IMPROVING THE DIGITAL ACCESSIBILITY OF HEALTH DATA VISUALIZATIONS TO BETTER SUPPORT THE GRAPHICACY SKILLS OF ADULTS AND OLDER ADOLESCENTS WITH DOWN SYNDROME

Abstract

Inaccessible digital health information can have serious consequences. As health systems becomemore data-driven, accurately interpreting visual representations of numbers is crucial. While health data visualizations (HDVs) can help some people understand complex information, others lack the necessary skills to use them. Data visualization literacy, or graphicacy, is an increasingly essential digital skill. For people managing chronic conditions or complex medical histories, inaccessible HDVs limit their ability to understand their health, act on information, and advocate for themselves. Although HDVs are common in digital health tools, they are rarely designed with cognitive accessibility in mind. For those with intellectual and developmental disabilities (IDDs), such as Down Syndrome (DS), inaccessible health data worsens existing healthcare disparities. This dissertation aims to improve the cognitive accessibility of HDVs with adults and older adolescents with DS. . In the first study, the author, together with a self-advocate with DS, co- developed accessible participant-facing materials, activities, and processes to ensure independent feedback. The second study, which used semi-structured interviews, found that most participants used devices independently and often provided tech support to others. The study also revealed that social features in health systems and non-adaptive parental controls often traded autonomy for digital safety. A researcher-administered questionnaire and semi-structured interview found that while participants performed well on basic graph reading, their comprehension declined as the graph-reading task became more complex or ambiguous. Grounded theory identified design and task barriers in HDVs that made inference-making harder. In three co-design workshops, participants grouped and sorted health objects (Talismans) and created graph-based art (Workshop 1), used improvisation to explore teaching and storytelling (Workshop 2), and played games like reverse Jeopardy to identify salient clues and connect ideas between questions and answers (Workshop 3). Reflexive Thematic Analysis (RTA) showed that participants’ subjective experiences influenced them most during unfamiliar or complex tasks. Many used strategies to manage cognitive load, but when they hit a personal limit—like tension or conflict with their understanding—they often switched from inference-making to sense-making or using shortcuts. These studies challenged assumptions about the abilities of people with Down Syndrome in HCI. These studies challenged commonly held assumptions in human-computer interaction (HCI) research about the abilities of people with Down Syndrome. The dissertation contributed a DS-specific cognitive accessibility profile for (health) data visualizations and its broader implications for HCI research.