Abstract
Purpose
Using the Wilson–Cleary model of patient outcomes as a conceptual framework, the impact of functional status on health-related quality of life (HRQoL) among older adults was examined, including tests of the mediation provided by life-space mobility.
Methods
Participants were enrollees in a population-based, longitudinal study of mobility among community-dwelling older adults. Data from four waves of the study equally spaced approximately 18 months apart (baseline, 18, 36, and 54 months) were used for participants who survived at least 1 year beyond the 54-month assessment (n = 677). Autoregressive mediation models using longitudinal data and cross-sectional mediation models using baseline data were evaluated and compared using structural equation modeling.
Results
The longitudinal autoregressive models supported the mediating role of life-space mobility and suggested that this effect is larger for the mental component summary score than the physical component summary score of the SF-12. Evidence for a reciprocal relationship over time between functional status, measured by ADL difficulty, and life-space mobility was suggested by modification indices; these model elaborations did not alter the substantive meaning of the mediation effects. Mediated effect estimates from longitudinal autoregressive models were generally larger than those from cross-sectional models, suggesting that mediating relationships would have been missed or were potentially underestimated in cross-sectional models.
Conclusions
These results support a mediating role for life-space mobility in the relationship between functional status and HRQoL. Functional status limitations might cause diminished HRQoL in part by limiting mobility. Mobility limitations may precede functional status limitations in addition to being a consequence thereof.
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Notes
Separate models were fit for PCS and MCS primarily for the ease of presentation of results and because each variable was considered a separate end point. It is possible to estimate one complete model containing both PCS and MCS. Given that the PCS and MCS were originally constructed to be orthogonal, there should be little difference in the results based on separate models and results from a single model considering both PCS and MCS. Indeed, when considering the final models reported in the current analysis, there were no substantive differences in findings when comparing separate estimation models for PCS and MCS and a single, combined model.
The nature of the calculation of the LSC score limits the applicability of internal consistency reliability and thus Cronbach’s alpha is not reported for the LSC; as stated earlier, past research reports high levels of test–retest reliability for this measure.
As suggested by one anonymous reviewer, in addition to examining chi-square change and model fit statistics for assessing measurement invariance, one should consider whether any meaningful differences in the estimates of interest occur between models with constrained invariant parameters and those where parameters are freely estimated. In the present study, the mediated effects were highly similar between models with measurement invariance for functional status and those with non-invariance. For example, the total mediated effect estimate for the PCS Model 2 assuming invariant thresholds and loadings (see Table 2) was −0.88 (p < 0.0001); this same effect was −0.90 (p < 0.0001) in a model with non-invariance. An examination of the time-specific indirect effects revealed similar results; p values for these indirect effects differed in the third decimal place, if at all.
References
Baker, P. S., Bodner, E. V., & Allman, R. M. (2003). Measuring life-space mobility in community-dwelling older adults. Journal of the American Geriatrics Society, 51(11), 1610–1614.
Peel, C., Baker, P. S., Roth, D. L., Brown, C. J., Bodner, E. V., & Allman, R. M. (2005). Assessing mobility in older adults: The UAB Study of Aging Life-Space Assessment. Physical Therapy, 85(10), 1008–1019.
Brown, C. J., Roth, D. L., Allman, R. M., Sawyer, P., Ritchie, C. S., & Roseman, J. M. (2009). Trajectories of life-space mobility after hospitalization. Annals of Internal Medicine, 150(6), 372–378.
Ware, J. E., Kosinski, M., & Keller, S. D. (2002). SF-12: How to score the SF-12 physical and mental health summary scales (4th ed.). Lincoln, RI: QualityMetric Incorporated.
Wilson, I. B., & Cleary, P. D. (1995). Linking clinical variables with health-related quality of life: A conceptual model of patient outcomes. JAMA, 273(1), 59–65.
Sousa, K., & Kwok, O. (2006). Putting Wilson and Cleary to the test: Analysis of a HRQOL conceptual model using structural equation modeling. Quality of Life Research, 15(4), 725–737.
Baker, S. R., Pankhurst, C. L., & Robinson, P. G. (2007). Testing relationships between clinical and non-clinical variables in xerostomia: A structural equation model of oral health-related quality of life. Quality of Life Research, 16(2), 297–308.
Ryu, E., West, S. G., & Sousa, K. H. (2007). Mediation and moderation: testing relationships between symptom status, functional health, and quality of life in HIV patients. Multivariate Behavioral Research, 44(2), 213–232.
Wyrwich, K., Harnam, N., Locklear, J., Svedsäter, H., & Revicki, D. (2011). Understanding the relationships between health outcomes in generalized anxiety disorder clinical trials. Quality of Life Research, 20(2), 255–262.
