Jung-Min Lee1, Youngwon Kim2, Yang Bai2, Glenn A Gaesser3, Gregory J Welk2. 1. School of Health, Physical Education and Recreation, University of Nebraska-Omaha, USA. Electronic address: jungminlee@unomaha.edu. 2. Department of Kinesiology, College of Human Sciences, Iowa State University, USA. 3. School of Nutrition & Health Promotion, Arizona State University, USA.
Abstract
OBJECTIVES: The purpose of the study is to evaluate the validity of different SenseWear software (algorithms v5.2 vs. algorithm v2.2) for estimating energy expenditure (EE) in children. DESIGN: Original research. METHODS: Forty-five children aged 7-13 years performed 12 randomly assigned activities (out of a set of 24) while wearing a SWA with simultaneous monitoring via portable calorimetry (IC). Each activity lasted 5min, with a 1min break between activities. The estimated EE values from the SWA were compared to the measured EE values from the IC using 3-way (Method×Algorithm×Activity) mixed model ANOVA. RESULTS: The analyses revealed a significant method (IC vs. SWA)×Algorithm (v5.2 vs. v2.2) interaction, with significantly smaller errors (IC-SWA) for the newer v5.2 algorithms (0.25±0.09kcalmin(-1)) than the older v2.2 algorithms (1.04±0.09kcalmin(-1)). The mean absolute percent error (MAPE) was 17.0±12.1% for SWA5.2 algorithm and 31.4±11.1% for SWA2.2 algorithm. The v5.2 algorithms yielded non-significant (p>0.5) differences in EE estimates for most of the walking related activities as well as for stationary cycling at moderate intensity (MAPE=14.5%). CONCLUSIONS: The smaller errors in estimated EE with the SenseWear v5.2 algorithms (compared to v2.2) demonstrate continued incremental improvements in estimates of EE for monitoring free-living activities in children.
OBJECTIVES: The purpose of the study is to evaluate the validity of different SenseWear software (algorithms v5.2 vs. algorithm v2.2) for estimating energy expenditure (EE) in children. DESIGN: Original research. METHODS: Forty-five children aged 7-13 years performed 12 randomly assigned activities (out of a set of 24) while wearing a SWA with simultaneous monitoring via portable calorimetry (IC). Each activity lasted 5min, with a 1min break between activities. The estimated EE values from the SWA were compared to the measured EE values from the IC using 3-way (Method×Algorithm×Activity) mixed model ANOVA. RESULTS: The analyses revealed a significant method (IC vs. SWA)×Algorithm (v5.2 vs. v2.2) interaction, with significantly smaller errors (IC-SWA) for the newer v5.2 algorithms (0.25±0.09kcalmin(-1)) than the older v2.2 algorithms (1.04±0.09kcalmin(-1)). The mean absolute percent error (MAPE) was 17.0±12.1% for SWA5.2 algorithm and 31.4±11.1% for SWA2.2 algorithm. The v5.2 algorithms yielded non-significant (p>0.5) differences in EE estimates for most of the walking related activities as well as for stationary cycling at moderate intensity (MAPE=14.5%). CONCLUSIONS: The smaller errors in estimated EE with the SenseWear v5.2 algorithms (compared to v2.2) demonstrate continued incremental improvements in estimates of EE for monitoring free-living activities in children.
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