ArticleClinical psychology in Europe2023
Building an Early Warning System for Depression: Rationale, Objectives, and Methods of the WARN-D Study.
Article in Clinical psychology in Europe, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07471529 (Activating Social Resources in Psychotherapy-Seeking Individuals), which is not on this map. Cited by 30 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Activating Social Resources in Psychotherapy-Seeking Individuals: Effectiveness and Timing of a Social Support Just-in-Time Adaptive Intervention
Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction models for longitudinal trajectories of depression and anxiety: a systematic review.Journal of affective disorders · 2026Pooled it
- Harnessing digital health interventions to address the heterogeneity of depression: a systematic review.Frontiers in digital health · 2025Pooled it
- Dynamical system modeling in schizophrenia: a narrative review of computational psychiatry frameworks.Dialogues in clinical neuroscience · 2026Review
- Article
- Clinicians' practices and attitudes toward depression assessment: a cross-sectional survey study.BMC psychology · 2026Article
- Article
- Using network science to examine temporal relationships between 24-hour movement behaviors and depression during the transition to college: protocol for a prospective cohort study.Journal of activity, sedentary and sleep behaviors · 2026Article
- Using Network Science to Examine Temporal Relationships between 24-Hour Movement Behaviors and Depression During the Transition to College: Protocol for a Prospective Cohort Study.Research square · 2026Article
- Vocal Markers of Schizophrenia: Assessing the Generalizability of Machine Learning Models and Their Clinical Applicability.Schizophrenia bulletin · 2026Article
- Using wearable data to detect depression severity across clinical and non-clinical samples.Scientific reports · 2026Article
- Identifying predictors of student depression through validated machine learning pipelines.Frontiers in medicine · 2026Article
- Optimizing the frequency of ecological momentary assessments using signal processing.Psychological medicine · 2025Article
- Article
- Understanding the game experiences and mental health of youth: protocol for the Game-in-Action Quebec cohort study.BMJ open · 2025Article
- Article
- Article
- Digital interventions in mental health: An overview and future perspectives.Internet interventions · 2025Review
- E-mental Health in the Age of AI: Data Safety, Privacy Regulations and Recommendations.Alpha psychiatry · 2025Article
- A mixed-methods investigation of an ecological momentary assessment protocol for cigarette-smoking youth: Psychometric properties and participant experiences.Drug and alcohol dependence reports · 2025Article
- Evolution of Core Symptoms of Depression Disorders Among Chinese Adolescents Across Different Grades.Depression and anxiety · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Depression is common, debilitating, often chronic, and affects young people disproportionately. Given that 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 prevention is to identify individuals at risk for depression in the near future. To close this gap, this protocol paper introduces the WARN-D study, our effort to build a personalized early warning system for depression. Method: To develop the system, we follow around 2,000 students over 2 years. Stage 1 comprises an extensive baseline assessment in which we collect a broad set of predictors for depression. Stage 2 lasts 3 months and zooms into participants' daily experiences that may predict depression; we use smartwatches to collect digital phenotype data such as sleep and activity, and we use a smartphone app to query participants about their experiences 4 times a day and once every Sunday. In Stage 3, we follow participants for 21 months, assessing transdiagnostic outcomes (including stress, functional impairment, anxiety, and depression) as well as additional predictors for future depression every 3 months. Collected data will be utilized to build a personalized prediction model for depression onset. Discussion: Overall, WARN-D will function similarly to a weather forecast, with the core difference that one can only seek shelter from a thunderstorm and clean up afterwards, while depression may be successfully prevented before it occurs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.