I recently started tracking my sleep with an Oura ring.
The detail and sheer volume of data is incredible — intimidating, almost.
But that, in itself, isn’t going to improve my quality of sleep. Simply having more data than I know what to do with isn’t going to make the numbers go up — in fact, ironically, paying too close attention to my sleep quality might just result in a mild form of sleep anxiety which derails the whole project.
Which is what prompted me to dive into this topic.
*Warning: this is going to be rather niche.
Despite sleep being the most universal of human activities, its immense impact on almost every aspect of life we care about, and the number of low-cost, high-quality sleep tracking tools out there — I understand that tracking your sleep and analyzing the data isn’t for everyone…
That said. If you are someone who collects sleep data on a nightly basis — odds are, you could be doing more with it.
More is the key word here. If you’re already tracking and reviewing your sleep data, that’s excellent — please continue.
But I’m suggesting that beyond this first level of analysis, there are at least two more steps we can be taking:
Sleep tracking apps and wearables
Some great tools for anyone interested in getting started are:
I’m sure there are many more. If you’re interested in this niche world of tracking and making use of your health data, but haven’t started yet — those are a good place to start.
I won’t be able to say it better than Matt Walker in his recent bestseller, Why We Sleep, so I won’t try. The short version is:
Sleep influences everything.
As a creator, knowledge worker, maker, freelancer or builder — how exactly it affects your work, creativity and productive output can be difficult to gauge. We intuit that more sleep is better, or we develop a sense of our chronotype and make decisions on that basis — yet the tangible results can be hard to pin down.
I’m not going to take up the task of convincing anyone that ‘sleep is important for your work’. If you’re someone who values their sleep enough to track it and try to influence its quality, then this is for you.
The first level of analysis is to simply read the patterns given to you.
Apps like SleepCycle and Oura’s web app have rather impressive charts and breakdowns built-in. Not only do they give the most recent activity, beautifully presented, but they provide relevant contextual information to learn more about the data set you’re reading.
Screenshot by author, taken in Oura web app.
This is an important first step. And admittedly, even this one gets skipped — so if you’re in the habit of at least checking your stats daily, that’s a great start.
But it is a passive approach.
Despite these companies having the best intentions and incredibly talented teams — they are appealing to a wide audience.
The stats and scores given need to apply to anyone from a shift worker to an elite athlete to a college student. For that reason, the insights and takeaways can only be so tailored. More often, what you’ll get are general takeaways.
It’s a great starting place, and highly worthwhile. But we can do more.
The second level is to go beyond passively observing the numbers given to you and to actively try to influence the readings.
If my Oura Sleep Score is low, I can view it and say, ‘Hmm, I guess I better take it easy today’.
But I can also decide: ‘I’m going to take steps to improve it over the next two weeks.’
From there, I’ll develop a couple of hypotheses about what’s influencing the score.
I might try to keep a more rigorous schedule and see if that improves my scores.
I might decide to get to bed an hour earlier than usual, for 14 nights, and see if that improves the rating.
This approach is actionable. It’s a process of taking on board the data given, making a hypothesis and testing. Great.
But it is still passive in one sense — the investigation isn’t tied to anything other than ‘the number’.
If I am simply tied to, ‘Number go up’, then once again, I’m linked to whatever decisions were made by the manufacturer or engineer.
Some of those decisions will be universally true, and so they’ll be helpful for me. But some of them won’t be.
This is where the final layer of sleep data analysis comes in.
Why did I start tracking my sleep data in the first place? Was it just a curiosity?
Pick at the thread a little further, and there’s likely some underlying intuition or desire:
I’d like to improve my mood through better sleep.
I’d like to reach a fitness goal.
I’d like to be more productive.
Figuring out the underlying objective is key. What’s it all for?
For example, I might decide that the most important thing for me is to be more productive, and so increasing ‘REM minutes’ might be the single most important factor.
In this case, ‘Total Hours Asleep’ may prove less important, even though it influences my sleep score, equally.
At the end of the day, what matters most to me is to increase the number of quality productive hours (including the effective learning that occurs during sleep after those productive hours). When I implement an intervention which improves REM sleep minutes and daily productive hours, I’ll know that it’s been a successful experiment.
If my overall Sleep Score happens to go down during the same period, I could really care less.
When you have the system working at level 3, this is the flow you’ll likely want to follow: