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mHealth use for people with dementia is fraught with factors influencing its implementation in care and daily life. A better understanding of these factors may provide guidelines to inclusive design. This study aimed to assess whether factors gathered in a literature-based model could be validated by opinions of experts. On basis of a questionnaire as part of a larger study, experts identified barriers that they considered to be related to aging and dementia influencing mHealth use. Nineteen barriers that were mentioned by the dementia experts were covered in our literature-based model. No adaptions to the model were required. The dementia experts acclaimed three barriers to mHealth use that could not be mapped onto the framework: the unavailability of (informal) caregivers to support the mHealth use, the stage and type of dementia of an mHealth user, and the fear of the unknown. These should be considered as prerequisites in the implementation phase of mHealth and explored more in future research.

The aim of this deliverable is to provide context for the CAPABLE system demonstration video. A brief description of the scope of the demonstration is followed by a detailed description of the scenario that will be used during the demo. The demonstration video was recorded in early 2023; the demo will also be shown during the consortium meeting that will happen on January 31st and February 1st, 2023. Given the decoupled nature of the CAPABLE architecture, the efforts that led to the completion of this demo were twofold: on the one hand, each involved partner carried out the development of the component under their own responsibility, on the other hand, there was a global effort to make the different components communicate with each other. All these efforts have been coordinated by Task Force 1, focused on the development of the final CAPABLE architecture., 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

In this deliverable, we describe the knowledge engineering process used to formalize the clinical practice guidelines implemented in CAPABLE. More precisely the following steps will be illustrated: Choice of the guidelines - selecting the most compatible guidelines for the system. Text processing mechanism - detecting recommendations and translating them into a set of computer-interpretable rules. Analysis of each recommendation - detecting what raw data type was involved, which vocabulary is mostly suitable for coding, and what abstractions need to be performed on this raw data. This allows for achieving clearer representation, at the same time producing reusable pieces of knowledge. The detected raw data also represents the minimum data set needed by the CAPABLE DSS (Decision Support System), so this step has been fundamental for the development of other CAPABLE components. Translation of each recommendation into a set of computer-interpretable rules, organization of rules into a logical flow, and their representation using the Composer tool, i.e., the authoring tool developed by one of the project partners. In this step, adaptations have been implemented to allow smooth guideline implementation in different healthcare settings. Validation of the represented knowledge by simulating realistic clinical scenarios (oncologists have been involved in this phase). Particular attention has been put to multimorbidity management, since running guidelines for different pathologies may lead to contradictory recommendations, which must be resolved Handover to the implementation team and refinement iterations with the team itself. The deliverable will then provide the actual description of the formalized guidelines, through a set of flowcharts, and links to their representation in Composer., 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

In this deliverable, we present the activities carried out in WP7 in preparation for the CAPABLE pilot studies. These activities include: the preparation of the preliminary documentation for study authorization in compliance to the current EU regulations in terms of data protection and medical devices usage, the preparation of a training plan for the users of the system and for the technical staff who will support users during the study, and the definition of the policies for the maintenance of the CAPABLE software and of the devices that are needed for it to be used. This document is organized into three Sections, each one related to the described activities. Being this a Demonstration deliverable, it includes the references to all the documentation that has been prepared during the last year of the project. The links to the available documentation are reported in the Annexes Section., 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

This deliverable shares the results of the third usability and user experience study of the CAPABLE prototypes as defined in previous deliverable D7.1. During these activities, the ongoing developed interfaces of the final system provided by WP6 have been evaluated by applying the two methods described in D7.1: Interviews with patients. These interviews gathered feedback from the end users of the Patient App: Melanoma and Kidney cancer patients during the treatment phase. Considering the broader applicability of the app to any type of cancer patient, this iteration also included other types of oncological patients, and cancer survivors (treatment finished in no more than 2 years). Overall, 17 patients have been interviewed. Interviews with healthcare professionals (HCPs). These interviews aimed to collect overall feedback about both doctors’ and patients’ solutions, understand if the clinical and patients’ needs are covered, and revise the core functionalities that have been proposed in the current prototype. A total of 10 health professionals have been interviewed. The protocols of each study are attached in the Annex. The document also presents the general conclusions from these validation activities and the next steps to follow in future developments to satisfy the user experience needs emerging in this process. Differently from the previous two rounds, the current pandemic situation allowed all the interviews to be performed in a presence. The following tools have been used: Online survey engine based on Limesurvey, used for the interviewer as a guide. Online access to the clinical dashboards to inspect functionalities for the Health Care Professionals. App installed on an Android device to test the functionalities for the patients. An Asus Vivowatch 5 smartwatch., 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

