Factors related to the use of a head-mounted display for individuals with low vision
Bibliographic record
Abstract
The decision-making process around the (non-)use of assistive technologies is multifactorial. The goal of the present study was to identify which factors predict or correlate with the use of a head-mounted magnification device for low vision (LV) (eSight Eyewear), by applying this multifactorial paradigm in order to tailor LV rehabilitation interventions to reduce device abandonment. Using a cross-sectional design, participants were recruited from 567 eSight Eyewear owners to complete a 45-min survey online including questions from standardized questionnaires classified into four families: personal, device-related, environmental, and interventional. Using current device use/nonuse as a binary outcome, logistic regression analyses were performed to identify the variables that predicted the highest percentage of variance in eSight use. The 109 (19.2%) respondents with complete data had a mean age of 47.7 years (SD = 25.4, range: 9–96), 51% self-reported a central visual impairment. The final regression model alternatives accounted for 84.7%, 68.7%, 83.7%, and 64.7% (Nagelkerke’s pseudo R2) of the variance in eSight use. The most consistently predictive variables of sustained device use across models were: higher scores on the Psychological Impact of Assistive Devices Scale (PIADS) and the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST) scale, and participants’ lack of experiencing headaches while using the device. None of the traditional clinical variables (demographics, ocular, or general health), or LV rehabilitation experience was predictive of sustained use of a head-mounted LV display. However, the administration of standardized device-impact questionnaires may be able to identify device users that could benefit from individualized attention during LV rehabilitation provision to reduce the probability of device abandonment.Implications for rehabilitationInvestigating the factors predicting (non-)use of head-mounted magnification devices for low vision (LV) is important to identify patients with a higher risk of device nonuse and to provide evidence for interventions designed to improve use.The optimal combinations of our statistical analysis models highlighted the importance of individualized attention focusing on the user during LV rehabilitation provision of, and training with, head-mounted devices.Standardized device-related quality of life measures were robust predictors of device use and may be able to identify individuals that could benefit from individualized attention during LV rehabilitation.The absence of headaches while using a head-mounted magnification device was a robust predictor of continued use.User follow-up service satisfaction strongly predicted continued devices use, indicating that manufacturers and rehabilitation service organizations need to maintain a high level of service. Investigating the factors predicting (non-)use of head-mounted magnification devices for low vision (LV) is important to identify patients with a higher risk of device nonuse and to provide evidence for interventions designed to improve use. The optimal combinations of our statistical analysis models highlighted the importance of individualized attention focusing on the user during LV rehabilitation provision of, and training with, head-mounted devices. Standardized device-related quality of life measures were robust predictors of device use and may be able to identify individuals that could benefit from individualized attention during LV rehabilitation. The absence of headaches while using a head-mounted magnification device was a robust predictor of continued use. User follow-up service satisfaction strongly predicted continued devices use, indicating that manufacturers and rehabilitation service organizations need to maintain a high level of service.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".