Constantino Martins

Major Depressive Disorder (MDD) stands as a leading cause of disability, demanding innovative self-management solutions. Despite advances in treatment and management, many still struggle with daily symptoms, highlighting the urgency for solutions that can offer scalable, personalized treatment to promote long-term well-being and reduce reliance on professional intervention. This study evaluates the effectiveness of BodyMindGlow, a digital platform designed for mental health self-management which records and monitors health status and provides recommendations to improve physical and mental well-being. App requirements were defined following a study involving 205 applications for mental health management, detailing current practices in Behavioral Management, Persuasive Systems Design (PSD), Behavioral Economics (BE) principles and Security and Privacy strategies. The study involved two groups: one with a clinical diagnosis of depression and/or anxiety and another without a diagnosis but interested in improving their general well-being. For two weeks, participants recorded mental and physical state daily and used services for emotional management and promotion of a healthy lifestyle. Follow-up was consulted daily through evolution graphs. Evaluation included initial assessment tests (PHQ-9 and GAD-7), daily records, a usability questionnaire divided into the System Usability Scale (SUS) and a personalized questionnaire. The results provided insights to guide future research and advancements in Digital Mental Health (DMH), demonstrating the effectiveness of an engaging solution that fits the fundamental and limiting needs of current mental health self-management. Creating this platform with advanced features, such as Artificial Intelligence, Gamification and SmartCoach, proved crucial to improve adherence and effectiveness of interventions.

Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals’ perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.

This chapter is devoted to examining and reflecting on the importance of artificial intelligence techniques and technologies applied to the construction of intelligent learning environments (ILE), specially, for remote teaching and learning purpose. Their potential for educational purpose goes hand in hand with the vision of the education as a pilar for a sustainable future (e.g. UNESCO, 2014), which aims at designing cost-effective and at the same time quality teaching and learning opportunities for all students. Intelligent tutoring systems (ITS) cases will receive a special focus as their potential for personalization, adaptation to the student, and automatic recommendations based on the student's profile, comprising important features for fostering adaptive and autonomous learning paths. All those principles combined with AI contributions for ITS design will be pointed out. Finally, future research directions in the field of intelligent learning environments are figured out.

UNSTRUCTURED Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals’ perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.