Acceptance of physical activity virtual reality games by residents of long-term care facilities: a scoping review
Bibliographic record
Abstract
PURPOSE: This scoping review aims to identify evidence on older adults' acceptance of PA VR games in LTC facilities, describe research designs used, define key acceptance concepts, and identify knowledge gaps for future research. MATERIALS AND METHODS: Following Arksey and O'Malley's framework, data from published and unpublished articles (Jan 2000-May 2023) were collected. Twelve databases and additional sources were searched for studies on LTC residents (≥65 years), PA video games (including VR and console games), acceptance, and attitudes. Data extraction included article details, design, population, intervention, outcomes, and limitations. RESULTS: Five studies met inclusion criteria from 1628 initial titles. They assessed acceptance of PA VR games among older adults in LTC facilities, showing varying levels of acceptance. Most studies used analytical designs, including RCTs. Key concepts of VR acceptance were poorly defined, with only one study using a validated TAM questionnaire. Knowledge gaps highlight the need for further research to understand PA VR acceptance among older adults in LTC facilities. CONCLUSION: Validated acceptance questionnaires are needed in study of VR acceptance by older adults. Use of qualitative and quantitative methods can enhance understanding of technology acceptance, alongside exploration of individual, environmental, and age-related factors. Detailed reporting of VR interventions is recommended to comprehend acceptance factors.
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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".