Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 May 17;19(1):146.
doi: 10.1186/s12955-021-01783-z.

Psychometric properties of the WHOQOL-BREF(PT) in a sample of elderly citizens

Affiliations

Psychometric properties of the WHOQOL-BREF(PT) in a sample of elderly citizens

Margarida Goes et al. Health Qual Life Outcomes. .

Abstract

Background: The goal of this article was to research the psychometric properties of the WHOQOL-BREF(PT) instrument in a sample of elderly citizens residing in a rural area in their own homes or at family members' or friends' homes and to compare the results: (i) to those reported by the team of Portuguese researchers that undertaken the instrument's translation/validation to the Portuguese language and (ii) to those reported internationally by the World Health Organization Quality of Life group. An overall quality of life scoring (QOL24-all facets) is also proposed in this article as novelty. The correlation level between QOL24 and the instrument's general facet was also investigated.

Methods: This was a cross-sectional study with a sample of 351 elderly citizens (46.4% males and 53.6% females) randomly selected from the official dataset of the Local Health Unit of Baixo Alentejo. All the data were collected by health professionals at the participants' homes following the structured interview methodology and using the WHOQOL-BREF(PT) instrument. Three different structural equation models were developed: (i) a first-order confirmatory factor analysis, to assess the instrument's psychometric properties; (ii) a hierarchical second-order confirmatory factor analysis model, to allow determining the QOL24 scoring; and (iii) a more generic structural equation model, to investigate the correlation level between QOL24 and the instrument's general facet.

Results: The WHOQOL-BREF(PT) showed an "almost very good" goodness of fit (comparative fit index of 0.949 and Tucker-Lewis index of 0.943), an adequate internal consistency (Cronbach's alpha: from 0.64 to 0.90; composite reliability: from 0.59 to 0.88) and tolerable convergent validity (average variance extracted: from 0.374 to 0.614). However, discriminant validity was not reached because strong correlations between the first-order factors (four QOL domains) were obtained, together with low values of the average variance extracted. The scoring of QOL domains and QOL24, determined as weighted averages (proposed in this article as novelty) were significantly different than those determined as unweighted averages. The standardized correlation coefficient between QOL24 and the instrument's general facet was of 0.89 (statistically highly significant).

Conclusions: The WHOQOL-BREF(PT) is a psychometrically sound instrument to assess the QOL of the considered population sample. However, the QOL domains were found strongly intertwined. More studies are necessary to validate the weighted average scoring strategy of QOL domains and QOL24. Concurrent validity between QOL24 and the instrument's general facet was considered as "strong".

Keywords: Elderly; Psychometric properties; Quality of life; Structural equation modelling; WHOQOL-BREF.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
First-order CFA model of the WHOQOL-BREF(PT)
Fig. 2
Fig. 2
H-second-order CFA model of the WHOQOL-BREF(PT)
Fig. 3
Fig. 3
SEMQOL24-GF model of the WHOQOL-BREF(PT)
Fig. 4
Fig. 4
Comparison between the average scores of the entire sample based on WHO strategy (unweighted average) and using the fswi predicted from the H-second-order CFA model (weighted average)

References

    1. Kyu H, Abate D, Abate K, Abay S, et al. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2018;392:1859–1922. doi: 10.1016/S0140-6736(18)32335-3. - DOI - PMC - PubMed
    1. WHO. World health statistics . Monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization; 2020. p. 2020.
    1. Raggi A, Corso B, Minicuci N, Quintas R, et al. Determinants of quality of life in ageing populations: results from a cross-sectional study in Finland Poland and Spain. PLoS ONE. 2016;11:e0159293. doi: 10.1371/journal.pone.0159293. - DOI - PMC - PubMed
    1. Goes M, Lopes M, Oliveira H, Fonseca C, et al. A nursing care intervention model for elderly people to ascertain general profiles of functionality and self care needs. Sci Rep Nat Res. 2020;10:1–11. doi: 10.1038/s41598-019-56847-4. - DOI - PMC - PubMed
    1. Roser, M, Ritchie, H. Burden of disease. 2016. https://ourworldindata.org/burden-of-disease. Accessed 25 Dec 2020.

MeSH terms

LinkOut - more resources