Validity of a Novel, Clinically Relevant Measure to Differentiate Functional Power and Movement Velocity and Discriminate Fall History Among Older Adults: A Pilot Investigation.

Abstract

Background and ObjectivesLower-body muscular power and movement velocity (MV) are associated with balance and physical function. The Tendo power analyzer (Tendo) is a portable device that calculates functional lower body power (FLBP) and MV. This reliable (Cronbach’s α = .98) method is validated against motion capture analysis of functional lower body sit-to-stand power and velocity (r = .76). However, the Tendo has not been utilized in discrimination or prediction of falls. We determined the discriminant validity of FLBP and MV among older adults based on the history of falls. These results lay the framework for longitudinal research in FLBP and MV in fall prediction/prevention.Research Design and MethodsCross-sectional investigation examining differences between FLBP and MV during 5 sit-to-stands of 98 community-dwelling older adults (aged 77.5 years, 61% female) classified by the history of fall (no = 59, yes = 39). Participants completed 5 consecutive sit-to-stands (60-second rest between each) with FLBP and MV measured by the Tendo. Multivariate analysis of variance modeling determined between-group differences in functional lower body sit-to-stand average velocity, peak velocity, relative average power, and relative peak power. Binary and forward conditional logistic regression models determined the ability of each measure to discriminate fall history.ResultsFLBP and MV were significantly lower in older adults with a fall history (p < .05). Relative average power and peak power were 15% and 16% lower and average and peak velocity were 18% and 14% slower, respectively among fallers. Logistic regression indicated average velocity was the best discriminator of fall history (p < .05).Discussion and ImplicationsThe Tendo detects differences in FLBP and MV during a sit-to-stand while discriminating fall history. Future longitudinal studies should determine efficacy in fall prediction and applicability toward clinically relevant interventions for fall prevention.