Optimising an immersive virtual reality behaviour change intervention to support retired and non-working adults to reduce their sedentary behaviour: a mini-focus group interview study
Bibliographic record
Abstract
OBJECTIVE: Prolonged sedentary behaviour is associated with numerous negative health outcomes. Immersive virtual reality (IVR) offers opportunities for retired and non-working adults to take part in meaningful non-sedentary activities that may not be available to them in their natural environment. Using the behaviour change wheel and theoretical domains framework, an IVR intervention prototype was developed. This study aimed to explore and optimise the prototype with retired and non-working adults. A secondary aim was to explore participants' perceptions of IVR more generally. METHODS AND MEASURES: Five semi-structured mini-focus group interviews were conducted with 12 retired and non-working adults. Each group explored the intervention prototype together and discussed their experiences afterwards. A rapid analysis and reflexive thematic analysis (TA) were conducted on the data. RESULTS: Several feasible intervention design changes were generated through the rapid analysis. The reflexive TA generated three themes relating to participants' past experiences reducing their time spent sedentary, how they experienced the intervention prototype as a means to reduce their time spent sedentary, and their perspectives on using such an intervention in the future. CONCLUSION: The results indicate that retired and non-working adults may enjoy using IVR to reduce sedentary time but generally favour natural experiences when possible.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".