A prospective observational study for a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment (FAITH): study protocol.

BACKGROUND: Depression is a common condition among cancer patients, across several points in the disease trajectory. Although presenting higher prevalence rates than the general population, it is often not reported or remains unnoticed. Moreover, somatic symptoms of depression are common in the oncological context and should not be dismissed as a general symptom of cancer. It becomes even more challenging to track psychological distress in the period after the treatment, where connection with the healthcare system typically becomes sporadic. The main goal of the FAITH project is to remotely identify and predict depressive symptoms in cancer survivors, based on a federated machine learning (ML) approach, towards optimization of privacy. METHODS: FAITH will remotely analyse depression markers, predicting their negative trends. These markers will be treated in distinct categories, namely nutrition, sleep, activity and voice, assessed in part through wearable technologies. The study will include 300 patients who have had a previous diagnosis of breast or lung cancer and will be recruited 1 to 5 years after the end of primary cancer. The study will be organized as a 12-month longitudinal prospective observational cohort study, with monthly assessments to evaluate depression symptoms and quality of life among cancer survivors. The primary endpoint is the severity of depressive symptoms as measured by the Hamilton Depression Rating Scale (Ham-D) at months 3, 6, 9 and 12. Secondary outcomes include self-reported anxiety and depression symptoms (HADS scale), and perceived quality of life (EORTC questionnaires), at baseline and monthly. Based on the predictive models gathered during the study, FAITH will also aim at further developing a conceptual federated learning framework, enabling to build machine learning models for the prediction and monitoring of depression without direct access to user's personal data. DISCUSSION: Improvements in the objectivity of psychiatric assessment are necessary. Wearable technologies can provide potential indicators of depression and anxiety and be used for biofeedback. If the FAITH application is effective, it will provide healthcare systems with a novel and innovative method to screen depressive symptoms in oncological settings. TRIAL REGISTRATION: Trial ID: ISRCTN10423782 . Date registered: 21/03/2022.

User Centered Design to Improve Information Exchange in Diabetes Care Through eHealth : Results from a Small Scale Exploratory Study.

Heterogeneity of people with diabetes makes maintaining blood glucose control and achieving therapy adherence a challenge. It is fundamental that patients get actively involved in the management of the disease in their living environments. The objective of this paper is to evaluate the use and acceptance of a self-management system for diabetes developed with User Centered Design Principles in community settings. Persons with diabetes and health professionals were involved the design, development and evaluation of the self-management system; which comprised three iterative cycles: scenario definition, user archetype definition and system development. A comprehensive system was developed integrating modules for the management of blood glucose levels, medication, food intake habits, physical activity, diabetes education and messaging. The system was adapted for two types of principal users (personas): Type 1 Diabetes user and Type 2 Diabetes user. The system was evaluated by assessing the use, the compliance, the attractiveness and perceived usefulness in a multicenter randomized pilot study involving 20 patients and 24 treating professionals for a period of four weeks. Usage and compliance of the co-designed system was compared during the first and the last two weeks of the study, showing a significantly improved behaviour of patients towards the system for each of the modules. This resulted in a successful adoption by both type of personas. Only the medication module showed a significantly different use and compliance (p= 0.01) which can be explained by the different therapeutic course of the two types of diabetes. The involvement of patients to make their own decisions and choices form design stages was key for the adoption of a self-management system for diabetes.

Empowering Citizens through Perceptual Sensing of Urban Environmental and Health Data Following a Participative Citizen Science Approach.

The growth of the urban population together with a high concentration of air pollution have important health impacts on citizens who are exposed to them, causing serious risks of the development and evolution of different chronic diseases. This paper presents the design and development of a novel participatory citizen science-based application and data ecosystem model. These developments are imperative and scientifically designed to gather and process perceptual sensing of urban, environmental, and health data. This data acquisition approach allows citizens to gather and generate environment- and health-related data through mobile devices. The sum of all citizens' data will continuously enrich and increase the volumes of data coming from the city sensors and sources across geographical locations. These scientifically generated data, coupled with data from the city sensors and sources, will enable specialized predictive analytic solutions to empower citizens with urban, environmental, and health recommendations, while enabling new data-driven policies. Although it is difficult for citizens to relate their personal behaviour to large-scale problems such as climate change, pollution, or public health, the developed ecosystem provides the necessary tools to enable a greener and healthier lifestyle, improve quality of life, and contribute towards a more sustainable local environment.

Evaluation of the Create@School Game-Based Learning-Teaching Approach.

The constructivist approach is interested in creating knowledge through active engagement and encourages students to build their knowledge from their experiences in the world. Learning through digital game making is a constructivist approach that allows students to learn by developing their own games, enhancing problem-solving skills and fostering creativity. In this context two tools, Create@School App and the Project Management Dashboard (PMD), were developed to enable students from different countries to be able to adapt their learning material by programming and designing games for their academic subjects, therefore integrating the game mechanics, dynamics, and aesthetics into the academic curriculum. This paper focuses on presenting the validation context as well as the evaluation of these tools. The Hassenzahl model and AttrakDiff survey were used for measuring users' experience and satisfaction, and for understanding emotional responses, thus providing information that enables testing of the acceptability and usability of the developed apps. After two years of usage of code-making apps (i.e., Create@School and its pre-design version Pocket Code), the pupils processed knowledge from their academic subjects spontaneously as game-based embedded knowledge. The students demonstrated creativity, a practical approach, and enthusiasm regarding making games focused on academic content that led them to learning, using mobile devices, sensors, images, and contextual information. This approach was widely accepted by students and teachers as part of their everyday class routines.