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Evaluation ViewAttendees 8

Suicide and Self-Injury
Session Type:: Symposia

Symposium 7 - Advancing Suicide EMA Research: Diverse Research Questions Call For Diverse Analytic Approaches

Friday, November 21, 2025
3:00 PM - 4:30 PM CST
Location: Celestin B, Level 3
CE: 1.5

Keywords: Suicide, Technology / Mobile Health, Methods
Level of Familiarity: Basic to Moderate
Recommended Readings: Sedano-Capdevila, A., Porras-Segovia, A., Bello, H. J., Baca-García, E., & Barrigon, M. L. (2021). Use of ecological momentary assessment to study suicidal thoughts and behavior: a systematic review. Current Psychiatry Reports, 23(7), 41. https://doi.org/10.1007/s11920-021-01255-7,   Nock, M. K., Kleiman, E. M., Abraham, M., Bentley, K. H., Brent, D. A., Buonopane, R. J., ... & Pearson, J. L. (2021). Consensus statement on ethical & safety practices for conducting digital monitoring studies with people at risk of suicide and related behaviors. Psychiatric Research and Clinical Practice, 3(2), 57-66. https://doi.org/10.1176/appi.prcp.20200029,   Kleiman, E. M., Glenn, C. R., & Liu, R. T. (2019). Real-Time Monitoring of Suicide Risk among Adolescents: Potential Barriers, Possible Solutions, and Future Directions. Journal of Clinical Child & Adolescent Psychology, 48(6), 934–946. https://doi.org/10.1080/15374416.2019.1666400,   Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1-32. https://doi.org/10.1146/annurev.clinpsy.3.022806.091415,  

The field of suicide research has witnessed an explosion of studies utilizing ecological momentary assessment (EMA) in the past 15 years—allowing researchers to observe suicide risk as it unfolds in real time, predict proximal outcomes, and begin developing novel just-in-time interventions. A leading cause of death, suicide is uniquely positioned to benefit from EMA given the transience of acute risk states and complex etiology. Intensive longitudinal study designs such as EMA produce data that could be analyzed in countless different ways. However, there is disproportionately little guidance on which types of research questions these various analytic approaches are best suited to answer. In other words, what do we do with EMA data once it is collected? This symposium showcases cutting-edge EMA-based studies that employ diverse analytic methods ranging from mixture modeling, idiographic network analysis, and machine learning, offering clinically relevant insights into integrating EMA to advance suicide risk assessment and prediction.

The first presentation examines discrepancies in suicidal ideation (SI) reporting between EMA and retrospective measures among adolescents, highlighting EMA’s ability to identify SI in individuals with lower clinical severity who might go undetected by traditional assessments. The second presentation explores the dynamic interplay between the desire to live (DTL) and the desire to die (DTD) in adolescents. Findings will reveal distinct profiles of DTL and DTD through EMA and their associations with suicidal behaviors, offering insights into phenotypic expressions of suicidal motivation. The third presentation utilizes idiographic network models to uncover person-specific pathways of SI risk among adults during the high-risk post-hospitalization period. This study reveals substantial heterogeneity across suicidal individuals in purported risk processes and indicates the importance of precision approaches for assessment and intervention. The fourth presentation applies Natural Language Processing (NLP) analysis to identify linguistic markers of acute changes in SI in daily life. Findings show that features like hostility, verbosity, and emotional expression vary across distinct trajectories of SI changes (e.g., transient vs. persistent elevation), suggesting the potential value of NLP-based tools for risk detection. Together, these studies underscore the value of real-time, individualized approaches to understanding and addressing suicide risk.

Finally, we are extremely fortunate to have a senior faculty member, who has pioneered the application of real-time monitoring assessments to suicidal individuals, as our discussant to share his reflections about this exciting direction of suicide research.

Learning Objectives:

At the end of this session, the learner will be able to:
  1. Discuss the benefits and challenges of applying ecological momentary assessment to suicide research.
  2. Provide examples of ecological momentary assessment designs and methodological considerations for assessing suicide risk.
  3. Explain how ecological momentary assessment can improve suicide risk assessment beyond traditional, retrospective assessments.
  4. Discuss the data-driven phenotypes of suicidal motivational states and their implications for interventions.
  5. Explain the advantages of idiographic analytic methods in examining suicide risk processes.
  6. Discuss how machine learning approaches can be applied to ecological momentary assessment data to improve suicide risk detection and prediction.

Presentations:

  • 3:06 PM - 3:27 PM CST
    (SYM 7) Who and What Retrospective Risk Assessments Miss: Examining Retrospective Denial of Momentary Suicidal Ideation in Adolescents

    Speaker: Ki Eun Shin, Ph.D. (she/her/hers) – Long Island University, Post

    Co-author: Ilana Gratch, M.S. – Columbia University

    Co-author: Christine B. Cha, Ph.D. (she/her/hers) – Yale University

  • 3:27 PM - 3:48 PM CST
    (SYM 7) Digital Phenotypes of Dynamics in Desire to Live and Die Among Suicidal Adolescents

    Speaker: Olivia C. Lawrence, M.A. (she/her/hers) – Columbia University

    Co-author: Ki Eun Shin, Ph.D. (she/her/hers) – Long Island University, Post

    Co-author: Da-Eun Lee, PhD (she/her/hers) – Yale School of Medicine

    Co-author: Christine B. Cha, Ph.D. (she/her/hers) – Yale University

  • 3:48 PM - 4:09 PM CST
    (SYM 7) Idiographic Network Models Reveal Highly Individualized Proximal Risk Processes for Suicidal Ideation in Adults After Hospitalization for a Suicide Attempt

    Speaker: Gemma T. Wallace, Ph.D. (she/her/hers) – Alpert Medical School of Brown University

    Co-author: Christopher D. Hughes, Ph.D. (he/him/his) – Alpert Medical School of Brown University

    Co-author: Heather Schatten, Ph.D. – Butler Hospital & Brown University

  • 4:09 PM - 4:30 PM CST
    (SYM 7) Linguistic Signals of Acute Suicidal Ideation in Major Depressive Disorder: NLP Analysis Reveals Contextual Sentiment Patterns in Ecological Momentary Assessments

    Speaker: Nicholas C. Jacobson, Ph.D. – Dartmouth College

    Co-author: Damien Lekkas, PhD (he/him/his) – Northwestern University

    Co-author: Amanda C. Collins, Ph.D. (she/her/hers) – Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States

    Co-author: Michael V. Heinz, M.D. – Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States

    Co-author: Tess Z. Griffin, Ph.D. – Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States

    Co-author: Arvind Pillai, M.S. – Department of Computer Science, Dartmouth College, Hanover, NH, United States

    Co-author: Subigya Nepal, Ph.D. – Stanford Institute for Human-Centered-AI, Stanford University, Palo Alto, CA, United States

    Co-author: Daniel M. Mackin, Ph.D. – Dartmouth-Hitchcock Medical Center and Dartmouth College

    Co-author: Andrew Campbell, PhD (he/him/his) – Dartmouth College