The burgeoning Quantified Self movement is a from-the-ground-up collective helping to perfect the marriage of technology with people’s self-improvement goals and usher in a new form of digital self-empowerment. But with the rise of self-measurement’s popularity come key questions about who’s minding the vast ocean of personal data coming out of it and how it is—and ought to be—used.
A thought experiment: close your eyes and picture yourself lying in bed in a state of salivating semi-sleep. At once, you’re startled up by an alarm set off by a sensor on your wrist notifying you that you’ve come to the end of your latest sleep cycle. Entering your kitchen, you find that the LED display on your refrigerator lists your day’s meal allotment based on the UPC codes of the food you ate the day before. As you pack a designated lunch, a yellow glow on the accelerometer bracelet on your other wrist notifies you that you’re now due for a jaunt on the treadmill to meet your resting heart rate reduction goal. You muster up a deep breath (or sigh) of inspiration and make your way over to a pre-assigned interval workout.
Now, coming to: does this scenario strike you as a perverse science fiction nightmare, or is this instead the ultimate fantasy of the day-to-day lifestyle of your ever-improving self? If you’re leaning more on the side of the latter, there’s good news—with advances in technology, probably already in your pocket, and the creative collaboration of the Quantified Self movement, this utopian morning may be closer than you think.
Measuring the Self—This Ain’t Your Mother’s Weight Watchers (Well, Sort of)
Ask any Olympic athlete—if you want to improve your performance, you have to measure yourself constantly on the road to optimal competitiveness. In the last century, professional athletes have benefited from the highest level of sports technologies, medical knowledge and even statistical inference (case in point: the film Moneyball) to record, analyze and compare performance to build customized regimens to achieve set goals. While the costs involved have been high, given the pride, glory, and—lest we forget—boatloads of cash on the line, investments in hardware, software and manpower would seem a no-brainer. This is notwithstanding, of course, the incredible brain-power behind this science.
Now, whereas during this time some of the analytic set have, in their own right, tried to self-measure with available technologies (e.g. the time-tested pen and pad), the idea of having the ‘Average Joe’ do the same has been a somewhat different story. Sure, postwar diet crazes begat the concept of calorie counting in the collective consciousness, and Weight Watchers would end up making an industry by simplifying this into an easy-to-digest point system for the masses, but this would not at all be taken up en masse.
While Weight Watchers was far from technological, it set a popular self-measurement precedent, serving as a form of the feedback loop, the mechanism behind most actionable self-improvement schemes. The feedback loop runs on a series of steps towards desired outcomes: ‘evidence’ (noting how many points you’re eating when you dig into a chocolate brownie), ‘relevance’ (understanding how many points you can have in total in a day), ‘consequence’ (understanding the negative consequences of exceeding the daily point allowance, which you are about to do in one fell swoop by putting that brownie in your mouth) and ‘action’ (putting the brownie down, popping in a piece of gum and trying to picture yourself multiple sizes thinner, if that helps).
We Have the Technology—Exponential Leaps in Looping
Fast forward just a wee bit to today, and thanks to some turn of century technological breakthroughs, there’s been an exponential increase in the access people have to feedback loop tools spanning multiple areas of self-measurement, the data that can be acquired and meaningfully interpreted from these tools at literally all hours of the day and the general awareness of these tools and the data in them as they have been (selectively) broadcast to the world. Any old consumer with a shred of disposable income now no longer has any excuse for chugging down a six pack and every reason to go turn their gut into one—for this they can thank cheaper-than-cheap sensors, big strides in big data and the ‘cloud,’ the extensive reach of social media and the handy thing that ties them together: mobility.
A proliferation of cheap-to-produce sensors within the last decade have allowed for a ubiquity of heart monitors, pedometers and accelerometers in small and mobile formats, all within a consumer’s budget.
Viewed in terms of the loop, when it comes to building up tons of evidence (and reliable unbiased evidence at that), a proliferation of cheap-to-produce sensors within the last decade have allowed for a ubiquity of heart monitors, pedometers and accelerometers in small and mobile formats, all within a consumer’s budget. These sensors automatically measure at a level of detail that would be impossible to compute with pen and pad, namely figuring out heart rate and generalized full-body activity levels (as does the Nike+ Fuelband) at every instant of the day. Once all this data is absorbed and stored in a device or in the cloud, growing data mining capability and a stronger-than-ever focus on insightful visual summaries of data insights help provide even more evidence, as well as relevance and consequence, particularly as users can find out how they are tracking relative to their reference group. And here’s where social media takes things to another level—reference groups need no longer be static tables of yesteryear, but dynamically built based on actual performance of a collaborative/competitive community of peers literally racing against you from anywhere in the world. These references may in turn up the game through ‘digital peer pressure,’ which may in turn affect your action, and the more one gets rewarded for exceeding goals and possibly exceeding their peers, the more appealing taking that action becomes. Of course, it’s not just the ‘friendly competition’ that keeps one on the ball, but the very enjoyment of turning self-measurement from a private activity to a social game to be enjoyed amongst friends that makes this networking effect such an exciting part of the new self-measurement.
