@article{ref1,
title="How steady is the STEADI? Inferential analysis of the CDC fall risk toolkit",
journal="Archives of gerontology and geriatrics",
year="2019",
author="Nithman, Robert W. and Vincenzo, Jennifer L.",
volume="83",
number="",
pages="185-194",
abstract="INTRODUCTION: The CDC developed the STEADI toolkit to assist providers with incorporating fall risk screening, assessment of modifiable risk factors, and implementing evidence-based treatment strategies. The purpose of this study was two-fold: analyze the STEADI algorithm for strengths/weaknesses based upon inferential data and provide recommendations for additional research and possible limitations of the STEADI toolkit from a physical therapy perspective. <br><br>METHODS: This investigation employed a quantitative, cross-sectional cohort design collating data from community-dwelling and retirement-facility seniors (n = 77) from two regions of the U.S. Data is reported based upon descriptive statistics, correlation, and validity of the STEADI algorithm, its subcomponent tests, and self-reported fall data. All participants completed the Stay Independent Brochure (SIB) and the algorithm's mobility, balance, and lower extremity strength tests regardless of risk categorization. <br><br>RESULTS: Sensitivity of the STEADI with discriminating fallers and predicting future falls was better among community-dwellers (73-80%) versus the retirement facility-dwellers (56-62%). The STEADI demonstrated high false negative rates among those categorized as low risk as 57% community-dwellers and 24% facility-dwellers fell in the prior 12 months and several fell within 6 months following participation. <br><br>RESULTS suggest that it is important to conduct more than one mobility or balance screening test, and indicate that elevated STEADI risk classification was not associated with advancing age. <br><br>CONCLUSIONS: Outcomes from this study suggest that cut-off scores and the selection of functional fall screening tests, as well as the relative weights and scoring of items on the SIB/3KQ be reevaluated to maximize discriminate and predictive validity of the algorithm.<br><br>Copyright © 2019 Elsevier B.V. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="0167-4943",
doi="10.1016/j.archger.2019.02.018",
url="http://dx.doi.org/10.1016/j.archger.2019.02.018"
}