alexa Validity of a Multi-Sensor Armband for Estimating Energy Expenditure during Eighteen Different Activities | OMICS International | Abstract
ISSN: 2165-7904

Journal of Obesity & Weight Loss Therapy
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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: [email protected]
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.

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