Elderly HealthMag: Systematic Building and Calibrating a Tool for Identifying and Evaluating Senior User Digital Health Software

2026-01-30

AI Summary

This study addresses the lack of inclusivity in current digital health software, which often stems from erroneous assumptions about older adults and fails to accommodate their unique needs arising from age-related and health-related challenges. To bridge this gap, the authors propose Elderly HealthMag—a novel approach that adapts the InclusiveMag framework to the domain of geriatric digital health by integrating HealthMag and AgeMag methodologies into a “dual-lens” analytical system. This system employs systematic mapping, calibration procedures, and cognitive walkthroughs to systematically identify, model, and evaluate the inclusivity requirements and barriers faced by older users. Empirical findings demonstrate that the proposed method effectively uncovers inclusivity biases in existing applications and offers significant practical utility and innovation across the stages of requirements analysis, design, and evaluation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Humans and AI: AI for Accessibility
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

Abstract

Digital health (DH) software is increasingly deployed to populations where many end users live with one or more health conditions. Yet, DH software development teams frequently operate using implicit, incorrect assumptions about these users, resulting in products that under-serve the specific requirements imposed by their age and health conditions. Consequently, while software may meet clinical objectives on paper, it often fails to be inclusive during actual user interaction. To address this, we propose \textbf{\textit{HealthMag}}, a tool inspired by GenderMag designed to help better elicit, model and evaluate requirements for digital health software. We developed HealthMag through systematic mapping and calibration following the InclusiveMag framework. Furthermore, we integrated this with a calibrated version of an existing AgeMag method to create a dual-lens approach: \textbf{\textit{Elderly HealthMag}}, designed to aid requirements, design and evaluation of mHealth software for senior end users. We demonstrate application and utility of Age HealthMag via cognitive walkthroughs in identifying inclusivity biases in current senior user-oriented digital health applications.

Problem

• digital health
• elderly users
• inclusive design
• health conditions
• usability

Innovation

• Elderly HealthMag
• inclusive design
• digital health
• cognitive walkthrough
• AgeMag