The Greeks had the right idea but the wrong equipment.
Know thyself, they said – but all they had to work with were the dusty corners of our angst-ridden psyches.
I’m talking a walk with my iPhone, a pedometer app ticking off my baby steps toward a different kind of self-knowledge – truer, some would say, because it’s brought down to numbers.
This isn’t a belated New Year’s resolution of personal improvement (the new gym membership is supposed to cover that), but my attempt to understand the rise of the Quantified Self – a movement that evolved from geeks filling in spreadsheets with every conceivable detail of their daily lives to automated apps and gadgets even an old lap-counter like me can try to figure out.
As I proceed, step by step, I could be tracing a journey to the kind of numerical utopia promised by products like Fitbit Force, one of the wireless “wearables” being pushed at the recent Consumer Electronics Show in Las Vegas. There are also apps like Sleep Cycle, which graphs the quality of your sleep (and adjusts your wake-up alarm accordingly), MoodPanda, to help you rate your feelings through an interactive diary, and 80Bites, for those who want to track their food consumption bite by bite.
All of them share the sort of data software once exclusive to research labs, sports franchises and Fortune 500 companies, but now designed to modernize the search for an elusive better self, stripped of intellectual waffling and philosophical posturing.
“Never before have we had the ability to self-improve to such a massive extent,” says Christopher Berry, chief science officer at Authintic, a company that helps companies make use of personal social data. “There’s always a difference between what somebody tells you they are and what they actually are.”
What we are, please note, not who we are.
And there’s the rub. Can a pedometer – or a sleep app or a mood tracker – tell me what I need to live a better life, to know myself in ways that the old forms of introspection missed? And how much should I trust the “what” – all that data that I uncover? Can all these newly generated numbers, much like the selective haziness of human memory, sometimes get it wrong?
Numbers can surprise you.
Walking has become a large part of my life, in part because it’s the kind of locomotion that invariably takes time, but also because it comes with its own pleasure. I started my foot-based rambling in Europe, where the more concentrated landscape offered constant rewards at walking speed, and then realized that it was as easy and rewarding to cover large swathes of ground in my day-to-day life.
Walking is liberation – no traffic jams, subway delays, cellphone conversations on a cramped bus, just a trip home from work that is also exercise and even spiritually cleansing. Bad weather, like the recent ice storm, raises the stakes but doesn’t diminish my sense of accomplishment. Unable to sleep one power-free night, but unwilling to sit in the frozen dark at home for hours, I set myself loose and walked to the warmth and light of an office that still had functioning electricity. But I didn’t know as much about my walking as I thought.
I had always assumed I walked at about 6.4 kilometres an hour, a brisk marching pace. The pedometer told me that I covered the seven-km trip at only 5.3 km/h – a disappointment until I worked in the slippery footing of the icy sidewalks (the pedometer showed my stride shortening as I struggled to keep my balance).
Because I kept the pedometer working the whole day, I discovered that I took 13,708 steps, counting my roaming around the workplace – far more than I would have imagined.
Once you outsource yourself to an app or device, human measurement quickly becomes much more precise. There’s a natural tendency to want to do more to earn the gold stars that come with big numbers, to keep up with your apps’ demands and expectations – at some level you become more accountable, because you have a standard to meet and an external monitor to remind you of how you’re doing.
Self-trackers who are more sociable and less private than I am don’t stop with the accumulation of personal data – they add to its meaning by uploading the numbers to social sites where friends and allies comment on their progress. Or they get together at Meetup groups to share their experiences.
Sacha Chua is a 30-year-old tech consultant who is active in Toronto’s Quantified Life group, – she storyboards their meetings through Sketchnotes – and who presented her personal time-tracking study at the 2012 global Quantified Self conference in Palo Alto, Calif.
“By sharing their data, people come to understand themselves better,” she says. “And they also inspire people to ask similar questions about themselves, play with the same tools and make new connections.”
Group sports like amateur cycling are going through a huge identity shift as riders use personal trackers like the Garmin bike computer to share their routes, their best times on climbs, their energy expenditure, and their overall sense of self.
“Data is a way of proving your self-worth,” says Anna Gustafson, a cycling enthusiast and writer who studied the growing quantification of her sport while researching a book called Your First Big Ride.
