INTRODUCTION: The SenseWear Pro Armband (SWA) has been shown to be a valid and practical tool to assess energy expenditure (EE) in adults. However, recent studies have reported significant errors in EE estimates when the algorithms are applied to children. The purpose of this study was to assess the validity of recently developed algorithms developed to take into account children's unique movement patterns. METHODS: Twenty-one healthy children (14 boys and 7 girls), averaging 9.4 (1.3) yr of age, participated in a range of activities while being monitored with the SWA and a metabolic analyzer. The activity protocol lasted 41 min and included resting, coloring, playing computer games, walking on a treadmill (2, 2.5, and 3 mph), and stationary bicycling. RESULTS: The original algorithms overestimated EE by 32%, but average error with the newly developed algorithm was only 1.7%. There were no significant differences in overall estimates of EE across the 41-min trial (P > 0.05), but there was some variability in agreement for specific activities (average absolute difference in EE estimates was 13%). The average errors in EE estimates with the new algorithms were -20.7%, -4.0%, -4.9%, -0.9%, 0.6%, 3.5%, and -25.1% for resting, coloring, computer games, walking on a treadmill (2, 2.5, and 3 mph), and biking, respectively. Biking was the only activity with significant differences in EE estimations (P < 0.001). Average minute-by-minute correlations across individuals was r = 0.71 +/- 1.3 indicating that the relationships were consistent across individuals. CONCLUSIONS: The newly developed algorithms demonstrate improved accuracy for assessing EE for typical activities in children-including accurate estimation of light activities.
INTRODUCTION: The SenseWear Pro Armband (SWA) has been shown to be a valid and practical tool to assess energy expenditure (EE) in adults. However, recent studies have reported significant errors in EE estimates when the algorithms are applied to children. The purpose of this study was to assess the validity of recently developed algorithms developed to take into account children's unique movement patterns. METHODS: Twenty-one healthy children (14 boys and 7 girls), averaging 9.4 (1.3) yr of age, participated in a range of activities while being monitored with the SWA and a metabolic analyzer. The activity protocol lasted 41 min and included resting, coloring, playing computer games, walking on a treadmill (2, 2.5, and 3 mph), and stationary bicycling. RESULTS: The original algorithms overestimated EE by 32%, but average error with the newly developed algorithm was only 1.7%. There were no significant differences in overall estimates of EE across the 41-min trial (P > 0.05), but there was some variability in agreement for specific activities (average absolute difference in EE estimates was 13%). The average errors in EE estimates with the new algorithms were -20.7%, -4.0%, -4.9%, -0.9%, 0.6%, 3.5%, and -25.1% for resting, coloring, computer games, walking on a treadmill (2, 2.5, and 3 mph), and biking, respectively. Biking was the only activity with significant differences in EE estimations (P < 0.001). Average minute-by-minute correlations across individuals was r = 0.71 +/- 1.3 indicating that the relationships were consistent across individuals. CONCLUSIONS: The newly developed algorithms demonstrate improved accuracy for assessing EE for typical activities in children-including accurate estimation of light activities.
Authors: Heidi B Iglayreger; Mark D Peterson; Dongmei Liu; Christine A Parker; Susan J Woolford; Bethany J Sallinen Gafka; Fauziya Hassan; Paul M Gordon Journal: J Pediatr Date: 2014-03-06 Impact factor: 4.406
Authors: Elissa Jelalian; E Whitney Evans; Diana Rancourt; Lisa Ranzenhofer; Neta Taylor; Chantelle Hart; Ronald Seifer; Kelly Klinepier; Gary D Foster Journal: Child Obes Date: 2019-12-18 Impact factor: 2.992
Authors: Louisa G Sylvia; Emily E Bernstein; Jane L Hubbard; Leigh Keating; Ellen J Anderson Journal: J Acad Nutr Diet Date: 2013-11-28 Impact factor: 4.910