Social Media and the Science of Health Behavior
2013; Lippincott Williams & Wilkins; Volume: 127; Issue: 21 Linguagem: Inglês
10.1161/circulationaha.112.101816
ISSN1524-4539
Autores Tópico(s)Social Media and Politics
ResumoHomeCirculationVol. 127, No. 21Social Media and the Science of Health Behavior Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessResearch ArticlePDF/EPUBSocial Media and the Science of Health Behavior Damon Centola, PhD Damon CentolaDamon Centola From the Massachusetts Institute of Technology, Cambridge, MA. Originally published28 May 2013https://doi.org/10.1161/CIRCULATIONAHA.112.101816Circulation. 2013;127:2135–2144IntroductionSocial influences are a primary factor in the adoption of health behaviors.1,2 Compliance with diet and nutrition programs, adherence to preventive screening recommendations, and maintenance of exercise routines all can depend on having contact with friends and family who also engage in these behaviors. In addition to a great deal of literature on peer effects,3 recent studies of large network data sets have made important advances in our understanding of how social networks influence the collective dynamics of health behavior.4,5 Research has shown that social influences can affect collective health outcomes ranging from epidemic obesity to smoking behaviors, which have important consequences both for theoretical models of social epidemiology and for the practical design of interventions and treatment strategies.6,7 These findings have direct implications for research aimed at understanding how social influences on dieting, exercising, medication use, and getting screenings can impact behavior change affecting cardiovascular disease. The large number of health domains affected by recent research on the spread of behaviors has made social diffusion a topic of growing interest for an increasing variety of researchers and practitioners who are concerned with understanding the social dimensions of health. This article discusses the development of new methods that use social media to study these health dynamics.Although there is widespread theoretical and practical interest in understanding how social influences affect health-related behaviors, empirical studies of the social dynamics of health face important methodological challenges. Large observational studies of population health have faced the limitation that they are unable to address problems of causal identification.8,9 Extant studies have been able to show conclusively that health-related traits such as smoking5 and weight gain4 correlate with social ties in a network, yet the data do not provide a clear assessment of the degree to which social network ties directly influence behaviors versus reveal shared exposure to common influences.9 Correlations of traits with social network ties can occur because people who are friends are exposed to the same media signals (ie, exogenous information), because connected individuals live in the same neighborhoods (ie, geographic constraints such as living near the same restaurants and gyms), because people who already have similar traits form social ties with one another (ie, choice homophily), or because connected individuals influence one another to adopt similar behaviors (ie, social influence).10,11 Although new techniques are being developed to discriminate between each of these causal mechanisms,4,12 determining the relative impact of these factors is very difficult and is made even more complicated by the lack of reliable data both on the timing of behavior change and on the actual social network structure of a given population.These difficulties motivate the need for new methods that can allow health researchers to identify the role of social networks in the real-time dynamics of behavior change. The goal of this article is to demonstrate that the rapid growth of peer-to-peer social media presents an important new resource for addressing these empirical challenges. Increasing levels of public participation in a diverse range of health-related social media create a new population of subjects whose natural, everyday engagement with health behaviors can be monitored and scientifically explored with a rapidly expanding repertoire of social technologies. Building on these new capacities, recent research has begun to study how social media can be used to experimentally evaluate the effects of social influence on behavior change. This approach to using social media entails a shift in focus from the interpersonal dimensions of social interactions to the community-wide effects of social network structure on the spread of behaviors through online populations.Social Media and HealthSocial media has become an indelible part of the public health landscape.13–15 From Web-based appointment scheduling to online coaching for smoking cessation and weight loss, the Internet provides an increasingly valuable resource for customers of health services.16 Although the majority of these efforts concentrate on organizational tools for providing clients with improved services, an equally important use of social media has come from the emergence of peer-driven health communities.Peer-to-peer interaction in the health sector has a long history, starting with the creation of support groups for alcohol and tobacco abstention, weight control, long-term treatment, and grief and trauma counseling.17 Much of the value of these peer-counseling organizations derives from personal and empathetic interaction.18 The logic of this kind of interaction has been extended into the virtual domain with the development of online tools for coaching and abstention.19 For instance, recent Web-based social support services such as QuitNet and Free and Clear provide peer-to-peer e-mail and instant messaging systems that offer newly abstaining smokers support and counseling from members with years of abstention experience.At first glance, it is remarkable that anonymous online communities can be effective environments