Smartphone-based Digital Phenotyping
Behavior has traditionally been a difficult phenotype to characterize because of its temporal nature and context dependence. Traditionally it has been captured using either self-reported accounts of behavior or clinician-administered instruments. Both are subjective, qualitative, and typically cross-sectional. We have previously defined digital phenotyping as the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices, in particular smartphones. Smartphone-based digital phenotyping can give rise to temporally dense, longitudinal measurement of behavioral in naturalistic or free-living settings. I will discuss the concept of digital phenotyping and will introduce Beiwe, our open source research platform for high-throughput, smartphone-based digital phenotyping. I will show some applications of Beiwe in various clinical settings. I will also consider some of the statistical and computational challenges that arise in this line of research.
Jukka-Pekka “JP” Onnela is Associate Professor of Biostatistics in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health, Harvard University. He is also the Director of the Master’s Program in Health Data Science, which is one of the three data science programs at Harvard. He obtained his doctorate in network science in physics in Finland, and his doctoral dissertation received the Dissertation of the Year Award from the university. Prior to starting his faculty position at Harvard in November 2011, he completed a junior research fellowship at the University of Oxford, a Fulbright scholarship at Harvard Kennedy School, and a postdoctoral fellowship at Harvard Medical School. His main interest is in developing quantitative methods in two areas: statistical network science, the study of network representations of social and biological systems, and digital phenotyping, the moment-by-moment quantification of the individual-level human phenotype using smartphone data. He received the prestigious NIH Director’s New Innovator Award in 2013 for his digital phenotyping project.