Eccentric Viewing Training for Balance in Older Adults with Visual Impairment
Bibliographic record
Abstract
Age-related macular degeneration, the second leading cause of vision loss in Canada, affects central vision, impairing reading, mobility, and daily functioning. This contributes to increased fear and risk of falling, highlighting the need for effective rehabilitation. Current low vision rehabilitation interventions primarily target reading, such as eccentric viewing training (EVT), which teaches clients to locate and use their preferred retinal locus to improve reading. Previous research has found correlations between fixation stability and perceived and objective measures of balance. However, the current thesis examined whether an EVT intervention that improves fixation stability can also improve balance. Sixteen participants (Mage = 85.35, SD = 7.1) completed an EVT program at the Lethbridge-Layton-Mackay Rehabilitation Centre in Montreal. Pre- and post-testing included visual measures (reading performance and fixation stability), and balance measures (global balance, dynamic balance during functional gait tasks, postural stability, and perceived balance confidence). Results indicated improvement post-EVT in fixation stability (g = -1.08) and reading accuracy (g = 0.684), but not in reading speed (g = -0.018). For balance, group-level improvements were observed in global balance (g = -0.496), medial-lateral postural stability (g = -0.782), and anterior-posterior stability (g = -0.42). Although dynamic balance (g = 0.306) and total postural path length (g = -0.499) did not show statistically significant improvements, certain participants improved, suggesting clinical significance. Improving mobility through EVT could potentially reduce falls and fear of falling in older adults, fostering greater independence, and ultimately improving quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".