To determine whether Sway, a sway-based mobile application, predicts falls and to evaluate its discriminatory sensitivity and specificity relative to other clinical measures in identifying fallers in individuals with Parkinson disease (PD).
Observational cross-sectional study.
A convenience sample of subjects with idiopathic PD in Hoehn and Yahr levels I-III (N=59).
Participants completed a balance assessment using Sway, the Movement Disorders Systems-Unified PD Rating Scale motor examination, 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
The most predictive logistic regression model included fall history, ABC Scale, 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 Scale were statistically significant (P<.02). Participants were 32 times more likely to fall in the future if they fell in the past. The ABC Scale and Mini Balance Evaluation Systems Test (Mini-BESTest) demonstrated greater accuracy than Sway (area under the curve=0.76, 0.72, and 0.65, respectively). Cutoff scores to identify fallers were 85% for the ABC Scale and 21 of 28 for the Mini-BESTest.
Sway did not improve the accuracy of predicting future fallers beyond common clinical measures and fall history.
List of abbreviations:ABC (Activities-specific Balance Confidence), AUC (area under the curve), H&Y (Hoehn and Yahr), Mini-BESTest (Mini Balance Evaluation Systems Test), mCTSIB (Modified Clinical Test of Sensory Interaction on Balance), MDS-UPDRS (Movement Disorder Society-Unified Parkinson’s Disease Rating Scale), PD (Parkinson’s disease), ROC (receiver operating characteristic)
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Published online: October 24, 2019
© 2019 by the American Congress of Rehabilitation Medicine