The Voice-EPI Framework: A Novel Voice-Enabled Digital Biomarker Platform for Dynamic Digital Precision Prevention

Abstract

Digital biomarkers have become central to the field of precision medicine and preventive care. Using technology to continuously assess behavioral, physiological, environmental, and psychological information can provide insight into the trajectory of a patient’s health, which is not possible through routine clinical visits. Digital biomarkers have many applications in cancer prevention, allowing the tracking of modifiable risk factors, screening activities, and health behavior adherence as well as assessing various aspects of the psychological domain. Nevertheless, current digital biomarker systems rely heavily on specific sensors, specialized medical devices, software platforms, and other technical requirements, which increase costs and make scaling difficult. This article presents a review of current methods of collecting digital biomarkers and introduces a novel concept of Voice-Enabled Dynamic Digital Precision Prevention (V-DDPP), which represents a new approach to collecting digital phenotypic information. The V-DDPP method combines voice recording with standardized conversations, reconstructed questionnaires, speech analysis, and artificial intelligence to produce multidimensional digital biomarkers using participant-owned smartphones. The V-DDPP framework provides a highly scalable, affordable, and globally applicable alternative to traditional wearable-based digital biomarker systems. This method will prove particularly valuable in implementing Dynamic Digital Precision Prevention programs in breast (DDPP-BC) and cervical cancer (DDPP-CC).