Quality of Life Research

, Volume 23, Issue 9, pp 2531–2543 | Cite as

Measurement of stable changes of self-management skills after rehabilitation: a latent state–trait analysis of the Health Education Impact Questionnaire (heiQ™)

  • M. Schuler
  • G. Musekamp
  • J. Bengel
  • M. Schwarze
  • K. Spanier
  • Chr. Gutenbrunner
  • I. Ehlebracht-König
  • S. Nolte
  • R. H. Osborne
  • H. Faller



To assess stable effects of self-management programs, measurement instruments should primarily capture the attributes of interest, for example, the self-management skills of the measured persons. However, measurements of psychological constructs are always influenced by both aspects of the situation (states) and aspects of the person (traits). This study tests whether the Health Education Impact Questionnaire (heiQ™), an instrument assessing a wide range of proximal outcomes of self-management programs, is primarily influenced by person factors instead of situational factors. Furthermore, measurement invariance over time, changes in traits and predictors of change for each heiQ™ scale were examined.


Subjects were N = 580 patients with rheumatism, asthma, orthopedic conditions or inflammatory bowel disease, who filled out the heiQ™ at the beginning, the end of and 3 months after a disease-specific inpatient rehabilitation program in Germany. Structural equation modeling techniques were used to estimate latent trait-change models and test for measurement invariance in each heiQ™ scale. Coefficients of consistency, occasion specificity and reliability were computed.


All scales showed scalar invariance over time. Reliability coefficients were high (0.80–0.94), and consistency coefficients (0.49–0.79) were always substantially higher than occasion specificity coefficients (0.14–0.38), indicating that the heiQ™ scales primarily capture person factors. Trait-changes with small to medium effect sizes were shown in five scales and were affected by sex, age and diagnostic group.


The heiQ™ can be used to assess stable effects in important outcomes of self-management programs over time, e.g., changes in self-management skills or emotional well-being.


Self-management Assessment Latent state–trait theory Latent trait-change model Measurement invariance Chronic disease 



The authors wish to thank our cooperation clinics: Rehabilitation Center Bad Eilsen, Hospital Bad Bramstedt, Hospital Bad Oexen, Hospital Bad Reichenhall, Hospital Norderney, Deegenberg Hospital Bad Kissingen and Rehabilitation Center Bad Mergentheim Hospital Taubertal. This project was funded by the German Federal Ministry of Education and Research (Bundesministerium fuer Bildung und Forschung). Professor Osborne was supported in part by an Australian National Health and Medical Research Council Population Health Career Development Award (#400391).

Conflict of interest

The authors state that there are no conflicts of interests.

Supplementary material

11136_2014_693_MOESM1_ESM.docx (27 kb)
Supplementary material 1 (DOCX 27 kb)


