title: Analyzing repeated data collected by mobile phones and frequent text messages. An example of low back pain measured weekly for 18 weeks.
abstract: BACKGROUND
  Repeated data collection is desirable when monitoring fluctuating conditions. Mobile phones can be used to gather such data from large groups of respondents by sending and receiving frequently repeated short questions and answers as text messages.The analysis of repeated data involves some challenges. Vital issues to consider are the within-subject correlation, the between measurement occasion correlation and the presence of missing values.The overall aim of this commentary is to describe different methods of analyzing repeated data. It is meant to give an overview for the clinical researcher in order for complex outcome measures to be interpreted in a clinically meaningful way.
  
  
  METHODS
  A model data set was formed using data from two clinical studies, where patients with low back pain were followed with weekly text messages for 18 weeks. Different research questions and analytic approaches were illustrated and discussed, as well as the handling of missing data. In the applications the weekly outcome "number of days with pain" was analyzed in relation to the patients' "previous duration of pain" (categorized as more or less than 30 days in the previous year).Research questions with appropriate analytical methods 1: How many days with pain do patients experience? This question was answered with data summaries. 2: What is the proportion of participants "recovered" at a specific time point? This question was answered using logistic regression analysis. 3: What is the time to recovery? This question was answered using survival analysis, illustrated in Kaplan-Meier curves, Proportional Hazard regression analyses and spline regression analyses. 4: How is the repeatedly measured data associated with baseline (predictor) variables? This question was answered using generalized Estimating Equations, Poisson regression and Mixed linear models analyses. 5: Are there subgroups of patients with similar courses of pain within the studied population? A visual approach and hierarchical cluster analyses revealed different subgroups using subsets of the model data.
  
  
  CONCLUSIONS
  We have illustrated several ways of analysing repeated measures with both traditional analytic approaches using standard statistical packages, as well as recently developed statistical methods that will utilize all the vital features inherent in the data.
authors: Institute of Environmental Medicine, Unit of Intervention and Implementation Research, Karolinska Institutet, Nobels v, 13, 171 77 Stockholm, Sweden. iben.axen@ki.se
authors: Iben
authors: I
authors: Axén
authors: Lennart
authors: L
authors: Bodin
authors: Alice
authors: A
authors: Kongsted
authors: Niels
authors: N
authors: Wedderkopp
authors: Irene
authors: I
authors: Jensen
authors: Gunnar
authors: G
authors: Bergström
mesh_terms: D000328:Adult; D016000:Cluster Analysis; D003289:Convalescence; D003625:Data Collection / Q000379:methods*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D015994:Incidence; D016014:Linear Models; D016015:Logistic Models; D017116:Low Back Pain / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D016012:Poisson Distribution; D060145:Text Messaging