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Abstract
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Smartphone accelerometer applications have become a quick, simple method of collecting clinical data on postural sway in athletes who have experienced a concussion. Normalized path length (NPL) and standard deviation (SD) have been used as measures of variability in alternative postural sway studies. The correlation between the two measures has yet to be established.
PURPOSE: The purpose of this study was to investigate the relationship between SD and NPL of net acceleration data as a measure of variability in postural sway in collegiate athletes.
METHODS: 8 individuals participated in this study including basketball, soccer, and lacrosse players, both male and female. Of the subjects, 4 had previously experienced a concussion, while 4 served as controls. Subjects were asked to complete a series of 17 trials composed of differing conditions including, open and closed base, tandem stance and single leg on foam or tile. These trials were completed with the eyes closed and open. Each trial was performed for 20 seconds and recorded using a smartphone accelerometer attached to the waist. NPL and SD of net acceleration were calculated to quantify the variability in postural sway exhibited by each subject. These two measures were then correlated to investigate their relationship.
RESULTS: The correlation of NPL and SD of net acceleration data using a smartphone accelerometer was significant (r = 0.977, p < 0.05). While the correlations remained significant when disaggregated (r =0.9755, p 0.1).
CONCLUSION: A strong correlation exists between NPL and SD of net acceleration data taken using a smartphone accelerometer. This correlation holds for both concussed subjects and controls. There is no significant difference in the strength of the correlation between the two groups. Therefore, both NPL and SD of net acceleration data can both be considered valid metrics for the measurement of variability in postural sway. Further research will correlate these measures with other indicators of variability in postural sway such as root mean squared and center of pressure data.
Language: en |