UC Institute for Prediction Technology and UCLA Center for Digital Behavior
Sean Young, PhD MS is the Founder and Director of the University of California Institute for Prediction Technology (UCIPT) and the Center for Digital Behavior at UCLA. We are a highly interdisciplinary collaborative formed to advance research on the use of digital and mobile technologies to understand, predict, and change human behavior. Our work has been routinely cited in top media outlets like The New York Times, NPR, Mashable, Yahoo Finance, Huffington Post, and CBS news.
Digital behavior research leverages the increasingly widespread adoption of new technologies, such as social media, wearable sensors, mobile applications, and “big data” from these technologies, to study human behavior (see Digitalbehavior.ucla.edu for more info). The significance of human behavior—what, when, why, or how people do, say, or think—extends far beyond psychology to many academic disciplines and to both the public and private sector. Because this work has wide-ranging applications in medicine, business, policy, entrepreneurship and more, I founded the UCLA Center for Digital Behavior to bring together researchers and experts from a diverse range of fields across UCLA and beyond. The Center now aims to engage government agencies, businesses, not-for-profits, and stakeholder groups in projects of mutual interest, to scale innovations for addressing matters of local to global importance.
Here are a few examples of the types of questions we study:
- How can we analyze big data generated by social media, wearable sensors, and mobile applications to monitor or predict behaviors and outcomes (from criminal activity surveillance to election forecasting)?
- How can we create and leverage digital and mobile platforms to change behaviors (from public health interventions to corporate leadership programs)?
- Why do some technologies and approaches influence behavior more successfully and sustainably than others (from recycling campaigns to branded games)?
Using "big data" for health and behavioral prediction
We created a method called Behavioral Insights on Big Data (BIBD) for using social data (social media, apps, wearable devices, etc) to predict events in health/medicine, politics, business, and social welfare.
Young, S.D. (2015). A "big data" approach to HIV Epidemiology and Prevention. Preventive Medicine, 70(17).
Young SD. Behavioral insights on big data: using social media for predicting biomedical outcomes. Cell: Trends Microbiol. 2014;22(11):601–2.
Young SD, Rivers C, Lewis B. Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes. Prev Med. 2014;63:112-5.
Young, SD, Dutta, D, Dometty, G. (2009). Extrapolating psychological insights from Facebook profiles: a study of religion and relationship status. Cyberpsychology and Behavior, 12(3), 347-50.
Use of social networking and online communities to improve health behaviors
We have designed a method for creating health communities that leads to high rates of patient engagement and health behavior change.
Krueger, E. & Young, S.D. (2015). Twitter: A Novel Tool for Studying the Health and Social Needs of Transgender Communities. JMIR Mental Health, 2(2), e16.
Young, SD, Cumberland, W.G., Lee, S.J., Jaganath, D., Szekeres, G., & Coates, T. (2013). Social Networking Technologies as an Emerging Tool for HIV Prevention: A Cluster Randomized Trial. Annals of Internal Medicine. 159(5), 318-324.
Young SD, Cumberland WG, Nianogo R, Menacho LA, Galea JT, Coates T. The HOPE social media intervention for global HIV prevention in Peru: a cluster randomised controlled trial. Lancet HIV. 2014;doi:10.1016/S2352-3018(14)00006-X.
Young SD, Holloway I, Jaganath D, Rice E, Westmoreland D, Coates T. Project HOPE: Online social network changes in an HIV prevention randomized controlled trial for African American and Latino men who have sex with men. Am J Public Health. 2014;104(9):1707-12.
Young, SD, & Jaganath, D. (2013). Online social networking for HIV education and prevention: a mixed-methods analysis. Sexually Transmitted Diseases, 40(2), 163-167.
Young, SD. (2012). Recommended guidelines on using social networking technologies for HIV prevention research. AIDS and Behavior, 16(7): 1743–1745. .
Global health studies
Chiu, J., Menacho, L. , Fisher, C., and Young, S.D. (2015). Ethics issues in social media-based HIV prevention in low- and middle-income countries. Cambridge Quarterly of Healthcare Ethics, 24(3). Chiu, C., Menacho, L., & Young, S.D. (2016). The Association Between Age and Ethics-Related Issues in Using Social Media for HIV Prevention in Peru. Ethics and Behavior, 26(2).
