Digital Twins and the Emerging Science of Self: Implications for Digital Health Experience Design and “Small” Data - Schwartz, Steven M.; Wildenhaus, Kevin; Bucher, Amy; Byrd, Brigid (Frontiers in Computer Science, 2020)
The technology currently available for quantifying various biometric, behavioral, emotional, cognitive and psychological aspects of daily life has become increasingly diverse, accurate and accessible. Continued improvements are ongoing. These burgeoning technologies can and will profoundly alter the way lifestyle, health, wellness and chronic diseases are managed in the future. For those pursuing the potential of such digital technologies in the creation of compelling and effective connected healthcare experiences, a number of new concepts have surfaced. We have taken these concepts (many of which originate in engineering) and extended them to be incorporated into managing health risk and health conditions via a blended digital health experience. For example, the advent of mobile technology for health has given rise to concepts such as ecological momentary assessment (EMA) and ecological momentary intervention (EMI) that assess the person’s (digital twin) status and delivers interventions as needed when needed. For such concepts to be fully realized the experience design of mHealth program(s) (aka connected care) should and now can actually guide the end user through a series of self-experiments directed by data-driven feedback from a version of their digital twin. As treatment development and testing moves towards the precision of individual differences inherent in every person and every treatment response (or non-response) group data and more recent big data approaches for generating new knowledge offer limited help to end-users (including practitioners) for helping individuals evaluate their own digital twin generated data and change over time under different conditions. This is the renaissance of N-of-1 or individual science. N-of-1 evaluation creates the opportunity to evaluate each individual uniquely. The rigor and logic of N-of-1 designs have been well articulated and expanded upon for over a half-century. For the clinician, this revitalized form of scientific and behavioral interaction evaluation can help validate or reject the impact a given treatment has for a given patient with increased efficiency and accuracy. Further, N-of-1 can incorporate biological (genomic), behavioral, psychological and digital health data such that users themselves can begin to evaluate the relationships of their own treatment response patterns and the contingencies that impact them. Thus, emerges the self-scientist.