Although the significance of big data and data science in predicting health outcomes and identifying causal factors is widely recognized, their application in health disparities research remains limited. Understanding health disparities in the visually impaired population requires examining their health behavior patterns and health literacy levels, which can longitudinally impact their health and well-being. In prior research, one of the authors of the study conducted an online survey with 2718 participants using 5 validated self-reported questionnaires, such as Health Promoting Lifestyle Profile II, Health Literacy Questionnaire, eHealth Literacy Questionnaire, General Self-Efficacy, and Center for Epidemiological Studies Depression. Analysis of the online survey data demonstrated that individuals with blindness exhibited significantly higher levels of health-promoting behaviors, health literacy, and eHealth literacy compared to those with moderate and severe low vision. In this study, the research team developed an R Shiny web application as a follow-up to the online survey to disseminate its findings reproducibly and interactively. The R Shiny web application is expected to facilitate reproducible as well as interactive data analysis and sharing more efficiently than traditional methods, such as appendices or Supplementary Materials, Supplemental Digital Content 2, http://links.lww.com/CIN/A463 in academic journals. Extending the research cycle with open datasets and reproducible data analysis can deepen our understanding of health disparities and foster greater collaboration among researchers with similar interests.
Choi et al. (Wed,) studied this question.