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Italian translation and psychometric validation of the Manual Ability Measure-36 (MAM-36) and its correlation with an objective measure of upper limb function in patients with multiple sclerosis

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Abstract

Background

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system with an unpredictable course. During its course, deficits affecting upper limb functions may occur. Hence, there is a need for self-administered scales providing a comprehensive assessment of upper limb functions. The Manual Ability Measure-36 (MAM-36), which investigates patients’ performance in activities of daily living requiring upper limb function, has not been adapted and validated in the Italian context.

Objectives

We develop an Italian translation and validation of the MAM-36 in a population of people with MS (PwMS), explore its psychometric properties and investigate its associations with clinical data and the Nine Hole Peg Test (9-HPT).

Research plan and methods

The multicentre study involved five Italian neurological centres. Subjects were evaluated using EDSS, 9-HPT and the MAM-36 scale. We used confirmatory factor analysis and Rasch analysis to investigate the properties of the MAM-36.

Results

We enrolled 218 PwMS. Results supported the unidimensionality of the MAM-36, and adequate functioning of rating scale and items. Additionally, the MAM-36 showed weak negative associations with age and disease duration, and moderate associations with EDSS and 9-HPT scores.

Discussion

The adapted MAM-36 showed adequate psychometric properties. However, indications of problematic targeting to PwMS with low disability emerged. For this reason, use of the scale appears to be more suitable among patients with moderate-to-severe disability.

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Notes

  1. Prior to conducting these analyses, we investigated the presence of non-independence among MAM-36 scores due to patients’ clustering into different centres. We computed the intraclass correlation coefficient (ICC) for MAM-36 scores via linear mixed model, controlling for demographic (age, gender) and clinical (disease course, disease duration, and clinician-rated EDSS) variables, i.e. we computed the residual ICC [22]. Based on our computation, the residual ICC for MAM-36 scores was 0.012, corresponding to a design effect of 1.61, which compared with the suggested cut-off (a design effect ≥ 2, [23]), indicated no significant clustering effect was present beyond that due to demographic and clinical variables. For this reason, we did not control for the effect of centres in subsequent analyses.

References

  1. McDonald I, Compston A (2006) The symptoms and signs of multiple sclerosis. In: Compston A, Ebers G, Lassmann H (eds) McAlpine’s multiple sclerosis, 4th edn. Curchill Livingstone, London, pp 287–346

    Chapter  Google Scholar 

  2. Johansson S, Ytterberg C, Claesson IM, Lindberg J, Hillert J, Andersson M, Widén Holmqvist L, von Koch L (2007) High concurrent presence of disability in multiple sclerosis. Associations with perceived health. J Neurol 254(6):767–773

    Article  Google Scholar 

  3. Lamers I, Feys P (2014) Assessing upper limb function in multiple sclerosis. Mult Scler 20:775–784

    Article  Google Scholar 

  4. Yalachkov Y, Soydaş D, Bergmann J, Frisch S, Behrens M, Foerch C, Gehrig J (2019) Determinants of quality of life in relapsing-remitting and progressive multiple sclerosis. Mult Scler Relat Disord May 30:33–37

    Article  Google Scholar 

  5. Kratz AL, Braley TJ, Foxen-Craft E, Scott E, Murphy JF 3rd, Murphy SL. (2017) How do pain, fatigue, depressive, and cognitive symptoms relate to well-being and social and physical functioning in the daily lives of individuals with multiple sclerosis? Arch Phys Med Rehabil. Nov;98(11):2160–2166

  6. Yozbatiran N, Baskurt F, Baskurt Z, Ozakbas S, Idiman E (2006) Motor assessment of upper extremity function and its relation with fatigue, cognitive function and quality of life in multiple sclerosis patients. J Neurol Sci 246(1-2):117–122

    Article  Google Scholar 

  7. Bertoni R, Lamers I, Chen CC, Feys P, Cattaneo D (2015) Unilateral and bilateral upper limb dysfunction at body functions, activity and participation levels in people with multiple sclerosis. Mult Scler 21(12):1566–1574

