An early warning system for depression
 
WARN-D

Onderzoek

Ons onderzoeksproject heeft tot doel het gepersonaliseerde systeem voor vroegtijdige waarschuwing WARN-D te ontwikkelen dat depressie bij studenten kan voorspellen voordat deze optreedt. Dit is belangrijk omdat de vroege volwassenheid een tijd is waarin psychische problemen hun hoogtepunt bereiken. Het is ook belangrijk omdat interventies slechts ongeveer 1 op de 2 mensen kunnen helpen en experts zijn het erover eens dat preventie de meest effectieve manier is om de wereldwijde ziektelast van depressie te veranderen. De grootste barrière voor preventie is het identificeren van degenen die in de nabije toekomst risico lopen op depressie. Ons project pakt deze uitdaging aan door WARN-D te ontwikkelen dat depressie betrouwbaar voorspelt voordat het zich voordoet, en belooft de wetenschap van depressiepreventie radicaal te transformeren. Om dit te doen, zullen we 2.000 studenten gedurende 2 jaar volgen en opkomende theoretische, meet- en modelleringsbenaderingen uit verschillende wetenschappelijke gebieden integreren. Deze omvatten complex systems theory, meting van de geestelijke gezondheid via smartphones en smartwatches, evenals netwerkmodellen en machine learning.

Het WARN-D-project wordt gefinancierd door de European Research Council (ERC) in het kader van het Horizon 2020-onderzoeks- en innovatieprogramma van de Europese Unie, subsidieovereenkomst nr. 949059.

Publicaties

Het project is gestart in april 2021 en onze eerste publicaties worden verwacht in het voorjaar van 2022. De publicaties van Dr. Eiko Fried vindt je hier.

Studenten scripties

  • 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”

Wetenschappelijke presentaties

  • 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, virtueel
  • 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, virtueel (geleid door 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”, virtueel (geleid door 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, virtueel; SiNAPSA Neuroscience Conference, virtueel

WARN-D in het nieuws

Videos

(Je kunt meer Engelse video’s vinden op onze Engelse website)