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

Lin Ge, Xinming An, Donglin Zeng, Samuel McLean, Ronald Kessler, Rui Song

Keywords: Continuous-time hidden Markov model, Mental health, Multivariate longitudinal data

Abstract: Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among veterans and millions of Americans after traumatic exposures, resulting in substantial health and financial burdens for trauma survivors, their families, 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 objective longitudinal mobile device data to identify homogeneous APNS states and study the dynamic transitions among them and potential risk factors after trauma exposure. To handle the unique challenges posed by longitudinal mobile device data, we developed a continuous-time hidden Markov factor model 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. A Python implementation of the proposed method is available at https://anonymous.4open.science/r/CTHMFM.

Track: 10. Digital health

Registration Id: LJNKLTQQR65

Submission Number: 191