Aspects of activity behavior as a determinant of the physical activity level.

This study investigated which aspects of the individuals' activity behavior determine the physical activity level (PAL). Habitual physical activity of 20 Dutch adults (age: 26-60 years, body mass index: 24.5 ± 2.7 kg/m(2)) was measured using a tri-axial accelerometer. Accelerometer output was used to identify the engagement in different types of daily activities with a classification tree algorithm. Activity behavior was described by the daily duration of sleeping, sedentary behavior (lying, sitting, and standing), walking, running, bicycling, and generic standing activities. Simultaneously, the total energy expenditure (TEE) was measured using doubly labeled water. PAL was calculated as TEE divided by sleeping metabolic rate. PAL was significantly associated (P<0.05) with sedentary time (R=-0.72), and the duration of walking (R=0.49), bicycling (R=0.77), and active standing (R=0.62). A negative association was observed between sedentary time and the duration of active standing (R=-0.87; P<0.001). A multiple-linear regression analysis showed that 75% of the variance in PAL could be predicted by the duration of bicycling (Partial R(2) =59%; P<0.01), walking (Partial R(2) =9%; P<0.05) and being sedentary (Partial R(2) =7%; P<0.05). In conclusion, there is objective evidence that sedentary time and activities related to transportation and commuting, such as walking and bicycling, contribute significantly to the average PAL.

Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer.

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.