    Article  CAS  Google Scholar 

  8. Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33(11):1444–1452

    CAS  PubMed  Google Scholar 

  9. Feys P, Lamers I, Francis G et al (2017) The Nine-Hole Peg Test as a manual dexterity performance measure for multiple sclerosis. Mult Scler 23(5):711–720

    Article  Google Scholar 

  10. Kierkegaard M, Einarsson U, Gottberg K, von Koch L, Holmqvist LW (2012) The relationship between walking, manual dexterity, cognition and activity/participation in persons with multiple sclerosis. Mult Scler 18(5):639–646

    Article  Google Scholar 

  11. Solaro C, Cattaneo D, Brichetto G, Castelli L, Tacchino A, Gervasoni E, Prosperini L (2019) Clinical correlates of 9-Hole Peg Test in a large population of people with multiple sclerosis. Mult Scler Relat Disord 30:1–8

    Article  Google Scholar 

  12. Lamers I, Kelchtermans S, Baert I, Feys P (2014) Upper limb assessment in multiple sclerosis: a systematic review of outcome measures and their psychometric properties. Arch Phys Med Rehabil 95(6):1184–1200

    Article  Google Scholar 

  13. Penta M, Thonnard JL, Tesio L (1998) ABILHAND: a Rasch-built measure of manual ability. Arch Phys Med Rehabil 79(9):1038–1042

    Article  CAS  Google Scholar 

  14. Brichetto G, Bragadin MM, Fiorini S, Battaglia MA, Konrad G, Ponzio M, Pedullà L, Verri A, Barla A, Tacchino A (2019) The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach. Neurol Sci:1–4

  15. Kahraman T (2018) Performance measures for upper extremity functions in persons with multiple sclerosis. Arch Neuropsychiatry 55(Suppl 1):S41–S45

    Google Scholar 

  16. Poole JL, Huffman M, Hunter A, Mares C, Siegel P (2015) Reliability and validity of the Manual Ability Measure-36 in persons with Charcot-Marie-Tooth disease. J Hand Ther 28(4):364

    Article  Google Scholar 

  17. Tsai CL, Lin CF, Lin HT, Liu MF, Chiu HY, Hsu HY, Kuo LC (2017) How kinematic disturbance in the deformed rheumatoid thumb impacts on hand function: a biomechanical and functional perspective. Disabil Rehabil 39(4):338–345

    Article  Google Scholar 

  18. Chen CC, Bode RK (2010) Psychometric validation of the Manual Ability Measure-36 (MAM-36) in patients with neurologic and musculoskeletal disorders. Arch Phys Med Rehabil 91:414–420

    Article  Google Scholar 

  19. Rallon CR, Chen CC (2008) Relationship between performance-based and self-reported assessment of hand function. Am J Occup Ther:62

  20. Chen CC, Kasven N, Karpatkin HI, Sylvester A (2007) Hand strength and perceived manual ability among patients with multiple sclerosis. Arch Phys Med Rehabil 88(6):794–797

    Article  Google Scholar 

  21. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292–302

    Article  Google Scholar 

  22. Snijders T, Bosker R (2012) Multilevel analysis: an introduction to basic and advanced multilevel modeling, 2nd edn. Sage, Thousand Oaks

    Google Scholar 

  23. Lai MHC, Kwok O (2015) Examining the rule of thumb of not using multilevel modeling: the “design effect smaller than two” rule. J Exp Educ 83:423–438

    Article  Google Scholar 

  24. Hu L, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model Multidiscip J 6(1):1–55

    Article  Google Scholar 

  25. Hair JF Jr, Black WC, Babin BJ, Anderson RE (2009) Multivariate data analysis, 7th edn. Prentice Hall, Upper Saddle River, p 761

    Google Scholar 

  26. McDonald RP (1985) Factor analysis and related methods. Lawrence Erlbaum Associates, Inc, Hillsdale

    Google Scholar 

  27. Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39–50

    Article  Google Scholar 

  28. Andrich D (1978) A rating formulation for ordered response categories. Psychometrika 43:561–573

    Article  Google Scholar 

  29. Linacre JM (2002a) Optimizing rating scale category effectiveness. J Appl Meas 3(1):85–106

    Google Scholar 

  30. Bond TG, Fox CM (2007) Applying the Rasch model: fundamental measurement in the human sciences. 2nd. Lawrence Erlbaum Associates Publishers. Chapter 12

