Circadian science Interviews Sleeping troubles

Interview with Dr. Amy Bender

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.

Circadian science Sleeping troubles

Visualizing MESA, pt. 1

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).

 Interested in using acceleration to track sleep? We’ve got a software package for that.

Circadian science

Book Of The Month (March)

When by Daniel H. Pink

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.

Get early App access!

Circadian science Technology

Scientific Tests

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.

Circadian science Lighting Sleeping troubles

Official Company Stance on Permanent DST



(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.)

Circadian science Interviews Shift Work Sleeping troubles

Interview with Dr. Louise O’Brien

Would you mind introducing yourself to our audience—where do you work, what do you do?

I’m Louise O’Brien, an Associate Professor at the Division of Sleep Medicine, Department of Neurology, at the University of Michigan. My work focuses mostly on sleep disruption in pregnant women and its consequences. I’m also interested in treatments and therapies available to intervene to improve the health of women and babies.

Your work largely centers around sleep and its connections to pregnancy and maternal health. What led you to this field of study?

That’s a great question. Going back a long time ago when I was a graduate student, I was really interested in SIDS (Sudden Infant Death Syndrome) and why seemingly healthy babies died suddenly at night. So, I was spending a lot of time monitoring babies overnight to understand what was going on physiologically. That led me to really want to understand more about what happens during sleep, because I realized I’m doing all this nocturnal monitoring, and I really don’t know that much about sleep. That brought me to the United States—to become trained in sleep.

What are some of the things that are really well-known about how sleep affects pregnancy?

I think most pregnant women know that sleep can be quite disrupted during pregnancy. Healthcare professionals can dismiss this as normal, or it’s the body’s way of getting ready for a baby, etc. But I think we’re now learning that certain types of sleep disruption, such as frequent snoring or obstructive sleep apnea can actually lead to poor health outcomes for mom and baby. Poor maternal sleep can lead to high blood pressure or diabetes in the mom, and can also result in poor fetal growth, preterm birth, even an increase in c-section deliveries.

We are learning more and more with the work that we do. For instance, in recent years we’re learning that sleep behaviors, like sleeping on your back, appear to be related to poor outcomes such as stillbirth. A woman who has a stillbirth in late pregnancy has been shown to be more than twice as likely to have fallen asleep on their back. So, this is a relatively new area, and an area that we’re very interested in. I think that behaviors such as sleep position are particularly interesting to me because they can be changed. And If we can change behaviors, that offers an opportunity for intervention that could potentially reduce poor outcomes.

What are some of the current research questions around sleep, circadian rhythms, and pregnancy that are most exciting to you?

I’m becoming really interested in the timing of sleep. A lot of my previous research has been on sleep disorders, like obstructive sleep apnea, which is a medical condition that can be treated. We all sleep, but we don’t all have a sleep disorder. And so, what we’re learning from the general non-pregnant population is, even if we get sufficient sleep (7-8 hours as an adult), if that sleep is mistimed against your body’s natural rhythm there appears to be an increase in blood pressure. So, I’m interested to take those findings to the pregnancy population and to see if mistiming sleep during a critical developmental window for a fetus has adverse consequences which impact the health of the mom , and also the health of the baby. Because we know that what goes on in utero can sometimes have long term effects decades later, potentially even transgenerational, this is an important area that we really don’t know anything about. So the timing of sleep is something I’m really getting interested in. Because, again, we can change it.

The obvious place we see mistimed sleep is in shift workers, but they may not be able to change so easily because they’re working shifts, and they’re working against their body’s natural rhythm. That’s an extreme example, but we know that miscarriage is higher in shift workers than non-shift workers. So the question is, what is mistimed sleep against our body’s natural rhythm really doing?

Since we’re a company that does wearable analytics: What’s the current state-of-the-art for wearable tracking during pregnancy?

