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Using structural equation modeling to detect response shift in quality of life in patients with Alzheimer's disease

Published online by Cambridge University Press:  03 May 2018

Xuxia Wang ,
Xiaomeng Xu ,
Hongjuan Han ,
Runlian He ,
Liye Zhou ,
Ruifeng Liang  and
Hongmei Yu
Xuxia Wang
Affiliation:
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
Xiaomeng Xu
Affiliation:
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
Hongjuan Han
Affiliation:
Department of Mathematics, School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China
Runlian He
Affiliation:
Department of Nursing, Taiyuan Central Hospital, Taiyuan, China
Liye Zhou
Affiliation:
Department of Mathematics, School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China
Ruifeng Liang
Affiliation:
Department of Environmental Health, School of Public Health, Shanxi Medical University, Taiyuan, China
Hongmei Yu*
Affiliation:
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
*
Correspondence should be addressed to: Hongmei Yu, Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan 030001, China. Phone: +86-351-4135049; Fax: +86-351-2027943. Email: yu@sxmu.edu.cn.
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Abstract

Background:

Our study aims to detect different types of response shifts (RS) and true changes of quality of life (QOL) measurement in patients with Alzheimer's disease (AD) using structural equation modeling (SEM) in domain level.

Methods:

Patients with AD aged over 60 years old were collected from the Department of Neurology and Geriatrics in Taiyuan Central Hospital, China. The 12-item Short Form (SF-12) Health Survey was measured in 238 patients with AD prior to hospitalization and one month following discharge. RS was detected by SEM approach. The statistical process consisted of four steps and fitted four models. We interpreted changes of parameters in models to detect RS and to assess true change.

Results:

The results showed reprioritization of social functioning (SF) (χ2 = 4.13, p < 0.05), reconceptualization of role limitations due to emotional problems (RE) (χ2 = 17.03, p < 0.001), uniform recalibration of bodily pain (BP) (χ2 = 12.24, p < 0.001), and non-uniform recalibration of mental health (MH) (χ2 = 4.41, p < 0.05), respectively. The true changes of common factors were deteriorated in general physical health (PHYS) (−0.10, χ2 = 8.30, p < 0.005) and improved in general mental health (MENT) (+0.29, χ2 = 20.95, p < 0.001). The effect-sizes of RS were only small.

Conclusion:

This study showed that patients with AD occurred three types of RS and true changes one month following discharge. RS had effects on the QOL of patients. Better understanding of potential changes in QOL in patients with AD is crucial.

Keywords

response shiftstructural equation modelingAlzheimer's diseasequality of life
Type
Original Research Article
Information
International Psychogeriatrics , Volume 31 , Issue 1: Issue Theme: Parkinson's Disease , January 2019 , pp. 123 - 132
Copyright
Copyright © International Psychogeriatric Association 2018 

