Using intensive longitudinal data to identify early predictors of suicide-related outcomes in high-risk adolescents: Practical and conceptual considerations

Mobile technology offers new possibilities for assessing suicidal ideation and behavior in real- or near-real-time. It remains unclear how intensive longitudinal data can be used to identify proximal risk and inform clinical decision making. In this study of adolescent psychiatric inpatients (N = 32, aged 13-17 years, 75% female), we illustrate the application of a three-step process to identify early signs of suicide-related crises using daily diaries. Using receiver operating characteristic (ROC) curve analyses, we considered the utility of 12 features-constructed using means and variances of daily ratings for six risk factors over the first 2 weeks postdischarge (observations = 360)-in identifying a suicidal crisis 2 weeks later. Models derived from single risk factors had modest predictive accuracy (area under the ROC curve [AUC] 0.46-0.80) while nearly all models derived from combinations of risk factors produced higher accuracy (AUCs 0.80-0.91). Based on this illustration, we discuss implications for clinical decision making and future research.