Heslin, K., Stein, J., Heinzerling, K., Pan, D., Magladry, C., & Hays, R. (2011). Clinical correlates of health-related quality of life among opioid-dependent patients. Quality of Life Research, 20(8), 1205–1213.
Wilson, I., & Kaplan, S. (1995). Clinical practice and patients’ health status: How are the two related? Medical Care, 33(4 Suppl), AS209–AS214.
Ferrans, C., Zerwic, J., Wilbur, J., & Larson, J. (2005). Conceptual model of health-related quality of life. Journal of Nursing Scholarship, 37(4), 336–342.
Maurice-Stam, H., Oort, F., Last, B., & Grootenhuis, M. (2009). A predictive model of health-related quality of life in young adult survivors of childhood cancer. European Journal of Cancer Care, 18(4), 339–349.
Schulz, T., Niesing, J., Stewart, R. E., Westerhuis, R., Hagedoorn, M., Ploeg, R. J., et al. (2012). The role of personal characteristics in the relationship between health and psychological distress among kidney transplant recipients. Social Science & Medicine. doi: 10.1016/j.socscimed.2012.05.028 [Epub ahead of print].
Newsom, J. T., & Schulz, R. (1996). Social support as a mediator in the relation between functional status and quality of life in older adults. Psychology and Aging, 11(1), 34–44.
Cheong, J., MacKinnon, D. P., & Khoo, S. T. (2003). Investigation of mediational processes using parallel process latent growth curve modeling. Structural Equation Modeling, 10(2), 238–262.
Cole, D. A., & Maxwell, S. E. (2003). Testing mediation models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577.
Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44.
Little, T. D., Preacher, K. J., Selig, J. P., & Card, N. A. (2007). New developments in latent variable panel analyses of longitudinal data. International Journal of Behavioral Development, 31(4), 357–365.
MacKinnon, D. P. (2008). Longitudinal mediation analysis. In D. P. MacKinnon (Ed.), Introduction to statistical mediation analysis (pp. 193–236). New York: Lawrence Erlbaum.
Liu, L. C., Flay, B. R., & Aban Aya Investigators. (2009). Evaluating mediation in longitudinal multivariate data: Mediation effects for the Aban Aya youth project drug prevention program. Prevention Science, 10(3), 197–207.
Audrain-McGovern, J., Rodriguez, D., & Kassel, J. D. (2009). Adolescent smoking and depression: Evidence for self-medication and peer smoking mediation. Addiction, 104(10), 1743–1756.
Selig, J. P., & Preacher, K. J. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6(2–3), 144–164.
Negriff, S., Ji, J., & Trickett, P. (2011). Exposure to peer delinquency as a mediator between self-report pubertal timing and delinquency: A longitudinal study of mediation. Development and Psychopathology, 23(1), 293–304.
Roth, D. L., & MacKinnon, D. P. (2012). Mediation analysis with longitudinal data. In J. T. Newsom, R. N. Jones, & S. M. Hofer (Eds.), Longitudinal data analysis: A practical guide for researchers in aging, health, and social sciences (pp. 181–216). New York: Routledge.
Wilson, I., & Cleary, P. (1997). Clinical predictors of declines in physical functioning in persons with AIDS: Results of a longitudinal study. Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology, 16(5), 343–349.
Mathisen, L., Andersen, M. H., Veenstra, M., Wahl, A. K., Hanestad, B. R., & Fosse, E. (2007). Quality of life can both influence and be an outcome of general health perceptions after heart surgery. Health and Quality of Life Outcomes, 5, 27.
Devine, K., Reed-Knight, B., Loiselle, K., Simons, L., Mee, L., & Blount, R. (2011). Predictors of long-term health-related quality of life in adolescent solid organ transplant recipients. Journal of Pediatric Psychology, 36(8), 891–901.
Collins, L. M., Graham, J. W., & Flaherty, B. P. (1998). An alternative framework for defining mediation. Multivariate Behavioral Research, 33(2), 295–312.
Allman, R. M., Sawyer, P., & Roseman, J. M. (2006). The UAB Study of Aging: Background and prospects for insights into life-space mobility among older African-Americans and Whites in rural and urban settings. Aging Health, 2(3), 417–428.
Wolinsky, F., Bentler, S., Hockenberry, J., Jones, M., Obrizan, M., Weigel, P., et al. (2011). Long-term declines in ADLs, IADLs, and mobility among older Medicare beneficiaries. BMC Geriatrics, 11, 43.
Parker, M., Baker, P. S., & Allman, R. M. (2002). A life-space approach to functional assessment of mobility in the elderly. Journal of Gerontological Social Work, 35(4), 35–55.
