The 88-item Multiple Sclerosis Spasticity Scale: a Rasch validation of the Italian version and suggestions for refinement of the original scale
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In multiple sclerosis (MS), the impact of spasticity on the patient’s life is a key issue, and it is fundamental that existing tools measuring the patient’s perspective undergo psychometric analysis and refinement to optimize confidence in their use in clinical practice and research.
We examined—by Rasch analysis (RA)—the main metric characteristics of the 88-item Multiple Sclerosis Spasticity Scale (MSSS-88) to: (i) further validate its Italian version (MSSS-88-IT), previously validated through classical test theory methods only and (ii) independently verify the measurement properties of the original scale.
MSSS-88 data from a convenience sample of 232 subjects with MS underwent RA, mainly examining item fit, reliability indices, test information function, dimensionality, local item independence, and differential item functioning (DIF).
Most items fitted the Rasch model, but 13/88 items showed a misfit in infit and/or outfit values. Rasch reliability indices were high (> 0.80). Test information functions in most subscales showed a sharp decrease in measurement precision as the ability level departs from the quite limited central range of maximal information. The unidimensionality of each subscale was confirmed. Thirteen item pairs showed local dependency (residual correlations > 0.30) and three items presented DIF.
Reliability, dimensionality and some internal construct validity characteristics of the MSSS-88-IT were confirmed. But, drawbacks of the original MSSS-88 emerged related to some item misfit, redundancy, or malfunctioning. Thus, further large independent studies are recommended, to verify the robustness of previous findings and examine the appropriateness of a few targeted item replacements.
KeywordsPsychometrics Rasch model Patient-reported outcome measure Spasticity Multiple sclerosis
This paper is based on research conducted by LP and MO during their PhD program in “Advanced Sciences and Technologies in Rehabilitation Medicine and Sports”, at the University of Rome “Tor Vergata”, Rome, Italy. They express special thanks to Prof. Calogero Foti and Prof. Diego Centonze for their helpful support in the PhD theses.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 6.Food and Drug Administration (FDA). (2009). Guidance for industry: Patient-reported outcome measures: Use in medical product development to support labeling claims. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM193282.pdf. Accessed 26 May 2018.
- 7.Hobart, J. C., Riazi, A., Thompson, A. J., Styles, I. M., Ingram, W., Vickery, P. J., Warner, M., Fox, P. J., & Zajicek, J. P. (2006). Getting the measure of spasticity in multiple sclerosis: The Multiple Sclerosis Spasticity Scale (MSSS-88). Brain, 129, 224–234. https://doi.org/10.1093/brain/awh675.CrossRefGoogle Scholar
- 9.Schyns, F., Paul, L., Finlay, K., Ferguson, C., & Noble, E. (2009). Vibration therapy in multiple sclerosis: A pilot study exploring its effects on tone, muscle force, sensation and functional performance. Clinical Rehabilitation, 23, 771–781. https://doi.org/10.1177/0269215508101758.CrossRefGoogle Scholar
- 11.Mori, F., Ljoka, C., Magni, E., Codecà, C., Kusayanagi, H., Monteleone, F., Sancesario, A., Bernardi, G., Koch, G., Foti, C., & Centonze, D. (2011). Transcranial magnetic stimulation primes the effects of exercise therapy in multiple sclerosis. Journal of Neurology, 258, 1281–1287. https://doi.org/10.1007/s00415-011-5924-1.CrossRefGoogle Scholar
- 13.Zajicek, J. P., Hobart, J. C., Slade, A., Barnes, D., & Mattison, P. G., MUSEC Research Group (2012). Multiple sclerosis and extract of cannabis: Results of the MUSEC trial. Journal of Neurology, Neurosurgery & Psychiatry, 83, 1125–1132. https://doi.org/10.1136/jnnp-2012-302468.CrossRefGoogle Scholar
- 14.Rodic, S. Z., Knezevic, T. I., Kisic-Tepavcevic, D. B., Dackovic, J. R., Dujmovic, I., Pekmezovic, T. D., Drulovic, J. S., & Konstantinovic, L. M. (2016). Validation of the Serbian version of Multiple Sclerosis Spasticity Scale 88 (MSSS-88). PLoS ONE, 11, e0147042. https://doi.org/10.1371/journal.pone.0147042.CrossRefGoogle Scholar
- 15.Henze, T., von Mackensen, S., Lehrieder, G., Zettl, U. K., Pfiffner, C., & Flachenecker, P. (2014). Linguistic and psychometric validation of the MSSS-88 questionnaire for patients with multiple sclerosis and spasticity in Germany. Health and Quality of Life Outcomes, 12, 119. https://doi.org/10.1186/s12955-014-0119-y.CrossRefGoogle Scholar
- 16.Ottonello, M., Pellicciari, L., Centonze, D., Foti, C., Pistarini, C., Albensi C., & Giordano, A. (2017). The cross-cultural adaptation and psychometric validation of the MSSS-88 for use in Italian patients with multiple sclerosis. Disability and Rehabilitation, 25, 1–7. https://doi.org/10.1080/09638288.2017.1393699.CrossRefGoogle Scholar
- 17.Ball, S., Vickery, J., Hobarth, J., Wright, D., Green, C., Shearer, J., Nunn, A., Cano, M. G., MacManus, D., Miller, D., Mallik, S., & Zajicek, J. (2015). The Cannabinoid Use in Progressive Inflammatory Brain Disease (CUPID) trial: A randomised double-blind placebo-controlled parallel-group multicentre trial and economic evaluation of cannabinoids to slow progression in multiple sclerosis. Health Technology Assessment, 19, 1–187. https://doi.org/10.3310/hta19120.CrossRefGoogle Scholar
- 18.Linacre, J. M. (2009). A User’s guide to Winsteps-ministep: Rasch-model computer programs. Program Manual 3.68.0. Chicago: Winsteps.Google Scholar
- 20.Linacre, J. M. (2002). Optimizing rating scale category effectiveness. Journal of Applied Measurement, 3, 85–106.Google Scholar
- 22.Wright, B. D., & Masters, G. N. (1982). Rating scale analysis. Chicago: Mesa Press.Google Scholar
- 26.Linacre, J. M. (2002). What do infit and outfit, mean-square and standardized mean? Rasch Measurement Transactions, 16(2), 878.Google Scholar
- 27.Wright, B. D. (1996). Local dependency, correlations and principal components. Rasch Measurement Transactions, 10, 509–511.Google Scholar
- 28.Wolfe, E. W., & Smith, E. V. Jr. (2000). Instrument development tools and activities for measure validation using Rasch models: Part II—Validation activities. Journal of Applied Measurement, 8, 204–234.Google Scholar