Improving movement confidence of people with dementia using gaming
Bibliographic record
Abstract
Abstract Background People living with dementia face increased risk of falls due to balance impairment and falling‐related psychological factors, such as fear of falling and lack of confidence. Improving movement confidence could improve balance and impact fall risk. This study set out to examine movement confidence of people living with dementia while playing a digital bowling game. Method Sixty‐six people (34F/32M) with dementia (mean MoCA 12.7/30), completed a formal balance measure (mini‐BEST or Sharpened Romberg) before and after participating in a 20‐session (2x per week) digital bowling game. Samples of video recordings of the bowling sessions were analysed by two raters, using a coding scheme developed to examine movement confidence. Result Formal balance measures confirmed that the majority of participants were substantially impaired in several domains, including reactive postural control (mini‐BEST) and balance with eyes open (Sharpened Romberg). By contrast movement confidence during game play started at a high level in several domains such as optimal walking and fluency of motion and remained high throughout. Conclusion Movement confidence can be observed when people with dementia are playing a physical game and contrasts with their performance on formal measures. This may be due to game playing focusing attention onto the game activity, whereas formal assessment focuses on completing the specific movements under assessment. The results highlight the potential of exergames to improve movement confidence and target balance to reduce fall risk of people living with dementia.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".