  Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among
veterans and millions of Americans after traumatic exposures, resulting in
substantial burdens for trauma survivors and society. Despite numerous studies
conducted on APNS over the past decades, there has been limited progress in
understanding the underlying neurobiological mechanisms due to several unique
challenges. One of these challenges is the reliance on subjective self-report
measures to assess APNS, which can easily result in measurement errors and
biases (e.g., recall bias). To mitigate this issue, in this paper, we
investigate the potential of leveraging the objective longitudinal mobile
device data to identify homogeneous APNS states and study the dynamic
transitions and potential risk factors of APNS after trauma exposure. To handle
specific challenges posed by longitudinal mobile device data, we developed
exploratory hidden Markov factor models and designed a Stabilized
Expectation-Maximization algorithm for parameter estimation. Simulation studies
were conducted to evaluate the performance of parameter estimation and model
selection. Finally, to demonstrate the practical utility of the method, we
applied it to mobile device data collected from the Advancing Understanding of
RecOvery afteR traumA (AURORA) study.
