Imagine you’re on a swing on a playground with a friend standing behind you.
This friend is not a jerk, so they’re going to push you when a normal person would push you on a swing—right when you’re at the end of your backwards motion and ready to start moving forward again. They, like a normal person, are going to stay out of your way the rest of the time.
If they push you a little bit late or a little bit early, no big deal. If they push you way early, or way late—like, for instance, when you’re still very much in the middle of swinging backwards— that’s a different story. Imagine having your blissful, carefree swing interrupted by smacking into someone standing right in your path. Imagine them actively pushing you back in the direction you came from, right when you least want to be pushed that way.
That person is light exposure. The case where they give you a boost, speed you up, get you to a bigger swing: that’s light exposure during the day. The case where they slow you down, get in your way, reduce the size of your swing: that’s light at night.
And okay, it’s never that simple. “Day” and “night” mean different things based on your body clock’s current time. Light exposure slowing down your body’s clock is part of a normal day. The same light can affect different people in different ways.
That said, I love this analogy because it captures something I think about a lot in the context of circadian rhythms. The secret of a good swing is having a clear difference between your forward and backwards motion. Shoves are good in the forward direction, and not-so-good in the other direction. Similarly, more and more it seems to me that the secret to healthy circadian rhythms is having a clear difference between the active and inactive parts of your day. When your body wants light, get lots and lots of light. When it’s time for dark, get the darkest dark you can.
I’m thinking about this today because I just read “Light at night in older age is associated with obesity, diabetes, and hypertension” by Kim et al. in the journal SLEEP. Using a dataset of 552 community-dwelling adults aged 63-84, they looked at how exposure to light at night is associated with cardiovascular disease risk factors. They found significant associations between light at night and obesity, diabetes, and hypertension, but no such associations between average light over the course of the 24-hour day and those same risk factors. In other words, the fact that it’s at night matters. When you get light at night, you muddle the difference between night and day. You lose that good swing.
This paper isn’t the only paper to look at light at night—manyothersexist—and I don’t want to get in trouble for confusing correlation and causation (the above are correlations only). That said, there are plenty of possible mechanisms by which light could increase your risk of cardiovascular disease.
One is by throwing off your internal clock, so your metabolic machinery is not firing on all cylinders, or is rising and falling in a way that’s mistimed relative to when you’re eating. Another is by suppressing production of melatonin, which your body produces naturally once a day, but won’t produce if it thinks it’s still light outside. Melatonin has a number of properties that are important for circulatory and metabolic health, so it makes sense that having less of it around may not be the greatest thing for your health.
But at the highest level, I find real value in thinking about that swing analogy. There are some things in our body that are meant to be dynamic and rhythmic: breathing, walking, heart rate. Our 24-hour rhythms are no different. Get that good swing by getting lots of light during the day. Keep that good swing by turning them all off at night.
Thanks to the authors of Kim et al. for their great work! We really enjoyed the read.
Back by popular demand, here is part two of the “We Rate Sleep Memes” blog series. If you’re just now tuning in (check out part one here), the title pretty much says it all. We take sleep-related memes we find online, we use them as an excuse to talk about sleep and circadian science, and we rate ’em. Let’s kick it off with:
Here’s the annoying thing: Resting is good. I’m not about to be out here telling you not to rest. In fact, a study that looked at people who regularly sleep less than five hours a night during the work week found that weekend catch-up sleep might help compensate for the bad effects of not sleeping during the week.
What’s annoying is that sleeping in on the weekend can cause its own problems. Basically, you end up jet lagging yourself without actually going anywhere. This “social jet lag” can mess with your mood, grades, metabolism, and lots of other important things.
So the real answer is probably to do everything you can to get to the point where you don’t need to recover sleep on the weekend. Keep to the same schedule, every day. Yeah, yeah, I know—easier said than done, especially with work and life commitments. But if you can make your sleep life more regular, expect it to make a lot of other things in your life better too.
Originality: ⅘ Nice callout to the weekend
Overall quality: ⅘ Dog very cute
Heads up: If you’re doing this, and doing it a lot, you miiight be building up an association between “being in bed” and “not going to sleep.”
Don’t get me wrong, creating imaginary situations that will probably never happen before bed is a time-honored tradition. As far as I know, pretty much everyone does it. Boromir, son of Denethor, isn’t incorrect here.
But it’s a problem if your brain’s whirring so much on your pillow that you start to think of bed more as “the place where I am stressed out about imaginary scenarios” than “the place where I sleep.” That association can make it harder and harder to actually fall asleep when you want to.
