Abstract
Quality of life (QOL) of a patient is usually computed as the (weighted) sum of items and analysed by means of multiple regressions to evaluate its relationships with various measured factors. The aim of the present study was to compare results derived under classical statistical method with those obtained under more appropriate statistical techniques for QOL. Analyses were applied to data from 4155 subjects participated in 2012 in a community based sample study in the French speaking part of Belgium and which completed a web-based questionnaire on their weight-related experience. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were carried out to derive QOL and to test direct/indirect effects of body mass index (BMI), age, body image discrepancy (BID), latent socio-economic (SOCIO) and latent subjective-norm (SN). No major differences were found under both SEM and the product of coefficients approach using SAS PROCESS macro developed by Hayes. Significant direct and indirect effects on physical and psychological dimensions of QOL were found for age, BMI and SOCIO while significant direct effects were found for BID and SN (p < 0.0001). Factor loadings were found to be significantly different according to gender (p < 0.0001). BID and SN are partially mediators on the relationships between BMI and QOL. The study also confirms the role of SOCIO on the (un)observable variables included in the model. However, the large sample size provided significant tests with small effect size and couldn’t highlight pertinent differences between both methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aghili, R., Ridderstråle, M., Kia, M., Ebrahim Valojerdi, A., Malek, M., Farshchi, A., & Khamseh, M. E. (2017). The challenge of living with diabetes in women and younger adults: a structural equation model. Primary Care Diabetes, 11(5), 467–473. https://doi.org/10.1016/j.pcd.2017.05.001.
Annette Alstadsæter, E. A., Alstadsæter, A., & Feiring, E. (2014). Does healthcare moderate the impact of socioeconomic status on Selfrated Health? Journal of Clinical Research & Bioethics, 05(01). https://doi.org/10.4172/2155-9627.1000169.
Arpey, N. C., Gaglioti, A. H., & Rosenbaum, M. E. (2017). How socioeconomic status affects patient perceptions of health care: a qualitative study. Journal of Primary Care & Community Health, 8(3), 169–175. https://doi.org/10.1177/2150131917697439.
Ashing-Giwa, K. T., & Lim, J.-W. (2008). Predicting health-related quality of life: testing the contextual model using structural equation modeling. Applied Research in Quality of Life, 3(3), 215–230. https://doi.org/10.1007/s11482-009-9057-y.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. http://www.ncbi.nlm.nih.gov/pubmed/3806354. Accessed 14 Nov 2017.
Barrett, P. (2007). Structural equation modelling: adjudging model fit. Personality and Individual Differences, 42(5), 815–824. https://doi.org/10.1016/J.PAID.2006.09.018.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. http://www.ncbi.nlm.nih.gov/pubmed/2320703. Accessed 15 Nov 2017.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588.
Blumenthal, S. J., & Kagen, J. (2002). The effects of socioeconomic status on health in rural and urban America. JAMA, 287(1), 109. https://doi.org/10.1001/jama.287.1.109-JMS0102-3-1.
Boehmer, S., & Luszczynska, A. (2006). Two kinds of items in quality of life instruments: ‘indicator and causal variables’ in the EORTC QLQ-C30. Quality of Life Research, 15(1), 131–141. https://doi.org/10.1007/s11136-005-8290-6.
Bofah, E. A., & Hannula, M. S. (2017). Home resources as a measure of socio-economic status in Ghana. Large-Scale Assessments in Education, 5(1), 1. https://doi.org/10.1186/s40536-017-0039-5.
Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005.
Cha, K. M., Chung, Y. K., Lim, K. Y., Noh, J. S., Chun, M., Hyun, S. Y., Kang, D. R., Oh, M. J., & Kim, N. H. (2017). Depression and insomnia as mediators of the relationship between distress and quality of life in cancer patients. Journal of Affective Disorders, 217, 260–265. https://doi.org/10.1016/j.jad.2017.04.020.
Costa, D. S. J. (2015). Reflective, causal, and composite indicators of quality of life: a conceptual or an empirical distinction? Quality of Life Research, 24(9), 2057–2065. https://doi.org/10.1007/s11136-015-0954-2.
Danner, D., Hagemann, D., & Fiedler, K. (2015). Mediation analysis with structural equation models: combining theory, design, and statistics. European Journal of Social Psychology, 45(4), 460–481. https://doi.org/10.1002/ejsp.2106.
Donneau, A. F., Mauer, M., Coens, C., Bottomley, A., & Albert, A. (2014). Longitudinal quality of life data: a comparison of continuous and ordinal approaches. Quality of Life Research, 23(10), 2873–2881. https://doi.org/10.1007/s11136-014-0730-8.
Dragan, A., & Akhtar-Danesh, N. (2007). Relation between body mass index and depression: a structural equation modeling approach. BMC Medical Research Methodology, 7(1), 17. https://doi.org/10.1186/1471-2288-7-17.
