ArticlePloS one2013
Assessing the online social environment for surveillance of obesity prevalence.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Social media data shed light on air-conditioning interest of heat-vulnerable regions and sociodemographic groups.One earth (Cambridge, Mass.) · 2023Article
- Reaching Populations at Risk for HIV Through Targeted Facebook Advertisements: Cost-Consequence Analysis.JMIR formative research · 2023Article
- Simulating Facebook Advertisements to Establish Cost per New HIV Diagnosis Using Routine and Targeted Models in a Local Population.Healthcare (Basel, Switzerland) · 2022Article
- Mapping Inequalities in the Physical, Built and Social Environment in Population-Based Studies of Brain Health.Frontiers in neuroimaging · 2022Review
- [Medical applications of social networks. Specific aspects of the COVID-19 pandemic].Medicine · 2020Article
- Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis.Journal of medical Internet research · 2020Article
- Role of the Built and Online Social Environments on Expression of Dining on Instagram.International journal of environmental research and public health · 2020Article
- Census Tract Food Tweets and Chronic Disease Outcomes in the U.S., 2015⁻2018.International journal of environmental research and public health · 2019Article
- Use of Social Media, Search Queries, and Demographic Data to Assess Obesity Prevalence in the United States.Palgrave communications · 2019Article
- Social media captures demographic and regional physical activity.BMJ open sport & exercise medicine · 2019Article
- How has big data contributed to obesity research? A review of the literature.International journal of obesity (2005) · 2018Review
- The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations.Child abuse & neglect · 2018Article
- Socio-spatial Self-organizing Maps: Using Social Media to Assess Relevant Geographies for Exposure to Social Processes.Proceedings of the ACM on human-computer interaction · 2018Article
- Online Health Monitoring using Facebook Advertisement Audience Estimates in the United States: Evaluation Study.JMIR public health and surveillance · 2018Article
- Twitter-derived neighborhood characteristics associated with obesity and diabetes.Scientific reports · 2017Article
- Adolescent and Young Adult Use of Social Media for Health and Its Implications.The Journal of adolescent health : official publication of the Society for Adolescent Medicine · 2017Article
- Characterizing Awareness of Schizophrenia Among Facebook Users by Leveraging Facebook Advertisement Estimates.Journal of medical Internet research · 2017Article
- Denominator Issues for Personally Generated Data in Population Health Monitoring.American journal of preventive medicine · 2017Article
- The Lexicocalorimeter: Gauging public health through caloric input and output on social media.PloS one · 2017Article
- Pokémon Go and Exposure to Mosquito-Borne Diseases: How Not to Catch 'Em All.PLoS currents · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundUnderstanding the social environmental around obesity has been limited by available data. One promising approach used to bridge similar gaps elsewhere is to use passively generated digital data. PURPOSE: This article explores the relationship between online social environment via web-based social networks and population obesity prevalence.
methodsWe performed a cross-sectional study using linear regression and cross validation to measure the relationship and predictive performance of user interests on the online social network Facebook to obesity prevalence in metros across the United States of America (USA) and neighborhoods within New York City (NYC). The outcomes, proportion of obese and/or overweight population in USA metros and NYC neighborhoods, were obtained via the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance and NYC EpiQuery systems. Predictors were geographically specific proportion of users with activity-related and sedentary-related interests on Facebook.
resultsHigher proportion of the population with activity-related interests on Facebook was associated with a significant 12.0% (95% Confidence Interval (CI) 11.9 to 12.1) lower predicted prevalence of obese and/or overweight people across USA metros and 7.2% (95% CI: 6.8 to 7.7) across NYC neighborhoods. Conversely, greater proportion of the population with interest in television was associated with higher prevalence of obese and/or overweight people of 3.9% (95% CI: 3.7 to 4.0) (USA) and 27.5% (95% CI: 27.1 to 27.9, significant) (NYC). For activity-interests and national obesity outcomes, the average root mean square prediction error from 10-fold cross validation was comparable to the average root mean square error of a model developed using the entire data set.
conclusionsActivity-related interests across the USA and sedentary-related interests across NYC were significantly associated with obesity prevalence. Further research is needed to understand how the online social environment relates to health outcomes and how it can be used to identify or target interventions.
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