for providing productive interactions that improve participants' health behaviors. However, the idea of social support from online interactions has been around since the inception of the Internet.19 Since the 1990s, Usenet groups and Listservs have provided tools for support groups and medical information sharing through patient networks. For instance, a long-standing Listserv for cancer patients and their families, ACOR, provides an open network for patients to share treatment experiences and to engage with an empathetic community.20The increasing popularity of social media sites like Facebook and Twitter has also given rise to commercial applications that offer radical new approaches to using social media for improved health. For instance, companies such Redbrick Health, StayWell, and Healthways have begun to use online social support platforms to help promote compliance with planned health regimens. Through widespread recruitment and regular interactions, these sites create communities that encourage increased participation in exercise and diet programs among their members. In a similar spirit, a recent Internet startup called PatientsLikeMe offers an extensive social media platform with online health profiles, patient information and disease histories, and interactive tools that allow members to share comprehensive reports with one another. Members of the site can participate in multiple disease-specific communities, allowing them to find information relevant to their individual medical needs. Not unlike the Listservs and patient support chat groups from the previous generation, patients can share information about their treatments and experiences, but with the important difference that the new, more sophisticated social media technologies allow participants to interact by comparing detailed records of ongoing health status, treatment programs, and recovery plans.In addition to the social support that participants receive, a significant motivation for participating in these environments is the new informational channels that they create across traditional health communities.21 A great deal of literature in social epidemiology studies the ways in which spatial, geographic, social, and economic constraints can significantly affect patients' health outcomes.22 This research has found that a major variable affecting population health can be knowledge about and access to medical treatments and technologies.23,24 For instance, studies have shown that physician practices can be highly localized, varying dramatically from one geographic region to another.22,25 Consequently, information about treatments, medicines, and screenings may be disproportionately available to some patient populations and not to others.26 The remarkable growth of Internet-based health and wellness communities allows patients from a variety of social and geographic backgrounds to share information about novel health resources, ranging from information about diet and nutrition to opportunities to learn about patient advocacy, preventive health screenings, and new treatment technologies.27,28Health ResearchThis growing variety of social technologies provides an array of new opportunities for health researchers. As shown in Table 1, extant technologies fall into 2 broad categories of social interactions and health: open forms of social media such as Facebook and Twitter and intentionally designed online health communities.Table 1. Examples of Open and Intentionally Designed Online Social NetworksTypes of Online NetworksExamplesOpen social networksFacebook, Twitter, Google+Intentionally designed social networksThe Healthy Lifestyle Network, QuitNet,PatientsLikeMeOpen networks such as Twitter and Facebook provide social interactions on any topic. Intentionally designed networks such as The Healthy Lifestyle Network and QuitNet provide participants with targeted interactions around health-related goals.Open technologies are large-scale virtual communication infrastructures that are designed for social interactions across many substantive domains. They are not specifically designed for health-related interactions, nor do they explicitly target any particular health community. Despite this, technologies such as Facebook, Twitter, Google+, and a variety of other social tools have created novel opportunities to trace the interactions between social connectivity and health. Recent work using open social technologies has provided important new insights into the dynamics of opinion propagation on health behaviors. For instance, studies on Twitter networks have found that sentiment about vaccines can be propagated through chains of Twitter feeds.15 Similarly, attitudes toward smoking, weight loss, and cholesterol and blood pressure medications can also have a viral quality. The reinforcement and diffusion of attitudes has important consequences for the kinds of preventative behaviors that people are willing to engage in, particularly when the behaviors are difficult (like smoking cessation and exercise), or costly (like new medications and better nutrition).29,30 Open social resources provide access to attitudinal records of hundreds of millions of people, which offers an unprecedented opportunity for large-scale inferences about the sentiments of the population at large, and the kinds of messaging strategies that may be most effective for reaching them. However, these large-scale data do not provide context-specific interactional observations or the capacity for clear causal identification of interaction patterns on population health. For this, we need to turn to intentionally designed online health networks.Intentionally designed health communities are composed of members with an explicit interest in health and health behaviors. They can serve a variety of ends, from promoting regular exercise routines to providing counseling for smoking cessation. For researchers interested in the social dynamics of health, these sites offer the novel opportunity