2021-2026

FNR PEARL

See our partners list

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.

2022-2025

FNR PEARL dHealthPD, FNR PREVENE

Gabriel Martinez Tirado, Stefano Sapienza, Patricia Martins Conde

Germany:SCAI, Luxembourg: LIH

This project ‘Data-driven clinical decision support tool for diagnosing Mild Cognitive Impairment in Parkinson’s disease’applies an integrative machine learning approach to improve the diagnosis of mild cognitive impairment (MCI) in Parkinson’s disease (PD). The goal is to develop a clinical decision support tool that identifies early MCI patients, enabling earlier detection and more personalized care.

By combining diverse clinical and patient data, the model aims to support healthcare professionals in tailoring interventions, refining diagnostic procedures, and ultimately enhancing outcomes for people with Parkinson’s disease.

2023-2027

FNR Industry fellowship

Alan Castro Mejia, Stefano Sapienza, Patricia Martins Conde

Luxembourg: 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 standardized 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.

FNR PEARL dHealthPD

Stefano Sapienza, Gaëtan Ragon

The project “Following Individualised Real-World Symptom Trajectories to Study Therapeutic Effect in Parkinson” is an open-end data collection whose aim is predicting the efficacy of treatment changes in Parkinson’s patients, starting from digital biomarker derived from wearable sensors, patient reported outcomes, and long-term disease progression.  To achieve this objective this study will work side by side with different neurologists in Luxembourg and their patients.  The doctors will decide medication changes and evaluate the effects. Their patients will be the participant in our study.

FNR PEARL dHealthPD

Patricia Martins Conde, Stefano Sapienza

Systems Biology group at the University of Luxembourg

Frailty is a state of increased vulnerability caused by a decline in physical function. Its clinical manifestation includes among others fatigue, slow deambulation, decreased strength, and falls. Different scales have been proposed to measure frailty in elder adults, but the evaluation of this condition remains unexplored in patients with Parkinson’s disease where neurodegeneration could lead to similar effects. The aim of this project is to develop a new frailty assessment scale for patient with Parkinson’s disease based on the LuxPark cohort, and to utilize statistical methods machine learning models to investigates its construct validity with respect to clinical variables that are known to be relevant for frailty individuals. 

FNR PEARL dHealthPD

Gelani Zelimkhanov

Luxembourg: TTM, CHL/LRC

Sensor-based biometric gait analysis, performed in supervised (lab-based) and unsupervised (home-based) environment for Parkinson’s patients, at risk cohorts (RBD) and healthy controls. 

The aim is to analyse gait characteristics and identify key patterns among PD patients, controls and at-risk cohorts, aiming to develop disease management and clinical decision-making support based on objective evaluation (sensor-derived data) and to identify and follow-up longitudinal disease progression trajectories using real-world data.

2024-2027

FNR PEARL dHealthPD

Francesca Boschi, Stefano Sapienza

Germany: University Hospital Erlangen

This study investigates whether gait parameters can serve as reliable predictors of mobility changes in Parkinson’s disease patients. We conducted a retrospective analysis of 111 clinical visits from 71 individuals treated at the Department of Molecular Neurology, University Hospital Erlangen. Patient progress was assessed using the UPDRS III gait subitem score across baseline and follow-up visits, with intervals ranging from 30 to 365 days.

Our objectives include classifying patients into Improvers, Stables, or Deteriorators, applying longitudinal analyses with multiple comparison corrections, and benchmarking results against existing literature. To advance predictive care, we also train machine learning models using clinical data alone and in combination with gait parameters, aiming to improve forecasting of mobility trajectories in Parkinson’s disease.

2020-2025

DFG

Stefano Sapienza, Marijus Giraitis, Gelani Zelimkhanov

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

Austria: 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. The primary outcome of the trial is the change after the intervention in self-selected gait velocity measured using wearable sensors. Secondary endpoints are changes in clinical scores, patient reported outcomes, functional metrics, and daily life mobility.

