Quality of Life Research

, Volume 21, Issue 7, pp 1205–1216 | Cite as

Validity, reliability and responsiveness of the EQ-5D in German stroke patients undergoing rehabilitation

  • Matthias Hunger
  • Carla Sabariego
  • Björn Stollenwerk
  • Alarcos Cieza
  • Reiner Leidl



To analyse the psychometric properties of the EQ-5D in German stroke survivors undergoing neurological rehabilitation.


The EQ-5D, the Hospital Anxiety and Depression Scale (HADS) and the Stroke Impact Scale (SIS) were completed before (210 subjects) and after (183 subjects) a patient education programme in seven rehabilitation clinics in Bavaria, Germany. A postal follow-up was conducted after 6 months. Acceptance, validity, reliability and responsiveness of the EQ-5D were tested. The SIS subscales were used as external anchors to classify the patients into change groups between the measurements.


The proportion of missing answers ranged from 4.7 to 8.6%. Between 16 and 19% reported no problems in any EQ-5D dimension. At baseline, correlations between EQ-5D index and the SIS subscales ranged from 0.15 (communication) to 0.60 (mobility). Correlations with the EQ VAS were slightly smaller. All scores were reliable in test–retest with intraclass correlations ranging from 0.67 to 0.81. EQ-5D index and EQ VAS were consistently responsive only to improvements in health, showing small- to medium effect sizes (0.27–0.42).


The EQ-5D has shown reasonable validity, reliability and, more limited, responsiveness in stroke patients with mild to moderate limitations of functional status, allowing it to be used in clinical trials in rehabilitation.


Stroke Outcome assessment Reproducibility of results Quality of life Psychometrics 



The RCT evaluating the ICF-based patient education was supported by the German Federal Ministry of Education and Research (BMBF). We acknowledge with thanks for time and information provided by the study participants.


