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:

SUBMITTER: Metcalf O 

PROVIDER: S-EPMC11948352 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature