Multidimensional fatigue inventory and post-polio syndrome - a Rasch analysis

Abstract

Background: Fatigue is a common symptom in post-polio syndrome (PPS) and can have a substantial impact on patients. There is a need for validated questionnaires to assess fatigue in PPS for use in clinical practice and research. The aim with this study was to assess the validity and reliability of the Swedish version of Multidimensional Fatigue Inventory (MFI-20) in patients with PPS using the Rasch model. Methods: A total of 231 patients diagnosed with PPS completed the Swedish MFI-20 questionnaire at post-polio out-patient clinics in Sweden. The mean age of participants was 62 years and 61% were females. Data were tested against assumptions of the Rasch measurement model (i.e. unidimensionality of the scale, good item fit, independency of items and absence of differential item functioning). Reliability was tested with the person separation index (PSI). A transformation of the ordinal total scale scores into an interval scale for use in parametric analysis was performed. Dummy cases with minimum and maximum scoring were used for the transformation table to achieve interval scores between 20 and 100, which are comprehensive limits for the MFI-20 scale. Results: An initial Rasch analysis of the full scale with 20 items showed misfit to the Rasch model (p < 0.001). Seven items showed slightly disordered thresholds and person estimates were not significantly improved by rescoring items. Analysis of MFI-20 scale with the 5 MFI-20 subscales as testlets showed good fit with a non-significant x 2 value (p = 0.089). PSI for the testlet solution was 0.86. Local dependency was present in all subscales and fit to the Rasch model was solved with testlets within each subscale. PSI ranged from 0.52 to 0.82 in the subscales. Conclusions: This study shows that the Swedish MFI-20 total scale and subscale scores yield valid and reliable measures of fatigue in persons with post-polio syndrome. The Rasch transformed total scores can be used for parametric statistical analyses in future clinical studies.