Can mini-BESTest predict recurrent falls in patients with Parkinson's disease? : a six months prospective study

Pao Yue-kong Library Electronic Theses Database

Can mini-BESTest predict recurrent falls in patients with Parkinson's disease? : a six months prospective study

 

Author: Lau, Nga Chi
Title: Can mini-BESTest predict recurrent falls in patients with Parkinson's disease? : a six months prospective study
Degree: M.Sc.
Year: 2012
Subject: Parkinson's disease -- Patients -- Health risk assessment.
Equilibrium (Physiology) -- Testing.
Falls (Accidents) -- Prevention.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Rehabilitation Sciences
Pages: ix, 67 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2507562
URI: http://theses.lib.polyu.edu.hk/handle/200/6679
Abstract: Background: Falls is common in patients with Parkinson's disease (PD), which leads to physical and functional decline were resulted as well as recurrent falls. Balance impairment is one of the major causes of falls in PD, however, the identification of clinical balance assessment on predicting future recurrent falls in PD patients is inadequate. Objectives: The present study aimed to examine the performance of mini-BESTest between recurrent and non-recurrent fallers and whether mini-BESTest could predict recurrent falls in PD patients. Method This was a prospective cohort study in individuals with PD. The baseline measurements of demographic data, together with functional assessments including miniBESTest, FOGQ and FTSTS test were examined and compared between PD recurrent and non-recurrent fallers. Logistic regression model was used to identify the significant predictors for PD recurrent fallers. The optimal cut-off score was determined from the receiver operating characteristic curve with the best overall sensitivity and specificity values shown. Result: A total of 113 subjects completed the study, 24 of them (21.2%) experienced more than one fall within the 6-month follow up period and classified as recurrent fallers. Results of univariate logistic regression showed that all the three functional tests including FOGQ, FTSTS and mini-BESTest significantly predicted recurrent falls in PD patients independently. In the multivariate logistic regressIOn, mini-BESTest together with fall history, gender and UPDRS III remained as a significant predictor (p=0.014) for future recurrent falls in PD patients with an overall accuracy of 86.4%. The suggested cutoff score of mini-BESTest was 19 (sensitivity of 0.79; specificity of 0.63) with the area under curve of 0.74. Conclusion: Mini-BESTest score is an objective balance assessment that allows clinicians to predict future recurrent falls in PD. Its systematic information on dynamic balance is useful in the establishment of tailored fall prevention program and exercise training for potential recurrent fallers in PD.

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