  31. Linacre JM (2002b) What do Infit and Outfit, mean-square and standardized mean? Rasch Measurement Transactions 16:878

    Google Scholar 

  32. Bland JM, Altman DG (1997) Cronbach’s alpha. BMJ 314(7080):572

    Article  CAS  Google Scholar 

  33. Fisher WJ (1992) Reliability, separation, strata statistics. Rasch Meas Trans 6:328

    Google Scholar 

  34. Linacre JM (2006) Data variance explained by Rasch measures. Rasch Measurement Transactions 20(1):1045

    Google Scholar 

  35. Smith EV Jr (2002) Detecting and evaluating the impact of multidimensionality using item fit statistics and principal component analysis of residuals. J Appl Meas 3(2):205–231

    PubMed  Google Scholar 

  36. Erasmus LP, Sarno S, Albrecht H, Schwecht M, Pöllmann W, König N (2001) Measurement of ataxic symptoms with a graphic tablet: standard values in controls and validity in multiple sclerosis patients. J Neurosci Methods 108(1):25–37

    Article  CAS  Google Scholar 

  37. Marengo D, Miceli R, Settanni M (2016) Do mixed item formats threaten test unidimensionality? Results from a standardized math achievement Test. TPM: Testing, Psychometrics, Methodology in Applied Psychology 23:25–36

    Article  Google Scholar 

  38. Bandalos DL (2014) Relative performance of categorical diagonally weighted least squares and robust maximum likelihood estimation. Struct Equ Model 21:102–116

    Article  Google Scholar 

  39. Suen HK (1990) Principles of test theories. Lawrence Erlbaum, Hillsdale

    Google Scholar 

  40. Smith AB, Rush R, Fallowfield LJ, Velikova G, Sharpe M (2008) Rasch fit statistics and sample size considerations for polytomous data. BMC Med Res Methodol 8(1):33

    Article  Google Scholar 

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Funding

This work was partly supported by a Grant 2014/R20 from the Italian Multiple Sclerosis Foundation (FISM).

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Authors and Affiliations

  1. Department of Rehabilitation, CRRF “Mons. Luigi Novarese”, Moncrivello, VC, Italy

    Claudio Solaro, Rachele Di Giovanni & Erica Grange

  2. Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy

    Giampaolo Brichetto, Margit Mueller & Andrea Tacchino

  3. IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy

    Rita Bertoni, Letizia Castelli & Davide Cattaneo

  4. MS Center Institute of Neurological Sciences, University of Catania, Catania, Italy

    Francesco Patti & Angelo Pappalardo

  5. Department of Neurosciences, S. Camillo-Forlanini Hospital, Rome, Italy

    Luca Prosperini

  6. Department of Psychology, University of Turin, Turin, Italy

    Rosalba Rosato & Davide Marengo

Authors
  1. Claudio Solaro
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  2. Rachele Di Giovanni
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  3. Erica Grange
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  4. Giampaolo Brichetto
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  5. Margit Mueller
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  6. Andrea Tacchino
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  7. Rita Bertoni
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  8. Francesco Patti
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  10. Luca Prosperini
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  11. Letizia Castelli
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  13. Davide Cattaneo
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  14. Davide Marengo
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Contributions

CS conceived the study and designed the study protocol. CS, DM, DC, RDG, EG, BG, MM, AT, RB, FP, AP, LP, LC and RR recruited the subjects and carried out the experiments. DM, DC and RR analysed the results. CS, DM, DC, RDG and EG wrote the manuscript. All authors have read and approved the final version of the manuscript.

Corresponding author

Correspondence to Davide Marengo.

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Solaro, C., Di Giovanni, R., Grange, E. et al. Italian translation and psychometric validation of the Manual Ability Measure-36 (MAM-36) and its correlation with an objective measure of upper limb function in patients with multiple sclerosis. Neurol Sci 41, 1539–1546 (2020). https://doi.org/10.1007/s10072-020-04263-2

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  • DOI: https://doi.org/10.1007/s10072-020-04263-2

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