity theory (SST) and technology acceptance models underpinned the theoretical foundations of this study. We conducted semi-structured interviews with participants. 19 Participants of a LTC were interviewed: five residents and ten tenants, aged 65 to 93 years (8 female and 7 male) and four staff members. Interviews ranged from 15 to 30 minutes and were transcribed verbatim and were analyzed using thematic analysis. RESULTS: We identified four themes based on older adults' responses that reflected their unique VR gaming experience, including (1) enjoyment, excitement, and the novel environment; (2) PA and motivation to exercise; (3) social connection and support; and (4) individual preferences and challenges. Three themes were developed based on the staff members' data to capture their perspective on the factors that influence the acceptance of VR among LTC resident including (1) relevance and personalization of the games; (2) training and guidance; and (3) organizational and individual barriers. CONCLUSIONS: VR gaming experiences are enjoyable exciting, and novel for LTC residents and tenants and can provide physical, cognitive, social, and motivational benefits for them. Proper guidance and personalized programs can increase understanding and familiarity with VR, leading to a higher level of acceptance and engagement. Our findings emphasize the significance of social connection and support in promoting acceptance and enjoyment of VR gaming among older adults. Incorporating social theories of aging helps to gain a better understanding of how aging-related changes influence technology acceptance among older adults. This approach can inform the development of technology that better meets their needs and preferences.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".