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

TL;DR

Elderly HealthMag is a systematic tool inspired by GenderMag, designed to identify and evaluate inclusivity biases in digital health software for seniors, improving requirements and design processes.

Contribution

The paper introduces Elderly HealthMag, a novel dual-lens method combining HealthMag and AgeMag to enhance inclusivity in senior digital health applications.

Findings

Identified biases in existing senior health apps using Elderly HealthMag.

Demonstrated the tool's effectiveness through cognitive walkthroughs.

Provided a framework for systematic evaluation of inclusivity in digital health software.

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.