Invisible Load: Uncovering the Challenges of Neurodivergent Women in Software Engineering

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 aims to identify the key factors influencing user preferences for mHealth adaptation designs and examine trade-offs users make when choosing between different adaptation features. 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.

Submission

ICSE-SEIS ’26

Citation

Munazza Zaib, Wei Wang, Dulaji Hidellaarachchi, and Isma Farah Siddiqui. 2026. Invisible Load: Uncovering the Challenges of Neurodivergent Women in Software Engineering. In 2026 IEEE/ACM 48th International Conference on Software Engineering (ICSE-SEIS ’26), April 12–18, 2026, Rio de Janeiro, Brazil. ACM, New York, NY, USA, 5 pages.