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References

Ahmed, S., Bourbeau, J., Maltais, F. and Mansour, A. (2009). The Oort structural equation modeling approach detected a response shift after a COPD self-management program not detected by the Schmitt technique. Journal of Clinical Epidemiology, 62, 1165–1172.Google Scholar
Banerjee, S. et al. (2009). What do we know about quality of life in dementia? A review of the emerging evidence on the predictive and explanatory value of disease specific measures of health related quality of life in people with dementia. International Journal of Geriatric Psychiatry, 24, 15–24.Google Scholar
Barca, M. L., Engedal, K., Laks, J. and Selbæk, G. (2011). Quality of life among elderly patients with dementia in institutions. Dementia and Geriatric Cognitive Disorders, 31, 435–442.Google Scholar
Barclay-Goddard, R., Epstein, J. D. and Mayo, N. E. (2009). Response shift: a brief overview and proposed research priorities. Quality of Life Research, 18, 335–346.Google Scholar
Barclaygoddard, R., Lix, L. M., Tate, R. B., Weinberg, L. and Mayo, N. E. (2009). Response shift was identified over multiple occasions with a structural equation modeling framework. Journal of Clinical Epidemiology, 62, 1181–1188.Google Scholar
Bollen, K. A. (1998). Structural Equations with Latent Variables (vol. 35, pp. 289–308). New York: John Wiley & Sons.Google Scholar
Brown, T. A. (2006). Confirmatory Factor Analysis for Applied Research. New York: Guilford Publication.Google Scholar
Browne, M. W. and Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21, 230–258.Google Scholar
Bullinger, M. et al. (2015). Evaluation of the American-English quality of life in short stature youth (QoLISSY) questionnaire in the United States. Health Qual Life Outcomes, 13, 43.Google Scholar
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd edn. Hillsdale, MI: Lawrence Erlbaum Associates.Google Scholar
Cummings, J. L., Mega, M., Gray, K., Rosenberg-Thompson, S., Carusi, D. A. and Gornbein, J. (1994). The neuropsychiatric inventory comprehensive assessment of psychopathology in dementia. Neurology, 44, 2308–2314.Google Scholar
Dabakuyo, T. S. et al. (2013). Response shift effects on measuring post-operative quality of life among breast cancer patients: a multicenter cohort study. Quality of Life Research, 22, 1–11.Google Scholar
Daltroy, L. H., Larson, M. G., Eaton, H. M., Phillips, C. B. and Liang, M. H. (1999). Discrepancies between self-reported and observed physical function in the elderly: the influence of response shift and other factors. Social Science & Medicine, 48, 1549–1561.Google Scholar
Folstein, M. F., Folstein, S. E. and Mchugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.Google Scholar
Hubanks, L. and Kuyken, W. (1994). Quality of Life Assessment: An Annotated Bibliography. Geneva: World Health Organization.Google Scholar
Huggins-Manley, A. C. and Algina, J. (2015). The partial credit model and generalized partial credit model as constrained nominal response models, with applications in Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 22, 308–318.Google Scholar
Jing, W., Willis, R. and Feng, Z. (2016). Factors influencing quality of life of elderly people with dementia and care implications: a systematic review. Archives of Gerontology and Geriatrics, 66, 23–41.Google Scholar
Joreskog, K. and Sorbom, D. (1996). LISREL8: User's Reference Guide. Chicago: Scientific Software International.Google Scholar
Kingkallimanis, B. L., Oort, F. J., Nolte, S., Schwartz, C. E. and Sprangers, M. A. G. (2011). Using structural equation modeling to detect response shift in performance and health-related quality of life scores of multiple sclerosis patients. Quality of Life Research, 20, 1527–1540.Google Scholar
Li, F. U., Deng, J., Wang, M. and Guan, Y. (2016). Effect of HABILITATION nursing model on the quality of life of patients with mild or moderate Alzheimer disease. Nursing Practice & Research, 19, 26–29.Google Scholar
McKhann, G. M. et al. (2011). The diagnosis of dementia due to Alzheimer's disease: recommendations from the national institute on Aging-Alzheimer's association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement, 7, 263–269.Google Scholar
Norman, G. (2003). Hi! How are you? Response shift, implicit theories and differing epistemologies. Quality of Life Research, 12, 239–249.Google Scholar
Oort, F. J. (2005). Using structural equation modeling to detect response shifts and true change. Quality of Life Research, 14, 587–598.Google Scholar
Oort, F. J., Visser, M. R. and Sprangers, M. A. (2005). An application of structural equation modeling to detect response shifts and true change in quality of life data from cancer patients undergoing invasive surgery. Quality of Life Research, 14, 599–609.Google Scholar
Peng, D. T. et al. (2005). Discussion on application of MMSE for senile dementia patients. Chinese Journal of Neuroimmunology & Neurology, 12, 187–190.Google Scholar
Ploughman, M., Austin, M., Stefanelli, M. and Godwin, M. (2010). Applying cognitive debriefing to pre-test patient-reported outcomes in older people with multiple sclerosis. Quality of Life Research, 19, 483.Google Scholar
Rabins, P. V. and Black, B. S. (2007). Measuring quality of life in dementia: purposes, goals, challenges and progress. International Psychogeriatrics, 19, 401–407.Google Scholar
Schwartz, C. E. and Sprangers, M. A. G. (2000). Methodological approaches for assessing response shift in longitudinal health-related quality-of-life research. Social Science & Medicine, 48, 1531–1548.Google Scholar
Schwartz, C. E., Bode, R. K., Repucci, N., Becker, J., Sprangers, M. A. G. and Fayers, P. M. (2006). The clinical significance of adaptation to changing health: a meta-analysis of response shift. Quality of Life Research, 15, 1533–1550.Google Scholar
Shou, J. et al. (2016). Reliability and validity of 12-item Short-Form health survey (SF-12) for the health status of Chinese community elderly population in Xujiahui district of Shanghai. Aging Clinical and Experimental Research, 28, 339–346.Google Scholar
Sprangers, M. A. G. and Schwartz, C. E. (1999). Integrating response shift into health-related quality of life research: a theoretical model. Social Science & Medicine, 48, 1507–1515.Google Scholar
Takyiwaa, E., Pham, T. and Khanlari, Z. (2015). Nurses approaches to improving the quality of life of Alzheimer's disease patients in Iran. Nihon Shokuhin Kougakukaishi, 6, 133–141.Google Scholar
Traa, M. J., Braeken, J., De Vries, J., Roukema, J. A., Orsini, R. G. and Den Oudsten, B. L. (2014). Evaluating quality of life and response shift from a couple-based perspective: a study among patients with colorectal cancer and their partners. Quality of Life Research, 24, 1431–1441.Google Scholar
Verdam, M. G. E., Oort, F. J., Der Linden, Y. M. V. and Sprangers, M. A. G. (2015). Taking into account the impact of attrition on the assessment of response shift and true change: a multigroup structural equation modeling approach. Quality of Life Research, 24, 541–551.Google Scholar
Verdam, M. G. E., Oort, F. J. and Sprangers, M. A. G. (2017). Structural equation modeling-based effect-size indices were used to evaluate and interpret the impact of response shift effects. Journal of Clinical Epidemiology, 85, 37–44.Google Scholar
Verdam, M. G. E., Oort, F. J., Visser, M. R. M. and Sprangers, M. A. G. (2012). Response shift detection through then-test and structural equation modelling: decomposing observed change and testing tacit assumptions. Netherlands Journal of Psychology, 67, 58–67.Google Scholar
Visser, M. R., Smets, E. M., Sprangers, M. A. and de Haes, H. J. (2000). How response shift may affect the measurement of change in fatigue. Journal of Pain and Symptom Management, 20, 12–18.Google Scholar
Wang, H. M., Liu, P. P., Patrick, D. L., Edwards, T. C. and Skalicky, A. M. (2010). Response shift in quality of life measurement among patients with hypertension in a community in China. Quality of Life Research, 19, 61–62.Google Scholar
Wang, T., Wang, L. and Wen, C. G. (2015). The effect of intensive care intervention on quality of life of hospitalized patients with Alzheimer's. Medical Innovation of China, 31, 94–97.Google Scholar
Ware, J Jr., Kosinski, M. and Keller, S. D. (1996). A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Medical Care, 34, 220–233.Google Scholar
Ware, J. E., Kosinski, M. A. and Keller, S. D. (1998). How to Score the SF-12 Physical and Mental Health Summary Scale (pp. 21–23). Boston, Massachusetts: The Health Institute, New England Medical Center.Google Scholar
Wilson, I. B. (1999). Clinical understanding and clinical implications of response shift. Social Science & Medicine, 48, 1577–1588.Google Scholar