Virtual Reality Mobility Assessment of Functional Vision in Retinal Disease

Cone-Rod Degeneration, Rod-Cone Degeneration

Cone-Rod Degeneration, Rod-Cone Degeneration, Eye, BCVA, OCT

Background: The retina is a thin layer of tissue at the back of the eye. Retinal disease usually reduces a person s mobility because it affects how he or she moves through familiar and unfamiliar environments. Researchers want to see if a virtual reality (VR) tool can provide an easier and more accurate way to assess mobility. Objective: To learn if researchers can track changes in mobility in people with retinal disease using a new VR tool. Eligibility: People aged 5 and older with retinal disease that affects their vision, and healthy volunteers. Design: Participants will have 2-3 clinic visits. Participants will wear goggles or sit in front of a screen while sitting. Using a game controller, they will navigate through 4 obstacle courses presented in VR. Participants will have a medical history exam. They will answer questions about their family history. They will fill out questionnaires about the vision and mobility issues they have in their daily lives. Participants will have a complete eye exam. They will read letters from a chart. Their eye pressure will be measured. Their pupils may be dilated with eye drops. Pictures of their eye will be taken. Lights will be shined in their eyes. Participants will take a visual field test. For this, they will look into a dome and press a button when they see a light. Participants will have an electroretinogram. For this, they will sit in the dark with their eyes patched. Then their eyes will be numbed with eye drops and they will wear contact lenses while watching flashing lights. Participants will have optical coherence tomography. This is a noninvasive procedure. It produces cross-sectional pictures of the retina....

Objective: Designing clinical trials for advanced retinal disease represents an especially difficult challenge due to the lack of suitable outcome measures. Clinical measures such as visual field and area of atrophy measured with multimodal imaging may be highly variable and/or difficult to measure in this population. A main contributor to disability in the visually impaired is poor mobility, which is a quality of life measure used to assess visually-guided behavior in low-vision patients. The goal of our study is to determine whether parameters from a recently developed virtual reality (VR) mobility assessment tool may serve as biomarkers of functional vision in participants with advanced retinal disease. The long-term goal will be to determine whether the VR mobility assessment tool parameters can document longitudinal changes in functional vision and serve as a suitable outcome measure for clinical trials in participants with advanced retinal disease. Study Population: Up to 120 participants with retinal disease and 45 healthy volunteers will be recruited. The upper limit of 120 participants with retinal disease was chosen to allow approximately equal groups of 60 participants with rod-cone degeneration (RCD) and 60 participants with cone-rod degeneration (CRD) to represent groups of participants with peripheral visual field constriction and central vision loss, respectively. A total of 60 per group was chosen to A) allow feasibility to be determined across age groups (e.g., 5-11 yrs., 12-50 years, over 50 years) and B) to allow for a sufficient range of disease severity to examine VR mobility test sensitivity. The number of healthy volunteers (N=45) was chosen to provide about 15 participants across each of three age groups. Design: In this multi-site observational study, VR mobility testing will be performed in participants with retinal disease. While the ultimate goal is to use this for advanced retinal disease, in the current study we will examine participants with a wide range of retinal disease severity to enable correlations between VR mobility parameters and markers of disease severity (e.g., field size, mobility scores from questionnaires). This analysis will also help determine the range of retinal disease severity for which VR mobility will be useful. Based on the simulation studies, we predict that participants should be able to repeat the VR course between four to eight times in a one-hour session. Testing will also include best corrected visual acuity (BCVA), visual fields, optical coherence tomography (OCT), autofluorescence imaging, ultra-widefield imaging and participant reported outcome (PRO) questionnaires. Two tests of photosensitivity, Visual Photosensitivity Threshold (VPT) and Palpebral Aperture Measurement (PAM) will also be recorded in a subset of participants known to be photosensitive (e.g., albinos, achromats, and CRD), and healthy volunteers at visit 001. Participants will be required to attend two to three clinic visits within three months. VR and photosensitivity testing will be the focus of the second and third clinic visit in order to A) examine the learning effect and B) quantify test-retest variability of VR and photosensitivity test parameters. Outcome Measures: The primary outcome is to determine whether parameters from a recently developed VR mobility tool can serve as biomarkers of functional vision in participants with retinal disease. To this end, we will examine the correlation between VR mobility test parameters (e.g., accuracy, task time) and the mobility score from a PRO questionnaire/s. A secondary outcome is to examine the correlation between the VR mobility test parameters and clinical measures of retinal structure and function (e.g., visual acuity, non-seeing area). Other secondary outcomes include quantifying the learning effect and test-retest variability of the VR test parameters, exploring the feasibility of the tool based on age and presence of physical disabilities, determining the sensitivity of VR mobility test parameters to the presence and severity of retinal disease, determining the brightest background at which participants who experience photoaversion can navigate the VR maze, and determining whether prior or present computer game playing (e.g., number of hours, type of games played, computer game platform) influences baseline performance on the VR mobility tool.

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Australia, United States