Review Article

Wearable-Derived Digital Biomarkers in Preventive and Personalized Medicine: Promise, Evidence, and Barriers to Clinical Translation

Abstract

Wearable technologies now permit near-continuous measurement of physiological and behavioral parameters under free-living conditions. Combined with advances in artificial intelligence (AI), these devices support the development of wearable-derived digital biomarkers that may shift healthcare from a reactive to a preventive and personalized model. This narrative review synthesizes current evidence on the technological foundations, AI-based signal processing, clinical applications, implementation challenges, and future directions of wearable-derived digital biomarkers. Evidence supports their use across cardiovascular disease, diabetes and obesity, neurological and mental health conditions, sleep and respiratory medicine, and remote patient monitoring, where continuous data streams can inform early detection, individualized risk prediction, treatment optimization, and clinical decision-making. We propose a conceptual framework describing how wearable sensing, continuous physiological data acquisition, and AI-based analytics translate into clinically actionable digital biomarkers. At the same time, we argue that enthusiasm has outpaced evidence: few candidate biomarkers have undergone prospective validation in diverse populations, analytical performance varies substantially across devices and skin tones, and demonstration of improved clinical outcomes remains rare. Data quality, validation, standardization, interoperability, algorithm transparency, privacy, cybersecurity, regulatory oversight, and equitable access all constrain clinical adoption. Emerging developments in explainable AI, multimodal data integration, digital twins, and predictive analytics may address some of these constraints. Wearable-derived digital biomarkers hold genuine potential for proactive, patient-centered, data-driven care, but realizing that potential will depend less on new sensors than on rigorous validation, standardization, and equitable implementation.

Authors affiliation:

Damilola Alabi (ORCID): College of Medicine, Ekiti State University, Ado-Ekiti, Nigeria
Anyebe Daniel Ameh (ORCID): College of Professional Studies, University of Denver, Denver, Colorado, United States.
Deborah Ave Okon (ORCID): Department of Public Health Sciences, New Mexico State University, Las Cruces, New Mexico, United States.

How to Cite: D. Alabi, A.D. Ameh and D.A. Okon. Wearable-Derived Digital Biomarkers in Preventive and Personalized Medicine: Promise, Evidence, and Barriers to Clinical Translation. Journal of Biomedicine and Biosensors, 6(3):25–36, 2026. https://doi.org/10.58613/jbb633