Project description DEENESFRITPL Forecasting depression to facilitate prevention Prevention is the most effective way to address depression, which affects around 40 million people in the European region. The primary cause of suicide deaths, depression limits a person’s ability to cope with daily life and to fully function at work or school. Studies show only 50 % of patients improve under initial treatment. In this context, the ERC-funded WARN-D project will develop a personalised early warning system to help identify people at risk and who should receive assistance. The project will follow 2 000 individuals in their daily lives for over 2 years. Numerous tools will be used to develop the system for forecasting depression before it occurs. Show the project objective Hide the project objective Objective 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. Fields of science natural sciencesmathematicsapplied mathematicsdynamical systemsengineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsmobile phonesnatural sciencescomputer and information sciencesartificial intelligencemachine learning Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-2020-STG - ERC STARTING GRANTS Call for proposal ERC-2020-STG See other projects for this call Funding Scheme ERC-STG - Starting Grant Host institution UNIVERSITEIT LEIDEN Net EU contribution € 1 500 000,00 Address RAPENBURG 70 2311 EZ Leiden Netherlands See on map Region West-Nederland Zuid-Holland Agglomeratie Leiden en Bollenstreek Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 1 500 000,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all UNIVERSITEIT LEIDEN Netherlands Net EU contribution € 1 500 000,00 Address RAPENBURG 70 2311 EZ Leiden See on map Region West-Nederland Zuid-Holland Agglomeratie Leiden en Bollenstreek Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 1 500 000,00
