Hostname: page-component-848d4c4894-tn8tq Total loading time: 0 Render date: 2024-06-30T21:10:20.623Z Has data issue: false hasContentIssue false
  • English
  • Français

Improving Sensitivity to Detect Mild Cognitive Impairment: Cognitive Load Dual-Task Gait Speed Assessment

Published online by Cambridge University Press:  17 April 2017

Rebecca K. MacAulay ,
Mark T. Wagner ,
Dana Szeles  and
Nicholas J. Milano
Rebecca K. MacAulay*
Affiliation:
Department of Psychology, University of Maine, Orono, Maine
Mark T. Wagner
Affiliation:
Department of Neurology, Medical University of South Carolina, Charleston, South Carolina
Dana Szeles
Affiliation:
Department of Neurology, Medical University of South Carolina, Charleston, South Carolina
Nicholas J. Milano
Affiliation:
Department of Neurology, Medical University of South Carolina, Charleston, South Carolina
*
Correspondence and reprint requests to: Rebecca K. MacAulay, Department of Psychology, University of Maine, 301 Little Hall, Orono, ME 04469. E-mail: rebecca.macaulay@maine.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Longitudinal research indicates that cognitive load dual-task gait assessment is predictive of cognitive decline and thus might provide a sensitive measure to screen for mild cognitive impairment (MCI). However, research among older adults being clinically evaluated for cognitive concerns, a defining feature of MCI, is lacking. The present study investigated the effect of performing a cognitive task on normal walking speed in patients presenting to a memory clinic with cognitive complaints. Methods: Sixty-one patients with a mean age of 68 years underwent comprehensive neuropsychological testing, clinical interview, and gait speed (simple- and dual-task conditions) assessments. Thirty-four of the 61 patients met criteria for MCI. Results: Repeated measure analyses of covariance revealed that greater age and MCI both significantly associated with slower gait speed, ps<.05. Follow-up analysis indicated that the MCI group had significantly slower dual-task gait speed but did not differ in simple-gait speed. Multivariate linear regression across groups found that executive attention performance accounted for 27.4% of the variance in dual-task gait speed beyond relevant demographic and health risk factors. Conclusions: The present study increases the external validity of dual-task gait assessment of MCI. Differences in dual-task gait speed appears to be largely attributable to executive attention processes. These findings have clinical implications as they demonstrate expected patterns of gait-brain behavior relationships in response to a cognitive dual task within a clinically representative population. Cognitive load dual-task gait assessment may provide a cost efficient and sensitive measure to detect older adults at high risk of a dementia disorder. (JINS, 2017, 23, 493–501)

Keywords

Cognitive agingDementiaAttentionCognitive reserveRiskSubjective Health complaint
Type
Special Section: Mild Cognitive Impairment
Information
Journal of the International Neuropsychological Society , Volume 23 , Issue 6 , July 2017 , pp. 493 - 501
Copyright
Copyright © The International Neuropsychological Society 2017 

