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Health Behavior Interventions in the Age of Facebook Nathan K. Cobb, MD, Amanda L. Graham, PhD B ehavioral interventions leveraging online net- works have the potential to affect population health dramatically, given their capacity to dis- seminate programs virally while simultaneously facilitat- ing social support and modifying norms. 1 Historically, intervention design has prioritized effect over reach, as- suming that dissemination can be achieved post hoc. The papers from Bull 2 and Cavallo 3 in this issue poten- tially point us in a different direction by using Face- book as an intervention approach for preventing sex- ually transmitted infection and promoting physical activity, respectively. The scale of Facebook’s online network is immense: an estimated 149 million Americans use it every month, each one connected to an average of 214 people. 4 Brands such as Starbucks have millions of people engage with their Pages; the social game (“App”) Words With Friends has 16 million active players every month. 5 For health behavior change intervention designers, Facebook offers something unprecedented— direct access to an individu- al’s social network, in real time, and without the need for tedious network enumeration by participants. In theory, this kind of access could support an optimal network intervention model where diffusion and social support are linked and synergistic. For example, an intervention for smoking cessation could spread from a target to their friends, inducing cessation while simultaneously aug- menting social support and reinforcing abstinence. Face- book provides tools for precisely these network effects: Pages and Groups (as used by Bull 2 and Cavallo, 3 respec- tively) allow self-affıliation and transmission of informa- tion through groups, while Apps provide direct access to an individual’s personal information, their friends, and distribution channels for diffusion. Despite these advantages, both papers demonstrate the challenge in selecting and implementing methods for network-based intervention. The advantages of Groups and Pages are the low barrier for affıliation (e.g., clicking a “Like” button) and the ability to immediately form ad hoc networks. However, these approaches are limited by the fact they do not allow access to an individual’s per- sonal information or his or her network data. Apps have broader mechanisms for social interaction, but are signif- icantly more diffıcult to develop and require formal con- sent. The literature on the use of ad hoc or “created” online networks for social support is well established, 6 with roots in traditional support groups. Individuals are encouraged to “subscribe” to a site, forum, page, or group where they interact primarily with people they do not otherwise know and are exposed to information, norms, and support not previously available. Still, ad hoc net- work interventions—like the use of Facebook Groups or Pages—are hampered by two issues: (1) the need to have an existing, functional, scaled support network prior to participant enrollment (to avoid an “empty room” phe- nomenon) and (2) the hesitancy of people to actively engage with strangers. These two issues may explain why Cavallo and col- leagues 3 found no benefıt from adding a Facebook group to what appears to have been a well-designed web inter- vention. Described as a feasibility study, they enrolled only 67 people in the intervention arm—less than half the number of friends of the typical Facebook user. For this intervention to have yielded signifıcant results, it may have required far more people in the Group with a greater degree of behavioral heterogeneity. Future research will need to clarify whether their “negative” results stem from theory and methods or merely from lack of scale in the social network. Bull and colleagues 2 used Facebook in a different way in their sexual health intervention. After recruiting seed individuals into Facebook Groups, they used respondent- driven sampling to essentially disseminate the interven- tion through the social networks of the seeds. They describe a large difference in the success of their recruitment strat- egy by condition—an average of an additional 1.04 vs 1.79 participants (resulting in over-recruitment in the inter- vention arm). This difference would appear to be due to the intervention itself—implemented by a professional From the Schroeder Institute for Tobacco Research and Policy Studies (Cobb, Graham), American Legacy Foundation; Division of Pulmonary and Critical Care, Department of Medicine (Cobb), Department of Oncol- ogy, Lombardi Comprehensive Cancer Center (Cobb, Graham), George- town University Medical Center, Washington, DC; and Department of Health, Behavior and Society (Cobb), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland Address correspondence to: Nathan K. Cobb, MD, American Legacy Foundation, Schroeder Institute for Tobacco Research and Policy Studies, 1724 Massachusetts Avenue, NW, Washington DC 20036. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2012.08.001 © 2012 American Journal of Preventive Medicine Published by Elsevier Inc. Am J Prev Med 2012;43(5):571–572 571

Health Behavior Interventions in the Age of Facebook

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    ehavioral interventions leveraging online net-works have the potential to affect populationhealth dramatically, given their capacity to dis-

    inate programs virally while simultaneously facilitat-social support and modifying norms.1 Historically,

    ervention design has prioritized effect over reach, as-ming that dissemination can be achieved post hoc.e papers from Bull2 and Cavallo3 in this issue poten-lly point us in a different direction by using Face-ok as an intervention approach for preventing sex-lly transmitted infection and promoting physicaltivity, respectively.The scale of Facebooks online network is immense: animated 149 million Americans use it every month,ch one connected to an average of 214 people.4 Brandsch as Starbucks have millions of people engage withir Pages; the social game (App)WordsWith Friendss 16 million active players every month.5 For healthhavior change intervention designers, Facebook offersmething unprecedenteddirect access to an individu-s social network, in real time, and without the need forious network enumeration by participants. In theory,s kind of access could support an optimal networkervention model where diffusion and social supportlinked and synergistic. For example, an interventionsmoking cessation could spread from a target to theirends, inducing cessation while simultaneously aug-nting social support and reinforcing abstinence. Face-ok provides tools for precisely these network effects:ges and Groups (as used by Bull2 and Cavallo,3 respec-ely) allow self-affliation and transmission of informa-n through groups, while Apps provide direct access toindividuals personal information, their friends, andtribution channels for diffusion.