2024-2026

FNR PEARL dHealthPD

Francesca Terranova, Stefano Sapienza, Alex Bisdorff,Mehdi Rhaddioui

Luxembourg: CHEM

Recent insights suggest that vestibular dysfunction can cause orthostatic hypotension, rather than merely result from it as traditionally assumed. However, because only a limited number of studies have explored this relationship, further research is needed to determine the prevalence of orthostatic hypotension in vestibular disorders and to clarify its clinical significance. The study on Orthostatic Hypotension as secondary consequence of Vestibular Dysfunction (OHVD) aims to advance understanding of this phenomenon.

Wearable medical device measuring cardiovascular parameters will enable us to collect data during vestibular manoeuvres. Unlike manual assessment, this device provides continuous monitoring of blood pressure, heart rate, and body position. This enables us to detect orthostatic hypotension events triggered by vestibular manoeuvres, by analysing variations of blood pressure and heart rate relative to body position and vestibular manoeuvre.

We will analyse and compare the frequency of orthostatic hypotension between patients with vestibular dysfunction and a control group, thereby contributing valuable insights into the interplay between vestibular pathology and cardiovascular regulation.

2022 – 2024

FNR/Ministry of Economy

Liyousew Borga, Isabel Schwaninger

LIH, LuxAI

This project develops and clinically validates an innovative social robot QTrobot (QT), designed to support assessment, therapy, and symptom alleviation for children on the autism spectrum. QT delivers a structured program to foster cognitive, social, language, communication, and autonomy skills during the critical first five years of development. A built-in reporting module enables continuous monitoring of progress across these domains, ensuring personalized and adaptive therapy.

Autism affects approximately one in 54 children worldwide, many of whom experience slower development in key skills and require intensive, structured interventions. While early interventions are proven to improve wellbeing, social competence, learning capacity, and IQ, saving an estimated €1.1 million per person in long-term costs, access to such therapies remains limited globally.

QT addresses this gap by empowering parents to deliver evidence-based therapies at home. By providing scalable, affordable, and effective digital therapeutic support, QTrobot helps ensure that children with autism receive the consistent, high-quality interventions they need to thrive.

2024 – 2027

FNR PEARL dHealthPD

Francesca Boschi, Francesca Terranova, Stefano Sapienza

This project aims to develop a pipeline for assessing mobility in daily life through the computation of micro, macro, and complexity parameters from inertial measurement units (IMU). The pipeline leverages algorithms from the GaitMab library, which calculate validated spatio-temporal gait parameters at the stride level. These features are subsequently aggregated according to the guidelines of the Mobilise D consortium to finally obtain metrics that characterize daily life mobility. The pipeline developed and tested on data collected from people with Parkinson’s disease (PD) and atypical parkinsonian disorder. By leveraging wearable sensor data, this pipeline provides with an objective assessment and deeper insights into gait characteristics and disease progression outside of clinical settings.

The pipeline is designed using object-oriented principles (OOP), ensuring modularity, clarity, and integration into broader analytical ecosystems. This structure also supports reproducibility and facilitates the generation of standardized, reusable datasets.

Through this approach, the project seeks to advance digital biomarkers for Parkinson’s disease, enabling more precise monitoring, personalized interventions, and improved understanding of mobility in everyday life.

2024 – 2026

FNR PEARL dHealthPD

Francesca Terranova, Stefano Sapienza, Marijus Giraitis, Gelani Zelimkhanov

Orthostatic hypotension (OH) is a frequent non-motor symptom of Parkinson’s disease (PD), characterised by a significant drop in blood pressure upon standing. Despite its prevalence, OH often goes under-diagnosed, complicating disease management and progression. Wearable medical devices offer a promising solution by enabling continuous monitoring in both clinical settings and daily life, supporting earlier detection of OH.