If this sounds like you: Get out of bed. Have your imaginary scenario thoughts in a nice chair in the living room somewhere. Keep the lights dim or off altogether as you do it. Wait to get into bed until you’re just about falling over yourself with sleepiness.
Then, when you look like this:
…land right on into bed.
Originality: 2/5 Yes, yes. We all know about Night Thoughts.
Overall quality: ⅘ Boromir very tragic and noble.
Facts o’clock: Your body’s internal clock sends different signals for sleep at different times of the day. These different signals mean you’ll sleep for different lengths of time depending on when you fall asleep.
And since your body’s clock is always updating and adjusting itself, it probably won’t send the same signal at the exact same time every day. This can make it hard to pick up a pattern in why you’re sleeping four hours one nap, and 20 minutes the next.
Another thing that can make it hard to find a pattern? How long you sleep also depends on how much you’ve been awake and asleep recently, on topof the signal from your body’s internal clock. So there are a lot of moving pieces, which can make it seem like you’re playing “nap roulette”, when it’s really “nap you could do a better job of predicting the duration of if you were keeping close tabs on when you’ve been sleeping and the time your body’s clock currently thinks it is.”
*green goblin voice* Listen here, Spiderman: biology is complicated and can seem random, but it might not be as random as you think.
Originality: 3.5/5. Definitely been done before as a topic, but “nap roulette” has strong brand energies.
Overall quality: ⅘. Nice meme. Now if you’ll please excuse me, I have a picture I need to sell to J. Jonah Jameson.
So you want to help shift workers feel better—sleep better, be safer, have fewer of the long term chronic health problems that go hand in hand with shift work. How do you do it? Where do you start?
As some of the most circadian-wrecked people around, shift workers have been the topic of no small amount of research. Yet one incontrovertible, “best” strategy has failed to emerge for what shift workers should do. There are plenty of reasons for this, but the short answer is: it’s complicated! There are a lot of possible shift schedules a person can be on, and a lot of variation from person to person in how those shifts will affect them. In this blog post, I’ll try to chip away at the complexity a bit by covering what’s currently known about strategies for shift work, and what shift workers might do in the future.
Rather conveniently, a lot of the ways you try to help shift workers can be framed as a choice between two alternatives. So let’s start with one of the biggest “versus” there is out there.
Homeostat vs Circadian Interventions
There are two main forces that conspire to make a person feel sleepy. One is your sleep hunger, or sleep homeostat—basically, a build up of “need for sleep” that accrues when you’re awake, and drains when you’re asleep.
The other is your body’s circadian clock, which sends an extra strong signal once a day to tell you to go to sleep. These aren’t the only things that make a person sleepy, but they explain a lot of the phenomena we see in shift work contexts. This way of thinking about sleepiness (homeostat plus circadian) is called the “two process model of sleep.”
You could classify the strategies around helping shift workers into two camps, based on which of these two forces—homeostat or circadian— they’re primarily targeting. If you want to have a low sleep homeostat going into the night shift, for instance, you probably want to sleep as close to before your shift as you can. So you might try staying up until 1:00 pm on the day after your shift, building up a ton of sleep pressure, then falling asleep for most of the afternoon and evening, waking up right when it’s time for work. Naps and caffeine would also fall under the header of “mostly targeting the sleep homeostat.”
Targeting the circadian clock, however, means moving your rhythms to promote sleep at a time you actually can sleep. This means phase shifting your clock, which can be achieved by doing the kinds of activities that matter to the clock (getting light exposure, avoiding light, exercise, etc.) at the right times.
These methods aren’t mutually exclusive by default, but they can be in conflict at times. A lot of what decides that is the direction you choose to move your clock in.
Advancing vs delaying the clock
A totally day-adjusted person will probably have their peak fatigue hours occur sometime in the early morning; say, 3:00 am. If they go on a night shift, those peak fatigue hours are happening right in the middle of work hours. (Not exactly ideal). So you could shift their rhythms so that their worst hours no longer happen at 3:00 am.
Way #1to Achieve This: Shift them later, or delay their clock. Move it so they’re feeling the biggest circadian drive to sleep at, say, 9:30 am, after they’re home from work.
Way #2: Shift them earlier, or advance their clock. Move it so they’re feeling the biggest circadian drive to sleep at, say, 5:00 pm, or before they go to work.
