Monitoring obesity prevalence in the United States through bookmarking activities in online food portals.

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  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Food* ; Food Preferences* ; Social Networking*; Internet/*statistics & numerical data ; Obesity/*epidemiology ; Obesity/*psychology; Humans ; Prevalence ; United States/epidemiology
    • Abstract:
      Studying the impact of food consumption on people's health is a serious matter for its implications on public policy, but it has traditionally been a slow process since it requires information gathered through expensive collection processes such as surveys, census and systematic reviews of research articles. We argue that this process could be supported and hastened using data collected via online social networks. In this work we investigate the relationships between the online traces left behind by users of a large US online food community and the prevalence of obesity in 47 states and 311 counties in the US. Using data associated with the recipes bookmarked over an 9-year period by 144,839 users of the Allrecipes.com food website residing throughout the US, several hierarchical regression models are created to (i) shed light on these relations and (ii) establish their magnitude. The results of our analysis provide strong evidence that bookmarking activities on recipes in online food communities can provide a signal allowing food and health related issues, such as obesity to be better understood and monitored. We discover that higher fat and sugar content in bookmarked recipes is associated with higher rates of obesity. The dataset is complicated, but strong temporal and geographical trends are identifiable. We show the importance of accounting for these trends in the modeling process.
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    • Publication Date:
      Date Created: 20170622 Date Completed: 20171006 Latest Revision: 20240327
    • Publication Date:
      20240327
    • Accession Number:
      PMC5479550
    • Accession Number:
      10.1371/journal.pone.0179144
    • Accession Number:
      28636665