The mission of the DDP Lab is to develop key expertises and skills in the entire chain of research in digital health, from the implementation of digital health projects to the analysis of big data using data-driven/artificial intelligence approaches.

Digitization of clinical & epidemiological research

Digital cohorts: development of large, international and digital cohort studies where participants are monitored with digital solutions (smartphone app, web platform, connected devices…).

Remote patient monitoring, ePROs: monitoring of patient between clinical visits, tracking of patient reported outcomes and real-life data collection.

Ethical & reglementary aspects: fast-track process for both prospective observational and intervention study development.

• IT infrastructures: in-house development of secured web platforms, cloud-based data lakes and research smartphone apps.

Patient & Public Involvement

Patient-centered research: use of digital technologies to carry research out “with” or “by” patients/study participants rather than “about” them.

• Innovative methods for study participation improvement: mixed methods, qualitative approaches, recordings to improve study participation rate and minimize attrition over time.

Devices, Digital Data & Biomarkers

Connected devices (Activity trackers, Accelerometers, Pill Organizers…): leveraging digital tools to collect meaningful data on lifestyle, clinical factor or key biomarkers with a limited burden for the patient/user.

• Social Media: social media listening for a better understanding of a population of interest in real life (perceptions, beliefs, concerns…) or for pharmacovigilance (side effects, weak signals…)

Voice: identification of vocal biomarkers of emotions, psychological factors or clinical outcomes for patient monitoring, diagnosis or high-risk population identification.


Digital Interventions (JITAIs): development of pragmatic just-in-time adaptive interventions that adapt the provision of support (type, timing, intensity) overtime to an individual's changing status and contexts using digital technologies

Data-driven & AI-based approaches: combination of hypothesis-driven with data-driven analyses of cohort or clinical trial data (clustering methods, prediction)

• Deep Digital Phenotyping: combination of heterogeneous sources of digital, clinical, biological, omic data to deeply characterize individuals and diseases.

Digital Twins: virtual patients/individuals with similar or close characteristics as patients seen in a consultation for whom the health status, risks of complications, and disease evolutions are known.

Digital epidemiology, ehealth, artificial intelligence, digitization of clinical and epidemiological research

The DDP Research Unit mission is to develop key expertises
and skills in the entire chain of
research in digital health.