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.

Instrumented Analysis of the Sit-to-Stand Movement for Geriatric Screening: A Systematic Review

“…The remaining study used accelerometery, utilising a smartphone worn on a waist belt [ 35 ]. The study in which a sensor was attached to the body used a linear position transducer that was attached to the belt by a cable [ 36 ]. The remaining three studies used a Microsoft Kinect sensor placed perpendicular to the chair [ 37 ], four force plates integrated into the chair [ 38 ], and a chair equipped with load cells and a light detection and ranging (LiDAR) sensor [ 39 ].…”

“…Similarly, with the differences between groups, the largest correlations were found for force/power and velocity variables, with moderate effects typically reported. Finally, three studies reported models with respect to discriminating or classifying between fallers and non-fallers [ 31 , 33 , 36 ]. Two machine learning algorithms, a support vector machine (SVM) and logistic regression (LR), were used for classification with a range of different parameters used in the models reported, meaning pooled estimates could not be produced.…”

“…This could indicate that, rather than there being a particular phase of the STS such as the sit-to-stand transition that is difficult for fallers to perform, it might be equally difficult to perform all phases. However, another reason for the lack of differences observed could be that many different methods of dividing the STS into phases were used, while some studies did not segment the STS at all [ 34 , 36 ]. The most phases identified in the STS was four [ 37 ], which included the sit-to-stand transition, stand-to-sit transition, and standing and sitting phases.…”

“…Classification of fallers and non-fallers was only reported by two studies, one of which did not include accuracy, making it difficult to compare. Although the accuracy from an IMU-based system was only moderate [ 31 ], an excellent odds ratio (OR) was obtained with the linear transducer [ 36 ] to distinguish fallers from non-fallers.…”

Velocity of the Five Sit-to-Stand Test in Older Adults: A Systematic Review (Velocidad de la prueba sentado-de pie de cinco repeticiones en adultos mayores: Una revisión sistemática)

“…Another study indicates that in sarcopenic conditions, MV has a value of 0.5 m/s (Glenn, Gray, Vincenzo, et al, 2017). Under conditions of fragility, population F presents a MV of 0.78 m/s (Ejupi et al, 2015), in addition, another study identifies a MV of 0.41 m/s and peak velocity of 0.64 m/s in this population (Vincenzo et al, 2018). In clinical conditions, maximum velocity values of 0.52 m/s prior to training and a velocity of 0.61 m/s post strength training are indicated (Regterschot et al, 2014).…”