It’s not very good. I think with lots of wearables out there that claim to be able to track your sleep, the reality is that none of them are really validated against the gold standard- which is an overnight sleep study. There is an algorithm that has been validated against polysomnography, a type of sleep study, but none have been validated in pregnant women. So, we just really don’t know. While there are lots of things out there that claim to track your sleep, there is nothing out there that tracks it accurately in pregnant women. There is definitely an opportunity for growth in this area, absolutely. Wearables let you look in your app, and it says “REM sleep or deep sleep”, but how accurate is that? We really do not know. So many people have wearables, and I think if we can somehow harness that technology and validate it, then we have a real opportunity to see how sleep across gestation impacts maternal and fetal health. Now this is a window of opportunity. We should be doing this now, because we could then make a huge difference to the lives of mothers and babies if we just had this data.

People sometimes use sleep and circadian rhythms interchangeably, even though they’re not the same. Are there any circadian-specific angles to pregnancy and delivery outcomes that you think are particularly important to call attention to?

I would go back to this idea of mistimed sleep. You can get sufficient sleep and still have poor outcomes, potentially if your sleep is mistimed. We’re learning that in the nonpregnant population now. So, the timing of when we sleep is really important. We already know that getting insufficient sleep is bad for us, but we just assume if we get 7-8 hours of sleep we must be fine. But, if we mistime that, then maybe we’re NOT so fine. I think this is a really interesting area, and how does that relate to pregnancy? We just don’t know, the data is not there. But, I think that this is going to be the next niche area.

Some literature that’s coming out now is adding another layer on top of that. For instance, our diet—WHEN we eat. What’s the impact of eating late at night or mistiming our eating, and how does that affect pregnancy? I think this is a more complicated area that’s going to get a lot of work in the next decade. This is where the field is going to go, and I would like to think that we would be in there somewhere you know, making some inroads into this really important area. I think it’s crucial that we understand what’s going on with our timing, and our eating, and how that’s impacting our own health and the health of that developing baby. Timing is everything, right?

Anything else you’d like to highlight, from your own work, or as an area that needs more attention?

One of the things I would like to mention is: how does sleep play into disparity in healthcare and disparities in outcomes? So for instance, we know that minority women have worse outcomes than caucasian women, we also know that minorities in general tend to have poorer sleep. How does this whole sleep, pregnancy, and disparities play together? That’s a little bit unknown at this point. This is another area that I think is really important—is there a role for sleep and addressing sleep issues in being able to improve outcomes for minority pregnant women? Outside of pregnancy, we know that minorities in general have worse sleep than caucasians, especially Black women. We also know that Black women have worse pregnancy outcomes. For instance, they have double the risk of having growth-restricted babies, and also have higher risk of preterm birth. Nobody’s really looked at pulling sleep into that. We’re looking at two parallel angles, and what I think we need to do is bring these things together to see if there is a role for sleep in these poor outcomes. Because if there is, then we can intervene.

Interested in beta testing our app? Send us an email!

Circadian science

Book Of The Month (February)

Sync by Steven Strogatz

For our second book of the month, we picked Sync: How Order Emerges from Chaos In the Universe, Nature, and Daily Life, by Steven Strogatz. This might seems like a bit of an oddball choice for a company that’s working on consumer apps in the health space. Why pick a math book if you’re a sleep, circadian rhythms, and well-being start-up?

We’ve got our reasons, but before we get into them, let’s back up a little. There are a lot of things in life we think of as incontrovertibly rhythmic. Walking, for instance. That’s rhythmic: there’s a beat to your steps. Swinging on a swing is another. Breathing, heartbeats, dancing to music—it’s weird to think of these without a rhythm. More bluntly, if these things don’t have a nice, clear rhythm, odds are pretty good that something’s pretty wrong.

For some reason, though, we don’t seem to care about the rhythms of our sleep. There’s this hyper-focus on eight hours of sleep a night, and nowhere near enough focus on when those eight hours are happening. Analogy time: imagine you’re listening to a weak, erratic heartbeat. You wouldn’t say that everything was fine, just so long as a certain number of beats happened each minute. You’d care that the rhythm was off.

