To Örebro University

PURPOSE: To validate the Computer Science and Application's (CSA) activity monitor for assessment of the total amount of physical activity during two school-weeks in 9-yr-old children and to develop equations to predict total energy expenditure (TEE) and activity energy expenditure (AEE) from activity counts and anthropometric variables.

METHODS: A total of 26 children (15 boys and 11 girls, mean age 9.1 +/- 0.3 yr) were monitored for 14 consecutive days. TEE was simultaneously measured by the doubly labeled water method. Averaged activity counts (counts.min(-1)) were compared with data on: 1) TEE, 2) AEE = TEE minus basal metabolic rate (BMR; estimated from predictive equations), and 3) daily physical activity level (PAL = TEE/BMR).

RESULTS: Physical activity determined by activity counts was significantly related to the data on energy expenditures: TEE (r = 0.39; P < 0.05), AEE (r = 0.54; P < 0.01), and PAL (r = 0.58; P < 0.01). Multiple stepwise regression analysis showed that TEE was significantly influenced by gender, body composition (body weight or fat free mass), and activity counts (R(2) = 0.54--0.60). AEE was significantly influenced by activity counts and gender (R(2) = 0.45). There were no significant differences between activity counts and PAL in discriminating among activity levels with "low" (PAL < 1.56), "moderate" (1.57 < or = PAL > or = 1.81), and "high" (PAL > 1.81) intensity.

CONCLUSION: Activity counts from the CSA activity monitor seems to be a useful measure of the total amount of physical activity in 9-yr-old children. Activity counts contributed significantly to the explained variation in TEE and was the best predictor of AEE.