ArticleEuropean archives of psychiatry and clinical neuroscience2024
Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment.
Article in European archives of psychiatry and clinical neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
What it found
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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.
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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.
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Who cites it
6 citing papers in PubMed.
- Using smartphone-based assessment of social experience to predict relapse in first-episode psychosis.Schizophrenia (Heidelberg, Germany) · 2026Article
- Personalizing ecological momentary intervention for substance use disorders through data-driven decision rules.Frontiers in psychiatry · 2026Review
- Reply to paper 'Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment'.European archives of psychiatry and clinical neuroscience · 2025Article
- Data analysis strategies for the Accelerating Medicines Partnership® Schizophrenia Program.Schizophrenia (Heidelberg, Germany) · 2025Article
- Modelling the impact of environmental and social determinants on mental health using generative agents.NPJ digital medicine · 2025Review
- Using Machine Learning and the HAMD-24 Scale to Predict Suicide Ideation in Depressed Patients.Psychology research and behavior management · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across different study groups. We investigated whether EMA combined with unsupervised machine learning can distinguish groups on the continuum of genetic risk toward psychotic illness and identify individuals with need for extended healthcare. Individuals with psychotic disorder (PD, N = 55), healthy individuals (HC, N = 25) and HC with first-degree relatives with psychosis (RE, N = 20) were assessed at two sites over 7 days using EMA. Cluster analysis determined subgroups based on similarities in longitudinal trajectories of psychotic symptom ratings in EMA, agnostic of study group assignment. Psychotic symptom ratings were calculated as average of items related to hallucinations and paranoid ideas. Prior to EMA we assessed symptoms using the Positive and Negative Syndrome Scale (PANSS) and the Community Assessment of Psychic Experience (CAPE) to characterize the EMA subgroups. We identified two clusters with distinct longitudinal EMA characteristics. Cluster 1 (N
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Registered trials
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