Skip to main content

Advertisement

Log in

Development of a cross-cultural item bank for measuring quality of life related to mental health in multiple sclerosis patients

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Objective

Quality of life (QoL) measurements are considered important outcome measures both for research on multiple sclerosis (MS) and in clinical practice. Computerized adaptive testing (CAT) can improve the precision of measurements made using QoL instruments while reducing the burden of testing on patients. Moreover, a cross-cultural approach is also necessary to guarantee the wide applicability of CAT. The aim of this preliminary study was to develop a calibrated item bank that is available in multiple languages and measures QoL related to mental health by combining one generic (SF-36) and one disease-specific questionnaire (MusiQoL).

Methods

Patients with MS were enrolled in this international, multicenter, cross-sectional study. The psychometric properties of the item bank were based on classical test and item response theories and approaches, including the evaluation of unidimensionality, item response theory model fitting, and analyses of differential item functioning (DIF). Convergent and discriminant validities of the item bank were examined according to socio-demographic, clinical, and QoL features.

Results

A total of 1992 patients with MS and from 15 countries were enrolled in this study to calibrate the 22-item bank developed in this study. The strict monotonicity of the Cronbach’s alpha curve, the high eigenvalue ratio estimator (5.50), and the adequate CFA model fit (RMSEA = 0.07 and CFI = 0.95) indicated that a strong assumption of unidimensionality was warranted. The infit mean square statistic ranged from 0.76 to 1.27, indicating a satisfactory item fit. DIF analyses revealed no item biases across geographical areas, confirming the cross-cultural equivalence of the item bank. External validity testing revealed that the item bank scores correlated significantly with QoL scores but also showed discriminant validity for socio-demographic and clinical characteristics.

Conclusion

This work demonstrated satisfactory psychometric characteristics for a QoL item bank for MS in multiple languages. This work may offer a common measure for the assessment of QoL in different cultural contexts and for international studies conducted on MS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Baumstarck, K., Boyer, L., Boucekine, M., Michel, P., Pelletier, J., & Auquier, P. (2013). Measuring the quality of life in patients with multiple sclerosis in clinical practice: A necessary challenge. Multiple Sclerosis International, 2013, 524894. doi:10.1155/2013/524894.

    Article  PubMed Central  PubMed  Google Scholar 

  2. Noble, J. G., Osborne, L. A., Jones, K. H., Middleton, R. M., & Ford, D. V. (2012). Commentary on “disability outcome measures in multiple sclerosis clinical trials”. Multiple Sclerosis Journal, 18, 1718–1720. doi:10.1177/1352458512457847.

    Article  PubMed  Google Scholar 

  3. Mitchell, A. J., Benito-León, J., González, J.-M. M., & Rivera-Navarro, J. (2005). Quality of life and its assessment in multiple sclerosis: Integrating physical and psychological components of wellbeing. Lancet Neurology, 4, 556–566. doi:10.1016/S1474-4422(05)70166-6.

    Article  PubMed  Google Scholar 

  4. Solari, A. (2005). Role of health-related quality of life measures in the routine care of people with multiple sclerosis. Health and Quality of Life Outcomes, 3, 16. doi:10.1186/1477-7525-3-16.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Ware, J. E., Snow, K. K., Kosinski, M., Gandek, B., & Institute, N. E. M. C. H. H. (1993). SF-36 health survey: Manual and interpretation guide. New England Medical Center: The Health Institute.

    Google Scholar 

  6. Patrick, D. L., & Deyo, R. A. (1989). Generic and disease-specific measures in assessing health status and quality of life. Medical Care, 27, S217–S232.

    Article  CAS  PubMed  Google Scholar 

  7. Simeoni, M. C., Auquier, P., Fernandez, O., Flachenecker, P., Stecchi, S., Constantinescu, C. S., et al. (2008). Validation of the multiple sclerosis international quality of life questionnaire. Multiple Sclerosis, 14, 219–230. doi:10.1177/1352458507080733.

