One of the things we’re interested in as scientists is what longitudinal, large-scale data collection can tell us about sleep. Along those lines, one of our research projects involves looking at how models of circadian rhythms, as well as different sleep regularity metrics, can help us understand different outcomes for different folks. And as part of all that, we wanted to make some pretty pictures.
We recently teamed up with Ryan Rezai, a data scientist and student at the University of Waterloo, to visualize some data from the Multi-Ethnic Study of Atherosclerosis (MESA). All the plots below were made by Ryan to showcase some of the high-level properties of the MESA dataset. We think that with beautiful, interactive datasets, the nuances of big data stories, like in MESA, can become a lot clearer. Let’s dive in!
Total sleep and outcomes of interest
How much does how much sleep you get correlate with your perceived sleepiness? Below shows average nightly sleep (from wrist actigraphy—a way of measuring sleep based on how much you move) and responses to the Epsworth Sleepiness Scale (ESS). Higher numbers mean more sleepiness. Right away there’s something interesting: this plot isn’t a line that starts high at low levels of sleep and goes straight down:
Instead, we see the famous U-shaped curves of sleep research. Short version of what we mean by that: lots of bad things (like sleepiness) are correlated with both short amounts of sleep and long amounts of sleep. One natural thing to think is that, for the extreme long sleepers, there’s something underlying both the bad thing and their propensity to sleep a lot (for instance, if you’re sick, you may sleep more and also generally feel sleepier all around). But it’s certainly the case that other things could be going on too, which is why it’s helpful to consider each case of a U-shape in isolation.
We’ll come back to that in a minute. In the meantime, here’s a case of a curve that looks… pretty flat:
This graph is showing total sleep from actigraphy, plus the fraction of respondents who said they had a diagnosis of a sleep disorder from a clinician. This is actually pretty weird: why is it highest for people who seem to have a lot of hours of sleep?
Our theory: This could be one of the pitfalls of wrist-acceleration sleep tracking. Any time you’re trying to measure someone’s sleepiness from their wrist, you run the risk of mistaking “them being very, very still but awake” for “them being asleep.” In this case, it may be that the people with the large amounts of sleep recorded are simply trying to fall asleep for longer (and staying mostly still for longer), but not actually managing to do it. That could mean that their recorded sleep is quite high, but their actual sleep is much lower. It could also be another explanation for what was going on with the U-shaped curve above: maybe some of the long sleepers in that plot weren’t true long sleepers.
Here’s one more interesting total sleep duration tidbit. The number of apnea events (from a night of polysomnography, or PSG) is much higher in people who were usually found to sleep a pretty long duration (around 10.5 hours):
You might expect this to be close to a straight line (with habitual short sleepers having shorter PSG nights and fewer hours in which to have an apnea event), but it doesn’t look particularly linear at all at the end there. Another suggestive hint that some people actigraphy is picking up as sleeping for a very long time probably aren’t sleeping very well after all. (And maybe also an indicator that there weren’t that many 10.5 hour sleepers in the dataset; see: widening standard error bounds around the line).
So let’s talk about actigraphic sleep
Disadvantages of using an acceleration-based device to track your sleep: It’ll probably mistake a lot of “awake but still” time for sleep. You’ll have questions, like the ones we had above, about how accurate it is for people who are immobile for a long time. Comes with the territory.
Advantages: it’s objective. It doesn’t care about being judged for saying you only slept two hours last night.
With these pros and cons in mind, a natural question to have is: how does an objective measure like actigraphy compare to subjective measures like “asking people how long they slept last night”? For starters, if they were perfectly correlated, you’d expect them to track along the line of slope one.
Spoiler alert: they don’t.
The orange curve shows self-reported versus actual sleep, while the gray line shows the line of slope one. Those lines are not the same! Instead, we see people who sleep a long time according to actigraphy saying they sleep less than actigraphy expects, and people who sleep only a short amount according to actigraphy saying they sleep more than actigraphy is saying. These differences are pretty wild.
And there’s another interesting difference to explore here. Among people who say they don’t get a lot of sleep (subjectively), ESS scores are really high, suggesting profound sleepiness. For people who don’t get a lot of sleep according to actigraphy, not so much.
Moral of the story
Whenever you want to understand data, make a picture out of it first. Are our long sleepers sleeping a long time because they’re otherwise not doing well, or are they not even sleeping a long time at all? What effect does the choice of sleep measure—subjective or objective—have on our results? Only way to know is to try it out and see. And that’s what we’ll be doing in the coming posts in this series. Stay tuned!
With thanks to these resources:
Zhang GQ, Cui L, Mueller R, Tao S, Kim M, Rueschman M, Mariani S, Mobley D, Redline S. The National Sleep Research Resource: towards a sleep data commons. J Am Med Inform Assoc. 2018 Oct 1;25(10):1351-1358. doi: 10.1093/jamia/ocy064. PMID: 29860441; PMCID: PMC6188513.
Chen X, Wang R, Zee P, Lutsey PL, Javaheri S, Alcántara C, Jackson CL, Williams MA, Redline S. Racial/Ethnic Differences in Sleep Disturbances: The Multi-Ethnic Study of Atherosclerosis (MESA). Sleep. 2015 Jun 1;38(6):877-88. doi: 10.5665/sleep.4732. PMID: 25409106; PMCID: PMC4434554.
The Multi-Ethnic Study of Atherosclerosis (MESA) Sleep Ancillary study was funded by NIH-NHLBI Association of Sleep Disorders with Cardiovascular Health Across Ethnic Groups (RO1 HL098433). MESA is supported by NHLBI funded contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by cooperative agreements UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 funded by NCATS. The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002).