Abstract
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.
Highlights
Digital TwinsThe vision of a “quantified self ” really began with Gary Wolf and Kevin Kelly ( editors at Wired magazine) in 2007 (Wolf, 2007)
Raymond lives with his wife Jeanne of 38 years
It is generally recognized that regardless of the health condition(s) being managed, individuals are a rich source of experiential information about the signs and symptoms of disease, common and unique responses to interventions, and successes and failures for self-managing health, all within the context of everyday living. These expert patients (Tattersall, 2002; Cordier, 2014) and their primary caregivers are the ultimate source of information for patient-centered processes and outcomes as they are shaped by each individual’s experience of illness, social circumstances, attitudes to risk, values, preferences, and problem solving
Summary
The vision of a “quantified self ” really began with Gary Wolf and Kevin Kelly ( editors at Wired magazine) in 2007 (Wolf, 2007). Imagine if people had access to a similar set of selfgenerated biobehavioral information via a dashboard connected to the increasingly sophisticated and diverse set of commercially available devices, biosensors, technologies, and related data representing their own operational health and lifestyle These digital twins could be the by-product of a networked set of biosensors, wearables, peripherals, smart pill dispensers, smart inhalers, ingestible smart pills, implantable devices (e.g., implantable cardio defibrillators), smart injectors, smartphone applications, and/or smart speakers all connected to an intelligent home ecosystem. If the concept of a digital twin is currently conceivable with existing commercially ready digital health and therapeutics technologies, data derived from such enhanced selfmonitoring technology represents the individual’s digital phenotype (Onnela and Rauch, 2016; Huckvale et al, 2019) As such, this digital phenotype is the sum of an individual’s ad libitum behavior expressed through digital media (sensors, tools, devices, apps, and related software, such as machine learning or artificial intelligence, etc.) in vivo and in situ. The history of intelligence testing and the eugenics movement is a sober reminder (Gould, 1981) of what happens when the interpretive lens is not considered
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