Effect of Ambient Light and Age‐Related Macular Degeneration on Precision Walking
Bibliographic record
Abstract
PURPOSE: To determine how age-related macular degeneration (AMD) and changes in ambient light affect the control of foot placement while walking. METHODS: Ten older adults with AMD and 11 normal-sighted controls performed a precision walking task under normal (∼600 lx), dim (∼0.7 lx), and after a sudden reduction (∼600 to 0.7 lx) of light. The precision walking task involved subjects walking and stepping to the center of a series of irregularly spaced, low-contrast targets. Habitual visual acuity and contrast sensitivity and visual field function were also assessed. RESULTS: There were no differences between groups when performing the walking task in normal light (p > 0.05). In reduced lighting, older adults with AMD were less accurate and more variable when stepping across the targets compared to controls (p < 0.05). A sudden reduction of light proved the most challenging for this population. In the AMD group, contrast sensitivity and visual acuity were not significantly correlated with walking performance. Visual field thresholds in the AMD group were only associated with greater foot placement error and variability in the dim light walking condition (r = -0.69 to -0.87, p < 0.05). CONCLUSIONS: While walking performance is similar between groups in normal light, poor ambient lighting results in decreased foot placement accuracy in older adults with AMD. Improper foot placement while walking can lead to a fall and possible injury. Thus, to improve the mobility of those with AMD, strategies to enhance the environment in reduced lighting situations are necessary.
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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 | 0.000 | 0.000 |
| 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".