Centering Disabled User Experience Replication Data

Study Information<b>Study Title:</b> <br>Centering the Disabled User Experience of Health Information in a World Driven by Artificial Intelligence: A Mixed Methods Investigation<br><b>Study Description:</b> <br>Disabled people are more susceptible to infectious diseases (like COVID-19) than nondisabled people; finding accurate, relevant health information is especially pressing. Internet search technologies are often touted as empowering disabled people who seek healthcare information online, but does this depiction reflect reality? This dissertation encompasses three studies assessing disabled user experiences in finding health information online: a structured literature review, a cross-sectional survey, and a concurrent think-aloud study. The literature review shows that, across 11 articles, disabled people were often not considered in public health messaging surrounding COVID-19. We then analyze 142 cross-sectional survey responses about usability and satisfaction regarding web-based COVID-19 information. Usability and satisfaction were both lower in people who had developmental or mental health disabilities (r=-0.21, p=0.0131 for usability; r=-0.24, p=0.0111 for satisfaction). Satisfaction was also lower among screen magnification or closed caption users (r=-0.21, p=0.0262). In the concurrent think-aloud study, ten participants were asked to internet search four prompts and narrate their experiences in real-time. Themes included concerns about accessibility/usability, AI-generated information, peer-reviewed articles, hospital or government webpages, news/advertising, and sentiment/trust. Participants also reported physical fatigue (n=5) and distracting layouts (n=5) while searching online. All participants encountered AI-generated information in their searches. This dissertation ends in reflection on how future work by scholars in health and information sciences should be shaped by the input of disabled people.<br><b>Study Author:<br></b>Sonal Sathe<br><br><b>Study URL: </b>https://hdl.handle.net/10919/139899Dataset Information<b>Dataset Title:<br></b>Replication Data for Centering the Disabled User Experience of Health Information in a World Driven by Artificial Intelligence<br><b>Dataset Description:<br></b>The data in this archive can be used to replicate reported findings for Chapter 3 in the dissertation listed above. Data were collected using an online QuestionPro survey of adults living in the US state of Virginia from March to May of 2025, with a goal of recruiting similar numbers of (self-identified) disabled and non-disabled participants. Respondents were recruited through a mixed convenience sample using fliers and email distribution lists through universities, healthcare offices, and disability groups in Virginia. The survey focused on respondent disability status and the usability of accessibility tools and health nformation tools, with particular reference to Covid-19 related information seeking. In addition to demographic questions and rosters of disability types and accessibility tool types, the survey included adapted versions of the System Usability Scale (Deshmukh &amp; Chalmeta, 2024) and Health-ITUES (Schnall et al, 2018).<br>Prior to archiving, data were anonymized by the removal of potentially identifying information and a reduction in the number of categories of certain demographic variables. Scales were also adjusted prior to archiving by reverse coding items scored in the negative direction. A full codebook with original question wordings and coded values can be found in `codebook.txt`.<br><b>Dataset correspondence to:</b> <br>Nathaniel D. Porter ([e-mail])File manifest`data_analysis.R`: R code to replicate analyses`data_preparation.R`: R code originally used to anonymize and clean the data (requires raw data not included in archive; provided for transparency)`readme.Rmd`: project and file information`codebook.txt`: plain text codebook file`Disabled_UXR_clean_data.csv`: anonymized replication datafileReferencesDeshmukh, A. M., &amp; Chalmeta, R. (2024). Validation of system usability scale as a usability metric to evaluate voice user interfaces. PeerJ Computer Science, 10, e1918. https://doi.org/10.7717/peerj-cs.1918<br>Schnall, R., Cho, H., &amp; Liu, J. (2018). Health Information Technology Usability Evaluation Scale (Health-ITUES) for Usability Assessment of Mobile Health Technology: Validation Study. JMIR mHealth and uHealth, 6(1), e4. https://doi.org/10.2196/mhealth.8851