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IPS 553

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The goal of this project is to mitigate the culture of sleep deprivation that pervades medical education.

Long hours and sleep deprivation are trademarks of being a student in any medical field. Lack of sleep is a “badge of honor”.

An unspoken ideological assumption is that medical education should be challenging enough require sacrifices to personal well-being and sleep.

Pressure and anxiety to do well abound, further impeding sleep.

While hard work and commitment are necessary, research demonstrates that lack of sleep harms both clinical and academic performance.

This course is intended for an audience of graduate health professional students. They will be diverse in demographics, disciplines and experience as well as diverse in their sleep patterns and attitudes!

Hosted in Blackboard

It leads the audience through three modules developed with andragogic principles in mind.

Module 1 addresses the neurophysiology of sleep, learning, and memory.

Module 2 explains research supporting the relationship between sleep and performance.

Module 3 offers  suggestions for encouraging student sleep and rest.

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Recognize the dangers of accepting that sleep deprivation is "normal" in both medical practice and medical training

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Improving Awareness, Diagnosis, and Treatment of Sleep Disorders

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Sleep is often viewed by the general public as a "perceptual hole in time"-during which nothing productive occurs (Dement and Vaughn, 1999). One only has to examine common colloquialisms such as "don't get caught napping," "if you snooze you loose," or "time is money" to gain a sense of the prevailing attitude that sleep is either optional, a luxury, or unimportant.

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Sleep Deprivation Epidemic:

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royal society for the encouragement of arts manufactures and commerce

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Matthew Walker on The Sleep Deprivation Epidemic - RSA

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RSA ANIMATES. The RSA Animate series is a unique way of illustrating and sharing the world-changing ideas from the RSA's free public events programme

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subscribe to Hidden Brain podcast

What does the song "Satisfaction" by The Rolling Stones have in common with the periodic table of elements? Both are the product of dreams.

Creative reawakening is just one of many boons provided by a full nights rest, argues neuroscientist Matthew Walker.

"Sleep is the Swiss army knife of health. When sleep is deficient, there is sickness and disease. And when sleep is abundant, there is vitality and health."

On this week's episode of Hidden Brain, the mind and body benefits of eight full hours of sleep - and yes, Matthew Walker says that's how many hours of rest you should be getting.

"If we didn't need eight hours of sleep and could survive on six, Mother Nature would have done away with 25 percent of our sleep time millions of years ago. Because when you think about it, sleep is an idiotic thing to do. If sleep does not provide a remarkable set of benefits, then it's the biggest mistake the evolutionary process has ever made."
Hidden Brain is hosted by Shankar Vedantam and produced by Maggie Penman, Jennifer Schmidt, Rhaina Cohen, Parth Shah, and Renee Klahr. Our supervising producer is Tara Boyle. You can also follow us on Twitter @hiddenbrain, and listen for Hidden Brain stories each week on your local public radio station.

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The "Swiss Army Knife" Of Health: A Good Night's Sleep

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subscribe to Hidden Brain podcast What does the song "Satisfaction" by The Rolling Stones have in common with the periodic table of elements? Both are the product of dreams. Creative reawakening is just one of many boons provided by a full nights rest, argues neuroscientist Matthew Walker.

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TedEd

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The benefits of a good night's sleep - Shai Marcu

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Sleep is your life-support system and Mother Nature’s best effort yet at immortality, says sleep scientist Matt Walker. In this deep dive into the science of slumber, Walker shares the wonderfully good things that happen when you get sleep -- and the alarmingly bad things that happen when you don’t, for both your brain and body. Learn more about sleep’s impact on your learning, memory, immune system and even your genetic code -- as well as some helpful tips for getting some shut-eye.