Rhythm is a fundamental property of our bodies and our health. Literally fundamental: you can write down equations to describe how molecules at the smallest scale interact in the body and have rhythms arise spontaneously from the physics of how they bind and bounce off each other. And in the messy, chaotic conditions of the real world, rhythms often try to match up with other rhythms. There’s something very foundational about synchronizing.

Or, to quote Sync:

“For reasons we don’t yet understand, the tendency to synchronize is one of the most pervasive drivers in the universe, extending from atoms to animals, from people to planets.”

Your internal clock tries to sync up with the rhythms of the sun. The rhythms of the clock in your stomach try to sync up with the rhythms from your brain, as well as the rhythms of the food you eat. If the brain rhythms and the food rhythms are telling two different stories—think, two pieces of music with different tempos playing at the same time—your stomach clock can struggle to find the beat.

Modern life pretty much makes it impossible for us to keep all our circadian systems in sync: There are going to be times when you have to stay up late; when your work, or life, or just being really hungry one night make it so your brain falls out of sync with the sun and your stomach. The solution isn’t never losing synchrony: it’s recovering it quickly whenever you do.

So why did we pick Sync for our book of the month? Well, #1, we’re fans of Steven Strogatz and applied math in general. Reason #2, we love how it calls attention to the fact that rhythms aren’t some optional add-on to life; they’re at the very root of it.

Or, to quote the book:

“[T]he capacity for sync does not depend on intelligence, or life, or natural selection. It springs from the deepest source of all: the law of mathematics and physics.”

As for Reason #3? There are just some beautiful pieces of writing. Take this one:

“Synchronized chaos brings us face-to-face with a dazzling new kind of order in the universe, or at least one never recognized before: a form of temporal artistry that we once thought uniquely human. It exposes sync as even more pervasive, and even more subtle, than we ever suspected.”

When people stop thinking of sleep as something to count, and start thinking of it as one instrument in the complex orchestra of the body’s rhythms, we think they’ll feel benefits they weren’t expecting. Our bodies are hardwired for rhythms. Let’s bring them into sync.

Circadian science Technology

Biophysics for Better Living 2.0

In my last blog post, I talked about the power and potential for biophysics engines to contribute to clinical care and ragged on my own ability to play video games. The short version of it, if you don’t have time to circle back: If you’re going to make a digital twin of somebody, base it on a system of differential equations that captures how the physics of the human body work. Also, I’m terrible at video games.

I talked in that blog post about how you can use a biophysics engine—a.k.a. known properties of how the body works, codified into equations— to estimate things that are hard to measure because they’re hard to reach. Things like the firing rates in your ventral and dorsal suprachiasmatic nucleus (SCN), which are tucked pretty deeply inside your brain. You’re not going to be able to easily peek and see what the SCN is up to (at least not right now, in 2022), but you can figure out what your SCN is likely up to passing the same inputs your real SCN got into a model of the SCN.

Woke up really early and got a blast of light? That probably sped up your clock. Stayed up late and kept the lights on overhead? That probably slowed your clock down. Where did your clock wind up at the end of it all? That’s a question that hinges on the details of when, how long, and how bright your light exposures were. In other words, it’s a question for the mathematical model. 

But there are other questions you can ask a model, beyond “where am I now?” You can ask “what if?” What if I were to stay up the entire night in bright light? What if I dimmed the lights at 3:15 pm? How would these changes affect the state of my internal clock? And how would that changed state affect downstream outputs, like the timing of my peak fatigue or my peak performance?

When we talk about what we do at Arcascope, we say we do circadian tracking and circadian recommendations. The tracking is using a biophysics model to answer the question “where is your circadian clock right now?” The recommendations come from repeatedly asking the model “what would happen if you did (insert series of behaviors here)?” Asking a lot of questions gets you a lot of answers, which you can then pick from based on which ones best meet your goals, whether that’s sleeping more, adjusting faster, or being maximally alert at a specific time. 

