title: Ecological Momentary Assessments and Automated Time Series Analysis to Promote Tailored Health Care: A Proof-of-Principle Study.
abstract: BACKGROUND
  Health promotion can be tailored by combining ecological momentary assessments (EMA) with time series analysis. This combined method allows for studying the temporal order of dynamic relationships among variables, which may provide concrete indications for intervention. However, application of this method in health care practice is hampered because analyses are conducted manually and advanced statistical expertise is required.
  
  
  OBJECTIVE
  This study aims to show how this limitation can be overcome by introducing automated vector autoregressive modeling (VAR) of EMA data and to evaluate its feasibility through comparisons with results of previously published manual analyses.
  
  
  METHODS
  We developed a Web-based open source application, called AutoVAR, which automates time series analyses of EMA data and provides output that is intended to be interpretable by nonexperts. The statistical technique we used was VAR. AutoVAR tests and evaluates all possible VAR models within a given combinatorial search space and summarizes their results, thereby replacing the researcher's tasks of conducting the analysis, making an informed selection of models, and choosing the best model. We compared the output of AutoVAR to the output of a previously published manual analysis (n=4).
  
  
  RESULTS
  An illustrative example consisting of 4 analyses was provided. Compared to the manual output, the AutoVAR output presents similar model characteristics and statistical results in terms of the Akaike information criterion, the Bayesian information criterion, and the test statistic of the Granger causality test.
  
  
  CONCLUSIONS
  Results suggest that automated analysis and interpretation of times series is feasible. Compared to a manual procedure, the automated procedure is more robust and can save days of time. These findings may pave the way for using time series analysis for health promotion on a larger scale. AutoVAR was evaluated using the results of a previously conducted manual analysis. Analysis of additional datasets is needed in order to validate and refine the application for general use.
authors.affiliation: University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Groningen, Netherlands. j.a.j.van.der.krieke@umcg.nl.
authors.forename: Lian
authors.identifier: http://orcid.org/0000-0001-6745-5913
authors.initials: L
authors.lastname: van der Krieke
authors.forename: Ando C
authors.identifier: http://orcid.org/0000-0002-3169-9753
authors.initials: AC
authors.lastname: Emerencia
authors.forename: Elisabeth H
authors.identifier: http://orcid.org/0000-0001-6501-9288
authors.initials: EH
authors.lastname: Bos
authors.forename: Judith Gm
authors.identifier: http://orcid.org/0000-0002-6393-0032
authors.initials: JG
authors.lastname: Rosmalen
authors.forename: Harriëtte
authors.identifier: http://orcid.org/0000-0003-3661-3151
authors.initials: H
authors.lastname: Riese
authors.forename: Marco
authors.identifier: http://orcid.org/0000-0002-0764-2124
authors.initials: M
authors.lastname: Aiello
authors.forename: Sjoerd
authors.identifier: http://orcid.org/0000-0001-7901-3338
authors.initials: S
authors.lastname: Sytema
authors.forename: Peter
authors.identifier: http://orcid.org/0000-0002-0866-6929
authors.initials: P
authors.lastname: de Jonge
keywords: Web-based, dynamic effects, automatization; ecological momentary assessment; tailored treatment; time series analysis; vector autoregressive modeling