to collect data on participants' recorded health behaviors (such as exercise minutes per week, or daily dietary intake), while also tracking those behaviors among the members' social networks. On some sites, these data may suffer from self-reporting bias, for instance the accuracy of daily exercise reports or diet entries may depend on a participant's memory or be colored by a desire to appear fit. However, many of these sites provide tools that solve these problems by allowing participants to upload digitally recorded exercise data, or real-time medical records, which eliminates self-report bias and provides a means for timely social interactions on relevant health behaviors. Further, subjects' levels of participation in these sites – eg, the detail of their entries, their engagement with other members, and the overall frequency of log-ins – provide a direct behavioral measure of participants' involvement with the health community. The data extracted from these sites can be useful for establishing correlations between features of participants' social networks and subjects' commitments to smoking cessation, exercise routines, or good nutrition practices. Since the social networks in these sites are completely known to researchers, the impact of social factors such as homophily (ie, the tendency for social contacts to be similar to one another), clustering (ie, the tendency for people's contacts to be connected to one another), and degree (ie, the number of contacts that each subject regularly engages with) can be measured and evaluated with precision.As the social value of these online technologies increases with scale, an important benefit for the medical community is the opportunity that they present for improving on traditional methods of health research. As shown in Table 2, traditional methods for studying the social dynamics of health have faced difficulties getting regular, reliable measurements of when and how behavior change takes place. These difficulties are compounded by the inability of traditional observational methods to get reliable network measurements, and to identify the causal impact of social influences on changes in behavior. Open social media technologies have begun to address these problems by introducing the possibility of collecting regular, reliable data on health activities in a way that seamlessly integrates with existing online behaviors. Combined with the ability to record accurate social network data, and trace behavior change over time, these open technologies create a capacity to improve not just the quantity, but also the quality of data available for health research. Yet, these technologies are primarily observational. Going one step further, intentionally designed communities create the opportunity to address the difficulties that have plagued traditional efforts to identify the causal impact of social factors on health behaviors.Table 2. Comparison of Methods for Studying Social Influences on Health BehaviorsTraditional Observational DataLaboratory ExperimentDigital Observational DataInternet ExperimentScale✓X✓✓MeasurementX✓✓✓Structural controlX✓X✓ReplicationX✓X✓Behavioral fidelity✓X✓✓Traditional observational data, laboratory experiments, and digital observational data each have complementary advantages and disadvantages. Internet experiments combine the advantages of each approach.The gold standard for medical research is the randomized, controlled trial. For scientific evaluation of medical treatments, this method provides an invaluable means of determining the relative efficacy of new pharmaceuticals. Small group social psychology has also made use of this method for testing the effects of interpersonal interactions on behavior change, identifying how key factors, such as status and gender, can affect social influence.31 However, large-scale social dynamics and network effects have been impossible to study in controlled settings due to significant practical barriers, such as limitations on behavioral realism and population size. The creation of intentionally designed health communities using peer-to-peer social media has begun to eliminate these barriers, allowing the development of new experimental methods for designing controlled studies of the real-time collective dynamics of health behaviors.To provide an overview of the methodological advances created by open and intentionally designed social media, I discuss these technologies in light of the limitations faced by previous methods of social and behavioral research on health (shown in Table 2). For each of the difficulties faced by traditional methods – scale, measurement, behavioral fidelity, structural control, and reproducibility – I identify how emerging social technologies offer new solutions.ScaleScale is an essential feature of studying the dynamics of behavior in social networks. This is because the effects of social interactions on collective outcomes are qualitatively different in small groups than they are in large networks. For instance, a population of twenty people with diverse opinions may be unable to reach a consensus on the risks of smoking; however mathematical models show that increasing the size of the population by two orders of magnitude can allow consensus on smoking risks to emerge.32 This is because while very different people will not influence each other, people with similar beliefs will. The more people there are in the population, the more likely it is that someone will find another person with a similar belief, which allows a regression to the mean of their beliefs. The more people who can interact, the easier coordination will be. These local interactions can generate a process of global consensus across the population.33,34 Changes in norms about obesity, beliefs about the need for diabetes mellitus screenings, and attitudes toward taking prescription medications to lower cholesterol all take place within large-scale social networks. Empirically studying these dynamics of belief