Balance, movement confidence, and cognition: Exploring the impacts of a group motion‐based technology intervention for people with dementia and mild cognitive impairment
Bibliographic record
Abstract
Abstract Background Exercise can impact balance, movement confidence, and cognition among people with dementia and mild cognitive impairment (MCI). However, engaging and accessible ways for this population to exercise are lacking. Motion‐based technology, which people with dementia and MCI can learn to use and enjoy using, could be a potential solution. The purpose of this study was to examine the impacts of a group motion‐based technology intervention on balance, movement confidence, and cognition among people with dementia and MCI. Method Participants with dementia and MCI (n=28; 53.6% female) were recruited from four community‐based adult day programs and invited to partake in a 10‐week Xbox Kinect bowling intervention. Before and after the intervention, participants completed the Montreal Cognitive Assessment (MoCA) and the Mini Balance Evaluation Systems Test (Mini‐BEST) to measure cognition and balance, respectively. Video recordings were taken during the first, middle, and final week of the intervention and coded using video analysis software, to measure movement confidence. Quantitative data from the MoCA, Mini‐BEST, and coded video recordings were compared using a series of related‐samples Wilcoxon signed rank tests. Result At baseline, participants with dementia and MCI were substantially impaired with regards to balance (mean Mini‐BEST: 14.58/28), cognition (mean MoCA: 12.86/30), and mobility (57.1% mobility device users). Among the participants who completed the study, there was no significant decline in balance (Z‐score=‐1.276, p=0.202), movement confidence (Z‐score=‐0.359, p=0.719), or cognition (Z‐score=‐0.060, p=0.952), suggesting a potential maintenance effect of the intervention. Conclusion Firstly, the findings highlight the prevalence of mobility and balance impairments among people with dementia and MCI, in addition to confirming their cognitive impairment. Secondly, this study also highlights the potential of using motion‐based technology to support the function and well‐being of people with dementia and MCI. That is, the finding of no significant decline in function among the participants can be considered a positive finding, given the generally progressive nature and trajectory of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".