Abstract
BACKGROUND: The typical American worker spends about two-thirds of their work day standing. Prolonged standing has been found to be associated with acute and chronic adverse health outcomes. There is considerable variability among existing methods of analysis for prolonged-standing data, and therefore difficulty interpreting and comparing results across studies. PURPOSE: The purpose of this study is to develop a bodyweight transfer analysis method that incorporates factors of both time and amplitude of loading. This method was then applied to actual prolonged-standing data, to understand how the results of this method are impacted by time spent standing, and how the results relate to previously-reported methods of analysis for weight shifting data. METHODS: Seven subjects (six male, one female) stood with each foot on one of two force plates for 6 h with a 5-min seated rest break between hours. Our new method identified two different types of events: fidgets and weight shifts. Center-of-pressure data were analyzed with the proposed method and three existing methods of analysis. RESULTS: Subjects utilized different quantities of fidgets and weight shifts over the course of the trials. Existing methods of analysis identified a wide range in number of events, with some methods consistently identifying more events than others. These existing methods significantly differed from the proposed method. Fidgets, weight shifts, and fidgets + weight shifts, as identified using the proposed method, had significant interactions with time, while only one of the existing methods showed a significant time interaction. CONCLUSIONS: The conclusions drawn from analysis of prolonged standing center-of-pressure data can differ significantly depending on the method of analysis used. The method proposed here accounts for the different sources of discomfort and the tissue characteristics of these sources. Future work should explore the relationships between physiologic parameters and fidgets and weight shifts, so that appropriate clinical interventions can be identified.