ABSTRACT: The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.
INTRODUCTION: The field of digital health has become a multibillion dollar market, powering a paradigm shift by enabling the continuous capture of multimodal data including activity, sleep, vital signs, and contextual information. Novel machine learning applications are pioneering the conversion of these multimodal data into measures for health-related quality of life (QOL)–relevant symptoms like fatigue , stress , and depression [3,4]. These insights have the potential to enable improved care delivery  and a deeper understanding of patients’ lived experiences and better, more personalized medicines. However, important barriers remain to realize these benefits, both in technical and social aspects of real-world adoption.
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