Description du projet
Anticiper la dépression pour faciliter sa prévention
La prévention est la manière la plus efficace de traiter la dépression, qui affecte environ 40 millions de personnes en Europe. Cause principale des suicides, la dépression limite la capacité d’une personne à faire face au quotidien et à être pleinement opérationnelle au travail ou à l’école. Des études montrent que seulement 50 % des patients vont mieux en suivant un premier traitement. Dans ce contexte, le projet WARN-D, financé par le CER, développera un système d’alerte précoce personnalisé qui permettra d’identifier les personnes à risque qui devraient bénéficier d’une assistance. Le projet suivra 2 000 personnes dans leur quotidien durant plus de deux ans. Différents outils seront utilisés pour développer le système de prévision de la dépression avant qu’elle ne survienne.
Objectif
Depression is common, debilitating, and often chronic. It severely compromises the capacity for independent living, and is the strongest predictor of suicide. Young people are disproportionately affected, and many will spend over 20% of their lives in a state of depression. Further, only 50% of patients improve under initial treatment. Experts agree that prevention is the most effective way to change depression’s global disease burden. The biggest barrier to successful personalized prevention is to identify those at risk for depression in the near future. My proposal aims to solve the challenge who should receive prevention, and when, by developing the personalized early warning system WARN-D. To implement personalized detection, I will follow 2,000 individuals over 2 years, and integrate emerging theoretical, measurement, and modelling approaches from different scientific fields so far unconnected. Regarding theory, I conceptualize depression as a complex dynamical system in which causal relations and vicious cycles between problems can move the system from a healthy to a clinical state, consistent with the Network Approach to Psychopathology that I co-developed. Regarding measurement, I will follow participants in their daily lives, and collect temporal dynamics of bio-psycho-social variables like mood, anxiety, stress, impairment, sleep, and activity via smart-phone based ecological momentary assessment (EMA) and smart-watch based digital phenotype data. I will use dynamical network models to study the relations among problems, and use parameters of these models, combined with baseline, EMA, and digital phenotype data, to construct the prediction model WARN-D via state-of-the-art machine learning models. The interdisciplinary project combines numerous modern tools to develop a tailored personalized early warning system that forecasts depression reliably before it occurs, promising to radically transform the science of depression detection.
Champ scientifique
- natural sciencesmathematicsapplied mathematicsdynamical systems
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsmobile phones
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Thème(s)
Régime de financement
ERC-STG - Starting GrantInstitution d’accueil
2311 EZ Leiden
Pays-Bas