Correlates of physical activity levels, muscle strength, working memory, and cognitive function in older adults
Bibliographic record
Abstract
Objective To explore the relationship between physical activity level, muscle strength, working memory and cognitive function in older adults. Methods A cross-sectional research design was employed to recruit 120 older adults individuals aged 70 and above. Participants were asked to complete the International Physical Activity Questionnaire-Short Form and the Montreal Cognitive Assessment Scale. Data on variables such as grip strength and performance on the N-back task were collected. Data analysis involved the use of independent samples t-tests, χ2 tests, linear regression analysis, Pearson correlation analysis, and one-way analysis of variance (ANOVA). Results The detection rate of cognitive dysfunction in older adults was 53.211%; 1-back correct rate had an explanatory power of 11.6% for the cognitive function scores of older adults (R 2 = 0.116, p < 0.001); grip strength showed a significant positive correlation with 1-back correct rate (r = 0.417, p < 0.001), and was significantly correlated with the 0-back response time (r = −0.478), 1 -back response time (r = −0.441) were significantly negatively correlated ( p < 0.001); physical activity level was significantly positively correlated with grip strength (r = 0.559, p < 0.001), and the difference in grip strength among older adults with different physical activity levels was statistically significant ( F = 19.685, p < 0.001). Conclusion Physical activity level, muscle strength, working memory, and cognitive function are closely related in older adults, and the relational pathway of physical activity → muscle strength → working memory → cognitive function may serve as a useful addition to promote the field of cognitive research in older adults. To identify and prevent cognitive decline in older adults, physical activity questionnaires, grip strength tests, and 1-back task tests can be extended to nursing homes and communities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".