Fit-Twin: A Digital Twin of a User with Wearables and Context as Input for Health Promotion

Abstract

Digital health contributes to health promotion by empowering the user with the holistic view of their health. Health promotion is to enable the user to take control over their health. The availability of wearables has contributed to the shift in healthcare, that is more connected, predictive, and proactive. Proactive in healthcare is to predict and prevent a situation, beforehand. This shift in healthcare puts the user in charge of most healthrelated decisions. Innovative technologies like AI already contribute to the cause by applying reasoning and negotiation to the collected health data to provide timely interventions to the user. The availability of realtime data from sensors that the user wears all the time allows more opportunities with new health insights. One such prospect is the use of digital twins, which provides personalization and precision. Digital twins also allow risk-free modelling for more accurate outcomes. A user digital twin is not just a virtual replica, but it combines all the factors that can impact the user. The context of the user is a prominent factor in healthcare. The paper establishes the need for digital twins in health promotion. In this paper, a Fit-twin is presented that mimics a user with wearables and the user context as input. The Fit-twin is implemented using Azure digital twins, Fitbit charge, and local context API. This allows one-way communication between the user and the Fit-twin. The outcome is a user digital twin that can be used for health promotion by applying predictive capabilities.