The Relationship between Cognitive Status and Retained Activity Participation among Community-Dwelling Older Adults
Bibliographic record
Abstract
Identifying retained activity participation to old age can improve age-related changes in balance and cognition function. Subjects ≥ 60 years were enrolled in this study. Balance and Cognitive function include working memory, executive function, and sustained and divided attention was evaluated with “Fullerton advanced balance”, “n-back”, “Wisconsin card sort”, “sustain and divided attention test”, respectively. In addition, retained activity participation was measured using the Activity Card Sort questionnaire. The univariate and multivariate regression analyses of different domains of retained activity participation were used as independent variables, including instrumental activity, low-effort leisure, high-effort leisure, and social activity on balance and specific domains of cognition. Seventy-seven subjects (65.3 ± 4.4 years, 61% female) were included. About 47% of older adults had a college education, 32.3% had a diploma, and 20.7% had elementary−middle education. These results show that retained instrumental activity had a relationship with working memory (β = 0.079, p < 0.05). In addition, we found that retained high-effort leisure activity can increase balance, divided attention, and executive function score (β = 0.1, β = 0.05, β = 0.02, p < 0.05). Moreover, there was a positive relationship between retained low-effort activity and sustained attention (β = 0.08, p < 0.05). In addition, the coefficient of determination (R2) for balance, working memory, executive function, sustained, and divided attention were 0.45, 0.25, 0.13, 0.11 and 0.18, respectively. The study suggests that retained activity participation types may have various effects on balance and some selective cognitive components in older people.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".