Do you have a FitBit story?

Last November, S came home with a Fitbit Flex. For those of you who don't have one of these increasingly ubiquitous devices, it's a small, plastic band that you wear on your wrist (there are other tracker options as well). It tracks the number of steps you take each day, and provides a means to track food and water intake, as well as sleep. You can set goals against these factors to help you reach health milestones. "Do you want one? Would you do this with me?" he asked while showing me around the website. I agreed, and a few days later, I had my own box of brightly colored bands and a small tracker.

It wasn’t hard for me to get into the competition for steps, and with that followed more precise record keeping, and a greater awareness of my relationship with food. But it didn't stop there. Like many current applications, there's a social component to FitBit, which allows you to compete with friends as well. And it wasn't long before I was looking to compete with coworkers and friends.

Wearable technology seems to be sweeping the market. While devices like the Fitbit are increasingly common, we’ve also been struggling to make watches smarter and maximize the potential of our phones. We might be thinking James Bond, but the truth is we've been wearing technology for thousands of years: armor, eyeglasses and contact lenses, SCUBA gear, and prosthetics are all examples of wearable technologies. They perform a service for us, but they're also steeped social symbolism. They help us curate our identities offline by allowing us to present ourselves as fulfilling normative expectations for a variety of contexts. For example, by putting on armor we claim membership with a group and broadcast that to others. That armor also allows us to affiliate ourselves with all beliefs of the group and helps us broadcast those beliefs to others. We strengthen the ties of the group and enhance its overall fitness through wearable technologies.

Wearable technology exists in offline spaces. The newest generation is digitally-enabled but they still have to exist and function in an offline context. Will this infusion of data change how we’ll use these devices?

The science of social pressure.

Among the Fitbit faithful in my group, our Fitbit stats are part of what we talk about in offline interactions. We compare stats, we encourage each other other, and we check-in when someone goes dormant. We’re a sub-network, and we’re creating a specific marker for our group. As we establish this sub-network, we encourage signs of associated behaviors online. Having a healthy meal? Someone will Like that. Going hiking, biking, or swimming? Someone else will Like that. In this way, the network that includes Fitbit users weaves together an experience that reinforces acceptable behaviors.

There is a science to social pressure that has been well documented. The work done by Nicholas Christakis and James Fowler who have used the Framingham body of data to trace the spread of obesity, smoking cessation, and happiness is one example. This data was the byproduct of a longitudinal medical study that collected information on the personal contacts of participants, which allowed researchers to map the participants' social networks. This work was meant to apply to offline networks, but given the way online networks have evolved—particularly clustered online networks such as Facebook where there is a great deal of overlap between connections—it has the potential to provide insights into the ways the actions of one individual can influence the actions of others within that online network as well.

In the past, Facebook has used this reasoning to spur initiatives like Rock the Vote which reportedly directly influenced 60,000 more people to vote in the 2010 election, and influenced an additional 280,000 people to vote. It's the story of this latter group that's particularly interesting because it reveals the potential of social networks to drive people to action. Approximately one percent of users in 2010 (611,000 people) received an informational banner at the top of their news feeds, which encouraged them to vote and provided a link to polling places. It also included a clickable "I voted" button and a counter of users who had clicked the button. Some sixty million (98%) of users received a social message, which had the added element of showing six random Facebook friends who had clicked the "I voted" button. The remaining one percent received no special messaging or incentives. Researchers compared the online behaviors of the different groups with publicly available voting records for the 6.3 million users who had been included in this experience to understand which group was more likely to vote.

While they found little difference in voting behavior between those who saw the informational message and those who didn't get a message at all, users who saw the social message (including the six friends who indicated that they had participated) were

2% more likely to click the "I voted" button,

0.3% more likely to look for information about a polling place than those who received the informational message,

and 4% more likely to head to a poll than the other groups.

Facebook was not asking you to share who you voted for, all it asked was that you pass along the message that you voted if you voted. In doing so, it established a normative experience, which became the basis for social pressure in this context.

A more recent example of this is the ALS Ice Bucket Challenge. While this event has resulted in record donations for the ALS Association, it has drawn criticism because it specifically calls people out to participate. The challenge involves nominating five people to complete the task of either dumping a bucket of ice water over their heads or donating $100.00 to ALS within 24 hours. Respondents then name five nominees of their own, and so it goes. It's a modern day chain letter.

There's a potential stigma attached to non-participation. In both of these instances, our willingness to participate tells others something about ourselves. It establishes empathy, and sympathy, and generosity, which are all qualities that are necessary to the survival of any social network. Our networks (both online and offline) help us establish a sense of what's acceptable. The more social reinforcement we receive that certain actions are appropriate, the more likely we are to adopt those actions ourselves.

