From Data to Decisions in Maternal Health: Wearables, Biomarkers and Intelligent Analytics Across the Care Continuum

Maternal health generates increasingly diverse sources of information, from electronic health records and laboratory biomarkers to wearable sensors, patient-reported outcomes, environmental exposures, and social determinants of health. Yet these data often remain fragmented across clinical encounters, technologies, and healthcare systems. The opportunity is therefore not simply to generate more maternal-health data, but to determine how heterogeneous and longitudinal information can be integrated, interpreted, and translated into timely, equitable, and actionable decisions across the maternal-care continuum. This Research Topic seeks interdisciplinary contributions examining the use of wearable and connected technologies, physiological and molecular biomarkers, digital biomarkers, data analytics, artificial intelligence, and decision-support approaches to improve maternal health from preconception and pregnancy through childbirth, postpartum recovery, and longer-term maternal health. Particular attention will be given to approaches that move beyond prediction alone toward clinically meaningful interpretation, early warning, personalized intervention, health-system integration, and measurable maternal health outcomes. Particular areas of interest include:Wearables and continuous maternal monitoring: maternal cardiovascular signals, activity, sleep, stress, glucose, blood pressure, temperature, and other longitudinal physiological measures. Biomarkers and multimodal maternal data: laboratory, metabolic, inflammatory, placental, genomic/omics, imaging, and physiological biomarkers integrated with clinical and behavioral information. Predictive and explainable analytics: ML, Bayesian models, time-series analytics, XAI, and multimodal models for early identification of maternal deterioration, hypertensive disorders, gestational diabetes, preterm birth, hemorrhage, infection, readmission, and other maternal risks. Digital biomarkers and early-warning systems: transforming continuous real-world signals into validated indicators of maternal risk, recovery, and changing health status. Decision intelligence and personalized maternal care: translating predictions into clinical decision support, care pathways, resource allocation, personalized interventions, and appropriate escalation. Implementation, equity, and trust: validation across populations, health disparities, accessibility, low-resource settings, privacy, data governance, responsible AI, clinician/patient trust, and integration into healthcare workflows. Topic editors: Mohammed Omar, Samer Ellahham Source: https://www.frontiersin.org/research-topics/86756/from-data-to-decisions-in-maternal-health-wearables-biomarkers-and-intelligent-analytics-across-the-care-continuum