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Identification of the ability to predict falls in older adults using a threashold based on the stance time variability of the gait

Abstract

Falls are the major cause of injuries and death in people with 65 years or more. The measure of gait variability parameters is an important assessment tool of risk of falls. Previous study identified that the stance time variability was able to discriminate older fallers and non-fallers based on the self-report of falls. According to this previous study it demonstrated that older adults with history of falls showed an increased variability of stance time of the gait (standard deviation of stance time of 40 strides > 0,102) and were correctly classified with 100% of sensibility and specificity using this cut off value that was found. However, it is still unclear the ability of that variable to predict falls in older population. Thus, the main objective of the present study will be to evaluate the capacity of this cut point value, based on stance time variability, to predict falls in older adults. Older adults (60-80 yr) living in a community setting will be evaluated. For the data collection the stance time of 40 strides and the preferred walking speed will be recorded. To calculate the stance time variability will be considered the standard deviation of this parameter in forty strides. Over 12 months, the volunteers will be contact, monthly, to identify the falls that occurred into this period. Thus, to determine the predictive capacity of this cut off value, based on stance time variability, the specificity and sensibility will be calculated using the ROC curve. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MARQUES, NISE RIBEIRO; SPINOSO, DEBORAH HEBLING; CARDOSO, BRUNA CARVALHO; MORENO, VINICIUS CHRISTIANINI; KURODA, MARINA HIROMI; NAVEGA, MARCELO TAVELLA. Is it possible to predict falls in older adults using gait kinematics?. CLINICAL BIOMECHANICS, v. 59, p. 15-18, NOV 2018. Web of Science Citations: 3.

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