This study evaluates the feasibility of using sensor-derived estimations of blood pressure and posture to identify OH during the Schellong test, with clinician diagnosis as the reference standard. Two devices will be employed: SOMNOtouch, which estimates blood pressure via electrocardiogram (ECG) and photoplethysmography (PPG), and Empatica EmbracePlus, which uses accelerometer and PPG sensors to monitor physiological and activity-related parameters. Together, these tools will provide the raw data needed to develop algorithms for improved cardiovascular regulation monitoring in PD.

2025-2027

FNR PEARL dHealthPD

Francesca Boschi, Stefano Sapienza, Alan Castro Meija

This analysis focuses on data from Luxembourg participants enrolled in the MobilityApp clinical trial. These patients provide daily self-assessment scores during the trial, including questions such as “How secure was your gait today?”. The aim of the analysis is to model these patient-reported outcome measures (PROMs) using objective measurements, specifically digital mobility outcomes (DMOs) derived from wearable-sensor recordings.

2025 – 2028

Marie Skłodowska-Curie Actions (MSCA) – AIPD

Svetlana Kuziakina, Stefano Sapienza

The project ‘Stratification of Clinical Disease Trajectories and Prediction of Individual Rate of Progression’ focuses on stratifying the clinical trajectories of patients with a confirmed Parkinson’s disease (PD) diagnosis and predicting their individual rate of progression from baseline. Leveraging large-scale datasets from PPMI, LuxPARK, and ICEBERG, we aim to identify distinct progression patterns and develop predictive models that capture patient-specific disease dynamics.

By stratifying patients according to their progression profiles, the study will explore the potential benefits for clinical trial design, including improved patient selection, tailored interventions, and more precise evaluation of therapeutic outcomes. Ultimately, this approach seeks to enhance the efficiency and effectiveness of PD research and care by aligning trial methodologies with the heterogeneity of disease progression.

2025-2027

FNR PEARL dHealthPD, Pelican Grant

Francesca Boschi, Stefano Sapienza

Syrine Slim (MaD Lab)

WE-MOVE PD (Weather Effects on Mobility in Parkinson’s Disease) is a collaboration with the Machine learning and Data analytics (MaD Lab, FAU) and the dMed (UniLU).
This project analyses digital mobility outcomes (DMOs) from people with Parkinson’s disease collected in Germany across 2023–2025 through DiGA and Selective Contracts studies. The main objective is to investigate how external factors, such as weather conditions, influence real-world mobility in Parkinson’s disease. By quantifying how these external factors affect gait and mobility patterns, the project aims to improve the interpretation of DMOs obtained in daily-life settings.

2023 – 2026

FNR PEARL dHealthPD

Francesca Terranova, Stefano Sapienza

Germany: Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen

USA: Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Boston

Parkinson’s disease (PD) symptoms fluctuate throughout daily life, yet traditional clinical assessments capture only brief snapshots. While wearables enable continuous monitoring, non-motor symptoms such as autonomic dysfunction remain difficult to assess in real-world settings. This proof-of-concept study introduces a hybrid sensing approach that combines wearable ECG with automated video-based posture detection to evaluate heart-rate (HR) responses during natural postural transitions (PTs). In a simulated multi-room apartment, we recorded participants using multiple cameras and ECG sensors and developed a semi-automated pipeline using multi-person re-identification and pose estimation to detect PTs. The extracted HR dynamics reveal physiological responses that may reflect autonomic impairment in PD. These findings demonstrate the feasibility of hybrid video–wearable monitoring for capturing everyday autonomic function beyond controlled clinical evaluations.

2023-2026

FNR PEARL dHealthPD

Messaline Fomo, Liyousew Borga, Isabel Schwaninger

The incurable and progressive nature of Parkinson disease (PD) warrants the use of novel digital tools to support patients in managing their symptoms and improving their overall wellbeing. Digital health tools (DHTs) are said to empower patients by giving them more control in their ability to manage their disease. However, existing research on patient empowerment have focused on the conceptualisation at a more general level for patients with chronic diseases, with limited insights into how disease-specific characteristics may interact with their empowerment and the role DHTs play in this process. This is particularly important for PwPD as physical and cognitive symptoms may interact with their empowerment and influence how they decide to use DHTs to achieve what matters to them in relation to their health and wellbeing. 