    Article  PubMed  Google Scholar 

  8. Vickrey, B. G., Hays, R. D., Harooni, R., Myers, L. W., & Ellison, G. W. (1995). A health-related quality of life measure for multiple sclerosis. Quality of Life Research, 4, 187–206.

    Article  CAS  PubMed  Google Scholar 

  9. Leong, K. P., Yeak, S. C. L., Saurajen, A. S. M., Mok, P. K. H., Earnest, A., Siow, J. K., et al. (2005). Why generic and disease-specific quality-of-life instruments should be used together for the evaluation of patients with persistent allergic rhinitis. Clinical and Experimental Allergy, 35, 288–298. doi:10.1111/j.1365-2222.2005.02201.x.

    Article  CAS  PubMed  Google Scholar 

  10. Riemsma, R. P., Forbes, C. A., Glanville, J. M., Eastwood, A. J., & Kleijnen, J. (2001). General health status measures for people with cognitive impairment: Learning disability and acquired brain injury. Health Technology Assessment (Winchester, England), 5, 1–100.

    CAS  Google Scholar 

  11. Lobentanz, I. S., Asenbaum, S., Vass, K., Sauter, C., Klösch, G., Kollegger, H., et al. (2004). Factors influencing quality of life in multiple sclerosis patients: Disability, depressive mood, fatigue and sleep quality. Acta Neurologica Scandinavica, 110, 6–13. doi:10.1111/j.1600-0404.2004.00257.x.

    Article  CAS  PubMed  Google Scholar 

  12. Morris, J., Perez, D., & McNoe, B. (1998). The use of quality of life data in clinical practice. Quality of Life Research, 7, 85–91.

    Article  CAS  PubMed  Google Scholar 

  13. Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. New Jersey: Lawrence Erlbaum Associates.

  14. Fayers, P., & Machin, D. (2007). Quality of life: The assessment, analysis and interpretation of patient-reported outcomes (2nd ed.). Chichester: John Wiley & Sons.

  15. Weiss, D. J. (2004). Computerized adaptive testing for effective and efficient measurement in counseling and education. Measurement and Evaluation in Counseling and Development, 37, 70–84.

    Google Scholar 

  16. Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., et al. (2007). Psychometric evaluation and calibration of health-related quality of life item banks: Plans for the patient-reported outcomes measurement information system (PROMIS). Medical Care, 45, S22–S31. doi:10.1097/01.mlr.0000250483.85507.04.

    Article  PubMed  Google Scholar 

  17. Hill, C. D., Edwards, M. C., Thissen, D., Langer, M. M., Wirth, R. J., Burwinkle, T. M., et al. (2007). Practical issues in the application of item response theory: A demonstration using items from the pediatric quality of life inventory (PedsQL) 4.0 generic core scales. Medical Care, 45, S39–S47. doi:10.1097/01.mlr.0000259879.05499.eb.

    Article  PubMed  Google Scholar 

  18. Cook, K. F., Bamer, A. M., Roddey, T. S., Kraft, G. H., Kim, J., & Amtmann, D. (2012). A PROMIS fatigue short form for use by individuals who have multiple sclerosis. Quality of Life Research, 21, 1021–1030. doi:10.1007/s11136-011-0011-8.

    Article  PubMed  Google Scholar 

  19. Becker, H., Stuifbergen, A., Lee, H., & Kullberg, V. (2014). Reliability and validity of PROMIS cognitive abilities and cognitive concerns scales among people with multiple sclerosis. International Journal of MS Care, 16, 1–8. doi:10.7224/1537-2073.2012-047.

    Article  PubMed Central  PubMed  Google Scholar 

  20. Senders, A., Hanes, D., Bourdette, D., Whitham, R., & Shinto, L. (2014). Reducing survey burden: Feasibility and validity of PROMIS measures in multiple sclerosis. Multiple Sclerosis (Houndmills, Basingstoke, England), 20, 1102–1111. doi:10.1177/1352458513517279.

