Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-25T08:27:20.929Z Has data issue: false hasContentIssue false
  • English
  • Français

Orientation and verbal fluency in the English Longitudinal Study of Ageing: modifiable risk factors for falls?

Published online by Cambridge University Press:  07 December 2018

T. O. Smith Open the ORCID record for T. O. Smith [Opens in a new window] ,
S. R. Neal ,
G. Peryer ,
K. J. Sheehan ,
M. P. Tan  and
P. K. Myint
T. O. Smith*
Affiliation:
Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK & NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
S. R. Neal
Affiliation:
Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
G. Peryer
Affiliation:
School of Health Sciences, University of East Anglia, Norwich, UK
K. J. Sheehan
Affiliation:
Department of Population Health Sciences, School of Population Health and Environmental Sciences, King’s College London, London, UK
M. P. Tan
Affiliation:
Ageing and Age-Associated Disorders Research Group, Department of Medicine, University of Malaya, Kuala Lumpur, Malaysia
P. K. Myint
Affiliation:
Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
*
Correspondence should be addressed to: Toby O. Smith, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Nuffield Orthopaedic Centre, University of Oxford, Windmill Road, Oxford OX3 7LD, UK. Email: toby.smith@ndorms.ox.ac.uk.
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives:

To determine the relationship between falls and deficits in specific cognitive domains in older adults.

Design:

An analysis of the English Longitudinal Study of Ageing (ELSA) cohort.

Setting:

United Kingdom community-based.

Participants:

5197 community-dwelling older adults recruited to a prospective longitudinal cohort study.

Measurements:

Data on the occurrence of falls and number of falls, which occurred during a 12-month follow-up period, were assessed against the specific cognitive domains of memory, numeracy skills, and executive function. Binomial logistic regression was performed to evaluate the association between each cognitive domain and the dichotomous outcome of falls in the preceding 12 months using unadjusted and adjusted models.

Results:

Of the 5197 participants included in the analysis, 1308 (25%) reported a fall in the preceding 12 months. There was no significant association between the occurrence of a fall and specific forms of cognitive dysfunction after adjusting for self-reported hearing, self-reported eyesight, and functional performance. After adjustment, only orientation (odds ratio [OR]: 0.80; 95% confidence intervals [CI]: 0.65–0.98, p = 0.03) and verbal fluency (adjusted OR: 0.98; 95% CI: 0.96–1.00; p = 0.05) remained significant for predicting recurrent falls.

Conclusions:

The cognitive phenotype rather than cognitive impairment per se may predict future falls in those presenting with more than one fall.

Keywords

memoryexecutive functioncognitive skillsfallinjuryaged
Type
Original Research Article
Information
International Psychogeriatrics , Volume 31 , Issue 10: Issue Theme: Dementia and the Society , October 2019 , pp. 1491 - 1498
Copyright
© International Psychogeriatric Association 2018 

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

Amboni, M., Barone, P. and Hausdorff, J. M. (2013). Cognitive contributions to gait and falls: evidence and implications. Movement Disorders, 28, 1520–1533. doi: 10.1002/mds.25674.CrossRefGoogle ScholarPubMed
Apolinario, D. et al. (2016). Using temporal orientation, category fluency, and word recall for detecting cognitive impairment: the 10-point cognitive screener (10-CS). International Journal of Geriatric Psychiatry, 31, 4–12. doi: 10.1002/gps.4282.CrossRefGoogle Scholar
Bergen, G., Stevens, M. R. and Burns, E. R. (2016). Falls and fall injuries among adults aged ≥65 years - United States, 2014. MMWR: Morbidity and Mortality Weekly Reports, 65, 993–998. doi: 10.15585/mmwr.mm6537a2.Google Scholar
Best, J. R., Davis, J. C. and 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, 1112–1120. doi: 10.1111/jgs.13444.CrossRefGoogle ScholarPubMed
Blumen, H. M. et al. (2018). Gray matter volume covariance patterns associated with gait speed in older adults: a multi-cohort MRI study. Brain Imaging and Behaviour. doi: 10.1007/s11682-018-9871-7.CrossRefGoogle Scholar
Eckstrom, E. et al. (2016). An interprofessional approach to reducing the risk of falls through enhanced collaborative practice. Journal of the American Geriatrics Society, 64, 1701–1707. doi: 10.1111/jgs.14178.CrossRefGoogle ScholarPubMed
Fleischman, D. A. et al. (2015). Physical activity, motor function, and white matter hyperintensity burden in healthy older adults. Neurology, 84, 1294–1300. doi: 10.1212/WNL.0000000000001417.CrossRefGoogle ScholarPubMed
Gale, C. R., Cooper, C. and Aihie Sayer, A. (2016). Prevalence and risk factors for falls in older men and women: the English Longitudinal Study of Ageing. Age and Ageing, 45, 789–794. doi: 10.1093/ageing/afw129.CrossRefGoogle ScholarPubMed
Gow, A. J. et al. (2012). Neuroprotective lifestyles and the aging brain: activity, atrophy, and white matter integrity. Neurology, 79, 1802–1808. doi: 10.1212/WNL.0b013e3182703fd2.CrossRefGoogle ScholarPubMed
Guralnik, J. M., Ferrucci, L., Simonsick, E. M., Sallive, M. E. and Wallace, R. B. (1995). Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. New England Journal of Medicine, 332, 556–562. doi: 10.1056/NEJM199503023320902.CrossRefGoogle ScholarPubMed
Hausdorff, J. M. (2005). Gait variability: methods, modeling and meaning. Journal of Neuroengineering Rehabilitation, 2, 19. doi: 10.1186/1743-0003-2-19.CrossRefGoogle ScholarPubMed
Hausdorff, J. M., Balash, J. and Giladi, N. (2003). Effects of cognitive challenge on gait variability in patients with Parkinson’s disease. Journal of Geriatrics Psychiatry and Neurology, 16, 53–58. doi: 10.1177/0891988702250580.CrossRefGoogle ScholarPubMed
Herman, T., Mirelman, A., Giladi, N., Schweiger, A. and Hausdorff, J. M. (2010). Executive control deficits as a prodrome to falls in healthy older adults: a prospective study linking thinking, walking, and falling. Journal of Gerontology A: Biological Sciences in Medical Sciences, 65A, 1086–1092. doi: 10.1093/gerona/glq077.Google Scholar
Hsu, C. L., Nagamatsu, L. S., Davis, J. C. and Liu-Ambrose, T. (2012). Examining the relationship between specific cognitive processes and falls risk in older adults: a systematic review. Osteoporosis International, 23, 2409–2424. doi: 10.1007/s00198-012-1992-z.CrossRefGoogle ScholarPubMed
Hunter, H., Rochester, L., Morris, R. and Lord, S. (2017). Longitudinal falls data in Parkinson’s disease: feasibility of fall diaries and effect of attrition. Disability and Rehabilitation, 40, 2236–2241. doi: 10.1080/09638288.2017.1329357.CrossRefGoogle ScholarPubMed
Lauretani, F., Meschi, T., Ticinesi, A. and Maggio, M. (2017). “Brain-muscle loop” in the fragility of older persons: from pathophysiology to new organizing models. Aging in Clinical Experimental Research, 29, 1305–1311. doi: 10.1007/s40520-017-0729-4.CrossRefGoogle ScholarPubMed
Llewellyn, D. J., Lang, I. A., Langa, K. M. and Huppert, F. A. (2008). Cognitive function and psychological well-being: findings from a population-based cohort. Age and Ageing, 37, 685–689. doi: 10.1093/ageing/afn194.CrossRefGoogle ScholarPubMed
Lord, S. R., Menz, H. B. and Tiedemann, A. (2003). A physiological profile approach to falls risk assessment and prevention. Physical Therapy, 83, 237–252. doi: 10.1093/ptj/83.3.237.Google ScholarPubMed
Montero-Odasso, M., Verghese, J., Beauchet, O. and Hausdorff, J. M. (2012). Gait and cognition: a complementary approach to understanding brain function and the risk of falling. Journal of American Geriatrics Society 60, 2127–2136. doi: 10.1111/j.1532-5415.2012.04209.x.CrossRefGoogle ScholarPubMed
Muir, S. W., Gopaul, K. and Montero Odasso, M. M. (2012). The role of cognitive impairment in fall risk among older adults: a systematic review and meta-analysis. Age and Ageing, 41, 299–308. doi: 10.1093/ageing/afs012.CrossRefGoogle ScholarPubMed
Pang, I., Okubo, Y., Sturnieks, D., Lord, S. R. and Brodie, M. A. (2018). Detection of near falls using wearable devices: a systematic review. Journal of Geriatric Physical Therapy. doi: 10.1519/JPT.0000000000000181.Google Scholar
Passarino, G. et al. (2007). A cluster analysis to define human aging phenotypes. Biogerontology, 8, 283–290. doi: 10.1007/s10522-006-9071-5.CrossRefGoogle ScholarPubMed
Reitan, R. (1978). Manual for administration of neuropsychological test batteries for adults and children. Tucson, AZ: Reitan Neuropsychology Laboratories.Google Scholar
Savica, R. et al. (2017). Comparison of gait parameters for predicting cognitive decline: the mayo clinic study of aging. Journal of Alzheimers Disease, 55, 559–567. doi: 10.3233/JAD-160697.CrossRefGoogle ScholarPubMed
Segev-Jacubovski, O., Herman, T., Yogev-Seligmann, G., Mirelman, A., Giladi, N. and Hausdorff, J. M. (2011). The interplay between gait, falls and cognition: can cognitive therapy reduce fall risk? Expert Reviews in Neurotherapy, 11, 1057–1075. doi: 10.1586/ern.11.69.CrossRefGoogle ScholarPubMed
Senden, R., Savelberg, H. H., Adam, J., Grimm, B., Heyligers, I. C. and Meijer, K. (2014). The influence of age, muscle strength and speed of information processing on recovery responses to external perturbations in gait. Gait and Posture, 39, 513–517. doi: 10.1016/j.gaitpost.2013.08.033.CrossRefGoogle Scholar
Shankar, A., McMunn, A., Banks, J. and Steptoe, A. (2011). Loneliness, social isolation, and behavioral and biological health indicators in older adults. Health Psychology, 30, 377–385. doi: 10.1037/a0022826.CrossRefGoogle ScholarPubMed
Steptoe, A., Breeze, E., Banks, J. and Nazroo, J. (2013). Cohort profile: the English longitudinal study of ageing. International Journal of Epidemiology, 42, 1640–1648. doi: 10.1093/ije/dys168.CrossRefGoogle ScholarPubMed