A visual telerehabilitation program in virtual reality in age-related macular degeneration: a randomized feasibility and proof-of-concept trial. (Preprint)
Bibliographic record
Abstract
Abstract Background Age-related macular degeneration (AMD) causes progressive central vision loss in older adults. Low-vision rehabilitation can improve functional vision by training the use of a preferred retinal locus, commonly through clinic-based biofeedback training (BFT). However, repeated supervised rehabilitation is burdensome, and functional gains may be difficult to sustain without home practice. Stand-alone virtual reality (VR) may enable home-based, remotely monitored visual stimulation, but feasibility, safety, and usability in older adults with AMD remain insufficiently characterized. Objective This study aimed to evaluate the feasibility and safety of adding home-based VR 3D single-object tracking (3D-SOT-VR) to conventional BFT in older adults with dry AMD in a parallel, randomized, single-blind (to assessors), controlled, formative trial and to generate exploratory functional hypotheses for a future trial. Methods Adults with dry AMD were recruited at the Low Vision Clinic, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada, from September 2021 to October 2023. Participants were randomized to BFT once weekly for 4 weeks (BFT group) or BFT plus home-based 3D-SOT-VR (BFT-VR group) every other day for 4 weeks. Experimental intervention consisted of tracking a single object among distractors moving at different speeds in a 3D virtual space in a VR headset. Primary feasibility and safety outcomes included recruitment, adoption, adherence, compliance, intervention completion, remote data transfer, usability, and VR-induced symptoms and effects. Secondary outcomes included visual acuity, contrast sensitivity, fixation stability, retinal sensitivity, reading speed, and low-vision quality of life. Exploratory outcomes assessed performance at 3D-SOT-VR and usage. Analyses were descriptive and exploratory, with CIs and denominators reported to reflect limited precision and missingness. Results Fourteen individuals were randomized (BFT, n=6; BFT-VR, n=8), below the planned sample size of 32. Recruitment was not achieved because of COVID-19–related interruptions and reduced onsite access. Eleven individuals were analyzed for the primary outcome (BFT n=6, BFT-VR n=5). Intervention completion was 100% in the BFT arm and 75% in the BFT-VR arm, below the prespecified BFT-VR threshold. Among participants who used VR, adherence to scheduled home sessions was acceptable, completed VR-session files were transmitted without loss, and no participant met the predefined cybersickness stopping rule. One BFT-VR participant discontinued because headset weight caused neck fatigue. Group-level visual outcomes did not provide significant effectiveness. Reading speed showed a clinically meaningful individual-level improvement in the BFT-VR arm and correlated with VR-task performance. The findings were not clearly durable at follow-up. Conclusions This pilot study provides formative evidence that clinic-based BFT combined with home-based, remotely monitored VR visual stimulation can be implemented safely in older adults with dry AMD, while identifying major contextual feasibility barriers. The intervention is innovative because it extends low-vision rehabilitation into the home using a connected device and objective performance monitoring. Recruitment, retention, missing data handling, and sustainability of functional gains must be addressed before effectiveness testing.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".