Separating fact from fiction.

But online networks differ from offline networks in one very important way: they make it easier to manipulate perception. Online identities are carefully curated experiences. We can choose the best of ourselves to share with others, and we often do: the most flattering photographs, check-ins at exclusive locations, statuses that are designed to showcase how much fun we're having and how likable we can be.

We want to share the best of ourselves, and it’s easy to do that online where we can pick and choose what we tell people. We can click that button to say we voted without actually voting. We can say we donated money to the ALS challenge without donating and without having to dump a bucket of water over our heads. Who would really know? We’re deceptive, and honestly that’s a part of belonging to a social group. We see instances of this in other social animals. We want to do what our friends are doing; we want to belong and confirm that we belong to our network. And to that end, deception is something deploy pretty frequently.

The data we’re sharing online may not necessarily tell the whole story. Think about it: When was the last time you changed your profile picture on Facebook? Do you really still look like that person? Have you put on a few pounds or let your hair grow out? Even if you are perfectly manicured all the time, do you share instances when you're not feeling that great? Photos of you curled up on the sofa when you're sick, or first thing in the morning when you've woken up? Statuses sharing that you're experiencing diarrhea? Probably not. We can also shape our networks to match our interests. We can filter out people who share things we don't agree with or who present selves that we don't want to associate with, even if we don't want to cut them from our social group entirely. We hide their updates, or passive aggressively post content in opposition to their opinions.

We’re deceptive offline as well but it’s more constrained because we hold each other accountable for those deceptions. Contact lenses are a great example of this: they allow us to lie about the state of our vision without compromising the fitness we add to the group. Contact lenses give the illusion that our vision isn’t enhanced by technology, but they help us see, which means that we’re adding visual data to the group overall. That means, if you want to get down into the nitty gritty, that if contact lenses allow me to see a predator coming, then I can warn you, and we both run away and live for another day. Of course, that's an extreme, but the sense of fitness is what allows the deception to persist.

Gadgets like the FitBit force us to walk a fine line of self-presentation. They merge online networks where we can create communities that match our interests and create specific reflections of self, and offline networks where we have to meet those associations and account for that representation. But this has always been a hallmark of wearable technology. It merges function with symbolism to help establish our sense of belonging.

What does the future look like?

As we look to build these devices, we can’t overlook our offline tendencies. We’re going to be deceptive, but the technology that will have longevity will be the devices that recognize deception but hold us accountable. The difference between a pedometer and a Fitbit is that our Fitbit results matter to other people. You can sit on the couch and shake your Fitbit to fake steps, but ultimately you’re going to have to see your friends, and if you’re consistently at the top of the Fitbit leaderboard, they’ll wonder that there isn’t a physical correlation to the data. And that raises questions about what you really believe because wearing this device tells people something about the values you place on physical activity and healthy eating. While you don’t need to lead the board every week to have a belief in those things, there is an offline connection that people will look for. That’s what the infusion of data gives us: a way to quantify symbolism.

We’re going to take these devices and assign meanings to them. They’ll become the basis for sub-communities within the neworks that we belong to, but the future of wearable tech isn’t bound to the data that these devices collect and share. It’s tied to the ways we define norms within our networks. Our networks are great at telling us what to do. There are trends we can identify: periods where we experience the same sorts of life events that reveal social expectations. Anecdotally, for example, in my late twenties, a substantial number of people in my feed got in engaged. For approximately two years following, my feed was filled with wedding photos. And then came the pregnancy announcements and baby photos. In this way, my network demonstrated appropriate and expected social paths. Of course, there are people who didn’t get married or could not get married, and people who have not had children, but the clustering of these actions added weight to their perceived appropriateness.

These types of trends illustrate a human story. And data alone won’t be able to do that. Our wearable technology will need to leave room for us to tell that story and define what it means to belong to a group. The future of wearable tech is not rooted in the data that we can capture, but in the offline behaviors that make that data credible.

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References:

Bond, Robert et. al. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489: 295-298 DOI: 10.1038/nature11421.

Christakis, N., & Fowler, J. (2007). The Spread of Obesity in a Large Social Network over 32 Years New England Journal of Medicine, 357 (4), 370-379 DOI:10.1056/NEJMsa066082

Christakis, N., & Fowler, J. (2008). The Collective Dynamics of Smoking in a Large Social Network New England Journal of Medicine, 358 (21), 2249-2258 DOI:10.1056/NEJMsa0706154

Rosenquist, J., Fowler, J., & Christakis, N. (2010). Social network determinants of depression Molecular Psychiatry, 16 (3), 273-281 DOI: 10.1038/mp.2010.13

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