Evolution of the digital biomarker ecosystem

The pursuit of digital biomarkers wherein signal outputs from biosensors are collated to inform health-care decisions continues to evolve at a rapid pace. In the field of neurodegenerative disorders, a goal is to augment subjective patient-reported inputs with patient-independent verifiable data that can be used to recommend interventive measures. For example, in the case of Alzheimer's disease, such tools might preselect patients in the presymptomatic and prodromal phases for definitive positron emission tomographic analysis, allowing accurate staging of disease and providing a reference point for subsequent therapeutic and other measures. Selection of appropriate and meaningful digital biomarkers to pursue, however, requires deep understanding of the disease state and its ecological relationship to the instrumental activities of daily living scale. Similar opportunities and challenges exist in a number of other chronic disease states including Parkinson's, Huntington's, and Duchenne's disease, multiple sclerosis, and cardiovascular disease. This review will highlight progress in device technology, the need for holistic approaches for data inputs, and regulatory pathways for adoption. The review focuses on published work from the period 2012–2017 derived from online searches of the most widely used abstracting portals.