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Hindi Research Article
Validity of a Multi-Sensor Armband for Estimating Energy Expenditure during Eighteen Different Activities
| Paige Dudley1, David R Bassett1*, Dinesh John2 and Scott E Crouter3 | |
| 1University of Tennessee, Knoxville, USA | |
| 2Northeastern University, Boston, USA | |
| 3University of Massachusetts, Boston, USA | |
| Corresponding Author : | David R Bassett Department of Kinesiology, Recreation, and Sport Studies The University of Tennessee 1914 Andy Holt Ave, Knoxville TN 37996-2700, USA Tel: 865-974-8766 Fax: 865-974-8981 E-mail: djohn1@kin.umass.edu |
| Received July 13, 2012; Accepted August 27, 2012; Published August 29, 2012 | |
| Citation: Dudley P, Bassett DR, John D, Crouter SE (2012) Validity of a Multi- Sensor Armband for Estimating Energy Expenditure during Eighteen Different Activities. J Obes Wt Loss Ther 2:146. doi:10.4172/2165-7904.1000146 | |
| Copyright: © 2012 Dudley P, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |
Abstract
Purpose: To examine the validity of an armband physical activity monitor in estimating energy expenditure (EE) over a wide range of physical activities. Methods: 68 participants (mean age=39.5 ± 13.0 yrs) performed one of three routines consisting of six activities (approximately 10 min each) while wearing the armband and the Cosmed K4b2 portable metabolic unit. Routine 1 (n=25) involved indoor home-based activities, routine 2 (n=22) involved miscellaneous activities, and routine 3 (n=21) involved outdoor aerobic activities. Results: Mean differences between the EE values in METs (criterion minus estimated) are as follows. Routine 1: watching TV (-0.1), reading (-0.1), laundry (0.1), ironing (-1.3), light cleaning (-0.4), and aerobics (0.4). Routine 2: driving (-0.6), Frisbee golf (-0.9), grass trimming (-0.5), gardening (-1.5), moving dirt with a wheelbarrow (-0.1), loading and unloading boxes (0.1); Routine 3: sidewalk walking (-1.0), track walking (-0.8), walking with a bag (-0.6), tennis (1.6), track running (2.2), and road running (2.1). The armband significantly overestimated EE during several light-to-moderate intensity activities such as driving (by 74%), ironing (by 70%), gardening (by 55%), light cleaning (by 15%), Frisbee golf (by 24%), and sidewalk walking (by 26%) (P<0.05). The arm band significantly underestimated high intensity activities including tennis (by 20%), and track or road running (by 20%). Conclusion: Although the armband provided mean EE estimates within 16% of the criterion for nine of the 18 activities, predictions for several activities were significantly different from the criterion. The armband prediction algorithms could be refined to increase the accuracy of EE estimations.
