Are You Being Misled by Your Quantified Self?

Are we letting the technology that records our lives shape what we see as important about them?

7 min read

A person wearing a suit of LED strips and other gadgets points at a board that is similarly illuminated.
The logo for How We Get To Next's Identity month–a piece of paper with a face made up of other faces.

Every morning, Ken Snyder measures his blood glucose using an Accu-Chek glucometer. He measures his weight and body composition using Withings Wi-Fi-enabled scales. During the day, he tracks his steps with a Fitbit step-counter.

“For sleep quality, I use Beddit,” he says. “[It’s] a sensor-rich sleeve that slips over the mattress, and the smartphone app displays a summary of my heart rate, breathing, and snoring patterns.”

Snyder is a co-organizer of the London Quantified Self Meetup Group, and he’s taking me through his self-tracking routine in the hope that I can glean an insider’s perspective of the quantified self movement. The QS phenomenon takes the idea of a tech-mediated life to the extreme–it involves self-tracking (often using wearables and connected objects), and immersion in data and metrics.

The intention is that better self-knowledge comes through data–gathering it, aggregating it, visualizing it, interpreting it, and using it to inform behavioral changes. The aspect I want to make some more sense of is what an extreme metrics mindset might be doing to us–and whether we’re letting the technology that records our lives shape our attitudes about what we deem important about them.

The Moves app knows whether Snyder’s walking, cycling, or running. He uses the Day One journaling app for life-logging and runs Rescue Time to monitor his productivity. When he’s running, his Garmin Fenix 3 watch and chest strap record his heart rate, vertical oscillations, and maximal oxygen uptake, as well as his pace and route. Once a week, he measures his blood pressure with a wireless Withings cuff. He regularly has his blood assayed, especially for C-reactive protein, triglycerides, and ApoB. As Snyder describes his habits, I catch myself anxiously wondering whether I should be tracking any of these metrics as well.

His motivation for all this measurement is that he’s training for an ultramarathon, but he’s also trying to improve his mental clarity and look after his longer-term health. More fundamentally, he enjoys the process of collecting and tracking data: “I love learning things about myself. It’s fascinating and it’s fun,” he says.

I’m curious about the links between the different types of data he tracks. Does he run experiments, looking for correlations between behavioral inputs and the physiological markers? Yes, sometimes, but it’s mainly about short-term feedback loops. He tries to do things to keep his blood glucose levels low, for example, both for short-term mental sharpness and long-term heart health.

“Intellectually, I might know that a lower blood glucose level reduces my risk of heart disease,” he explains, “but what really motivates me is getting a read on my energy system and seeing what’s affecting me. Short-term feedback loops are intensely motivating.” The short-term benefit is a fitter, healthier mind and body–but it’s seeing all those numbers that’s the real boost to his motivation.

As well as the power of numbers, I’m struck by the phrase “getting a read on my energy system.” At first I’m confused–it sounds like something you might say about a building–and then I think about times I’ve been aware of my own body as a mechanical system. How, when I’m sick, I might rely on a thermometer to validate my own, already feverish experience.

I wonder about this kind of biofeedback, and Snyder tells me about a commercially available headband to wear during self-hypnosis, which monitors changing electrical activity in the brain. There is a real-time display showing which stage of hypnosis has been reached. Snyder says people like it because they “like to know that something is happening.” It makes me curious about the interplay between data from sensors and private, subjective experience. When we experience both kinds of sensing, how do we balance the two?

For those of us negotiating tech-mediated lives–even without embracing every wearable or connected object–it can feel like a fundamental but insidious shift is taking place. Living and interacting with increasingly pervasive consumer technology, in an increasingly data-driven world, must be affecting the way we relate to the rest of what we experience in our lives.

The historian Yuval Noal Harari calls this new worship of all things data “Dataism,” where “proponents perceive the entire universe as a flow of data, see organisms as little more than biochemical algorithms, and believe that humanity’s cosmic vocation is to create an all-encompassing data-processing system–and then merge into it.”

The key issue in my mind is whether the more this data-centric view takes hold of us, the more we come to assume that the things that can be counted–or converted to data–are the things that are important. In fact, it sometimes feels even stronger than that: Do we intuitively think that the act of measuring, recording, or tracking something makes it real?


Deborah Lupton, a digital sociologist from the University of Canberra, says that she often hears self-trackers say: “If I didn’t track it, it didn’t happen.”

For many people she’s researched, she’s found that the experience itself–for example, a cycle ride–only becomes valid and real through the act of tracking. If the sensor failed during a ride, it’s like the whole thing never happened.

I can relate to this. It can sometimes seem as though things which have been recorded, transformed into data, and shared are more real than the actual experience. By creating and sharing photos, videos, and status updates, we’re not only communicating what those experiences were like, but actually demonstrating–both to ourselves and other people–that they happened.

There are parallels in the way that data-centrism invites us to cultivate a mindset where more is always better. So much of the online economy now operates under the assumption that quantification of experiences is normal. It serves advertisers’ interests that our networks be broken down into quantifiable things: Quantify your friends on Facebook or Twitter, quantify your professional contacts on LinkedIn, quantify your photography skills on Instagram, and so on. While we know this is reductive, how can we be sure we’re not being influenced to feel, against our better judgement, that the number of experiences or relationships is more important than their quality or depth?

More fundamentally, too, this data-centrism reinforces a way of viewing the world where our default for interacting with it is to measure it and compare relational parts of it to each other. Our cultural age is beginning to privilege action and doing over experience and being. Does self-tracking reinforce this assumption? Does it encourage us to expand the tracking mindset beyond the domain of what’s being tracked? If we insist on measuring everything, we could be forgetting that an essential part of experiencing things is that they come with ambiguity.


