Main Article Content
Background: Over the past decade, the use of Web-based data in public health issues has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information and has been applied to several topics with the most focused subject related to health and medicine. However, the most cited articles and the popular medical subject headings(MESH terms) on health behaviors in Google Trends research remain unknown. The web-based behavior requires to monitor and analyze on-line data for examining actual human behavior to predict and even prevent health-related issues that constantly arise in daily life.
Objective: This systematic review aimed at reporting and further presenting the most cited articles and the popular MESH terms on health behaviors in Google Trends (infodemiology) researches in health-related topics since 2009 to provide an overview of the topic burst for future research on the subject of health behavior.
Methods: Following the Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends[Title]” in PubMed databases since 2009, applying specific criteria for types of journal articles. A total of 86 published papers were extracted, excluding those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to MESH terms using social network analysis(SNA)and selected the most cited articles that related to the health behavior in Google Trends.
Results: The most cited articles are those from the US in 2009(PMID= 19845471 cited 88 times) and the UK in 2013(PMID= 23619126 cited 74 times). The MESH term represented by Internet earns the highest impact factor(IF) and presents significantly different among term clusters(F(3,20)=15.79, p<0.001). The most number of citing journals is from PloS One. The most number of author affiliations is from the US.
Conclusions: The monitoring of online queries can provide insight into human behavior, as the phenomenon is significantly and continuously growing at present and in the future for assessing behavioral changes in health topics.
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- Al Nuaimi E, Al Neyadi H, Mohamed N, et al. Applications of big data to smart cities. Journal of Internet Services and Applications, 2015, 6(25): 1-15. https://doi.org/10.1186/s13174-015-0041-5
- Hilbert M and Lpez P. The world’s technological capacity to store, communicate, and compute information. Science, 2011, 332(6025): 60-65. https://doi.org/10.1126/science.1200970
- Philip Chen CL and Zhang CY. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 2014, 275(10): 314-347. https://doi.org/10.1016/j.ins.2014.01.015
- Jin XL, Wah BW, Cheng X, et al. Significance and Challenges of Big Data Research. Big Data Research, 2015, 2(2): 59-64. https://doi.org/10.1016/j.bdr.2015.01.006
- Fosso Wamba S, Akter S, Edwards A, et al. How big data can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 2015, 165: 234-246. https://doi.org/10.1016/j.ijpe.2014.12.031
- Chang RM, Kauffman RJ and Kwon YO. Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 2014, 63: 67- 80. https://doi.org/10.1016/j.dss.2013.08.008
- Gandomi A and Haider M. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 2015, 35(2): 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
- Hsu CF, Chien TW, Chow JC, et al. The most highlycited authors who published papers on the topic of health behavior: A Bibliometric Analysis. Adv Health Behavior, 2018,1(1),24