Use of a Web-Based Physical Activity Record System to Analyze Behavior in a Large Population: Cross-Sectional Study

BACKGROUND: The use of Web-based physical activity systems has been proposed as an easy method for collecting physical activity data. We have developed a system that has exhibited high accuracy as assessed by the doubly labeled water method. OBJECTIVE: The purpose of this study was to collect behavi...

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Autores principales: Namba, Hideyuki, Yamada, Yosuke, Ishida, Mika, Takase, Hideto, Kimura, Misaka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications Inc. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383835/
https://www.ncbi.nlm.nih.gov/pubmed/25794109
http://dx.doi.org/10.2196/jmir.3923
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author Namba, Hideyuki
Yamada, Yosuke
Ishida, Mika
Takase, Hideto
Kimura, Misaka
author_facet Namba, Hideyuki
Yamada, Yosuke
Ishida, Mika
Takase, Hideto
Kimura, Misaka
author_sort Namba, Hideyuki
collection PubMed
description BACKGROUND: The use of Web-based physical activity systems has been proposed as an easy method for collecting physical activity data. We have developed a system that has exhibited high accuracy as assessed by the doubly labeled water method. OBJECTIVE: The purpose of this study was to collect behavioral data from a large population using our Web-based physical activity record system and assess the physical activity of the population based on these data. In this paper, we address the difference in physical activity for each urban scale. METHODS: In total, 2046 participants (aged 30-59 years; 1105 men and 941 women) participated in the study. They were asked to complete data entry before bedtime using their personal computer on 1 weekday and 1 weekend day. Their residential information was categorized as urban, urban-rural, or rural. Participant responses expressed the intensity of each activity at 15-minute increments and were recorded on a Web server. Residential areas were compared and multiple regression analysis was performed. RESULTS: Most participants had a metabolic equivalent (MET) ranging from 1.4 to 1.8, and the mean MET was 1.60 (SD 0.28). The median value of moderate-to-vigorous physical activity (MVPA, ≥3 MET) was 7.92 MET-hours/day. A 1-way ANCOVA showed that total physical activity differed depending on the type of residential area (F(2,2027)=5.19, P=.006). The urban areas (n=950) had the lowest MET-hours/day (mean 37.8, SD, 6.0), followed by urban-rural areas (n=432; mean 38.6, SD 6.5; P=.04), and rural areas (n=664; mean 38.8, SD 7.4; P=.002). Two-way ANCOVA showed a significant interaction between sex and area of residence on the urban scale (F(2,2036)=4.53, P=.01). Men in urban areas had the lowest MET-hours/day (MVPA, ≥3 MET) at mean 7.9 (SD 8.7); men in rural areas had a MET-hours/day (MVPA, ≥3 MET) of mean 10.8 (SD 12.1, P=.002). No significant difference was noted in women among the 3 residential areas. Multiple regression analysis showed that physical activity consisting of standing while working was the highest contributor to MVPA, regardless of sex. CONCLUSIONS: We were able to compile a detailed comparison of physical activity because our Web-based physical activity record system allowed for the simultaneous evaluation of physical activity from 2046 Japanese people. We found that rural residents had greater total physical activity than urban residents and that working and transportation behaviors differed depending on region type. Multiple regression analysis showed that the behaviors affected MVPA. People are less physically active while working, and sports and active transportation might be effective ways of increasing physical activity levels.
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spelling pubmed-43838352015-04-10 Use of a Web-Based Physical Activity Record System to Analyze Behavior in a Large Population: Cross-Sectional Study Namba, Hideyuki Yamada, Yosuke Ishida, Mika Takase, Hideto Kimura, Misaka J Med Internet Res Original Paper BACKGROUND: The use of Web-based physical activity systems has been proposed as an easy method for collecting physical activity data. We have developed a system that has exhibited high accuracy as assessed by the doubly labeled water method. OBJECTIVE: The purpose of this study was to collect behavioral data from a large population using our Web-based physical activity record system and assess the physical activity of the population based on these data. In this paper, we address the difference in physical activity for each urban scale. METHODS: In total, 2046 participants (aged 30-59 years; 1105 men and 941 women) participated in the study. They were asked to complete data entry before bedtime using their personal computer on 1 weekday and 1 weekend day. Their residential information was categorized as urban, urban-rural, or rural. Participant responses expressed the intensity of each activity at 15-minute increments and were recorded on a Web server. Residential areas were compared and multiple regression analysis was performed. RESULTS: Most participants had a metabolic equivalent (MET) ranging from 1.4 to 1.8, and the mean MET was 1.60 (SD 0.28). The median value of moderate-to-vigorous physical activity (MVPA, ≥3 MET) was 7.92 MET-hours/day. A 1-way ANCOVA showed that total physical activity differed depending on the type of residential area (F(2,2027)=5.19, P=.006). The urban areas (n=950) had the lowest MET-hours/day (mean 37.8, SD, 6.0), followed by urban-rural areas (n=432; mean 38.6, SD 6.5; P=.04), and rural areas (n=664; mean 38.8, SD 7.4; P=.002). Two-way ANCOVA showed a significant interaction between sex and area of residence on the urban scale (F(2,2036)=4.53, P=.01). Men in urban areas had the lowest MET-hours/day (MVPA, ≥3 MET) at mean 7.9 (SD 8.7); men in rural areas had a MET-hours/day (MVPA, ≥3 MET) of mean 10.8 (SD 12.1, P=.002). No significant difference was noted in women among the 3 residential areas. Multiple regression analysis showed that physical activity consisting of standing while working was the highest contributor to MVPA, regardless of sex. CONCLUSIONS: We were able to compile a detailed comparison of physical activity because our Web-based physical activity record system allowed for the simultaneous evaluation of physical activity from 2046 Japanese people. We found that rural residents had greater total physical activity than urban residents and that working and transportation behaviors differed depending on region type. Multiple regression analysis showed that the behaviors affected MVPA. People are less physically active while working, and sports and active transportation might be effective ways of increasing physical activity levels. JMIR Publications Inc. 2015-03-19 /pmc/articles/PMC4383835/ /pubmed/25794109 http://dx.doi.org/10.2196/jmir.3923 Text en ©Hideyuki Namba, Yosuke Yamada, Mika Ishida, Hideto Takase, Misaka Kimura. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 19.03.2015. 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, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Namba, Hideyuki
Yamada, Yosuke
Ishida, Mika
Takase, Hideto
Kimura, Misaka
Use of a Web-Based Physical Activity Record System to Analyze Behavior in a Large Population: Cross-Sectional Study
title Use of a Web-Based Physical Activity Record System to Analyze Behavior in a Large Population: Cross-Sectional Study
title_full Use of a Web-Based Physical Activity Record System to Analyze Behavior in a Large Population: Cross-Sectional Study
title_fullStr Use of a Web-Based Physical Activity Record System to Analyze Behavior in a Large Population: Cross-Sectional Study
title_full_unstemmed Use of a Web-Based Physical Activity Record System to Analyze Behavior in a Large Population: Cross-Sectional Study
title_short Use of a Web-Based Physical Activity Record System to Analyze Behavior in a Large Population: Cross-Sectional Study
title_sort use of a web-based physical activity record system to analyze behavior in a large population: cross-sectional study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383835/
https://www.ncbi.nlm.nih.gov/pubmed/25794109
http://dx.doi.org/10.2196/jmir.3923
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