Way #1, or delaying the clock, is often called “compromise phase position.” The idea is that it’s a compromise for the night shift life—you’re not totally shifting to a nocturnal schedule, but you are getting the time of day when your clock maximally promotes sleep to be outside your work hours. You can do this by blasting yourself with light in phase delay portion of your body’s daily rhythms, which for a person who’s still pretty adjusted to the day schedule is going to be in the afternoon and evening. Note that this is where we start to conflict a bit with the homeostat-targeting interventions: If you’re keeping yourself in a super bright environment in the hours before your shift, you’re probably not sleeping the whole time you’re at it.
Way #2, or advancing the clock, does not come with the same homeostat conflict. To advance the clock, a person still relatively well-adjusted to a day schedule would want to avoid evening/afternoon light and get tons of it in the morning. A “sleep after 1:00 pm” intervention in which people were also dosed with bright light in the latter part of their shift saw a 3 hour shift in the timing of the circadian rhythm biomarker, dim light melatonin onset (DLMO). In other words, you can target the homeostat right before a shift and promote an earlier phase shift at the same time.
There’s evidence that both strategies can improve upon a baseline of undirected, “do what you want” advice to shift workers. Advancing the clock plays nicely with “sleep before shift” strategies, but you could also take a pre-shift nap, while mostly delaying yourself in the lead up to it. You could also try splitting your sleep—sleeping right after your shift, and then again right beforehand, and using your non-sleep time to steer your clock in one direction or another (though depending on what your personal time zone is, this may be a bit difficult—those hours might be times when you’re more or less insensitive to light).
So how do we begin to choose a strategy to recommend? Well, there’s one missing dimension to all the research touched on so far that we haven’t discussed yet.
Non-shift workers vs. shift workers
All of the shift working studies cited above looked at non-shift workers who were brought into the lab and put on simulated shift work protocols. Typically, being a shift worker was an exclusion criteria for the study: No real shift workers allowed.
There’s a very good reason for this, which is that shift workers have wonky circadian rhythms. You bring shift workers into a lab and look at their dim light melatonin onset timings, and you can see coverage over almost all the 24-hour clock. This means that you wouldn’t expect a nice clean scientific result to come out of putting them all on the same schedule: What’s good for someone would almost certainly be terrible for another. Focusing only on non-night shift workers (who are, it should be said, a good model for “just starting out on the night shift workers”) means you’re able to better parse a signal from the noise.
But it also means that you miss out on a very important piece of information: Namely, that only a tiny fraction of shift workers phase advance themselves in the real world. Many of them don’t follow particularly great strategies, but the ones who are better adapted tend to be very delayed.
This result comes from work in night shift nurses that looked at the different strategies that real nurses employ. In that research, the “most adapted” nurses were the ones who basically did this compromise phase position strategy, where they were very late types on their off days. Nurses who stayed up all night before a shift or napped during the day on their off days tended to be worse adapted— worse mood, increased cardiovascular risk, you name it. Counterintuitively, the least adapted nurses also tended to be older and more experienced on the job.
When you step back and think about shift work in a vacuum, the truly best strategy from a health perspective would probably be for shift workers to shift their lives entirely to align with night work, sleeping during the day even on the days they have off. In that sense, it would be like living in the United States but pretending you worked the same hours as a person living in Tokyo. With good enough blackout curtains and strong enough willpower to ignore the FOMO of diurnal life, you truly could fully adapt to a night-living lifestyle.
A tiny fraction of real shift workers do this. But most don’t, and the vast majority want to sleep at night during the days they’re not working. The better adapted nurses in the Vanderbilt study achieved this by being pretty extreme night owls on their days off. The poorly adapted nurses, the older ones who tended to stay up all night or nap on off days— they might be the ones to benefit most from a phase advancing schedule, which appears to have worse discoverability (nobody really does it in the real world) than the delaying schedules. In other words, if one direction isn’t working—as it appears not to for the ill-adapted shift workers—try going the other way.
Time now for my caveat that this is all, once again, pretty complicated. You can be an extreme night owl on your off days right up until the moment you have to work a 7am to 7pm shift. Your actions during your off days and off hours are constantly shifting your circadian profile, so that the thing that works for you one week might not work for you the next week. None of these studies could look at DLMO changing day-by-day in the real world, because none of them had the ability to track DLMO cheaply and in real-time. What do you want to prioritize—safety on the commute? Safety during shift? Ability to sleep well and feel good? Putting one of these above the other can give you a different answer. It’s a lot.
Enough already! What should I do?