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

Allali, G., Ayers, E.I., & Verghese, J. (2016). Motoric cognitive risk syndrome subtypes and cognitive profiles. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 71(3), 378–384.Google Scholar
Allali, G., Kressig, R.W., Assal, F., Herrmann, F.R., Dubost, V., & Beauchet, O. (2007). Changes in gait while backward counting in demented older adults with frontal lobe dysfunction. Gait & Posture, 26(4), 572–576.Google Scholar
Al-Yahya, E., Dawes, H., Smith, L., Dennis, A., Howells, K., & Cockburn, J. (2011). Cognitive motor interference while walking: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 35(3), 715–728.Google Scholar
Andersen, R.A., Snyder, L.H., Bradley, D.C., & Xing, J. (1997). Multimodal representation of space in the posterior parietal cortex and its use in planning movements. Annual Review of Neuroscience, 20(1), 303–330.Google Scholar
Baezner, H., Blahak, C., Poggesi, A., Pantoni, L., Inzitari, D., & Chabriat, H., . . . LADIS Study Group. (2008). Association of gait and balance disorders with age-related white matter changes The LADIS Study. Neurology, 70(12), 935–942.Google Scholar
Beck, A.T., Steer, R.A., & Brown, G.K. (1996). Manual for the beck depression inventory-II. San Antonio, TX: Psychological Corporation.Google Scholar
Benedict, R.H., Schretlen, D., Groninger, L., Dobraski, M., & Shpritz, B. (1996). Revision of the Brief Visuospatial Memory Test: Studies of normal performance, reliability, and validity. Psychological Assessment, 8(2), 145.Google Scholar
Benedict, R.H., Schretlen, D., Groninger, L., & Brandt, J. (1998). Hopkins Verbal Learning Test–Revised: Normative data and analysis of inter-form and test-retest reliability. The Clinical Neuropsychologist, 12(1), 43–55.CrossRefGoogle Scholar
Best, J.R., Davis, J.C., & Liu‐Ambrose, T. (2015). Longitudinal analysis of physical performance, functional status, physical activity, and mood in relation to executive function in older adults who fall. Journal of the American Geriatrics Society, 63(6), 1112–1120.Google Scholar
Coppin, A.K., Shumway-Cook, A., Saczynski, J.S., Patel, K.V., Ble, A., Ferrucci, L., & Guralnik, J.M. (2006). Association of executive function and performance of dual-task physical tests among older adults: Analyses from the InChianti study. Age and Ageing, 35(6), 619–624.Google Scholar
Della Sala, S., & Logie, R.H. (2001). Theoretical and practical implications of dual-task performance in Alzheimer’s disease. Brain, 124(8), 1479–1481.Google Scholar
Folstein, M.F., Folstein, S.E., & McHugh, P.R. (1975). Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198.Google Scholar
Hayhoe, B., Majeed, A., & Perneczky, R. (2016). General practitioner referrals to memory clinics: Are referral criteria delaying the diagnosis of dementia? Journal of the Royal Society of Medicine, 109(11), 410–415.Google Scholar
Herman, T., Mirelman, A., Giladi, N., Schweiger, A., & Hausdorff, J.M. (2010). Executive control deficits as a prodrome to falls in healthy older adults: A prospective study linking thinking, walking, and falling. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 65(10), 1086–1092.CrossRefGoogle ScholarPubMed
Hollman, J.H., Kovash, F.M., Kubik, J.J., & Linbo, R.A. (2007). Age-related differences in spatiotemporal markers of gait stability during dual task walking. Gait & Posture, 26(1), 113–119.Google Scholar
Holtzer, R., Burright, R.G., & Donovick, P.J. (2004). The sensitivity of dual-task performance to cognitive status in aging. Journal of the International Neuropsychological Society, 10(02), 230–238.CrossRefGoogle ScholarPubMed
Holtzer, R., Epstein, N., Mahoney, J.R., Izzetoglu, M., & Blumen, H.M. (2014). Neuroimaging of mobility in aging: A targeted review. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 69(11), 1375–1388.Google Scholar
