Actionable Biosensing for Real-Time Monitoring and Decision Support in Inflammatory Rheumatic Diseases

Inflammatory rheumatic diseases exhibit dynamic and heterogeneous inflammatory activity, yet clinical monitoring remains episodic and temporally sparse, limiting early intervention and delaying treatment adjustment. Advances in biosensing technologies, wearable monitoring, and computational modeling offer opportunities to transition toward continuous, data-driven disease assessment. In this review, we synthesize evidence across rheumatology, immunology, biosensing, and digital health to examine how multimodal measurement approaches can support clinically actionable decision-making. We introduce a structured framework—the “Measurement Stack”—that links three components: biological signal domains (systemic, synovial, imaging-derived, and physiological), sensing platforms with distinct temporal and specificity trade-offs, and computational inference layers including feature extraction, multimodal data integration, and predictive modeling. We emphasize that the clinical value of biomarkers depends not on association alone but on actionability, defined by temporal sensitivity, repeatability, robustness to heterogeneity and signal noise, and alignment with clinical decisions. Key methodological considerations include feature engineering for sparse and continuous data, handling missingness and signal drift, calibration-aware validation, temporal and external validation, and decision-curve analysis for clinical utility. A decision-centric mapping aligns measurement and modeling strategies with clinical tasks such as early flare detection, differentiation of flare from infection, therapy switching or tapering, and monitoring of treatment response. By integrating biosensing advances with clinically grounded evaluation standards, this review outlines pathways toward interpretable, deployment-ready monitoring systems enabling proactive and personalized management of inflammatory rheumatic disease.