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Citation
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Fiems CL, Combs-Miller SA, Buchanan N, Knowles E, Larson E, Snow R, Moore ES. Arch. Phys. Med. Rehabil. 2019; ePub(ePub): ePub. |
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Abstract
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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).
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 R2) 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.
CONCLUSION: SWAY did not improve the accuracy of predicting future fallers beyond common clinical measures and fall history.
Copyright © 2019. Published by Elsevier Inc.
Language: en |