Fitzpatrick, R., Fletcher, A., Gore, S., Jones, D., Spiegelhalter, D., & Cox, D. (1992). Quality of life measures in health care. I: applications and issues in assessment. BMJ (Clinical research ed.), 305(6861), 1074–1077. http://www.ncbi.nlm.nih.gov/pubmed/1467690. Accessed 5 Dec 2017.
Hayes, A. F. (1986). PROCESS: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling 1. http://www.afhayes.com/. Accessed 23 March 2018.
Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal; AMJ, 25(1), 76–81. https://doi.org/10.1016/j.ausmj.2017.02.001.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.
Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: evidence that structural equations models perform better than regressions. Journal of Consumer Psychology, 17(2), 139–153. https://doi.org/10.1016/S1057-7408(07)70020-7.
Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: Guilford Press.
Krieger, N., Williams, D. R., & Moss, N. E. (1997). Measuring social class in US public health research: concepts, methodologies, and guidelines. Annual Review of Public Health, 18(1), 341–378. https://doi.org/10.1146/annurev.publhealth.18.1.341.
Litwin, H., & Sapir, E. V. (2009). Perceived income adequacy among older adults in 12 countries: findings from the survey of health, ageing, and retirement in Europe. The Gerontologist, 49(3), 397–406. https://doi.org/10.1093/geront/gnp036.
MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614. https://doi.org/10.1146/annurev.psych.58.110405.085542.
Marmot, M. (2013). Report on social determinants of health and the health divide in the WHO European region. World Health Organization., 234. http://www.euro.who.int/__data/assets/pdf_file/0004/251878/Review-of-social-determinants-and-the-health-divide-in-the-WHO-European-Region-FINAL-REPORT.pdf. Accessed 14 Nov 2017.
Martinez, S. A., Beebe, L. A., Thompson, D. M., Wagener, T. L., Terrell, D. R., & Campbell, J. E. (2018). A structural equation modeling approach to understanding pathways that connect socioeconomic status and smoking. PLoS One, 13(2), e0192451. https://doi.org/10.1371/journal.pone.0192451.
Meuleners, L. B., Lee, A. H., Binns, C. W., & Lower, A. (2003). Quality of life for adolescents: Assessing measurement properties using structural equation modelling. Quality of Life Research: an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 12(3), 283–290. http://www.ncbi.nlm.nih.gov/pubmed/12769141. Accessed 17 Nov 2017.
Moon, J. R., Cho, Y. A., Huh, J., Kang, I.-S., & Kim, D.-K. (2016). Structural equation modeling of the quality of life for patients with marfan syndrome. Health and Quality of Life Outcomes, 14(1), 83. https://doi.org/10.1186/s12955-016-0488-5.
Niyonsenga, T., Trepka, M. J., Lieb, S., & Maddox, L. M. (2013). Measuring socioeconomic inequality in the incidence of AIDS: rural-urban considerations. AIDS and Behavior, 17(2), 700–709. https://doi.org/10.1007/s10461-012-0236-8.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.
Øvrum, A. (2011). Socioeconomic status and lifestyle choices: evidence from latent class analysis. Health Economics, 20(8), 971–984. https://doi.org/10.1002/hec.1662.
Pétré, B., Donneau, A.-F., Crutze, C., Husson, E., Scheen, A., & Guillaume, M. (2015). Obese subjects involvement in a population-based survey: the use of information and communication technologies (ICT) to avoid stigmatization. Quality of Life Research, 24(5), 1131–1135. https://doi.org/10.1007/s11136-014-0800-y.
Pétré, B., Scheen, A., Ziegler, O., Donneau, A.-F., Dardenne, N., Husson, E., Albert, A., & Guillaume, M. (2016). Body image discrepancy and subjective norm as mediators and moderators of the relationship between body mass index and quality of life. Patient Preference and Adherence, 10, 2261–2270. https://doi.org/10.2147/PPA.S112639.
Post, M. W. M. (2014). Definitions of quality of life: what has happened and how to move on. Topics in Spinal Cord Injury Rehabilitation, 20(3), 167–180. https://doi.org/10.1310/sci2003-167.
Ribeiro, M. R. C., da Silva, A. A. M., de Britto e Alves, M. T. S. S., Batista, R. F. L., Ribeiro, C. C. C., Schraiber, L. B., et al. (2017). Effects of socioeconomic status and social support on violence against pregnant women: a structural equation modeling analysis. PLoS One, 12(1), e0170469. https://doi.org/10.1371/journal.pone.0170469.
Rosseel, Y. (2012). Lavaan : an R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02.
Rosseel, Y. (2014). Structural equation modeling with lavaan. Using R for personality research, 1–127. https://personality-project.org/r/tutorials/summerschool.14/rosseel_sem_intro.pdf. Accessed 14 Nov 2017.
