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

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.

💡 Research Summary

The paper addresses a critical gap in digital health (DH) software development: the frequent neglect of the specific needs of older adults who also live with various health conditions. While many DH products meet clinical objectives, they often fail to be inclusive during real‑world use because development teams rely on implicit, sometimes incorrect assumptions about users’ capabilities and preferences. To remedy this, the authors introduce HealthMag, a new “Magnifier” method inspired by GenderMag, and combine it with a calibrated version of AgeMag to create a dual‑lens framework called Elderly HealthMag.

Methodology
The research proceeds in three parallel streams.

1. HealthMag development: A systematic literature review (SLR) of over 130 studies spanning digital‑health surveys, psychology, and social‑science research identified 16 candidate health‑related facets (e.g., health self‑efficacy, trust/privacy concerns, technology proficiency, continuity of care, fatigue/pain). Through expert interviews and iterative ranking, these were narrowed to a concise set of five core facets, each defined with clear value ranges (low, medium, high). These facets capture interaction‑relevant consequences of health status without requiring clinical diagnoses.
2. Elderly AgeMag calibration: Existing AgeMag, which already models five age‑related facets (cognitive load, sensory ability, motor ability, tech familiarity, social support), was refined for the 65+ population, adjusting definitions and value scales to reflect typical age‑related changes.
3. Persona creation: Large language models generated initial persona drafts based on the combined facet sets and population data (e.g., Australian Institute of Health and Welfare statistics). Domain experts and software practitioners then reviewed, edited, and validated the drafts, resulting in three “flexible” personas that each embody a distinct combination of health and age facet values.

The three outputs are integrated into Elderly HealthMag, which pairs the health‑focused and age‑focused lenses, attaches the personas, and provides a specialized cognitive‑walkthrough questionnaire. The questionnaire prompts evaluators at each sub‑task to ask facet‑specific questions (e.g., “If the user has low health self‑efficacy, does the error‑recovery flow provide additional guidance?”). By doing so, the method makes hidden biases observable and traceable to concrete design requirements.

Empirical Evaluation
The authors applied Elderly HealthMag to two widely used DH applications targeting older adults: (1) a medication‑adherence app and (2) a remote‑consultation scheduling app. Using the dual‑lens walkthrough, they identified 27 inclusivity bugs; 18 of these emerged only when health facets were considered alongside age facets, demonstrating the value of the intersectional approach. Typical issues included:

• Overly complex error‑recovery steps for users with low health self‑efficacy.
• Privacy‑setting dialogs that were too intricate for users with high privacy concern and limited continuity of care, leading to abandonment.
• Long waiting times or unresponsive feedback that exacerbate fatigue or pain, causing disengagement.

For each bug, the authors proposed concrete design remedies (e.g., progressive disclosure of guidance, simplified privacy controls, adaptive timers that shorten waiting periods when fatigue is detected). The walkthrough also generated updated requirement statements reflecting the combined health‑age constraints.

Contributions

1. A synthesized evidence base of 130+ studies yielding five validated health facets relevant at interaction time.
2. The HealthMag framework and its integration with a calibrated Elderly AgeMag, forming the first dual‑lens Magnifier for senior DH users.
3. Three reusable, facet‑aligned personas that can be adapted across DH projects.
4. Demonstrated utility through cognitive walkthroughs that uncovered previously hidden bias and guided actionable design changes.

Limitations and Future Work
The study focuses on Australian older adults; cross‑cultural validation is needed. Facet value calibration relies heavily on expert judgment, which may limit generalizability. Future research should (a) test Elderly HealthMag in diverse linguistic and cultural contexts, (b) explore automation (e.g., tool support that flags potential bias during backlog grooming), and (c) assess long‑term clinical outcomes of designs informed by the method.

In summary, Elderly HealthMag offers a systematic, evidence‑based, and pragmatic approach for software engineers and UX professionals to capture health‑related requirements, detect assumption‑driven bias, and design more inclusive digital health solutions for the growing senior population.