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Automated Ecological Assessment of Physical Activity: Advancing Direct Observation

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised...

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Detalles Bibliográficos
Autores principales: Carlson, Jordan A., Liu, Bo, Sallis, James F., Kerr, Jacqueline, Hipp, J. Aaron, Staggs, Vincent S., Papa, Amy, Dean, Kelsey, Vasconcelos, Nuno M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750905/
https://www.ncbi.nlm.nih.gov/pubmed/29194358
http://dx.doi.org/10.3390/ijerph14121487
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author Carlson, Jordan A.
Liu, Bo
Sallis, James F.
Kerr, Jacqueline
Hipp, J. Aaron
Staggs, Vincent S.
Papa, Amy
Dean, Kelsey
Vasconcelos, Nuno M.
author_facet Carlson, Jordan A.
Liu, Bo
Sallis, James F.
Kerr, Jacqueline
Hipp, J. Aaron
Staggs, Vincent S.
Papa, Amy
Dean, Kelsey
Vasconcelos, Nuno M.
author_sort Carlson, Jordan A.
collection PubMed
description Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82–0.98). Total MET-minutes were slightly underestimated by 9.3–17.1% and the ICCs were good (0.68–0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings.
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spelling pubmed-57509052018-01-10 Automated Ecological Assessment of Physical Activity: Advancing Direct Observation Carlson, Jordan A. Liu, Bo Sallis, James F. Kerr, Jacqueline Hipp, J. Aaron Staggs, Vincent S. Papa, Amy Dean, Kelsey Vasconcelos, Nuno M. Int J Environ Res Public Health Article Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82–0.98). Total MET-minutes were slightly underestimated by 9.3–17.1% and the ICCs were good (0.68–0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings. MDPI 2017-12-01 2017-12 /pmc/articles/PMC5750905/ /pubmed/29194358 http://dx.doi.org/10.3390/ijerph14121487 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Carlson, Jordan A.
Liu, Bo
Sallis, James F.
Kerr, Jacqueline
Hipp, J. Aaron
Staggs, Vincent S.
Papa, Amy
Dean, Kelsey
Vasconcelos, Nuno M.
Automated Ecological Assessment of Physical Activity: Advancing Direct Observation
title Automated Ecological Assessment of Physical Activity: Advancing Direct Observation
title_full Automated Ecological Assessment of Physical Activity: Advancing Direct Observation
title_fullStr Automated Ecological Assessment of Physical Activity: Advancing Direct Observation
title_full_unstemmed Automated Ecological Assessment of Physical Activity: Advancing Direct Observation
title_short Automated Ecological Assessment of Physical Activity: Advancing Direct Observation
title_sort automated ecological assessment of physical activity: advancing direct observation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750905/
https://www.ncbi.nlm.nih.gov/pubmed/29194358
http://dx.doi.org/10.3390/ijerph14121487
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