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.

翻译：数字健康软件正越来越多地部署于众多终端用户患有一种或多种健康状况的人群中。然而，数字健康软件开发团队在运作时常常基于对这些用户的隐性且不正确的假设，导致产品无法充分满足其年龄和健康状况所施加的特定需求。因此，尽管软件在纸面上可能达到临床目标，但在实际用户交互过程中往往缺乏包容性。为解决此问题，我们提出了**HealthMag**，这是一个受GenderMag启发而设计的工具，旨在帮助更好地引出、建模和评估数字健康软件的需求。我们遵循InclusiveMag框架，通过系统化映射与校准开发了HealthMag。此外，我们将其与现有AgeMag方法的校准版本相结合，创建了一种双重视角方法：**Elderly HealthMag**，旨在辅助面向老年终端用户的移动健康软件的需求分析、设计与评估。我们通过认知走查展示了Age HealthMag在识别当前面向老年用户的数字健康应用中的包容性偏见方面的应用与效用。