Having affordable access to all of the above might excite a certain tranche of early adopters, but the beauty is that nearly all this hardware and software naturally come together in the modern smartphone, something people are buying in droves and carrying with them at all times of the day anyway. Smartphones come with built-in sensors, access to the cloud and the Internet, and surprisingly nuanced applications, ones built to not just take sensor data but manually inputted data (the phone is, after all, a notepad too) and generate fully insightful, often previously unrecognized trends and insights based on the performance of yourself and your peers. And while smartphone users may never have clamored for traditional statistical applications like Statistical Package for the Social Sciences (SPSS) back in the day, they’re getting a kick out of apps like Runkeeper which tracks runner speeds, distances and times, which can be shared with a grander community, Fitocracy which takes competition further through gaining points and badges for performance, and Gympact which takes things even further by putting wagers on the line for exercise competition. Exercise of course is not the limit of all this—Weight Watchers’ second coming may now be in the form of Massive Health’s The Eatery, an app built to not just store what you’ve eaten but go beyond and show you insights and counterintuitive trends regarding what you eat and when. Rather than counting calories, one takes a picture of their meal, rates it and posts it. The meal in turn gets rated and reviewed by the community, providing a peer influence factor, and within a short period of time, eating trends can be generated and displayed (in beautiful retina display). Meanwhile, some apps are benefitting from external hardware, as is the case with Jawbone’s Up; this combines elements of fitness tracking and data mining/relationship seek through a sensing bracelet that measures nearly everything about you, including even your sleep, to pass onto your smartphone at all hours of the day.
Movement Tracking—Built from the Ground Up
So who is responsible for bringing these tools into the hands of Joe Six Pack (through the ‘Trojan Horse’ of the smartphone)? Well, in the most superficial sense, technology builders and software publishers, but ultimately their work stems from a movement, and the objective is not intrusive mind control as the paranoid might fear but rather self-empowerment in its most unbridled and arguably nerdiest form: the Quantified Self movement.
Uniting around the QuantifiedSelf.com site, Wired editor Gary Wolf and multidisciplinary author-cum-cataloguer Kevin Kelly have overseen a veritably global community ‘jam session’ on building and using self-measurement tools like the ones described above. Communication takes place through housed blogs and forums, as well as show and tell meetings where stories and experiences are shared, and conferences which focus primarily on the building of the tools themselves. The community has been built up in the collaborative environment akin to open source or the creative commons, growing out of the personal passion of those inspired to measure and catalogue themselves on a regular basis. One can only imagine how Alfred Kinsey would have applied these technologies.
The Brand Opportunity—Working from the Top Down
All the while, it can’t be ignored that corporate interests have existed in the self-measurement world and are only geared to ramp up. As technologies grow in adoption and more users climb aboard, those that own the ‘keys’ to the these applications have the benefits of gaining reach and visibility, building engagement and gaining reams and reams of behavioral data. And while some companies are starting to keep an eye on the potential from the sidelines (even Quantified Self has its own corporate sponsors including the likes of Intel and Autodesk), others have already been actively involved in the frontlines, in both hardware and software capacities. For example, Nike has already shown their presence through its creation of Nike+, an effective exercise social network and collaboration with Apple ongoing since 2006. A social network of millions has been built around friendly and competitive fitness as measured through monitors (including plug-ins to Apple mobile products, the phones themselves and brand new monitoring devices like the Fuelband, Nike’s response to Up). Nike has built an engaged community communicating with each other, created way more ‘touchpoints’ with their customers than in the world of footwear alone, and have amassed a massive aggregate of data—so much so that the network has broken down and had to be apologized for earlier in the year.
Then there’s the even clearer stakeholder in health and behavioral issues—insurance companies. Auto-insurer Progressive was a pioneer in introducing their MyRate device which, upon customers’ voluntary installation in their cars, wirelessly transmit all driving information to the company and allows Progressive to offer a new rate based on the driver’s performance every six months. This gets around the false negatives (or positives?) of precious actuarial tables and seems to provide ‘fairer’ pricing and certainly greater motivation for better driving. Or does it?
Big Brother Might Be Watching—But Can He Keep a Secret?