Lupton also describes the concept of “data sense”–how digital data, especially from sensors, needs to be understood. Data must be interpreted by including knowledge that comes to us through our human senses. We need to negotiate between these different forms of knowledge–from digital sensors and from our physical felt senses–to interpret what data might be telling us. (As Snyder says: “Our gut feel is pretty poor; it does not tell us what our weight is.”)

More widely, there seems to be a general recognition that it can be difficult to tune into the body’s felt sense, or that it isn’t always reliable: Disconnection from bodily experiences is now even recognized in academic–typically cerebral–circles. Many people are turning to practices like mindfulness in response.

While affective computing–the branch of computer science that studies how computers can understand subjective human experiences–has always recognized that emotions are not reducible to physiological markers or observable signals, things get more nuanced with constructs like stress. As self-tracker Bob Troia said in an interview with PBS NewsHour (and quoted in Lupton’s book The Quantified Self): “I can look down at my phone at any point in the day and see, kind of, how stressed I am.” Our emphasis on external sensors, overloading us with sensory data from tracking products, could actually make it harder to experience our own natural sensations.

That said, there’s more to QS than just sensors and fitness–emotional well-being is a key area, and many initiatives involve self-reported data. This can take the form of qualitative data as well as numbers.

With apps like MoodPanda people can rate their moods from one to 10, and share their ratings with the app’s community, including with contextual comments. Looking through that feedback from users suggests it’s a community that’s very supportive. (One typical review from a user: “I cannot imagine my life without MoodPanda. It has saved me.”)

Or there’s Dr. Liz Miller’s Mood Mapping, also designed for describing and communicating mood. Users plot their subjective “energy levels” against “positivity,” and Miller herself runs meetups where mood-mappers can share their findings. These kinds of examples feel like particularly humanizing illustrations of how some people self-track, emphasizing subjective experience and human connection.

It strikes me that along with supporting a change in behavior, another key attraction of self-tracking is that it can create the impression that things are under control. Data seems to promise certainty. Sometimes, though, self-tracking can struggle with the gap between what we may know intellectually and the messy truth we know is hiding beneath. The recent Nike+ FuelBand settlement–wherein Nike lost a lawsuit brought by disgruntled customers, angry that the device was less accurate at gathering data than the company claimed–centered around how accurately the band tracked steps taken and calories burned. The accuracy of heart rate measurements in the Fitbit Charge HR and Surge has also been questioned. As consumer wellness products, fitness trackers are not subject to regulation like medical devices, but there’s still a temptation to assume that precise-looking readings must be accurate.

Still, there is this sense that you may be more in control of things when tracking them. The Hawthorne effect, where people who know they’re being watched try harder to perform tasks well, is relevant here and may apply even when people are watching themselves through data. Of course, the link between tracking and desired outcome is not always straightforward. For example, this study by health researchers from the University of Pittsburgh found that self-tracking may not offer an advantage in helping people lose weight, compared to typical diets and exercise.

Hamed Haddadi, senior lecturer in digital media from the School of Electronic Engineering and Computer Science at Queen Mary University of London, explains more to me on this: “The wearables sector has struggled quite a bit. We need to go beyond the plotting of steps. After a couple of weeks, people get bored of seeing their step count. The data is not contextualized; it does not include swimming, gym, skiing, or other activities. This is a key shortcoming, that the devices are failing to enable people to interact with them.”

The sheer quantity of data, and its ubiquity, can create pressure for all of us to be self-diagnosing, self-managing consumers. Snyder talks about the importance of working with fitness professionals when analyzing self-tracked data. He respects trained professionals, and their experiences and practices, rather than assuming that only data is what counts. For him, “engaging with professionals to help achieve health outcomes has been a more statistically effective way of producing behavior change than trying to do it alone.”

Choosing to share data with professionals is one thing, but an important issue to consider is privacy, particularly with wearables. The companies and people with access to the data our wearables gather can be unknown, or unknowable. This is another of Haddadi’s research areas–the privacy aspects of wearable technology, as well as the Internet of Things and the personal data ecosystem more broadly–and he believes that “users feel that they have lost the privacy battle.”

And maybe they truly have–just think about involuntary self-tracking, especially of minors, through school-championed fitness programs. Or think about the value of all that healthcare data to both hackers (who could exploit it for malicious purposes) and insurance companies or governments (that may want to deny certain lifestyles appropriate healthcare).


Reflecting on what I’ve glimpsed of QS and self-tracking, what strikes me most is how much I’d underestimated the collaborative aspect. Lupton mentions “communal tracking” and “social fitness,” where groups of athletes share findings with each other; in QS meetups, there are show-and-tell sessions for people to share what they’ve learned. Within the various online communities, I’d expected to find a competitive atmosphere, but the ethos generally feels mutually supportive and open. It does not feel like the self-obsessed, narcissistic QS movement we read so much about.

I’m now curious to reexamine some of my concerns. Maybe QS, at its best, is less about reducing human experience to data, and more about using the very human–and non-algorithmic–skills of interpretation and teamwork to unlock relevant insights from self-tracking. Given how overwhelming the data deluge can feel, that’s inspiring. I might even try it.

The logo for How We Get To Next's Identity month–a piece of paper with a face made up of other faces.

How We Get To Next was a magazine that explored the future of science, technology, and culture from 2014 to 2019. This article is part of our Identity section, which looks at how new technologies influence how we understand ourselves. Click the logo to read more.