Understanding Lifestyle Decisions Based on Patient Historical Data: A Latent Growth Modeling Approach

  • Ronald FreezeEmail author
  • T. S. Raghu
  • Ajay Vinze
Part of the Annals of Information Systems book series (AOIS, volume 19)


Healthcare issues related to chronic disease conditions and management does not have easy or immediate solutions. Evidence based decision making in such contexts requires long-term tracking and analysis of patient data in order to provide patient choices that produce extended quality of life. Using Latent Growth Modeling (LGM), we present a planning perspective to analyze underlying patterns of long-term chronic data related to the progression of Multiple Sclerosis (MS). Using the North American Research Committee on Multiple Sclerosis (NARCOMS) patient driven initiative that collects survey data on a biannual basis for the purpose of clinical trial recruitment and epidemiological research, this study analyzes three temporal data points spanning 3 years. Two LGM models are presented that identify patient traits correlating with disease progression. The traits analyzed are both patient and physician controlled.


Health outcomes Structural equation modeling Latent growth modeling Healthcare Multiple sclerosis Lifestyle decisions 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of ArkansasFayettevilleUSA
  2. 2.Arizona State UniversityTempeUSA

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