TY  - JOUR
AU  - Melia, Ruth
AU  - Musacchio Schafer, Katherine
AU  - Rogers, Megan L
AU  - Wilson-Lemoine, Emma
AU  - Joiner, Thomas Ellis
PY  - 2025
DA  - 2025/4/17
TI  - The Application of AI to Ecological Momentary Assessment Data in Suicide Research: Systematic Review
JO  - J Med Internet Res
SP  - e63192
VL  - 27
KW  - ecological momentary assessment
KW  - artificial intelligence
KW  - machine learning
KW  - suicidal thoughts and behaviors
KW  - mobile health
KW  - mHealth
AB  - Background: Ecological 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. Objective: This 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. Methods: PsycINFO, 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). Results: In 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. Conclusions: Findings 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 Registration: Open Science Framework (OSF); https://doi.org/10.17605/OSF.IO/NZWUJ and PROSPERO CRD42023440218; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023440218 
SN  - 1438-8871
UR  - https://www.jmir.org/2025/1/e63192
UR  - https://doi.org/10.2196/63192
DO  - 10.2196/63192
ID  - info:doi/10.2196/63192
ER  -