  1. 1.
    Lorig, K. R. (2003). Self-management education: More than a nice extra. Medical Care, 41(6), 699–701.PubMedGoogle Scholar
  2. 2.
    Lorig, K. R., & Holman, H. (2003). Self-management education: History, definition, outcomes, and mechanisms. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine, 26(1), 1–7.CrossRefGoogle Scholar
  3. 3.
    Osborne, R. H., Spinks, J. M., & Wicks, I. P. (2004). Patient education and self-management programs in arthritis. Medical Journal of Australia, 180(5 Suppl), S23–S26.PubMedGoogle Scholar
  4. 4.
    Faller, H., Reusch, A., & Meng, K. (2011). DGRW-Update: Patientenschulung. Rehabilitation, 50(5), 284–291.PubMedCrossRefGoogle Scholar
  5. 5.
    Steyer, R., Schmitt, M., & Eid, M. (1999). Latent state–trait theory and research in personality and individual differences. European Journal of Personality, 13(5, Spec Issue), 389–408.CrossRefGoogle Scholar
  6. 6.
    Nesselroade, J. R. (2004). Intraindividual variability and short-term change. Gerontology, 50(1), 44–47.PubMedCrossRefGoogle Scholar
  7. 7.
    Eid, M. (1995). Modelle der Messung von Personen in Situationen. Weinheim: Psychologie Verlags Union.Google Scholar
  8. 8.
    Steyer, R., Ferring, D., & Schmitt, M. J. (1992). States and traits in psychological assessment. European Journal of Psychological Assessment, 8(2), 79–98.Google Scholar
  9. 9.
    Steyer, R., Geiser, C., & Fiege, C. (2012). Latent state–trait models. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology (Vol. 3, pp. 291–308)., Data analysis and research publication Washington, DC: American Psychological Association.Google Scholar
  10. 10.
    Geiser, C., & Lockhart, G. (2012). A comparison of four approaches to account for method effects in latent state–trait analyses. Psychological Methods, 17(2), 255–283.PubMedCrossRefPubMedCentralGoogle Scholar
  11. 11.
    Courvoisier, D. S., Agoritsas, T., Glauser, J., Michaud, K., Wolfe, F., Cantoni, E., et al. (2012). Pain as an important predictor of psychosocial health in patients with rheumatoid arthritis. Arthritis Care & Research, 64(2), 190–196.CrossRefGoogle Scholar
  12. 12.
    Courvoisier, D. S., Eid, M., & Nussbeck, F. W. (2007). Mixture distribution latent state–trait analysis: Basic ideas and applications. Psychological Methods, 12(1), 80–104.PubMedCrossRefGoogle Scholar
  13. 13.
    Eid, M., & Hoffmann, L. (1998). Measuring variability and change with an item response model for polytomous variables. Journal of Educational and Behavioral Statistics, 23(3), 193–215.CrossRefGoogle Scholar
  14. 14.
    Steyer, R., Krambeer, S., & Hannöver, W. (2004). Modeling latent trait-change. In K. Van Montfort, H. Oud, & A. Satorra (Eds.), Recent developments on structural equation modeling: Theory and applications (pp. 337–357). Amsterdam: Kluwer Academic Press.CrossRefGoogle Scholar
  15. 15.
    Osborne, R. H., Elsworth, G. R., & Whitfield, K. (2007). The Health Education Impact Questionnaire (heiQ): An outcomes and evaluation measure for patient education and self-management interventions for people with chronic conditions. Patient Education and Counseling, 66(2), 192–201.PubMedCrossRefGoogle Scholar
  16. 16.
    Osborne, R. H., Batterham, R., & Livingston, J. (2011). The evaluation of chronic disease self-management support across settings: The international experience of the health education impact questionnaire quality monitoring system. The Nursing clinics of North America, 46(3), 255–270.PubMedCrossRefGoogle Scholar
  17. 17.
    Epstein, J., Osborne, R. H., Elsworth, G. R., Beaton, D. E., & Guillemin, F. (2013). Cross-cultural adaptation of the Health Education Impact Questionnaire: Experimental study showed expert committee, not back-translation, added value. Journal of Clinical Epidemiology. doi:  10.1016/j.jclinepi.2013.07.013
  18. 18.
