Large-scale assessment of needs in low vision individuals using the Aira assistive technology

Abstract

Purpose: To systematically evaluate the needs of low vision individuals through call data obtained through the Aira assistive technology system. Patients and methods: Aira (Aira Tech Corporation, La Jolla, CA, USA) is an on-demand assistive wearable technology designed for individuals with low vision. The user wears glasses with an integrated front-facing video camera that connects with a remote human agent who assists the user with the specified task. Call types, temporal characteristics, and duration of call were compared by gender and vision status (low vision, light perception, and blind). Chi-square tests, t-tests, ANOVA, linear regression and Poisson regression analyses were performed. Results: 878 subscribers placed 10,022 total calls (4759 female, 5263 male) over 3 months. The most common categories were reading (35%), navigation (33%), and home management (16%). The distribution of categories (χ2=49.3, p<0.001), duration (t=-7.59, p<0.0001) and time of call (χ2=37.4, p<0.001) differed by gender. The distribution of categories (χ2=61, p<0.001), duration (F=13.7, p<0.0001), and time of call (χ2=36.9, p<0.001) differed by vision status. Blind [adjusted IRR=1.68 (95% CI: 1.56-1.79)] and light perception users [adjusted IRR=1.43 (95% CI: 1.32-1.53)] had increased usage compared to low vision users. Women had higher usage than men [adjusted IRR=1.09 (95% CI: 1.04-1.13)]. Conclusion: To our knowledge, this is the first large-scale needs assessment of 878 low vision individuals over 10,022 calls. The most common categories were reading, navigation, and home management. Distribution of call types, duration, and time of call differed significantly by gender and vision status. Blind and light perception users had higher usage rates than those with low vision. Women had higher usage rates than men. This large-scale needs analysis of low vision individuals provides insight into utilization patterns across varying levels of vision loss and gender, which will guide future evolutions of assistive technology by tailoring future hardware and software upgrades.