Crowdsourced health data: Comparability to a US national survey, 2013-2015

American Journal of Public Health

Volumn 107, Issue 8, 2017, Pages 1283-1289

  Yank, Veronica ^a   Agarwal, Sanjhavi ^a   Loftus, Pooja ^b   Asch, Steven ^b,c   Rehkopf, David ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b STANFORD UNIVERSITY   (United States)

^c VA PALO ALTO HEALTH CARE SYSTEM   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADOLESCENT; ADULT; AGED; BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM; CARDIOVASCULAR DISEASES; COMPARATIVE STUDY; CROWDSOURCING; DEFENSE MECHANISM; FEMALE; HEALTH STATUS; HUMAN; MALE; MIDDLE AGED; PROCEDURES; PUBLIC HEALTH; YOUNG ADULT;

ADOLESCENT; ADULT; AGED; BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM; CARDIOVASCULAR DISEASES; CROWDSOURCING; FEMALE; HEALTH STATUS; HUMANS; MALE; MIDDLE AGED; PUBLIC HEALTH; REGRESSION (PSYCHOLOGY); YOUNG ADULT;

EID: 85024375992     PISSN: 00900036     EISSN: 15410048     Source Type: Journal    
DOI: 10.2105/AJPH.2017.303824     Document Type: Article

References (36)

1. Wolf G. The data-driven life. The New York Times Sunday Magazine. 2010:MM38.
2. Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data. 2013; 1(2):85-99.
3. Comstock J. Apple and Google's different, but complementary, approaches to medical research. 2015. Available at: http://mobihealthnews.com/41189/appleand-googles-different-but-complementary-approachesto-medical-research. Accessed May 9, 2016.
4. Cavazos-Rehg PA, Krauss M, Spitznagel E, et al. Monitoring of non-cigarette tobacco use using Google Trends. Tob Control. 2015;24(3):249-255.
5. Houle JN, Collins JM, Schmeiser MD. Flu and finances: influenza outbreaks and loan defaults in US cities, 2004-2012. Am J Public Health. 2015;105(9):e75-e80.
6. Wilson RE, Gosling SD, Graham LT. A review of Facebook research in the social sciences. Perspect Psychol Sci. 2012;7(3):203-220.
7. Capurro D, Cole K, Echavarria MI, Joe J, Neogi T, Turner AM. The use of social networking sites for public health practice and research: a systematic review. J Med Internet Res. 2014;16(3):e79.
8. Bahk CY, Cumming M, Paushter L, Madoff LC, Thomson A, Brownstein JS. Publicly available online tool facilitates real-time monitoring of vaccine conversations and sentiments. Health Aff (Millwood). 2016;35(2): 341-347.
9. Merkatz RB, Temple R, Subel S, Feiden K, Kessler DA; Working Group on Women in Clinical Trials. Women in clinical trials of new drugs. A change in Food and Drug Administration policy. N Engl J Med. 1993; 329(4):292-296.
10. National Institutes of Health. NIH guidelines on the inclusion of women and minorities as subjects in clinical research. Fed Regist. 1994;59(59):11146-11151.
11. Manrai AK, Funke BH, Rehm HL, et al. Genetic misdiagnoses and the potential for health disparities. N Engl J Med. 2016;375(7):655-665.
12. Burchard EG. Medical research: missing patients. Nature. 2014;513(7518):301-302.
13. Swan M. Crowdsourced health research studies: an important emerging complement to clinical trials in the public health research ecosystem. J Med Internet Res. 2012;14(2):e46.
14. Goodman JK, Cryder CE, Cheema A.Data collection in a flat world: the strengths and weaknesses of Mechanical Turk samples. J Behav Decis Making. 2013;26(3):213-224.
15. Chandler J, Shapiro D. Conducting clinical research using crowdsourced convenience samples. Annu Rev Clin Psychol. 2016;12:53-81.
16. Naslund JA, Aschbrenner KA, Marsch LA, McHugo GJ,Bartels SJ. Crowdsourcing for conducting randomized trials of internet delivered interventions in people with serious mental illness: a systematic review. Contemp Clin Trials. 2015;44:77-88.
17. Ranard BL, Ha YP, Meisel ZF, et al. Crowdsourcing-harnessing the masses to advance health and medicine, a systematic review. J Gen InternMed. 2014;29(1):187-203.
18. Precision Medicine Initiative (PMI) Working Group. The Precision Medicine Initiative Cohort Program -Building a research foundation for 21st century medicine. 2015. Available at: https://www.nih.gov/sites/default/files/research-training/initiatives/pmi/pmi-working-groupreport-20150917-2.pdf. Accessed November 14, 2016.
19. World Health Organization. WHO fact sheet: cardiovascular diseases (CVDs). Available at: http://www.who.int/mediacentre/factsheets/fs317/en. Accessed November 18, 2016.
20. Pierannunzi C, Hu SS, Balluz L. A systematic review of publications assessing reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004-2011. BMC Med Res Methodol. 2013;13:49.
21. WHO EC. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157-163.
22. King K. Geoloaction and federalism of the internet: cutting internet gambling's Gordian Knot. Columbia Sci Technol Law Rev. 2010;XI:41-75.
23. US 2010 Census. Available at: http://www.census.gov/2010census/data. Accessed October 28, 2015.
24. Zambom AZ, Dias R. A review of kernel density estimation with applications to econometrics. 2012. Available at: https://arxiv.org/abs/1212.2812. Accessed November 14, 2016.
25. Growing Older in America. The Health and Retirement Study. Bethesda: National Institute on Aging; 2015.
26. Huff C, Tingley D. "Who are these people?" Evaluating the demographic characteristics and political preferences of MTurk survey respondents. Res Politics. 2015;2(3):1-12.
27. Wilmot EG, Edwardson CL, Achana FA, et al. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia. 2012;55(11):2895-2905.
28. Buhrmester M, Kwang T, Gosling SD. Amazon's Mechanical Turk: a new source of inexpensive, yet high-quality, data? Perspect Psychol Sci. 2011;6(1):3-5.
29. Choi JH, Lee JS. Online social networks for crowdsourced multimedia-involved behavioral testing: an empirical study. Front Psychol. 2016;6:1991.
30. Kristan J, Suffoletto B. Using online crowdsourcing to understand young adult attitudes toward expert-authored messages aimed at reducing hazardous alcohol consumption and to collect peer-authored messages. Transl Behav Med. 2015;5(1):45-52.
31. Burkill S, Copas A, Couper MP, et al. Using the web to collect data on sensitive behaviours: a study looking at mode effects on the British National Survey of Sexual Attitudes and Lifestyles. PLoS One. 2016;11(2):e0147983.
32. Brabham DC, Ribisl KM, Kirchner TR, Bernhardt JM. Crowdsourcing applications for public health. Am J Prev Med. 2014;46(2):179-187.
33. Smolinski MS, Crawley AW, Baltrusaitis K, et al. Flu near you: crowdsourced symptom reporting spanning 2 influenza seasons. Am J Public Health. 2015;105(10): 2124-2130.
34. Santiago-Rivas M, Schnur JB, Jandorf L. Sun protection belief clusters: analysis of Amazon Mechanical Turk data. J Cancer Educ. 2016;31(4):673-678.
35. Merchant RM, Griffis HM, Ha YP, et al. Hidden in plain sight: a crowdsourced public art contest to make automated external defibrillators more visible. Am J Public Health. 2014;104(12):2306-2312.
36. Vayena E, Mastroianni A, Kahn J. Ethical issues in health research with novel online sources. Am J Public Health. 2012;102(12):2225-2230.