Listen, if there was a one-size-fits-all easy solution to all of this, we wouldn’t have made an app for it. I would just have emailed everyone this blog post and done that thing where you brush your hands together in the international sign of “all done here.”
Here’s one rule-of-thumb, though: If you’re adjusted to a day schedule, and you’ve got a one-off night shift tonight before going back to the day schedule, you’re not going to be able to meaningfully shift your body’s circadian clock in the next 8 hours. You’re going to want to bank as much sleep as you can in the hours leading up to it and be aware of when your peak fatigue hours are going to occur. Our app can help you with that.
For everyone else, this is where our app comes in. Shift builds on this history of research to design plans unique to your body clock. You can choose which ones to try, and give feedback on the ones you like and don’t like. Want to help us move the needle on getting shift workers to a healthier place? Reach out for early access.
“If I go to bed at 10:30 pm, I often wake up at 4:30 or 5:30 am and can’t go back to sleep. But if I go to bed at 1:00 am, I fall asleep easily and can sleep until 9:00 am. Throughout the work week I’m running on 5-6 hours of sleep, and only on the weekend do I get to recover. Could it be that my natural cycle is just different from what social norms say?”
Reader, I pushed my glasses up my nose so fast and so hard that they are now deeply embedded into my face. A small price to pay for this gift of a question.
But before I can get into why I was so excited about my friend’s pain and suffering, I need to tell you about the two-process model of sleep.
The Two-Process Model, 10,000-ft-View Edition.
When you’re awake, you build up a “hunger” for sleep. This is your sleep homeostat, and it grows when you’re awake and drains when you’re asleep.
At the same time, you’ve got a circadian drive for alertness, which for day-adjusted folks tends to send its strongest signal for sleep in the middle of the night. The effect of both of these together is your sleep drive.
When the sleep drive hits an upper threshold, your body switches into “wants sleep” mode and tries to get you to go to bed. When it drops below a threshold, your body switches into “wants wake” mode and tries to get you to wake up. You can visualize this phenomenon as the zig-zag of the homeostat bopping between time-varying upper and lower thresholds set by the circadian clock. Hit the upper limit, fall asleep. Hit the lower limit, wake up.
Still with me? Ok, here’s the most important picture you’ll see in this whole blog post:
Well, there you have it! My friend’s sleep problems in a nutshell. No further explanation needed.
I showed this graph to another friend of my mine, and he sent back this:
Okay, so maybe a little more explanation needed.
Simple model. Complex phenomena.
Let’s talk about the two-process model in the real world. For one, people don’t instantly pass out when they hit the upper threshold for sleep. People can push sleep back in a lot of ways— staying out of bed, keeping the lights on, or, as was the case in one old sleep study, repeatedly dunking their heads in ice water. We can call this a “wake effort”—making an effort to stay awake in the face of the tyrannical rule of the two-process model. In fact, it might not really feel like that much effort if you’re having fun on the internet.
The blue-dashed line in the plot below shows what would happen if my friend stayed up a couple hours later than her body necessarily wanted to, moving her bedtime from roughly 10:00 pm (no wake effort) to a bit after midnight (with wake effort). The blue shaded region shows her sleep when she delays her bedtime in this way.
Here’s the thing: That blue shaded sleep area, the one that starts a little after hour 24 (midnight) and goes until a little bit after 8:00 am? It’s wider than the shaded purple sleep area, which starts around 10:00 pm (hour 22) and goes until maybe 4:30 am. In other words, she’s sleeping more by staying up later—almost two hours longer.
You can eyeball it if you look where the lines are hitting the red waveform on the bottom. The purple and blue curves are chasing it as it’s going down, and the “stay up later” blue curve is hitting it at a significantly later point. That means more sleep overall (“good,” in theory), despite a later bedtime (“bad,” in theory).
What does this mean? Well, for starters, it hints at the enormous complexity you can start to get at when you mix two waveforms and thresholding conditions. The wiggly line of circadian sleep drive and the zig-zag line of homeostatic sleep drive, while deceptively simple, can interact to generate some wild phenomena.
Let’s explore a bit. I’m starting my friend’s sleep homeostat at a fairly high value, because she told me she tends to need an alarm to wake up during the work week (and feels pretty tired all of the time). If I started her at a lower sleep homeostat (waking up better rested), the difference between the two sleep durations becomes a lot smaller (purple: 7.7 hours of sleep, blue: 7.9 hours).
