Leveraging Discrete Choice Experiments for User-Centric Requirements Prioritization in mHealth Applications

TL;DR

This study applies a Discrete Choice Experiment to quantify how chronic-disease patients value Adaptive User Interfaces in mHealth apps, capturing trade-offs between six design attributes. Using a mixed logit model on 186 participants, it shows usability as the dominant driver, with controllability, adaptation frequency, and granularity also shaping adoption, while caregiver involvement and frequent changes can hinder acceptance. The analysis reveals substantial preference heterogeneity across health status, gender, age, and coping styles, and quantifies cross-attribute trade-offs via Marginal Rate of Substitution, informing practical design guidance for future adaptive mHealth applications. By framing requirements prioritization as a data-driven process, the work demonstrates a concrete pathway for integrating user preferences into SE design decisions and highlights avenues for extending DCEs to non-functional requirements and demographic-specific design paradigms.

Abstract

Mobile health (mHealth) applications are widely used for chronic disease management, but usability and accessibility challenges persist due to the diverse needs of users. Adaptive User Interfaces (AUIs) offer a personalized solution to enhance user experience, yet barriers to adoption remain. Understanding user preferences and trade-offs is essential to ensure widespread acceptance of adaptation designs. This study identifies key factors influencing user preferences and trade-offs in mHealth adaptation design. A Discrete Choice Experiment (DCE) was conducted with 186 participants who have chronic diseases and use mHealth applications. Participants were asked to select preferred adaptation designs from choices featuring six attributes with varying levels. A mixed logit model was used to analyze preference heterogeneity and determine the factors most likely influencing adoption. Additionally, subgroup analyses were performed to explore differences by age, gender, health conditions, and coping mechanisms. Maintaining usability while ensuring controllability over adaptations, infrequent adaptations, and small-scale changes are key factors that facilitate the adoption of adaptive mHealth app designs. In contrast, frequently used functions and caregiver involvement can diminish the perceived value of such adaptations. This study employs a data-driven approach to quantify user preferences, identify key trade-offs, and reveal variations across demographic and behavioral subgroups through preference heterogeneity modeling. Furthermore, our results offer valuable guidance for developing future adaptive mHealth applications and lay the groundwork for continued exploration into requirements prioritization within the field of software engineering.

Figures (4)

• Figure 1: Examples of prototype of content adaptation.
• Figure 2: Example question of the DCE
• Figure 3: Radar chart for subgroup comparison: This radar chart illustrates the Relative Attribute Importance (RAI) for three key attributes—controllability over adaptations, usability of the app, and granularity of adaptations—across subgroups based on gender, health condition, age, and coping mechanisms. Lines represent the relative weight each subgroup placed on each attribute. Values are point estimates for descriptive comparison only; no statistical tests were conducted.
• Figure 4: MRS relative to usability and its implications (N/A indicates no significant difference in preference for that attribute across subgroups).