Use of Virtual Reality Simulation to Identify Vision-Related Disability in Patients With Glaucoma

Abstract

Importance: Clinical assessment of vision-related disability is hampered by the lack of instruments to assess visual performance in real-world situations. Interactive virtual reality (VR) environments displayed in a binocular stereoscopic VR headset have been designed, presumably simulating day-to-day activities to evaluate vision-related disability.

Objective: To investigate the application of VR to identify vision-related disability in patients with glaucoma.

Design, setting, and participants: In a cross-sectional study, 98 patients with glaucoma and 50 healthy individuals were consecutively recruited from a university eye clinic; all participants were Chinese. The study was conducted between August 30, 2016, and July 31, 2017; data analysis was performed from December 1, 2017, to October 30, 2018.

Exposures: Measurements of visual acuity, contrast sensitivity, visual field (VF), National Eye Institute 25-item Visual Function Questionnaire Rasch score, and VR disability scores determined from 5 VR simulations: supermarket shopping, stair and city navigations in daytime, and stair and city navigations in nighttime. Duration required to complete the simulation, number of items incorrectly identified, and number of collisions were measured to compute task-specific and overall VR disability scores. Vision-related disability was identified when the VR disability score was outside the normal age-adjusted 95% confidence region.

Main outcomes and measures: Virtual reality disability score.

Results: In the 98 patients with glaucoma, mean (SD) age was 49.8 (11.6) years and 60 were men (61.2%); in the 50 healthy individuals, mean (SD) age was 48.3 (14.8) years and 16 were men (32.0%). The patients with glaucoma had different degrees of VF loss (122 eyes [62.2%] had moderate or advanced VF defects). The time required to complete the activities by patients with glaucoma vs healthy individuals was longer by 15.2 seconds (95% CI, 5.5-24.9 seconds) or 34.1% (95% CI, 12.4%-55.7%) for the shopping simulation, 72.8 seconds (95% CI, 23.0-122.6 seconds) or 33.8% (95% CI, 10.7%-56.9%) for the nighttime stair navigation, and 38.1 seconds (95% CI, 10.9-65.2 seconds) or 30.8% (95% CI, 8.8%-52.8%) for the nighttime city navigation. The mean (SD) duration was not significantly different between the glaucoma and healthy groups in daytime stair (203.7 [93.7] vs 192.9 [89.1] seconds, P = .52) and city (118.7 [41.5] vs 117.0 [52.3] seconds, P = .85) navigation. For each decibel decrease in binocular VF sensitivity, the risk of collision increased by 15% in nighttime stair (hazard ratio [HR], 1.15; 95% CI, 1.08-1.22) and city (HR, 1.15; 95% CI, 1.08-1.23) navigations. Fifty-eight patients (59.1%) with glaucoma had vision-related disability in at least 1 simulated daily task; a higher proportion of patients had vision-related disability in nighttime city (27 of 88 [30.7%]) and stair (27 of 90 [30.0%]) navigation than in daytime city (7 of 88 [8.0%]) and stair (19 of 96 [19.8%]) navigation. The overall VR disability score was associated with the National Eye Institute 25-item Visual Function Questionnaire Rasch score (R2 = 0.207).

Conclusions and relevance: These findings suggest that vision-related disability is associated with lighting condition and task in patients with glaucoma. Virtual reality may allow eye care professionals to understand the patients' perspectives on how visual impairment imparts disability in daily living and provide a new paradigm to augment the assessment of vision-related disability.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Lam reported receiving grants from the Innovation and Technology Commission, the Government of Hong Kong Special Administration Region (HKSAR), and The Chinese University of Hong Kong during the conduct of the study; in addition, Dr Lam reported having had patent application US Non-Provisional Application No. 15/466,348 pending, related to the virtual reality technology used in this study. Dr To reported receiving grants from the Innovation and Technology Commission Innovation and Technology Fund, Technology Start-up Support Scheme for Universities, The Chinese University of Hong Kong Knowledge Transfer Project Fund, and The Chinese University of Hong Kong Technology and Business Fund during the conduct of the study; in addition, Dr To reported having had patent application US Non-Provisional Application No. 15/466,348 pending, related to the virtual reality technology used in this study. Dr Weinreb reported receiving personal fees from Aerie Pharmaceuticals, Allergan, Eyenovia, and Implantdata; and nonfinancial support from Heidelberg Engineering, Carl Zeiss Meditec, Genentech, Konan, Optovue, Topcon, Optos, Centervue, and Bausch & Lomb outside the submitted work. Dr Leung reported receiving grants from HKSAR Innovation and Technology Commission and The Chinese University of Hong Kong during the conduct of the study; in addition, Dr Leung reported having had patent application US Non-Provisional Application No. 15/466,348 pending, related to the virtual reality technology used in this study. No other disclosures were reported.

Figures

Figure 1.. Virtual Reality Performance in the Supermarket Shopping Simulation

A, Screen captures from the supermarket shopping virtual reality simulation. B, Comparison of the duration to complete the simulation between patients with glaucoma and healthy individuals. C, Comparison of the number of incorrect selections between patients with glaucoma and healthy individuals. Error bars indicate SE.

Figure 2.. Virtual Reality Simulations of Stair Navigation

Top view of stair navigation path and screen captures (shown in inserts) from the daytime (A) and nighttime (B) virtual reality simulations. To minimize learning effects, the types and locations of the obstacles in the navigation tasks vary in each round of simulation (shown in inserts). Comparisons of the duration to complete the navigation (C) and the number of collisions (D) between the glaucoma and healthy groups in the daytime and nighttime simulations show that patients with glaucoma fared worse in nighttime than daytime navigation. Error bars indicate SE.

Figure 3.. Virtual Reality Simulations of City Navigation

Top view of city navigation path and screen captures (shown in inserts) from the daytime (A) and the nighttime (B) virtual reality simulations. To minimize learning effects, the types and locations of the obstacles in the navigation tasks vary in each round of simulation (shown in inserts). Comparisons of the duration to complete the navigation (C) and the number of collisions (D) between the glaucoma and healthy group in the daytime and nighttime simulations show that patients with glaucoma fared worse in nighttime than daytime navigation. Error bars indicate SE.

Figure 4.. Identifying Patients With Vision-Related Disability

The example shows the performance locations of all healthy individuals (blue dots) with reference to a multivariate, 3-dimensional space constructed from the variables measured from the supermarket shopping simulation and age. The 95% confidence region is demarcated by an ellipsoid. The performance locations of 2 patients with glaucoma (aged 60 years) (red dots) are outside the age-adjusted boundary (black curvilinear line), suggesting that they had vision-related disability. The severity of vision-related disability can be inferred from the Mahalanobis distance (dotted red lines), which is a unitless, scale-invariant measure taking the correlations of the different parameters in the multivariate space into consideration. The Mahalanobis distance is measured between the performance location of an individual and the centroid axis of the healthy group at a specific age.