It’s all still biophysics models, but in one use-case, we’re putting them to work to capture reality, while in the other, we’re trying to capture a whole swath of possible futures. A list of potential realities to choose from as you scope out the rest of your week. Better living by picking the best answers to “what if?”— I think that’s pretty neat. And it keeps me up at night thinking—what if we could do this for other systems, besides sleep and wake, too?

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Circadian science Technology

Biophysics for Better Living

One of the highest stakes moments of my life was the time in graduate school when I was (badly) playing Donkey Kong at Pinball Pete’s Arcade, and a very, very good Donkey Kong player came over to watch me play in silence. I’d just spent six dollars worth in quarters dying repeatedly on the first floor of the first level, but something about his judging, appraising eyes summoned the fireball-hopper within. I made it past the first level, then the second, then the third—an absolute record for me—and I had almost cleared the fourth when the pressure finally got the best of me. Still, it felt like a tremendous achievement. I like to think the good player and I shared a nod of mutual respect as he elbowed me out of the way and proceeded to spend the next forty-five minutes on an uninterrupted victory run. 

Besides letting everyone know that I need to get out more, this story is an opportunity to talk about a kind of math that should be more integrated into clinical care, and isn’t— yet. There was a model of physics deciding what happened in that game, a system of differential equations for converting my button mashing into movement across a screen. And just like the inputs I gave the system could be transformed into outputs that captured the physics of motion, so too can the inputs we give our bodies be transformed by a realistic biophysics model to give us outputs that capture the ways our bodies work without the need for invasive tests. 

Let’s back up: At a very high level, what’s going on in a video game is that you’ve got prescribed rules for how to update all the positions of all the moving pieces from one frame to the next. In the next blink of an eye, where should Mario go? If I’m pushing the joystick to the left in that instant, nudge him a little to the left. If I’ve just hit the jump button, nudge him a little bit upwards. If I’m mid-jump, make the force of gravity and the speed he took off the ground fight it out to see if he should be nudged a little up or a little down. 

The whole set of rules for making these decisions on how to nudge can be called a physics engine. Passing the history of my joystick spins and button presses into this physics engine gets you an output that describes how Mario moves, how he jumps, and how far I make it in the game before an untimely demise. This type of math is all over the place in games, CGI, and user interaction elements (like views that scroll and bounce). But there’s no reason a physics engine has to be restricted to visuals, or video games, or the physics of movement. You can have a physics engine that captures ion channels open and close, for instance, or how neurons talk to each other in the brain. 

Which brings us to your body’s internal clock. Circadian rhythms are a massive pain to measure experimentally; it’s one of their defining properties. It’s hard to know what’s going on in the brain without looking at it, and it’s hard to get a glimpse of it without getting pretty invasive. But if you know the rules for how the clock part of your brain works— “the differential equations describing the suprachiasmatic nucleus”— you can pass through a history of inputs to the system, and get out an output that describes how the neurons are communicating with each other at a specific point in time. Pass in what my lighting history has looked like over the last four weeks, and I can tell you what time my brain would cue melatonin to start rising in my body tonight. 

So here’s why I think we should be using differential equations in medicine more: they can help you see things you otherwise wouldn’t be able to see. You can use a system of differential equations to track how the brain would react to the inputs you give it, even if you can’t readily experimentally measure brain state, just how a person with access to Donkey Kong’s code could track how far I got on my amazing run, even if someone blocked off the console screen with a tarp. Knowing the rules means you can measure things in the dark. 

