2021-2026

FNR PEARL

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The vision of dHealthPD (FNR-PEARL programme) is to create a new “ecosystem of digital medicine” by developing a “digital health triangle” consisting of a) an IT-platform supported integrated healthcare model for PD, b) new patient-centred outcome parameters by wearable devices, and c) predictive data modelling for individualised patient care.

The Digital medicine group will address the following topics:

• Clinical evaluation and validation of innovative real-life target parameters derived from clinical-grade wearables for PD. Wearable sensors providing objective outcome measures will be tested in patients under laboratory and real-life (e.g. home monitoring) settings. Different sensor-types addressing the major body functions according to the international Classification of Functioning Disability and Health (ICF) will be evaluated ranging from gait and mobility, cardio-vascular function, breathing and sleep.
• Digital HealthCare applications. Wearable sensors providing objective outcomes will be integrated into the trans-sectoral healthcare workflow to support personalised clinical decisions in PD.
• Connectivity of real-life patients and their medical data in PD. Generate innovative digital health pathways for PD patients including the implementation of wearable sensors and apps, as well as digitally connecting integrated healthcare provider in Luxembourg.
• Outcomes generated by clinical-grade wearables for PD. The project will focus on developing and evaluating sensor-technologies measuring disease-related functional impairment in PD, such as sensor-based gait analysis, cardiopulmonary regulation, cognitive function and sleep patterns. Technical and clinical validation of the distinct technologies will be conducted to prove their technology readiness for clinical applications.
• Personalised Intelligence developed using “Digital Health pathways” for personalised medicine in PD. The usability, applicability, integration into multidisciplinary care concepts and health-economic efficiency will be analysed and individualised data prediction models will be generated to provide personalised clinical decision support for PD patient care.

2021-2024

ERA-PERMED (H2020)

LCSB, Clinical Research Center for Neurosciences at the Institut du Cerveau et de la moelle epiniere, E-health research at Telecom SudParis (France), Fraunhofer SCAI, Portabiles HealthCare Technologies (P-HCT) (Germany), University Namur (Belgium)

DIGIPD is a European funded project (ERA-PERMED (H2020)) with partners from Germany, France and Luxembourg.

This DIGIPD project is validating digital biomarkers to improve individualised diagnosis and prognosis for Parkinson patients. To better predict disease progression and therefore disease management, impairment of gait, voice and face movement is measured using digital technology to adapt treatment. Depending on disease progression patients can be stratified into subgroups for clinical trials to reveal new or better drugs.

The project is coordinated by the Fraunhofer Institute for Algorithms and Scientific Computing SCAI.

2021-2025

DFG

LCSB, FAU (Erlangen), TUM (Munich), UKER (Erlangen)

EmpkinS will investigate novel radar, wireless, depth camera, and photonics-based sensor technologies as well as body function models and algorithms. The primary objective of EmpkinS is to capture human motion parameters remotely with wave-based sensors to enable the identification and analysis of physiological and behavioral states and body functions. To this end, EmpkinS aims to develop sensor technologies and facilitate the collection of motion data for the human body. Based on this data of hitherto unknown quantity and quality, EmpkinS will lead to unprecedented new insights regarding biomechanical, medical, and psychophysiological body function models and mechanisms of action as well as their interdependencies.
The main focus of EmpkinS is on capturing human motion parameters at the macroscopic level (the human body or segments thereof and the cardiopulmonary function) and at the microscopic level (facial expressions and fasciculations). The acquired data are captured remotely in a minimally disturbing and non-invasive manner and with very high resolution. The physiological and behavioural states underlying the motion pattern are then reconstructed algorithmically from this data, using biomechanical, neuromotor, and psychomotor body function models. The sensors, body function models, and the inversion of mechanisms of action establish a link between the internal biomedical body layers and the outer biomedical technology layers. Research into this link is highly innovative, extraordinarily complex, and many of its facets have not been investigated so far.
To address the numerous and multifaceted research challenges, the EmpkinS CRC is designed as an interdisciplinary research program. The research program is coherently aligned along the sensor chain (see figure 1) from the primary sensor technology (Project Area A) over signal and data processing (Project Areas B and C) and the associated modelling of the internal body functions and processes (Project Areas C and D) to the psychological and medical interpretation of the sensor data (Project Area D). Ethics research (Project Area E) is an integral part of the research programme to ensure responsible research and ethical use of EmpkinS technology.

