OBJECTIVES: To assess validity evidence of TracmorD to determine energy used for physical activity in 3-4-year-old children. DESIGN AND METHODS: Participants were randomly selected from GECKO Drenthe cohort (n = 30, age 3.4 ± 0.3 years). Total energy expenditure (TEE) was measured using the doubly labeled water method. Sleeping metabolic rate (SMR) was measured by indirect calorimetry (Deltatrac). TEE and SMR were used to calculate physical activity level (PAL) and activity energy expenditure (AEE). Physical activity was monitored using a DirectLife triaxial accelerometer, TracmorD with activity counts per minute (ACM) and activity counts per day (ACD) as outcome measures. RESULTS: The best predictor for PAL was ACM with gender and weight, the best predictor for AEE was ACM alone (backward regression, R(2) = 0.50, P = 0.010 and R2 = 0.31, P = 0.011, respectively). With ACD, the prediction model for PAL included ACD, height, gender, and sleep duration (R2 = 0.48, P = 0.033), the prediction model for AEE included ACD, gender and sleep duration (R2 = 0.39, P = 0.042). The accelerometer was worn for 5 days, but 3 days did not give a different estimated PAL. CONCLUSION: TracmorD provides moderate-to-strong validity evidence that supports its use to evaluate energy used for physical activity in 3-4-year-old children.
OBJECTIVES: To assess validity evidence of TracmorD to determine energy used for physical activity in 3-4-year-old children. DESIGN AND METHODS: Participants were randomly selected from GECKO Drenthe cohort (n = 30, age 3.4 ± 0.3 years). Total energy expenditure (TEE) was measured using the doubly labeled water method. Sleeping metabolic rate (SMR) was measured by indirect calorimetry (Deltatrac). TEE and SMR were used to calculate physical activity level (PAL) and activity energy expenditure (AEE). Physical activity was monitored using a DirectLife triaxial accelerometer, TracmorD with activity counts per minute (ACM) and activity counts per day (ACD) as outcome measures. RESULTS: The best predictor for PAL was ACM with gender and weight, the best predictor for AEE was ACM alone (backward regression, R(2) = 0.50, P = 0.010 and R2 = 0.31, P = 0.011, respectively). With ACD, the prediction model for PAL included ACD, height, gender, and sleep duration (R2 = 0.48, P = 0.033), the prediction model for AEE included ACD, gender and sleep duration (R2 = 0.39, P = 0.042). The accelerometer was worn for 5 days, but 3 days did not give a different estimated PAL. CONCLUSION: TracmorD provides moderate-to-strong validity evidence that supports its use to evaluate energy used for physical activity in 3-4-year-old children.
Authors: C Delisle Nyström; J Pomeroy; P Henriksson; E Forsum; F B Ortega; R Maddison; J H Migueles; M Löf Journal: Eur J Clin Nutr Date: 2017-07-26 Impact factor: 4.016
Authors: Marco Giurgiu; Simon Kolb; Carina Nigg; Alexander Burchartz; Irina Timm; Marlissa Becker; Ellen Rulf; Ann-Kathrin Doster; Elena Koch; Johannes B J Bussmann; Claudio Nigg; Ulrich W Ebner-Priemer; Alexander Woll Journal: BMJ Open Sport Exerc Med Date: 2022-05-12
Authors: Annelinde Lettink; Teatske M Altenburg; Jelle Arts; Vincent T van Hees; Mai J M Chinapaw Journal: Int J Behav Nutr Phys Act Date: 2022-09-08 Impact factor: 8.915