Response shift and disease activity in inflammatory bowel disease
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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.
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.
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.
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.
KeywordsDisease activity Group comparisons Health-related quality of life Longitudinal Measurement invariance Structural equation modeling
- 7.Visser, M. R., Oort, F. J., van Lanschot, J. J., Velden, J., Kloek, J. J., Gouma, D. J., et al. (2013). The role of recalibration response shift in explaining bodily pain in cancer patients undergoing invasive surgery: An empirical investigation of the Sprangers and Schwartz model. Psychooncology, 22, 515–522.CrossRefPubMedGoogle Scholar
- 8.Barclay-Goddard, R., King, J., Dubouloz, C. J., & Schwartz, C. E. (2012). Building on transformative learning and response shift theory to investigate health-related quality of life changes over time in individuals with chronic health conditions and disability. Archives of Physical Medicine and Rehabilitation, 93, 214–220.CrossRefPubMedGoogle Scholar
- 10.Osborne, R. H., Hawkins, M., & Sprangers, M. A. (2006). Change of perspective: A measurable and desired outcome of chronic disease self-management intervention programs that violates the premise of preintervention/postintervention assessment. Arthritis and Rheumatism, 55, 458–465.CrossRefPubMedGoogle Scholar
- 11.King-Kallimanis, B. L., Oort, F. J., Nolte, S., Schwartz, C. E., & Sprangers, M. A. (2011). Using structural equation modeling to detect response shift in performance and health-related quality of life scores of multiple sclerosis patients. Quality of Life Research, 20, 1527–1540.CrossRefPubMedPubMedCentralGoogle Scholar
- 13.Lix, L. M., Graff, L. A., Walker, J. R., Clara, I., Rawsthorne, P., Rogala, L., et al. (2008). Longitudinal study of quality of life and psychological functioning for active, fluctuating, and inactive disease patterns in inflammatory bowel disease. Inflammatory Bowel Diseases, 14, 1575–1584.CrossRefPubMedGoogle Scholar
- 15.Bernklev, T., Jahnsen, J., Lygren, I., Henriksen, M., Vatn, M., & Moum, B. (2005). Health-related quality of life in patients with inflammatory bowel disease measured with the short form-36: Psychometric assessments and a comparison with general population norms. Inflammatory Bowel Diseases, 11, 909–918.CrossRefPubMedGoogle Scholar
- 19.Maruish, M. (2011). User’s manual for the SF-36 health survey (3rd ed.). Lincoln: QualityMetric Inc.Google Scholar
- 26.Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale: Routledge Academic.Google Scholar
- 27.Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: The Guilford Press.Google Scholar
- 30.Asparouhov, T., & Muthen, B. (2006). Robust Chi square difference testing with mean and variance adjusted test statistics. Los Angeles: MPlus Web Notes.Google Scholar
- 31.Ware, J. E, Jr, Snow, K. K., Kosinski, M., & Gandek, B. (1993). SF-36 health survey: Manual and interpretation guide. Boston: The Health Institute, New England Medical Center.Google Scholar
- 36.Borsboom, G. J., Korfage, I. J., Essink-Bot, M. L., & Duivenvoorden, H. J. (2007). The structural equation modeling technique did not show a response shift, contrary to the results of the then test and the individualized approaches. Journal of Clinical Epidemiology, 60, 426–427.CrossRefPubMedGoogle Scholar
- 41.Guilleux, A., Blanchin, M., Vanier, A., Guillemin, F., Falissard, B., Schwartz, C. E., et al. (2015). RespOnse Shift Algorithm in Item response theory (ROSALI) for response shift detection with missing data in longitudinal patient-reported outcome studies. Quality of Life Research, 24, 553–564.CrossRefPubMedGoogle Scholar