With Big Data comes big responsibilities, and as big brands and companies build audiences around self-measurement tools, there is the capability for mismanagement or usage that favors the company at the expense of the user.
One natural way in which such data sets is used is marketing and effectively the sale of eyeball inventory to other brands. Runkeeper is an example of an app that’s created a community to which it can offer premium exposure to third-party advertisers, and the more targeted the advertisement given the user data provided, the more valuable that inventory becomes. The second is that of the use of the data itself, and this pertains to the ways in which agencies, particularly those of insurance and government, are able to make use of this information to categorize people, control them by requiring certain behaviors of them to meet certain standards of insurability and, as per the flipside of the Progressive case made above, punish them for behavior that they find undesirable.
If consumers have to date been hesitant to join or continue with networks because companies were making use of their demographic profiles and selling them, then one can expect even more resistance for companies that are capable of using this information to affect key life factors like life and health insurance eligibility (massive ticket items, particularly in the USA), let alone just purely being ‘freaked out’ that such intimate pieces of information are available to be seen or even automatically acted upon by external parties (while many have acquiesced to it, the idea that Gmail was robotically ‘reading’ emails to serve ads has continued to irk some).
Tit for tat—Striking a Fine Balance Between Data Maker and Data Holder
Ultimately, the issue comes down to the true value that the company is able to provide its users in exchange for the data that it takes in. If in the example of marketing the company uses whitehat or transparent practices that provide users with relevant targeting, notifying them of services or capabilities that they would be interested in, then over time that should be rewarded. If on the other hand blackhat techniques are used to subvert the trust of the company, then there can be massive backlashes (Facebook’s infamous Beacon fiasco, where Amazon purchases of users were broadcast unwittingly to their friends, served as a primary early backlash against non-transparent privacy intrusion).
Likewise, in the case of the second type of usage, if the data is used in a way to ‘punish’ the user, either by finding out information about them that causes their status with the company to worsen or, in the most extrapolated version of the science fiction scenario up top, requires the users to run incessantly on treadmills just to qualify for coverage, then it’s bound to harbour very negative brand feelings in the community. If on the other hand, the management of the data is seen less as a necessary evil or leap of faith in the company’s blackbox data management, but rather a value add by having the data stored externally (in the same way one might pay for hosting), with analysis and variations added to boot, then that might entirely change the attitude. Even at the level of housing strictly personal, non-anonymous information, if the data has the capability to be ported back to the user for her own personal use and/or she has the option to selectively share certain pieces of the data with other sources (for example, imagine if the user were able to choose which of these pieces of data she would like to share with her doctor before her upcoming appointment), then the value of communally stored data becomes much more of a boon and much less of a roadblock to adoption. Ultimately, gaining a win-win relationship will come down to finding the sweet spot between ease-of-use, unobtrusive data collection, insightful formatting of tracked data and building and engaging a lively community that provides support and helps deal with the emotional side of things.
In a few short decades, we’ve gone from a world where a handful of real and armchair scientists have self-monitored and theorized on foolscap to a world in the not-too-distant future where a connected ecosystem of ubiquitous sensors, carried and/or implanted mobile devices provide auto-generated and unbiased user data for whole communities at a time.
Conclusion—Working Off the Adoption Curves
In a few short decades, we’ve gone from a world where a handful of real and armchair scientists have self-monitored and theorized on foolscap to a world in the not-too-distant future where a connected ecosystem of ubiquitous sensors, carried and/or implanted mobile devices provide auto-generated and unbiased user data for whole communities at a time. These apps can spit back real-time and post-mortem insights through engaging visual presentations of counterintuitive correlations, helping users reach long sought-after fitness goals, save money and in general enrich the quality of their lives. And if that were not enough, the costs associated with this could go from a few hundred dollars at this very day to somewhere in the neighborhood of free.
But while some will embrace the movement, others will question whether or not it is truly ‘free’—why should they give up this data to escape to who knows where? It’s one thing to write down some data, it’s another when it’s involuntary, genetically-dictated and might have large implications for stakeholders that are not necessarily on your side. Perhaps they have a very good point. But perhaps this is the same level of objection we’ve seen with nearly every technology, objections that were overcome with time and desensitization. After all, how long did it take after seeing your first Bluetooth conversation at Starbucks, where you thought the speaker was a soliloquizing schizophrenic, before you were having your own without even flinching? How many queasy status updates did it take before you had tested the waters and were happy to tweet your heart out indiscriminately? When the need and the value is there, it’s only a matter of time before some critical mass gets to it. And when the advantages can help with such major issues on the Western agenda like getting rid of some critical mass, signing up becomes all the harder to turn down.