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Social media captures demographic and regional physical activity

OBJECTIVES: We examined the use of data from social media for surveillance of physical activity prevalence in the USA. METHODS: We obtained data from the social media site Twitter from April 2015 to March 2016. The data consisted of 1 382 284 geotagged physical activity tweets from 481 146 users (55...

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Autores principales: Cesare, Nina, Nguyen, Quynh C, Grant, Christan, Nsoesie, Elaine O
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6678033/
https://www.ncbi.nlm.nih.gov/pubmed/31423323
http://dx.doi.org/10.1136/bmjsem-2019-000567
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author Cesare, Nina
Nguyen, Quynh C
Grant, Christan
Nsoesie, Elaine O
author_facet Cesare, Nina
Nguyen, Quynh C
Grant, Christan
Nsoesie, Elaine O
author_sort Cesare, Nina
collection PubMed
description OBJECTIVES: We examined the use of data from social media for surveillance of physical activity prevalence in the USA. METHODS: We obtained data from the social media site Twitter from April 2015 to March 2016. The data consisted of 1 382 284 geotagged physical activity tweets from 481 146 users (55.7% men and 44.3% women) in more than 2900 counties. We applied machine learning and statistical modelling to demonstrate sex and regional variations in preferred exercises, and assessed the association between reports of physical activity on Twitter and population-level inactivity prevalence from the US Centers for Disease Control and Prevention. RESULTS: The association between physical inactivity tweet patterns and physical activity prevalence varied by sex and region. Walking was the most popular physical activity for both men and women across all regions (15.94% (95% CI 15.85% to 16.02%) and 18.74% (95% CI 18.64% to 18.88%) of tweets, respectively). Men and women mentioned performing gym-based activities at approximately the same rates (4.68% (95% CI 4.63% to 4.72%) and 4.13% (95% CI 4.08% to 4.18%) of tweets, respectively). CrossFit was most popular among men (14.91% (95% CI 14.52% to 15.31%)) among gym-based tweets, whereas yoga was most popular among women (26.66% (95% CI 26.03% to 27.19%)). Men mentioned engaging in higher intensity activities than women. Overall, counties with higher physical activity tweets also had lower leisure-time physical inactivity prevalence for both sexes. CONCLUSIONS: The regional-specific and sex-specific activity patterns captured on Twitter may allow public health officials to identify changes in health behaviours at small geographical scales and to design interventions best suited for specific populations.
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spelling pubmed-66780332019-08-16 Social media captures demographic and regional physical activity Cesare, Nina Nguyen, Quynh C Grant, Christan Nsoesie, Elaine O BMJ Open Sport Exerc Med Original Article OBJECTIVES: We examined the use of data from social media for surveillance of physical activity prevalence in the USA. METHODS: We obtained data from the social media site Twitter from April 2015 to March 2016. The data consisted of 1 382 284 geotagged physical activity tweets from 481 146 users (55.7% men and 44.3% women) in more than 2900 counties. We applied machine learning and statistical modelling to demonstrate sex and regional variations in preferred exercises, and assessed the association between reports of physical activity on Twitter and population-level inactivity prevalence from the US Centers for Disease Control and Prevention. RESULTS: The association between physical inactivity tweet patterns and physical activity prevalence varied by sex and region. Walking was the most popular physical activity for both men and women across all regions (15.94% (95% CI 15.85% to 16.02%) and 18.74% (95% CI 18.64% to 18.88%) of tweets, respectively). Men and women mentioned performing gym-based activities at approximately the same rates (4.68% (95% CI 4.63% to 4.72%) and 4.13% (95% CI 4.08% to 4.18%) of tweets, respectively). CrossFit was most popular among men (14.91% (95% CI 14.52% to 15.31%)) among gym-based tweets, whereas yoga was most popular among women (26.66% (95% CI 26.03% to 27.19%)). Men mentioned engaging in higher intensity activities than women. Overall, counties with higher physical activity tweets also had lower leisure-time physical inactivity prevalence for both sexes. CONCLUSIONS: The regional-specific and sex-specific activity patterns captured on Twitter may allow public health officials to identify changes in health behaviours at small geographical scales and to design interventions best suited for specific populations. BMJ Publishing Group 2019-07-14 /pmc/articles/PMC6678033/ /pubmed/31423323 http://dx.doi.org/10.1136/bmjsem-2019-000567 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Original Article
Cesare, Nina
Nguyen, Quynh C
Grant, Christan
Nsoesie, Elaine O
Social media captures demographic and regional physical activity
title Social media captures demographic and regional physical activity
title_full Social media captures demographic and regional physical activity
title_fullStr Social media captures demographic and regional physical activity
title_full_unstemmed Social media captures demographic and regional physical activity
title_short Social media captures demographic and regional physical activity
title_sort social media captures demographic and regional physical activity
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6678033/
https://www.ncbi.nlm.nih.gov/pubmed/31423323
http://dx.doi.org/10.1136/bmjsem-2019-000567
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