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.

翻译：创伤后不良神经精神后遗症（APNS）在退伍军人及数百万经历创伤暴露的美国民众中普遍存在，给创伤幸存者和社会带来沉重负担。尽管过去数十年间针对APNS开展了大量研究，但由于若干独特挑战，其潜在神经生物学机制的理解进展有限。其中一个挑战是依赖主观自我报告测量来评估APNS，这容易导致测量误差和偏差（如回忆偏差）。为缓解这一问题，本文探索利用客观的纵向移动设备数据来识别同质性的APNS状态，研究创伤暴露后APNS的动态转换及其潜在风险因素。针对纵向移动设备数据的具体挑战，我们开发了探索性隐马尔可夫因子模型，并设计了稳定化期望最大化算法进行参数估计。通过模拟实验评估了参数估计和模型选择的性能。最后，为展示该方法的应用价值，我们将其应用于从“深化创伤后恢复理解（AURORA）”研究收集的移动设备数据。