LAUSR

Digital phenotyping consists of moment-by-moment quantification of behavioral data from individual people, typically collected passively from smartphones and other sensors. Within the evolving context of precision health, digital phenotyping can advance the use of mobile health -based self-management tools and interventions by enabling more accurate prediction for prevention and treatment, facilitating supportive strategies, and informing the development of features to motivate self-management behaviors within real-world conditions. This represents an advancement in self-management science: with digital phenotyping, nurse scientists have opportunities to tailor interventions with increased precision. In this paper, we discuss the emergence of digital phenotyping, the historical background of ecological momentary assessment, and the current state of the science of digital phenotyping, with implications for research design, computational requirements, and ethical considerations in self-management science, as well as limitations.