The effectiveness of public health advertisements to promote health: a randomized-controlled trial on 794,000 participants

npj Digital Medicine

Volumn 1, Issue 1, 2018, Pages

  Yom Tov, Elad ^a   Shembekar, Jinia ^b   Barclay, Sarah ^b   Muennig, Peter ^c  

^a MICROSOFT RESEARCH   (United States)

^b J Walter Thompson Company   (United States)

^c Department of Medicine   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL GROUPS; DAILY ACTIVITY; EXPOSED TO; INDIVIDUAL-BASED; INTERNET USERS; POST INTERVENTIONS; RANDOMIZED CONTROLLED TRIAL; SOCIO-DEMOGRAPHIC CHARACTERISTICS; STATUS QUO; WEIGHT LOSS;

PUBLIC HEALTH;

ADULT; ARTICLE; BODY WEIGHT LOSS; CONTROLLED STUDY; DAILY LIFE ACTIVITY; DRUG EFFICACY; EXERCISE; FEMALE; HUMAN; INTERNET; LIFESTYLE; MAJOR CLINICAL STUDY; MALE; POPULATION HEALTH; PUBLIC HEALTH MESSAGE; RANDOMIZED CONTROLLED TRIAL; SCIENTIST;

EID: 85103681505     PISSN: None     EISSN: 23986352     Source Type: Journal    
DOI: 10.1038/s41746-018-0031-7     Document Type: Article

References (23)

1. Atlantis, E., Salmon, J. & Bauman, A. Acute effects of advertisements on children’s choices, preferences, and ratings of liking for physical activities and sedentary behaviours: a randomised controlled pilot study. J. Sci. Med. Sport 11, 553–557 (2008).
2. Berger, J. & Rand, L. Shifting signals to help health: using identity signaling to reduce risky health behaviors. J. Consum. Res. 35, 509–518 (2008).
3. Snyder, L. B. Health communication campaigns and their impact on behavior. J. Nutr. Educ. Behav. 39, S32–S40 (2007).
4. Snyder, L. B. et al. A meta-analysis of the effect of mediated health communication campaigns on behavior change in the United States. J. Health Commun. 9, 71–96 (2004).
5. Witte, K. & Allen, M. A meta-analysis of fear appeals: Implications for effective public health campaigns. Health Educ. Behav. 27, 591–615 (2000).
6. Nutbeam, D. Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century. Health Promot. Int. 15, 259–267 (2000).
7. Mathieson, S. A. DH doubled ad spending to £60m. The Guardian https://www.theguardian.com/healthcare-network/2011/jan/13/department-health-doubled-advertising-spending-60m (2017).
8. Rice, R. E. & Atkin, C. K. Public Communication Campaigns. (Sage, Thousand Oaks, CA, 2012).
9. Grady, C. Enduring and emerging challenges of informed consent. N. Engl. J. Med. 372, 855–862 (2015).
10. Kramer, A. D., Guillory, J. E. & Hancock, J. T. Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl. Acad. Sci. USA 111, 8788–8790 (2014).
11. Zuboff, S. Big other: surveillance capitalism and the prospects of an information civilization. J. Inform. Technol. 30, 75–89 (2015).
12. Andreu-Perez, J., Poon, C. C., Merrifield, R. D., Wong, S. T. & Yang, G.-Z. Big data for health. IEEE J. Biomed. Health 19, 1193–1208 (2015).
13. Ruggeri, K., Yoon, H., Kácha, O., van der Linden, S. & Muennig, P. Policy and population behavior in the age of Big Data. Curr. Opin. Behav. Sci. 18, 1–6 (2017).
14. Kohavi, R., Crook, T., Longbotham, R. Online Experimentation at Microsoft. Third Workshop on Data Mining Case Studies and Practice Prize. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (Association of Computing Machinery (ACM), San Jose, CA, 2009).
15. Lewis, R. A. & Reily, D. H. Online Ads and onine sales: measuring the effects of retail advertising via a controlled experiment on Yahoo! QME-Quant. Mark. Econ. 12, 235–266 (2014).
16. Yom-Tov, E., Muennig, P. & El-Sayed, A. M. Web-based antismoking advertising to promote smoking cessation: a randomized controlled trial. J. Med. Internet Res. 8, e306 (2016).
17. Yom-Tov, E. Crowdsourced Health: How What You Do on the Internet Will Improve Medicine. (MIT Press, Cambridge, MA, 2016.)
18. Yom-Tov, E. & Lev-Ran, S. Adverse reactions associated with cannabis consumption as evident from search engine queries. JMIR Public Health Surveill. 3, e77 (2017).
19. Yom-Tov, E. & Gabrilovich, E. Postmarket drug surveillance without trial costs: discovery of adverse drug reactions through large-scale analysis of web search queries. J. Med. Internet Res. 15, e124 (2013).
20. Fourney, A., White, R. W., Horvitz, E. Exploring time-dependent concerns about pregnancy and childbirth from search logs. 33rd Annual ACM Conference on Human Factors in Computing Systems, 737–746 (Seoul, Republic of Korea, 2015).
21. US Bureau of the Census. Census 2010. http://www.census.gov/main/www/cen2000.html (2010).
22. Fogg, B. J. Fogg Behavior Model. http://www-personal.umich.edu/~mrother/KATA_Files/FBM.pdf (The author, 2007).
23. Fogg, B. J. A behavior model for persuasive design. Proceedings of the 4th International Conference on Persuasive Technology, Claremont, CA (Association of Computing Machinery (ACM), New York, NY, 2009).