Understanding the mechanisms of instability can aid in reducing fall risk. As a sensitive measure of fall risk, the distance between the center of pressure (COP) and center of mass (COM) is currently assessed through discrete points assumed to represent physiological important fall mechanisms. However, it is unclear if these discrete points are appropriate measures of fall risk. Statistical parametric mapping (SPM) is a waveform analysis technique that removes this possibly biased a priori approach. Sixteen healthy young adults (8 males, 8 females; Age: 29 ± 3.6 years, Height: 1.7 ± 0.9 m, Mass: 75 ± 16 kg) performed two tasks that disturbed dynamic stability: voluntary stepping at different step lengths, and forward perturbations at different accelerations. COP-COM distance magnitudes were extracted during the first step in both tasks at discrete points typically assessed in previous research. Discrete point analysis (DPA) was performed on these discrete points and SPM analysis was completed on the COP-COM distance waveform. The results from the study found that SPM analysis identified equivalent significant differences to DPA and identified additional significant differences elsewhere in the COP-COM distance waveform that were not able to be detected by DPA. Two key advantages from using SPM: (1) reduction of possibly biased a priori selection, and (2) increased efficiency and reduced time-cost in data post-processing as inherent variability can limit the detection of discrete points resulting in identifying physiologically different discrete points across trials. This study suggests the use of SPM as a sensitive data analysis approach in detecting fall risk as an alternative to DPA.
Keywords: Discrete point analysis; Fall risk; Perturbation; Statistical parametric mapping; Step length; Waveform analysis.
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