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

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.

A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data.

The availability of mobile health (mHealth) technology has enabled increased collection of intensive longitudinal data (ILD). ILD have potential to capture rapid fluctuations in outcomes that may be associated with changes in the risk of an event. However, existing methods for jointly modeling longitudinal and event-time outcomes are not well-equipped to handle ILD due to the high computational cost.

A latent variable approach to jointly modeling longitudinal and cumulative event data using a weighted two-stage method.

Ecological momentary assessment (EMA), a data collection method commonly employed in mHealth studies, allows for repeated real-time sampling of individuals' psychological, behavioral, and contextual states. Due to the frequent measurements, data collected using EMA are useful for understanding both the temporal dynamics in individuals' states and how these states relate to adverse health events. Motivated by data from a smoking cessation study, we propose a joint model for analyzing longitudinal EMA data to determine whether certain latent psychological states are associated with repeated cigarette use.

A Robust Mixed-Effects Bandit Algorithm for Assessing Mobile Health Interventions.

Mobile health leverages personalized, contextually-tailored interventions optimized through bandit and reinforcement learning algorithms. Despite its promise, challenges like participant heterogeneity, nonstationarity, and nonlinearity in rewards hinder algorithm performance. We propose a robust contextual bandit algorithm, termed "DML-TS-NNR", that simultaneously addresses these challenges via (1) modeling the differential reward with user- and time-specific incidental parameters, (2) network cohesion penalties, and (3) debiased machine learning for flexible estimation of baseline rewards.

iSurvive: An Interpretable, Event-time Prediction Model for mHealth.

An important mobile health (mHealth) task is the use of multimodal data, such as sensor streams and self-report, to construct interpretable time-to-event predictions of, for example, lapse to alcohol or illicit drug use. Interpretability of the prediction model is important for acceptance and adoption by domain scientists, enabling model outputs and parameters to inform theory and guide intervention design. Temporal latent state models are therefore attractive, and so we adopt the continuous time hidden Markov model (CT-HMM) due to its ability to describe irregular arrival times of event data.