Does a Sway-Based Mobile Application Predict Future Falls in People With Parkinson Disease?

Objective: 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).

Design: Observational cross-sectional study.

Setting: Community.

Participants: A convenience sample of subjects with idiopathic PD in Hoehn and Yahr levels I-III (N=59).

Interventions: 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: Sway score.

Results: The most predictive logistic regression model included fall history, ABC Scale, and Sway (P<.001). This model explained 61% (Nagelkerke R) 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.

Conclusion: Sway did not improve the accuracy of predicting future fallers beyond common clinical measures and fall history.