Holtzer, R., Verghese, J., Xue, X., & Lipton, R.B. (2006). Cognitive processes related to gait velocity: Results from the Einstein Aging Study. Neuropsychology, 20(2), 215–223.CrossRefGoogle ScholarPubMed
Holtzer, R., Wang, C., & Verghese, J. (2012). The relationship between attention and gait in aging: Facts and fallacies. Motor Control, 16(1), 64–80.CrossRefGoogle Scholar
Larner, A.J. (2012). Screening utility of the Montreal Cognitive Assessment (MoCA): In place of–or as well as–the MMSE? International Psychogeriatrics, 24(03), 391–396.Google Scholar
Lezak, M.D., Howieson, D.B., & Loring, D.W. (2004). Neuropsychological assessment. New York: Oxford University Press.Google Scholar
Kaplan, E., Goodglass, H., & Weintraub, S.T. (1976). Boston Naming Test, Experimental Edition Veterans Administrations Hospital, Boston. Neuropsychologia, 27, 8.Google Scholar
MacAulay, R.K., Allaire, T.D., Brouillette, R.M., Foil, H.C., Bruce-Keller, A.J., Han, H., & Keller, J.N. (2015). Longitudinal assessment of neuropsychological and temporal/spatial gait characteristics of elderly fallers: Taking it all in stride. Frontiers in Aging Neuroscience, 7, 34.Google Scholar
MacAulay, R.K., Allaire, T., Brouillette, R., Foil, H., Bruce-Keller, A.J., & Keller, J.N. (2016). Apolipoprotein E genotype linked to spatial gait characteristics: Predictors of cognitive dual task gait change. PLoS One, 11(8), e0156732.Google Scholar
MacAulay, R.K., Brouillette, R.M., Foil, H.C., Bruce-Keller, A.J., & Keller, J.N. (2014). A longitudinal study on dual-tasking effects on gait: Cognitive change predicts gait variance in the elderly. PLoS One, 9(6), e99436.Google Scholar
McLennan, S.N., Mathias, J.L., Brennan, L.C., & Stewart, S. (2011). Validity of the Montreal Cognitive Assessment (MoCA) as a screening test for mild cognitive impairment (MCI) in a cardiovascular population. Journal of Geriatric Psychiatry and Neurology, (24)1, 33–38.Google Scholar
Mielke, M.M., Roberts, R.O., Savica, R., Cha, R., Drubach, D.I., Christianson, T., & Knopman, D.S. (2013). Assessing the temporal relationship between cognition and gait: Slow gait predicts cognitive decline in the Mayo Clinic Study of Aging. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 68(8), 929–937.CrossRefGoogle ScholarPubMed
Mitchell, A.J. (2009). A meta-analysis of the accuracy of the mini-mental state examination in the detection of dementia and mild cognitive impairment. Journal of Psychiatric Research, 43(4), 411–431.Google Scholar
Muir, S.W., Speechley, M., Wells, J., Borrie, M., Gopaul, K., & Montero-Odasso, M. (2012). Gait assessment in mild cognitive impairment and Alzheimer’s disease: The effect of dual-task challenges across the cognitive spectrum. Gait & Posture, 35(1), 96–100.CrossRefGoogle ScholarPubMed
Nadkarni, N.K., Lopez, O.L., Perera, S., Studenski, S.A., Snitz, B.E., Erickson, K.I., & Klunk, W.E. (2016). Cerebral amyloid deposition and dual-tasking in cognitively normal, mobility unimpaired older adults. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences. doi: 10.1093/gerona/glw211 CrossRefGoogle Scholar
Petersen, R.C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256(3), 183–194.Google Scholar
Randolph, C., Tierney, M.C., Mohr, E., & Chase, T.N. (1998). The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): Preliminary clinical validity. Journal of clinical and Experimental Neuropsychology, 20(3), 310–319.CrossRefGoogle ScholarPubMed
Reitan, R.M, & Wolfson, D. (1993). The Halstead-Reitan Neuropsychological Test Battery: Theory and clinical interpretation (2nd ed). Tucson, AZ: Neuropsychology Press.Google Scholar
Ritchie, K. (2004). Mild cognitive impairment: An epidemiological perspective. Dialogues Clinical Neuroscience, 6(4), 401–408.Google Scholar
