A Life in Data: Finding Change through Habit Tracking

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It was 2009. I was 18 years old, in university (but not particularly stimulated by it), and spending vast quantities of time on the internet. By some mechanism, I came upon the work of Nicholas Felton.

Nicholas is an information designer who created a series of annual reports in which he precisely measured the minutiae of his life — how many beverages he had, how far he travelled, even how much time he had sunk into Grand Theft Auto IV. I was equal parts fascinated and bewildered; I could only imagine the sheer time commitment that would have gone into tracking absolutely everything in such detail.

I remember dabbling in the use of one of Nicholas’ projects, Daytum, a web platform that allowed people to upload data from their own lives and, depending on what class of data it was, make the appropriate type of visualization. I was a broke college kid, so while I appreciated what the free version offered, it wasn’t enough to satisfy my curiosity, my want to track my own life and see what correlations I could draw.

Outside of a mention here or there (like in 2012, when I heard he had created an application to assist with the tracking process), I didn’t really keep tabs on what Nicholas was up to. The work that had been so riveting and inspiring for me wasn’t even granted the importance to become a distant memory. Life happened, and I forgot.

In December 2015, again through some vague mechanism, I learned that Nicholas had produced his last annual report the year prior. Having wrapped up almost ten years of hosting a radio program earlier in the year myself, I could understand his rationale for wanting to break away from the project that he had nurtured for a decade.

More recently (mid-March, to be precise), I noticed that he had posted a class about data visualization to Skillshare. I tweeted my excitement, and was a bit surprised that he replied to me. In my experience, my close friends often don’t reply to my tweets, so I was honestly taken aback that a famed designer with a following would respond to an off-the-cuff comment from me.

I took the course and instantly fell in love with the idea of creating relationships between disparate entities. Nicholas was even willing to answer a few of my questions.

Imagine my surprise when, less than a week later, I discovered that the application that had been created to assist with his 2012 report was available for public use. Reporter surveys users at random points throughout the day, asking pre-established questions that each individual can specify. I was finally able to dive into the hidden depths that my life, and I was excited by the thought of finding some hidden link that would make me a better version of myself.

Sometimes, circumstance can be very helpful. Due to the removal of overall activity totals in favour of daily averages over a time period in the Health app as part of the 9.3 update, I found myself upgrading a spreadsheet that I hadn’t touched since the beginning of the year with my health and fitness data. I had been, and still am, trying to be as thorough as possible with the logistics of my journey to lose weight to see it if may provide any valuable insights, though that’s a story for another time.

This increasing familiarity with the intricacies of spreadsheets — which makes everything sound far more boring than it actually is — was an asset when I began to parse the data I had collected over a 31-day period. I wanted the collection period to have a definitive beginning and ending, because while allowing for more time to elapse might have generated new insights and provided additional context to some behaviours, it would eventually grow to the point where it was no longer an accurate reflection of a specific point in time.

I asked myself 12 unique questions a day, six of which would only be asked either when I woke up or when I went to bed. Two of the morning questions were “How did you sleep?” and “Did you wake up overnight?”. For my purposes, I considered “waking up overnight” any event in which I needed to get out of bed. In the couple of weeks before this project, I hadn’t been sleeping well, and I wondered if one had anything to do with the other. What I discovered was that yes, I tended to rate my sleep as being poorer when I woke up overnight.

Based on this data, I decided to discover the root of the issue. It seems like common sense in retrospect, but in the above-outlined fitness pursuits, one of my goals has been to drink more water. I found that whenever I drank a lot within three hours or so before I went to bed, I would invariably need to get up to go to the bathroom, thus causing the disruption in my sleep cycle. The obvious solution was to change my drinking habits, and when I had done so I woke up completely rested, for the first time in a long time. A single small change created a positive benefit.

Not all issues are that straightforward, of course. On my list of questions, I included “I”, more out of a general curiosity than anything. The list of people has 19 unique entries on it, but the clear winner that appears in 89% of the data is that I am alone. It makes sense; I live alone, but I also didn’t consider time at work as being time with coworkers unless I was specifically interacting with them. What’s more, while family does make some token appearances here and there, I don’t actually have any friends on my list — the vast majority is coworkers, with some other notable people included as a result of coffee meetings or networking events.

My average mood was neutrally happy (4 out of 5). A reporting of “Bad” typically correlated with headaches or other physical pain, but a few instances were recorded when I was feeling emotionally vulnerable.

This particular point displays the restraints and limits of this data set, and the approach of random sampling in general, painting a very depressing picture in the process. The reality of the situation is that even if I don’t get a chance to see them as much as I would like, I almost exclusively text my friends, and there are several I speak with several times a day. If the data set was presented alone, without context, it would seem as though I may as well be living on the moon.

However, the additional context doesn’t make the data any less valid, and it makes me wonder if anything would be impacted by better maintaining my relationships.

Lastly, as I’m me, I was curious about productivity in terms of my work performance. Unfortunately, the project management software in use at my office is not a particularly good measuring stick for non-development roles. Often, many pieces of content are condensed into a single ticket, leading to a bit of frustration wherein even if something gets finished, it’s not “done” because there is more to it to do.

Lest I digress, I wanted to investigate my burn rate — how much would I get done (or at least, according to the system) over an average month? I grappled with the idea of including weekends because occasionally I do some work, but given that they are not entirely dedicated to work I was concerned that their inclusion would throw off the data set.

On average, I closed about 1.04 tickets per day, but I didn’t close any tickets on 34.8% of the days in the data set. This was offset by my closing two or more tickets 30.4% of the time. What interests me about this data is the small margin between the two extremes — there is only a 4.4% difference in the odds that I will close either no tickets or two or more tickets.

For the sake of completion, I closed one ticket in a day the same amount of time that I didn’t close any tickets — 34.8%.

From this experiment, did I learn anything insightful, anything that could potentially change my life, or the lives of others? I would have to say not particularly, but only given the limited nature of the data set. I reported 172 times, enough to get a high-level look at my life but not enough to delve into specifics. When Nicholas used Reporter for his 2012 Annual Report, he reported an average of every 76 minutes, or 12.9 times a day — a far cry from the six times per day I had the application set to notify me.

With a greater data set, and more fine-tuned questions, there may be deeper and more meaningful correlations that could be found in the depths of life. Do I sleep longer when I’ve exercised more, whether in distance or intensity? Am I more productive when I’m working on things for myself, as opposed to my job?

Would I report again? I would, because not only did I enjoy using the application, seeing the relationships between my everyday activities was very fascinating. Maybe not on such a scale as Nicholas (4739 reports in a year is probably just a bit too much for me), but this experiment was not so much about the specifics of the data that I collected, but about the possibilities, the interactions and correlations, that can be made evident by reporting our daily activities, interactions, and feelings.

If there was one great thing to come out of the month, it was that I don’t need to feel tired ever again.

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