Give her a higher starting homeostat, on the other hand, and the no-resistance-to-sleep-drive curve (purple) has her falling asleep around 7:30pm, waking up at midnight, and staying awake all night until she passes out again in the early morning, while the “wake effort” curve in blue mostly stays the same:
These are wildly different scenarios, and they’re arising from me gently nudging a number up or down a little bit. Imagine what happens if we make those upper and lower thresholds—representing the circadian drive for alertness—act the way a real circadian rhythm does: shifting, stretching, and bending in response to the signals you give it during the day. Imagine weakening the signal from the body’s clock, or adding the effects of noise:
Mathematicians who study this kind of stuff have done some prettygreatwork looking at just how much complexity can arise from the simple rules of a sleep homeostat and a circadian rhythm. In my own life, I think of the two process model most often when I wake up in the middle of the night. Ok, I tell myself. My circadian sleep drive is probably running a bit late. If I hang out in the dark a while, it’ll swing in and kick me back into sleep. And what’s great is that it reliably does.
“Are my natural cycles just different from social norms?”
But let’s get back to my friend’s question. If I had to make a guess as to what was going on with her based on the two-process model, I’d say her circadian clock is delayed relative to where she wants it to be (and that she’s often waking up in the morning with a high sleep homeostat due to the chronic sleep restriction from her work.)
That might seem to be a vote for her natural rhythms just not jiving with her work hours, and there’s probably some merit to this. She’s described herself as light sensitive, which may be a trait that predisposes you to being more of a night owl. That could be shifting her rhythms later, and making it so her circadian clock isn’t where it needs to be when her sleep homeostat drops to a low value.
Yet one of the great things about circadian rhythms is that they can change. If they didn’t, we’d never get over jet lag. We’d stay on our starting time zone schedule for all time, regardless of where we went, when we slept, or when we went outside. The fact that our clocks can be disrupted also means they can be fixed.
Another way of thinking about it: My friend’s weekend rhythms would make her a pretty darn early bird if we abruptly transplanted her three time zones west to California. What’s the difference between living in California versus her home city on the East Coast? Well, a lot of things, but the most important one for her circadian clock is the difference in her light exposure.
So I’ve got her trying out a new circadian regimen to help herself sleep during the work week. My hope is that the two-process model, along with the fancier flavors of it that have spun up in research and at our company, can help people in the real world understand their sleep better. Simple rules can interact to make complicated behaviors happen, but at the end of the day they’re still simple rules. And simple rules might just have straightforward solutions, too.
We’ve noticed a lot of shift worker communities online. I’m not talking about just a few here—I’m talking about hundreds. These communities also each tend to have thousands of active followers. Surprising, right? Until you remember that over 30 million people in the U.S. currently live with shift work.
From Instagram accounts to Twitter hashtags, Facebook pages to subreddits, it’s clear that shift workers are looking for spaces online to connect. Some of these communities are full of funny and relatable memes, hoping to get a few laughs out of their followers. Others are centered around giving general advice and support. When we took a closer look, we noticed something else: Questions and concerns from shift workers that (directly or not) involve circadian rhythms. That inspired us to start this very blog series. It also involves our favorite thing: helping people with science.
By addressing some of these FAQs that we see online, we hope to be an additional resource for the shift work community. If you have a question you would like us to answer next in this series, feel free to email us at firstname.lastname@example.org.
1: “I would experience this weird sensation where all the sudden I feel more tired around 1:30am – 3:30am, they tend to vary but it’s really annoying, not sure what is going on…”
Ah, we know exactly what’s going on here. Around 3am is an interesting time to be awake (and no, we’re not talking about the “witching hour”). It’s because your body’s internal clock is often trying its absolute best to force you into sleep around this time. If you continue to stay awake and push through it, your clock will eventually back off and stop sending quite so strong a signal for sleep. This is probably what’s going on with the “weird sensation” this particular shift worker is experiencing.
Another fact regarding this time of night is that, due to the strong urge your body is sending for sleep, your likelihood of making a mistake goes way up. Which is another reason why it’s so important to have an internal clock that is in sync with your unique work schedule. By adjusting your internal clock, you can eliminate this excessive sleepiness that occurs in the middle of the night and be more awake on your shift.
2 “I need to lose weight and I feel like it’s so much harder to do on night shift. Some of my coworkers don’t eat at all during shift but I end up getting hungry. Yet I also will wake up feeling snackish throughout the day so I basically end up eating round the clock with no “fast” like normal people have at night. So many people say intermittent fasting is the easiest diet but I can’t figure it out with this schedule.”