    m the Schroeder Institute for Tobacco Research and Policy Studiesthehttp://dx.doi.org/10.1016/j.amepre.2012.08.001

    012 American Journal of Preventive Medicine Published by Elsevierventions in theebookda L. Graham, PhD

    Despite these advantages, both papers demonstrate theallenge in selecting and implementing methods fortwork-based intervention. The advantages of Groupsd Pages are the low barrier for affliation (e.g., clickingLike button) and the ability to immediately form adc networks. However, these approaches are limited byfact they do not allow access to an individuals per-

    nal information or his or her network data. Apps haveoadermechanisms for social interaction, but are signif-ntly more diffcult to develop and require formal con-t. The literature on the use of ad hoc or createdline networks for social support is well established,6

    th roots in traditional support groups. Individuals arecouraged to subscribe to a site, forum, page, or groupere they interact primarily with people they do noterwise know and are exposed to information, norms,d support not previously available. Still, ad hoc net-rk interventionslike the use of Facebook Groups orgesare hampered by two issues: (1) the need to haveexisting, functional, scaled support network prior torticipant enrollment (to avoid an empty room phe-menon) and (2) the hesitancy of people to activelygage with strangers.These two issues may explain why Cavallo and col-gues3 found no beneft from adding a Facebook groupwhat appears to have been a well-designed web inter-ntion. Described as a feasibility study, they enrolledly 67 people in the intervention armless than half thember of friends of the typical Facebook user. For thiservention to have yielded signifcant results, it mayve required farmore people in theGroupwith a greatergree of behavioral heterogeneity. Future research willed to clarify whether their negative results stem fromory and methods or merely from lack of scale in thecial network.Bull and colleagues2 used Facebook in a different wayintervention itselfimplemented by a professional

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    572 Cobb and Graham / Am J Prev Med 2012;43(5):571572Groups with thousands of active participants acrosshavioral stages of change) or developers need to ac-ire the capacity and infrastructure to develop Appspable of tapping into an individuals network, commu-ating across it, and spreading virally. Both approachesuire multidisciplinary teams that include social mediaecialists, marketers, and software developers as equalrtners in design and intervention development. Build-such teams undoubtedly will require changes to tra-

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    Visit www.ajpmo2011. arxiv.org/abs/1111.4503.Words with friendsFacebook application metrics from AppData.www.appdata.com/apps/facebook/168378113211268-words-with-friends.Archived at:www.webcitation.org/69XkpIB1a.Eysenbach G, Powell J, Englesakis M, Rizo C, Stern A. Healthrelated virtual communities and electronic support groups: sys-tematic review of the effects of online peer to peer interactions.Br Med J 2004;328(7449):1166.Heffernan JM, Smith RJ, Wahl LM. Perspectives on the basicreproductive ratio. J R Soc Interface 2005;2(4):28193.

    w?alth sciences journalsEDLINE,bsite..org today!organizationbeing simply more viral.ally is quantifed by the reproductive ratioderived from epidemiology that refers tof new respondents (infections) directlya single seed recruit.7 From the three-wavebers provided, we can estimate this num-.55 for the control and 0.75 for the activehis difference may appear to be small, butntion with the capacity to propagate, itponentially. More importantly, an R valuedicates a tipping point and potential diffu-ut a population without requiring newompanies repeatedly have created conta-ts; a health behavior team that built such anould have cracked the thorniest of dissem-ms.s suggest that if the use of existing ties fore and diffusion are important to interven-ey need to be included at the inception ofplications are daunting: Either designerson the development and maintenance ofnetwork populations (e.g., Facebook Pages

    ditional funding and developmentmodels, but thtial is too large to be ignored or minimized. Cavaand their collaborators are to be commended fothe way.

    No fnancial disclosures were reported by the authopaper.

    References1. Cobb NK, Graham AL, Byron MJ, Niaura RS, Ab

    Workshop Participants. Online social networks andcessation: a scientifc research agenda. J Med In2011;13(4):e119.

    2. Bull SS, Levine DL, Black SR, Schmiege S, Santelmediadelivered sexual health intervention: a clusteized controlled trial. Am J Prev Med 2012;43(5):467

    3. CavalloDN,TateDF, RiesAV, Brown JD,DeVellis RFman AS. A social mediabased physical activity interandomized controlled trial. Am J Prev Med 2527532.

    4. Ugander J, Karrer B, Backstrom L, Marlow C. The athe Facebook social graph. Arxiv Preprint ArXivwww.ajpmonline.org

    Health Behavior Interventions in the Age of FacebookReferences