formation, social influence, and behavior change in large social networks is impossible in a laboratory setting since only a small number of subjects can interact at a time. By contrast, thousands, even tens of thousands of people can interact on social media websites, where participants learn about new ideas, information, and behaviors from one another. These online environments can thus allow researchers to observe the large-scale dynamics of social influence in real, connected populations.MeasurementAnother essential factor for studying the collective dynamics of health behavior is the ability to measure behavior change. What behaviors do people adopt, in what order, and because of which social influences? Detailed measurements of population connectivity, the sequences of adoption, and the real-time social influences on both adopters and nonadopters alike are necessary in order to understand the effects of social factors on behavior change. For instance, one of the most well-known empirical challenges in measuring behavior change is identifying individual thresholds for adopting new behaviors.35–38 In mathematical models of behavior change, thresholds are typically represented as the number of adopters an individual needs to be exposed to before she will be convinced to also adopt. However, in order to measure individual thresholds researchers must count the number of social exposures that each person has. This requires knowing both the number of connections that each subject has, and who among those connections adopted before the subject. Getting accurate data for these simple facts has turned out to be a very hard problem to solve. By contrast, in social media networks, every connection and every action is time-stamped and recorded. So, the state of each individual, the connectedness of the population, and the path of behavior change through the population are all known quantities.Behavioral FidelitySocial media can allow us to study social interactions at scale and with precise measurement. Yet, how do we know that the networks measured in the online space are accurate representations of the actual influences on the behaviors of interest? A third feature of social media is the remarkable, global importation of daily social experiences into the online domain. While traditional laboratory studies of social behavior have faced the difficulty that measured behaviors and social interactions are explicitly artificial, in online settings detailed measurements of interactions over social media record people's natural behaviors of people in online settings. Open social technologies such as Facebook, Twitter, Google+ and other popular online venues have provided a minimal infrastructure for everyday social life, which allows individuals to interact seamlessly offline and online, blurring the distinction between the two worlds. This flexibility allows online environments to take on a remarkable familiarity, which offers researchers a high-fidelity record of subjects' everyday online interactions.In addition to recording real social interactions, online social media increasingly provides an accurate record of health behaviors. As traditional health-related activities such as shopping for health and beauty products, signing up for gym memberships, contacting doctors, and making appointments for routine checkups become increasingly routine online activities, these digital traces of everyday life provide direct observations of the health-related behaviors that people engage in. Moreover, as these activities give rise to new intentionally designed online health communities, larger numbers of people maintain their own personal records of exercise and diet behavior in online form, providing as accurate a record of daily health behavior as there has ever been. Combined with the increasing trend toward online integration of physician records of medication maintenance, along with regular weight and cardiovascular reports, connections between individualized online health records and health-tailored social media may well become a new gold standard for accurate measures of real-time changes in population health.Structural ControlObservational studies of any ilk, even well-designed natural experiments and field experiments, typically face the difficulty that population structure is not controlled. Consequently, specific features of social connectivity or confounding interactions between variables such as social familiarity, the structure of the social network, and the similarity of social contacts cannot be explicitly controlled or varied independently. This makes it essentially impossible to causally identify which social factors directly affect collective changes in health behaviors. However, as new, intentionally designed health communities and social media applications are developed, it is increasingly common for certain health-relevant relationships to take place entirely within these online environments. This means that the full sequence of messages, notifications, and contacts related to a specific behavior can be recorded and studied. For behavioral and medical scientists, this invites a new kind of research opportunity. Individual interactions not only can be precisely monitored but also can be seamlessly and strategically structured to study how changes in the pattern of social interactions influence changes in behavior. This also suggests that new kinds of behavioral interventions may be able to be deployed through social media tools, which can turn the online record of interactions and behaviors into a scientifically comprehensive record of how social structures affect the diffusion of health outcomes. These innovations suggest the possibility of laboratory-quality measurements that can, for instance, offer direct causal evidence on how alterations to the i) kinds of social contacts that subjects are exposed to ii) how many people they interact with, or iii) the connectedness of their network clusters can systematically