The current study builds on a recent scoping review where we conceptualized how DHTs and their functional components contribute to patient empowerment in the context of chronic disease management from the perspective of patients. The objective of this study is to validate this conceptual framework among PwPD by identifying which aspects of empowerment are valued by patients and highlight any differences between the conceptual framework and the preferences for PwPD. The study will be cross-sectional study with a sequential mixed method, integrating both qualitative and quantitative study design. Data will be collected via semi-structured interviews and surveys with PwPD.

2024-2027

FNR PEARL dHealthPD

Niloofar Kheradbin, Isabel Schwaninger, Patricia Martins Conde

Newly diagnosed People with Parkinson’s Disease (PwPD) frequently experience emotional stress following the realization of the new diagnosis. They are left alone to cope with negative emotions such as fear and anxiety, which could be overlaid by disease-specific non-motor symptoms such as depression and fatigue. We aim to design and develop MoodAid,  a digital health coaching tool that supports this vulnerable population in managing their current mood changes and adopting preventive self-care strategies.

In this research, we will identify the evidence-based educational content and user engagement elements through a dual thematic analysis of literature review results and by mapping to capability approach to patient empowerment and COM-B model for behaviour change. In another phase of the study, we will perform a mixed methods clustering analysis on the Luxembourg Parkinson Study cohort (LuxPARK) to understand the profiles of mood changes and define the user personas. In the final step, we will design tailored educational content using Participatory Design approaches, i.e., co-design workshops and adopt coaching concepts such as Motivational Interviewing (MI).

The goal is to enhance mood management and increase awareness of prevention strategies, thus leading to a higher Quality of Life (QoL) for PwPD.

2024-2026

FNR PEARL dHealthPD

Isabel Schwaninger

App stores offer access to an increasing number of digital heath apps, where users such as people with Parkinson’s disease (PD) and Alzheimer’s disease (AD) can retrieve apps to support their daily living, for disease management, and wellness. However, while an increasing number of apps are available on these online marketplaces, there is a lack of systematic understanding of available apps and their quality, which cannot easily identified in app stores by users. This project investigates app information related to AD/PD apps available via app stores. Doing so, we apply descriptive statistics and text mining approaches to explore and extract quality information in textual app descriptions and user reviews. Drawing on health technology assessment (HTA) concepts and patient-reported outcome measures and experience measures (PROMs/PREMs), we discuss how to provide clearer, user-centred quality indicators for digital health applications in the AD/PD context.

2023-2026

FNR PEARL dHealthPD

Marijus Giraitis, Isabel Schwaninger, Patricia Martins Conde

Luxembourg: CHL, LCTR, Germany: OnCare GmbH

PDiary-App is a patient-centred electronic diary application designed to strengthen digital connectivity in Parkinson’s disease (PD) care. Its core purpose is to empower patients to actively track and share their symptoms, disease progression, and treatment experiences, while simultaneously supporting healthcare professionals with structured, clinically relevant information.

Developed collaboratively with patients and clinicians, the app supports individuals from the earliest stages of PD. Through its diary function, patients record relevant health and treatment data, which is structured according to expert input and shared with therapists. This ensures that both patients and care providers have access to up-to-date, clinically meaningful information, fostering personalised, coordinated, and adaptive care throughout the course of the disease journey.

2024-2027

FNR PEARL dHealthPD

Patricia Martins Conde

Luxembourg: CHL, TTM

Connected Care PD is a collaborative initiative aimed at advancing the quality, coordination, and efficiency of Parkinson’s disease care within the Réseau de Compétences Maladies Neurodégénératives (RdC-MN). Building on the proven success of ParkinsonNet Luxembourg,the project will implement an integrated care pathway concept that combines digital connectivity, structured interprofessional communication, and continuous quality monitoring.