  1. 1.
    Golomb, B. A., Vickrey, B. G., & Hays, R. D. (2001). A review of health-related quality-of-life measures in stroke. Pharmacoeconomics, 19(2), 155–185.PubMedCrossRefGoogle Scholar
  2. 2.
    Salter, K. L., Moses, M. B., Foley, N. C., & Teasell, R. W. (2008). Health-related quality of life after stroke: What are we measuring? International Journal of Rehabilitation Research, 31(2), 111–117.PubMedCrossRefGoogle Scholar
  3. 3.
    The EuroQol Group. (1990). EuroQol—a new facility for the measurement of health-related quality of life. Health Policy, 16(3), 199–208.CrossRefGoogle Scholar
  4. 4.
    Schweikert, B., Hahmann, H., & Leidl, R. (2006). Validation of the EuroQol questionnaire in cardiac rehabilitation. Heart, 92(1), 62–67.PubMedCrossRefGoogle Scholar
  5. 5.
    Petrou, S., & Hockley, C. (2005). An investigation into the empirical validity of the EQ-5D and SF-6D based on hypothetical preferences in a general population. Health Economics, 14(11), 1169–1189.PubMedCrossRefGoogle Scholar
  6. 6.
    Dorman, P., Slattery, J., Farrell, B., Dennis, M., & Sandercock, P. (1998). Qualitative comparison of the reliability of health status assessments with the EuroQol and SF-36 questionnaires after stroke. United Kingdom Collaborators in the International Stroke Trial. Stroke, 29(1), 63–68.PubMedCrossRefGoogle Scholar
  7. 7.
    Dorman, P. J., Dennis, M., & Sandercock, P. (1999). How do scores on the EuroQol relate to scores on the SF-36 after stroke? Stroke, 30(10), 2146–2151.PubMedCrossRefGoogle Scholar
  8. 8.
    Dorman, P. J., Waddell, F., Slattery, J., Dennis, M., & Sandercock, P. (1997). Is the EuroQol a valid measure of health-related quality of life after stroke? Stroke, 28(10), 1876–1882.PubMedCrossRefGoogle Scholar
  9. 9.
    van Exel, N. J., Scholte op Reimer, W. J., & Koopmanschap, M. A. (2004). Assessment of post-stroke quality of life in cost-effectiveness studies: The usefulness of the Barthel Index and the EuroQoL-5D. Quality of Life Research, 13(2), 427–433.PubMedCrossRefGoogle Scholar
  10. 10.
    Pickard, A. S., Johnson, J. A., & Feeny, D. H. (2005). Responsiveness of generic health-related quality of life measures in stroke. Quality of Life Research, 14(1), 207–219.PubMedCrossRefGoogle Scholar
  11. 11.
    Neubert, S., Sabariego, C., Stier-Jarmer, M., & Cieza, A. (2011). Development of an ICF-based patient education program. Patient Education and Counseling, 84(2), e13–e17.PubMedCrossRefGoogle Scholar
  12. 12.
    Hensler, S., Hoidn, S., & Jork, K. (2006). DEGAM practice guideline for stroke. Zeitschrift für Allgemeinmedizin, 82, 404–408.CrossRefGoogle Scholar
  13. 13.
    Rollnik, J. D., & Janosch, U. (2010). Current trends in the length of stay in neurological early rehabilitation. Deutsches Ärzteblatt international, 107(16), 286–292.PubMedGoogle Scholar
  14. 14.
    Schupp, W. (1995). Concept for a functional status and handicap-adjustment treatment and rehabilitation service chain in neurologic and neurosurgical management in Germany (“phase model”). Der Nervenarzt, 66(12), 907–914.PubMedGoogle Scholar
  15. 15.
    Stier-Jarmer, M., Koenig, E., & Stucki, G. (2002). Structures of Early Neurological Rehabilitation (Phase B) in Germany. Physikalische Medizin, Rehabilitationsmedizin, Kurortmedizin, 12, 260–271.CrossRefGoogle Scholar
  16. 16.
    Leidl, R., & Reitmeir, P. (2011). A value set for the EQ-5D based on experienced health states: Development and testing for the german population. Pharmacoeconomics, 29(6), 521–534.PubMedCrossRefGoogle Scholar
  17. 17.
    Herrmann, C., Buss, U., & Snaith, R. P. (1995). HADS-D—Hospital Anxiety and Depression Scale—Deutsche Version: Ein Fragebogen zur Erfassung von Angst und Depressivität in der somatischen Medizin. Bern: Huber.Google Scholar
  18. 18.
    Zigmond, A. S., & Snaith, R. P. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67(6), 361–370.PubMedCrossRefGoogle Scholar
  19. 19.
    Aben, I., Verhey, F., Lousberg, R., Lodder, J., & Honig, A. (2002). Validity of the beck depression inventory, hospital anxiety and depression scale, SCL-90, and Hamilton depression rating scale as screening instruments for depression in stroke patients. Psychosomatics, 43(5), 386–393.PubMedCrossRefGoogle Scholar
  20. 20.
    Duncan, P. W., Wallace, D., Lai, S. M., Johnson, D., Embretson, S., & Laster, L. J. (1999). The stroke impact scale version 2.0. Evaluation of reliability, validity, and sensitivity to change. Stroke, 30(10), 2131–2140.PubMedCrossRefGoogle Scholar
  21. 21.
    Petersen, C., Morfeld, M., & Bullinger, M. (2001). Testing and validation of the German version of the Stroke Impact Scale. Fortschritte der Neurologie-Psychiatrie, 69(6), 284–290.PubMedGoogle Scholar
