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

Int Psychogeriatr. 2019 Jan;31(1):123-132. doi: 10.1017/S1041610218000595. Epub 2018 May 3.

Abstract

ABSTRACTBackground: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: Alzheimer's disease; quality of life; response shift; structural equation modeling.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Alzheimer Disease / psychology*
  • Alzheimer Disease / therapy*
  • China
  • Female
  • Health Surveys
  • Humans
  • Latent Class Analysis*
  • Male
  • Mental Health
  • Quality of Life / psychology*