Literature DB >> 26861693

Walking ability to predict future cognitive decline in old adults: A scoping review.

Lisette H J Kikkert1, Nicolas Vuillerme2, Jos P van Campen3, Tibor Hortobágyi4, Claudine J Lamoth5.   

Abstract

Early identification of individuals at risk for cognitive decline may facilitate the selection of those who benefit most from interventions. Current models predicting cognitive decline include neuropsychological and/or biological markers. Additional markers based on walking ability might improve accuracy and specificity of these models because motor and cognitive functions share neuroanatomical structures and psychological processes. We reviewed the relationship between walking ability at one point of (mid) life and cognitive decline at follow-up. A systematic literature search identified 20 longitudinal studies. The average follow-up time was 4.5 years. Gait speed quantified walking ability in most studies (n=18). Additional gait measures (n=4) were step frequency, variability and step-length. Despite methodological weaknesses, results revealed that gait slowing (0.68-1.1 m/sec) preceded cognitive decline and the presence of dementia syndromes (maximal odds and hazard ratios of 10.4 and 11.1, respectively). The results indicate that measures of walking ability could serve as additional markers to predict cognitive decline. However, gait speed alone might lack specificity. We recommend gait analysis, including dynamic gait parameters, in clinical evaluations of patients with suspected cognitive decline. Future studies should focus on examining the specificity and accuracy of various gait characteristics to predict future cognitive decline.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Biomarker; Cognitive impairment; Dementia; Gait; MCI; Prediction models

Mesh:

Year:  2016        PMID: 26861693     DOI: 10.1016/j.arr.2016.02.001

Source DB:  PubMed          Journal:  Ageing Res Rev        ISSN: 1568-1637            Impact factor:   10.895


  43 in total

1.  Associations of Skeletal Muscle Mass, Lower-Extremity Functioning, and Cognitive Impairment in Community-Dwelling Older People in Japan.

Authors:  H Ishii; H Makizako; T Doi; K Tsutsumimoto; H Shimada
Journal:  J Nutr Health Aging       Date:  2019       Impact factor: 4.075

2.  The Associations Between Grey Matter Volume Covariance Patterns and Gait Variability-The Tasmanian Study of Cognition and Gait.

Authors:  Helena M Blumen; Michele L Callisaya; Oshadi Jayakody; Monique Breslin; Richard Beare; Velandai K Srikanth
Journal:  Brain Topogr       Date:  2021-04-29       Impact factor: 3.020

Review 3.  Walking Pace and the Risk of Cognitive Decline and Dementia in Elderly Populations: A Meta-analysis of Prospective Cohort Studies.

Authors:  Minghui Quan; Pengcheng Xun; Cheng Chen; Ju Wen; Yiyu Wang; Ru Wang; Peijie Chen; Ka He
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2016-12-07       Impact factor: 6.053

4.  A combined stepping and visual tracking task predicts cognitive decline in older adults better than gait or visual tracking tasks alone: a prospective study.

Authors:  Yosuke Osuka; Hunkyung Kim; Yutaka Watanabe; Yu Taniguchi; Narumi Kojima; Satoshi Seino; Hisashi Kawai; Ryota Sakurai; Hiroki Inagaki; Shuichi Awata; Shoji Shinkai
Journal:  Aging Clin Exp Res       Date:  2020-09-23       Impact factor: 3.636

5.  Shifting from Declines to Improvements: Associations between a Meaningful Walking Speed Change and Cognitive Evolution over Three Years in Older Adults.

Authors:  K Pothier; P de Souto Barreto; M Maltais; Y Rolland; B Vellas
Journal:  J Nutr Health Aging       Date:  2018       Impact factor: 4.075

6.  Home-Based Gait Speed Assessment: Normative Data and Racial/Ethnic Correlates Among Older Adults.

Authors:  David A Boulifard; Emmeline Ayers; Joe Verghese
Journal:  J Am Med Dir Assoc       Date:  2019-08-05       Impact factor: 4.669

7.  XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes.

Authors:  Byungjoo Noh; Changhong Youm; Eunkyoung Goh; Myeounggon Lee; Hwayoung Park; Hyojeong Jeon; Oh Yoen Kim
Journal:  Sci Rep       Date:  2021-06-09       Impact factor: 4.379

8.  Impact of an individual personalised rehabilitation program on mobility performance in older-old people.

Authors:  Guy Rincé; Catherine Couturier; Gilles Berrut; Anthony Dylis; Manuel Montero-Odasso; Thibault Deschamps
Journal:  Aging Clin Exp Res       Date:  2021-02-24       Impact factor: 3.636

9.  The Association of Gait Speed and Frontal Lobe among Various Cognitive Domains: The Korean Frailty and Aging Cohort Study (KFACS).

Authors:  M Seo; C W Won; S Kim; J H Yoo; Y H Kim; B S Kim
Journal:  J Nutr Health Aging       Date:  2020       Impact factor: 4.075

10.  Beam Walking to Assess Dynamic Balance in Health and Disease: A Protocol for the "BEAM" Multicenter Observational Study.

Authors:  Tibor Hortobágyi; Azusa Uematsu; Lianne Sanders; Reinhold Kliegl; József Tollár; Renato Moraes; Urs Granacher
Journal:  Gerontology       Date:  2018-10-18       Impact factor: 5.140

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