Quality of Life Research

, Volume 25, Issue 7, pp 1751–1760 | Cite as

Response shift and disease activity in inflammatory bowel disease

  • Lisa M. Lix
  • Eric K. H. Chan
  • Richard Sawatzky
  • Tolulope T. Sajobi
  • Juxin Liu
  • Wilma Hopman
  • Nancy Mayo
Article

Abstract

Purpose

Response shift (RS) may mask true change in health-related quality of life in longitudinal studies. People with chronic conditions may experience RS as they adapt to their disease, but it is unknown whether fluctuations in disease activity will influence the presence of RS. The study purpose was to test for RS in individuals with inflammatory bowel disease (IBD), a condition characterized by periods of symptom flares and remission.

Methods

Data were from the Manitoba IBD Cohort Study (N = 388). Multi-group confirmatory factor analysis (MG-CFA) and a RS detection method based on structural equation modeling were used to test for reconceptualization, reprioritization, and recalibration RS in participants with consistent active, consistent inactive, and inconsistent disease activity over a 6-month period on the SF-36.

Results

The MG-CFA revealed that a weak invariance model with equal factor loadings across groups was the best fit to the baseline SF-36 data. Reconceptualization, uniform recalibration, and non-uniform recalibration RS was detected in the consistent active group, but effect sizes were small. For the consistent inactive group, recalibration RS was observed and effect sizes were small to moderate. For the inconsistent disease activity group, small-to-moderate recalibration RS effects were observed. There was no evidence of reprioritization.

Conclusions

Individuals with a chronic disease may exhibit RS even if they are not actively experiencing symptoms on a consistent basis. Heterogeneity in the type and magnitude of RS effects may be observed in chronic disease patients who experience changes in disease symptoms.

Keywords

Disease activity Group comparisons Health-related quality of life Longitudinal Measurement invariance Structural equation modeling 

Supplementary material

11136_2015_1188_MOESM1_ESM.docx (24 kb)
Supplementary material 1 (DOCX 23 kb)

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lisa M. Lix
    • 1
  • Eric K. H. Chan
    • 2
    • 3
  • Richard Sawatzky
    • 3
    • 4
  • Tolulope T. Sajobi
    • 5
    • 6
  • Juxin Liu
    • 7
  • Wilma Hopman
    • 8
  • Nancy Mayo
    • 9
  1. 1.Department of Community Health Sciences, College of MedicineUniversity of ManitobaWinnipegCanada
  2. 2.Measurement, Evaluation, and Research Methodology (MERM) ProgramUniversity of British ColumbiaVancouverCanada
  3. 3.School of NursingTrinity Western UniversityLangleyCanada
  4. 4.Centre for Health Evaluation and Outcome SciencesProvidence Health CareVancouverCanada
  5. 5.Department of Community Health SciencesUniversity of CalgaryCalgaryCanada
  6. 6.O’Brien Institute for Public HealthUniversity of CalgaryCalgaryCanada
  7. 7.Department of Mathematics and StatisticsUniversity of SaskatchewanSaskatoonCanada
  8. 8.Queen’s UniversityKingstonCanada
  9. 9.McGill University Health CentreMontrealCanada

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