Examining the utility of digital phenotyping for the prediction of intrusive experiences.

Background: Intrusive experiences related to witnessing a traumatic event are the core symptom of post-traumatic stress disorder (PTSD), and have been shown to be predicted by peritraumatic emotional arousal. However, research into the role of peritraumatic arousal in the development of intrusions has been limited by a reliance on self-report scales.Objective: This study aimed to examine whether facial, focal, and language phenotypes of peritraumatic emotional arousal could also contribute to the prediction of intrusive experiences.Method: This longitudinal study recruited university students (N = 81), who were recorded describing their response to an analogue trauma (car accident video). Participants' facial, vocal, and linguistic features were extracted from these recordings, as well as their self-reported emotional experiences, and input into machine learning (random forest regression) models to predict whether they would have intrusive experiences of the analogue trauma three days after their exposure to it.Results: Random forest regression models were able to predict variance in responsiveness to triggers that reminded individuals of the analogue trauma (R2 = 0.23) and whether they had vivid memories of the analogue trauma (R2 = 0.55). Vocal and language features made the greatest contribution to the prediction of responsiveness to triggers, while initial self-reported memory vividness made the greatest contribution to the prediction of three-day memory vividness.Conclusions: These findings suggest that facial, vocal, and language phenotypes, in combination with self-report measures, can have utility for predicting the occurrence of specific intrusive experiences following exposure to an analogue trauma in a university sample. Further research into this approach in clinical samples is required to demonstrate its utility for predicting intrusions in those with PTSD.