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Validity of a Multi-Sensor Armband for Estimating Energy Expenditure during Eighteen Different Activities

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...

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Autores principales: Dudley, Paige, Bassett, David R, John, Dinesh, Crouter, Scott E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7288981/
https://www.ncbi.nlm.nih.gov/pubmed/32528742
http://dx.doi.org/10.4172/2165-7904.1000146
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author Dudley, Paige
Bassett, David R
John, Dinesh
Crouter, Scott E
author_facet Dudley, Paige
Bassett, David R
John, Dinesh
Crouter, Scott E
author_sort Dudley, Paige
collection PubMed
description 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 K4b(2) 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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spelling pubmed-72889812020-06-11 Validity of a Multi-Sensor Armband for Estimating Energy Expenditure during Eighteen Different Activities Dudley, Paige Bassett, David R John, Dinesh Crouter, Scott E J Obes Weight Loss Ther Article 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 K4b(2) 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. 2012-08-29 2012 /pmc/articles/PMC7288981/ /pubmed/32528742 http://dx.doi.org/10.4172/2165-7904.1000146 Text en http://creativecommons.org/licenses/by-nc/3.0/ 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.
spellingShingle Article
Dudley, Paige
Bassett, David R
John, Dinesh
Crouter, Scott E
Validity of a Multi-Sensor Armband for Estimating Energy Expenditure during Eighteen Different Activities
title Validity of a Multi-Sensor Armband for Estimating Energy Expenditure during Eighteen Different Activities
title_full Validity of a Multi-Sensor Armband for Estimating Energy Expenditure during Eighteen Different Activities
title_fullStr Validity of a Multi-Sensor Armband for Estimating Energy Expenditure during Eighteen Different Activities
title_full_unstemmed Validity of a Multi-Sensor Armband for Estimating Energy Expenditure during Eighteen Different Activities
title_short Validity of a Multi-Sensor Armband for Estimating Energy Expenditure during Eighteen Different Activities
title_sort validity of a multi-sensor armband for estimating energy expenditure during eighteen different activities
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7288981/
https://www.ncbi.nlm.nih.gov/pubmed/32528742
http://dx.doi.org/10.4172/2165-7904.1000146
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