ArticlePsychological medicine2025
Smartphone-based text obtained via passive sensing as it relates to direct suicide risk assessment.
Article in Psychological medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Fine-tuning large language models for detecting negative self-referential language in smartphone text among youth with depression.Journal of psychopathology and clinical science · 2026Article
- Conceptualization From the Sensors to Suicide-Related Outcomes: Scoping Review Based on Layered Hierarchical Sensemaking Framework.JMIR mHealth and uHealth · 2026Article
- Towards naturalistic social neuroscience: A multi-level framework integrating real-world phenotyping, neurobiology, and computational mechanisms.Cognitive, affective & behavioral neuroscience · 2026Review
- Surfacing Suicidal Risk Through Simulated Social Interaction: Per-Person Language Model Agents as Communicative Stress Tests.medRxiv : the preprint server for health sciences · 2026Article
- Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study.JMIR mental health · 2026Article
- In their own words: case studies of adolescent smartphone language preceding suicide-related hospitalizations.NPP - digital psychiatry and neuroscience · 2026Article
- Apologies in Crisis: The Link Between Perceived Burdensomeness and Suicide Risk in Online Text Communication.Suicide & life-threatening behavior · 2025Article
- Integrating wearable biosensing with clinical interviews for suicide risk detection in adolescents.Digital healthArticle
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Authors and funding
9 authors.
Funding
Abstract
backgroundRecent research highlights the dynamics of suicide risk, resulting in a shift toward real-time methodologies, such as ecological momentary assessment (EMA), to improve suicide risk identification. However, EMA's reliance on active self-reporting introduces challenges, including participant burden and reduced response rates during crises. This study explores the potential of Screenomics-a passive digital phenotyping method that captures intensive, real-time smartphone screenshots-to detect suicide risk through text-based analysis.
methodSeventy-nine participants with past-month suicidal ideation or behavior completed daily EMA prompts and provided smartphone data over 28 days, resulting in approximately 7.5 million screenshots. Text from screenshots was analyzed using a validated dictionary encompassing suicide-related and general risk language.
resultsResults indicated significant associations between passive and active suicidal ideation and suicide planning with specific language patterns. Detection of words related to suicidal thoughts and general risk-related words strongly correlated with self-reported suicide risk, with distinct between- and within-person effects highlighting the dynamic nature of suicide risk factors.
conclusionsThis study demonstrates the feasibility of leveraging smartphone text data for real-time suicide risk detection, offering a scalable, low-burden alternative to traditional methods. Findings suggest that dynamic, individualized monitoring via passive data collection could enhance suicide prevention efforts by enabling timely, tailored interventions. Future research should refine language models and explore diverse populations to extend the generalizability of this innovative approach.
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