How to track your life without losing your mind.
I’ve tracked personal data like weight, heart rate, productivity, and sleep for years now. As a data-junkie, it’s been a fun resource for experiments. At first, it took a lot of effort, and I gave up on a handful of datasets. Now, I spend less than a workday per year (total) working on what’s become a very rich and useful collection.
Collecting rich data about my life has given me an objective way to look at my past. It’s also become a significant part of how I make decisions about the future (maybe I’ll cover that in another post). If you’re curious about self-tracking, but you’re not sure if you have the energy to keep it up, maybe this tip will make the difference for you too.
Active vs. Passive Tracking
I’ve been tracking my weight for close to eight years. At first, I was reporting by actively entering my weight into an app called WeightBot. A few years ago, I switched to passive reporting using a smart scale (Withings Body Cardio) that automatically logs my weight when I step on it.
You can see the difference in detail on the chart below (numbers removed for privacy).
When I reviewed the data, it was a revelation. The richer and more objective passive portion of the chart was so rich that it read like a journal. What’s more, it took less effort to generate than the active one.
The passive data tells a story. I remember the first dip when I left my job to live in Montreal and started running every day. I can re-live the gradual climb — my stress was growing as I was starting a new business. I can take pride in the recovery as work began to stabilize, and I could start building a team.
The active data is sporadic and not even as clear as my memory of that period. There are also some hints that it was biased, which I’ll cover next.
The total range of motion here is relatively broad (≈16% of body weight), so I’m sure I would have noticed these changes without tracking them. Still, it’s been incredibly valuable to be able to look back at the past objectively. Reflecting on a lot of objective data like this has impacted some big decisions in my life.
This insight drove me to rebuild my tracking stack. I became obsessed with removing actively tracked data and finding passive approaches to tracking anything I could.
The Bias of Active Tracking
I think of active tracking as “tracking that takes real-time (or near real-time) effort.” This is in contrast to passive tracking, which happens without intervention.
The first section of the chart is active because I had to decide to log my weight in WeightBot each day. Though that process was easy, I fell out of the habit for months at a time. What’s more, those weeks and months when I was least motivated to measure my weight were likely the ones I was gaining the most.
Even if your measurements are accurate, your choice of when to measure can bias the data. If you look closely at the chart’s active section, you’ll notice that the areas where I tracked most frequently were the areas of sharpest decline. I was more likely to measure my weight when I was losing it, and I might have stopped measuring entirely when I was at my actual maximum.
Active tracking can be useful for changing your behaviour, or for short-term experiments. It’s a great way to know what happened during some period, but it’s a poor sample of what the data looked like before and after. Anyone who’s kept a food journal will know this effect. Simply writing down everything you eat turns out to be a great way to eat less junk for a week or two.
When Passive Isn’t Possible
Sometimes there is no good option for passively tracking something. I’ve found this with productivity tracking. There are tools that try to track and classify your behaviour automatically, but I’ve found them too inaccurate to be useful.
I’ve tried a few of the personal tracking options available, and apps like Toggl or Timely for my work. Eventually, I landed on Timing for the mix of pricing, features, and a local-only option.
Timing creates a timeline of calendar events and computer usage (e.g., the title of each active window). I still have to bucket this data by project and activity manually, but it’s a fairly objective process since I’m working from passive data. I like to think of this as “assisted” passive tracking.
You can see a chart of thousands of productive hours below (slightly obfuscated for privacy). The different shades represent different projects and activities.
This chart is grouped by day, but I have roughly 15-minute precision across thousands of hours. I know when I’m most productive, when I tend to overwork, and which activities take up the largest share of my time. If you look carefully, you’ll even see that I took my first real vacation in years near the end of the timeline (hah!).
I’ve set a reminder every week to categorize my data. It’s a helpful mini-review to see how I spent my last week and decide how I’d like to spend the next one. At times I’ll become too busy and put off classifying data for weeks. Still, the app continues to track passively, and the resolution of that data is more than enough to maintain high accuracy when I’m ready to classify it again.
My total time spent in the app over the past year is only roughly 8 hours. Besides activities like journaling and goal-setting, this is the most time-intensive part of my dataset. Still, it’s provided me with some of the most valuable insights.
The trick with assisted passive data is to find a process that will keep reviews objective and minimize the cost of missing one. If you can passively track data in a way that you can still accurately classify it within a month or so of recording, you’ll have a much higher chance of keeping it up.
Wrapping Up
After years of self-tracking, I’ve found that the more passive a tracking process is, the more likely you are to derive value from it. Early on, it’s easy to get excited about your data and put in the work to keep active processes going, but as your stack matures, you realize how much more value there is in your passive measurements.
Here are some guidelines to follow:
- Track things passively whenever possible. Even if you’re not sure when the data will be useful, it’s likely worth doing if it’s cheap or free to collect long term.
- If the data can’t be collected passively, consider an assisted passive approach. Gather as much information as you can passively and then augment it with an objective manual process.
- Active tracking is the last resort, and might not be worth doing at all. It takes a lot of effort to keep it up on the scale of years. Keep in mind that the process is subjective. Don’t treat the data as an unbiased sample.
If you’re just getting started with tracking personal data, I hope you find this helpful. Trust me, it’s well worth exploring!
