Development and psychometric properties of the Multiple Sclerosis Knowledge Assessment Scale: Rasch analysis of a novel tool for evaluating MS knowledge

Abstract

Background:

Multiple sclerosis (MS)-related knowledge is an important evaluation metric for health education interventions. However, few MS knowledge assessment tools are currently available for use.

Objectives:

This study aims to develop a reliable and valid Multiple Sclerosis Knowledge Assessment Scale (MSKAS) for use in the MS community and the general public.

Methods:

The MSKAS was developed using a Delphi study methodology and was administered to participants in the first open enrolment of the Understanding Multiple Sclerosis (UMS) online course. Rasch analysis was used to examine its psychometric properties and develop the final scale.

Results:

Experts from across the MS community participated in the development of the MSKAS, resulting in an initial scale of 42 items. Five hundred and forty-three UMS participants completed the MSKAS; 89% were female and 30% were people with MS. The final unidimensional 22-item scale has a person separation index of 2.16, a person reliability index of 0.82, an item separation index of 11.19, and a Cronbach’s alpha (kr-20) test reliability of 0.87.

Conclusion:

The MSKAS is a unidimensional scale with good construct validity and internal consistency. The MSKAS has the potential to be useful for the assessment of MS knowledge in research and clinical practice.

Keywords

Multiple sclerosis Rasch analysis psychometric properties knowledge assessment

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References

Wilkins

. Progressive multiple sclerosis. New York: Springer, 2017.

GBD 2016 Multiple Sclerosis Collaborators. Global, regional, and national burden of multiple sclerosis 1990–2016: A systematic analysis for the global burden of disease study 2016. Lancet Neurol 2019; 18(3): 269–285.

Campbell

Simpson

Ahmad

, et al. Change in multiple sclerosis prevalence over time in Australia 2010-2017 utilising disease-modifying therapy prescription data. Mult Scler J 2020; 26(11): 1315–1328.

Ahmad

Campbell

van der Mei

, et al. Health economic impact of multiple sclerosis in Australia in 2017: An analysis of MS Research Australia’s platform: The Australian MS Longitudinal Study (AMSLS), 2018, http://ecite.utas.edu.au/132297

Rieckmann

Centonze

Elovaara

, et al. Unmet needs, burden of treatment, and patient engagement in multiple sclerosis: A combined perspective from the MS in the 21st Century Steering Group. Mult Scler Relat Disord 2018; 19: 153–160.

Colligan

Metzler

Tiryaki

. Shared decision-making in multiple sclerosis. Mult Scler J 2017; 23(2): 185–190.

Brown

Gabriele

White

. Physician and patient treatment decision-making in relapsing-remitting multiple sclerosis in Europe and the USA. Neurodegener Dis Manag 2018; 8(6): 371–376.

Köpke

e S

Kasper

Mühlhauser

, et al. Patient education program to enhance decision autonomy in multiple sclerosis relapse management: A randomized-controlled trial. Mult Scler 2009; 15(1): 96–104.

Kasper

Köpke

Mühlhauser

, et al. Informed shared decision making about immunotherapy for patients with multiple sclerosis (ISDIMS): A randomized controlled trial. Eur J Neurol 2008; 15(12): 1345–1352.

10.

Heesen

Kasper

Segal

, et al. Decisional role preferences, risk knowledge and information interests in patients with multiple sclerosis. Mult Scler 2004; 10(6): 643–650.

11.

Solari

Martinelli

Trojano

, et al. An information aid for newly diagnosed multiple sclerosis patients improves disease knowledge and satisfaction with care. Mult Scler 2010; 16(11): 1393–1405.

12.

Köpke

Solari

Rahn

, et al. Information provision for people with multiple sclerosis. Cochrane Database Syst Rev 2018; 10: CD008757.

13.

Giordano

Liethmann

Köpke

, et al. Risk knowledge of people with relapsing-remitting multiple sclerosis – Results of an international survey. PLoS ONE 2018; 13(11): e0208004.

14.

Abolfazli

Elyasi

Javadi

, et al. Knowledge and attitude assessment of Iranian multiple sclerosis patients receiving interferon beta. Iran J Neurol 2014; 13(3): 160.

