A1851
Title: Sequential mixed-methods evaluation of AI-assisted digital health platforms in dementia care
Authors: Qiping Fan - Clemson University (United States) [presenting]
Abstract: A sequential mixed-methods statistical framework was developed and applied to evaluate AI-assisted digital health platforms in dementia care. Grounded in the Build-Measure-Learn iterative design, the framework integrated qualitative and quantitative approaches across iterative development cycles. The framework was implemented across Two study phases spanning 2022 to 2025. Phase One employed qualitative thematic analysis from in-depth interviews to characterize caregiver needs across financial, legal, and functional domains, followed by instrument-based usability assessment to guide platform refinement. Phase Two applied regression-based modeling to examine technology acceptance as a function of sociodemographic and behavioral covariates among a nationally recruited sample, complemented by qualitative thematic analysis to capture user-reported experiences and attitudes toward AI integration. Quantitative and qualitative findings were integrated at the interpretation stage to assess platform usability and acceptance across successive development iterations. Implications for statistical methodology in digital health platform evaluation within neurodegenerative disease and neuroscience research contexts are discussed.
