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Expanding Our Scope to Include Biomechanics Education and Outreach

Kimberly Bigelow and Michael L. Madigan

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Subtle Differences During Posturography Testing Can Influence Postural Sway Results: The Effects of Talking, Time Before Data Acquisition, and Visual Fixation

Melissa R. Taylor, Erin E. Sutton, Wiebke S. Diestelkamp, and Kimberly Edginton Bigelow

The goal of this study was to examine the effects of 3 factors and their interactions on posturography: a period of time to become accustomed to the force platform before the initiation of data collection, presence of a visual fixation point, and participant talking during testing. The postural stability of 30 young adults and 30 older adults was evaluated to determine whether any observed effects were confounded with age. Analysis of variance techniques were used to test all possible combinations of the 3 factors. We hypothesized that all 3 factors would significantly affect postural stability. For both participant groups, the results suggest that a period of time to become accustomed to the force platform before the initiation of data collection and a visual fixation point significantly affect postural control measures, while brief participant talking does not. Despite this, no significant interactions existed suggesting that the effects of these factors, which may occur in clinical testing, do not depend on each other. Our results suggest that inconsistencies in posturography testing methods have the potential to significantly affect the results of posturography, underscoring the importance of developing a standardized testing methodology.

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Age Stratification and Sample Entropy Analysis Enhance the Limits of Stability Tests for Older Adults

Senia Smoot Reinert, Allison L. Kinney, Kurt Jackson, Wiebke Diestelkamp, and Kimberly Bigelow

The objective of this study was to determine if a foam testing condition and/or nonlinear analysis methods can be used to identify differences between age stratified subgroups of older adults when conducting the Limits of Stability assessment. Ninety older adults participated in this study. A force plate was used to record center of pressure data during Limits of Stability testing on a firm and foam surface. Participants were grouped into age-stratified subgroups: young-old (60–69 years), middle-old (70–79 years), and old-old (80+ years). Anterior-posterior (A/P) and medial-lateral (M/L) sway ranges and sample entropy values were calculated. The young-old group had significantly larger A/P and M/L sway ranges than the old-old group on both surfaces. A/P sample entropy increased significantly with age. M/L sample entropy increased significantly with age between the young-old and old-old and the middle-old and old-old groups. Sample entropy values between surfaces significantly differed for all groups. These results indicate Limits of Stability differences occur between older adults of different age groups and should be taken into consideration for clinical and research testing. Nonlinear analysis may help further identify differences in Limits of Stability performance while use of a foam surface is of limited additional value.

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Manual and Cognitive Dual Tasks Contribute to Fall-Risk Differentiation in Posturography Measures

Renee Beach Sample, Kurt Jackson, Allison L. Kinney, Wiebke S. Diestelkamp, Senia Smoot Reinert, and Kimberly Edginton Bigelow

Falls occur in 33% of older adults each year, some leading to moderate to severe injuries. To reduce falls and fall-related injuries, it is important to identify individuals with subtle risk factors elevating their likelihood of falling. The objective of this study was to determine how postural sway measures differed between fallers and nonfallers under standard and dual-task conditions. Quietstanding posturography measures were collected from 150 older adults during standard, cognitive, manual, and cognitive+manual tasks, and analyzed through traditional and nonlinear analyses. Of the traditional measures, M/L sway range and 95% confidence ellipse sway area showed statistically significant differences in all 4 test conditions between fallers and nonfallers. Although the manual dual task showed the most stable balance, effect sizes demonstrated larger differences between fallers and nonfallers. Nonlinear analysis revealed M/L sample entropy and M/L α-scaling exponent differentiating between fallers and nonfallers, with the cognitive task demonstrating larger differences. Based on the results, it is recommended to: (1) apply M/L sway range and 95% confidence ellipse area, (2) use the manual task to differentiate between fallers and nonfallers when using traditional analyses, and (3) use the cognitive task and M/L alpha and M/L sample entropy when using nonlinear analyses.