Psychiatry Investig.  2025 Feb;22(2):156-166. 10.30773/pi.2024.0257.

Machine Learning Models to Identify Individuals With Imminent Suicide Risk Using a Wearable Device: A Pilot Study

• Um J
• Park J
• Lee DE
• Ahn JE
• Baek JH

• KMID: 2564768
• DOI: http://doi.org/10.30773/pi.2024.0257

Abstract

Objective
We aimed to determine whether individuals at immediate risk of suicide could be identified using data from a commercially available wearable device.
Methods
Thirty-nine participants experiencing acute depressive episodes and 20 age- and sex-matched healthy controls wore a commercially available wearable device (Galaxy Watch Active2) for two months. We collected data on activities, sleep, and physiological metrics like heart rate and heart rate variability using the wearable device. Participants rated their mood spontaneously twice daily on a Likert scale displayed on the device. Mood ratings by clinicians were performed at weeks 0, 2, 4, and 8. The suicide risk was assessed using the Hamilton Depression Rating Scale’s suicide item score (HAMD-3). We developed two predictive models using machine learning: a single-level model that processed all data simultaneously to identify those at immediate suicide risk (HAMD-3 scores ≥1) and a multilevel model. We compared the predictions of imminent suicide risk from both models.
Results
Both the single-step and multi-step models effectively predicted imminent suicide risk. The multi-step model outperformed the single-step model in predicting imminent suicide risk with area under the curve scores of 0.89 compared to 0.88. In the multi-step model, the HAMD total score and heart rate variability were most significant, whereas in the single-step model, the HAMD total score and diagnosis were key predictors.
Conclusion
Wearable devices are a promising tool for identifying individuals at immediate risk of suicide. Future research with more refined temporal resolution is recommended.

Keyword

Suicide; Depression; Daily mood monitoring; Imminent suicide risk; Wearable device; Risk monitoring