    Schuler, M., Musekamp, G., Faller, H., Ehlebracht-Konig, I., Gutenbrunner, C., Kirchhof, R., et al. (2013). Assessment of proximal outcomes of self-management programs: Translation and psychometric evaluation of a German version of the Health Education Impact Questionnaire (heiQ). Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 22(6), 1391–1403.CrossRefGoogle Scholar
  19. 19.
    Ehlebracht-König, I., & Bönisch, A. (2007). Beispiel einer qualitätsgesicherten Schulung bei Patienten mit Spondylitis ankylosans. Praxis Klinische Verhaltensmedizin und Rehabilitation, 75, 15–20.Google Scholar
  20. 20.
    Ehlebracht-König, I., Bönisch, A., & Pönicke, J. (2009). Fraktionierte rehabilitation: Ergebnisse einer randomisierten, kontrollierten Studie. Rehabilitation, 48, 30–38.PubMedCrossRefGoogle Scholar
  21. 21.
    Schultz, K., Petro, W., Müller, C., & Schwiersch, M. (2000). Asthma-Verhaltenstraining mit Erwachsenen: Konzepte und Materialien. In F. Petermann & P. Warschburger (Eds.), Asthma bronchiale (pp. 275–294). Göttingen: Hogrefe.Google Scholar
  22. 22.
    Schultz, K., Schwiersch, M., Petro, W., Mühlig, S., & Petermann, F. (2000). Individualisiertes, modular strukturiertes Patientenverhaltenstraining bei obstruktiven Atemwegserkrankungen in der stationären Rehabilitation. Pneumologie, 54, 296–305.PubMedCrossRefGoogle Scholar
  23. 23.
    Deutsche Rentenversicherung Bund. (2011). Reha Therapiestandards Chronischer Rückenschmerz. Berlin: Deutsche Rentenversicherung Bund.Google Scholar
  24. 24.
    Widaman, K. F., Ferrer, E., & Conger, R. D. (2010). Factorial invariance within longitudinal structural equation models: Measuring the same construct across time. Child Development Perspectives, 4(1), 10–18.PubMedCrossRefPubMedCentralGoogle Scholar
  25. 25.
    Jelitte, M., & Schuler, M. (2012). Messen wir immer das Gleiche? Zur Invarianz von Messungen und Response-Shift in der Rehabilitation - Teil 2. Die Rehabilitation, 51(6), 415–423.PubMedCrossRefGoogle Scholar
  26. 26.
    Meredith, W., & Horn, J. (2001). The role of factorial invariance in modeling growth and change. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 203–240). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  27. 27.
    Schuler, M., & Jelitte, M. (2012). Messen wir bei allen Personen das Gleiche? Zur Invarianz von Messungen und Response Shift in der Rehabilitation - Teil 1. Die Rehabilitation, 51(5), 332–339.PubMedCrossRefGoogle Scholar
  28. 28.
    Geiser, C., Eid, M., West, S. G., Lischetzke, T., & Nussbeck, F. W. (2012). A comparison of method effects in two confirmatory factor models for structurally different methods. Structural Equation Modeling, 19(3), 409–436.CrossRefGoogle Scholar
  29. 29.
    Eid, M. (2000). A multitrait-multimethod model with minimal assumptions. Psychometrika, 65(2), 241–261.CrossRefGoogle Scholar
  30. 30.
    Yoon, M., & Millsap, R. E. (2007). Detecting violations of factorial invariance using data-based specification searches: A Monte Carlo study. Structural Equation Modeling, 14(3), 453–463.Google Scholar
  31. 31.
    Oberski, D. J. (2009). Jrule for Mplus (Version 0.91).
  32. 32.
    Steinmetz, H. (2013). Analyzing observed composite differences across groups is partial measurement invariance enough? Methodology-European Journal of Research Methods for the Behavioral and Social Sciences, 9(1), 1–12.CrossRefGoogle Scholar
  33. 33.
    Saris, W. E., Satorra, A., & van der Veld, W. M. (2009). Testing structural equation models or detection of misspecifications? Structural Equation Modeling, 16(4), 561–582.CrossRefGoogle Scholar
  34. 34.
    van der Veld, W. M., & Saris, W. E. (2011). Causes of generalized social trust. In E. Davidov, P. Schmidt, & J. Billiet (Eds.), European association for methodology series (pp. 207–247). New York, NY: Routledge/Taylor & Francis Group.Google Scholar
  35. 35.
