Factors related to the use of a head-mounted display for individuals with low vision
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».