  22. 22.
    Geyh, S., Cieza, A., & Stucki, G. (2009). Evaluation of the German translation of the Stroke Impact Scale using Rasch analysis. The Clinical Neuropsychologist, 23(6), 978–995.PubMedCrossRefGoogle Scholar
  23. 23.
    Hamilton, B., & Granger, C. V. (1987). A uniform national data system for medical rehabilitation. In M. Fuhrer (Ed.), Rehabilitation outcomes: analysis and measurement (pp. 137–147). Baltimore, MD: Brookes Publishing Co.Google Scholar
  24. 24.
    van der Putten, J. J., Hobart, J. C., Freeman, J. A., & Thompson, A. J. (1999). Measuring change in disability after inpatient rehabilitation: Comparison of the responsiveness of the Barthel index and the functional independence measure. Journal of Neurology, Neurosurgery and Psychiatry, 66(4), 480–484.CrossRefGoogle Scholar
  25. 25.
    Hall, K. M., Hamilton, B. B., Gordon, W. A., & Zasler, N. D. (1993). Characteristics and comparisons of functional assessment indices: Disability Rating Scale, functional independence measure, and functional assessment measure. The Journal of Head Trauma Rehabilitation, 8(2), 60–74.CrossRefGoogle Scholar
  26. 26.
    Hobart, J. C., Lamping, D. L., Freeman, J. A., Langdon, D. W., McLellan, D. L., Greenwood, R. J., et al. (2001). Evidence-based measurement: Which disability scale for neurologic rehabilitation? Neurology, 57(4), 639–644.PubMedCrossRefGoogle Scholar
  27. 27.
    Hsueh, I. P., Lin, J. H., Jeng, J. S., & Hsieh, C. L. (2002). Comparison of the psychometric characteristics of the functional independence measure, 5 item Barthel index, and 10 item Barthel index in patients with stroke. Journal of Neurology, Neurosurgery and Psychiatry, 73(2), 188–190.CrossRefGoogle Scholar
  28. 28.
    Duncan, P. W., Reker, D. M., Horner, R. D., Samsa, G. P., Hoenig, H., LaClair, B. J., et al. (2002). Performance of a mail-administered version of a stroke-specific outcome measure, the Stroke Impact Scale. Clinical Rehabilitation, 16(5), 493–505.PubMedCrossRefGoogle Scholar
  29. 29.
    Carod-Artal, F. J., Coral, L. F., Trizotto, D. S., & Moreira, C. M. (2008). The stroke impact scale 3.0: Evaluation of acceptability, reliability, and validity of the Brazilian version. Stroke, 39(9), 2477–2484.PubMedCrossRefGoogle Scholar
  30. 30.
    Carod-Artal, J., Egido, J. A., Gonzalez, J. L., & Varela de Seijas, E. (2000). Quality of life among stroke survivors evaluated 1 year after stroke: Experience of a stroke unit. Stroke, 31(12), 2995–3000.PubMedCrossRefGoogle Scholar
  31. 31.
    Revicki, D., Hays, R. D., Cella, D., & Sloan, J. (2008). Recommended methods for determining responsiveness and minimally important differences for patient-reported outcomes. Journal of Clinical Epidemiology, 61(2), 102–109.PubMedCrossRefGoogle Scholar
  32. 32.
    Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.PubMedCrossRefGoogle Scholar
  33. 33.
    Konig, H. H., Bernert, S., Angermeyer, M. C., Matschinger, H., Martinez, M., Vilagut, G., et al. (2009). Comparison of population health status in six European countries: Results of a representative survey using the EQ-5D questionnaire. Medical Care, 47(2), 255–261.PubMedCrossRefGoogle Scholar
  34. 34.
    Ahlsio, B., Britton, M., Murray, V., & Theorell, T. (1984). Disablement and quality of life after stroke. Stroke, 15(5), 886–890.PubMedCrossRefGoogle Scholar
  35. 35.
    Streiner, D. L., & Geoffrey, R. N. (1995). Health Measurement Scales. A practical guide to their development and use (2nd ed.). New York: Oxford University Press.Google Scholar
  36. 36.
    Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.PubMedCrossRefGoogle Scholar
  37. 37.
    Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.PubMedCrossRefGoogle Scholar
  38. 38.
    Dolan, P., Gudex, C., Kind, P., & Williams, A. (1996). The time trade-off method: results from a general population study. Health Economics, 5(2), 141–154.PubMedCrossRefGoogle Scholar
  39. 39.
    Greiner, W., Claes, C., Busschbach, J. J., & von der Schulenburg, J. M. (2005). Validating the EQ-5D with time trade off for the German population. European Journal of Health Economics, 6(2), 124–130.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Matthias Hunger
    • 1
  • Carla Sabariego
    • 2
  • Björn Stollenwerk
    • 1
  • Alarcos Cieza
    • 2
  • Reiner Leidl
    • 1
    • 3
  1. 1.Helmholtz Zentrum München, German Research Center for Environmental HealthInstitute of Health Economics and Health Care ManagementNeuherbergGermany
  2. 2.Ludwig-Maximilians-UniversityInstitute for Public Health and Health Care ResearchMunichGermany
  3. 3.Ludwig-Maximilians-University, Munich Center of Health SciencesMunichGermany

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