Human Factors in Healthcare (Jun 2024)
Developing feedback visualizations to support older adults’ medication adherence
Abstract
Background: Blood pressure control is critical for older adults because the prevalence of hypertension and resulting cardiovascular illness increases with age. Antihypertension medications are central to blood pressure treatment. However, nonadherence to antihypertension medications is high. Health technology such as smartphone apps provide an opportunity for users to manage their medication regimen and support processes related to medication-taking. Objective: We implemented a user-centered evaluation approach to develop and refine adherence feedback visualizations for the MEDSReM© medication adherence app for older adults with hypertension. Methods: We conducted a literature review and iterative usability testing to achieve this objective. We identified adherence goals, information needs, as well as design guidelines by reviewing theoretical frameworks and existing scientific evidence. We then used a two-phase iterative user-centered study and subject matter expert evaluation. Both quantitative and qualitative data were used to select and improve the current prototype and evolve to the next prototype. Results: The need for daily, weekly, and monthly adherence performance information as well as visualization formats for conveying this information was identified from the literature review. Overall, the information shown in visualization prototypes was successfully interpreted by participants. Comprehension issues of visualizations were identified and addressed from visual prototype revisions. Insights from both user and subject matter expert groups were used to select and refine the prototypes for the MEDSReM app. Conclusion: Evidence-based and user-centered approaches were effective for developing visualizations about adherence performance feedback in the MEDSReM app and provided insight into how the app can be made easy to understand and use by older adults with hypertension, which will be evaluated in future effectiveness testing.
