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Titre : | Does a Sway-Based Mobile Application Predict Future Falls in People With Parkinson Disease? (2020) |
Auteurs : | Connie M. Fiems ; Stéphanie A. Miller ; Nathan Buchanan |
Type de document : | Article |
Dans : | Archives of Physical Medicine and Rehabilitation (Vol. 101, n° 3, 2020) |
Article en page(s) : | p. 472-478 |
Note générale : | https://doi.org/10.1016/j.apmr.2019.09.013 |
Langues: | Anglais |
Descripteurs : |
HE Vinci Équilibre postural ; Maladie de Parkinson ; Réadaptation ; Technologie biomédicale |
Résumé : |
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 r2 of the variance in fall prediction and correctly classified fallers. however only history abc scale were statistically significant participants times more likely to future if they fell past. mini balance evaluation systems test demonstrated greater accuracy than sway under curve="0.76," respectively cutoff scores identify fallers for mini-bestest.> Conclusion Sway did not improve the accuracy of predicting future fallers beyond common clinical measures and fall history. |
Disponible en ligne : | Oui |
En ligne : | https://login.ezproxy.vinci.be/login?url=https://www.sciencedirect.com/science/article/pii/S0003999319313115 |