affect collective changes in their health behaviors.ReproducibilityThe logic of experimental replication over independent observations provides the foundation for causal inference. But, how can we reproduce a behavioral epidemic? Or how can we independently replicate the diffusion of a health technology? Despite its intuitive appeal, the logic of replication has traditionally eluded empirical studies of social dynamics. The reason is that most large-scale observational studies cannot be reproduced under identical structural circumstances, with the same measurement capabilities, and with equivalent distributions of subject populations. However, the ability to design and control studies of behavioral dynamics with intentionally designed social media provides a way of overcoming these obstacles. With a controlled experimental design, subjects participating in intentionally designed sites can be randomized to independent trials in which entire populations can be independently "treated" with socially targeted interventions. These population-sized trials can thus be replicated by randomizing pools of subjects to completely independent social worlds with demonstrably divergent health outcomes. Extending the randomized, controlled designs from medicine and psychology, health scientists can gather repeated observations not just of individuals but of entire populations.The first-order question that has traditionally occupied researchers of health behavior is whether it is possible to identify social influences on behavior change.4,8,9 However, the union of these 5 features of social media—scale, measurement, behavioral fidelity, control, and reproducibility— creates the novel capacity not only to identify, but to investigate, the dynamics of behavioral influence on health in large populations. Once methodological barriers to identification are overcome, the number and variety of important new questions that can be studied increase dramatically. For instance, theoretical research in social epidemiology suggests not only that social influence takes place but also that changes to the pattern of network ties (or network topology) in a population may dramatically alter whether and how influence occurs. These theories of social influence suggest compelling new ideas about how network theory might be used to promote changes in population health. Could introducing changes into people's social networks really have large-scale impacts on their weight, cholesterol levels, and smoking behaviors? It is an intriguing proposition; however, when it comes to empirically evaluating these theories, traditional observational techniques do not offer many solutions. Here, new social media-based methods for behavioral research may allow researchers to empirically investigate for the first time how theoretically proposed changes to people's social networks might promote significantly different outcomes for population health.A Case Study: Social Networks and DiffusionSocial epidemiologists have long been familiar with a truism called "the strength of weak ties."36 The basic idea is that if you want to find information about new forms of medical treatment, innovations in health technology, or recent trends in diets that lower cholesterol and blood pressure, the best people to talk to are not the people whom you know well but rather your casual acquaintances. People you know well (ie, your close friends and family) are referred to as your affectively "strong ties" because of the strong emotional bond in the relationship. Correspondingly, casual acquaintances are called "weak ties" because there is very little affect in these relationships.By definition, you do not know your weak ties well. So, you are not likely to know who their close friends or family are. You probably would not ask them to watch your children or offer to lend them a large amount of money. However, they provide an important social service. They connect you to parts of the social network that are far away. In other words, weak ties are also long-distance ties in the social sense that they connect people in the social network who would otherwise be socially remote. In contrast, close ties tend to know each other's friends, and their friends know each other. This creates triangles in the social network (see Figure 1). Each triangle is connected to other triangles, which are embedded in still more strong-tie triangles. An important consequence of this is that if someone tells a few of her close friends that she is looking to find out about alternative treatments for high blood pressure, and they pass the word on to their friends, chances are that many of them will wind up repeating this information to each other.Download figureDownload PowerPointFigure 1. Clustered network with a single weak tie. Each individual's neighbors in the clustered network share neighbors with each other, creating triangles in the network. The addition of a weak tie into the network (dashed line) creates a link that connects otherwise distant individuals, allowing new ideas and information to spread more quickly.From the point of view of social diffusion,a eg, getting the word out about a search for medical treatment options, these redundant messages shared among close friends are wasted signals because the search will get circulated among many of the same people without reaching new sources of information. In a diffusion process, each time a piece of information gets repeated to someone who has already heard it, the social link fails to create a new exposure, and the search travels comparatively more slowly than it would have if the link had gone to an unknown person. Consequently, an informational search winds up bouncing around networks of people who have many of the same ideas about how to find health resources, similar exposure to medical treatments, and similar beliefs about the available treatments.In contrast, if none of your social contacts have
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