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INDIVIDUAL DIFFERENCES IN MEMORY FUNCTION

INDIVIDUAL DIFFERENCES IN MEMORY FUNCTION. Richard N. Jones, John N. Morris, Adrienne Rosenberg, Paul Malloy HRCA Research and Training Institute, Boston Ken Kleinman Harvard Medical School, Boston MA Jason Allaire Wayne State University, Institute of Gerontology, Detroit MI

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INDIVIDUAL DIFFERENCES IN MEMORY FUNCTION

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  1. INDIVIDUAL DIFFERENCES IN MEMORY FUNCTION Richard N. Jones, John N. Morris, Adrienne Rosenberg, Paul Malloy HRCA Research and Training Institute, Boston Ken Kleinman Harvard Medical School, Boston MA Jason Allaire Wayne State University, Institute of Gerontology, Detroit MI Michael Marsiske University of Florida, Institute on Aging, Gainesville

  2. ACTIVE Advanced Cognitive Training for Vital and Independent Elderly funded as a cooperative agreement with the National Institute on Aging and the National Institute of Nursing Research. Participating Institutions & Principal Investigators:Hebrew Rehabilitation Center for Aged John Morris, Ph.D.Indiana University David Smith, M.D.Johns Hopkins University George Rebok, Ph.D.Pennsylvania State University Sherry Willis, Ph.D.University of Alabama at Birmingham Karolin Ball, Ph.D.Wayne State University Michael Marsiske, Ph.D.Coordinating CenterNew England Research Institutes Sharon Tennstedt, Ph.D.

  3. Objectives • Characterize performance on memory task in cognitively normal older adults • Use Latent Growth Curve Modeling (LGM) techniques • Separately model initial recall and learning • Explore association of background variables and recall, learning

  4. Rey AVLTAuditory Verbal Learning Test(Lezak, 1983; Rey, 1964) • 15 unrelated word list, read at 2 sec. intervals • Participants asked to recall words • List re-read four times • Repeated recall trials • Intrusion list • Delayed Recall and Recognition task

  5. Latent Growth Modeling • Psychometric approach to repeated measures data • Individual differences in intercept and slope over time captured in latent variables (initial recall, learning) • Certain parameterizations equivalent to random coefficient models, mixed models, hierarchical linear models

  6. Methods: Participants • JHU • in and around urban geriatric center • urban senior high-rise housing • suburban retirement community • urban & rural medical centers • PSU • PACE enrollees • HRCA • elder housing • IU • community centers • UAB • drivers licenses, ID cards • eye clinic attendees • WSU • community organizations • urban residential settings

  7. Eligibility Criteria age ³ 65 living in non-institutional setting available for duration of trial Exclusion Criteria vision impairment hearing loss impaired communication reported disability Alzheimer’s dx or MMSE<23 cancer dx w/ limited life expectancy current chemo/radiation CVA in previous 12 months in other cognitive training high level depressive sx (CESD>16) Eligibility and Exclusion Criteria

  8. Demographics Age Sex Ethnicity Education Cognitive Functioning General (MMSE) Reasoning ability (STAMAT) Vocabulary (Kit) Digit symbol substitution (WAIS) Background Variables

  9. LGM Parameterization without Background Variables

  10. Alternative Functional Forms of AVLT Growth

  11. Logarithmic Growth Function

  12. Model implied trajectories

  13. LGM Parameterization with Background Variables

  14. Conclusion • LGM provides a comprehensible model of initial recall and learning performance on the AVLT task • Alternative summary measures are suggested by considering effect of background variables on initial recall and rate learning

  15. LGM Alternative Parameterization without Background Variables

  16. Drum Curtain Bell Coffee School Parent Moon Garden Hat Farmer Nose Turkey Color House River Word List Used in ACTIVE

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