@article{ref1,
title="A postural assessment utilizing machine learning prospectively identifies older adults at a high risk of falling",
journal="Frontiers in medicine",
year="2020",
author="Adams, Sasha D. and Wirfel, Kelly L. and Forth, Katharine E. and Madansingh, Stefan I. and Lieberman Aiden, Erez and Rianon, Nahid J.",
volume="7",
number="",
pages="e591517-e591517",
abstract="INTRODUCTION: Falls are the leading cause of accidental death in older adults. Each year, 28.7% of US adults over 65 years experience a fall resulting in over 300,000  hip fractures and $50 billion in medical costs. Annual fall risk assessments have  become part of the standard care plan for older adults. However, the effectiveness  of these assessments in identifying at-risk individuals remains limited. This study  characterizes the performance of a commercially available, automated method, for  assessing fall risk using machine learning. <br><br>METHODS: Participants (N = 209) were  recruited from eight senior living facilities and from adults living in the  community (five local community centers in Houston, TX) to participate in a 12-month  retrospective and a 12-month prospective cohort study. Upon enrollment, each  participant stood for 60 s, with eyes open, on a commercial balance measurement  platform which uses force-plate technology to capture center-of-pressure (60 Hz  frequency). Linear and non-linear components of the center-of-pressure were analyzed  using a machine-learning algorithm resulting in a postural stability (PS) score  (range 1-10). A higher PS score indicated greater stability. Participants were  contacted monthly for a year to track fall events and determine fall circumstances. Reliability among repeated trials, past and future fall prediction, as well as  survival analyses, were assessed. <br><br>RESULTS: Measurement reliability was found to be  high (ICC(2,1) [95% CI]=0.78 [0.76-0.81]). Individuals in the high-risk range (1-3)  were three times more likely to fall within a year than those in low-risk (7-10). They were also an order of magnitude more likely (12/104 vs. 1/105) to suffer a  spontaneous fall i.e., a fall where no cause was self-reported. Survival analyses  suggests a fall event within 9 months (median) for high risk individuals. <br><br>CONCLUSIONS: We demonstrate that an easy-to-use, automated method for assessing fall  risk can reliably predict falls a year in advance. <br><br>OBJECTIVE identification of  at-risk patients will aid clinicians in providing individualized fall prevention  care.<p /> <p>Language: en</p>",
language="en",
issn="2296-858X",
doi="10.3389/fmed.2020.591517",
url="http://dx.doi.org/10.3389/fmed.2020.591517"
}