Activity Engagement Across Stages of Cognitive Health
Bibliographic record
Abstract
Objective: This study aims to examine activity engagement across stages of cognitive health among older adults. Methods: We used a cross-sectional study analysis of baseline data collected as part of the prospective Microbiome in Aging of Gut and Brain (MiaGB) longitudinal study; the study period was August 2022 to December 2023. Health history and activity engagement questionnaires and the Montreal Cognitive Assessment (MoCA) were used to examine the study objective. One-way ANOVA and chi-squared tests, with Bonferroni post hoc analyses, assessed group differences. Results: The weighted samples reflected 417 participants: 54% females, 70.7% White, with an average age of 72 (±8.7) years, 90% with at least high school education, and 75% self-reported medium income status. Results suggested that individuals who scored ≤17 points on the MoCA had an average age of 84 years, were White, non-Hispanic, female, had less than a high school education, and medium income status (p < 0.001). Significant differences were found in active engagement in all health behaviors (p < 0.05). The frequency of engagement in activities was all statistically significant (p < 0.05), except the frequency of looking after grandchildren (p > 0.05). Older adults who scored ≤17 MoCA points reported higher rates of hypertension, osteoarthritis, and depression compared with individuals who scored higher on the MoCA assessment. Conclusions: Older adults with lower cognitive status report a higher rate of clinical ailments and have less engagement in meaningful activities. We should promote meaningful activities to improve the quality of life in older adults with decreased cognition. We make recommendations for appropriate modifications for activity engagement across cognitive health levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".