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Cheryl M. Corcoran

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Open access Jul 2026

Relationship among Sleep Disturbance, Stress, and Suicidal Ideation in Clinical High Risk for Psychosis

Abstract Background and Hypothesis Sleep disturbance is a well-established risk factor for suicide, though few studies to date have examined whether sleep disturbance contributes to suicide risk among individuals at clinical high risk for psychosis (CHR). The current study addressed this gap in the literature. We hypothesized that sleep disturbance would have a unique relationship with suicidal ideation/attempts when accounting for other variables in the model. We also hypothesized that the interaction between sleep disturbance/attenuated positive symptoms and sleep disturbance/stress would be related to suicidal ideation/attempts in CHR. Study Design The current study used data generated by the Accelerating Medicines Partnership® Schizophrenia Observational Study. The total sample included 1,048 participants (827 CHR and 221 community controls). Participants completed measures of suicidal ideation/attempts, attenuated positive symptoms, depressive symptoms, perceived stress, and sleep disturbance. Study Results Results supported a relationship between sleep disturbance and suicidal ideation/attempts in CHR, with participants who had lifetime ideation and attempts experiencing more sleep disturbance than those with no ideation or attempts. We also found small, but significant positive correlations between sleep disturbance and suicide risk in CHR. When accounting for other variables in the model, the effect of sleep disturbance remained significant for past month ideation, but not lifetime ideation or attempts. Both interaction models were non-significant. Conclusions Our findings highlight the potential value of sleep measures in early identification and treatment of suicide risk in CHR. Further research in this area is warranted.

H. Wastler, Aubrey M. Moe, Alexandra M Blouin et al. · 0 citations
Conference Jul 2026

From Lived Experience to Digital Biomarkers: Methods for Scaling Narrative Data with Large Language Models in Mental Health

Lived experience narratives provide a rich account of how individuals interpret and organize their mental health, capturing dimensions of meaning and context that are often missed by structured assessments and traditional language-based features. Despite their importance, the unstructured nature of these data has limited their systematic use. Recent advances in large language models (LLMs) enable scalable analysis of narrative data, allowing for the extraction of thematic and structural features across large datasets. These approaches position lived experience as a promising digital biomarker, with the potential to capture early, ecologically valid signals and complement existing clinical measures. However, challenges related to validation, interpretability, bias, and data governance remain. We outline emerging methodological frameworks and discuss how LLMbased approaches can support more scalable, longitudinal, and person-centered models of mental health.

Jenna M. Reinen, Cheryl M. Corcoran, René S. Kahn et al. · 0 citations