A Modular Digital Health Architecture for Longitudinal Menstrual Cycle Monitoring: System Design and Formative Usability Evaluation

Background: Longitudinal menstrual cycle monitoring requires digital health systems capable of handling individual variability, irregular sampling, and incomplete real-world observations. Most consumer-focused menstrual-tracking applications depend on simplified calendar-based logic, offering limited support for transparent longitudinal data handling, interoperability, and the management of irregular real-world observations. Objective: This study presents the design, implementation, and formative evaluation of a non-clinical digital health infrastructure for longitudinal menstrual cycle monitoring, with an emphasis on modular system architecture, longitudinal data processing, and user-perceived usability. Methods: A modular digital health system was developed in accordance with separation-of-concerns and privacy-by-design principles, combining a backend analytical infrastructure with a mobile application interface. The architecture was designed to support longitudinal data acquisition, variability-aware processing, and extensibility while remaining independent of proprietary analytical services. System evaluation included technical and functional verification, formative usability assessment, and quality evaluation using the user version of the Mobile App Rating Scale (uMARS). Results: In the uMARS evaluation (N = 63), the mean total score across core domains was 3.11 ± 0.76. Information quality (3.44 ± 0.85) and functionality (3.27 ± 0.88) received the highest ratings, whereas engagement (2.83 ± 0.84) received the lowest, consistent with the system’s prototype character. Internal consistency was high (Cronbach’s α = 0.91), and sensitivity analysis restricted to female participants yielded results comparable to those of the full sample. Conclusions: The proposed system demonstrates the technical and functional feasibility of a modular digital health architecture for longitudinal menstrual cycle monitoring under heterogeneous real-world data conditions. The findings support the use of variability-aware and extensible monitoring infrastructures as a foundation for future applied research and iterative development of women’s digital health systems without making diagnostic or predictive clinical claims.