“It used to be defined in terms of backbone or intestinal fortitude, but now you have to show someone your data. The guys I ride with, the noises go off on their bike computer when their maximum heart rate is met, and if you don’t have yours hooked up, they think you’re never hitting your target rate. It’s proof that you’re not just messing around, that you’re a serious athlete.”
Or, as Mr. Berry says, “You are what you are by the actual evidence of the universe. Nature is knowable and data is one part of the equation.”
Adding it up
The meaning of life, in the quantified world, is inextricably bound up with our materialistic culture and its dissatisfactions: how much we eat, sleep, work out, stare hypnotically at work-avoidance websites, delegate time to family and friends, spend money on this and that, and wonder how it all adds up.
Traditional thinkers would say that these material matters and the numbers we use to track them are trivial compared to the deeper questions of existence. But they miss the connectedness of all these data points, which can lead to something greater than the sum of quantified parts – a realization of who we really are, complete with limitations that need to be acknowledged.
Ms. Chua is bent on a life of creative semi-retirement, but how can she achieve it? Personal frugality is a necessary part of her long-term plan, so she scans all her grocery receipts to break down spending on, say, vegetables versus ice cream, and maxes out her library card to the point where she can quantify the money she has saved through borrowing rather than buying – $1,000 in November alone.
And because she is trying to use her time more efficiently, with less attention to the things that don’t provide mental stimulation, she’s a dedicated self-quantifier who clocks her activities during the day and measures the duration of her sleep at night – all in search of the perfect ratio.
While other people natter inconclusively about work-life balance, she can head straight to her 2013 data and give you the hours she allocated to work, hobbies, relaxation, writing and sleeping.
“I can see from the numbers that I generally need 81/2 to 9 hours sleep, so it’s not like I can resolve to get by with four hours a night in 2014. The time records let me accept my limitations – I can’t just talk myself into the answer I think I should have.”
Fitbit might not use it as a slogan, but, if nothing else, the data-driven life forces us to face the repeated misunderstandings we have about ourselves. We can no longer pretend lapses in daily life don’t affect our higher virtues or goals when those lapses become the pattern of life itself.
Unless the numbers are wrong.
The narrowness of the data you seek out, the numbers you’re able to gather, the numbers you omit, the third parties with whom you choose to share your information – any of this can skew the so-called science of quantification.
“If you’ve got bad data, you’ve got bad analysis,” says Andrew McIntosh, a software developer at Uptime Software. “People who track their dreams can only track the dreams they remember and write down in their tracker. Or people try to skew the game, and our data becomes only what we want ourselves to be. You wear your Fitbit on the days you’re going to exercise, but you don’t wear it on the days when you’re sitting at the office.”
The numbers should always be subordinate to a more introspective understanding of behaviour, Mr. McIntosh says. “The data doesn’t prove anything or give you any deep insights quantitatively. It just helps you look at yourself better – it gives you the direction you’re going, and then it’s up to you if you want to change.”
Ms. Chua agrees with this qualified approach to quantifying: Her apps simply help her make connections that she otherwise might have missed. Sometimes her starting-point for self-improvement is an odd pattern in her graphs, but just as often it’s vague intuition.
“Something funny’s going on. So then you start digging round in the data, and it brings different things together – you take a look at how much sleep you’re getting versus the sunset/sunrise pattern. You’re not necessarily holding yourself to strict scientific standards, but you can still say, I think it’s got something to do with this.”
Numbers for her are not an end in themselves but a starting-point for reflection. “It’s not so much about productivity, about squeezing as much as I can out of every second, and more about asking, what do I really value?”
A good analogy here is the contentious approach to player assessment in baseball, a sport that has undergone a data-driven revolution. And so I phone Keith Law, an MBA and former analyst for the Toronto Blue Jays who now covers baseball for ESPN.
Mr. Law is a statistical savant with a special talent for ranking young prospects. But with the proliferation of available numbers, a significant part of his expertise depends on sorting out the data that matter from the data that don’t.
Smart baseball analysts, for example, used to rank pitchers according to the batting-average they allowed for balls hit into play. “But that probably isn’t all that much in their control. Once the ball is hit into play, the pitcher’s role is over,” Mr. Law says.