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Diverse

Interprofessional

Possible Future Medical Educators

Graduate level

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Sleep health: can we define it? Does it matter? - PubMed - NCBI

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Creating a Medical Culture that Values Sleep
How Better Sleep Relates to Outcomes

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Marc Effron, president of the Talent Strategy Group, looked at the scientific literature behind high performance at work and identified eight steps we can all take to get an edge. Among those steps is taking care of your body - sleep, exercise, and nutrition. But the most important is sleep. He offers some practical advice on getting more and better rest, and making time to exercise. Effron is the author of the new book, 8 Steps to High Performance: Focus On What You Can Change (Ignore the Rest).
Download this podcast TRANSCRIPT
SARAH GREEN CARMICHAEL: Welcome to the HBR IdeaCast from Harvard Business Review. I'm Sarah Green Carmichael.

All of us want to perform to our utmost level at work - whether we're a new employee or the CEO.

But a lot of the self-help books and articles out there don't start with science, says our guest today, Marc Effron.

MARC EFFRON: To me, high performance is someone who delivers both results and behaviors at the 75 percentile, compared to their peers, over time. So it's not that you had a good month or good year but year in year out you are delivering results and behaviors better than most of the other people that you work with.

In his new book, Effron actually looks at eight different ways that you can become a high performer at work - with the studies to back up those claims.

Those include things like setting big goals, relying on yourself to develop new skills instead of expecting your company to help you, and growing your network.

But the step I found most interesting and surprising - and that Effron says is entirely within our control - is taking care of your physical body.

Marc Effron is president at The Talent Strategy Group, and the author of the book "8 Steps to High Performance: Focus on What You Can Change (Ignore the Rest)."

Marc, thanks so much for being here.

MARC EFFRON: My pleasure. Happy to be here, Sarah.

SARAH GREEN CARMICHAEL: You know, I must have read hundreds of leadership books while I've been hosting this show and not a whole lot of them talk about the importance of physical well-being. Why was that something that you felt was really important to talk about?

MARC EFFRON: Well, I didn't go into writing the book with a preconceived notion that I should write about the body and how the body contributes to high performance. I sorted through thousands of academic articles that said that they had some nugget of proof in them that connected something with something else. And it just so happened that there was a lot that popped up around sleep; a little bit around exercise. And so it caused me to include this one of the eight steps because it really emerged as a very powerful force in our performance.

SARAH GREEN CARMICHAEL: Yeah, it's interesting that you say there's a lot about sleep and not as much about some of the other things, because I too have looked for research on say, nutrition or exercise, and there really isn't as much research on that. Do you know why that's the case? Is it just harder to measure that other stuff or is sleep really that much more important?

MARC EFFRON: Well, there have been plenty of experiments done on exercise and performance, so it's not for lack of looking. In fact, that was probably the area where I was most surprised, being a bit of a gym rat myself. I was looking for some great proof that that would make me even more successful if I just spent one more day in the gym. And unfortunately, if you go through all of the academic literature, there just really isn't anything there. Obviously if you eat right and exercise you will be in great shape and probably a happier individual, but scientists could find no link between those two things and your performance at work.

SARAH GREEN CARMICHAEL: You know, I - as someone who doesn't always eat very well and definitely doesn't exercise enough, I find that oddly reassuring. So, you kind of break sleep into two different parts: quantity and quality. So let's start with quantity. We all know we're supposed to get six to eight hours of sleep, but is there a number that's the absolute minimum? Like, if six to eight just feels impossible. Is there a number that you should aim for as the absolute least you can get and still be effective?

MARC EFFRON: Yeah. That's part of the big challenge, Sarah, is that the advice on sleep is really all over the place. In fact, the National Sleep Foundation says it's somewhere between six and 10 hours. That's a four hour gap where you could do a lot of other things.

And if you look across the collection of research on sleep, it really narrows in on about six to seven hours being the ideal. That ideal is going to vary for everyone. The science also says we each likely have a set sleep point; that minimum number of hours that we need, and we can try as hard as we want to get less than that and train ourselves, it's not going to work.

So the challenge with quantity is that there's not a great set of research into what's the exact right number. We at least have a range, but we can likely narrow it down to that six to seven hour

Point.

