Relationship Between Visual Acuity and Cognitive Functions in Older Adults With Visual Impairment: The Mediating Role of Frailty
Bibliographic record
Abstract
BACKGROUND: There is a complex and interacting relationship between visual acuity, cognitive dysfunction, and frailty. It is suggested that frailty may mediate the relationship between visual acuity and cognitive impairment in older adults. OBJECTIVES: This study aimed to examine the mediating role of frailty in the relationship between visual acuity and cognitive function in older adults with visual impairment. METHODS: This cross-sectional correlational study was conducted with 116 participants with visual impairment at an ophthalmology clinic in Turkey between January and February 2025. Data were collected using an information form, the Standardized Mini-Mental Test, and the Edmonton Frailty Scale. Visual acuity was expressed as a logMAR score, ranging from 1.00 to -0.30. RESULTS: The mean age of the participants was 70.92 ± 5.92. The logMAR score positively and significantly associated with frailty levels (p < 0.001). Frailty was found to have a significant negative effect on cognitive function (p < 0.001). In addition, a significant negative relationship was identified between cognitive function and the logMAR score (p < 0.01). Both the total and direct effects of the logMAR score on cognitive function were significant (p < 0.001). The indirect effect, tested using the bootstrap method, was also significant (Coeff = -5.304, BootSE = 1.079, 95% CI [-7.652, -3.462]). CONCLUSIONS: These results suggest that the deterioration of visual acuity strongly impacts cognitive function and that frailty may mediate this relationship. Protecting visual health and preventing frailty in older adults may play a critical role in reducing the risk of cognitive impairment. Regular eye examinations, early intervention for vision disorders, and frailty prevention strategies can contribute to maintaining cognitive health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".