At its core, Connected Care PD strengthens collaboration among healthcare professionals through standardized care processes, shared clinical insights, and real-time digital communication tools. By integrating data-driven quality indicators into routine practice, the project aims to ensure consistent, evidence-based care delivery and transparent performance monitoring across the network.

The long-term ambition is to establish a reimbursed, sustainable integrated care model that leverages digital solutions to support coordinated care, optimize patient outcomes, and enhance system-wide efficiency. Through this transformation, the Réseau de Compétences Maladies Neurodégénératives will be positioned as a leading model for digitally supported, high-quality, multidisciplinary Parkinson’s disease management.

2026-2027

FNR PEARL dHealthPD

Maryam Talebi, Liyousew Borga

The project ‘Health-Related Quality of Life in Parkinson’s Disease Patients in Luxembourg’ aims to generate the first Luxembourg-specific utility estimates for individuals living with Parkinson’s disease (PD), offering an evidence base for future health technology assessments and cost-effectiveness evaluations. Building on the LUXPARK dataset and leveraging validated mapping techniques, the study will estimate health-related quality of life (HRQoL) across different methods for PD severity staging by translating PDQ-39 scores into EQ-5D utility values. At its core, the project addresses an unmet need in Luxembourg: the absence of local, preference-based HRQoL metrics that reflect the cultural, clinical, and healthcare-system specificities of PD patients. By systematically applying and comparing multiple international PDQ-39 to EQ-5D mapping algorithms, the research will provide robust stage-specific utility values and quantify the incremental decline in quality of life associated with disease progression.

2024-2026

FNR PEARL dHealthPD

Liyousew Borga

Luxembourg: CHL/TTM/IGSS

 This project aims to assess the financial implications of implementing a digitally connected integrated care model for Parkinson’s disease within the Luxembourg healthcare system. The model is designed to improve coordination between healthcare professionals and to align care delivery with the specific needs of patients at different stages of the disease. To support this, the project introduces a newly defined set of clinical stages for Parkinson’s disease that more accurately reflects patient needs and the intensity of care required as the condition progresses.

Based on these stages, the project will map all relevant care activities provided by neurologists, nurses, physiotherapists, occupational therapists, and other professionals. For each activity, the team will identify, and document associated medical costs, non-medical costs, and indirect costs such as caregiver time. These data will be used to construct a multi-year budget impact model that compares the financial demands of the integrated care model with those of the current standard of care in Luxembourg. The model will incorporate estimates of disease prevalence, expected transitions between disease stages, adherence to treatment plans, and patterns of healthcare resource use.

The overall goal of the project is to provide policymakers and healthcare stakeholders with a transparent and evidence-based assessment of how adopting an integrated care model would affect national healthcare expenditures. The results will offer a foundation for future decisions on service organisation, reimbursement structures, and potential implementation of coordinated care approaches for Parkinson’s disease in Luxembourg.

2024-2027

FNR PEARL dHealthPD

Patricia Martins Conde, Liyousew Borga

Luxembourg: IGSS

The project Evaluation of quality of care for individuals with PD. PDCare investigates how socioeconomic factors, healthcare utilization, and costs shape the quality of care for people with Parkinson’s disease (PwP). Using pseudonymized data from social security, insurance claims, and hospital and long-term care records, the aim is to assess the prevalence and socioeconomic determinants of PD and its economic burden, analyse care pathways and variations in healthcare use (outpatient, inpatient, and long-term care) and their impact on outcomes and examine the role of teleconsultations for PwP and healthcare professionals, particularly during COVID-19.

By integrating these insights, the project seeks to advance evidence-based strategies for improving care quality and efficiency in Parkinson’s disease management.

2023-2026

FNR PEARL dHealthPD

Patricia Martins Conde, Marijus Giraitis

The project Impact and Acceptance of Digitally Enabled Integrated Care ‘PDQuality’ evaluates the acceptance and impact of a digitally enabled integrated care model, such as Réseau de Compétences – Maladies Neurodégénératives (RdC-MN), in supporting people with Parkinson’s disease (PwP) and healthcare professionals (HCPs).