SARAH GREEN CARMICHAEL: I remember a few years ago there was a fascinating Wall Street Journal article about what they called the "sleepless elite," this tiny, tiny, tiny fraction of people who really can function, on not very much sleep at all. I think the problem is that a lot of us think we're in this group and we're not. We really do need more sleep. But why is it so tempting to think that we can sort of cut corners there?

MARC EFFRON: Well, I think any high performer wants to do whatever he or she can do to continue being a high performer. And so we're always looking for a shortcut and while we've got those wasted seven hours, we're just lying in bed - we're not working at all during that time.

Therefore, why don't I just take one of those hours and apply it to work? It's a very tempting thought, until you dig into the research and you find out it just doesn't work that way. In fact, I remember reading that article and thinking, "I bet they have a couple of hints at the end about how I can become one of those sleepless elites."

They did not and there are certainly no hints in the academic literature to help people do that. We are unfortunately somewhat hardwired into our sleep patterns; both the number of hours we need, but also the time that we wake up. So those advocating for, you know, a 4:00 AM wake up and get a great start to the day - it's likely that their brains are hardwired to that and it would be very difficult for somebody else to shift and engage in that exact same pattern.

SARAH GREEN CARMICHEL: So what happens to our brains when we don't get enough sleep?

MARC EFFRON: Well, this is what's interesting is you would naturally think, "Well, if I don't get enough sleep, I'm not going to show up as smart, as clever as I would if I got a full night's sleep." But what the science says is, actually, that's not what deteriorates. It's our more fundamental skills that deteriorate.

We'll forget people's names. We won't remember where the car keys are. We might not be alert on the way to work and swerve or get into an accident. So it's actually not the higher motor functions, the higher cognitive functions that suffer, it's the much more fundamental ones. So you'll still be just as bright in that meeting, you'll just be really crabby or irritable.

SARAH GREEN CARMICHAEL: Which can be a really bad thing if you're supposed to be running the meeting.

MARC EFFRON: It does not contribute to success in some cases.

SARAH GREEN CARMICHAEL: So it's not just the quantity of sleep you get - the number of hours. You also make the case that it's the quality of sleep that really can affect your mood and your performance at work. So I'm wondering to hear a little bit more about maybe some things you do to make sure that the high quality of sleep is there?

MARC EFFRON: And that was one of the most interesting findings when I was sorting through all those academic papers, was the fact that it is quality, not quantity that matters most. And the challenge for anyone who lives a real life is that the recommendations around getting high quality sleep sounds something like: you should go to bed in a completely dark room with no device reading beforehand, no animals in the room, the room set to about 65 degrees and no other disturbances.

And if you're a hermit, that's probably a lovely way to get a full night's sleep. But for those of us who probably have all of those things going on, it makes it a bit challenging. If nothing else, it at least lays out: here's what's required to get higher quality sleep. And the two big ones are a dark room and a cold room. Those are actually pretty controllable, and those can certainly help to improve the quality. You can get five hours of quality sleep, which will allow you to be a higher performer than 10 hours of poor quality sleep.

SARAH GREEN CARMICHAEL: I don't know if this is a study you ran across, but I do remember reading once that people, when you make your bed, you get higher quality sleep. And that is a study that, regardless of how scientific it is, I always cite as a chronic bed maker.

MARC EFFRON: Well Sarah, there is what we call a replication crisis in this field. And I'm just going to guess that mountain might not stand up twice. But it doesn't hurt, I'm sure.

SARAH GREEN CARMICHAEL: It was probably done by someone who was nagging their spouse to make the bed.

MARC EFFRON: Exactly, or who was in the sheet business - one of the two.

SARAH GREEN CARMICHAEL: Exactly. So if you, for whatever reason, have not managed to get enough sleep or you have not managed to be in that dark, cool room, are there some ways that you can make up for it after the fact?

