Validation of the SenseWear Pro Armband Algorithms in Children
Validation of the SenseWear Pro Armband Algorithms in Children
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.
Physical activity is a complex behavior that is quite difficult to assess in field-based research. Accelerometry-based activity monitors have become the most widely used strategy for assessing physical activity under free-living conditions, but despite considerable work, many challenges remain. Researchers interested in youth's physical activity have additional challenges to overcome including the complexities associated with the more sporadic and intermittent physical activity patterns in children and the inherent variability due to growth and maturation.
The recent developments of pattern-recognition activity monitors offer considerable promise for improving the accuracy of physical activity assessment techniques. The SenseWear Pro Armband® (SWA; BodyMedia, Pittsburgh, PA) monitor, for example, integrates motion sensor data with a variety of heat-related sensors to estimate the energy cost of free-living activity. The SWA is similar in cost to most accelerometers (~$400) and can be worn comfortably on the upper arm for extended periods. An advantage of this multichannel approach is that the heat-related sensors provide additional information that cannot be obtained solely from movement sensors. The heat-related sensors, for example, provide a way to assess the energy cost of complex, nonambulatory activities. The sensors can also detect the increased work required to walk up a grade or to carry a load. A final advantage of the SWA is that the SWA automatically reports actual wear time (thereby avoiding the considerable challenge in determining whether a monitor was, in fact, worn as directed). These features provide several advantages over traditional uniaxial accelerometers for assessing physical activity in the field. The validity of energy expenditure (EE) estimates from the SWA has been supported in studies using both indirect calorimetry (IC) and doubly labeled water. Furthermore, recent research has demonstrated potential advantages in accuracy when compared with traditional accelerometry-based monitors.
Although the validity studies in adults are fairly consistent, results of validation studies in youth have been more equivocal. Arvidsson et al. reported that the SWA significantly underestimated EE for a variety of standardized physical activities in a sample of 20 children. In contrast, Dorminy et al. reported consistent overestimation of EE with the SWA in a sample of 21 youth. The nature of the discrepancies in these results is not clear but is likely due to the use of algorithms that were not developed specifically for children (personal communication, November 2008). A unique characteristic of the SWA armband is that the company continually upgrades the algorithms as new data become integrated into the pattern recognition system. The purpose of this study was to assess the validity of new proprietary algorithms that were developed specifically from children's data. Comparisons are made between estimates from the old algorithms (versions 4.2 or earlier) and recently released algorithms (versions 6.1) to clarify the nature of the errors reported in previous studies.
Abstract and Introduction
Abstract
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
Physical activity is a complex behavior that is quite difficult to assess in field-based research. Accelerometry-based activity monitors have become the most widely used strategy for assessing physical activity under free-living conditions, but despite considerable work, many challenges remain. Researchers interested in youth's physical activity have additional challenges to overcome including the complexities associated with the more sporadic and intermittent physical activity patterns in children and the inherent variability due to growth and maturation.
The recent developments of pattern-recognition activity monitors offer considerable promise for improving the accuracy of physical activity assessment techniques. The SenseWear Pro Armband® (SWA; BodyMedia, Pittsburgh, PA) monitor, for example, integrates motion sensor data with a variety of heat-related sensors to estimate the energy cost of free-living activity. The SWA is similar in cost to most accelerometers (~$400) and can be worn comfortably on the upper arm for extended periods. An advantage of this multichannel approach is that the heat-related sensors provide additional information that cannot be obtained solely from movement sensors. The heat-related sensors, for example, provide a way to assess the energy cost of complex, nonambulatory activities. The sensors can also detect the increased work required to walk up a grade or to carry a load. A final advantage of the SWA is that the SWA automatically reports actual wear time (thereby avoiding the considerable challenge in determining whether a monitor was, in fact, worn as directed). These features provide several advantages over traditional uniaxial accelerometers for assessing physical activity in the field. The validity of energy expenditure (EE) estimates from the SWA has been supported in studies using both indirect calorimetry (IC) and doubly labeled water. Furthermore, recent research has demonstrated potential advantages in accuracy when compared with traditional accelerometry-based monitors.
Although the validity studies in adults are fairly consistent, results of validation studies in youth have been more equivocal. Arvidsson et al. reported that the SWA significantly underestimated EE for a variety of standardized physical activities in a sample of 20 children. In contrast, Dorminy et al. reported consistent overestimation of EE with the SWA in a sample of 21 youth. The nature of the discrepancies in these results is not clear but is likely due to the use of algorithms that were not developed specifically for children (personal communication, November 2008). A unique characteristic of the SWA armband is that the company continually upgrades the algorithms as new data become integrated into the pattern recognition system. The purpose of this study was to assess the validity of new proprietary algorithms that were developed specifically from children's data. Comparisons are made between estimates from the old algorithms (versions 4.2 or earlier) and recently released algorithms (versions 6.1) to clarify the nature of the errors reported in previous studies.