Movement disorders related to perception and action, such as stroke, significantly impair functioning in daily living, severely impacting the life of affected individuals and having a huge economic impact. Although rehabilitation practice aims to restore functional ability through re-learning perception-action couplings, it leads to marginal improvements of daily functioning at best. Here it is argued that to improve effectiveness of rehabilitation a systems approach should be adopted to understand processes underlying perception-action disorders. A systems approach takes into account nonlinear interactions between components, enabling to explain for instance why patients do not respond stereotypical to rehabilitation training. REPAIRS (RE-learning Perception-Action In Rehabilitation from a Systems perspective) is a unique and timely training-through-research school, aiming to improve rehabilitation effectiveness. REPAIRS starts from systems-based fundamental knowledge on learning perception-action couplings to build applications to rehabilitation, while exploiting recent technology advancements. This research school provides the required critical mass of top-level researchers connecting European academic, clinical and technology experts to train the next generation of researchers and entrepreneurs in this perspective. REPAIRS studies interaction between four levels of the perception-action cycle: brain, muscles & joints, agent-environment and social, which is integrated with requirements on translation from clinical, technology and philosophical domains. The focus on interactions between levels and domains naturally ensures an interdisciplinary and intersectoral training. Integrating this with a high-level training of transferrable skills, dissemination and communication while exploiting an Experiential Skill Learning Workshop, will boost the employability of the young researchers involved and the innovation potential of Europe through reshaping rehabilitation.

Most adults who try to lose weight fail to maintain it. Obesity is a key economic and healthcare challenge for Europe. Effective interventions and commercial programmes for weight loss are widely available, but most people re-gain their lost weight. Currently few comprehensive solutions exist to help Europeans manage weight loss maintenance (WLM). Current research suggests the most promising evidence-based behaviour change techniques for WLM are self-monitoring, goal setting, action control, building self-efficacy and intrinsic motivation. Recent research also suggests that stress management and emotion regulation skills are key enablers of relapse prevention and weight-regain. Information technology offers attractive tools for teaching and supporting these techniques, some of which are currently delivered through resource-intensive face-to-face therapies. ICT-delivery includes networked-wireless tracking technologies, weighing-scales and activity sensors, online tools and smart-phone apps, multi-media resources and internet-based support. A broad choice of tools is most likely to be acceptable to users, who can pick and choose their own preferred technologies. The NoHoW project tests whether ICT-based delivery of the most promising evidence-based behavior change techniques is effective for WLM. We will carry out a large-scale international 3-centre trial of information technology tools that implement the most up-to-date behavioural science research. This trial will establish the effectiveness of these ICT tools in supporting WLM, linked to studies of European consumer needs and behaviour. Impact: The project will directly feed results into development of new products and services from the UK’s largest commercial weight-loss provider, Slimming World providing immediate benefit to 500,000+ consumers. Commercialisation of project results will provide much needed WLM services that promote health education and long-term weight management programmes.

Transition from healthy status to Parkinson’s Disease (PD) is vaguely tractable, since symptoms can be so subtle in the early stages that they go unnoticed. Lack of biomarkers and/or findings on routine MRI and CT scans, PD is left undiagnosed for years, gradually affecting the life of over 6.5 million of older adults (>55-60 yrs) worldwide, increasing the risk of their health deterioration. Epidemiological studies conclude that early intervention could have an inverse relation with the PD-related risks of progressive frailty, falls and emotional shift towards depression. Based on this evidence, the cardinal objective of i-PROGNOSIS is the development of (i) an ICT-based behavioural analysis approach for capturing, as early as possible, the PD symptoms appearance, and (ii) the application of ICT-based interventions countering identified risks. To achieve this, awareness initiatives will be employed, so as to construct i-PROGNOSIS community, targeting > 5000 older individuals within the duration of the project, in order to unobtrusively sense large scale behavioural data from its members, acquired from their natural use of mobile devices (smartphone/smartwatch). Ensuring anonymisation and secure Cloud archiving, i-PROGNOSIS will develop and employ advanced big data analytics and machine learning techniques, in a distributed and privacy aware fashion, so as to instantiate a PD Behavioural Model and construct reliable early PD symptoms detection alarms. To those identified and clinically validated as early stage PD patients, ICT-based interventions will be provided via the i-PROGNOSIS Intervention Platform, including: a) a Personalised Game Suite (ExerGames, DietaryGames, EmoGames, Handwriting/VoiceGames) for physical/emotional support, b) targeted nocturnal intervention to increase relaxation/sleep quality and c) assistive interventions for voice enhancement and gait rhythm guidance. In this way, i-PROGNOSIS will constructively contribute to active and healthy ageing.

Parkinson’s disease (PD) is the most common neurodegenerative movement disorder, with a multifactorial aetiology, heterogeneous manifestation of motor and non-motor symptoms, and no cure. PD is often missed or misdiagnosed, as early symptoms are subtle and common with other diseases, allowing for considerable damage to occur before treatment. Moreover, selecting the optimal medication regimen is usually a lengthy, “trial and error” process, leading to critical, costly non-adherence. Following a trustworthy and inclusive approach to AI development and based on multidisciplinary expertise and broad stakeholder engagement, AI-PROGNOSIS aims to advance PD diagnosis and care by: 1) developing novel, predictive AI models for personalised PD risk assessment and prognosis (in terms of time to higher disability transition and response to medication) based on multi-source patient records and databases, including in-depth health, phenotypic and genetic data, 2) implementing a system of biomarkers informing the AI models by tracking key risk/progression markers in daily living, and ultimately 3) translating the models and digital biomarkers into a validated, privacy-aware AI-driven toolkit, supporting healthcare professionals (HCPs) in disease screening, monitoring and treatment optimization via quantitative, explainable evidence, and empowering individuals with/without PD with tailored insights for informed health management.

Focusing on chronic respiratory conditions (CRDs), LMICs pay an unfairly high cost in terms of morbidity and mortality. Along with the existence of CRDs, other multiple long-term chronic conditions co-exist, including ischemic heart disease, hypertension, diabetes, osteoporosis and depression. Current care in these countries presents significant shortcomings such as the limited availability of affordable and user-friendly digital tools for remote monitoring and patient engagement, while care fragmentation is even more evident creating substantial inequality to the health services and outcomes, greatly affecting their Universal Health Coverage. In this direction, the 48-month implementation research-driven MULTIPULM project aims to apply an integrated care digital-based ecosystem, consisting of evidence-based tools, for effectively managing long-term conditions and enhancing secondary prevention of multimorbid patients suffering from chronic respiratory conditions with the focus on LMIC countries of Brazil, Serbia and Türkiye. MULTIPULM approach and offerings aim to improve the QoL of patients and their families as well as tackle the social, financial, political and organizational disparities linked with high challenging management of multimorbidity, formulating a reference paradigm for other LMICs to follow. The successful implementation of the integrated care models paradigms will be orchestrated by a meticulous detailed implementation roadmap and local capacity building activities for the creation of a critical mass of local stakeholders. In this light, MULTIPULM will be validated at large-scale with 3200 multimorbid patients in the 3 targeted LMIC countries. Last but not least, a socio-economic and financial assessment is scheduled to support local policy makers in the continuation of the project activities beyond its duration, contributing to the long-term sustainability of its benefits and to the improvement of the targeted countries’ UHC index.