Rosseel, Y. (2017). The lavaan tutorial. http://lavaan.ugent.be/tutorial/tutorial.pdf. Accessed 15 Nov 2017.
Sartipi, M., Nedjat, S., Mansournia, M. A., Baigi, V., & Fotouhi, A. (2016). Assets as a socioeconomic status index: categorical principal components analysis vs. latent class analysis. Archives of Iranian Medicine, 19(11), 791–796 doi:0161911/AIM.009.
Scheen, A., Bourguignon, J. P., Hubermont, G., Ziegler, O., Böhme, P., Collin, J. F., Romain, M. L., Lair, M., de Beaufort, C., Michel, G., & Guillaume, M. (2010). Éducation thérapeutique et préventive face au diabète et à l’obésité à risque chez l’adulte et l’adolescent : le projet Interreg IV EDUDORA2. Diabetes & Metabolism, 36, A84. https://doi.org/10.1016/S1262-3636(10)70341-4.
Sideridis, G., Simos, P., Papanicolaou, A., & Fletcher, J. (2014). Using structural equation modeling to assess functional connectivity in the brain: power and sample size considerations. Educational and Psychological Measurement, 74(5), 733–758. https://doi.org/10.1177/0013164414525397.
Steen, J., Loeys, T., Moerkerke, B., & Vansteelandt, S. (2017). Medflex: an R package for flexible mediation analysis using natural effect models. Journal of Statistical Software, 76(11), 1–46. https://doi.org/10.18637/jss.v076.i11.
Stephenson, M. T., Holbert, R. L., & Zimmerman, R. S. (2006). On the use of structural equation modeling in health communication research. Health Communication, 20(2), 159–167. https://doi.org/10.1207/s15327027hc2002_7.
Stevens, J. (2009). Applied multivariate statistics for the social sciences. In J. P. Stevens (Ed.), Version details - Trove (5th ed.). New York : Routledge, c2009. http://trove.nla.gov.au/work/17113022?q&sort=holdings+desc&_=1510743660016&versionId=44804159+218188866. Accessed 15 Nov 2017.
Stunkard, A. J., Sørensen, T., & Schulsinger, F. (1983). Use of the Danish adoption register for the study of obesity and thinness. Research Publications - Association for Research in Nervous and Mental Disease, 60, 115–120. http://www.ncbi.nlm.nih.gov/pubmed/6823524. Accessed 14 Nov 2017.
VanderWeele, T. J. (2016). Mediation analysis: a practitioner’s guide. Annual Review of Public Health, 37(1), 17–32. https://doi.org/10.1146/annurev-publhealth-032315-021402.
Verdugo, M. A., Schalock, R. L., Keith, K. D., & Stancliffe, R. J. (2005). Quality of life and its measurement: important principles and guidelines. Journal of Intellectual Disability Research, 49(10), 707–717. https://doi.org/10.1111/j.1365-2788.2005.00739.x.
Wagstaff, A., & Watanabe, N. (2003). What difference does the choice of SES make in health inequality measurement? Health Economics, 12(10), 885–890. https://doi.org/10.1002/hec.805.
Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, Bias, and solution propriety. Educational and Psychological Measurement, 76(6), 913–934. https://doi.org/10.1177/0013164413495237.
Yang Hansen, K., & Munck, I. (2012). Exploring the measurement profiles of socioeconomic background indicators and their differences in reading achievement: A two-level latent class analysis. IERI Monograph Series, 5. http://www.ierinstitute.org/fileadmin/Documents/IERI_Monograph/IERI_Monograph_Volume_05_Chapter_3.pdf. Accessed 27 Feb 2018.
Yanuar, F., Ibrahim, K., & Jemain, A. A. (2010). On the application of structural equation modeling for the construction of a health index. Environmental Health and Preventive Medicine, 15(5), 285–291. https://doi.org/10.1007/s12199-010-0140-7.
Ziegler, O., Filipecki, J., Girod, I., & Guillemin, F. (2005). Development and validation of a French obesity-specific quality of life questionnaire: Quality of life, obesity and dietetics (QOLOD) rating scale. Diabetes & Metabolism, 31(3 Pt 1), 273–283. http://www.ncbi.nlm.nih.gov/pubmed/16142018. Accessed 14 Nov 2017.
Funding
This work was supported by the European Regional Development Fund and the Belgian public health authorities (Program INTERREG IV – 50WLL/3/3/136).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors report no conflicts of interest in this work.
Ethical Approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed Consent
As collected data were anonymous, no written informed consent was required by the ethics committee of the University hospital of Liège, patient consent was assumed based on their voluntary participation.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dardenne, N., Pétré, B., Husson, E. et al. Assessing Quality of Life in an Obesity Observational Study: a Structural Equation Modeling Approach. Applied Research Quality Life 15, 1117–1133 (2020). https://doi.org/10.1007/s11482-019-09725-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11482-019-09725-0