Head‐mounted Visual Assistive Technology–related Quality of Life Changes after Telerehabilitation
Bibliographic record
Abstract
SIGNIFICANCE: Head-mounted low vision devices have become a viable alternative to enhance residual vision. This study supports the use of a head-mounted display to improve aspects of functional vision and quality of life. Much is still unknown regarding the required frequency, duration, or potential effectiveness of this telerehabilitation training protocol or what characteristics best identify optimal users. PURPOSE: A randomized study explored the effect of telerehabilitation on quality of life and functional vision in individuals with low vision using a head-mounted display. METHODS: We recruited 57 participants (age, 21 to 82 years; mean, 54.5 years) among new prospective eSight Eyewear users, randomized 1:1 into two parallel groups; the experimental group received the telerehabilitation training provided by a low vision therapist, whereas the control group received the self-training standard offered by the device manufacturer and without involvement of a low vision therapist. The primary outcome measures were the impact of telerehabilitation on validated measures of assistive technology-related quality of life: the Psychosocial Impact of Assistive Devices Scale and the Quebec User Evaluation of Satisfaction with Assistive Technology scale. Exploratory outcomes were the assessment of self-reported functional vision using the Veterans Affairs Low Vision Visual Functioning Questionnaire-48 and cybersickness associated with head-mounted display use with the Simulator Sickness Questionnaire. RESULTS: Assistive technology-related quality of life was improved when measured by the satisfaction scale but not the psychosocial scale within the first 3 months, independently of training type. Overall, functional vision improvement was observed within the first 2 weeks of device use and maintained during the 6-month study, independently of group type. Cybersickness outcomes were similar between training groups and did not change significantly for 6 months. CONCLUSIONS: eSight Eyewear, either with telerehabilitation or with the manufacturer self-training comparison, improved functional vision and increased users' quality of life within the initial 3 months of device training and practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".