Physical Versus Cognitive Impairment in Stroke‐Free Older Adults Living in Rural Settings: Relative Contributions to Decreased Functionality
Bibliographic record
Abstract
OBJECTIVES: Both physical and cognitive impairments contribute to disability. However, their relative impact on functional decline among older adults in low-resource settings has not been adequately studied. This study aims to explore the role of these factors on functionality. METHODS: Following a population-based, cross-sectional design, older adults living in rural Ecuador underwent a handgrip strength (HGS) test for physical performance, the Montreal Cognitive Assessment (MoCA) to evaluate cognitive performance, and a Functional Assessment Questionnaire (FAQ) assessed functionality. A generalized structural equation modeling (GSEM) approach was utilized to evaluate a predefined set of causal assumptions and integrate hypothesized latent constructs, providing a comprehensive explanation of the relationships among multiple interconnected variables and their association with dysfunctionality. RESULTS: We included 603 individuals (mean age: 67.5 ± 7 years; 54% women). According to the GSEM approach, a 10% rise in HGS (2.4 kg) was associated with a 3.68% improvement in functionality (p < 0.001), whereas a 10% increase in MoCA scores resulted in a 2.06% improvement in functionality (p < 0.001). One standard deviation (SD) in HGS comprised 18% of the distribution, which yielded a 6.5% shift in functionality. Similarly, one SD difference in MoCA scores accounted for 19% of the distribution, and a 3.91% change in functionality. CONCLUSIONS: Both physical and cognitive impairments significantly contribute to decreased functionality. However, physical performance exerts a greater influence on functional independence than cognitive performance. These findings highlight the importance of a holistic approach to interventions aimed at enhancing quality of life in older adults residing in low-resource rural settings.
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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 | 0.003 |
| 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 | 0.001 |
| 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".