    Article  Google Scholar 

  21. Cella, D., Lai, J.-S., Nowinski, C. J., Victorson, D., Peterman, A., Miller, D., et al. (2012). Neuro-QOL: Brief measures of health-related quality of life for clinical research in neurology. Neurology, 78, 1860–1867. doi:10.1212/WNL.0b013e318258f744.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Cella, D., Nowinski, C., Peterman, A., Victorson, D., Miller, D., Lai, J.-S., et al. (2011). The neurology quality-of-life measurement initiative. Archives of Physical Medicine and Rehabilitation, 92, S28–S36. doi:10.1016/j.apmr.2011.01.025.

    Article  PubMed Central  PubMed  Google Scholar 

  23. McDonald, W. I., Compston, A., Edan, G., Goodkin, D., Hartung, H. P., Lublin, F. D., et al. (2001). Recommended diagnostic criteria for multiple sclerosis: Guidelines from the international panel on the diagnosis of multiple sclerosis. Annals of Neurology, 50, 121–127.

    Article  CAS  PubMed  Google Scholar 

  24. Lublin, F. D., & Reingold, S. C. (1996). Defining the clinical course of multiple sclerosis: Results of an international survey. National multiple sclerosis society (USA) advisory committee on clinical trials of new agents in multiple sclerosis. Neurology, 46, 907–911.

    Article  CAS  PubMed  Google Scholar 

  25. Kurtzke, J. F. (1983). Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology, 33, 1444–1452.

    Article  CAS  PubMed  Google Scholar 

  26. Leplège, A., Ecosse, E., Verdier, A., & Perneger, T. V. (1998). The French SF-36 health survey: Translation, cultural adaptation and preliminary psychometric evaluation. Journal of Clinical Epidemiology, 51, 1013–1023.

    Article  PubMed  Google Scholar 

  27. Rose, M., Bjorner, J. B., Becker, J., Fries, J. F., & Ware, J. E. (2008). Evaluation of a preliminary physical function item bank supported the expected advantages of the patient-reported outcomes measurement information system (PROMIS). Journal of Clinical Epidemiology, 61, 17–33. doi:10.1016/j.jclinepi.2006.06.025.

    Article  CAS  PubMed  Google Scholar 

  28. Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., et al. (2010). The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63, 1179–1194. doi:10.1016/j.jclinepi.2010.04.011.

    Article  PubMed Central  PubMed  Google Scholar 

  29. DeWalt, D. A., Rothrock, N., Yount, S., & Stone, A. A. (2007). PROMIS cooperative group. evaluation of item candidates: The PROMIS qualitative item review. Medical Care, 45, S12–S21. doi:10.1097/01.mlr.0000254567.79743.e2.

    Article  PubMed Central  PubMed  Google Scholar 

  30. Leplege, A., Ecosse, E., Pouchot, J., Coste, J., & Perneger, T. (2001). MOS SF36 Questionnaire. Manual and Guidelines for Scores’ Interpretation. Paris: Editions Estem.

    Google Scholar 

  31. Zheng, Y., Chang, C.-H., & Chang, H.-H. (2013). Content-balancing strategy in bifactor computerized adaptive patient-reported outcome measurement. Quality of Life Research, 22, 491–499. doi:10.1007/s11136-012-0179-6.

    Article  PubMed  Google Scholar 

  32. Cameletti, M., Caviezel, V., (2010). CMC: Cronbach-Mesbah Curve.

  33. Ahn, S. C., & Horenstein, A. R. (2013). Eigenvalue ratio test for the number of factors. Econometrica, 81, 1203–1227. doi:10.3982/ECTA8968.

    Article  Google Scholar 

  34. Muthén L., Muthén, B. (2012). MPlus user’s guide. 7th Ed. Muthén Muthén.

  35. Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electron Journal of Business Research Methods, 6, 53–60.

