Using motion‐based technology to measure movement confidence among people with dementia and mild cognitive impairment
Bibliographic record
Abstract
Abstract Background Movement confidence is defined as “a person’s feeling or sense of adequacy in a movement situation.” It is proposed that those who are ‘movement confident’ are more likely to partake in movement situations (e.g., exercise) and enjoy doing so. However, the concept and measurement of movement confidence has not been explored among people with dementia and MCI, who can benefit from participating in activities involving physical movement, such as exercise. Method Participants with dementia and MCI (n=28; 53.6% female) were recruited from four community‐based adult day programs to participate in a 10‐week Xbox Kinect bowling intervention to see whether the intervention impacted their balance, movement confidence, and cognition. As part of the data collection procedure, video recordings were taken during the first, middle, and final week of the intervention and coded using video analysis software, to measure movement confidence. Count and percentage data were examined, and the coding scheme used to create a categorical measure of movement confidence. Count, percentage, and categorical data were analyzed descriptively and also compared across the study using a series of related‐samples Wilcoxon signed rank tests. Result Among those who completed the study, movement confidence was high at the start and did not significantly decline over time (Z‐score=‐0.359, p=0.719), suggesting a potential maintenance effect of the intervention. Various behaviours signifying movement confidence during the motion‐based activity, such as hip shifting, knee bending, change in base of support, and willingness to move were captured using both the movement confidence coding scheme and categorial measure. The coding scheme, categorical measure, and implications will be further described. Conclusion This study is the first to examine the complex construct of movement confidence among people with dementia and MCI, paving the way for future investigations. This study is also the first to create an observational, categorical measure of movement confidence which could potentially be used by clinicians (e.g., physiotherapists) to quickly score the movement confidence of people with dementia and MCI in various movement situations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".