Robert W Nithman1, Jennifer L Vincenzo2. 1. Doctor of Physical Therapy Program, College of Health Sciences, Midwestern University, Glendale, AZ, United States. Electronic address: RWNithmanPhD@protonmail.com. 2. Doctor of Physical Therapy Program, University of Arkansas for Medical Sciences, Fayetteville, AR, United States.
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. 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. 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. 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. 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.
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. 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. 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. 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. 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.
Authors: Thelma J Mielenz; Sneha Kannoth; Haomiao Jia; Kristin Pullyblank; Julie Sorensen; Paul Estabrooks; Judy A Stevens; David Strogatz Journal: Front Public Health Date: 2020-08-13
Authors: Jennifer L Vincenzo; Susan K Patton; Leanne L Lefler; Pearl A McElfish; Jeanne Wei; Geoffrey M Curran Journal: Int J Environ Res Public Health Date: 2022-02-09 Impact factor: 3.390
Authors: Jennifer L Vincenzo; Lori A Schrodt; Colleen Hergott; Subashan Perera; Jennifer Tripken; Tiffany E Shubert; Jennifer S Brach Journal: Int J Environ Res Public Health Date: 2022-01-26 Impact factor: 3.390