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Comparison of two different physical activity monitors

BACKGROUND: Understanding the relationships between physical activity (PA) and disease has become a major area of research interest. Activity monitors, devices that quantify free-living PA for prolonged periods of time (days or weeks), are increasingly being used to estimate PA. A range of different...

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Autores principales: Paul, David R, Kramer, Matthew, Moshfegh, Alanna J, Baer, David J, Rumpler, William V
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1925107/
https://www.ncbi.nlm.nih.gov/pubmed/17592631
http://dx.doi.org/10.1186/1471-2288-7-26
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author Paul, David R
Kramer, Matthew
Moshfegh, Alanna J
Baer, David J
Rumpler, William V
author_facet Paul, David R
Kramer, Matthew
Moshfegh, Alanna J
Baer, David J
Rumpler, William V
author_sort Paul, David R
collection PubMed
description BACKGROUND: Understanding the relationships between physical activity (PA) and disease has become a major area of research interest. Activity monitors, devices that quantify free-living PA for prolonged periods of time (days or weeks), are increasingly being used to estimate PA. A range of different activity monitors brands are available for investigators to use, but little is known about how they respond to different levels of PA in the field, nor if data conversion between brands is possible. METHODS: 56 women and men were fitted with two different activity monitors, the Actigraph™ (Actigraph LLC; AGR) and the Actical™ (Mini-Mitter Co.; MM) for 15 days. Both activity monitors were fixed to an elasticized belt worn over the hip, with the anterior and posterior position of the activity monitors randomized. Differences between activity monitors and the validity of brand inter-conversion were measured by t-tests, Pearson correlations, Bland-Altman plots, and coefficients of variation (CV). RESULTS: The AGR detected a significantly greater amount of daily PA (216.2 ± 106.2 vs. 188.0 ± 101.1 counts/min, P < 0.0001). The average difference between activity monitors expressed as a CV were 3.1 and 15.5% for log-transformed and raw data, respectively. When a conversion equation was applied to convert datasets from one brand to another, the differences were no longer significant, with CV's of 2.2 and 11.7%, log-transformed and raw data, respectively. CONCLUSION: Although activity monitors predict PA on the same scale (counts/min), the results between these two brands are not directly comparable. However, the data are comparable if a conversion equation is applied, with better results for log-transformed data.
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spelling pubmed-19251072007-07-20 Comparison of two different physical activity monitors Paul, David R Kramer, Matthew Moshfegh, Alanna J Baer, David J Rumpler, William V BMC Med Res Methodol Research Article BACKGROUND: Understanding the relationships between physical activity (PA) and disease has become a major area of research interest. Activity monitors, devices that quantify free-living PA for prolonged periods of time (days or weeks), are increasingly being used to estimate PA. A range of different activity monitors brands are available for investigators to use, but little is known about how they respond to different levels of PA in the field, nor if data conversion between brands is possible. METHODS: 56 women and men were fitted with two different activity monitors, the Actigraph™ (Actigraph LLC; AGR) and the Actical™ (Mini-Mitter Co.; MM) for 15 days. Both activity monitors were fixed to an elasticized belt worn over the hip, with the anterior and posterior position of the activity monitors randomized. Differences between activity monitors and the validity of brand inter-conversion were measured by t-tests, Pearson correlations, Bland-Altman plots, and coefficients of variation (CV). RESULTS: The AGR detected a significantly greater amount of daily PA (216.2 ± 106.2 vs. 188.0 ± 101.1 counts/min, P < 0.0001). The average difference between activity monitors expressed as a CV were 3.1 and 15.5% for log-transformed and raw data, respectively. When a conversion equation was applied to convert datasets from one brand to another, the differences were no longer significant, with CV's of 2.2 and 11.7%, log-transformed and raw data, respectively. CONCLUSION: Although activity monitors predict PA on the same scale (counts/min), the results between these two brands are not directly comparable. However, the data are comparable if a conversion equation is applied, with better results for log-transformed data. BioMed Central 2007-06-25 /pmc/articles/PMC1925107/ /pubmed/17592631 http://dx.doi.org/10.1186/1471-2288-7-26 Text en Copyright © 2007 Paul et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Paul, David R
Kramer, Matthew
Moshfegh, Alanna J
Baer, David J
Rumpler, William V
Comparison of two different physical activity monitors
title Comparison of two different physical activity monitors
title_full Comparison of two different physical activity monitors
title_fullStr Comparison of two different physical activity monitors
title_full_unstemmed Comparison of two different physical activity monitors
title_short Comparison of two different physical activity monitors
title_sort comparison of two different physical activity monitors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1925107/
https://www.ncbi.nlm.nih.gov/pubmed/17592631
http://dx.doi.org/10.1186/1471-2288-7-26
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