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Search Query Data to Monitor Interest in Behavior Change: Application for Public Health

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  • Lucas J Carr
  • Shira I Dunsiger

Abstract

Objectives: This study explored patterns of search query activity for the terms ‘weight’, ‘diet’, ‘fitness’, and ‘smoking’ using Google Insights for Search. Methods: Search activity for ‘weight’, ‘diet’, ‘fitness’, and ‘smoking’ conducted within the United States via Google between January 4th, 2004 (first date data was available) and November 28th, 2011 (date of data download and analysis) were analyzed. Using a generalized linear model, we explored the effects of time (month) on mean relative search volume for all four terms. Results: Models suggest a significant effect of month on mean search volume for all four terms. Search activity for all four terms was highest in January with observable declines throughout the remainder of the year. Conclusions: These findings demonstrate discernable temporal patterns of search activity for four areas of behavior change. These findings could be used to inform the timing, location and messaging of interventions, campaigns and policies targeting these behaviors.

Suggested Citation

  • Lucas J Carr & Shira I Dunsiger, 2012. "Search Query Data to Monitor Interest in Behavior Change: Application for Public Health," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-3, October.
    • Handle: RePEc:plo:pone00:0048158
    DOI: 10.1371/journal.pone.0048158
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    References listed on IDEAS

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    1. John W Ayers & Kurt Ribisl & John S Brownstein, 2011. "Using Search Query Surveillance to Monitor Tax Avoidance and Smoking Cessation following the United States' 2009 “SCHIP” Cigarette Tax Increase," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-7, March.
    2. Goodarz Danaei & Eric L Ding & Dariush Mozaffarian & Ben Taylor & Jürgen Rehm & Christopher J L Murray & Majid Ezzati, 2009. "The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors," PLOS Medicine, Public Library of Science, vol. 6(4), pages 1-23, April.
    3. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
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    1. Sean Coogan & Zhixian Sui & David Raubenheimer, 2018. "Gluttony and guilt: monthly trends in internet search query data are comparable with national-level energy intake and dieting behavior," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-9, December.

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