Correlation Between Hypertension, Type 2 Diabetes Mellitus, Physical Activity, and Cognitive Function in Elderly Population At Primary Healthcare
Bibliographic record
Abstract
Background: Cognitive impairment is broadly connected as one of the factors that cause disability in the elderly. This study seeks to ascertain the correlation amidst hypertension, diabetes mellitus, physical activity, and cognitive function in the elderly. Methods: A cross-sectional analytical study was carried out using a consecutive sampling method. The data were collected by interviewing elderly patients with Montreal Cognitive Assessment (MoCA) Indonesia and Rapid Assessment of Physical Activity (RAPA) while assessing patient Vascular Metabolism Factor (VMF) such as hypertension, type 2 diabetes mellitus (T2DM), and past history of heart disease. The Statistical Package for the Social Sciences (SPSS) was utilized to analyze the correlation between the aforementioned variables. Results: A total of 277 elderly participants were recruited from February to July 2022. There was a significant correlation between hypertension (OR= 4.8; 95% CI: 2.5-9.1; p < 0.001) and cognitive impairment as well as physical activity (OR= 1.7; 95% CI: 1.3-2.4; p < 0.001). Despite no meaningful correlation amidst diabetes status and cognitive impairment (OR= 1.7; 95% CI: 0.8-3.8; p = 0.14), the number of participants with impaired cognitive function was elevated in the diabetes group (80.7%). Further analysis revealed that the interaction between diabetes, hypertension, and physical activity has a significant correlation with cognitive impairment (p < 0.001; r2= 0.216). Conclusion: This study demonstrated a notable correlation amidst hypertension status, physical activity intensity, and cognitive function in the elderly population at Hative Kecil Public Health Center.
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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 | 0.000 | 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".