MARC EFFRON: Yeah, I think there are a few different strategies. One is: recognize that if you do not get either the quality or the quantity of sleep that you want, you are not going to be your best. Let someone in the office that you interact with regularly, and who you trust, know that. Not as an excuse, not: "Hey Sarah, I had a bad night's sleep, so please put up with me being a jerk today." But: "Hey Sarah, I had a bad night sleep. We've got that big meeting today. Can you make sure that I'm well-paced, I'm not rushing my words? Give me a little hand signal if I'm being too crabby?"

Ask others for help with recovering from that. But also, the science is really clear that two things help a lot. One is naps. Now, not everybody has the luxury of taking a nap, but the science is super clear that a 10 minute nap - not five, not 15, not an hour - a 10 minute nap can make up for as much as one hour of lost sleep.

So if you can find some place to put your head down for 10 minutes, it has amazingly restorative benefits. And then our old favorite caffeine. Caffeine is proven to increase alertness - not a surprise to any of us.

And so if you don't get that good quality or quantity of sleep, come into the office. Let your trusted friend know, "Hey, keep an eye on me today. I want to make sure I am a high performer even though I haven't had a great night's sleep." If you can find time to take a 10 minute nap to get that boost and a couple of cups of coffee or tea, that's a magical recipe for faking that you actually have had a great night's sleep.

SARAH GREEN CARMICHAEL: Is there a danger with caffeine that it actually makes your sleep problem worse? Like if you have caffeine because you didn't sleep, but then caffeine keeps you awake so then you get another night's bad sleep - are you kind of in a vicious cycle.?

MARC EFFRON: Yeah, there's definitely a challenge there. And most of us underestimate the half life of caffeine. So. people often will say, "I can have a double espresso and fall right asleep." Well, that's great, but quality of sleep is a very likely going to suffer, with caffeine speeding up your brain and your body. And so what the science would suggest is that caffeine within about six hours of bedtime is probably going to have a negative effect on both quality and quantity of sleep.

SARAH GREEN CARMICHAEL: What if you are someone who is chronically sleep-deprived? Maybe you have a new baby at home and they keep waking up no matter what you do, but for whatever reason, you are not in a position where you can really improve your sleep quality and it it's not just like, oh, today's a bad day. It's like every day is a bad day. What would your advice be to someone in that situation?

MARC EFFRON: I think I would go with one of the first things that I mentioned, which is you simply need to let other folks know, try as I might, I am not going to be at my sharpest over the next week, month, year - whatever that period is and ask for people to - if it's a long time understand, hey, I've got a new baby at home, you know, try as I might, I can't control their crying, therefore... But also let them know: here's what it's going to end. So no one wants to have an employee come to them and say for the next 12 years I'm probably not going to show up to well. So it's probably helpful to say here's the period during which I think I probably won't be as sharp as I'd like to be, but then also recognize things like ating - well while it might not be a high performance strategy - is certainly going to give you the fuel to perform well during the hours that you are at work.

And we've already talked about caffeine, a well-proven strategy around that. And that's part of the challenge with any of these elements of being a high performer is sometimes we simply can't get that particular step done. That's why there are eight steps in total. If you say, "Guess what, I'm, I'm just not going to get the sleep that I need." There are still seven other things you can do that can help you be a higher performer.

SARAH GREEN CARMICHAEL: So I want to talk a little bit more about some of these things that are beyond sleep. But staying in the physical realm, you mentioned that you're kind of a gym rat and you were really hoping to find strong evidence that working out would improve your performance at work. What did you find? You said you didn't quite find that, but you must've found a couple things. I'm curious to know what you found.

MARC EFFRON: There is a lot of research and about the best that I could find that talked about exercise and its impact on performance was: One, more vigorous - meaning weights, not aerobic exercise - tended to yield slightly higher performance benefits on the same day only. And exercising in the morning tended to yield higher performance benefits then exercising at night. That was about it. So if you combine those two little factoids, what you get is some really hard exercise early in the morning can give you a very small performance boost during the rest of the day.

SARAH GREEN CARMICHAEL: So I do think there is some really good research that shows that there is a connection between even moderate amounts of exercise and mental health. So could you then make a transitive leap that moderate amounts of exercise improve mental health and having better mental health surely helps you at work or is that too much of a stretch?

