667
Views
36
CrossRef citations to date
0
Altmetric
Original Article

The Timed Up and Go test: Predicting falls in ALS

, , , , &
Pages 292-295 | Received 05 Feb 2007, Accepted 04 May 2007, Published online: 10 Jul 2009
 

Abstract

There are few functionally meaningful clinical measures used to guide management of patients with ALS. Falls are common, can be debilitating, and result in increased health care costs. We assessed the performance and ability to predict falls of the Timed Up and Go (TUG) test, which quantifies walking ability, in a prospective longitudinal study.

Thirty‐one patients underwent six monthly TUG, ALSFRS‐R, forced vital capacity, muscle testing (MMT) and quality of life assessments. Linear and generalized linear mixed effects models assessed the associations among variables and ability to predict falls. The increase in TUG time was linear over six months, and TUG time was negatively associated with ALSFRS‐R (p⩽0.001) and MMT scores (p⩽0.001). The TUG test was the only variable that was associated with the chance of falling (p = 0.024); patients with TUG times of 14 s had a 10% chance of falling during the study. In conclusion, TUG performance declined linearly in this longitudinal study, was correlated with standard outcome measures, and predicted falls. The TUG test can guide management of patients with ALS; a time of 14 s can be used to prompt the recommendation for mobility aids to prevent falls.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 478.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.