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Activity Number: 12 - Statistical Methods in Mobile Health: New Directions and Innovation
Type: Invited
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Mental Health Statistics Section
Abstract #300116
Title: Stochastic Dynamics in Behavioral Mobile Health: Joint Modeling of Dynamic Health and Engagement Outcomes
Author(s): Walter Dempsey*
Companies: Harvard University
Keywords: Hierarchical Bayesian model; partially-observable Gaussian process; mixed-type measurement model; micro-randomized trials; mobile health; engagement

Advances in mobile technology have enabled scientists to study behavior in the individual's natural environment. In these settings, mechanistic theory is based on latent constructs. In a behavioral mHealth study, multimodal data is collected both passively and actively via mobile devices and sensors. Often these measurements are directly linked to the latent constructs coming out of behavioral theory. In mHealth, a critical composite, dynamic, hypothetical latent construct is engagement. A joint model for the dynamic health behavior and engagement outcomes is required to inform their dynamic interrelationship. I will introduce a hierarchical, partially observable Gaussian process model that depends on dynamic exogenous variables and multiple noisy measures of the latent construct. The model accounts for interventions that can lead to increased step counts and decreased engagement as well as alter their correlation. We present analysis of HeartSteps, a mobile health intervention study aimed at increasing physical activity.

Authors who are presenting talks have a * after their name.

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