Abstract
We compared the accuracy of five objective methods, including two newly developed methods combining accelerometry and activity type recognition (Acti4), against indirect calorimetry, to estimate total energy expenditure (EE) of different activities in semi-standardized settings. Fourteen participants performed a standardized and semi-standardized protocol including seven daily life activity types, while having their EE measured by indirect calorimetry. Simultaneously, physical activity was quantified by an ActivPAL3, two ActiGraph GT3X+’s and an Actiheart. EE was estimated by the standard ActivPAL3 software (ActivPAL), ActiGraph GT3X+ (ActiGraph) and Actiheart (Actiheart), and by a combination of activity type recognition via Acti4 software and activity counts per minute (CPM) of either a hip- or thigh-worn ActiGraph GT3X+ (AGhip + Acti4 and AGthigh + Acti4). At group level, estimated physical activities EE by Actiheart (MSE = 2.05) and AGthigh + Acti4 (MSE = 0.25) were not significantly different from measured EE by indirect calorimetry, while significantly underestimated by ActiGraph, ActivPAL and AGhip + Acti4. AGthigh + Acti4 and Actiheart explained 77% and 45%, of the individual variations in measured physical activity EE by indirect calorimetry, respectively. This study concludes that combining accelerometer data from a thigh-worn ActiGraph GT3X+ with activity type recognition improved the accuracy of activity specific EE estimation against indirect calorimetry in semi-standardized settings compared to previously validated methods using CPM only.
Highlights
Physical inactivity is a major global risk factor for chronic diseases [1,2] and mortality [3]
Results are shown as MET ± SD (MSE). * = p < 0.05 for MET compared to COSMED
The results of this study showed that combining accelerometry with activity type recognition in the AGthigh + Acti4 method improved the accuracy of physical activity EE estimation at group and individual level in a standardized and semi-standardized protocol, compared to three previously used and validated methods
Summary
Physical inactivity is a major global risk factor for chronic diseases [1,2] and mortality [3]. A high level of physical activity reduces the risk of a range of chronic diseases and premature deaths [4,5]. The documentation of the health effects of physical activity is mainly based on studies using self-reported physical activity [6,7,8]. Self-reported physical activity has been shown to be imprecise and potentially biased, and may not provide valid information on physical activity. Objective measures of physical activity are recommended [9,10,11]. Measurement of EE is the most frequently used objective approach for estimating physical activity. There are several challenges related to the measurement of physical activity EE [11,13,14]. Physical activity is the main determinant of variations in total EE of a single individual in time [16], while physical activity and body size are the main determinants for differences in EE between individuals [13,17]
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