MARC EFFRON: Yeah, I think you get to what we call a three way interaction: If A causes B, does B cause C? It's a lot easier to measure both of those things separately. So if you show up to work without good mental health, then it's probably going to impact your performance. But it's a bit of a stretch to be able to connect all of those things together.

SARAH GREEN CARMICHAEL: And sort of the third leg of this whole stool is diet, right? The stuff that we eat. It seems like there's really not a lot of evidence out there though, that what you eat really affects your performance at work. Did that surprise you?

MARC EFFRON: It did. I really thought I'd find some magical connection to at least what we know about, you know, decent balanced diets and higher performance. And again, it wasn't for lack of studies being out there. But it was more of a stretch to say food leads to higher performance, rather than food leads to good health and good health might be a platform or a foundation for higher performance. We're back to that three way interaction where it probably contributes, but it probably contributes in such a small way that it's going to be really difficult to kind of parse that out of everything else that we're studying.

SARAH GREEN CARMICHAEL: And maybe, you know, individual results might vary. If you're someone who has pizza for lunch and then notices that you fall into a food coma, maybe that's just you and you can learn from that, but that doesn't mean that it's like, science.

MARC EFFRON: And I would also guess - I didn't find anything that proved this - but I would guess there's far more downside risk on food than there is upside. So if I eat a perfect diet, will I be a higher performer? Probably not according to the science. But if I eat a really horrible diet, will I have health problems that will likely take me away from work or reduce my focus on my performance? Absolutely.

SARAH GREEN CARMICHAEL: And maybe this is something that should go without saying, we all have our own definitions of high performance, but I'm curious to know how did you define high performance and what do you mean when you use that term?

MARC EFFRON: To me, high performance is someone who delivers both results and behaviors at the 75th percentile, compared to their peers, over time. And a few magical phrase s in there: One is "compared to their peers." So all performance is relative. You might be great, but how great are your peers? So you're better than three out of four of your peers - and over time - so it's not that you had a good month or a good year, but year in, year out, you are delivering results and behaviors better than most of the other people that you work with.

SARAH GREEN CARMICHAEL: So that kind of reminds me of that old joke about the lawyers where they're running from the tiger, and it's like, "I don't have to outrun the tiger, I just have to outrun you."

MARC EFFRON: One of my favorite jokes.

SARAH GREEN CARMICHAEL: It's a piece of life wisdom, like I keep coming back to.

MARC EFFRON: But it's true - performance is relative. And I think that's part of the challenge with a lot of the changes that we've seen in performance management systems at companies recently is there was a lot of noise with people saying, "Well, I did a good job and I want a prize."

Well that might be great, Marc, that you want a prize, but Sarah did a better job, so she's going to get a better prize. So it's not simply that you're doing a good job. Life is relative. It's always going to be relative.

And that's what high performers understand, is it's not just about me being good, it's about me understanding what is the highest possible standard for the work that I'm doing and how can I meet that highest possible standard because somebody else is trying really hard to get there.

SARAH GREEN CARMICHAEL: There's so much about work performance and being better than your peers that is out of your control. You don't really control who your peers are. You don't really sometimes control what your company culture is, although you can try. And one of the things you wrote in the chapter on managing your physical health is how you manage your body is a completely controllable factor. And that is both appealing, but I also have to admit that it doesn't always feel that way to me as a person with a body. I'm just curious to know sort of how it feels to you.

MARC EFFRON: Sure. Well, I would suggest all of these steps are completely controllable. Now, that still means you're working in an ecosystem where other things are happening. So you might set big goals, and somebody else sets bigger goals. You might set big goals and your boss doesn't like those goals.

So it's not saying that you can completely control the outcome, but you can completely control that step. And part of it - again, let's go back to the exercise piece. If you say you want to exercise an hour every other day, will something else need to not get done in that hour? Absolutely.

