@article{ref1,
title="Does a sway-based mobile application predict future falls in people with Parkinson disease?",
journal="Archives of physical medicine and rehabilitation",
year="2019",
author="Fiems, Connie L. and Combs-Miller, Stephanie A. and Buchanan, Nathan and Knowles, Erin and Larson, Elizabeth and Snow, Rachel and Moore, Elizabeth S.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="OBJECTIVE: To determine whether a sway-based mobile application (SWAY) predicts falls and to evaluate its discriminatory sensitivity and specificity relative to other clinical measures in identifying fallers in individuals with Parkinson disease (PD). <br><br>DESIGN: Observational cross-sectional study SETTING: Community PARTICIPANTS: A convenience sample of 59 subjects with idiopathic PD in Hoehn & Yahr levels I-III. INTERVENTIONS: Participants completed a balance assessment using SWAY, the Movement Disorders Systems-Unified PD Rating Scale motor exam, Mini-BESTest, Activities-specific Balance Confidence (ABC) Scale and reported 6 month fall history. Participants also reported falls for each of the following 6 months. Binomial logistic regression was used to identify significant predictors of future fall status. Cutoff scores, sensitivity and specificity were based on receiver operating characteristic plots. MAIN OUTCOME MEASURES: SWAY score RESULTS: The most predictive logistic regression model included fall history, ABC, and SWAY (P <.001). This model explained 61% (Nagelkerke R<sup>2</sup>) of the variance in fall prediction and correctly classified 85% of fallers. However, only fall history and ABC were statistically significant (P <.02). Using this model, participants were 32 times more likely to fall in the future if they fell in the past. The ABC and Mini-BESTest demonstrated greater accuracy than SWAY (AUC =.76,.72 and.65 respectively). Cutoff scores to identify fallers were 85% for the ABC and 21/28 for the Mini-BESTest. <br><br>CONCLUSION: SWAY did not improve the accuracy of predicting future fallers beyond common clinical measures and fall history.<br><br>Copyright © 2019. Published by Elsevier Inc.<p /> <p>Language: en</p>",
language="en",
issn="0003-9993",
doi="10.1016/j.apmr.2019.09.013",
url="http://dx.doi.org/10.1016/j.apmr.2019.09.013"
}