The aim of this deliverable is to demonstrate the final version of the Virtual Coach (VC) component including all clinical and non-clinical coaching interventions that are used at both sites – ICSM and NKI – participating in clinical tests of the CAPABLE system. We would like to note that VC provides coaching related to adverse events associated with therapies of various cancer types, thus it goes beyond the two initially assumed use cases of renal cell carcinoma (RCC) and melanoma. The detailed presentation of VC at the release candidate (RC) stage, including the actor-model-based architecture and the two-level hierarchy of computer-interpretable guidelines (CIGs), was provided in deliverable D5.5, therefore for the sake of brevity in this document, we focus on new elements introduced in the last phase of the development and refer the reader to relevant parts in D5.5 if necessary. Specifically, these new elements include motivational and administrative workflows and rules (e.g., motivational messages for My Usual Walk capsule, rewards for performing capsules, psychological and nutritional support), as well as architectural components and external tools for improving the reliability of VC. This demonstration comprises a textual description and a video. The text in this document summarizes the final state of VC and provides details on the new developments mentioned above. Selected – most relevant and interesting – new elements are then presented in detail and demonstrated in the video available on the CAPABLE project website. The structure of this document is as follows. In Section 3 we define the scope of demonstration. In Section 4 we provide an overview of the final version of VC considering its architecture, available CIGs and possible intervention and data enquiry types. In Section 5 we present the external monitoring and notification system that oversees VC in addition to its internal architectural components. Finally, we conclude with the summary in Section 6.

This deliverable is a part of a project receiving funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 875052