    Google Scholar 

  36. Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual Differences, 42, 893–898. doi:10.1016/j.paid.2006.09.017.

    Article  Google Scholar 

  37. Bjorner, J. B., Kosinski, M, Jr, & Ware, J. E. (2003). Calibration of an item pool for assessing the burden of headaches: An application of item response theory to the headache impact test (HITTM). Quality of Life Research, 12, 913–933. doi:10.1023/A:1026163113446.

    Article  PubMed  Google Scholar 

  38. Bjorner, J. B., Chang, C.-H., Thissen, D., & Reeve, B. B. (2007). Developing tailored instruments: Item banking and computerized adaptive assessment. Quality of Life Research, 16(Suppl 1), 95–108. doi:10.1007/s11136-007-9168-6.

    Article  PubMed  Google Scholar 

  39. Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149–174. doi:10.1007/BF02296272.

    Article  Google Scholar 

  40. Andersen, J., (1970). Asymptotic properties of conditional maximum likelihood estimators. Journal of the Royal Statistical Society B, 32, 283–301.

  41. Mair, P., Hatzinger, R., Extended rasch modeling: The eRm package for the application of IRT Models in R 2007. http://epub.wu.ac.at/332/ (accessed April 18, 2014).

  42. Akaike, H. (1998). Information Theory and an Extension of the Maximum Likelihood Principle. In E. Parzen, K. Tanabe, & G. Kitagawa (Eds.), Selected papers of Hirotugu Akaike (pp. 199–213). New York: Springer.

    Chapter  Google Scholar 

  43. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464. doi:10.1214/aos/1176344136.

    Article  Google Scholar 

  44. Wright, B. D., & Linacre, J. M. (1994). Reasonable mean-square fit values. Rasch Measurement Transactions, 8, 370.

    Google Scholar 

  45. Lord, F. M. (1980). Applications of Item response theory to practical testing problems. Routledge.

  46. Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika, 35(1), p. 139.

  47. Choi, S. W., Gibbons, L. E., & Crane, P. K. (2011). Lordif: An R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and monte carlo simulations. Journal of Statistical Software, 39, 1–30.

    PubMed Central  PubMed  Google Scholar 

  48. Zumbo, B. D. (1999). A Handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and likert-type (ordinal) item scores. Ottawa: Directorate of Human Resources Research and Evaluation, Department of National Defense.

    Google Scholar 

  49. SPSS Inc. Released 2008. SPSS statistics for windows, Version 17.0. Chicago: SPSS Inc.

  50. R Core Team. R. (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.

  51. Benito-León, J., Morales, J. M., Rivera-Navarro, J., & Mitchell, A. (2003). A review about the impact of multiple sclerosis on health-related quality of life. Disability and Rehabilitation, 25, 1291–1303. doi:10.1080/09638280310001608591.

    Article  PubMed  Google Scholar 

  52. Myers, J. A., McPherson, K. M., Taylor, W. J., Weatherall, M., & McNaughton, H. K. (2003). Duration of condition is unrelated to health-state valuation on the EuroQoL. Clinical Rehabilitation, 17, 209–215.

    Article  PubMed  Google Scholar 

  53. Marrie, R. A., Miller, D. M., Chelune, G. J., & Cohen, J. A. (2003). Validity and reliability of the MSQLI in cognitively impaired patients with multiple sclerosis. Multiple Sclerosis (Houndmills Basingstoke England), 9, 621–626.

    Article  Google Scholar 

  54. Boyer, L., Simeoni, M.-C., Loundou, A., D’Amato, T., Reine, G., Lancon, C., et al. (2010). The development of the S-QoL 18: A shortened quality of life questionnaire for patients with schizophrenia. Schizophrenia Research, 121, 241–250. doi:10.1016/j.schres.2010.05.019.

    Article  PubMed  Google Scholar 

  55. Kleinman, A., Eisenberg, L., & Good, B. (1978). Culture, illness, and care: Clinical lessons from anthropologic and cross-cultural research. Annals of Internal Medicine, 88, 251–258.

