The role of physical activity in preventing cognitive decline among U.S. older adults with diabetes and prediabetes: a cross-sectional study
Bibliographic record
Abstract
Background: Physical activity (PA) has been widely recognized as a key strategy to slow age-related cognitive decline. However, its specific effects on older adults with diabetes or prediabetes remain poorly understood. Therefore, we investigated the association between different levels of PA and cognitive function among older Americans with diabetes and prediabetes. Methods: This cross-sectional study used data from the 2011-2014 National Health and Nutrition Examination Survey (NHANES) and included a total of 1,299 older adults aged ≥60 years. The PA levels were determined by calculating the weekly metabolic equivalent of task time (MET-min/week). The participants' cognitive abilities were assessed using the Consortium to Establish a Registry for Alzheimer's disease (CERAD) Word Learning Test, Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). Multivariable logistic regression models were used to analyze the association between different PA levels and cognitive function in patients with diabetes and prediabetes. The study utilized the restricted cubic spline (RCS) models to explore the nonlinear correlation of PA with cognitive function. Results: = 0.039). According to the RCS models, PA showed a significant nonlinear correlation with cognitive function, and the risk of cognitive decline decreased with the increase of PA levels. Conclusion: In older adults with diabetes and prediabetes, moderate and high levels of physical activity are associated with a lower risk of cognitive decline. Clinicians should encourage patients to participate actively in exercise to maximize the benefits of PA.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".