Physical Activity and Cognitive Function in the Elderly Population
Bibliographic record
Abstract
Background: Old age is accompanied by impaired musculoskeletal and nervous system, which may result in low mobility and cognitive problems.Objectives: This study aims to evaluate the relationship between Physical Activity (PA) and Cognitive Function (CF) among the elderly population. Materials & Methods:This is a descriptive cross-sectional study conducted on 200 old people who were members of retirement clubs in Mashhad City, Iran in 2017.They were selected using purposeful sampling method.To collect data, International Physical Activity Questionnaires (IPAQ), and Montreal Cognitive Assessment (MoCA) tools were employed.The Pearson correlation test and hierarchical regression analysis were used to determine the relationship and predictability of CF with PA, respectively after controlling intervening variable (age).Moreover, one-way Analysis of Covariance (ANCOVA) analysis was used to examine difference between CF scores in different PA levels.Results: There was a positive and significant relationship between PA and CF in the elderly (r=0.63,P<0.0001).After controlling the age factor, PA was able to explain 42% of CF variance (F 2,197 =72.17,P<0.0001).Moreover, ANCOVA results indicated that cognitive impairment was higher in the elderly with low PA (F 2,197 =54.40,P<0.0001).Conclusion: This study showed that lower PA was associated with higher cognitive impairment and older people with moderate and high PA had better CF than elderly with low PA.Therefore, suitable physical activity should be planned for the elderly to improve their ability in performing cognitive tasks.
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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 | 0.001 |
| 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 | 0.000 | 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".