Improving Gait Ability and Cognition Function through Action Observation Training in Elderly with Dementia
Bibliographic record
Abstract
BackgroundDementia is one of the most prevalent conditions among older adults.ObjectiveThis study aimed to investigate the effect of action observation training (AOT) on gait ability and cognition in older individuals with dementia.MethodsA total of 36 participants were randomly assigned to the experimental (n=18) or control (n=18) group. The 5-week intervention involved 45-min sessions. The participants engaged in general group exercises for 30 min. The experimental group watched a training video for 5 min, while the control group watched a scenic video. Subsequently, both groups underwent functional training for 10 min. All participants were assessed using a gait analyzer (G-walk), Dynamic Gait Index (DGI), timed up and go (TUG) test, and the Korean version of the Montreal Cognitive Assessment (MoCA-K) before and after the intervention.ResultsThe two groups showed significant within-group changes in gait velocity, cadence, stride length, DGI, and MoCA-K scores (p<0.05). However, the TUG test showed a significant differences only in the experimental group (p<0.05). A significant difference was observed between the experimental and control groups regarding the changes in gait velocity, cadence, DGI, TUG test, and MoCA-K score after the intervention (p<0.05).ConclusionsThe study suggest that AOT is effective in improving the gait and cognitive abilities of older individuals 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".