SynthesisJournal of medical Internet research2025
The Application of AI to Ecological Momentary Assessment Data in Suicide Research: Systematic Review.
Synthesis in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of AI to Ecological Momentary Assessment Data in Suicide Research: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Balancing burden and resolution: Effects of EMA survey schedule on compliance and study evaluation in suicide-focused research.Journal of psychiatric research · 2026Article
- Predicting suicide attempts in a high-risk clinical cohort of adolescents using machine-learning.European child & adolescent psychiatry · 2026Article
- Position paper on symbiotic intelligence in healthcare: Can AI help us better understand suicidal behavior and prevent suicide?Frontiers in medicine · 2026Article
- Recent Advances in AI-Driven Mobile Health Enhancing Healthcare-Narrative Insights into Latest Progress.Bioengineering (Basel, Switzerland) · 2025Review
- Association Between Current Suicidal Ideation and Personality Traits: Analysis of the Personality Inventory for DSM-5 in a Community Mental Health Sample.Medicina (Kaunas, Lithuania) · 2025Article
- Deep learning in obsessive-compulsive disorder: a narrative review.Frontiers in psychiatry · 2025Review
- Advances in the assessment and study of suicide in late-life depression.Frontiers in psychiatry · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEcological momentary assessment (EMA) captures dynamic processes suitable to the study of suicidal ideation and behaviors. Artificial intelligence (AI) has increasingly been applied to EMA data in the study of suicidal processes.
objectiveThis review aims to (1) synthesize empirical research applying AI strategies to EMA data in the study of suicidal ideation and behaviors; (2) identify methodologies and data collection procedures used, suicide outcomes studied, AI applied, and results reported; and (3) develop a standardized reporting framework for researchers applying AI to EMA data in the future.
methodsPsycINFO, PubMed, Scopus, and Embase were searched for published articles applying AI to EMA data in the investigation of suicide outcomes. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were used to identify studies while minimizing bias. Quality appraisal was performed using CREMAS (adapted STROBE [Strengthening the Reporting of Observational Studies in Epidemiology] Checklist for Reporting Ecological Momentary Assessment Studies).
resultsIn total, 1201 records were identified across databases. After a full-text review, 12 (1%) articles, comprising 4398 participants, were included. In the application of AI to EMA data to predict suicidal ideation, studies reported mean area under the curve (0.74-0.86), sensitivity (0.64-0.81), specificity (0.73-0.86), and positive predictive values (0.72-0.77). Studies met between 4 and 13 of the 16 recommended CREMAS reporting standards, with an average of 7 items met across studies. Studies performed poorly in reporting EMA training procedures and treatment of missing data.
conclusionsFindings indicate the promise of AI applied to self-report EMA in the prediction of near-term suicidal ideation. The application of AI to EMA data within suicide research is a burgeoning area hampered by variations in data collection and reporting procedures. The development of an adapted reporting framework by the research team aims to address this.
trial registrationOpen Science Framework (OSF); https://doi.org/10.17605/OSF.IO/NZWUJ and PROSPERO CRD42023440218; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023440218.
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Registered trials
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.