15.

Giordano

Uccelli

Pucci

, et al. The Multiple Sclerosis knowledge questionnaire: A self-administered instrument for recently diagnosed patients. Mult Scler 2010; 16(1): 100–111.

16.

Heesen

Kasper

Fischer

, et al. Risk knowledge in relapsing multiple sclerosis (RIKNO 1.0): Development of an outcome instrument for educational interventions. PLoS ONE 2015; 10(10): e0138364.

17.

Heesen

Pottgen

Rahn

, et al. What should a person with relapsing-remitting multiple sclerosis know? – Focus group and survey data of a risk knowledge questionnaire (RIKNO 2.0). Mult Scler Relat Disord 2017; 18: 186–195.

18.

Uccelli

Traversa

Trojano

, et al. Lack of information about multiple sclerosis in children can impact parents’ sense of competency and satisfaction within the couple. J Neurol Sci 2013; 324(1-2): 100–105.

19.

Maybury

Brewin

. Social relationships, knowledge and adjustment to multiple sclerosis. J Neurol Neurosurg Psychiatry 1984; 47(4): 372–376.

20.

de Bruin

Fischhoff

. The effect of question format on measured HIV/AIDS knowledge: Detention center teens, high school students, and adults. AIDS Educ Prev 2000; 12(3): 187–198.

21.

Put your thinking cap on: Detecting cognitive load using EEG during learning. In: Proceedings of the seventh international learning analytics & knowledge conference, Vancouver, BC, Canada, 13–17 March 2017.

22.

Frisbie

. Multiple choice versus true-false: A comparison of reliabilities and concurrent validities. J Educ Meas 1973; 10(4): 297–304.

23.

Claflin

Gates

Maher

, et al. Building a successful massive open online course (MOOC): A description of our process and lessons learned. JMIR, accepted.

24.

Linacre

. Winsteps® Rasch measurement computer program user’s guide. Beaverton, OR: Winsteps Com, 2019.

25.

Bond

Fox

. Applying the Rasch model: Fundamental measurement in the human sciences. 3rd ed. New York: Routledge, 2015.

26.

Boone

Staver

Yale

. Rasch analysis in the human sciences. New York: Springer, 2013.

27.

Tennant

Conaghan

. The Rasch measurement model in rheumatology: What is it and why use it? When should it be applied, and what should one look for in a Rasch paper? Arthrit Care Res 2007; 57(8): 1358–1362.

28.

Linacre

. Predicting responses from Rasch measures. J Appl Meas 2010; 11(1): 1–10.

29.

Pallant

Tennant

. An introduction to the Rasch measurement model: An example using the hospital anxiety and depression Scale (HADS). Br J Clin Psychol 2007; 46(Pt. 1): 1–18.

30.

Hepworth

Rowe

Burnside

. Development of a patient reported outcome measures for measuring the impact of visual impairment following stroke. BMC Health Serv Res 2019; 19(1): 348.

31.

Annear

Toye

Eccleston

, et al. Dementia knowledge assessment scale: Development and preliminary psychometric properties. J Am Geriatr Soc 2015; 63(11): 2375–2381.

32.

Pellicciari

Ottonello

Giordano

, et al. The 88-item multiple sclerosis spasticity scale: A Rasch validation of the Italian version and suggestions for refinement of the original scale. Qual Life Res 2019; 28(1): 221–231.

33.

Souza

MAP

Coster

Mancini

, et al. Rasch analysis of the participation scale (P-scale): Usefulness of the P-scale to a rehabilitation services network. BMC Public Health 2017; 17(1): 934.

34.

Honan

McDonald

Sufani

, et al. The awareness of social inference test: Development of a shortened version for use in adults with acquired brain injury. Clin Neuropsychol 2016; 30(2): 243–264.

35.

Dutt

Tan

Alagumalai

, et al. Development and validation of the ability in behavior assessment and interventions for teachers using Delphi technique and Rasch analysis. J Autism Dev Disord 2019; 49(5): 1976–1987.

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