Longitudinal Patterns and Predictors of Cognitive Impairment Classification Stability
| dc.contributor.author | McDowell, Cynthia | |
| dc.contributor.supervisor | MacDonald, Stuart Warren Swain | |
| dc.date.accessioned | 2022-08-19T20:39:58Z | |
| dc.date.available | 2022-08-19T20:39:58Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022-08-19 | |
| dc.degree.department | Department of Psychology | |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | Introduction: Classifications such as Mild Cognitive Impairment (MCI) and Cognitive Impairment, No Dementia (CIND) are thought to represent the transitory, pre-clinical phase of dementia. However, increasing research demonstrates that MCI and CIND represent nonlinear and unstable entities that do not always lead to imminent dementia. Despite an increase in research examining patterns and predictors of cognitive impairment classification stability, this concept is still poorly understood, and the research remains limited. The present study was designed to address the existing limitations within the literature by utilizing a longitudinal repeated measures design to gain a more thorough understanding of CIND classification stability patterns, as well as identify predictors of future stability. Objectives: The objectives were to i) explore patterns of longitudinal stability in cognitive status across multiple assessments, and ii) investigate whether select baseline variables could predict 6-year cognitive status stability patterns. Methods: Participants included 259 older adults from Project MIND, a six-year longitudinal repeated measures design in which participants were classified as either Normal Cognition (NC) or CIND at each assessment. A latent transition analysis approach was adapted in order to identify and characterize transitions in CIND status across annual assessments. Participants were classified as either Stable NC, Stable CIND, Progressers, Reverters, or Fluctuaters. Multinomial logistic regression was then employed to test whether baseline predictors were associated with cognitive status stability patterns. Results: The sample demonstrated high rates of reversion and fluctuation in CIND status across years of study. Additionally, premorbid IQ, total number of medications, presence of arthritis, and CIND severity at baseline were all significantly associated with select CIND stability outcomes. Conclusion: CIND status was unstable for several years following baseline assessment, and factors such as cognitive reserve may delay or protect against demonstrable cognitive impairment. Further, considering cognitive impairment severity (i.e., single versus multidomain impairment) at the time of initial classification may improve CIND classifications. Continued research on CIND stability is recommended to improve classification methodology and provide a framework for future identification and prevention. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/14115 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Cognitive Impairment | en_US |
| dc.subject | Stability vs Reversion | en_US |
| dc.subject | Patterns and Predictors | en_US |
| dc.title | Longitudinal Patterns and Predictors of Cognitive Impairment Classification Stability | en_US |
| dc.type | Thesis | en_US |