Validation and Comparison of Two Methods to Assess Human Energy Expenditure during Free-Living Activities

Abstract

BackgroundThe measurement of activity energy expenditure (AEE) via accelerometry is the most commonly used objective method for assessing human daily physical activity and has gained increasing importance in the medical, sports and psychological science research in recent years.ObjectiveThe purpose of this study was to determine which of the following procedures is more accurate to determine the energy cost during the most common everyday life activities; a single regression or an activity based approach. For this we used a device that utilizes single regression models (GT3X, ActiGraph Manufacturing Technology Inc., FL., USA) and a device using activity-dependent calculation models (move II, movisens GmbH, Karlsruhe, Germany).Material and MethodsNineteen adults (11 male, 8 female; 30.4±9.0 years) wore the activity monitors attached to the waist and a portable indirect calorimeter (IC) as reference measure for AEE while performing several typical daily activities. The accuracy of the two devices for estimating AEE was assessed as the mean differences between their output and the reference and evaluated using Bland-Altman analysis.ResultsThe GT3X overestimated the AEE of walking (GT3X minus reference, 1.26 kcal/min), walking fast (1.72 kcal/min), walking up−/downhill (1.45 kcal/min) and walking upstairs (1.92 kcal/min) and underestimated the AEE of jogging (−1.30 kcal/min) and walking upstairs (−2.46 kcal/min). The errors for move II were smaller than those for GT3X for all activities. The move II overestimated AEE of walking (move II minus reference, 0.21 kcal/min), walking up−/downhill (0.06 kcal/min) and stair walking (upstairs: 0.13 kcal/min; downstairs: 0.29 kcal/min) and underestimated AEE of walking fast (−0.11 kcal/min) and jogging (−0.93 kcal/min).ConclusionsOur data suggest that the activity monitor using activity-dependent calculation models is more appropriate for predicting AEE in daily life than the activity monitor using a single regression model.

Energy Expenditure During Incline Walking – Benefits of Integrating a Barometer into Activity Monitors

“…Furthermore, the move II uses data of acceleration and pressure for estimating EE. The latter approach was found to be more accurate for estimating EE on inclines, which is in agreement with previous studies [7,16,17].…”

“…The study of Anastasopoulou et al using a conventional accelerometer and a device equipped with a triaxial accelerometer and a barometer confirmed these findings. Herein, walking up-/downhill was realized by use of a flat bridge (up to 5%), but EEs were not calculated separately [17]. The weakness of the above-introduced studies is that they employed relatively slight inclines and, most important, the undifferentiated view of uphill and downhill movements using a triaxial accelerometer and a barometer.…”

Momentary mood predicts upcoming real‐life sedentary behavior

“…Both high‐pass filter (0.25 Hz) and low‐pass filter (11 Hz) were used to eliminate gravitational components and to exclude artifacts from the acceleration data. Anastasopoulou et al 26 showed that move accelerometers used in this study are appropriate for assessing humans’ energy expenditure.…”

Relationships between incidental physical activity, exercise, and sports with subsequent mood in adolescents

“…The participants' average IA within the study week was 40.86 mg/participant/wk (range = 13.32-74.78; SD = 11.87). For the sake of comparison, sedentariness relates to approximately 7 mg and walking to approximately 367 mg. 34 Sixty-eight participants (60%) exercised within the study week resulting in the total of 84 sports and 82 exercise sessions. The participants' mean mood was 5.59 (valence; SD = 0.54), 4.55 (energetic arousal; SD = 0.65), and 5.13 (calmness; SD = 0.63; see Table 1).…”