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

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 p

SUBMITTER: Um J 

PROVIDER: S-EPMC11878142 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature