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Integrative bottom-up approach to HRQOL measurement

  • Ralf Pukrop
  • Andreas Bechdolf
Chapter

Abstract

HRQOL is characterized by definitional uncertainty and a vast amount of heterogeneous assessment tools, and there is no really satisfying integrative theoretical model. The present chapter describes two studies using a bottom-up approach to establish an integrative model of HRQOL. The first study compared seven (inter)nationally validated HRQOL questionnaires in mentally healthy subjects (n=479), patients with major depression (n=171), and patients with schizophrenia (n=139) to explore convergent and divergent aspects of 7 instruments, 45 subscales, and a great number of single items. Multivariate analyses have been primarily conducted by a nonparametric multidimensional scaling technique (faceted similarity structure analysis). A set of seven reliable QOL domains could be extracted by simultaneous analysis of all assessment tools. This set consists of one ‘G-factor’ of general well-being vs. depressed mood and six specific QOL dimensions. This basic structure represents a core module which holds for nonclinical and clinical samples, and which can be completed by specific modules for particular subpopulations (e.g. persons with family, partnership, profession). Reliability, validity, and sensitivity to change (from admission to discharge and 4-month-follow-up) of this Modular System for QOL were investigated in a second study in healthy controls (N=346), patients with depression ( N=114), and patients with schizophrenia (N=91) using the SF-36 as a comparison standard. Results and major conclusions will be discussed with an emphasis on the clear impact of depressive symptoms and current mood on HRQOL, and the implications given by this relationship

Key words

HRQOL Depression Schizophrenia Facet analysis 

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Copyright information

© springer 2007

Authors and Affiliations

  • Ralf Pukrop
    • 1
  • Andreas Bechdolf
    • 1
  1. 1.Department of Psychiatry and PsychotherapyUniversity of CologneKerpenerstrGermany

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