Exercise games (exergames) can help COPD patients stay active and prevent exacerbation. When evaluating such exergames, patient values are an important variable to take into account. In this study, seven COPD patients used an exergame technology at the physiotherapist for six months. Their values regarding treatment and the exergames were identified in interviews. Values were very stable throughout the study, and closely interconnected. Personal Guidance and Independence were important values. Additionally, patients sometimes held conflicting values that they prioritized differently at different times or based on specific events. As the study identified important values that appeared stable over a period of time, albeit with different prioritization, they are worth considering when designing new technology. However, values cannot be looked at in isolation because of the strong connection between values.

The availability of mHealth for people with dementia is increasing. Various mHealth design guidelines for this population have been proposed. In this study, we developed a binary checklist with evaluation statements to assess the implementation of twenty design suggestions in seven currently available mHealth apps for people with dementia. Between 17%–65% of the evaluation statements in the checklist were implemented in these apps. Not all statements were considered applicable for each assessed mHealth app, which resulted in dividing the criteria in two groups as either key evaluation statements or optional evaluation statements. In future work we want to augment this checklist to contribute to the future design of mHealth for people with dementia.

The Swiss Ophthalmic Image Network (SOIN) is part of the Swiss Personalized Health Network (SPHN). SOIN contains a collaborative, clinical research environment, MI Data Lab, which allows privacy-preserving, data-driven, research. Personalized care of chronic ocular disease, based on Machine Learning (ML) and medical imaging, can dramatically improve quality of life and reduce the burden on health and social care systems. MI Data Lab allows research partners to consolidate their data in a space where doctors and data scientists cooperate to design novel ML algorithms, on curated datasets. To date, we have created several algorithms to detect ocular biomarkers automatically, and applied such tools to 100k+ retinal images. MI Data Lab enables the development of predictive models, the extraction novel traits to be explored in terms of -omic associations, treatment outcome, and priors for disease progression.

Developing effective digital interventions to help patients form healthy habits is a challenging goal. IDEAS is a step-by-step framework that allows developers to draw ideas from intended users and behavioral theories, and ideate implementation strategies for them, followed by rapid prototype development. Based on our long experience with developing generic knowledge-based clinical decision support systems (CDSS) and integrating them with electronic health records (EHR) to deliver patient-specific advice, we observed a challenge that IDEAS is not addressing: the semantic detailing of the clinical knowledge behind the digital intervention and relevant patient data that could be used to personalize the digital intervention. To close the gap, we augmented two steps of IDEAS with an ontology that structures the target behavior as classes, derived from HL7 Fast Healthcare Interoperability Resources standard. We exemplify the augmented IDEAS with a case study taken from the Horizon 2020 CAPABLE project, that uses Fogg's Tiny Habits behavioral model to improve the sleep of cancer patients via Tai Chi.

Artificial Intelligence (AI) in healthcare promises to improve the accuracy of diagnosis and screening, support clinical care, and assist in various public health interventions such as disease surveillance, outbreak response, and health system management. But the increasing importance of AI in healthcare means that trustworthy AI is vital to achieve the beneficial impacts on health anticipated by both health professionals and patients.This book presents the proceedings of the 32nd Medical Informatics Europe Conference (MIE2022), organized by the European Federation for Medical Informatics (EFMI) and held from 27 - 30 May 2022 in Nice, France. The theme of the conference was Challenges of Trustable AI and Added-Value on Health. Over 400 submissions were received from 43 countries, and were reviewed in a thorough process by at least three reviewers before being assessed by an SPC co-chair, with papers requiring major revision undergoing further review. Included here are 147 full papers (acceptance rate 54%), 23 short papers and 79 posters from the conference. Topics covered include the usual sub-domains of biomedical informatics: decision support and clinical information systems; clinical research informatics; knowledge management and representation; consumer health informatics; natural language processing; public health informatics; and privacy, ethical and societal aspects, but also innovative approaches to the collection, such as organization and analysis of data and knowledge related to health and wellbeing, as well as theoretical and applied contributions to AI methods and algorithms.Providing an overview of the latest developments in medical informatics, the book will be of interest to all those involved in the development and provision of healthcare today.

