Validation of an instrument to evaluate quality of life in the aging population: WHOQOL-AGE

Francisco Félix Caballero, Marta Miret, Mick Power, Somnath Chatterji, Beata Tobiasz-Adamczyk, Seppo Koskinen, Matilde Leonardi, Beatriz Olaya, Josep Maria Haro, José Luis Ayuso-Mateos, Francisco Félix Caballero, Marta Miret, Mick Power, Somnath Chatterji, Beata Tobiasz-Adamczyk, Seppo Koskinen, Matilde Leonardi, Beatriz Olaya, Josep Maria Haro, José Luis Ayuso-Mateos

Abstract

Background: There is a need for short, specific instruments that assess quality of life (QOL) adequately in the older adult population. The aims of the present study were to obtain evidence on the validity of the inferences that could be drawn from an instrument to measure QOL in the aging population (people 50+ years old), and to test its psychometric properties.

Methods: The instrument, WHOQOL-AGE, comprised 13 positive items, assessed on a five-point rating scale, and was administered to nationally representative samples (n = 9987) from Finland, Poland, and Spain. Cronbach's alpha was employed to assess internal consistency reliability, whereas the validity of the questionnaire was assessed by means of factor analysis, graded response model, Pearson's correlation coefficient and unpaired t-test. Normative values were calculated across countries and for different age groups.

Results: The satisfactory goodness-of-fit indices confirmed that the factorial structure of WHOQOL-AGE comprises two first-order factors. Cronbach's alpha was 0.88 for factor 1, and 0.84 for factor 2. Evidence supporting a global score was found with a second-order factor model, according to the goodness-of-fit indices: CFI = 0.93, TLI = 0.91, RMSEA = 0.073. Convergent validity was estimated at r = 0.75 and adequate discriminant validity was also found. Significant differences were found between healthy individuals (74.19 ± 13.21) and individuals with at least one chronic condition (64.29 ± 16.29), supporting adequate known-groups validity.

Conclusions: WHOQOL-AGE has shown good psychometric properties in Finland, Poland, and Spain. Therefore, considerable support is provided to using the WHOQOL-AGE to measure QOL in older adults in these countries, and to compare the QOL of older and younger adults.

Figures

Figure 1
Figure 1
Item Response Category Characteristic Curves associated with items Q3 and Q5.
Figure 2
Figure 2
Item Response Category Characteristic Curves associated with items Q9, Q10, and Q11.
Figure 3
Figure 3
Smoothed Gaussian cumulative distribution functions of the WHOQOL-AGE scores across the population aged 18–49 years and the population aged 50 and over.