    Article  CAS  PubMed  Google Scholar 

  56. Aaronson, N. K., Acquadro, C., Alonso, J., Apolone, G., Bucquet, D., Bullinger, M., et al. (1992). International quality of life assessment (IQOLA) project. Quality of Life Research, 1, 349–351.

    Article  CAS  PubMed  Google Scholar 

  57. Bullinger, M., Alonso, J., Apolone, G., Leplège, A., Sullivan, M., Wood-Dauphinee, S., et al. (1998). Translating health status questionnaires and evaluating their quality: The IQOLA project approach. International quality of life assessment. Journal of Clinical Epidemiology, 51, 913–923.

    Article  CAS  PubMed  Google Scholar 

  58. Guillemin, F., Bombardier, C., & Beaton, D. (1993). Cross-cultural adaptation of health-related quality of life measures: Literature review and proposed guidelines. Journal of Clinical Epidemiology, 46, 1417–1432.

    Article  CAS  PubMed  Google Scholar 

  59. Al-Tahan, A. M., Al-Jumah, M. A., Bohlega, S. M., Al-Shammari, S. N., Al-Sharoqi, I. A., Dahdaleh, M. P., et al. (2011). The importance of quality-of-life assessment in the management of patients with multiple sclerosis. Recommendations from the Middle East MS advisory group. Neurosciences (Riyadh Saudi Arab), 16, 109–113.

    Google Scholar 

  60. Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. Journal of the National Cancer Institute, 85, 365–376.

    Article  CAS  PubMed  Google Scholar 

  61. Guarnaccia, J. B., Aslan, M., O’Connor, T. Z., Hope, M., Kazis, L., Kashner, C. M., et al. (2006). Quality of life for veterans with multiple sclerosis on disease-modifying agents: Relationship to disability. Journal of Rehabilitation Research and Development, 43, 35–44.

    Article  PubMed  Google Scholar 

  62. Visschedijk, M. A. J., Uitdehaag, B. M. J., Klein, M., van der Ploeg, E., Collette, E. H., Vleugels, L., et al. (2004). Value of health-related quality of life to predict disability course in multiple sclerosis. Neurology, 63, 2046–2050.

    Article  CAS  PubMed  Google Scholar 

  63. Nortvedt, M. W., Riise, T., Myhr, K. M., & Nyland, H. I. (2000). Quality of life as a predictor for change in disability in MS. Neurology, 55, 51–54.

    Article  CAS  PubMed  Google Scholar 

  64. Kazis, L. E., Anderson, J. J., & Meenan, R. F. (1989). Effect sizes for interpreting changes in health status. Medical Care, 27, S178–S189.

    Article  CAS  PubMed  Google Scholar 

  65. Baumstarck, K., Butzkueven, H., Fernández, O., Flachenecker, P., Stecchi, S., Idiman, E., et al. (2013). Responsiveness of the multiple sclerosis international quality of life questionnaire to disability change: A longitudinal study. Health and Quality of Life Outcomes, 11, 127. doi:10.1186/1477-7525-11-127.

    Article  PubMed Central  PubMed  Google Scholar 

  66. Ware, J. E., Bjorner, J. B., & Kosinski, M. (2000). Practical implications of item response theory and computerized adaptive testing: A brief summary of ongoing studies of widely used headache impact scales. Medical Care, 38, II73–II82.

    Article  PubMed  Google Scholar 

  67. Ostini, R., Nering, M. L. (Eds.). (2006). Polytomous item response theory models. Thousand Oaks, CA: SAGE Publications Inc.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pierre Michel.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Michel, P., Auquier, P., Baumstarck, K. et al. Development of a cross-cultural item bank for measuring quality of life related to mental health in multiple sclerosis patients. Qual Life Res 24, 2261–2271 (2015). https://doi.org/10.1007/s11136-015-0948-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-015-0948-0

Keywords

Navigation