A Continuous-Time Dynamic Factor Model for Intensive Longitudinal Data Arising from Mobile Health Studies.

ABSTRACT: Intensive longitudinal data (ILD) collected in mobile health (mHealth) studies contain rich information on the dynamics of multiple outcomes measured frequently over time. Motivated by an mHealth study in which participants self-report the intensity of many emotions multiple times per day, we describe a dynamic factor model that summarizes ILD as a low-dimensional, interpretable latent process. This model consists of (i) a measurement submodel-a factor model-that summarizes the multivariate longitudinal outcome as lower-dimensional latent variables and (ii) a structural submodel-an Ornstein-Uhlenbeck (OU) stochastic process-that captures the dynamics of the multivariate latent process in continuous time. We derive a closed-form likelihood for the marginal distribution of the outcome and th

SUBMITTER: Abbott MR 

PROVIDER: S-EPMC12439307 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature