Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-17T14:04:49.817Z Has data issue: false hasContentIssue false
  • English
  • Français

Normal rates of cognitive change in successful aging: The Freedom House Study

Published online by Cambridge University Press:  16 December 2005

DONALD R. ROYALL ,
RAYMOND PALMER ,
LAURA K. CHIODO  and
MARSHA J. POLK
DONALD R. ROYALL
Affiliation:
Department of Psychiatry, University of Texas Health Science Center, San Antonio, Texas Department of Medicine, University of Texas Health Science Center, San Antonio, Texas and the South Texas Veterans' Health System Audie L. Murphy Division, Geriatric Research Education and Clinical Center (GRECC) Department of Pharmacology, University of Texas Health Science Center, San Antonio, Texas
RAYMOND PALMER
Affiliation:
Department of Family and Community Medicine, University of Texas Health Science Center, San Antonio, Texas
LAURA K. CHIODO
Affiliation:
Department of Medicine, University of Texas Health Science Center, San Antonio, Texas and the South Texas Veterans' Health System Audie L. Murphy Division, Geriatric Research Education and Clinical Center (GRECC)
MARSHA J. POLK
Affiliation:
Department of Medicine, University of Texas Health Science Center, San Antonio, Texas and the South Texas Veterans' Health System Audie L. Murphy Division, Geriatric Research Education and Clinical Center (GRECC)
Get access Rights & Permissions [Opens in a new window]

Abstract

We determined the rates of cognitive change associated with twenty individual measures. Participants included 547 noninstitutionalized septuagenarians and octogenarian residents of a comprehensive care retirement community who were studied over three years. Latent growth curves (LGC) of multiple cognitive measures were compared to a LGC model of the rates of change in Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). All curves were standardized relative to each variable's baseline distribution. Baseline scores were within their expected normal age-specific ranges. Most measures showed significant rates of change over time. There was also significant variability about those rates, suggesting clinical heterogeneity. Many deteriorated over time, as did ADLs and IADLs. However, performance on some measures improved, consistent with learning effects. The rates of change in two measures, the Executive Interview and the Trail Making Test, were closely related to decline in IADLs. These results suggest that age-related cognitive decline is a dynamic longitudinal process affecting multiple cognitive domains. Heterogeneity in the rates of cognitive change may reflect the summed effects of age and comorbid conditions affecting cognition. Some measures may be ill-suited for measuring age-related changes in cognition, either because they are insensitive to change, or hindered by learning effects. Nonverbal measures appear to be particularly well suited for the prediction of age-related functional decline. These observations are relevant to the definition and diagnosis of “dementing” conditions. (JINS, 2005, 11, 899–909.)

Keywords

AgingAssessmentDisabilityDementiaLongitudinalExecutive function
Type
Research Article
Information
Journal of the International Neuropsychological Society , Volume 11 , Issue 7 , November 2005 , pp. 899 - 909
Copyright
© 2005 The International Neuropsychological Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Allen, S.C., Jain, M., Ragab, S., & Malik, N. (2003). Acquisition and short-term retention of inhaler techniques require intact executive function in elderly participants. Age Ageing, 32, 299–302.CrossRefGoogle Scholar
Anstey, K.J., Hofer, S.M., & Luszcz, M.A. (2003). A latent growth curve analysis of late-life sensory and cognitive function over 8 years: Evidence for specific and common factors underlying change. Psychology and Aging, 18, 714–726.CrossRefGoogle Scholar
Arbuckle, J. (1994). Analysis of Moment Structures–AMOS. Chicago: Small Waters Corporation.
Arbuckle, J.L. (1996). Full information estimation in the presence of incomplete data. In G.A. Marcoulides & R.E. Schumaker (Eds.), Advanced structural equation modeling: Issues and techniques. Mahwah, NJ: Erlbaum.
Artero, S., Touchon, J., & Ritchie, K. (2001). Disability and mild cognitive impairment: A longitudinal population-based study. International Journal of Geriatric Psychiatry, 16, 1092–1097.CrossRefGoogle Scholar
Basso, M.R., Bornstein, R.A., & Lang, J.M. (1999). Practice effects on commonly used measures of executive function across twelve months. Clinical Neuropsychologist, 13, 283–292.CrossRefGoogle Scholar
Basso, M.R., Lowery, N., Ghormley, C., & Bornstein, R.A. (2001). Practice effects on the WCST-64 card version across 12 months. Clinical Neuropsychologist, 5, 471–478.CrossRefGoogle Scholar
Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.CrossRefGoogle Scholar
Benton, A. & Hamsher, K. (1989). Multilingual Aphasia Examination. Iowa City, IA: AJA Associates.
Bollen, K.A. & Long, J.S. (1993). Testing structural equation models. Thousand Oaks, CA: Sage Publications..
Braak, H. & Braak, E. (1997). Frequency of stages of Alzheimer-related lesions in different age categories. Neurobiology of Aging, 18, 351–357.CrossRefGoogle Scholar
Braak, H. & Braak, E. (1998). Evolution of neuronal changes in the course of Alzheimer's disease. Journal of Neural Transmission, 53, 127–140.CrossRefGoogle Scholar
Browne, M. & Cudeck, R. (1993). Alternative ways of assessing model fit. In K.A. Bollen & J.S. Long (Eds.), Testing structural equation models (pp. 136–162). Thousand Oaks, CA: Sage Publications.
Comijs, H.C., Dik, M.G., Deer, D.J.H., & Jonker, C. (2004). The course of cognitive decline in older persons: Results from the Longitudinal Aging Study of Amsterdam. Dementia and Geriatric Cognitive Disorders, 17, 136–142.CrossRefGoogle Scholar
Delis, D.C., Kramer, J.H., Kaplan, E., & Ober, B.A. (1987). California Verbal Learning Test: Adult version manual. San Antonio, TX: The Psychological Corporation.
Dymek, M.P., Atchison, P., Harrell, L., & Marson, D.C. (2001). Competency to consent to medical treatment in cognitively impaired patients with Parkinson's disease. Neurology, 56, 17–24.CrossRefGoogle Scholar
Elias, J.W. (1979). Lifespan perspective on cerebral asymmetry and information processing with an emphasis on the aging adult. Century Systems and Communication in the Elderly, 18, 187–201.Google Scholar
Elias, M.F., Sullivan, L.M., D'Agostino, R.B., Elias, P.K., Besier, A., Au, R., Seshadri, A., DeCarli, C., & Wolf, P.A. (2004). Framingham Stroke Risk Profile and lowered cognitive performance. Stroke, 35, 404–409.CrossRefGoogle Scholar
Ferrer, E., Salthouse, T.A., Stewart, W.F., & Schwartz, B.S. (2004). Modeling age and retest processes in longitudinal studies of cognitive abilities. Psychology and Aging, 19, 243–259.CrossRefGoogle Scholar
Fillenbaum, G.G. (1978). Validity and reliability of the Multidimensional Functional Assessment Questionnaire. In The OARS methodology. Duke University Center for the Study of Aging and Human Development. Durham, NC: Duke University.
Folstein, M., Folstein, S., & McHugh, P.R. (1975). ‘Mini-Mental State’: A practical method for grading the cognitive state of patients for the clinician. Psychiatric Research, 12, 189–198.CrossRefGoogle Scholar
Folstein, M.F., Folstein, S.E., McHugh, P.R., & Fanjiang, G. (2001). Mini-Mental State Examination user's guide. Odessa, FL: Psychological Assessment Resources.
Frerichs, R.J. & Tuokko, H.A. (2005). A comparison of methods for measuring cognitive change in older adults. Archives of Clinical Neuropsychology, 20, 321–333.CrossRefGoogle Scholar
Ghisletta, P. & Lindenberger, U. (2004). Static and dynamic longitudinal structural analyses of cognitive changes in old age. Gerontology, 50, 12–16.CrossRefGoogle Scholar
Haaland, K.Y., Vranes, L.F., Goowdwin, J.S., & Garry, P.J. (1987). Wisconsin Card Sorting Test performance in a healthy elderly population. Journal of Gerontology, 42, 345–346.CrossRefGoogle Scholar
Heaton, R.K., Chelune, G.J., Talley, J.L., Kay, G.G., & Curtiss, G. (1993). Wisconsin Card Sorting Test manual–Revised and expanded (pp. 40–41). Odessa, FL: Psychological Assessment Resources.
Hertzog, C., Dixon, R.A., Hultsch, D.F., & MacDonald, S.W. (2003). Latent change models of adult cognition: Are changes in processing speed and working memory associated with changes in episodic memory? Psychology and Aging, 18, 755–769.Google Scholar
Jacqmin-Gadda, H., Fabrigoule, C., Commenges, D., & Dartigues, J.F. (1997). A 5-year longitudinal study of the Mini-Mental State Examination in normal aging. American Journal of Epidemiology, 145, 498–506.CrossRefGoogle Scholar
Jehkonen, M., Ahonen, J.P., Dastidar, P., Laippala, P., & Vilkki, J. (2000). Unawareness of deficits after right hemisphere stroke: Double-dissociations of anosognosias. Acta Neurologica Scandinavica, 102, 378–384.CrossRefGoogle Scholar
Johnson, S.C., Saykin, A., Flashman, L.A., McAllister, T.W., & Sparling, M.B. (2001). Brain activation on fMRI and verbal memory ability: Functional anatomic correlates of CVLT performance. Journal of the International Neuropsychological Society, 7, 55–62.CrossRefGoogle Scholar
Kaplan, E.F., Goodglass, H., & Weintraub, S. (1983). The Boston Naming Test (2nd ed.). Philadelphia: Lea & Febiger.
Kongs, S.K., Thompson, L.L., Iverson, G.L., & Heaton, R.K. (2000). WCST-64 card version professional manual. Odessa, FL: Psychological Assessment Resources.
Kraemer, H.C., Yesavage, J.A., Taylor, J.L., & Kupfer, D. (2000). How can we learn about developmental processes from cross-sectional studies, or can we? American Journal of Psychiatry, 157, 163–171.Google Scholar
Lineweaver, T.T., Bond, M.W., Thomas, R.G., & Salmon, D.P. (1999). A normative study of Nelson's (1976) modified version of the Wisconsin card sorting test in healthy older adults. Clinical Neuropsychologist, 13, 328–347.CrossRefGoogle Scholar
Linn, R.T., Wolf, P.A., Bachman, D.L., Knoefel, J.E., Cobb, J.L., Belanger, A.J., Kaplan, E.F., & D'Agostino, R.B. (1995). The “Preclinical” Phase” of probable Alzheimer's disease: A 13-year prospective study of the Framingham cohort. Archives of Neurology, 52, 485–490.CrossRefGoogle Scholar
Mattis, S. (1988). Dementia Rating Scale: Professional manual. Odessa, FL: Psychological Assessment Resources.
Maixner, S.M., Burke, W.J., Roccaforte, W.H., Wengel, S.P., & Potter, J.F. (1995). A comparison of two depression scales in a geriatric assessment clinic. American Journal of Geriatric Psychiatry, 3, 60–67.CrossRefGoogle Scholar
Mann, L.S., Westlake, T., Wise, T.N., Beckman, A., Beckman, P., & Portez, D. (1999). Executive functioning and compliance in HIV patients. Psychological Reports, 84, 319–322.CrossRefGoogle Scholar
McArdle, J. & Hamagami, F. (1992). Modeling incomplete longitudinal and cross sectional data using latent growth structural models. Experimental Aging Research, 18, 145–166.CrossRefGoogle Scholar
McGue, M. & Christensen, K. (2002). Heritability of level of and rate of change in cognitive functioning in Danish twins aged 70 years and older. Experimental Aging Research, 28, 435–451.CrossRefGoogle Scholar
Miyake, A., Naomi, P., Freidman, D.A., Rettinger, P.S., & Hegarty, M. (2001). How are visuospatial working memory, executive functioning, and spatial abilities related? A latent variable analysis. Journal of Experimental Neuropsychology, 130, 621–640.Google Scholar
Petersen, R.C., Doody, R., Kurz, A., Mohs, R.C., Morris, J.C., Rabins, P.V., Ritchie, K., Russor, W., Thal, L., & Winblad, B. (2001). Current concepts in mild cognitive impairment. Archives of Neurology, 58, 1985–92.CrossRefGoogle Scholar
Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., & Kokmen, E. (1999). Mild Cognitive Impairment: Clinical characteristics and outcomes. Archives of Neurology, 56, 303–308.CrossRefGoogle Scholar
Rapp, S.R., Espeland, M.A., Hogan, P., Jones, B.N., & Dugan, E. (2003). Baseline experience with Modified Mini-Mental State Exam: The Women's Health Initiative Memory Study (WHIMS). Aging and Mental Health, 7, 217–213.CrossRefGoogle Scholar
Ratcliff, G., Dodge, H., Birzescu, M., & Ganguli, M. (2003). Tracking cognitive functioning over time: Ten-year longitudinal data from a community-based study. Applied Neuropsychology, 10, 76–88.CrossRefGoogle Scholar
Reitan, R.M. (1958). Validity of the Trail Making test as an indicator of organic brain damage. Perceptual and Motor Skills, 8, 271–276.CrossRefGoogle Scholar
Reynolds, C.A., Finkle, D., Gatz, M., & Pedersen, N.L. (2002). Sources of influence on rate of cognitive change over time in Swedish twins: An application of latent growth models. Experimental Aging Research, 28, 407–433.CrossRefGoogle Scholar
Royall, D.R. (2005). “Silent stroke”: An oxymoron meaning “dementia”. Seminars in Cerebrovascular Diseases and Stroke, 4, 97–101.Google Scholar
Royall, D.R., Cordes, J., & Polk, M.J. (1997). Executive control and the comprehension of medical information by elderly retirees. Experimental Aging Research, 23, 301–313.CrossRefGoogle Scholar
Royall, D.R., Cabello, M., & Polk, M.J. (1998a). Executive dyscontrol: An important factor affecting the level of care received by elderly retirees. Journal of the American Geriatrics Society, 46, 1519–1524.Google Scholar
Royall, D.R., Cordes, J.A., & Polk, M. (1998b). CLOX: An executive clock drawing task. Journal of Neurology, Neurosurgery and Psychiatry, 64, 588–594.Google Scholar
Royall, D.R., Chiodo, L.K., & Polk, M.J. (2000). Correlates of disability among elderly retirees with “sub-clinical” cognitive impairment. Journals of Gerontology, Series A, Biological Sciences and Medical Sciences, 55, M541–546.CrossRefGoogle Scholar
Royall, D.R., Chiodo, L.K., & Polk, M. (2003). Executive dyscontrol in normal aging: Normative data, factor structure, and clinical correlates. Current Neurology and Neuroscience Reports, 3, 487–493.CrossRefGoogle Scholar
Royall, D.R., Chiodo, L.K., & Polk, M.J. (2004). Misclassification is likely in the assessment of Mild Cognitive Impairment. Neuroepidemiology, 23, 185–191.CrossRefGoogle Scholar
Royall, D.R., Mahurin, R.K., & Gray, K.F. (1992). Bedside assessment of executive cognitive impairment: The Executive Interview (EXIT). Journal of the American Geriatrics Society, 40, 1221–1226.CrossRefGoogle Scholar
Royall, D.R., Mahurin, R.K., True, J.E., Anderson, B., Brock, I.P. 3rd, Freeburger, L., & Miller, A. (1993). Executive impairment among the functionally dependent: Comparisons between schizophrenic and elderly subjects. American Journal of Psychiatry, 12, 1813–1819.Google Scholar
Royall, D.R., Mulroy, A., Chiodo, L.K., & Polk, M.J. (1999). Clock drawing is sensitive to executive control: A comparison of six methods. Journals of Gerontology Series B, Psychological Sciences and Social Sciences, 54, 328–333.CrossRefGoogle Scholar
Royall, D.R., Palmer, R., Chiodo, L.K., & Polk, M.J. (2004). Declining executive control in normal aging predicts change in functional status: The Freedom House Study. Journal of the American Geriatrics Society, 52, 346–352.CrossRefGoogle Scholar
Royall, D.R., Palmer, R., Chiodo, L.K., & Polk, M.J. (in press). Wisconsin Card Sort performance fails to predict change in functional status in old age: The Freedom House Study. Journal of Clinical and Experimental Neuropsychology.