We reference your body’s “internal clock” frequently. This is your central circadian clock and it’s responsible for many of your internal functions throughout the day. But did you know that your stomach also has a clock of its own?
It’s true, and your stomach is better prepared to digest food at certain times of the day versus others. You can think of this as your “ideal meal window.” The time of this window depends on the signals that come from your body’s internal clock. In other words, your specific “best” meal window is unique to you. This is why it’s important to remember that there is no one-size-fits-all solution when it comes to timing your eating. But how do you know when that window is? Well, our app, Shift, has the answer for you.
In a recent interview with Dr. Amy Bender, she talks about the importance of improving the sleep of professional athletes.
Could you introduce yourself and tell us a little bit about what you work on?
I’m the Director of Clinical Sleep Science at Cerebra. We’re a sleep technology company focused on better diagnosis and treatment of sleep disorders, but also focused on work to help the everyday person sleep better. I lead our research department on initiatives related to those key areas of better diagnosis and treatment of sleep disorders and sleep improvement.
What got you interested in sleep in general, but also sleep and performance?
My aunt was a sleep technologist and she invited me out to her lab. She hooked up a patient with electrodes, and showed me the translation of those physiological signals onto the screen—I was instantly hooked. After that I pretty much called every sleep lab that I could when I got back home and found a place where I could volunteer.
As it turned out, the manager of the place I was volunteering at was on the hiring committee to hire the Director of the Sleep and Performance Research Center at Washington State University. So there was kind of a collaboration there already. They were looking for a sleep technologist, then ended up hiring me as a sleep technologist. At the lab we focused on sleep deprivation and the impact on cognition and the sleep EEG. I started off there for about 4 years as the sleep technologist and was fascinated by the science so I applied to graduate school.
I ended up getting into a dual Master’s PhD program focused on experimental psychology while continuing to work at the lab. Having the sleep technologist background that I do, I wanted to focus on the impact of sleep deprivation on the EEG. After my Masters and PhD I ended up doing a postdoc at the University of Calgary where I was focused on Canadian Olympic team athletes and how to improve their sleep. Because I was a former athlete myself (I played college basketball, Ironman, I did some mountaineering as well), there was kind of a love for sports and performance already. Doing that postdoc at the University of Calgary was like a combination of both of my passions. Since then I have worked with a number of college athletes, professional athletes, and Olympic athletes.
It seems like the importance of sleep for sports performance is getting more recognition these days. What shifts in perception have you seen in your career?
Well, I see more of an emphasis on sleep in sports teams for sure. Previously, the coach would only focus on things that they had control over with their players while at the facility. Things like sport-specific skills, conditioning, and strength. This has since expanded into nutrition on and off the field, sports psychology, and sleep. Once we started to realize how important sleep was for performance, I think the teams and athletes started listening. We still do have a long way to go, there’s only a handful of us out there working with teams and elite athletes and so I think it can certainly grow a lot more.
For example, Dr. Cheri Mah’s study on sleep extension in Stanford basketball players and how that impacted reaction time, mood, and sprint times—I mean people started to listen and I think we’re finally getting there. If a team or an athlete isn’t thinking about sleep, then they’re really missing out on a huge area of performance.
Our CEO actually wrote a blog post about this during the Olympics. Discussing how athletes can entrain to their new time zone and for their specific competition time.
Oh absolutely that is important, I recently went on a trip overseas, and it’s apparent. I tried to do all that I could to shift my rhythms earlier (I was traveling to Europe) so I was trying to get lots of light in the morning, get up earlier, block light at night, go to bed early, you know—just trying to shift my rhythms about three days before the trip. Even doing all that, being the sleep scientist that I am, I still had jet lag upon arrival. It was a quicker recovery, but still: people need to be thinking about If they’re traveling across time zones. They may bank on the fact that “I’ll get there a week ahead of time and I’ll be adjusted by the time the competition starts”, but I think the training leading into that competition is also important for being fresh and ready and alert. It’s definitely a factor for teams and athletes traveling across multiple time zones, and there’s a lot they can do ahead of time to help prepare for that.
We’re betting people ask you for sleep tips pretty regularly. Is there anything where you’re like “people still haven’t realized how big an impact this could have for them”?
You all are a circadian optimization company, and so one of the things is that light is so important. For example, I’m in my office right now in low light, it’s only between 100 and 200 lux, and so I think it’s important for people to understand that the indoor environment isn’t necessarily optimized for circadian optimization. Trying to get outside in the morning is key for me, even on a cloudy day where light could be up to 13,000 lux or so.
