Incorporating Excess Post-exercise Oxygen Consumption into Accelerometer Energy Expenditure Estimation Algorithms

Accelerometers are objective monitors of physical activity (PA) that can be used to estimate energy expenditure (EE). Most accelerometer EE estimation equations are based on steady-state data and do not consider excess post-exercise oxygen consumption (EPOC) after exercise. PURPOSE: To quantify the error in accelerometer EE estimates due to EPOC after varying durations of high-intensity treadmill running. METHODS: Nine young, healthy, recreationally active males participated in three study visits. Visit 1 included a treadmill VO2 peak test to determine the treadmill speed correlating to 80% VO2 peak for visits 2 and 3. Visit 2 included a seated 20-min baseline and three short (30s, 60s, 120s) vigorous treadmill running bouts each followed by 20 minutes of seated rest. Visit 3 included a supine 60-min baseline and a 30-min treadmill running bout followed by 3 hours of supine rest. Twelve EE estimation equations each using either a non-dominant wrist or right hip ActiGraph GT3X+ accelerometer were compared to the true EE measured by the Parvomedics TrueOne 2400 indirect calorimeter. RESULTS: The Freedson 1998 EE estimation equation overestimated EE during the 20min post-exercise period after each exercise bout (mean kCals [95% CIs]; 30s: 19.3 [11.4, 27.2], 60s: 16.6 [8.5, 24.7], 120s: 13.4 [5.74, 21.1], 30min: 15.1 [6.69, 23.5]). The Crouter 2009 branching algorithm underestimated EE during the 20min post-exercise period after each exercise bout (mean kCals [95% CIs]; 30s: -8.59 [-10.6, -6.62], 60s: -11.6 [-13.7, -9.38], 120s: -15.0 [-18.1, -11.8], 30min: -11.0 [-14.3, -7.77]), but was partially corrected by adding in the measured EPOC. CONCLUSION: Estimated EE during lying or seated rest from linear accelerometer equations was heavily dependent on the y-intercept of the equation, which represents the estimated resting EE of the wearer, with the Crouter calibration study being the only one to directly measure resting EE. More sophisticated approaches, like the Crouter 2009 and newer machine learning algorithms, have better potential to more accurately estimate EE across various activity types. New accelerometer EE estimations should include resting in their calibration protocols in order to more accurately estimate EE during rest