Royall, D.R., Palmer, R., Chiodo, L.K., & Polk, M.J. (2005). Executive control mediates memory's association with change in functional status: The Freedom House Study. Journal of the American Geriatrics Society, 53, 11–17.CrossRefGoogle Scholar
Royall, D.R., Palmer, R., Mulroy, A., Polk, M.J., Román, G.C., David, J-P., & Delacourte, A. (2002). Pathological determinants of clinical dementia in Alzheimer's disease. Experimental Aging Research, 28, 143–162.CrossRefGoogle Scholar
Royall, D.R. & Polk, M. (1998). Dementias that present with and without posterior cortical features: An important clinical distinction. Journal of the American Geriatrics Society, 46, 98–105.CrossRefGoogle Scholar
Royall, D.R., Rauch, R., Román, G.C., Cordes, J.A., & Polk, M.J. (2001). MRI findings associated with impairment on the Executive Interview (EXIT25). Experimental Aging Research, 7, 293–308.CrossRefGoogle Scholar
Salthouse, T.A. (1999). Theories of cognition. In V.L. Bengtson & K.W. Schaie (Eds.), Handbook of theories of aging (pp. 196–208). New York: Springer.
Saykin, A.J., Johnson, S.C., Flashman, L.A., McAllister, T.W., Sparling, M., Darcey, T.M., Moritz, C.H., Guerin, S.J., Weaver, A., & Mamourian, A. (1999). Functional differentiation of medial temporal and frontal regions involved in processing novel and familiar words. Brain, 122, 1963–1971.CrossRefGoogle Scholar
Schafer, J. (1997). Analysis of incomplete multivariate data. New York: Chapman & Hall.CrossRef
Sheikh, J.I. & Yesavage, J.A. (1986). Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. Clinical Gerontologist, 5, 165–173.Google Scholar
Singer, T., Verhaeghen, P., Ghisletta, P., Lindenberger, U., & Baltes, P.B. (2003). The fate of cognition in very old age: Six-year longitudinal findings in the Berlin Aging Study (BASE). Psychology and Aging, 18, 318–331.CrossRefGoogle Scholar
Sliwinski, M.J., Hofer, S.M., Hall, C., Buschke, H., & Liptom, R.B. (2003). Modeling memory decline in older adults: The importance of preclinical dementia, attrition, and chronological age. Psychology and Aging, 18, 658–671.CrossRefGoogle Scholar
Suh, G.-H., Ju, Y.-S., Yeon, B.K., & Shah, A. (2004). A longitudinal study of Alzheimer's disease: Rates of cognitive and functional decline. International Journal of Geriatric Psychiatry, 19, 817–824.CrossRefGoogle Scholar
Thompson, P.M., Hayshi, K.M., de Zubicaray, G., Janke, A.L., Rose, S.E., Semple, J., Herman, D., Hong, M.S., Dittmer, S.S., Doddrell, D.M., & Toga, A.W. (2003). Dynamics of gray matter loss in Alzheimer's disease. Journal of Neuroscience, 23, 994–1005.Google Scholar
Wechsler, D. (1981). Wechsler Adult Intelligence Scale–Revised. New York: The Psychological Corporation.
Willet, J. & Sayer, A. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time Psychological Bulletin, 116, 363–381.Google Scholar
Wothke, W. (1999). Longitudinal and multi-group modeling with missing data. In T.D. Little, K.U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and multiple group data: Practical issues, applied approaches, and specific examples. Mahwah, NJ: Erlbaum.