• Research on the spread of health-centered behavior describes how the structure of social networks can influence how behaviors travel through a population.

    These figures show experimentally manipulated on-line social networks. The first community (left) has a clustered network structure, while the second one is a more "random" casual contact network. Node colors indicate people who adopted a behavior (blue) and those who did not (white), with lighted links showing the active pathways of communication. The clustered networks spread the behavior to more people than the casual contact networks.

    Image: Damon Centola

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  • Damon Centola, an assistant professor of system dynamics and economic sociology at the MIT Sloan School of Management

    Photo: Melanie Gonick

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Better health through social networking

Research shows how the nature of your social network influences your health behavior.


Scientists have long thought that social networks featuring many distant connections, or “long ties” — where individuals know a lot of people, but not well — produce large-scale changes most quickly. But in a new study, Damon Centola, an assistant professor of system dynamics and economic sociology at the MIT Sloan School of Management, has reached a different conclusion: People are more likely to acquire new health practices while living in networks with dense clusters of connections — that is, when in close contact with people they already know well.

Researchers often regard these dense clusters of connections to be redundant when it comes to spreading information; networks featuring such clusters are considered less efficient than networks with a greater proportion of long ties. But getting people to change ingrained habits, Centola found, requires the extra reinforcement that comes from those redundancies. In other words, people need to hear a new idea multiple times before making a change.

“For about 35 years, wisdom in the social sciences has been that the more long ties there are in a network, the faster a thing will spread,” says Centola. “It’s startling to see that this is not always the case.” Centola’s paper on the subject, “The Spread of Behavior in an Online Social Network Experiment,” is published in the Sept. 3 issue of the journal Science.

The buddy system

To see what difference the form of a social network makes, Centola ran a series of experiments using an Internet-based health community he developed. The 1,528 people in the study had anonymous online profiles and a series of health interests; they were matched with other participants sharing the same interests — “health buddies,” as Centola calls them in the paper. Participants received e-mail updates notifying them about the activities of their health buddies.

Centola placed participants into one of two distinct kinds of networks — those oriented around long ties, and those featuring larger clusters of people — and ran six separate trials over a period of a few weeks to see which groups were more likely to register for an online health forum website offering ratings of health resources.

Overall, 54 percent of the people in clustered networks registered for the health forum, compared to 38 percent in the networks oriented around longer ties; the rate of adoption in the clustered networks was also four times as fast. Moreover, people were more likely to participate regularly in the health forum if they had more health buddies who registered for it. Only 15 percent of forum participants with one friend in the forum returned to it, but more than 30 percent of subjects with two friends returned to it, and over 40 percent with three friends in the forum made repeat visits.

“Social reinforcement from multiple health buddies made participants much more willing to adopt the behavior,” notes Centola in the paper. Significantly, he writes, this effect on individuals “translates into a system-level phenomenon whereby large-scale diffusion can reach more people, and spread more quickly, in clustered networks than in random networks.”

Centola thinks the existence of this effect has important implications for health officials. A “simple contagion,” in network theory, can spread with a single contact; a “complex contagion” requires multiple exposures for transmission. A disease, Centola suggests, can spread as a simple contagion, but behavior that can prevent the disease — such as going to a clinic for a vaccination — might spread only as a complex contagion, thus needing to be spurred by reinforcement from multiple neighbors in a social network.

“If there is a significant difference between simple and complex contagions, that actually matters for our policy interventions,” says Centola. The public promotion of screenings and other forms of disease prevention might best be aimed at communities and groups that act as closely clustered networks.

Studying communities, online and off

Colleagues in the field find the study to be of both theoretical and practical interest. “It’s interesting work because it shows that for the diffusion of certain kinds of things, you really need reinforcing,” says David Lazer, an associate professor of public policy at Harvard’s Kennedy School of Government. “You need wide bridges to transmit complex information like health data, and that is different from the traditional picture of how things spread in a network.”

As Centola acknowledges, the study has limitations. Joining an online health forum has little cost in time or money, unlike many other kinds of health behavior, from vaccinations to changing one’s diet or adopting an exercise routine. “Getting a colonoscopy is hard,” Centola says. “Just hearing about it is probably not going to convince you to do it.” The rate of adoption would likely vary widely for many forms of health behavior, and be relatively low when notable costs are involved.

On the other hand, Centola notes, the existence of those costs implies that social reinforcement, such as having multiple friends and relatives who get colonoscopies, may be especially important. “These redundant signals are necessary to make people adopt the behavior,” Centola says.  

Further fieldwork may help determine how resistant people are to changing particular forms of health behavior. “One thing this study begs, in a good way, is more research in natural settings,” says Lazer. To see the effectiveness of public-health measures, he suggests, “You might try to target two neighborhoods in different ways, and then assess which is more effective.”

For his part, Centola thinks there is also further work to be done evaluating the effects of online social networks on behavior. “There is a natural implication in terms of what this means for designing online communities,” says Centola. His new research, building on his current paper, aims to find new designs for online communities, in order to promote good health practices.


Topics: Health, Health care, Networks, Social networks, Systems design

Comments

@sonomabuzz on Twitter => This may explain why AA / NA works. It may even explain dynamics of starting using v stopping. http://bit.ly/9OfgMW
It would be very difficult to find out hidden links between cause and effect in terms of influence on social network. His study seems to be worth noting in terms of having tried to find out some hidden links, even though there are more work to do as he mentioned. I'm wondering if this result could be applicable to other factors such as money, education, etc. to affect our life. Thanks.
Thanks for sharing your FINDINGS!
interesting article. But what is also good to point out is that online social networks also reinforce unhealthy behaviour. And the phrase "dense clusters of connections" does not imply online social networks like facebook i guess. Because most peoples social networks on facebook does not fit the description "in close contact with people they already know well". And what about the health issues regarding peer-pressure? Or that maybe a different article of course. with regards M
There are two overlapping social networks that are at work - one network which includes people related to you (let's call it the Family-Net) and one which includes people who are your acquaintances (let's call it the Friendly-Net). In today's world, they do overlap - typically, the overlap varies between 15 % and 35 %, depending on an individual's geographic migration index. Remember, the Family-Net also shares more of the genetic code, and therefore have more of the common cause. My Family-Net for instance has for long had diabetes. A good understanding of how this is being tackled by different people in the Family-Net is very helpful - diet, exercise etc. Similarly, location-based diseases can be another subject for study.
This POV has bearing on a closely-related hot topics today - electronic health records. In fact it's not too wild a thought to conceive that the "social medical record" of the future may become a bigger driver of personal wellness than the purely patient-centric EHRs/PHRs that we are advocating today. What if people could enable specific components of their EHR to be selectively sharable with other people - similar to the granularity offered by Facebook privacy settings. This can support the system of socially-influenced wellness that exists in the "offline" world today (offline examples include mall runners' clubs, Alcoholics Anonymous, diabetes support groups, etc.)
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