06 Jan 2014
What the Quantified Self needs now is context
As the Quantified Self movement grows, we're finding—and creating—more ways to track our lives. Although measurement is a step closer to improvement, it's not enough on its own.
The data we have
I'm a little more eager to track than most, but I'm not covered in trackers like some. I do wear two fitness trackers currently (a Misfit Shine and a Jawbone UP), and I use my phone to track the places I go, what I spend money on, how often I go running and the books I read, just for a start. I've even used my phone as a fitness tracker with the Moves app and Fitbit's recent addition of MobileTrack to the iPhone app.
It's a long list of data points I'm collecting, and I've certainly had to create some new habits just to keep on top of it all. For instance, every time I hand over some cash or my bank card, a reminder triggers in my brain that I need to add this amount to my money-tracking app.
At the end of the day, I could see how many coffees I drank and the distance I ran in one app. I could look at my sleep stats and my daily activity in another. The rest of my stats are disparate, one-per-app. I have no idea whether there's a connection between my weight and my sleep quality, or how much money I spend and where I go. I can't even get a good idea of how the time of day or day of the week affect my habits. Do I spend more on weekends? Do I sleep better on Monday nights? Who would know.
So what's missing? The Quantified Self movement has brought us this far. We're encouraged to track everything we can. But doing something with the data is a whole other story.
For those who care enough to put in the time and effort, manual tracking and analysis can be done, at least for two data points at a time. I could make a spreadsheet of my sleep quality, or length in hours, and include how many coffee I had the previous day. I'd have to visually examine the data to decide whether a correlation exists, and I'd have no idea whether it was statistically significant or not. Plus, other data points like the weather or my stress levels might likewise be affecting my sleep, but I'd have no record of that.
Self-tracking is getting easier and more accessible but analysing the data to make sense of it—and, more importantly, to make use of it to understand and change our lives—is not. And without this step, few of us will make it through the number fatigue of long-term tracking.
The next step is context
To get more out of the data we're creating, we need to add context to it. This issue is what we're trying to solve with Exist.
Here's an example: I'm running three times a week on average, but in a recent week I only ran twice. Both times I ran slower than usual, and afterwards I felt more tired than normal.
Because I tracked all of that data, I can now look back on that week and see how my pattern changed. What I can't see is why. This is because the context is missing.
There could be various factors affecting my running. When I donate blood, for instance, I have less energy for running and I get tired faster. A Foursquare check-in at the blood bank at the start of this week could provide that context.
Another thing that affects my running is my diet. If I'm on an intermittent fast the morning before a run, that could mean I struggle to run as far, or as fast. Tracking my caloric intake alongside my running data would add context in that case.
Or perhaps it was simply a hot day. I don't like running in the heat so much, and it often makes me tired and slow. Adding weather data to each of my tracked runs would help me to understand how the temperature (or other factors, like wind speed) affect my running.
In the long-term, this kind of context can be even more useful. I might see over time that running is not helping me to lose weight, if I track my weight against regular running vs. previous months when I didn't run every week. If weight loss was my aim, this information could prompt me to adjust my exercise routine or to discover other factors helping or hampering my weight loss.
Something else I'm really interested in tracking is my mood. Over long periods, this will hopefully tell me about how different foods or drinks like alcohol and coffee affect my mood, as well as how exercise and good (or bad) quality sleep affect me the next day.
Adding even one more data point can sometimes point out telling correlations that tell us a story about our data. Adding even more can tell us a truly rich story, much closer to that of what's actually happening. Our lives our rarely affected by one specific cause—rather, many causes blend together to bring about a particular outcome.
In This Will Make You Smarter, Nigel Goldenfeld's essay Because explains how there is often a web of causation at play in complex systems, rather than a single cause-and-effect:
For every event that occurs, there are a multitude of possible causes, and the extent to which each contributes to the event is not clear, not even after the fact!
John Tooby, founder of the field of evolutionary psychology, goes further in his essay Nexus Causality, Moral Warfare, and Misattribution Arbitrage. He explains the "web of causality" as being a nexus:
Causality itself is an evolved conceptual tool that simplifies, schematizes, and focuses our representation of situations. This cognitive machinery guides us to think in terms of the cause—of an outcome's having a single cause. Yet for enlarged understanding, it is more accurate to represent outcomes as caused by an intersection, or nexus, of factors.
With Exist, we're not only making it easy to bring multiple data points together, but we're also adding much-needed analysis to find the trends and correlations that exist between those data points.
Users should not have to analyse graphs or calculate averages. We have computers to do this for us.
With Exist, we'll make this as easy as possible so you can get the benefits of understanding your body and your habits without needing to spend hours pouring over numbers.
Our plan for Exist is to eventually make it into a smart assistant that can help us make informed decisions about our lives and build or change habits. With the amount of data and the various kinds we can pull together, Exist will be able to get to know us intimately to offer personalised, helpful suggestions.
To start with, these might be as simple as showing us that we're more likely to be more active on days we get out of bed earlier, or we're more likely to choose healthy meals when we've had a good night's sleep.
As the algorithms behind Exist and our data analysis become smarter, we'll be able to go so far as to offer pre-emptive suggestions, before you even realise you need them.
Tracking your life is a lot of effort, but we hope to make it worthwhile by providing the context needed to use all of this data. Once we can implement insights and suggestions that actually lead to changes in our daily lives, the effort of tracking will hopefully be a small price to pay.