2020-2023

DFG

Germany: FAU (Erlangen), UKER (Erlangen), PHCT

Austrlia: Medizinische Universität Innsbruck (i-med), Tirol Kliniken

Switzerland: Chuv (Le Centre hospitalier universitaire vaudois), École polytechnique fédérale de Lausanne (EPFL)

Italy: Südtiroler Sanitätsbetrieb (Sabes)

This project is a randomized controlled trial to determine whether gait focused versus standard PT and home based exercises result in greater improvement of mobility in parkinsonian disorders including PD, MSA-P and PSP-RS. With the help of the e-diary (Mobility-APP) patients are reminded to practise the home-based training.
Home-Physiotherapy Intervention in atypical PD patients – effects measured with Gait Parameters
Austria-Innsbruck/Erlangen/Switzerland-Lausanne/Italy-Bologna

2019-2024

EU-funded IMI (Innovated Medicine Initiative) project

LCSB
150 professionals from 34 participating universities, hospitals, and industry

The Mobilise-D project demonstrated that Digital Mobility Outcomes (DMOs) can quantitatively assess symptom expression levels. A subset of these spatiotemporal digital parameters is validated as DMOs and ready for clinical application in real-world healthcare procedures. Nevertheless, a persisting limitation is the lack of expert consensus regarding the clinical context and assessment paradigm where DMOs may be applied. Specifically, questions remain regarding the interpretability within and between diseases, strengths, weaknesses, and clinical utility of the DMOs for personalized clinical decision support – as a major dissemination strategy of the Mobilise-D consortium.

The purpose of this project is to frame Mobilise-D findings by identifying distinct clinical care contexts that enable the application of DMOs for personalized clinical decision support. Furthermore, we propose establishing consensus across Mobilise-D experts to disseminate expert-opinion guidelines that specify the context and assessment paradigm for the clinical use of DMO.

2022-

FNR (July 2022- June 2023)

It is well known in literature that development of cognitive impairment in Parkinson’s disease (PD) is associated with deterioration into more advanced stages but also with the early development of other related dementia. Thus, assessing mild-cognitive impairment (MCI) in PD is a key target for clinical workup. Unfortunately, nowadays the diagnosis is based on a complex and subjective examination procedure. Therefore, the aim of this project is to facilitate the diagnosis procedure of MCI by proposing a predictive risk stratification model of cognitive impairment in PD which applies machine learning methods to integrate genomic, health history and clinical data. Predictive risk stratification is emerging as an important tool to prospectively identify individuals who may benefit from preventive or therapeutic interventions at different disease stages in chronic disease management. However, due to the heterogeneity and complexity of genomic and medical data integration, the application in clinical care remained extremely challenging until new opportunities have arisen from the application of machine learning for personalized medicine, with its ability to seamlessly integrate multi/high dimensional and longitudinal data and quickly identify trends and patterns in the population. In this study we want to fully exploit these new powerful tools, creating a model that would enable researchers and physicians in the MCI diagnosis, taking full advantage of the wealth of information which is hidden in the available data but whose exploitation for knowledge discovery is beyond current conventional methods.

2022-2024

Public-private partnership FNR Meco

LCSB, LIH, LuxAI

We propose the development and clinical validation of an innovative digital therapeutic device, the social robot QTrobot (QT), for the purpose of assessment, therapy, and alleviation of symptoms of patients on the Autism Spectrum Disorder. QT provides a comprehensive program to elicit and improve cognitive, social, language, communication and autonomy skills of the first 5 years of development. It also provides a reporting module to assess and monitor the progress of children regarding their cognitive, social, language and communication skills. Autism is a neurodevelopmental condition affecting one in every 54 children. Children with autism often have slower development in language, social, cognitive and communication skills and require structured and intensive interventions that can help them to gain age-appropriate skills and improved autonomy. Early intensive interventions have been shown effective in improving both the wellbeing and social competence of children with autism and their learning capabilities and IQ, as well as resulting in the average cost saving of €1.1 million per person. However, there is a high shortage of the availability of such interventions worldwide, children often do not receive sufficient therapy hours and require costly and longterm social assistance and support for daily living. QT can address the shortage of autism professionals by empowering parents to deliver standard and evidence-based therapies at home, providing effective, scalable, and affordable therapy for autistic children. This project is a collaboration between the Luxembourg based company LuxAI, developer and manufacturer of the made-in-Luxembourg QT and the Luxembourg Institute of Health, bringing together the specialties required for the development and clinical validation of QT as a medical device for at-home therapy and assessment of autistic children. The project also brings together autism scientists and clinicians from LU, DE, FR, IT and UK, proposing a novel and unique clinical study in terms of its scope, scale (190 participants in 5 countries) and duration (10-month intervention). The project can have high social and economic impacts, on the business of LuxAI, on the fields of autism and robotics research, and on addressing the huge challenges of governments to provide cost-effective and scalable autism interventions. Examples are LU, UK and UAE authorities who have shown interest to use/finance QT for addressing their autism challenges.

2022-2023

LCSB

CHEM

Parkinson’s disease (PD) is a chronic degenerative disorder of the central nervous system, and it constitutes one of the neurological disorders with the fastest-growing prevalence. It is a complex condition characterised by a range of motor and non-motor symptoms that exhibit considerable variability among patients. Non-motor symptoms are increasingly recognised as being fundamental in early-stage diagnosis, potentially contributing to a slower disease progression rate. Among them, orthostatic hypotension (OH) stands out as a significant concern for individuals with PD. OH is characterised by a sustained drop in blood pressure (BP) upon transitioning from a seated or supine position to a standing one, leading to dizziness, weakness and increasing the risk of falls.

