EVALUATING A CO-DESIGNED EXERGAMING PLATFORM (MOUVMAT) FOR OLDER ADULTS LIVING IN LONG-TERM CARE HOMES
Bibliographic record
Abstract
Abstract MouvMat is an exergaming technology with an interactive digital gaming surface that was co-designed with a user-centered design approach for older adults (OA) living in long term care homes. The purpose of the study was to evaluate the acceptability and efficacy of MouvMat to improve mobility, cognitive function and psychosocial wellbeing among OAs. The study was conducted as a multi-site pilot randomised controlled trial where OAs in the intervention group attended 1-hour group sessions 3 times per week for 6 weeks. The control group received standard recreational programing. All participants underwent assessments (TUG, 2MW test, UCLA Loneliness Scale, Cornell Scale of Depression, digit span, TMT-A, TMT-B, alternating sequences, and verbal fluency) at baseline, mid-intervention, and end of intervention. Participant ratings indicated that the MouvMat was acceptable and OAs with a range of comorbid health conditions were able to attend sessions and engage with the intervention. Efficacy findings were varied and should be interpreted in the context of the heterogeneous physical and cognitive abilities of participants living in a long-term care setting. Future studies will focus on larger sample size and improvement in MouvMat’s design and interface.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".