Continuous-Time Hidden Markov Factor Model for Mobile Health Data: Application to Adverse Posttraumatic...

Each year, a significant portion of the 40 million individuals in the United States who seek care in emergency departments (EDs) following traumatic experiences develop adverse posttraumatic neuropsychiatric sequelae (APNS). This highlights the widespread impact of trauma and the critical need for effective interventions to address the health outcomes of these events. Despite significant research efforts, advancements in understanding the neurobiological mechanisms of APNS have been hindered, primarily due to reliance on subjective self-reports, which are susceptible to recall biases and careless responses. To overcome this limitation, we investigate the use of objective, longitudinal mobile device data to identify consistent APNS states and examine their dynamic transitions over time. To address the complexity of longitudinal mobile data, we developed a continuous-time hidden Markov factor model and applied it to mobile device data from the Advancing Understanding of Recovery after Trauma (AURORA) study. Findings from this study highlight the great potential of leveraging mobile device data for continuous health monitoring and for guiding personalized treatment approaches through mobile health initiatives.