It’s important for people to get outside light and go on a walk in the morning to help entrain their circadian rhythms to be more on that normal schedule. Many people don’t realize it, they think that their office environment is perfect for light. But getting the right amount at the right time, starting in the morning, is very important. Then also trying to dim the lights at night and maybe wear blue light blocking glasses in the evening are good tips for people to follow.
There’s been some work looking at office lighting, having bright white light in the morning and then as the evening approaches kind of transitioning to more of that orange kind of sunset lighting. And they do find improvements in sleep, in performance, and even mood.
Like you mentioned, we’re a circadian rhythms company first and foremost, so we gotta ask: What do you think the future holds for circadian rhythms research in the world of elite performance? How about just overall health?
I think there’s a lot to uncover here, and in particular I’m really interested in the individual, their own chronotype, their own circadian rhythm, and optimizing training times based on when they would perform the best. For example, if they’re more of an early bird but they have evening competition, how can we optimize our circadian rhythms to shift more towards an optimal performance time in the evening? I think this is a fruitful area that has a lot to be explored, and there are hints of it in the research right now. I think we could do a lot more to shift circadian rhythms for optimal performance at a certain time.
A while back, there was a realization that strength and conditioning is important, and so sports teams would add a strength coach. Then there was a realization that nutrition is also important, so they would add a nutritionist to the team. Now (potentially) I think that you might see more sleep coaches helping out teams. There’s a lot of work out there that we aren’t necessarily taking advantage of and I think that could be an area where maybe more sleep coaches will pop-up for different teams and different athletes.
Any research you’re excited about or want to highlight?
At Cerebra, we’re working on developing a kind of a miniature EEG wearable device that you could potentially wear on the forehead or even measuring in-ear EEG with one of our partners that we are working with. We want to pair that with an app to be able to figure out for the individual what their triggers are for sleep quality. We have a way to measure sleep quality using ORP (which is a metric of sleep depth which micro-analyses the EEG). We did a study recently where we had 20 people do 20 nights with our current device while tracking their lifestyle factors such as, caffeine, exercise, alcohol use, and how much they got outside. We’re really seeing some interesting results with some of those lifestyle factors and how that impacts sleep quality, and also how that impacts next day performance. Additionally, we did a reaction time test for all those individuals, we’re just finding some really interesting results and I think we want to go way beyond the “general sleep hygiene” advice for people and make it more personal and individualized.
For example, I might be a high or a fast metabolizer of caffeine, and so a coffee at 1 p.m. won’t necessarily impact my sleep quality vs someone who may be more of a slow metabolizer – where it would impact their sleep quality. I think it’s really exciting for us to really try and personalize sleep optimization for different individuals.
Actually, I was listening to a recent podcast that Olivia (CEO of Arcascope) was a guest on, and she mentioned that sleep at night starts with what you do during the day. A lot of these activities, stressors, or anxiety that you experience during the day can then impact your sleep quality at night.
Actually, before I started working at Arcascope, I had no idea that what I did during the day impacted my ability to fall asleep and stay asleep. Having experienced sleeping troubles throughout my life, I wish I had this knowledge sooner!
For sure, that brings up an important point. If you are struggling with your sleep, and you have tried different things but it doesn’t seem to impact your sleep quality, try and get help from a sleep professional. If you’ve been struggling multiple times for weeks you’ve tried everything, don’t try and solve it on your own but really try and reach out to sleep professionals who can help.
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!
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).
Interested in using acceleration to track sleep? We’ve got a software package for that.
Another month, another Book of the Month! Starting with its title, When by Daniel Pink hones in on just how simple the idea of timing things better really is. The question of “when” is in the same fundamental category as where, why, how, and what.
Yet, as Pink notes:
“We simply don’t take issues of when as seriously as we take questions of what.”
That’s starting to change. One of our favorite article titles ever is “Medicine in the Fourth Dimension” by Cederroth et al. In it, the authors highlight the growing awareness of how circadian biology influences drug efficacy and tolerability. At Arcascope, we think that so many aspects of health and wellbeing could benefit from taking the dimension of time into account, and that dosing time could come to be seen to be as fundamental as dosing amount.
But we didn’t just enjoy this book because it recognizes that timing matters. We also liked it for highlighting something we experience every day as we work on product: Sometimes you’re alert, and sometimes you’re not, and you should take breaks when you’re not instead of powering through. As he writes in his conclusion:
I used to believe that lunch breaks, naps, and taking walks were niceties. Now I believe they’re necessities.
