Can motion‐based technology impact balance, movement confidence, and cognitive function among people with cognitive impairment?
Bibliographic record
Abstract
Abstract Background Balance, movement confidence, and cognitive function are related to falls among people with cognitive impairment (PwCI; e.g. dementia). Motion‐based technologies (MBT; e.g. Xbox Kinect) are increasingly being explored to encourage exercise participation among PwCI, which can prevent falls. This study is examing the impacts of a group MBT intervention on balance, movement confidence, and cognitive function among PwCI. Methods Twenty‐four PwCI played Xbox Kinect bowling twice weekly for ten weeks at four adult day programs. The Mini Balance Evaluation Systems Test (Mini‐BEST) and the Montreal Cognitive Assessment (MoCA) were completed pre‐ and post‐intervention. Video recordings were taken during weeks one, five, and ten used to capture behavioural indicators of movement confidence (e.g. fluency of motion). Quantitative data collected through the Mini‐BEST and MoCA are being compared from pre‐ to post‐test using paired t‐tests. An ANCOVA is also being performed to account for covariates (e.g. number of intervention sessions attended). Count and percentage data are being extracted from coded video recordings to examine movement confidence. Results This study will answer questions regarding the potential of a group MBT intervention to impact balance, movement confidence, and cognitive function among PwCI. This could assess the feasibility and potential benefits of using MBT to deliver exercise interventions to PwCI. Finally, this work can be used as the basis for developing specific software and future exercise programs using MBT for PwCI. Conclusion The findings will be used to inform future MBT applications to deliver exercise to PwCI.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".