Predicting suicidal and self-harm ideation using ecological momentary assessment: deep learning analysis in a general population sample

Abstract

Background Suicidal and self-harm ideation are major risk factors for suicide but are often difficult to detect, particularly in non-clinical populations. Ecological Momentary Assessment (EMA) offers a real-time, low-burden method for monitoring psychological states, yet its predictive value outside clinical settings remains unclear. Objective To evaluate whether brief, indirect daily EMA data collected via a smartphone app can predict suicidal and self-harm ideation two weeks later in a general population sample. Methods A total of 499 adults in Korea completed 28 days of EMA using the BIG4 + app, reporting on seven daily items related to mood, sleep, appetite, concentration, fatigue, and loneliness. Suicidal and self-harm ideation were assessed using the CESD-R at baseline, 2 weeks, and 4 weeks. A recurrent neural network with Long Short-Term Memory (LSTM) architecture was trained on two-week EMA sequences, using 10-fold cross-validation. Results The combined model using EMA and baseline data achieved an AUC of 0.873 for suicidal ideation and 0.821 for self-harm ideation. Predictive accuracy exceeded an AUC of 0.75 by day 6. Participants with ideation consistently showed lower scores on all EMA items. The study achieved a 94% compliance rate. Conclusions Brief, indirect EMA data can predict near-term suicidal and self-harm ideation in a general population. These findings support the feasibility of smartphone-based EMA as a scalable and non-intrusive tool for early detection of suicide risk. Clinical trial number Not applicable.