BACKGROUND: Accelerometer-based devices are objective monitors of physical activity (PA) used to estimate energy expenditure (EE). Most EE estimation algorithms are based on steady-state data and do not consider excess post-exercise oxygen consumption (EPOC) after exercise. The purpose of this study was to quantify the error in accelerometer-based EE estimates due to EPOC after varying durations of high-intensity treadmill running. METHODS: Nine healthy, recreationally active adult males participated in 3 visits. Visit 1: treadmill VO2 peak test to determine treadmill speed at 80% VO2 peak for visits 2 and 3. Visit 2: seated 20min baseline and 3 vigorous treadmill running bouts (30s, 60s, 120s) each followed by 20min seated rest. Visit 3: supine 60min baseline and a 30min treadmill running bout followed by 3 hrs of supine rest. Fifteen EE estimation algorithms using a non-dominant wrist or right hip ActiGraph GT3X+ accelerometer (AG) were compared to measured EE using indirect calorimetry (IC). The 95% confidence interval (CI) of EE bias (AG - IC) was used to determine significance. RESULTS: The 11 linear regression EE algorithms tended to overestimate EE at rest after each exercise bout (mean bias kCals [95% CIs]; 30s: 12.5 [3.30, 21.6], 60s: 9.6 [0.37, 18.9], 120s: 6.5 [-2.74, 15.7], 30min: 177.5 [84.6, 262.1]). Adding criterion measured EPOC to the linear EE estimates often resulted in additional EE overestimation. The 4 non-linear algorithms underestimated EE after the short bouts and demonstrated no significant bias after the 30min bout (30s: -7.91 [-10.2, -5.57], 60s: -10.9 [-14.0, -7.88], 120s: -15.4 [-15.9, -14.8], 30min: 14.83 [-39.5, 69.2]). Adding EPOC to the non-linear EE estimates reduced bias after the short bouts but resulted in overestimation after the 30min bout. CONCLUSION: Linear regression algorithm estimated EE after exercise was dependent on the y-intercept, which was often higher than the measured resting EE in this study resulting in EE overestimation. Therefore, for most methods, the addition of measured EPOC to the estimated values increased the amount of EE overestimation during the post-exercise period. When calculating EE estimates from wearable-device data, researchers must be deliberate in selecting an appropriate algorithm based on the activities the algorithm is being applied to and the activities included in the calibration of that algorithm.

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