Bonomi AG, Plasqui G, Goris AH, Westerterp KR. Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. J Appl Physiol 107: 655-661, 2009. First published June 25, 2009 doi:10.1152/japplphysiol.00150.2009Accelerometers are often used to quantify the acceleration of the body in arbitrary units (counts) to measure physical activity (PA) and to estimate energy expenditure. The present study investigated whether the identification of types of PA with one accelerometer could improve the estimation of energy expenditure compared with activity counts. Total energy expenditure (TEE) of 15 subjects was measured with the use of doublelabeled water. The physical activity level (PAL) was derived by dividing TEE by sleeping metabolic rate. Simultaneously, PA was measured with one accelerometer. Accelerometer output was processed to calculate activity counts per day (AC D) and to determine the daily duration of six types of common activities identified with a classification tree model. A daily metabolic value (MET D) was calculated as mean of the MET compendium value of each activity type weighed by the daily duration. TEE was predicted by AC D and body weight and by ACD and fat-free mass, with a standard error of estimate (SEE) of 1.47 MJ/day, and 1.2 MJ/day, respectively. The replacement in these models of AC D with MET D increased the explained variation in TEE by 9%, decreasing SEE by 0.14 MJ/day and 0.18 MJ/day, respectively. The correlation between PAL and MET D (R 2 ϭ 51%) was higher than that between PAL and ACD (R 2 ϭ 46%). We conclude that identification of activity types combined with MET intensity values improves the assessment of energy expenditure compared with activity counts. Future studies could develop models to objectively assess activity type and intensity to further increase accuracy of the energy expenditure estimation. double-labeled water; motion sensor; classification tree; activity recognition IN MANY METABOLIC DISORDERS there is a need to measure daily energy expenditure. The main determinants of energy expenditure are body size and physical activity (PA) (30). Although body size can be easily determined, the assessment of PA represents a challenge because of diversified individual behaviors and because of the complex nature of human activities. Several methods have been proposed to objectively measure PA (18). Ideally, PA should be measured in free-living conditions, over a period of time representative for the habitual activity level, and with minimal discomfort to the subject. Accelerometers reasonably satisfy these requirements and, therefore, have been used widely for the assessment of PA (16, 18) . Traditionally, accelerometer output has been expressed as activity counts to quantify PA. This measure of the acceleration of the body is commonly defined as the area under the rectified acceleration signal measured over a fixed time interval like 1 min (4). Activity counts have been used to describe the pattern of PA, i.e., the frequency, the duration, and the intensity of PA. Furthermore, activity counts proved to be linearly related to the total energy expenditure (TEE), to the activityrelated energy expenditure (AEE), and to the physical activity level (PAL) as measured with the use of double-labeled water (8, 14, 21) . TEE is defined as the daily metabolic rate, while AEE corresponds to the portion of TEE consumed for PA. PAL is also commonly used to describe the amount of energy consumed for PA as a fraction of the energy required to maintain basal metabolic functions. Linear models have been developed to predict TEE and AEE, using activity counts and subject characteristics such as body weight as independent variables (8, 20) . In contrast, when indirect calorimetry was used to assess the metabolic rate during specific activities, the relationship between the intraindividual variability in AEE and activity counts varied according to the type of activity (19). Similar to TEE and AEE, PAL has been repeatedly predicted by linear models based on activity counts. However, as shown for AEE, the relationship between PAL and activity counts depends on the type of activity (19). Thus prediction models that account for the type of activity performed could result in more accurate estimates of TEE, AEE, and PAL. In recent years, accelerometers have been used in combination with classification models to identify types of PA by evaluating information (features) derived from the acceleration of the body (3, 9, 22, 28, 32) . Classification trees (9), neural networks (32), and hidden Markov models (22) are some of the classification models used to identify activity type. Zhang et al. (32) developed a neural network to identify up to 32 human movements, recording the acceleration of the body with five accelerometers. In more recent studies, the identification of activity types was based on the acceleration features measured with a single accelerometer (9, 12). However, the simplification of the measurement system, using one accelerometer, implied a decrease in the number of activities that could be accurately identified by the classification model. In this study PA was measured with a single accelerometer during daily life in a population of healthy adults. Simultaneously, TEE was assessed with the gold standard technique of double-labeled water. The aim was to investigate whether the identification of activity type combined with a simple methodology to define activity type intensity could improve the estimation of TEE, AEE, and PAL compared with daily activity counts.
However, doing so generally decreases the number of activities that can be recognized accurately REF .
Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer
Journal of applied physiology
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