ABSTRACT
OBJECTIVES: To examine trajectories of change in everyday function for individuals with cognitive deficits suggestive of mild cognitive impairment (MCI). DESIGN: Using data from the longitudinal, multisite Advanced Cognitive Training for Independent and Vital Elderly Study allowed for post hoc classification of MCI status at baseline using psychometric definitions for amnestic MCI, nonamnestic MCI, multidomain MCI, and no MCI. SETTING: Six U.S. cities. PARTICIPANTS: Two thousand eight hundred thirty-two volunteers (mean age 74; 26% African American) living independently, recruited from senior housing, community centers, hospitals, and clinics. MEASUREMENTS: Mixed-effect models examined changes in self-reported activities of daily living and instrumental activities of daily living (IADLs) from the Minimum Data Set Home Care Interview in 2,358 participants over a 3-year period. RESULTS: In models for IADL performance, IADL difficulty, and a daily functioning composite, there was a significant time by MCI classification interaction for each MCI subtype, indicating that all MCI groups showed faster rates of decline in everyday function than cognitively normal participants with no MCI. CONCLUSION: Results demonstrate the importance of MCI as a clinical entity that not only predicts progression to dementia, but also predicts functional declines in activities that are key to autonomy and quality of life. MCI classification guidelines should allow for functional changes in MCI, and clinicians should monitor for such changes. Preservation of function may serve as a meaningful outcome for intervention efforts.
Subject(s)
Activities of Daily Living , Cognition Disorders/classification , Cognition Disorders/physiopathology , Aged , Female , Humans , Male , Psychometrics , Severity of Illness IndexABSTRACT
The objective of this study was to model recall and learning on the Auditory Verbal Learning Test using latent growth curve techniques. Participants were older adults recruited for the ACTIVE cognitive intervention pilot. A series of nested models revealed that an approximately logarithmic growth curve model provided optimal fit to the data. Although recall and learning factors were statistically uncorrelated, a fitted multivariate model suggested that initial recall was significantly associated with demographic characteristics but unrelated to health factors and cognitive abilities. Individual differences in learning were related to race/ethnicity, speed of processing, verbal knowledge, and global cognitive function level. These results suggest that failing to recognize initial recall and learning as distinct constructs clouds the interpretation of supraspan memory tasks.