🤖 AI Summary
This study addresses the limitations of traditional health definitions—namely their inability to capture health’s dynamic, complex, and context-dependent nature. Methodologically, it integrates physiological sensing, behavioral tracking, and environmental contextual data to construct a continuous, multidimensional framework for health representation via multimodal digital biomarkers (MDBs). The contribution is twofold: first, MDBs are theorized not merely as technical extensions of digital biomarkers but as catalysts for an ontological and epistemological shift—from health as a static state to a variable process, and from an individual attribute to a relational phenomenon. Second, the study identifies critical ethical challenges arising from this shift, including the reconfiguration of epistemic authority in data-driven healthcare, ambiguity in accountability, and gaps in governance. Accordingly, it proposes an interdisciplinary theoretical framework that balances technical feasibility with ethical robustness, offering actionable guidance for policy development, clinical implementation, and algorithmic design. (149 words)
📝 Abstract
Multimodal digital biomarkers (MDBs) integrate diverse physiological, behavioral, and contextual data to provide continuous representations of health. This paper argues that MDBs expand the concept of digital biomarkers along the dimensions of variability, complexity and abstraction, producing an ontological shift that datafies health and an epistemic shift that redefines health relevance. These transformations entail ethical implications for knowledge, responsibility, and governance in data-driven, preventive medicine.