Personalized Telerehabilitation for a Head‐mounted Low Vision Aid: A Randomized Feasibility Study
Bibliographic record
Abstract
SIGNIFICANCE: A recent trend in low vision rehabilitation has been the use of portable head-mounted displays to enhance residual vision. Our study confirms the feasibility of telerehabilitation and informs the development of evidence-based recommendations to improve telerehabilitation interventions to reduce device abandonment. PURPOSE: To develop evidence-based recommendations for telerehabilitation, we conducted a feasibility study in preparation for a future randomized trial on the use of head-mounted displays. METHODS: We recruited novice eSight Eyewear users, randomized 1:1: the experimental group received telerehabilitation by a low vision therapist using video conferencing; the control group completed at home self-training provided by the device manufacturer. The primary feasibility outcomes were whether the recruitment goal of 60 participants (30/group) was attainable within 1 year and how participants judged the accessibility and acceptability of the telerehabilitation. An exploratory outcome was the impact of telerehabilitation on eSight Eyewear use behavior. RESULTS: Among 333 eSight users, 57 participants were enrolled, of which 35% withdrew from the study, whereas the remainder completed the 6-month follow-up. The withdrawal rate was higher in the control group but did not differ significantly from the experimental group. High accessibility (93% of participants accessed the platform) and global acceptability (100% overall satisfaction) were reported among those who completed the telerehabilitation protocol. The therapist had no difficulty judging the participants' reading performances qualitatively while participants used their device to read their eSkills and VisExc guides. Most participants improved their daily activities, based on qualitative reports of the attained goals. Seventy-nine percent of individuals declined to participate, whereas 16% of participants decided not to use eSight Eyewear anymore. CONCLUSIONS: The data demonstrated the feasibility of a randomized controlled telerehabilitation study for people with low vision using a head-mounted display. Positive feedback from the participants and the therapist suggests the potential value of this modality for low vision services.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".