Social Indicators Research

, Volume 119, Issue 3, pp 1557–1570 | Cite as

Quality of Life Predictors and Normative Data

  • Brígida Patrício
  • Luis M. T. Jesus
  • Madeline Cruice
  • Andreia Hall


This study identifies predictors and normative data for quality of life (QOL) in a sample of Portuguese adults from general population. A cross-sectional correlational study was undertaken with two hundred and fifty-five (N = 255) individuals from Portuguese general population (mean age 43 years, range 25–84 years; 148 females, 107 males). Participants completed the European Portuguese version of the World Health Organization Quality of Life short-form instrument and the European Portuguese version of the Center for Epidemiologic Studies Depression Scale. Demographic information was also collected. Portuguese adults reported their QOL as good. The physical, psychological and environmental domains predicted 44 % of the variance of QOL. The strongest predictor was the physical domain and the weakest was social relationships. Age, educational level, socioeconomic status and emotional status were significantly correlated with QOL and explained 25 % of the variance of QOL. The strongest predictor of QOL was emotional status followed by education and age. QOL was significantly different according to: marital status; living place (mainland or islands); type of cohabitants; occupation; health. The sample of adults from general Portuguese population reported high levels of QOL. The life domain that better explained QOL was the physical domain. Among other variables, emotional status best predicted QOL. Further variables influenced overall QOL. These findings inform our understanding on adults from Portuguese general population QOL and can be helpful for researchers and practitioners using this assessment tool to compare their results with normative data.


Quality of life Predictors Portuguese general population WHOQOL-Bref 



This work was developed during the PhD of the first author at the University of Aveiro, Portugal. This work was partially funded by FEDER through the Operational Program Competitiveness Factors—COMPETE and by National Funds through FCT—Foundation for Science and Technology in the context of the project FCOMP-01-0124-FEDER-022682 (FCT reference PEst-C/EEI/UI0127/2011). This research has been partly supported by a Doctoral grant (Programa de Formação Avançada de Docentes) from Instituto Politécnico do Porto (IPP) to Brígida Patrício.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Brígida Patrício
    • 1
    • 2
  • Luis M. T. Jesus
    • 2
    • 3
  • Madeline Cruice
    • 4
  • Andreia Hall
    • 2
    • 5
  1. 1.Escola Superior de Tecnologia da Saúde do Porto (ESTSP)Instituto Politécnico do Porto (IPP)PortoPortugal
  2. 2.Institute of Electronics and Informatics Engineering of Aveiro (IEETA)University of AveiroAveiroPortugal
  3. 3.School of Health Sciences (ESSUA)University of AveiroAveiroPortugal
  4. 4.School of Health SciencesCity University LondonLondonUK
  5. 5.Department of MathematicsUniversity of AveiroAveiroPortugal

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