Of course, you can’t always take a break when you need one most. But there should be more recognition—from employers, schedulers, and from ourselves—that human beings aren’t constant in time over the course of a day. We change from dawn to dusk, and our needs change too. Maybe that means timing light or exercise to help yourself adjust to a shift faster, or maybe it means giving yourself a break so you’re not hammering away at a problem when your brain just isn’t having it.
Timing things right is what Arcascope is about. Want to see When we tell you to do stuff? Reach out about getting on our early app access list.
Algorithms are really easy to mess up. Take your pick for how: overfitting to training data, having bad training data, having too little training data, encoding human bias from your training data in the model and calling it “objective”. Feeding in new data that’s in the wrong format. Typos, subtle typos, nightmarishly subtle typos. Your cat stepping on the keyboard when you’re out of the room.
Having done this for a while, my first impulse any time I get amazing performance with an algorithm is to be deeply suspicious. This isn’t because algorithms can’t be incredibly powerful; you really can get amazing performance if you do them right. But you can also get seemingly amazing performance if you do them wrong, and there are a lot of ways to be wrong.
The core issue, I think, is that there are so many choices involved in the making and maintaining of an algorithm, and if the algorithm is trying to do something complicated, those choices can have complicated downstream effects. You can’t readily anticipate what these effects might be (this problem is hard enough that you’re building an algorithm for it, after all), but your brain tells you that there can’t be that much of a difference between a threshold being 0.3 and that same threshold being set to 0.4. So you blithely make the change to 0.4, expecting minimal effects, and then the whole thing just collapses underneath you.
I’m saying this because, while developers on the whole have gotten pretty on board with the concept of unit tests and test coverage for code, I’m not as sure about what currently exists around tests for data science algorithms in biology, medicine, and health. I’m not talking about tests that confirm a function gives the number we expect it to based on an old run of it (e.g. asserting that f(3.4) = 6.83 because we ran it once with 3.4 and got 6.83 as the answer). Those kinds of tests basically act like a flag that something’s changed, and if you change your function f, you can just paste in the new output from f to make the test pass.
I’m talking about using a lot of data — a representative sample of what you’ve collected— in your code’s tests, to assert that some macro-property of the algorithm’s output is preserved. If you make a change to the algorithm, and that macro-property changes, your code should let you know about it. If this sounds like one flavor of functional testing, that’s because it is. But the key thing I’m arguing for is that the performance of data science-components of a product— code two steps removed from sklearn or torch or R—be tested functionally as well.
Let’s talk through an example. Imagine you’ve got an algorithm for distinguishing sleep from wake over the course of the night. In my own experience, algorithms of this kind can have an unfortunate tendency to flip quickly, in response to a threshold being changed, from thinking a person slept a lot over the night, to thinking that person barely slept at all. There are ways to address this, and it’s not always an issue, but it’s still something to look out for.
So how could you look out for it? Take a representative sample of data from a group of sleeping people, and add a test that runs your sleep algorithm on all of them and asserts that everyone gets detected to have at least some baseline amount of sleep. This way you don’t have to worry that changes to the algorithm made some people better but other people unexpectedly, dramatically worse. You can rest easy because assurances of this kind are built into your development pipeline.
At Arcascope, a lot of our scientific tests center around the mean absolute error of our prediction of melatonin onset. We want to predict when somebody’s melatonin onset is happening so we can map it to other quantities of interest: minimum core body temperature, peak athletic performance, peak fatigue, you name it. But we also want to do continual development work on these algorithms, to keep bringing the mean absolute error down over time. How can we make sure that our changes actually make things better? How can we make sure that a change that improves performance for some people isn’t sabotaging others? Tests that confirm the properties we care about are preserved, no matter what we do in the backend.
I mentioned above that I don’t know what’s out there elsewhere in digital health, and I don’t. Maybe a lot of people are writing tests of this kind in their code! All I know is that when we realized we could do this— add fundamental performance checks for the algorithms that make up our backend, embedding deidentified human data from our studies directly into our testing suite— it was a very cool moment. It helps me sleep a lot better at night knowing those tests are there.
Which is good, because sleeping more means I’m introducing fewer nightmarishly subtle typos to our code. Wins all around.
(To hear our actual stance on permanent DST, check out this blog post. Short version: we love getting rid of the seasonal time change, as long as we end up on permanent standard time, not permanent DST.)