Translational Behavioral Medicine

, Volume 5, Issue 1, pp 45–52 | Cite as

Using online crowdsourcing to understand young adult attitudes toward expert-authored messages aimed at reducing hazardous alcohol consumption and to collect peer-authored messages

Original Research


Text message delivered prevention interventions have the potential to improve health behaviors on a large scale, including reducing hazardous alcohol consumption in young adults. Online crowdsourcing can be used to efficiently develop relevant messages, but remains largely understudied. This study aims to use online crowdsourcing to evaluate young adult attitudes toward expert-authored messages and to collect peer-authored messages. We designed an online survey with four drinking scenarios and a demographic questionnaire. We made it available to people who reported age 18–25 years, residence in the US, and any lifetime alcohol consumption via the Amazon Mechanical Turk crowdsourcing platform. Participants rated 71 sample text messages on instrumental (helpful) and affective (interesting) attitude scales and generated their own messages. All messages were coded as informational, motivational, or strategy facilitating. We examined differences in attitudes by message type and by drinking status and sex. We surveyed 272 participants in 48 h, and 222 were included in analysis for a total participant payment cost of $178. Sample mean age was 23 years old, with 50 % being female, 65 % being of white race, and 78 % scored as hazardous drinkers. Informational messages were rated the most helpful, whereas motivational messages were rated the most interesting. Hazardous drinkers rated informational messages less helpful than non-hazardous drinkers. Men reported messages less helpful and interesting than women for most categories. Young adults authored 161 messages, with the highest proportion being motivational. Young adults had variable instrumental and affective attitudes toward expert-authored messages. They generated a substantial number of peer-authored messages that could enhance relevance of future alcohol prevention interventions.