And there’s another reason, too—personalization. By tweaking the rules of your video game physics engine, you can change the way the gameplay works. Cut gravity in half, and suddenly Mario’s soaring with every jump. Double it, and he’s a sitting duck for everything Donkey Kong throws at him. As the importance of individual differences in light sensitivity and fatigue management become better understood, biophysics engines are ready-made for customization. Want to better fit the data of someone who’s hyper-sensitive to light? Crank up the light sensitivity parameters. Got genetic data on them? Plug that into a molecular model of their rhythms

Biophysics engines can be the key to a true digital twin, helping us look inside a simulation of ourselves in ways we can’t look inside our real selves. There’s potential for these quantitative methods in medicine beyond sleep and circadian rhythms, too. Who knows? Maybe someday a model of this kind could be useful for understanding how the (fun) stress of my epic Donkey Kong run affected my mood and memory. Or at the very least, how my body processed the enormous bubble tea I drank in triumph right after. 

Want to learn why we don’t think the answer to circadian estimation in the real world is to just throw an artificial neural net at it? Check out this blog post by CTO Kevin Hannay. 

Circadian science Sleeping troubles

Naps, part one.

The first thing I want to say about naps is that I’m almost always for them. Naps can help you recover from sleep deprivation. Naps are good.

But let’s talk about that almost always. When might you want to avoid napping? 

Well, maybe you’re trying to shift your personal time zone and are at a point in your internal circadian day where getting light exposure will be very, very helpful to achieving that shift. Closing your eyes to take a nap will block photons from reaching your retina, which means your brain won’t have the photic momentum it needs to push through a shift in your rhythms. Probably not a big setback if the nap is short, but a multi-hour nap at the wrong time could end up slowing down how quickly you adjust.

Or maybe you really, really need to be alert right at the moment when you’d be waking up from a nap. In that case, you might worry about sleep inertia, the phenomenon of general grogginess and impaired performance that can persist for several hours after waking. This, too, might make you want to hold off on a nap. 

And then there is the classic “I napped, ergo, I cannot sleep now.” Your transition into sleep is driven both by your circadian clock as well as your accumulated “hunger for sleep” (or sleep drive). Feed that hunger for sleep right before bed, and you might not have enough sleep pressure built up to flip your switch from on to off. This is one of the reasons why avoiding naps in the evening is a common component of cognitive behavioral therapy for insomnia

So: You’re sleepy. You’ve got other stuff to do today. Do you take a nap? If so, how long? 

Answer: You probably want a ten minute nap. 

This, like all science boiled down to a single tidbit, is a big ole simplification. It matters what your internal time is (does your body think it’s day or night?) and what your recent sleep/wake history is. 

But multiple studies have found that a 10 minute nap during the day improves performance right off the bat, while longer naps mean that you have to sink time into recovering from your nap after you wake up. A 20 or 30 minute nap in these studies was still found to be better than staying awake, but participants could still be shrugging off the effects of sleep inertia more than two hours after waking, while a 5 minute nap was generally not enough for much of an effect.

Would you ever want to take a longer nap? You might, if your goal is not so much “perform better for the next two hours” as it is “don’t fall asleep in the next ten hours.” In a classic study from 1986, researchers kept subjects up all night, let them take a morning nap, and then measured how readily they fell asleep at different points over the rest of the day. Here, a 15 minute nap was barely better than no nap at slowing down how rapidly people fell back asleep, while a 60 minute nap had alerting effects that persisted 4 to 8 hours later. The benefits didn’t keep increasing past 60 minutes, though: a 120 minute nap didn’t get you anything more than a 60 minute one did. 

Another reason to consider a longer nap is memory. People who get a 60 minute nap do a better job at remembering words they’ve been exposed to than people who don’t get a nap. That said, a 6 minute nap is also enough to see a significant memory boost— for one tenth the time investment. 

In conclusion: You probably want a short nap. You might want a longer nap, though, if you don’t need to be super alert for the next few hours, but you do need to stay awake later in the day. 

Lastly, you might benefit from a nap, even if you don’t think of them as particularly helpful for you. The benefits of napping that show up in objective reaction time tests often aren’t reflected in how people subjectively rate their own sleepiness. You might get more of a boost from naps than you think, and you may also need less of a nap than you’d expect to see that boost.

Much of this blog post was helped along by this review. Thanks to the authors for the great resource!