Patient and public involvement (PPI) is increasingly used for improving quality of the research. There are many barriers in translating PPI into practice, including lacking examples of good practices. Frameworks that have been developed in one setting do not readily transfer to other settings. In this paper, we examine the implementation of PPI in the context of a digital health research project that explores the design, development and use of mHealth for persons with Multiple Sclerosis taking an iterative user-centered design approach. Methods: Instrumental case study to describe the PPI process on a digital health research project. Results: Overall experience was positive. We found 3 roles for PPI involvement: strategic members; design and development partners; and expert members. Challenges lay on unclear PPI terminology; managing roles and expectations; and ensuring accessibility.

Integration of digital self-management solutions into health care processes requires the involvement of health care professionals in the adoption and use of the solutions as part of the care pathway. We conducted 23 interviews with diverse profiles of health care professionals participating in the treatment of chronic patients in three different countries. Our results indicate that health care professionals appeared relatively motivated at the prospect of having access to patient-generated data. Nevertheless, they appeared less confident in weighing what types of data could be collected efficiently through mobile devices and how it could be presented in ways that would provide value to the care process. Our results identify four broad categories for how patient-generated health data could be useful: monitoring, prevention, research, and transparency of condition parameters.

The aim of this deliverable is to define the requirements of the CAPABLE system. These requirements include clinical requirements regarding the clinical scope of decision-support and its content (knowledge base), patient and clinician user needs, and technical system requirements for the different system components. Following our iterative development approach for the CAPABLE system, this document presents the functional and non-functional requirements. This document opens with a literature review (Section 2, corresponding to Task T2.1) that we have performed to identify the state of the art of data-science-based methods and systems for supporting the quality of life of cancer patients, starting with data collection and data integration methods, and continuing to machine-learning-based prediction models and patient coaching systems. The literature review allowed us to identify best practices that we plan to adopt in the CAPABLE system, as well as open challenges, many of which we plan to address. Going from the state of the art to our own CAPABLE system, in Section 3 we present the overall system architecture, along with the different components of the system. In addition to this structural description of the system, we also provide a workflow presenting the process of care supported by the CAPABLE system, from patient enrollment and initial setup of the physician-facing dashboard and patient-facing mobile app, to the ongoing services provided by the systems to these users, including patient monitoring and decision-support. The following chapters present a description of the methods that we used to collect different requirements, as well as the requirements themselves. Section 4 presents patient requirements (Task T2.1) and Section 5 ��� clinical requirements (Task T2.3), relating to the needs of the clinicians as well as a selection of clinical practice guidelines, monitoring data to be collected by patients at home, and certification/barriers to market. Section 6 (corresponding to Task T2.6) starts with a set of Sequence Diagrams that specify how the system components interact with each other in order to achieve the M12 Demo Scenario. Then we present all functional and non-functional requirements of the different system components (Data integration components [Case Manager, Data Platform, and the Knowledge-Data Ontology Mapper (KDOM)], decision-support [Computer-interpretable Guideline (CIG) Execution Engine and Knowledge Base, Multimorbidity Controller, Coaching System ��� all three rely on the existing decision model of the PROforma formalism], user interfaces [Patient APP GUI, Doctor APP GUI], sensors) as well as security/privacy). Section 7 presents the Technical requirements for data representation, integration, quality, and exchange (corresponding to Task T2.4). Section 8 presents the Technical requirements for the AI data processing and analysis methods (corresponding to Task T2.5), including predictive model and knowledge discovery (pattern detection). Technical detailed requirements for security and privacy will be formulated in WP4. WP2 continued to meet in Year 2 to complete the formulation of requirements. Issues that were elaborated in Year 2 include defining the structure and content of the pdf summary for the general practitioners [Section 4, Req. 5.3.1], additional requirements related to security by design [Section 6.12], and the final choice of phones and sensors [Section 6.8]. Based on feedback from the M12 Demo from all stakeholders and on the 1st technical review, we added one requirement [Req 4.5] and elaborated the user interface screens and for all requirements related to the clinicians' app and the patients' app [Sections 4.7 and 5.9]., 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