1 Department of Human Biology, Maastricht University, Maastricht; and 2 Group Care and Health Applications and 3 DirectLife New Wellness Solutions, Philips Research Laboratories, Eindhoven, The Netherlands Submitted 11 February 2009 ; accepted in final form 23 June 2009 Accelerometers 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 double-labeled 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 AC D 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 = 51%) was higher than that between PAL and AC D ( R 2 = 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 Address for reprint requests and other correspondence: A. G. Bonomi, Human Biology, Maastricht Univ., PO Box 616, 6200 MD Maastricht, The Netherlands (e-mail: a.bonomi{at}HB.unimaas.nl )