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Background: Recognised as essential for high-quality dementia service, person-centred care aims to understand and respect the unique needs of each individual. Self-experience practices may offer caregivers an opportunity to acquire knowledge, empathy, and skills related to person-centred care, especially through recreating experiences similar to dementia. Given the need to enhance the understanding of self-experience practices in dementia care, a more comprehensive investigation of these training interventions for (future) caregivers is needed. Methods: We conducted a scoping review to map the evidence on the use of self-experience practices in dementia training. We systematically searched Cochrane Library, MEDLINE via PubMed, CINAHL, and Web of Science. We also searched for grey literature, as well as registry entries, and conducted backward citation tracking of included reviews. We analysed data on intervention characteristics, factors influencing the implementation, and learning outcomes based on Kirkpatrick's model. Results: We included 44 reports across 30 intervention programmes. The majority of reports (91%) were published from 2016 onwards, with 32% originating from the USA and 25% from the UK. We identified passive, interactive, immersive, and multicomponent self-experience interventions in dementia education and training. Learning outcomes based on Kirkpatrick's model were fairly distributed across all identified modalities. Both consumers and providers emphasised aspects related to the development and implementation of practices, particularly organisational-related considerations such as temporal and spatial planning of trainings. Conclusions: Our review highlights diverse interventions incorporating self-experience practices, with an increasing role for technological tools. While self-experience interventions engage participants, the impact on individuals with dementia and organisational levels remain largely unreported. Our overview, informed by current literature, underscores unique considerations and challenges associated with dementia-related self-experience practices. Implementing and evaluating complex training interventions using self-experience practices should consider ethical aspects. Trial registration: Registry: Registered within the Open Science Framework (available at https://osf.io/fycxa/). [ABSTRACT FROM AUTHOR]

Background: The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD. Objective: The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis. Methods: The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers. Results: The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 –Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy). Conclusions: The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models. [ABSTRACT FROM AUTHOR]