Vestibular dysfunction, on the other hand, is another condition known to be associated with OH. Emerging research indicates that vestibular dysfunction both causes and results from orthostatic hypotension. The vestibular system plays a crucial role in regulating the autonomic nervous system, particularly in postural-related blood pressure control. Studies have shown that damage to the peripheral vestibular system can lead to a drop in blood pressure upon standing, suggesting a direct link between the vestibular system and autonomic regulation.

OH events are frequently missed during routine visits and are often detected when it has already reached a critical stage. For this reason, it is essential to adopt a multi-perspective approach that enables continuous monitoring both in controlled clinical environments and in the real-world setting where patients spend the majority of their lives. Nevertheless, this leads us to the immediate question of how to effectively address this issue.

The primary objective of this feasibility study is to investigate the detection of OH episodes during their routine daily activities in the home environment. With this purpose, we will focus on two patient groups: those with PD and those with vestibular dysfunction.

We will achieve this through continuous monitoring using a wearable device.

The recognition of vestibular dysfunction as a potential cause of orthostatic hypotension in addition to its known association with Parkinson’s disease is of great importance. This understanding could lead to improved diagnostic approaches and better management of these disorders, ultimately preventing syncopal episodes, recurrent falls, and significant injuries.

2023-2027

FNR Industry fellowship

ZithaSenior

Geriatric assessments are inherently complex due to multidimensional factors (ageing, comorbidities, functional and cognitive states, and adverse effects arising from treatment or care) influencing general health and well-being. Current clinical approaches rely on standardised scales and rater-dependent batteries for comprehensive patient assessment. Unfortunately, these tools are time-consuming, resource-intensive, and lack patient-reported outcomes –making them unfeasible for clinical monitoring or decision support tools. A potential solution to address these issues is to leverage two growing trends in geriatrics: patient-reported outcome measures and objective mobility monitoring. The former provides information on healthcare effectiveness, while the latter provides an objective remote measure of spatio-temporal gait parameters when using wearable sensors.

The Digital Medicine Group and ZithaSenior SA understand the need to complement current clinical batteries with objective and patient-reported outcomes measures to reduce the complexity of geriatric assessments and develop a clinical decision-support tool to evaluate the quality of care provided and monitor patient outcomes effectively.

2023-2025

EIT Health flagship

Spain: IESE Barcelona, DKV Services, Opinno Healthcare, Associació  Diabetes  de  Catalunya, F3T Project (EIT Health Spain), DTx consortium

Switzeland: Roche

Luxmbourg: Patient  ambassador of  ParkinsonNet Luxembourg, Luxinnovation

The EU is taking firm but still scattered steps forward in the development and implementation of Digital Medical Devices (DMD), with many initiatives being implemented in different countries to foster its regulation, reimbursement and adoption. These efforts are directed towards defining the path to follow by DMD developers. However, aside from patients, it is healthcare professionals who will ultimately be the final adopters of DMDs. The DMD Summer School is a blended program conceived to train future healthcare professionals and innovators who will be active players in the development of DMDs and future adopters. The DMD SS will provide an understanding of the DMD “world”, its potential to solve clinical unmet needs, knowledge on how digital medicine is being realised in the EU and will prepare them to make informed decisions and take an active role in the development and adoption of DMDs in their future work reality.

2023-2025

EIT Health Flagship Innovation Project (NMDH)

Portabiles HCT, Machine Learning and Data Analytics Lab at FAU, Radboud university medical center (Radboudumc), University Hospital Erlangen (UKER), University hospital Marburg (UKGM), VAG Contractual working group, DKV Servicios, Servicio Madrileño de Salud (SERMAS),Medical Valley Digital Health Application Center (DMAC)

Parkinson’s disease is a global burden with 10 M patients worldwide (1.2 M in Europe) and is the fastest-growing neurological disease (doubling by 2040). Even in countries with high healthcare standards, Parkinson’s care is often ‘unsafe, delayed, and inefficient’.

According to key opinion leaders, today’s healthcare provision for patients with Parkinson’s disease (PD) is fragmented and suffers from poor interdisciplinary collaboration. Timely access to healthcare services is often not granted, patients suffer from a worsening of their healthcare status until a therapeutic intervention is applied.

PDnetGo is an innovative combination of

a) best practice of multidisciplinary clinical care from the ParkinsonNet concept

b) telemonitoring with wearable devices (ParkinsonGo)

c) AI-driven workflows and dashboards.

PDnetGo is a unique approach aiming to optimize the efficiency of the overall care process by automatically analyzing and benchmarking the clinical outcome measures.

PDnetGo offers an easy-to-implement modular standalone solution that will improve healthcare for patients and their healthcare professionals.