Descrizione del progetto
Prevedere la depressione per agevolare la prevenzione
La prevenzione rappresenta il modo più efficace per affrontare la depressione, che colpisce circa 40 milioni di persone nella regione europea. La depressione, principale causa di decessi per suicidio, limita la capacità di una persona di gestire la vita quotidiana e di agire pienamente in ambito lavorativo o scolastico. Gli studi dimostrano che solo il 50 % dei pazienti migliorano nella fase iniziale di trattamento. In tale contesto, il progetto WARN-D, finanziato dal CER, svilupperà un sistema di allerta precoce personalizzato per contribuire all’individuazione delle persone a rischio e che dovrebbero ricevere assistenza. Il progetto seguirà 2 000 soggetti nella loro vita quotidiana per oltre 2 anni. Saranno utilizzati vari strumenti per sviluppare il sistema di previsione della depressione prima del suo sviluppo.
Obiettivo
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.
Campo scientifico
- natural sciencesmathematicsapplied mathematicsdynamical systems
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsmobile phones
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-STG - Starting GrantIstituzione ospitante
2311 EZ Leiden
Paesi Bassi