Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson’s disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient’s physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.

Background: Living Labs are user-centered, open innovation ecosystems based on a systematic user cocreation approach, which integrates research and innovation processes in real-life communities and settings. The Horizon 2020 Project VITALISE (Virtual Health and Wellbeing Living Lab Infrastructure) unites 19 partners across 11 countries. The project aims to harmonize Living Lab procedures and enable effective and convenient transnational and virtual access to key European health and well-being research infrastructures, which are governed by Living Labs. The VITALISE consortium will conduct joint research activities in the fields included in the care pathway of patients: rehabilitation, transitional care, and everyday living environments for older adults. This protocol focuses on health and well-being research in everyday living environments. Objective: The main aim of this study is to cocreate and test a harmonized research protocol for developing big data–driven hybrid persona, which are hypothetical user archetypes created to represent a user community. In addition, the use and applicability of innovative technologies will be investigated in the context of various everyday living and Living Lab environments. Methods: In phase 1, surveys and structured interviews will be used to identify the most suitable Living Lab methods, tools, and instruments for health-related research among VITALISE project Living Labs (N=10). A series of web-based cocreation workshops and iterative cowriting processes will be applied to define the initial protocols. In phase 2, five small-scale case studies will be conducted to test the cocreated research protocols in various real-life everyday living settings and Living Lab infrastructures. In phase 3, a cross-case analysis grounded on semistructured interviews will be conducted to identify the challenges and benefits of using the proposed research protocols. Furthermore, a series of cocreation workshops and the consensus seeking Delphi study process will be conducted in parallel to cocreate and validate the acceptance of the defined harmonized research protocols among wider Living Lab communities. Results: As of September 30, 2021, project deliverables Ethics and safety manual and Living lab standard version 1 have been submitted to the European Commission review process. The study will be finished by March 2024. Conclusions: The outcome of this research will lead to harmonized procedures and protocols in the context of big data–driven hybrid persona development among health and well-being Living Labs in Europe and beyond. Harmonized protocols enable Living Labs to exploit similar research protocols, devices, hardware, and software for interventions and complex data collection purposes. Economies of scale and improved use of resources will speed up and improve research quality and offer novel possibilities for open data sharing, multidisciplinary research, and comparative studies beyond current practices. Case studies will also provide novel insights for implementing innovative technologies in the context of everyday Living Lab research. International Registered Report Identifier (IRRID): DERR1-10.2196/34567

Background and Objectives: Population rapid growth and demographic shift is leading to a rise in neurodegenerative disorders such as dementia and mild cognitive impairment (MCI). Evidence indicates that MCI is not the earliest phase of prodromal AD. Subjective Memory Decline (SMD) refers to a self-perceived decline in cognitive abilities compared to previous functioning levels in individuals with normal cognition. Language impairment represents a critical marker of neurodegenerative disorders and early memory decline in healthy older adults. Methods: This review was conducted in accordance with PRISMA Statement guidelines. The inclusion criteria of the selection process were set as follows: (1) All studies analyzed spontaneous speech samples in individuals with SMD or individuals with +αβ amyloid. (2) Studies reported language performance indicators (e.g., lexical, syntactic, semantic, phonetic, or fluency measures) derived from spontaneous speech. (3) The study population included participants with SMD based on recognized diagnostic criteria or self-reported cognitive complaints without objective cognitive impairment. (4) Studies were written in English. (5) The time frame of studies was 5 years. Results: The present work is a review of speech features—particularly from spontaneous and narrative speech—and methods that can serve as sensitive indicators of early cognitive changes due to AD pathology. Conclusions: Spontaneous speech analysis, through acoustic and temporal parameters such as silence duration, phrasal segment length, and speech segment frequency, offers a rich window into the subtle cognitive and linguistic changes that reflect early memory decline in healthy older adults. Spontaneous speech performance could be a scalable, low-cost, and non-invasive diagnostic tool in proactive cognitive health.

