Using Smartphone Surveys to Predict Next-Week Suicide Attempts

Clinicians are tasked with predicting and preventing suicidal behavior among their patients; however, there is currently no method for accurately predicting whether a person will make a suicide attempt (SA) in the near future. We tested whether brief, smartphone-based surveys, combined with passively collected survey metadata, could predict the occurrence of suicidal behavior over the next 7 days among those at elevated risk. Participants were 619 patients presenting to the hospital with suicidal thoughts/behavior. They were sent brief (20-item) smartphone-based surveys 6 times/day for 3 months. Survey responses (N = 79,448) and metadata (e.g., time since last survey submission) were used as predictors of next-week SA and suicide-related event (SRE; which also included hospitalization to prevent an SA) in a series of machine learning models. The most accurate prediction was achieved using bidirectional long short-term memory and simple lasso-penalized logistic regression models, with the best performing model using bidirectional long short-term memory to predict SRE, which with specificity at .90, had area under the curve = .94, sensitivity = .87, and positive predictive value = .30, and SAs with area under the curve = .90, sensitivity = .74, positive predictive value = .16. Prediction accuracy was higher than has been achieved in prior studies and was strongest for models that predicted SREs (vs. SAs), included more sources of data, focused on adults (vs. adolescents), and when participants' own data were included in the model training process (vs. being held out). The strongest and most consistent predictors of next-week SA included within-study history of SREs (from adult lasso regression: OR = 1.47) and self-reported agitation (OR = 1.11), whereas odds of next-week SA were decreased for surveys submitted on weekends (OR = 0.87) and in the context of feelings that one could resist suicidal urges (OR = 0.88-0.96). Brief smartphone-based surveys can predict next-week SAs/SREs with a fairly high degree of accuracy. Future work is needed to further improve accuracy and test just-in-time interventions targeting high-risk periods. (PsycInfo Database Record (c) 2026 APA, all rights reserved).