Feasibility and effect of cognitive-based board game and multi-component exercise interventions on older adults with dementia
Bibliographic record
Abstract
BACKGROUND: Taiwan is an aging society, and the number of people with dementia is rapidly increasing. Due to a decline in cognitive and physical function, older adults with dementia not only gradually lose the ability to complete daily living tasks on their own, but are also at a higher risk of falls and injurious falls. It is important to develop interventions that combine cognitive and exercise training for older adults with dementia to promote or maintain their cognitive and physical functions and reduce their risk of falls. This study aimed to investigate the feasibility and effect of cognitive-based board games and multi-component exercise interventions on cognitive function, physical fitness, and fall risk in older adults with dementia. METHODS: This was a quasi-experimental study with a single-group pretest and post-test design. The study participants were 41 community-dwelling older adults with mild to moderate dementia. They received cognitive-based board games and multi-component exercise interventions once a week for 12 weeks. The interventions included 1 hour of exercise training and 1 hour of cognitive training. Scores for the Taiwan version of the Montreal Cognitive Assessment (MoCA-T), physical fitness, and the St. Thomas Risk Assessment Tool for Falling Elderly Inpatients (STRATIFY) were measured as outcome indicators at baseline and after the 12-week period. RESULTS: The overall MoCA-T score increased significantly (effect size = 0.402), with participants with mild dementia showing a greater increase (effect size = 0.522) than those with moderate dementia (effect size = 0.310). Participants' physical fitness performance improved. Female participants exhibited significant improvements in the 30-second chair stand test (effect size = 0.483) and 8-foot up-and-go test (effect size = 0.437). The fall risk score decreased by 0.05 points, the change was not significant. CONCLUSION: The cognitive-based board game and multi-component exercise interventions used in this study are beneficial for improving cognitive function and physical fitness in older adults with dementia. These interventions are feasible and suitable for promotion among community-dwelling and institution-dwelling older adults with mild cognitive impairment or dementia to delay the decline in cognitive and physical function.
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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 | 0.003 | 0.006 |
| 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 | 0.000 | 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".