期刊
PHYSICAL THERAPY
卷 93, 期 4, 页码 542-550出版社
OXFORD UNIV PRESS INC
DOI: 10.2522/ptj.20120302
关键词
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资金
- Davis Phinney Foundation
- Parkinson's Disease Foundation
- NIH [UL1 TR000448]
Background. The newly developed Brief-Balance Evaluation System Test (Brief-BESTest) may be useful for measuring balance and predicting falls in individuals with Parkinson disease (PD). Objectives. The purposes of this study were: (1) to describe the balance performance of those with PD using the Brief-BESTest, (2) to determine the relationships among the scores derived from the 3 versions of the BESTest (ie, full BESTest, Mini-BESTest, and Brief-BESTest), and (3) to compare the accuracy of the Brief-BESTest with that of the Mini-BESTest and BESTest in identifying recurrent fallers among people with PD. Design. This was a prospective cohort study. Methods. Eighty participants with PD completed a baseline balance assessment. All participants reported a fall history during the previous 6 months. Fall history was again collected 6 months (n=51) and 12 months (n=40) later. Results. At baseline, participants, had varying levels of balance impairment, and Brief-BESTest scores were significantly correlated with Mini-BESTest (r=.94, P<.001) and BESTest (r=.95, P<.001) scores. Six-month retrospective fall prediction accuracy of the Brief-BESTest was moderately high (area under the curve [AUC]=0.82, sensitivity=0.76, and specificity=0.84). Prospective fall prediction accuracy over 6 months was similarly accurate (AUC=0.88, sensitivity=0.71, and specificity=0.87), but was less sensitive over 12 months (AUC=0.76, sensitivity=0.53, and specificity=0.93). Limitations. The sample included primarily individuals with mild to moderate PD. Also, there was a moderate dropout rate at 6 and 12 months. Conclusions. All versions of the BESTest were reasonably accurate in identifying future recurrent fallers, especially during the 6 months following assessment. Clinicians can reasonably rely on the Brief-BESTest for predicting falls, particularly when time and equipment constraints are of concern.
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