References

    1. The WHOQOL group. The world health organization quality of life assessment (WHOQOL): development and general psychometric properties. Soc Sci Med. 1998;11:1569–1585. doi: 10.1016/S0277-9536(98)00009-4.
    1. Power M, Quinn K, Schmidt S. Development of the WHOQOL-old module. Qual Life Res. 2005;11:2197–2214. doi: 10.1007/s11136-005-7380-9.
    1. Skevington SM, Lotfy M, O’Connell KA. The world health Organization’s WHOQOL-BREF quality of life assessment: psychometric properties and results of the international field trial. A report from the WHOQOL group. Qual Life Res. 2004;11:299–310.
    1. Schmidt S, Muhlan H, Power M. The EUROHIS-QOL 8-item index: psychometric results of a cross-cultural field study. Eur J Public Health. 2006;11:420–428. doi: 10.1093/eurpub/cki155.
    1. Paschoal SM, Jacob FW, Litvoc J. Development of elderly quality of life index - EqoLI: item reduction and distribution into dimensions. Clinics (Sao Paulo) 2008;11:179–188.
    1. Hoshino K, Yamada H, Endo H, Nagura E. [An preliminary study on quality of life scale for elderly: an examination in terms of psychological satisfaction] Shinrigaku Kenkyu. 1996;11:134–140. doi: 10.4992/jjpsy.67.134.
    1. Paschoal SM, Filho WJ, Litvoc J. Development of elderly quality of life index - EQOLI: theoretical-conceptual framework, chosen methodology, and relevant items generation. Clinics (Sao Paulo) 2007;11:279–288. doi: 10.1590/S1807-59322007000300012.
    1. Fang J, Power M, Lin Y, Zhang J, Hao Y, Chatterji S. Development of short versions for the WHOQOL-OLD module. Gerontologist. 2012;11:66–78. doi: 10.1093/geront/gnr085.
    1. Börsch-Supan A, Hank K, Jürges H. A new comprehensive and international view on ageing: introducing the “Survey of Health, Ageing and Retirement in Europe”. Eur J Ageing. 2005;11:245–253. doi: 10.1007/s10433-005-0014-9.
    1. Juster FT, Suzman R. An overview of the health and retirement study. J Hum Resour. 1995;11:S7–S56.
    1. Marmot M, Banks J, Blundell R, Lessof C, Nazroo J. Health, Wealth, and Lifestyles of the Older Population in England. The 2002 English Longitudinal Study of Ageing. London: Institute for fiscal studies; 2003.
    1. Whelan BJ, Savva GM. Design and methodology of the irish longitudinal study on ageing. J Am Geriatr Soc. 2013;11(Suppl 2):S625–S628.
    1. Wong R, Espinoza M, Palloni A. [Mexican older adults with a wide socioeconomic perspective: health and aging] Salud Publica Mex. 2007;11(Suppl 4):S436–S447.
    1. Kowal P, Chatterji S, Naidoo N, Biritwum R, Fan W, Lopez RR. et al.Data resource profile: the world health organization study on global AGEing and adult health (SAGE) Int J Epidemiol. 2012;11:1639–1649. doi: 10.1093/ije/dys210.
    1. Kahneman D, Krueger AB, Schkade DA, Schwarz N, Stone AA. A survey method for characterizing daily life experience: the day reconstruction method. Science. 2004;11:1776–1780. doi: 10.1126/science.1103572.
    1. Ayuso-Mateos JL, Miret M, Caballero FF, Olaya B, Haro JM, Kowal P. et al.Multi-country evaluation of affective experience: validation of an abbreviated version of the day reconstruction method in seven countries. PLoS ONE. 2013;11:e61534. doi: 10.1371/journal.pone.0061534.
    1. Miret M, Caballero FF, Mathur A, Naidoo N, Kowal P, Ayuso-Mateos JL. et al.Validation of a measure of subjective well-being: an abbreviated version of the day reconstruction method. PLoS ONE. 2012;11:e43887. doi: 10.1371/journal.pone.0043887.
    1. Velicer WF. Determining the number of components from the matrix of partial correlations. Psychometrika. 1976;11:321–337. doi: 10.1007/BF02293557.
    1. Tucker LR. A Method for Synthesis of Factor Analysis Studies. Washington, D.C: Department of the Army; 1951. (Personnel Research Sections Report 984).
    1. Mulaik SA. The Foundations of Factor Analysis. New York: Mc Graw-Hill; 1972.
    1. Hu LT, Bentler PM. Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model. 1999;11:1–55. doi: 10.1080/10705519909540118.
    1. Reise SP, Widaman KF, Pugh RH. Confirmatory factor analysis and item response theory: two approaches for exploring measurement invariance. Psychol Bull. 1993;11:552–566.
    1. Steiger JH. Understanding the limitations of global fit assessment in structural equation modelling. Personal Individ Differ. 2007;11:893–898. doi: 10.1016/j.paid.2006.09.017.
    1. Schreider JB, Stage FK, King J, Nora A, Barlow EA. Reporting structural equation modeling and confirmatory factor analysis results: a review. J Educ Res. 2006;11:323–337. doi: 10.3200/JOER.99.6.323-338.
    1. Schumacker RE, Lomax RG. A biginner’s Guide to Structural Equation Modeling. Mahwah, N.J.: Lawrence Erlbaum Associates; 2004.
    1. Burnham KP, Anderson DR. Model Selection and Multimodel Inference: a Practical Information-Theoretic Approach. New York: Springer; 2011.
    1. Schwarz G. Estimating dimension of a model. Ann Stat. 1978;11:461–464. doi: 10.1214/aos/1176344136.
    1. Samejima F. In: Handbook of Modern Item Response Theory. Linden WV, Hambleton RK, editor. New York: Springer; 1997. Graded Response Model; pp. 85–100.
    1. Rindskopf D, Rose T. Some theory and applications of confirmatory second-order factor analysis. Multivar Behav Res. 1988;11:51–67. doi: 10.1207/s15327906mbr2301_3.
    1. Bland JM, Altman DG. Statistics notes: Cronbach’s alpha. Br Med J. 1997;11:572. doi: 10.1136/bmj.314.7080.572.
    1. Raykov T. Evaluation of convergent and discriminant validity with multitrait-multimethod correlations. Br J Math Stat Psychol. 2011;11:38–52. doi: 10.1348/000711009X478616.
    1. Campbell DT, Fiske DW. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol Bull. 1959;11:81–105.
    1. Rizopoulos D. ltm: an R package for latent variable modeling and item response theory analyses. J Stat Softw. 2006;11(5):1–25.
    1. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2008.
    1. Muthén LK, Muthén BO. Mplus User’s Guide. 4. Muthén & Muthén: Los Angeles, CA; 2010.
    1. StataCorp. Stata Statistical Software. Release 11. : College Station, TX: Stata Corporation; 2010.
    1. Draugalis JR, Coons SJ, Plaza CM. Best practices for survey research reports: a synopsis for authors and reviewers. Am J Pharm Educ. 2008;11:Article 11.
    1. Fuchs C, Diamantopoulos A. Using single-item measures for construct measurement in management research. DWS. 2009;11:195–210.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa