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

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Abstract

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.

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Acknowledgments

This research was funded by a Grant from the National Institute of Neurological Disorders and Stroke (NS054050). Y.L. was recipient of a Du Pré Award from the Multiple Sclerosis International Federation. R.M. and E.M. acquired funding and initiated the overall study. Y.L., E.H. and R.M. undertook statistical analysis. Y.L. wrote the draft manuscript. E.H., R.M. and E.M. revised the draft manuscript. All authors gave approval of the final submitted version. The authors wish to thank all participants in this research and all staff and students involved in data collection and analysis.

Conflict of interest

The authors report no conflict of interest.

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Authors and Affiliations

  1. Department of Kinesiology and Community Health, University of Illinois at Urbana–Champaign, 906 S Goodwin Ave, Freer Hall, Urbana, IL, 61801, USA

    Y. C. Learmonth, E. A. Hubbard, E. McAuley & R. W. Motl

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  1. Y. C. Learmonth
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  2. E. A. Hubbard
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  3. E. McAuley
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  4. R. W. Motl
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Correspondence to R. W. Motl.

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Learmonth, Y.C., Hubbard, E.A., McAuley, E. et al. Psychometric properties of quality of life and health-related quality of life assessments in people with multiple sclerosis. Qual Life Res 23, 2015–2023 (2014). https://doi.org/10.1007/s11136-014-0639-2

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