Ritchie, C., Locher, J., Roth, D., McVie, T., Sawyer, P., & Allman, R. (2008). Unintentional weight loss predicts decline in activities of daily living function and life-space mobility over 4 years among community-dwelling older adults. Journal of Gerontology. Series A, Biological Sciences and Medical Sciences, 63(1), 67–75.
Okonkwo, O. C., Roth, D. L., Pulley, L., & Howard, G. (2010). Confirmatory factor analysis of the validity of the SF-12 for persons with and without a history of stroke. Quality of Life Research, 19(9), 1323–1331.
Charlson, M., Pompei, P., Ales, K., & MacKenzie, C. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40(5), 373–383.
Hoyle, R. H., & Kenny, D. A. (1999). Sample size, reliability, and tests of statistical mediation. In R. H. Hoyle (Ed.), Statistical strategies for small sample research (pp. 195–222). Thousand Oaks, CA: Sage.
MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York: Lawrence Erlbaum.
Marsh, H. W. (1993). Stability of individual differences in multiwave panel studies: Comparison of simplex models and one-factor models. Journal of Educational Measurement, 30(2), 157–183.
Curran, P. J., & Bollen, K. A. (2001). The best of both worlds: Combining autoregressive and latent curve models. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 107–135). Washington, DC: American Psychological Association.
Gollob, H. F., & Reichardt, C. S. (1991). Interpreting and estimating indirect effects assuming time lags really matter. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: recent advances, unanswered questions, future directions (pp. 243–259). Washington, DC: American Psychological Association.
Anderson, S. E., & Williams, L. J. (1992). Assumptions about unmeasured variables with studies of reciprocal relationships: The case of employee attitudes. Journal of Applied Psychology, 77(5), 638–650.
Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312.
MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39(1), 99–128.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.
Yu, C. Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Doctoral dissertation, University of California, Los Angeles.
Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115–132.
Asparouhov, T., & Muthén, B. (2010). Weighted least squares estimation with missing data. http://www.statmodel.com/download/GstrucMissingRevision.pdf. Accessed 1 Aug 2011.
Muthén, L. K., & Muthén, B. O. (1998–2010). Mplus user’s guide (6th edn). Los Angeles, CA: Muthén & Muthén.
Millsap, R. E. (2005). Four unresolved problems in studies of factorial invariance. In A. Maydeu-Olivares & J. J. McArdle (Eds.), Contemporary psychometrics: A festschrift to Roderick P. McDonald (pp. 153–171). Mahwah, NJ: Erlbaum.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: The Guilford Press.
Andersen, C., Wittrup-Jensen, K., Lolk, A., Andersen, K., & Kragh-Sørensen, P. (2004). Ability to perform activities of daily living is the main factor affecting quality of life in patients with dementia. Health and Quality of Life Outcomes, 2, 52.
Vest, M., Murphy, T., Araujo, K., & Pisani, M. (2011). Disability in activities of daily living, depression, and quality of life among older medical ICU survivors: A prospective cohort study. Health and Quality of Life Outcomes, 9, 9.
Höfer, S., Benzer, W., Alber, H., Ruttmann, E., Kopp, M., Schüssler, G., et al. (2005). Determinants of health-related quality of life in coronary artery disease patients: A prospective study generating a structural equation model. Psychosomatics, 46(3), 212–223.
Heo, S., Moser, D., Riegel, B., Hall, L., & Christman, N. (2005). Testing a published model of health-related quality of life in heart failure. Journal of Cardiac Failure, 11(5), 372–379.
Saban, K., Penckofer, S., Androwich, I., & Bryant, F. (2007). Health-related quality of life of patients following selected types of lumbar spinal surgery: A pilot study. Health and Quality of Life Outcomes, 5, 71.
Collins, L. M., & Graham, J. W. (2002). The effect of the timing and spacing of observations in longitudinal studies of tobacco and other drug use: Temporal design considerations. Drug and Alcohol Dependence, 68(Suppl1), S85–S96.
Schafer, J. L. (1997). Analysis of incomplete multivariate data. London: Chapman & Hall.
Hedeker, D., & Gibbons, R. D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2(1), 64–78.
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The UAB Study of Aging is funded through a grant from the National Institute on Aging (R01 AG015062).
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Bentley, J.P., Brown, C.J., McGwin, G. et al. Functional status, life-space mobility, and quality of life: a longitudinal mediation analysis. Qual Life Res 22, 1621–1632 (2013). https://doi.org/10.1007/s11136-012-0315-3
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DOI: https://doi.org/10.1007/s11136-012-0315-3