Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS

Informazioni relative al progetto

i-PROGNOSIS

ID dell’accordo di sovvenzione: 690494

DOI

10.3030/690494

Progetto chiuso

Data della firma CE 10 Novembre 2015

Data di avvio 1 Febbraio 2016

Data di completamento 31 Gennaio 2020

Finanziato da

• SOCIETAL CHALLENGES - Health, demographic change and well-being

Costo totale

€ 3 921 302,50

Contributo UE

€ 3 921 302,50

Coordinato da

ARISTOTELIO PANEPISTIMIO THESSALONIKIS
Greece

Periodic Reporting for period 3 - i-PROGNOSIS (Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS)

Periodo di rendicontazione: 2018-08-01 al 2020-01-31

Sintesi del contesto e degli obiettivi generali del progetto

Parkinson’s Disease (PD) is one of the commonest neurodegenerative diseases causing progressive disability that results in a burden of ~2.2 million disability-adjusted life years (DALYs), exhibiting the greatest loss of quality-adjusted life years (QALYs) among 29 major chronic conditions. PD is a progressive and chronic neurological disease that often begins with mild symptoms in walking, talking and writing, emotional stability, that advance gradually over time. Symptoms can be so subtle in the early stages that they go unnoticed, as there are no PD-related biomarkers (e.g. blood tests) and findings on routine magnetic resonance imaging and computed tomography scans are unremarkable, leaving the disease undiagnosed for years. Motivated by the aforementioned, i-PROGNOSIS proposes an intelligent ICT-based approach for early PD symptoms detection and early intervention in older adult’s everyday life, promoting active and healthy ageing, by introducing new ways of health self-managing tools, set within a collaborative care context with health professionals. The overall objectives of i-PROGNOSIS are: i) The introduction of new early diagnostic tests for PD symptoms based on features extracted from securely Cloud-stored behavioural and sensorial data, unobtrusively collected by smart devices (e.g. smartphone, smartwatch), wearable biosensors and IoTs, and processed by advanced big data analytics and machine learning techniques. ii) Design and implementation of novel ICT-based adaptive, gamified, and personalized interventions, along with assistive interventions, promoting health self-management by providing dynamic feedback towards the improvement of older adult’s skills and functionalities for reduction of the PD-related risks of frailty, depression and falls. iii) Foster social awareness for volunteerism in early PD detection and construction of socio-economic and informed behavioural models for new cost-effective ICT-based PD early detection and related risks-reduction intervention practices and policies for the sustainability of health and care systems and the benefit of the older adults. All these objectives were accomplished and very promising results towards early PD symptoms detection and PD treatment and management have emerged.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

WP1: Establishment and implementation of a project management and quality assessment plan, progress reporting, monitoring of compliance of ongoing studies with ethical guidelines, preparation and submission of ethical applications; WP2: Identification of the user requirements, usages scenarios and business processes, along with the specification of the data collection and medical evaluation protocols governing all phases of data collection and system evaluation during the project lifespan; WP3: Design and implementation of data acquisition and protection methods for G/SData and Intervention phases, development of Smart Belt, implementation of G/SData analysis and machine learning algorithms, collection of big real-world data, evaluation of the effectiveness of G/SData analysis components; WP4: Design and development of a Personalised Game Suite (PGS), design of the Targeted Nocturnal Intervention (TNI), design and development of Voice Enhancement (VE) and Gait Rhythmic Guidance (GRG) interventions, development of PD-related behavioural models, development of PGS adaptation algorithms; WP5: Establishment of the Cloud-based i-PROGNOSIS data management infrastructure, realisation of the i-PROGNOSIS mobile applications (i.e. iPrognosis and iPrognosis Wear) for acquiring data from the everyday use of smartphone and other wearable devices, design and development of the interventions web-platfor, production of an Open Data Management Plan; WP6: Pilot data collection before G/S/IData studies, G/S/IData collection studies implementation, definition of the medical evaluation protocols, medical evaluation of the i-PROGNOSIS system in terms of PD symptoms detection, medical evaluation of the interventions; WP7: Design of detailed and holistic assessment plan and evaluation methodology, technical assessment and user acceptance evaluation of the iPrognosis and iPrognosis Wear apps and interventions, MAFEIP-based impact assessment of the interventions WP8: Scaffolding of social awareness via dissemination activities towards the construction and growth of i-PROGNOSIS community and network of stakeholders, dissemination of scientific results, development of detailed exploitation plan and intellectual property rights identification. The described workload and results, clearly support the three basic pillars of i-PROGNOSIS, i.e. PD early detection, novel PD-related interventions and social responsiveness to participation, data donation and PD awareness. The aforementioned work resulted to the following main outcomes: i) smartphone and smart watch applications for unobtrusive behavioral data collection, ii) machine learning algorithms for PD symptoms detection, iii) PGS and assistive interventions.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

The progress beyond the state-of-the-art of the i-PROGNOSIS refers to aspects of its three pillars. For the early PD detection, a mobile i-PROGNOSIS application, incorporating a novel smart keyboard, for unobtrusively acquiring the GData from the everyday use of the smartphone, along with SData (based on other wearable smart devices), archived in the Cloud, has been constructed for the first time. In addition to this, a smart belt for noninvasively capturing of the bowel sounds and, thus, monitoring constipation, was constructed, facilitating SData acquisition. The impact of this is the offering of the innovative possibility to undertake large-scale, accurate, longitudinal health-based research - “crowd sourced researching” towards the introduction of PD predictors. This is used to generate large amounts of data previously unavailable as part of a knowledge base health economy for any company in the Silver Market, as well as providing an ecologically valid alternative for costly random controlled trials. For the PD novel interventions, a PGS, encapsulating, for the first time, the most profound intervention activities for PD patients via a gamified environment, along with additional supporting interventions have been developed. All these provide the opportunity for home-based, patient-centric interventions that target PD-related risks via lifestyle behavioural change program and places a positive impact to them improving the experience of living with PD, enabling better self-manage of their health status, serving as a powerful and compelling approach to sustainable healthcare and active and healthy ageing. For the PD social awareness, a core mass of the i-PROGNOSIS community has been established for the first time, via the dissemination activities undertaken, providing the basis to deploy the novel acquisition tools and increase the social responsiveness to data donation and participation in the healthy and active ageing initiatives. The stakeholders networking evokes the transformation from an “I think” culture to a “we know” culture, leading to more informed and validated interventions supporting active and healthy ageing back to the community and society, as its extension. This closes the feedback loop and increases levels of older adults’ and stakeholders’ engagement, allowing them to understand that their participation actually makes a difference to how the early PD prediction progresses.

Development data (IMU and typing) collection by AUTH

"Screenshots from the ""iPrognosis"" mobile application"

Prof. Hadjileontiadis at the 2nd European Summit on Digital Innovation for Active and Healthy Ageing

The consortium

Prof. Hadjileontiadis at TISHW2016 conference presenting i-PROGNOGNOSIS

i-PROGNOSIS at the MDS Congress 2019 in Nice, France

i-PROGNOSIS in various events

i-PROGNOSIS ecosystem

The logo of the i-PROGNOSIS project