The use of GPS to predict energy expenditure for outdoor walking

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Montana State University - Bozeman, College of Education, Health & Human Development


The purpose of this study was to determine the ability of GPS-reported position and elevation to estimate actual energy expenditure (EEACT) for outdoor walking. An accurate method for assessing EE in the field could greatly influence the scope of future studies of free-living activities. Thirteen subjects (8 male, 5 female) completed a 2303 m course of varying grades at slow and fast self-selected paces. Data from a portable metabolic unit was used to compare the GPS-predicted EE (EEGPS). Calculations of EEGPS were made by compiling an equation accounting for ground speed, grade, (Minetti, et al., 2002) and wind resistance (Pugh, 1970). Differences between EEACT and EEGPS were statistically and practically significant for the slow walking trials. Fast trials showed no significant differences. The combined data differed significantly from EEACT, but was similar to the error for accelerometer-based activity monitors. The wrist and hip-worn GPS monitors provided similar results for EEGPS throughout the data set. Separating the data by grade type showed that EEGPS was most problematic for uphill walking.
Additional analysis performed on the data showed no meaningful changes in the results. These analysis included increasing the sampling interval for the GPS monitors and implementing rudimentary smoothing techniques in an effort to reduce the error in the GPS-reported elevation data. The GPS monitor data were able to estimate EEACT for fast walking and the combined data within the error range consistently reported for activity monitors (±5 - 25%). The EEGPS for the slow walking trials could not reasonably predict actual EE. Despite the troubles with predicting EEACT, the two GPS monitors tested provided very similar estimates of EE when compared to each other, ruling out reliability as a potential source of error. The addition of wind resistance to the EEGPS equation accounted for less than 2% of the differences between actual and GPS-predicted EE. Sources of error include the accuracy of GPS technology, and the suitability of the equation for calculating EE based on speed and grade. Future studies should focus on the use of other GPS monitors, or creating a custom algorithm for estimating EE for walking.




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