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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.

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.