Abstract
To compare the objective performance, acceptance and usability of head-mounted displays (HMDs) to provide evidence-based data that could be used to increase the efficiency of device referrals based upon a person's vision loss and functional needs. A cross-sectional, counterbalanced, individually controlled crossover study was performed on 15 adults with various eye conditions. Performance was measured when using four HMDs: eSight4, Eyedaptic EYE3, Eyedaptic EYE4 and IrisVision Inspire. Performance on clinical visual acuity tests and contrast were assessed, as well as vision-related activities of daily living (ADL) which were divided into three categories: Reading, Searching & Identifying and Eye-hand Coordination. User-experience was also assessed. Logistic regression analyses, Friedman one-way repeated measure analyses of variance by ranks and multivariate permutation testing were used for analysis. There was a significant improvement in visual acuity when using all devices. For contrast tasks, only the eSight4 and Eyedaptic EYE3 improved performance relative to baseline. For most Reading and Searching & Identifying tasks, the odds of being able to perform the tasks were significantly higher while using the devices. However, the actual performance with most devices (e.g., number of words read or reading speed) did not improve significantly over baseline for most tasks. For the Eye-hand Coordination tasks, participants performed equivalent to or significantly poorer than baseline when using the devices. No demographic or clinical predictors of outcomes were identified. Participants expressed dissatisfaction with the devices' effectiveness, acceptability and usability. While performance on clinical tests was better when using the devices, performance on most real-world ADLs was equal to or worse than baseline. No single device improved performance on all tasks, and performance on any one task was not improved with all the devices. The overall dissatisfaction with the devices paralleled the lack of objective improvement in the performance of real-world tasks.
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