The main objectives are to examine PwP and HCP acceptance of digital technologies and personal health records (PHR) and identify factors influencing adoption.

To assess the impact of the RdC ParkinsonNet model on patient capabilities (health literacy, self-management, shared decision-making, access and coordination of care) and functioning (quality of life, participation, satisfaction with care), as well as on HCP satisfaction.

And to analyse PHR usage patterns among PwP and HCPs and determine how PHR use contributes to achieving patient capabilities. By integrating these insights, the project aims to strengthen patient empowerment, improve care coordination, and enhance the overall quality of Parkinson’s care

2022-2027

FNR PEARL dHealthPD

Sijmen van Schagen

Luxembourg: Hive Services

UK: ORCHA

The project ‘Societal Acceptance of Digital Medical Devices’ responds to the lack of frameworks that help align Digital Medical Devices (DMDs) with societal (patient and healthcare provider) needs and healthcare system requirements. It is based on the hypothesis that broad societal acceptance of DMDs requires a comprehensive Application Readiness (AR) framework and tailored guidance for innovators. The project aims to define and validate this framework from a societal perspective, benchmark it against existing models, and develop a practical support tool (development checklist) to guide DMD innovators. By supporting alignment with stakeholder expectations (e.g. patients, healthcare providers, and payers) the framework will help ensure effective integration of DMDs into real-world care.

2024-2027

FNR PEARL dHealthPD

Patricia Martins Conde

The survey study Use and Acceptance of Digital Medical Devices for Health Care Professionals in Réseau de Compétence pour les Maladies Neurodégénératives (RdC-MN) explores how healthcare professionals (HCPs) within the RdC-MN accept, adopt and engage with digital medical technologies. By analysing attitudes, usage patterns, and experiences, the survey aims to uncover the key barriers and facilitators influencing the acceptance of digital medicine.

The findings will provide valuable insights into how digital tools can be better integrated into clinical practice, ultimately supporting more efficient, patient-centred care for individuals with Parkinson’s disease in Luxembourg.

2025

EIT Health DMD Fellowship module 2025

Patricia Martins Conde

Spain: IESE Barcelona IESE

UK: ORCHA

The Market Access for Digital Medical Devices module addresses a critical need in the rapidly evolving Digital Medical Device (DMD) sector: navigating Europe’s complex and fragmented regulatory and reimbursement landscape within the new European Health Data Space (EHDS). With varied frameworks like Germany’s DIGA and France’s PECAN, gaining market access requires understanding of each country’s unique compliance, evidence, and certification processes. This online module offers a foundational overview to diverse range of stakeholders – including entrepreneurs, data scientists, industry representatives and economists- to understand and compare key frameworks, manage certification, and implement necessary quality assurance processes, while equipping strategies that enhance competitive advantage and build user trust, ultimately streamlining market access for innovative DMDs across the EU.

2023-2025

EIT Health flagship

Patricia Martins Conde

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

Switzerland: Roche

Germany: Merck

Luxembourg: 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 realized 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.

2025

EIT Health DMD Fellowship module 2025

Spain: IESE

UK: ORCHA

The ‘Labelling Health and Wellness Apps under EHDS module’ addresses a critical need in the evolving digital health landscape:

understanding the regulatory requirements imposed by the European Health Data Space (EHDS) on health and wellness apps. As the market expands, navigating the complexities of app labelling and compliance is essential for ensuring interoperability, quality, safety, and user trust. This online module offers a foundational overview for diverse stakeholders, including entrepreneurs, developers, healthcare professionals, and industry representatives. Participants will learn the key regulatory and quality requirements specific to the EHDS for health and wellness apps, understand how labelling impacts the acceptance, and develop strategies to leverage labelling for competitive advantage and to build user trust. Guided by top experts in app labelling, this module equips participants with essential knowledge and skills, enhancing the reliability and effectiveness of innovative digital health solutions across EU.