Although the impact of post-acute COVID-19 syndrome (PACS) on patients and public health is undeniably significant, its etiology remains largely unclear. Much research has been conducted on the pathophysiology, shedding light on various aspects; however, due to the multitude of symptoms and clinical conditions that directly or indirectly define PACS, it is challenging to establish definitive causations. In this exploration, through systematically reviewing the latest pathophysiological findings related to the neurological symptoms of the syndrome, we aim to examine how psychosocial and neuropsychological symptoms may overlap with neurological ones, and how they may not only serve as risk factors but also contribute to the persistence of some primary symptoms of the disorder. Findings from our synthesis suggest that psychological and psychosocial factors, such as anxiety, depression, and loneliness, may interact with neurological symptoms in a self-reinforcing feedback loop. This cycle seems to be affecting both physical and psychological distress, potentially increasing the persistence and severity of PACS symptoms. By pointing out this interaction, in this review study, we attempt to offer a new perspective on the interconnected nature of psychological, psychosocial, and neurological factors, emphasizing the importance of integrated treatment approaches to disrupt this cycle and improve outcomes when possible.

The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. To effectively monitor the health of those affected, maintaining up-to-date health records is essential, and digital health informatics apps for surveillance play a pivotal role. In this review, we overview the existing literature on identifying and characterizing long COVID manifestations through hierarchical classification based on Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) initiative in artificial intelligence (AI) to identify long COVID. Through knowledge exploration, we present a concept map of clinical pathways for long COVID, which offers insights into the data required and explores innovative frameworks for health informatics apps for tackling the long-term effects of COVID-19. This study achieves two main objectives by comprehensively reviewing long COVID identification and characterization techniques, making it the first paper to explore incorporating long COVID as a variable risk factor within a digital health informatics application. By achieving these objectives, it provides valuable insights on long COVID’s challenges and impact on public health.

The growing prevalence of chronic diseases such as diabetes, cardiovascular diseases (CVDs), and hypertension calls for innovative strategies for disease surveillance and management. Digital health technologies (DHTs) offer a transformative approach to chronic disease management through real-time, personalized health monitoring and enhanced patient engagement; however, they also present inherent limitations. This narrative review synthesizes current applications of DHTs in the management of diabetes, CVDs, and hypertension, with a focus on advancements in artificial intelligence, digital therapeutics, and remote patient monitoring. A framework of solutions is proposed, comprising six key pillars: (1) data accuracy and standardization through artificial intelligence–enhanced calibration and multisensor fusion; (2) user engagement and compliance supported by gamification and behavioral science; (3) interoperability and clinical integration through Fast Healthcare Interoperability Resources and Health Level Seven standards; (4) accessibility and cost barriers addressed through frugal innovation, open-source platforms, reimbursement policies, and user education; (5) privacy and ethics, employing blockchain and federated learning for secure data governance; and (6) regulatory and policy considerations, advocating for global harmonization and supportive reimbursement models. This framework aims to enhance the reliability, accessibility, and effectiveness of DHTs in chronic disease management. The review provides actionable insights for stakeholders, including developers, healthcare professionals, and policymakers, on optimizing the design, implementation, and regulation of DHTs, ultimately improving chronic disease outcomes.

In the context of modern healthcare, the concept of patient-centered care (PCC) has emerged as a crucial aspect, prompting the need for innovative frameworks to empower patients and enhance healthcare outcomes. This paper introduces the Patient-Oriented Medical Ontology Conceptual Framework (POMOCF), a powerful framework aimed at giving patients control over their personal and healthcare information. It prioritizes patient independence by allowing for direct control of electronic health records (EHRs) and facilitates the gathering of information through document upload or scanning. In the POMOCF front-end framework, we propose to maintain scalability, along with robust authorization, authentication, and secure data protocols, all of which are detailed within the framework. This approach enhances data accuracy and security, improving data management efficiency. As part of our ongoing development, we are planning to implement an innovative inference system that will automatically analyze and extract relevant details from the uploaded authorized document, provide real-time updates, and maintain data accuracy. This approach further strengthens data accuracy and prevents unauthorized modifications, thereby making the data management process more efficient. POMOCF leverages medical ontology principles to ensure standardized clinical concepts and improved semantic interoperability. Our integration plans involve integrating genotypic data for personalized medicine initiatives and strengthening the front-end infrastructure. By linking front-end interfaces with advanced technological solutions, we aim to provide effective healthcare delivery, promoting personalized care and enhancing patient outcomes.

Beginning with an overview of the current state of Quality of Life (QoL), this narrative review provides a synthesis of thirty articles published between 2007 and 2026 to provide insight into the evolution of QoL frameworks, how QoL data tracking has been operationalized by these frameworks, and where the gaps are. While the literature has moved from a focus on deficits and impairment to a strengths-based approach centered around the child, and aligned with the principles of the United Nations Convention on the Rights of Persons with Disabilities (CRPD), one theoretical framework (Schalock & Verdugo’s Eight Domain Model) was identified as being used most often by researchers. In addition, several instruments were also identified as commonly used to measure QoL, which include generic health related QoL frameworks, family QoL frameworks, and Ecological Momentary Assessment (EMA). However, several recurring limitations were identified across the reviewed frameworks, including: (1) use of proxy reports; (2) static or cross-sectional design; (3) fragmentation in terms of number of domains assessed, informant type and assessment methodology; (4) lack of sensitivity to changes in QoL over time in children who experience severe levels of disability; and (5) large gaps between new technology available to support QoL assessments and traditional periodic assessments. Therefore, the reviewed literature suggests that no single framework adequately captures the full range of factors influencing QoL for children with neurodevelopmental and learning disabilities, highlighting the need for a more integrated and comprehensive approach. As such, the findings suggest that digital twin approaches may provide a promising direction for the development of next-generation QoL frameworks. No study identified in this review applied a digital twin approach to QoL assessment in this population. Digital twin approaches are therefore considered a prospective direction for further investigation rather than an empirically established solution, with their potential application to pediatric QoL requiring future implementation and validation.

The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. As a result, to monitor the health of individuals affected by these conditions, they must maintain up-to-date health records using digital health informatics apps for surveillance. In this review, we provide an overview of the existing literature on identifying long COVID manifestations through hierarchical classification and the characterization of long COVID by different hierarchical groups based on the Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) in artificial intelligence (AI) to identify long COVID. Knowledge exploration, using the concept map for the clinical pathways of long COVID presented in this paper, provides an overview of the data needed to explore tackling the long-term effect of COVID-19 by integrating innovative cohesive frameworks and designing health informatics-based applications. To the best of our knowledge, this is the first paper to explore the potential incorporation of long COVID as a variable risk factor within a digital health informatics application.