“You have to understand what the data means,” he continues. He could be talking about baseball or cholesterol levels because in this over-quantified world, the wise approach has to be the same. “More data is better but you have to have the temperament and self-discipline to take all that data, put it in the appropriate context and understand how not to react to it.”
Danger in numbers
We’ve always counted the bits and pieces of our lives – how many Hail Marys will expiate our sins – but finding use for the immense data we now can gather makes this ability to discern much more important. Do food apps make us healthier, or eating-obsessed? Do heart-rate monitors motivate healthy exercise, or push us over the edge? Should we trust the selective data that carries the illusion of science simply becaue it’s numerical?
“Detailed monitoring and quantification can be harmful,” says Nav Persaud, a doctor and lecturer at the University of Toronto. “Even when we’re quantifying a purely subjective state like pain or mood, a number gives the impression of scientific rigour and thoroughness. It’s pretty hard to explain what a mood is – people could disagree about it. And yet once you’ve applied a number, it’s difficult to ignore.”
Research has shown that frequent pain readings will increase the amount of pain people feel. A routine stress test can actually cause more harm than good. A question about anxiety will make a person more anxious.
Rating a patient’s mood on a scale from 1 to 10 may fit clinical guidelines. And yet, says Dr. Persaud, “how does the number a patient reports relate to how they’re functioning and how much they’re suffering?”
When I added a sleep app to my pedometer, I was surprised to see that at first I was sleeping more restfully than I imagined – which created a tension between two understandings of reality that I haven’t yet resolved. I became more regular in my sleeping habits (no compulsive reading of the latest Jack Reacher thriller until 2 a.m.) but I also found myself tensing up as my week of self-monitoring progressed, sleeping more fitfully as I thought about the all-seeing app resting on the mattress inches from my head.
Dr. Persaud studies the concept of disease-mongering, the fabrication of imagined sickness – and a marketable cure – through the manipulation of numbers. So he’s skeptical of apps that generate data to tell you what you should be able to figure out on your own.
“You should know whether you feel well-rested in the morning; you shouldn’t have to check your iPhone to find out how you slept.”
And yet we continue to check our devices, as if they knew best.
We also rely on our devices to make connections with those who are plugged into their own. But those connections, too, are more complex than they first appear. Once we begin to share data, it’s available to be sampled and commercialized much more widely.
“If people undervalue their own privacy,” says Christopher Berry, “and you offer them utility, and that utility is free, than those people become the product. But I don’t think people get this.”
Perhaps that’s harmless enough – certainly it’s happening all the time on social media. Mr. Berry is more concerned about the consequences of increasingly intimate data grabs – of personal genome tests, for example.
“I might know that I have an augmented risk for heart attack or Alzheimer’s and to me that type of connection is unbelievable. … And I think people would change the way they’re living and become better if they knew that type of information. But the ethical constraint is having that sit on a server in the United States, accessible and storable by the NSA and people I didn’t give permission to. I totally want all the utility but I don’t want any of the radioactivity that goes with it.”
Electronic spying and self-quantifying, no surprise, turn out to be part of the same technological package.
At the most extreme end of the Quantified Life is Feltron Annual Reports, an aesthetically beguiling collection of personal inputs that tell you where a meticulous infographer named Nicholas Felton spent the past year – indexed by activity, diet, location, people encountered (graphed by frequency/intimacy), photos taken (2,801 by iPhone), apparel worn (bow tie accomplished at 7:20 p.m., June 28, in Brooklyn).
How much utility is there in these numbers for anyone other than Nicholas Felton? And yet the people who collect data study him almost as if he were a heightened version of themselves – with the same fascination and attention to extreme detail that ancient Greeks would have recognized in the painstakingly rendered sculpture of a god or hero.
Do I see a kinship here – me trudging along with my pedometer while some quantified Apollo is adjusting his bow tie for the world? At a basic level, there’s a form of awareness, an attention to self that could veer off into narcissism for sure, but can also be a kind of appreciation – knowledge through contemplation.
Perhaps we don’t need to make overly innovative claims for the power of data or obsess over its existential implications: There’s simply pleasure and beauty and vicarious humanity in such a fully formed version of an individual.
“Words can be used to describe someone’s history,” Mr. Berry concedes, “but actual behaviour is far more interesting.”
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