Our research project aims to develop the personalized early warning system WARN-D that can predict depression in students before it occurs. This is important because early adulthood is a time in which mental health problems peak. It is also important because interventions can only help about 1 in 2 people, and experts agree that prevention is the most effective way to change depression’s global disease burden. The biggest barrier to prevention is to identify those at risk for depression in the near future. Our project tackles this challenge by developing WARN-D that forecasts depression reliably before it occurs, promising to radically transform the science of depression prevention. To do so, we will follow 2,000 students over 2 years, and integrate emerging theoretical, measurement, and modelling approaches from different scientific fields. These include complex systems theory, mental health measurement via smartphones and smartwatches, as well as network models and machine learning.

The WARN-D project is funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme, Grant agreement No. 949059.

Scientific publications

The protocol paper of our study is now submitted to a scientific journal, and we posted it online as a preprint (that means it is not peer-reviewed yet). You can find other publications of Dr. Eiko Fried here.

Student theses

Students have been able to analyze WARN-D data for their theses in many different ways, with topics ranging from childhood experiences/adversity, to social media use, to compliance on filling in daily surveys. Below you will find each thesis that has been written so far. For more detailed information on each thesis, see here.

  • KJ Gorrisen (2022): “The relationship between childhood adversity and personality in students: A network analysis”
  • A Rimpler (2022): “Generating Feedback Reports for Ecological Momentary Assessment Data”
  • NM Platania (2022): “Examining Links Between Individual Depressive Symptoms, Indicators of Socioeconomic Status, and Stressors”
  • J Essen (2022): “Non-compliance in an Ecological Momentary Assessment Study on Students’ Mental Health”
  • H Çağlayan (2022): “Sample Representativeness of the First Cohort of the WARN-D Project Dataset”
  • H Boekestijn (2022): “Towards an understanding of resilience and symptoms of generalized anxiety disorder (GAD) in students: a network analysis”
  • TF Steiniger (2022): “Chronotype and Functioning: A Network Analysis and Comparison between Early and Late Chronotypes among University Students”
  • S Meyer (2022): “Network Analysis on the correlation between Preliminary HiTOP Items of Maladaptive Personality Traits in the Internalizing Spectrum and Symptoms of Depression, Anxiety & Stress”
  • T Scheltinga (2022): “Towards a suicide safety net: the relationship between suicidal ideation, depressive symptoms and social support”
  • F Ouska (2022): “Untangling the Web: A Network Approach to the Antecedents and Consequences of Bullying”
  • C Claessen (2022): “Substance Use, Personality and Pathology: a Network Approach”
  • A Symeonidou (2022): “Risk and Protective Coping Factors in Depression: A Network Analysis”
  • K Gorissen (2022): “The relationship between childhood adversities and personality: a network analysis”
  • C Haneveld (2022): “The Influence of Child Maltreatment, Depression and Protective Factors on the Risk of Suicidal Behaviors and Ideation”
  • A Röttgers (2022): “Investigating the Role of Physical Activity, Coping Mechanisms and Depression: A Network Perspective”
  • G Koehler (2022): “Social media use, symptoms of depression, and personality: A network analysis”
  • E Diehl (2022): “Network analysis of multiple risk factors for depression in university students”
  • R Lipka (2022): “Resilience as A Mereological Concept: A Network Perspective on Resilience Factors”
  • M Moreira da Silva (2023): “Prediction of depressed mood using machine learning on time series data”

Scientific presentations

  • Eiko Fried (2023): “Developing an early warning system for depression”. Society for Ambulatory Assessment (SAA), Amsterdam (NL)
  • Eiko Fried & Aljoscha Rimpler (2023): “FRED: Generating Feedback Reports for Ecological Momentary Assessment Data”. Association for Psychological Science (APS), Washington D.C. (US)
  • Carlotta Rieble, Ricarda Proppert, & Björn Siepe (2023): “Augmenting Self-Reports With Passive Sensor Data to Understand Changes in Mental Health?”. Association for Psychological Science (APS), Washington D.C. (US)
  • Björn Siepe (2023): “Item Validation in the WARN-D Study”. Association for Psychological Science (APS), Washington D.C. (US)
  • Ricarda Proppert & Björn Siepe (2023): “Using passive sensor data to understand changes in mental health outcomes”. International Convention of Psychological Science (ICPS), Brussels (BE)
  • Carlotta Rieble (2023): “Measuring Changes in Depression: Do Different Ways to Self-Report Agree?”. International Convention of Psychological Science (ICPS), Brussels (BE)
  • Eiko Fried (2022): “Developing an early warning system for depression”. Stanford Center for Precision Mental Health and Wellness, virtual (led by Stanford University, US); GET DIGITAL, virtual; Annual Technology in Psychiatry Summit, virtual
  • Eiko Fried (2022): “Using network models to describe, predict, understand, and treat mental disorders”. Dutch Network Science Society Symposium, Leiden (NL); Norwegian Centre for Mental Disorders Research, virtual (led by the University of Oslo, NOR)
  • Carlotta Rieble (2022): “Early Warning Signals for Depression”. DGPPN Congress, Berlin (DE)
  • Ricarda Proppert (2022): “Survival of the fittest? Assessing bias in compliance to ecological momentary assessment protocols”. Association for Psychological Science (APS), Chicago (US)
  • Ricarda Proppert (2022): “Validating subjective EMA measures with objective activity tracking”. Association for Psychological Science (APS), Chicago (US)
  • Carlotta Rieble & Aljoscha Rimpler (2022): “Providing non-clinical feedback to motivate EMA participants”. Association for Psychological Science (APS), Chicago (US)
  • Ricarda Proppert & Carlotta Rieble (2021): “WARN-D – an early warning system to forecast depression in students”. EUniWell Symposium “Good Practices on Student Well-being”, virtual (led by Leiden University, NL)
  • Eiko Fried (2021): “Mental health: studying systems instead of syndromes”. Transdiagnostic Approaches to Mental Health Conference, virtual (led by the University of Manchester, UK); Presidential Symposium at Society for Computation in Psychology, virtual; SiNAPSA Neuroscience Conference, virtual

WARN-D in the news


We have made a few introductory videos about the project in the last months which we will list below.

1. General overview video

2. One minute summary of the project, using Legos

3. Leiden University portray about WARN-D