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.