Rosano, C., Studenski, S.A., Aizenstein, H.J., Boudreau, R.M., Longstreth, W.T., & Newman, A.B. (2012). Slower gait, slower information processing and smaller prefrontal area in older adults. Age and Ageing, 41(1), 58–64.Google Scholar
Rosso, A.L., Sanders, J.L., Arnold, A.M., Boudreau, R.M., Hirsch, C.H., Carlson, M.C., & Newman, A.B. (2015). Multisystem physiologic impairments and changes in gait speed of older adults. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 70(3), 317–322.Google Scholar
Rubin, D.B. (1996). Multiple Imputation after 18+ Years. Journal of the American Statistical Association, 91(434), 473–489.CrossRefGoogle Scholar
Sheridan, P.L., & Hausdorff, J.M. (2007). The role of higher-level cognitive function in gait: executive dysfunction contributes to fall risk in Alzheimer’s disease. Dementia and Geriatric Cognitive Disorders, 24(2), 125–137.Google Scholar
Spangenberg, K.B., Henderson, S., & Wagner, M.T. (1997). Validity of a recall and recognition condition to assess visual memory in the CERAD battery. Applied Neuropsychology, 4(3), 154–159.Google Scholar
Sperling, R.A., Aisen, P.S., Beckett, L.A., Bennett, D.A., Craft, S., Fagan, A.M., & Park, D.C. (2011). Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 280–292.Google Scholar
Strauss, E., Sherman, E.M., & Spreen, O.A (2006). A compendium of neuropsychological tests: Administration, norms, and commentary. New York: Oxford University Press.Google Scholar
Studenski, S., Perera, S., Wallace, D., Chandler, J.M., Duncan, P.W., Rooney, E., & Guralnik, J.M. (2003). Physical performance measures in the clinical setting. Journal of the American Geriatrics Society, 51(3), 314–322.CrossRefGoogle ScholarPubMed
Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8(03), 448–460.CrossRefGoogle ScholarPubMed
Stern, Y. (2006). Cognitive reserve and Alzheimer disease. Alzheimer Disease & Associated Disorders, 20(2), 112–117.Google Scholar
Verghese, J., Wang, C., Lipton, R.B., Holtzer, R., & Xue, X. (2007). Quantitative gait dysfunction and risk of cognitive decline and dementia. Journal of Neurology, Neurosurgery, & Psychiatry, 78(9), 929–935.Google Scholar
Verghese, J., Annweiler, C., Ayers, E., Barzilai, N., Beauchet, O., Bennett, D.A., & Capistrant, B. (2014). Motoric cognitive risk syndrome Multicountry prevalence and dementia risk. Neurology, 83(8), 718–726.Google Scholar
Wadley, V.G., Unverzagt, F.W., McGuire, L.C., Moy, C.S., Go, R., Kissela, B., & Howard, G. (2011). Incident cognitive impairment is elevated in the stroke belt: The REGARDS study. Annals of Neurology, 70(2), 229–236.Google Scholar
Wechsler, D. (2008). Wechsler adult intelligence scale-fourth. San Antonio, TX: Pearson.Google Scholar
Welsh, K.A., Butters, N., Hughes, J.P., Mohs, R.C., & Heyman, A (1992). Detection and staging of dementia in Alzheimer’s disease: Use of the neuropsychological measures developed for the Consortium to Establish a Registry for Alzheimer’s Disease. Archives of Neurology, 49(5), 448–452.CrossRefGoogle ScholarPubMed
Welsh-Bohmer, K.A., Østbye, T., Sanders, L., Pieper, C.F., Hayden, K.M., & Tschanz, J.T., … Cache Country Study Group. (2009). Neuropsychological performance in advanced age: Influences of demographic factors and apolipoprotein E: Findings from the Cache County Memory Study. The Clinical Neuropsychologist, 23(1), 77–99.Google Scholar
Yesavage, J.A., Brink, T.L., Rose, T.L., Lum, O., Huang, V., Adey, M., & Leirer, V.O. (1983). Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research, 17(1), 37–49.Google Scholar
Yogev‐Seligmann, G., Hausdorff, J.M., & Giladi, N. (2008). The role of executive function and attention in gait. Movement Disorders, 23(3), 329–342.Google Scholar
Yuan, J., Blumen, H.M., Verghese, J., & Holtzer, R. (2015). Functional connectivity associated with gait velocity during walking and walking‐while‐talking in aging: A resting‐state fMRI study. Human Brain Mapping, 36(4), 1484–1493.Google Scholar