Alex Malioukis

Introduction: Agitation is a common and burdensome symptom in people with dementia, particularly when compounded by impaired communication, making early detection and effective management difficult. Wearable sensor technologies may offer a promising avenue for supporting real-time behavioral monitoring and personalized care in this context. Objective: This study aims to examine the current clinical and technological capabilities of wearable sensor systems for detecting and managing agitation in persons with dementia, to assess whether these technologies can effectively support personalized care. Additionally, it seeks to identify key challenges and opportunities in applying human-centered design principles and tailored interventions to improve outcomes for both patients and caregivers. Methods: We conducted a scoping literature review, registered on OSF ¹ and guided by PRISMA-ScR guidelines. Five databases—Google Scholar, Scopus, PubMed, PsycINFO, and ACM Digital Library—were searched for English-language peer-reviewed studies published between 2016 and early 2025. From an initial pool of 798 articles, a multi-phase screening process led to a final inclusion of 13 studies that met predefined criteria. Results: The reviewed studies demonstrated that wearable sensors, particularly those employing multimodal data and personalized machine learning models, enable reliable detection of agitation symptoms and support timely, tailored interventions. The concept of digital phenotyping emerged as a promising approach for capturing complex behavioral signatures, while user-centered design was identified as essential for adoption and long-term compliance. Discussion: The evidence identified in this scoping review indicates that wearable and multimodal sensor technologies may offer promising approaches for monitoring agitation in dementia, while acknowledging that the research remains in early stages. We recommend future research focus on large-scale, longitudinal validation and the expansion of these tools to other populations with communication challenges, such as individuals with autism spectrum disorder or traumatic brain injury. Systematic review registration: https://doi.org/10.17605/OSF.IO/DNHYM .

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.

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.