    Byrne, B. M., Shavelson, R. J., & Muthen, B. (1989). Testing for the equivalence of factor covariance and mean structures—The issue of partial measurement invariance. Psychological Bulletin, 105(3), 456–466.CrossRefGoogle Scholar
  36. 36.
    Steyer, R., Eid, M., & Schwenkmezger, P. (1997). Modeling true intraindividual change: True change as a latent variable. Methods of Psychological Research, 2(1), 21–33.Google Scholar
  37. 37.
    Leonhart, R. (2004). Effektgrößenberechnung bei Interventionsstudien. Die Rehabilitation, 43(4), 241–246.PubMedCrossRefGoogle Scholar
  38. 38.
    Kazis, L. E., Anderson, J. J., & Meenan, R. F. (1989). Effect sizes for interpreting changes in health status. Medical Care, 27(3 Suppl), S178–S189.PubMedCrossRefGoogle Scholar
  39. 39.
    Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2., 2 print. ed.). Hillsdale, NJ u.a.: Erlbaum.Google Scholar
  40. 40.
    West, S. G., Aiken, L. S., & Krull, J. L. (1996). Experimental personality designs: Analyzing categorical by continuous variable interactions. Journal of Personality, 64(1), 1–48.PubMedCrossRefGoogle Scholar
  41. 41.
    Eid, M., & Diener, E. (2004). Global judgments of subjective well-being: Situational variability and long-term stability. Social Indicators Research, 65(3), 245–277.CrossRefGoogle Scholar
  42. 42.
    Eid, M., & Diener, E. (1999). Intraindividual variability in affect: Reliability, validity, and personality correlates. Journal of Personality and Social Psychology, 76(4), 662–676.CrossRefGoogle Scholar
  43. 43.
    Muthén, L. K., & Muthén, B. (2010). Mplus user;s guide. Los Angeles: Muthén & Muthén.Google Scholar
  44. 44.
    Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.CrossRefGoogle Scholar
  45. 45.
    Chen, F., Bollen, K. A., Paxton, P., Curran, P. J., & Kirby, J. B. (2001). Improper solutions in structural equation models: Causes, consequences, and strategies. Sociological Methods & Research, 29(4), 468–508.CrossRefGoogle Scholar
  46. 46.
    Mohiyeddini, C., Hautzinger, M., & Bauer, S. (2002). A latent state–trait analysis on assessing trait and state components of three instruments for measuring depression: ADS, BDI, and SDS. Diagnostica, 48(1), 12–18.CrossRefGoogle Scholar
  47. 47.
    Kolenikov, S., & Bollen, K. A. (2012). Testing negative error variances: Is a Heywood case a symptom of misspecification? Sociological Methods & Research, 41(1), 124–167.CrossRefGoogle Scholar
  48. 48.
    Muhlig, S., Schultz, K., de Vries, U., & Petermann, F. (2000). Grundlagen der Patientenschulung bei Asthma. In F. Petermann & P. Warschburger (Eds.), Asthma bronchiale (pp. Seiten 147–174). Gottingen: Hogrefe.Google Scholar
  49. 49.
    Nolte, S., Elsworth, G. R., Sinclair, A. J., & Osborne, R. H. (2009). Tests of measurement invariance failed to support the application of the “then-test”. Journal of Clinical Epidemiology, 62(11), 1173–1180.PubMedCrossRefGoogle Scholar
  50. 50.
    Oort, F. J. (2005). Using structural equation modeling to detect response shifts and true change. Quality of Life Research, 14(3), 587–598.PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • M. Schuler
    • 1
  • G. Musekamp
    • 1
  • J. Bengel
    • 2
  • M. Schwarze
    • 3
  • K. Spanier
    • 3
  • Chr. Gutenbrunner
    • 3
  • I. Ehlebracht-König
    • 4
  • S. Nolte
    • 5
    • 6
  • R. H. Osborne
    • 6
  • H. Faller
    • 1
  1. 1.Department of Medical Psychology, Medical Sociology, and Rehabilitation SciencesUniversity of WürzburgWürzburgGermany
  2. 2.Department of PsychologyUniversity of FreiburgFreiburgGermany
  3. 3.Hospital for Rehabilitation MedicineMedical School HannoverHannoverGermany
  4. 4.Rehabilitation Center Bad EilsenBad EilsenGermany
  5. 5.Department of Psychosomatic Medicine, Medical ClinicCharité – Universitätsmedizin BerlinBerlinGermany
  6. 6.School of Health and Social Development, Population Health Strategic Research CentreDeakin UniversityMelbourneAustralia

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