Predicting high anger intensity using ecological momentary assessment and wearable-derived physiological data in a trauma-affected sample

Abstract

Background: Digital technologies offer tremendous potential to predict dysregulated mood and behavior within an individual’s environment, and in doing so can support the development of new digital health interventions. However, no prediction models have been built in trauma-exposed populations that leverage real-world data. Objective: This project aimed to determine if wearable-derived physiological data can predict anger intensity in trauma-exposed adults. Method: Heart rate variability (i.e. a commercial wearable stress score) was combined with ecological momentary assessment (EMA) data collected over 10 days (n = 84). Five summary measures from stress scores collected 10 min prior to each EMA were selected using factor analysis of 24 candidates. Results: A high area under the receiver operating curve (AUC) was found for a logistic mixed effects model including these measures as predictors, ranging 0.761 (95% CI:0.569–0.921) to 0.899 (95% CI:0.784–0.980) across cross-validation methods. Conclusions: While the predictive performance may be overly optimistic due to the outcome prevalence (13.8%) and requires replication with larger datasets, our promising findings have significant methodological and clinical implications for researchers looking to build novel prediction and treatment approaches to respond to posttraumatic mental health.