Background/Objective: The use of Large Language Models (LLMs) has recently gained significant interest from the research community toward the development and adoption of Generative Artificial Intelligence (GenAI) solutions for healthcare. The present work introduces the first meta-review (i.e., review of systematic reviews) in the field of LLMs for chronic diseases, focusing particularly on cardiovascular, cancer, and mental diseases, to identify their value in patient care, and challenges for their implementation and clinical application. Methods: A literature search in the bibliographic databases of PubMed and Scopus was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to identify systematic reviews incorporating LLMs. The original studies included in the reviews were synthesized according to their target disease, specific application, LLMs used, data sources, accuracy, and key outcomes. Results: The literature search identified 5 systematic reviews respecting our inclusion and exclusion criteria, which examined 81 unique LLM-based solutions. The highest percentage of the solutions targeted mental disease (86%), followed by cancer (7%) and cardiovascular disease (6%), implying a large research focus in mental health. Generative Pre-trained Transformer (GPT)-family models were used most frequently (~55%), followed by Bidirectional Encoder Representations from Transformers (BERT) variants (~40%). Key application areas included depression detection and classification (38%), suicidal ideation detection (7%), question answering based on treatment guidelines and recommendations (7%), and emotion classification (5%). Study aims and designs were highly heterogeneous, and methodological quality was generally moderate with frequent risk-of-bias concerns. Reported performance varied widely across domains and datasets, and many evaluations relied on fictional vignettes or non-representative data, limiting generalisability. The most significant found challenges in the development and evaluation of LLMs include inconsistent accuracy, bias detection and mitigation, model transparency, data privacy, need for continual human oversight, ethical concerns and guidelines, as well as the design and conduction of high-quality studies. Conclusions: While LLMs show promise for screening, triage, decision support, and patient education—particularly in mental health—the current literature is descriptive and constrained by data, transparency, and safety gaps. We recommend prioritizing rigorous real-world evaluations, diverse benchmark datasets, bias-auditing, and governance frameworks before LLM clinical deployment and large adoption.

Background: Virtual Health and Wellbeing Living Lab Infrastructure is a Horizon 2020 project that aims to harmonize Living Lab procedures and facilitate access to European health and well-being research infrastructures. In this context, this study presents a joint research activity that will be conducted within Virtual Health and Wellbeing Living Lab Infrastructure in the transitional care domain to test and validate the harmonized Living Lab procedures and infrastructures. The collection of data from various sources (information and communications technology and clinical and patient-reported outcome measures) demonstrated the capacity to assess risk and support decisions during care transitions, but there is no harmonized way of combining this information. Objective: This study primarily aims to evaluate the feasibility and benefit of collecting multichannel data across Living Labs on the topic of transitional care and to harmonize data processes and collection. In addition, the authors aim to investigate the collection and use of digital biomarkers and explore initial patterns in the data that demonstrate the potential to predict transition outcomes, such as readmissions and adverse events. Methods: The current research protocol presents a multicenter, prospective, observational cohort study that will consist of three phases, running consecutively in multiple sites: a cocreation phase, a testing and simulation phase, and a transnational pilot phase. The cocreation phase aims to build a common understanding among different sites, investigate the differences in hospitalization discharge management among countries, and the willingness of different stakeholders to use technological solutions in the transitional care process. The testing and simulation phase aims to explore ways of integrating observation of a patient’s clinical condition, patient involvement, and discharge education in transitional care. The objective of the simulation phase is to evaluate the feasibility and the barriers faced by health care professionals in assessing transition readiness. Results: The cocreation phase will be completed by April 2022. The testing and simulation phase will begin in September 2022 and will partially overlap with the deployment of the transnational pilot phase that will start in the same month. The data collection of the transnational pilots will be finalized by the end of June 2023. Data processing is expected to be completed by March 2024. The results will consist of guidelines and implementation pathways for large-scale studies and an analysis for identifying initial patterns in the acquired data. Conclusions: The knowledge acquired through this research will lead to harmonized procedures and data collection for Living Labs that support transitions in care. International Registered Report Identifier (IRRID): PRR1-10.2196/34573