Android-Centric Data Integration Framework for Accessible Mobile Health Monitoring Applications

Remote patient monitoring (RPM) increasingly relies on mobile platforms for continuous health data capture, yet many Android-based mHealth applications remain inaccessible to users with disabilities due to fragmented interaction design and insufficient integration of multimodal data sources. This paper presents an Android-centric data integration and accessibility framework that unifies adaptive user interfaces, voice-driven interaction, and wearable sensor data pipelines to support inclusive remote patient care. First, a domain-specific accessibility barrier taxonomy is developed to characterize challenges across perceptual, motor, cognitive, and communicative dimensions in Android mHealth environments. Second, a data integration architecture is proposed that combines real-time wearable data ingestion, voice interaction services, and context-aware UI adaptation driven by a user capability model. Third, an Android-based prototype implementation is evaluated across multiple disability cohorts, demonstrating significant improvements in task completion rates, interaction efficiency, and error reduction compared to baseline applications. Results show improvements of over 30–50% in task success rates and substantial reductions in interaction time, highlighting the importance of tightly coupling data integration with adaptive interface design. The findings establish that accessibility in Android mHealth systems is not solely a compliance requirement but a system-level design objective, where integrated data pipelines and adaptive interaction models are essential for delivering reliable and inclusive healthcare services.