User-Centric Requirements Prioritization in mHealth Applications: Insights from a Discrete Choice Experiment

TL;DR

This study uses a discrete choice experiment to identify key user preferences and trade-offs influencing the adoption of adaptive features in mHealth applications for chronic disease management.

Contribution

It provides a data-driven analysis of user preferences, highlighting factors that promote or hinder adoption of adaptive mHealth interfaces, with insights into subgroup differences.

Findings

Usability, controllability, and infrequent adaptations promote adoption.

Frequent functions and caregiver involvement can reduce perceived value.

Preference heterogeneity varies across demographic groups.

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.