Crowdsourcing Young adult Intervention 


  1. 1.
    Free C, Phillips G, Galli L, et al. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med. 2013;10(1):1001362. doi:10.1371/journal.pmed.1001362.CrossRefGoogle Scholar
  2. 2.
    Rodgers A. Do u smoke after txt? Results of a randomised trial of smoking cessation using mobile phone text messaging. Tob Control. 2005;14(4):255-261. doi:10.1136/tc.2005.01157.CrossRefPubMedCentralPubMedGoogle Scholar
  3. 3.
    Foreman KF, Stockl KM, Le LB, et al. Impact of a text messaging pilot program on patient medication adherence. Clin Ther. 2012;34(5):1084-1091. doi:10.1016/j.clinthera.2012.04.007.CrossRefPubMedGoogle Scholar
  4. 4.
    Suffoletto B, Callaway C, Kristan J, Kraemer K, Clark DB. Text-message-based drinking assessments and brief interventions for young adults discharged from the emergency department. Alcoholism: Clin Exp Res. 2012;36(3):552-560. doi:10.1111/j.1530-0277.2011.01646.CrossRefGoogle Scholar
  5. 5.
    Suffoletto B, Kristan J, Callaway CW, et al. A text-message alcohol intervention for young adult emergency department patients: a randomized clinical trial. Ann Emerg Med. 2014. doi:10.1016/j.annemergmed.2014.06.010.PubMedCentralGoogle Scholar
  6. 6.
    Noar SM, Benac CN, Harris MS. Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychol Bull. 2007;133(4):673-693. doi:10.1037/0033-2909.133.4.673.CrossRefPubMedGoogle Scholar
  7. 7.
    van Gemert-Pijnen JE, Nijland N, van Limburg M, et al. A holistic framework to improve the uptake and impact of eHealth technologies. J Med Internet Res. 2011;13(4):e111. doi:10.2196/jmir.1672.CrossRefPubMedCentralPubMedGoogle Scholar
  8. 8.
    Kreuter MW, Wray RJ. Tailored and targeted health communication: strategies for enhancing information relevance. Am J Health Behav. 2003;27(3):227-232.CrossRefGoogle Scholar
  9. 9.
    Paolacci G, Chandler J, Ipeirotis PG. Running experiments on Amazon Mechanical Turk. Fuzzy Optim Decis Making. 2010;5(5):411-4119.Google Scholar
  10. 10.
    Behrend TS, Sharek DJ, Meade AW, Wiebe EN. The viability of crowdsourcing for survey research. Behav Res Methods. 2011;43(3):800-813. doi:10.3758/s13428-011-0081-0.CrossRefPubMedGoogle Scholar
  11. 11.
    Turner AM, Kirchhoff K, Capurro D. Using crowdsourcing technology for testing multilingual public health promotion materials. J Med Internet Res. 2012;14(3):e79. doi:10.2196/jmir.2063.CrossRefPubMedCentralPubMedGoogle Scholar
  12. 12.
    Muench F, van Stolk-Cooke K, Morgenstern J, Kuerbis AN, Markle K. Understanding messaging preferences to inform development of mobile goal-directed behavioral interventions. J Med Internet Res. 2014;16(2):e14. doi:10.2196/jmir.2945.CrossRefPubMedCentralPubMedGoogle Scholar
  13. 13.
    Carey KB, Henson JM, Carey MP, Maisto SA. Which heavy drinking college students benefit from a brief motivational intervention? J Consult Clin Psychol. 2007;75(4):663-669. doi:10.1037/0022-006×.75.4.663.CrossRefPubMedCentralPubMedGoogle Scholar
  14. 14.
    Bradley KA, DeBenedetti AF, Volk RJ, Williams EC, Frank D, Kivlahan DR. AUDIT-C as a brief screen for alcohol misuse in primary care. Alcoholism: Clin Exp Res. 2007;31(7):1208-1217.CrossRefGoogle Scholar
  15. 15.
    Dawson DA, Grant BF, Stinson FS, Zhou Y. Effectiveness of the derived alcohol use disorders identification test (AUDIT-C) in screening for alcohol use disorders and risk drinking in the US general population. Alcoholism: Clin Exp Res. 2005;29(5):844-854. doi:10.1097/01.ALC.0000164374.32229.CrossRefGoogle Scholar
  16. 16.
    Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50:179-211.CrossRefGoogle Scholar
  17. 17.
    Fisher JD, Fisher WA. Changing AIDS-risk behavior. Psychol Bull. 1992;111:455-474.CrossRefPubMedGoogle Scholar
  18. 18.
    Coley HL, Sadasivam RS, Williams JH, et al. Crowdsourced peer- versus expert-written smoking-cessation messages. Am J Prev Med. 2013;45(5):543-550. doi:10.1016/j.amepre.2013.07.004.CrossRefPubMedGoogle Scholar
  19. 19.
    Walters ST, Bennett ME, Noto JV. Drinking on campus: what do we know about reducing alcohol use among college students? J Subst Abus Treat. 2000;19(3):223-228. doi:10.1016/S0740-5472(00)00101-X.CrossRefGoogle Scholar
  20. 20.
    Ziemelis A, Bucknam RB, Elfessi AM. Prevention efforts underlying decreases in binge drinking at institutions of higher education. J American College Health. 2002;50(5):238-252. doi:10.1080/074484802095957157.CrossRefGoogle Scholar
  21. 21.
    Lehto T, Oinas-Kukkonen H. Persuasive features in web-based alcohol and smoking interventions: a systematic review of the literature. J Med Internet Res. 2011;13(3):e46. doi:10.2196/jmir.1559.CrossRefPubMedCentralPubMedGoogle Scholar
  22. 22.
    Miller MB, Leffingwell TR. What do college student drinkers want to know? Student perceptions of alcohol-related feedback. Psychol Addict Behav. 2013;27(1):214. doi:10.1037/a0031380.CrossRefPubMedGoogle Scholar
  23. 23.
    Walters ST, Roudsari BS, Vader AM, Harris TR. Correlates of protective behavior utilization among heavy-drinking college students. Addict Behav. 2007;32:2633-2644. doi:10.1016/j.addbeh.2007.06.022.CrossRefPubMedCentralPubMedGoogle Scholar
  24. 24.
    Norman P, Armitage CJ, Quigley C. The theory of planned behavior and binge drinking: assessing the impact of binge drinker prototypes. Addict Behav. 2007;32(9):1753-1768. doi:10.1016/j.addbeh.2006.12.009.CrossRefPubMedGoogle Scholar
  25. 25.
    Norman P, Conner M. The theory of planned behaviour and binge drinking: assessing the moderating role of past behaviour within the theory of planned behaviour. Br J Health Psychol. 2006;11(1):55-70. doi:10.1016/j.addbeh.2006.12.009.CrossRefPubMedGoogle Scholar
  26. 26.
    Hagger MS, Lonsdale A, Chatzisarantis NL. A theory-based intervention to reduce alcohol drinking in excess of guideline limits among undergraduate students. Br J Health Psychol. 2012;17(1):18-43. doi:10.1111/j.2044-8287.2010.02011.CrossRefPubMedGoogle Scholar
  27. 27.
    Collins L, Murphy S, Strecher V. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART) new methods for more potent eHealth interventions. Am J Prev Med. 2007;32(5):S112-S118. doi:10.1016/j.amepre.2007.01.022.CrossRefPubMedCentralPubMedGoogle Scholar

Copyright information

© Society of Behavioral Medicine 2014

Authors and Affiliations

  1. 1.Department of Emergency MedicineUniversity of Pittsburgh School of MedicinePittsburghUSA

Personalised recommendations