Design of Smart Head–Mounted Display Technology: A Convergent Mixed-Methods Study

Abstract

Introduction: The purpose of this study was to characterize functional impairments and human factor considerations that affect perceptions and preferences for head-mounted display (HMD) technology for adults with low vision and chronic eye disease. Methods: Through a convergent mixed-methods design, participants with visual impairments (age-related macular degeneration, diabetic retinopathy, glaucoma, or retinitis pigmentosa) were recruited. Participants completed the Impact of Vision Impairment (IVI) questionnaire, used commercially available HMDs (eSight, NuEyes, and Epson Moverio), and were interviewed. The IVI was used to identify groups with low, moderate, and high vision–related quality of life (VRQOL). Transcribed interviews were analyzed using a thematic approach. The survey and qualitative findings were integrated using mixed-methods joint display analysis. Results: Twenty-one participants were enrolled (mean age of 58.2 years, 57% male, median Snellen acuity of 20/40 [range: 20/20–hand movement]). An equal number ( n = 9) expressed a preference for eSight and NuEyes, while ( n = 3) preferred the Moverio. Participants emphasized ease of use, including HMD controls and screen, as common reasons for preference. Those with lower IVI well-being scores preferred eSight due to vision improvement. Those with moderate IVI well-being scores preferred NuEyes due to comfort and size. Those with high IVI well-being scores cited usability as the most important feature. Discussion: User preferences for HMD features were associated with VRQOL. A mixed-methods approach explained how varying degrees of visual impairment and HMD preferences were qualitatively related to usability at the individual level. Implications for Practitioners: To increase acceptance, new HMD development for low vision should focus on performance, usability, and human factors engineering. Although HMD technology can benefit individuals with low vision, device features and functions vary in meaningful ways based on vision parameters. Practitioners should be aware of how patient and device variations influence preferences when they recommend wearable systems and optimize training to harness these systems.