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Investigation of fall prediction factors in elderly population using isokinetic assessment of the lower extremities

Keywords: Fall prediction , elderly , isokinetic assessment , lower extremities

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Abstract:

The purpose of the present study was to investigate for predictive factors for falls in the elderly population after lower extremity strength assessment in an isokinetic dynamometer and to construct a discrimination model for the classification of the elderly in a fallers and non-fallers group. 59 community dwelling elderly men and women aged60 years and older with or without a history of falls all in good health were selected to participate in the study. For the assessment, we used six concentric and two eccentric speeds isokinetically (60°, 90°, 120°, 180°, 240°, 300°/secand 60°, 120°/sec respectively) and four different angular positions isometrically (75°, 60°, 45°, 30° of knee flexion,0°=anatomical zero). All measures were conducted with a Cybex Norm Isokinetic Dynamometer in the knee joint for both legs in flexion and extension. T-test was used in order to identify differences between fallers and non-fallers. Step-wise discriminant analysis performed in order to reveal the factors for classification of the elderly in fallers/non-fallers group. Statistical significant differences (p<0.05) were ob-served for strength and power measures in 42 of the 60variables, between the two groups (fallers/non-fallers), with non-fallers performing better in all examined variables. Three variables out of the initially 42 that included in the discriminant analysis were present in the discriminant model: Mean peak torque in 120°/sec in Extension, mean total work in 60°/secin flexion and mean total work ratio (Flexors/Extensors) in 60°/sec. These three variables classified correctly the 91.4% of the cases in the fallers/non-fallers groups. The conclusion of the researchers was that isokinetic and isometric assessment of the lower extremities strength in the elderly population can discriminate fallers and non fallers. As a result we can therefore classify older people in prevention programs according to their probability for falls.

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