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Use of social media, search queries, and demographic data to assess obesity prevalence in the United States
- Source :
- Palgrave Communications, Vol 5, Iss 1, Pp 1-9 (2019), Palgrave Commun
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- Obesity is a global epidemic affecting millions. Implementation of interventions to curb obesity rates requires timely surveillance. In this study, we estimated sex-specific obesity prevalence using social media, search queries, demographics and built environment variables. We collected 3,817,125 and 1,382,284 geolocated tweets on food and exercise respectively, from Twitter’s streaming API from April 2015 to March 2016. We also obtained searches related to physical activity and diet from Google Search Trends for the same time period. Next, we inferred the gender of Twitter users using machine learning methods and applied mixed-effects state-level linear regression models to estimate obesity prevalence. We observed differences in discussions of physical activity and foods, with males reporting higher intensity physical activities and lower caloric foods across 40 and 48 states, respectively. In addition, counties with the highest percentage of exercise and food tweets had lower male and female obesity prevalence. Lastly, our models separately captured overall male and female spatial trends in obesity prevalence. The average correlation between actual and estimated obesity prevalence was 0.797(95% CI, 0.796, 0.798) and 0.830 (95% CI, 0.830, 0.831) for males and females, respectively. Social media can provide timely community-level data on health information seeking and changes in behaviors, sentiments and norms. Social media data can also be combined with other data types such as, demographics, built environment variables, diet and physical activity indicators from other digital sources (e.g., mobile applications and wearables) to monitor health behaviors at different geographic scales, and to supplement delayed estimates from traditional surveillance systems.
- Subjects :
- Disease surveillance
020205 medical informatics
General Arts and Humanities
Physical activity
Psychological intervention
General Social Sciences
02 engineering and technology
medicine.disease
Obesity
Article
lcsh:Social Sciences
lcsh:H
Correlation
03 medical and health sciences
0302 clinical medicine
Geography
Environmental health
Linear regression
0202 electrical engineering, electronic engineering, information engineering
medicine
Social media
030212 general & internal medicine
General Economics, Econometrics and Finance
General Psychology
Built environment
Subjects
Details
- ISSN :
- 20551045
- Volume :
- 5
- Database :
- OpenAIRE
- Journal :
- Palgrave Communications
- Accession number :
- edsair.doi.dedup.....36782c58f5386488be1f614d98296caf