Psychometric properties of quality of life and health-related quality of life assessments in people with multiple sclerosis

PURPOSE: There is substantial interest in testing interventions for improving quality of life (QOL) and health-related quality of life (HRQOL) in people with multiple sclerosis (MS). Yet, there is limited research on the psychometric properties of QOL [e.g., Satisfaction with Life Scale (SWLS), Leeds MS Quality of Life Scale (LMSQOL)] and HRQOL [e.g., Short Form 12 Health Survey (SF-12) and Multiple Sclerosis Impact Scale-29 (MSIS-29)] measures in this population. Such research is important for designing and interpreting interventions. We examined the test-retest reliability, measurement error, and interpretability of QOL (i.e., SWLS and LMSQOL) and HRQOL (i.e., SF-12 and MSIS-29) measures over 6 months in people with MS. METHODS: Individuals with MS (n = 274) completed the SWLS, LMSQOL, SF-12 and MSIS-29 on two occasions, 6 months apart. We estimated test-retest reliability [intraclass correlation coefficient (ICC)], measurement error [standard error of measurement (SEM) and coefficient of variation] and interpretability [smallest detectable change (SDC)]. RESULTS: Intraclass correlation coefficient values ranged between moderate and good (ICC range = 0.669-0.883); the MSIS-29 physical component had the best reliability, and the SF-12 mental component had the worst reliability. Measurement error, based on percent SEM, varied among measures; the physical and mental components of the SF-12 (%SEM = 4.6 and 5.3, respectively) had the best measurement error, and the MSIS-29 mental component (%SEM = 13.2) and the SWLS (%SEM = 12.7) had the worst measurement error. Interpretability, based on percent SDC, varied among measures; interpretability was best for the physical and mental components of the SF-12 (%SDC = 12.7 and 14.7, respectively) and worst for the MSIS-29 mental component (%SDC = 36.7) and the SWLS (%SDC = 35). CONCLUSION: We provide novel data for helping researchers and clinicians select and interpret QOL and HRQOL measures and scores for interventions among people with MS. Such information will better inform our understanding of intervention effectiveness