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Development of methods to objectively identify time spent using active and motorised modes of travel to work: how do self-reported measures compare?

BACKGROUND: Active commuting may make an important contribution to population health. Accurate measures of these behaviours are required, but it is unknown how self-reported estimates compare to those derived from objective measures. We sought to develop methods for objectively deriving time spent i...

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Autores principales: Panter, Jenna, Costa, Silvia, Dalton, Alice, Jones, Andy, Ogilvie, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177527/
https://www.ncbi.nlm.nih.gov/pubmed/25231500
http://dx.doi.org/10.1186/s12966-014-0116-x
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author Panter, Jenna
Costa, Silvia
Dalton, Alice
Jones, Andy
Ogilvie, David
author_facet Panter, Jenna
Costa, Silvia
Dalton, Alice
Jones, Andy
Ogilvie, David
author_sort Panter, Jenna
collection PubMed
description BACKGROUND: Active commuting may make an important contribution to population health. Accurate measures of these behaviours are required, but it is unknown how self-reported estimates compare to those derived from objective measures. We sought to develop methods for objectively deriving time spent in specific travel behaviours from a combination of locational and activity data, and to assess the convergent validity of two self-reported estimates. METHODS: In 2010 and 2011, a sub-sample of participants from the Commuting and Health in Cambridge study concurrently completed objective monitoring using combined heart rate and movement sensors and global positioning system devices and reported their past-week commuting in a questionnaire (modes used, and usual time spent walking and cycling per trip) and in a day-by-day diary (all modes and durations). Automated and manual approaches were used to objectively identify total time spent using active and motorised modes. Agreement between self-reported and objectively-derived times was assessed using Lin’s concordance coefficients, Bland-Altman plots and signed-rank tests. RESULTS: Compared to objective assessments, day-by-day diary estimates of time spent using active modes on the commute were overestimated by a mean of 1.1 minutes/trip (95% limits of agreement (LOA): −7.7 to 9.9, p < 0.001). The magnitude of overestimation was slightly larger, but not significant (p = 0.247), when walking or cycling was used alone (mean: 2.4 minutes/trip, 95% LOA: −6.8 to 11.5). Total time spent on the commute was overestimated by a mean of 1.9 minutes/trip (95% LOA: −15.3 to 19.0, p < 0.001). The mean differences between self-reported usual time and objective estimates were −1.1 minutes/trip (95% LOA: −8.7 to 6.4) for cycling and +2.4 minutes/trip (95% LOA: −10.9 to 15.7) for walking. Mean differences between usual and daily estimates of time were <1 minute/trip for both walking and cycling. CONCLUSIONS: We developed a novel method of combining objective data to identify time spent using active and motorised modes, and total time spent commuting. Compared to objectively-derived times, self-reported times spent active commuting were slightly overestimated with wide LOA, suggesting that they should be used with caution to infer aggregate weekly quantities of activity on the commute at the individual level. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12966-014-0116-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-41775272014-09-29 Development of methods to objectively identify time spent using active and motorised modes of travel to work: how do self-reported measures compare? Panter, Jenna Costa, Silvia Dalton, Alice Jones, Andy Ogilvie, David Int J Behav Nutr Phys Act Research BACKGROUND: Active commuting may make an important contribution to population health. Accurate measures of these behaviours are required, but it is unknown how self-reported estimates compare to those derived from objective measures. We sought to develop methods for objectively deriving time spent in specific travel behaviours from a combination of locational and activity data, and to assess the convergent validity of two self-reported estimates. METHODS: In 2010 and 2011, a sub-sample of participants from the Commuting and Health in Cambridge study concurrently completed objective monitoring using combined heart rate and movement sensors and global positioning system devices and reported their past-week commuting in a questionnaire (modes used, and usual time spent walking and cycling per trip) and in a day-by-day diary (all modes and durations). Automated and manual approaches were used to objectively identify total time spent using active and motorised modes. Agreement between self-reported and objectively-derived times was assessed using Lin’s concordance coefficients, Bland-Altman plots and signed-rank tests. RESULTS: Compared to objective assessments, day-by-day diary estimates of time spent using active modes on the commute were overestimated by a mean of 1.1 minutes/trip (95% limits of agreement (LOA): −7.7 to 9.9, p < 0.001). The magnitude of overestimation was slightly larger, but not significant (p = 0.247), when walking or cycling was used alone (mean: 2.4 minutes/trip, 95% LOA: −6.8 to 11.5). Total time spent on the commute was overestimated by a mean of 1.9 minutes/trip (95% LOA: −15.3 to 19.0, p < 0.001). The mean differences between self-reported usual time and objective estimates were −1.1 minutes/trip (95% LOA: −8.7 to 6.4) for cycling and +2.4 minutes/trip (95% LOA: −10.9 to 15.7) for walking. Mean differences between usual and daily estimates of time were <1 minute/trip for both walking and cycling. CONCLUSIONS: We developed a novel method of combining objective data to identify time spent using active and motorised modes, and total time spent commuting. Compared to objectively-derived times, self-reported times spent active commuting were slightly overestimated with wide LOA, suggesting that they should be used with caution to infer aggregate weekly quantities of activity on the commute at the individual level. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12966-014-0116-x) contains supplementary material, which is available to authorized users. BioMed Central 2014-09-19 /pmc/articles/PMC4177527/ /pubmed/25231500 http://dx.doi.org/10.1186/s12966-014-0116-x Text en © Panter et al.; licensee BioMed Central Ltd. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Panter, Jenna
Costa, Silvia
Dalton, Alice
Jones, Andy
Ogilvie, David
Development of methods to objectively identify time spent using active and motorised modes of travel to work: how do self-reported measures compare?
title Development of methods to objectively identify time spent using active and motorised modes of travel to work: how do self-reported measures compare?
title_full Development of methods to objectively identify time spent using active and motorised modes of travel to work: how do self-reported measures compare?
title_fullStr Development of methods to objectively identify time spent using active and motorised modes of travel to work: how do self-reported measures compare?
title_full_unstemmed Development of methods to objectively identify time spent using active and motorised modes of travel to work: how do self-reported measures compare?
title_short Development of methods to objectively identify time spent using active and motorised modes of travel to work: how do self-reported measures compare?
title_sort development of methods to objectively identify time spent using active and motorised modes of travel to work: how do self-reported measures compare?
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177527/
https://www.ncbi.nlm.nih.gov/pubmed/25231500
http://dx.doi.org/10.1186/s12966-014-0116-x
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