Young, SD, Konda, K., Caceres, C., Galea, J., Lee, S., Salazar, X., & Coates, T. (2011). Effect of a community popular opinion leader HIV/STI intervention on stigma in urban, coastal Peru. AIDS and Behavior, 15(5), 930-7. PMCID: PMC3110996
Young, S.D., Hlavka, Z., Modiba, P., & Gray, G., van Rooyen, H., Szekeres, G., & Coates, T., (2010). HIV-related stigma, social norms, and HIV testing in Soweto and Vulindlela, South Africa: National Institutes of Mental Health Project Accept (HPTN 043). JAIDS, 55(5), 620-4. PMCID: PMC3136617
Young SD, Shakiba A, Kwok J, Montazeri MS. The influence of social networking technologies on female religious veil-wearing behavior in Iran. Cyberpsychol Behav Soc Netw. 2014;17(5):317-21.
Bendavid, E., Young, S.D., Katzenstein, D.A., Bayoumi, A., Sanders, G., & Owens, D. (2008). Cost-effectiveness of HIV monitoring strategies in resource-limited settings: a southern African analysis. Archives of Internal Medicine.
General behavior change, health, and social media studies
We continue to collaborate with new investigators in fields that relate to our work. For example, we are currently studying how social networking influences religious and health behavior among women in Iran, and how looking at your friends' Facebook pictures could impact your own health behaviors.
Young, S.D., & Jordan, A. (2013). The influence of social networking photos on social norms and sexual health behaviors. Cyberpsychology, Behavior, and Social Networking, 16(4): 243–247.
Smith A, Young SD. At the intersection of marketing, technology, and psychology: designing mobile technologies to change health behaviors. J Consum Health Internet. (In Press).
Young, S.D., Nussbaum, D., & Monin, B. (2007). Potential moral stigma and reactions to sexually transmitted diseases: evidence for a disjunction fallacy. Personality and Social Psychology Bulletin, 33(6), 789-799.
Young, S.D., Dutta, D. & Dommety, G. (2009). Extrapolating psychological insights from Facebook profiles: a study of religion and relationship status. CyberPsychology and Behavior, 12(3), 347-350.
Recommendations and guidelines on use of technologies in health care and for behavior change
We are involved in decisions and recommendations regarding the effectiveness, ethics, and use of technologies in public health and medicine.
Garett, R., Smith, J., and Young, S.D. (2016). A Review of Social Media Technologies Across the Global HIV Care Continuum. Current Opinion in Psychology, 1(9).
Young, S.D. (2011). Recommendations for Using Online Social Networking Technologies to Reduce Inaccurate Online Health Information. OJHAS, 10(2).
Young, S.D. (2012). Recommended guidelines on using social networking technologies for HIV prevention research. AIDS and Behavior, 16(7): 1743–1745. .
Gil, H., Gil, N., & Young, S.D. (In press). A review of the use of social media for health education and behavior change. Journal of Consumer Health on the Internet.
Innovative community-based participatory research
We believe that it is important to work with community organizations and clinics to improve community health. We currently partner with many local organizations to evaluate innovative health solutions, such as through our work testing the use of electronic vending machines to distribute rapid, oral-fluid HIV self-testing kits.
Young.S.D., Klausner, J., Flynn, R., Bolan, R. (2014). Electronic vending machines for dispensing rapid HIV self-testing kits: A case study. AIDS Care, 26(2):267-9.
Young., S.D. & Oppenheimer, D. (2006). Different methods of presenting risk information and their influence on medication compliance intentions: results of three studies. Clinical Therapeutics ;28(1):129-39.
Clinical Work and Patient Technology Development
I assemble teams and build technologies to improve patient care at the UCLA Department of Family Medicine. Current projects:
- Applying our Harnessing Online Peer Education (HOPE) social media intervention to build technologies to improve patient care
- Technologies to improve adherence
I believe it is important to support and guide the work of future researchers, community and health care specialists, entrepreneurs, and corporate/political leaders. I also love getting to share in their success.
Clinical and postdoctoral fellows, medical and graduate students, undergraduates, and internships: