I watch SEM: continuous time dynamic models with N≥1 smart watch data.

Abstract

Since smart devices have become useful tools in monitoring health, we use the applied part of this article for explaining how to retrieve N=1 bivariate ILD from popular smart watches and how to prepare them for CTSEM (including N>1 multivariate extensions). We show how to specify a cross-lagged panel CTSEM using the R package ctsem, how to fit the specified model to the retrieved data, and how to interpret the results. Limitations of CTSEM are discussed, too. Monitoring and forecasting industrial health represent important issues for organizations. In the theoretical part of this article, we provide a brief introduction to different types of repeated measure designs and methods to analyze repeatedly measured data, with a particular focus on continuous time modelling of intensive longitudinal data (ILD) with N≥1 analysis. We built on the distinction between within-person versus between-person effects, and how this is addressed in static versus dynamic models. Further, we elaborate on the distinction between discrete time dynamic models versus continuous time dynamic models. In particular, we deal with continuous time structural equation models (CTSEM), and we provide a brief introduction into the underlying math.