Acute suicidal ideation in context: highlighting sentiment-based markers through the diary entries of a clinically...

Despite major strides in conceptualizing and modeling the multifaceted nature of suicidal thought and behavior (STB) over the past few decades, the overall predictability of STB has not improved. This may be partly due to the dynamic nature of suicidal ideation (SI), which often fluctuates over hours, yet is largely overlooked in studies. Bolstered by the application and promise of natural language processing (NLP) across the mental health field, efforts toward richer operationalization of acute SI may include analyses on written data that occur alongside changes in SI, thus offering a better understanding of STB as it unfolds.

Ecological momentary assessment (EMA) data from 268 participants with major depressive disorder (MDD) were utilized to investigate acute changes in SI. Data consisted of thrice-daily SI severity scores measured through self-report responses to item 9 of the Patient Health Questionnaire mobile version (MPHQ-9) as well as free-form diary text. Using difference scores and probability of acute change thresholds, eleven acute SI phase trajectory types were defined to label change in SI over three consecutive EMAs. In total, 5,938 acute SI trajectories were paired with the temporally centered diary entries. The Sentiment Analysis and Cognition Engine (SEANCE) tool was applied to quantify the written content of each diary entry across eight established lexica. Entry results were grouped based on phase trajectory type, and the Kruskal-Wallis test was employed with post-hoc multiple hypothesis correction to statistically compare SEANCE features between all group pairs.

There were 131 statistically significant (adjusted p-value < 0.05) pairwise differences between acute SI phase trajectory groups, implicating 31 NLP features. Consistent with the literature, results highlighted qualities of writing that are generally associated with heightened SI, including personal pronoun usage, passivity, and negative valence. Patterns of significance also uncovered novel contextual nuance in terms of how characteristics such as verbosity, hostility, anger, and pleasantness present in relation to SI over short change trajectories.

This work provides an accessible exploratory framework that capitalizes on the benefits of dense EMA sampling and NLP to profile and quantify acute SI trajectories. The use of the MPHQ's item 9 to quantify SI is an important limitation as it is designed to also capture precursory SI, passive SI, and SI-adjacent behaviors, potentially overestimating the SI expressed by participants. Nonetheless, future research should continue to focus on short timeframes as there are likely important signals and interpretative nuances to SI expression that have yet to be fully detailed.