And that could be talking to your friends, it could be reading a book, it could be enjoying your favorite show on TV. All those things would simply get sacrificed, but it's making choices to say, "In the 24 hours that you have to spend, how would you like to spend them?" High performers say I'm going to spend more of those hours on things that deliver high performance and willingly sacrifice other things that might be very enjoyable, but that I don't see adding up to that higher performance.
SARAH GREEN CARMICHAEL: And don't sacrifice your sleep!

MARC EFFRON: Keep at least six hours.

SARAH GREEN CARMICHAEL: Yes. Well, Marc, this has been really fun. Thank you so much for spending some time with us.

MARC EFFRON: My pleasure. I enjoyed it.

SARAH GREEN CARMICHAEL: That was Marc Effron. He's president at The Talent Strategy Group, and author of the new book "8 Steps to High Performance: Focus on What You Can Change (Ignore the Rest)."

This episode was produced by Mary Dooe and Curt Nickisch. We got technical and production help from Alexie Rodriguez. Adam Buchholz is our audio product manager.

Thanks for listening to the HBR IdeaCast. I'm Sarah Green Carmichael.

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The Science Behind Sleep and High Performance

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Marc Effron, president of the Talent Strategy Group, looked at the scientific literature behind high performance at work and identified eight steps we can all take to get an edge. Among those steps is taking care of your body - sleep, exercise, and nutrition. But the most important is sleep.

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Hong, H., Schotland, H., & Perez, I. A. (2018). Sleep and performance. American Journal of Respiratory and Critical Care Medicine, 197(8), P13-P14. doi:10.1164/rccm.1978P13

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"Nature" Journal Article (2018): Rest to Learn! Awake Quiescence Aids Memory: 

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"Nature" Journal Article (2018): Rest to Learn! Awake Quiescence Aids Memory: 

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Abstract

Newly encoded memories are labile and consolidate over time. The importance of sleep in memory consolidation has been well known for almost a decade. However, recent research has shown that awake quiescence, too, can support consolidation: people remember more new memories if they quietly rest after encoding than if they engage in a task. It is not yet known how exactly this rest-related consolidation benefits new memories, and whether it affects the fine detail of new memories. Using a sensitive picture recognition task, we show that awake quiescence aids the fine detail of new memories. Young adults were significantly better at discriminating recently encoded target pictures from similar lure pictures when the initial encoding of target pictures had been followed immediately by 10 minutes of awake quiescence than an unrelated perceptual task. This novel finding indicates that, in addition to influencing how much we remember, our behavioural state during wakeful consolidation determines, at least in part, the level of fine detail of our new memories. Thus, our results suggest that rest-related consolidation protects the fine detail of new memories, allowing us to retain detailed memories.

Introduction

In order for labile new memories to be remembered over the longer term they must be consolidated, i.e. strengthened and stabilised, over time1,2. Research in humans and non-human animals suggests that an important mechanism of consolidation is the automatic neural reactivation of newly encoded memories in the minutes that follow their formation3,4,5,6,7,8,9,10,11,12,13,14,15. Memory reactivation is observed especially during periods of post-encoding sleep and awake quiescence (quiet resting), where the magnitude of reactivation predicts subsequent memory3,4,6,9,16, and the electrophysiological disruption of reactivation results in reduced memory11. Sleep and awake quiescence may be especially conducive to memory reactivation due to reductions in sensory input and task engagement, which could otherwise impact this process.

Behavioural research in humans supports this hypothesis, demonstrating better memory retention after post-encoding sleep19,20,21,22 and post-encoding awake quiescence18,23,24,25, relative to a filled period of wakefulness. While the effect of sleep in memory is well established, research into the effects of awake quiescence is still in its infancy. To date the latter research has shown that humans retain more words and associative information after a 15-30 minute delay and a 7-day delay26,27 when they rest quietly in the minutes immediately after memory encoding than when they engage in an unrelated perceptual task. This effect cannot be explained away by intentional rehearsal during the rest period, because people remember more memories after awake quiescence than task conditions, even when the memories cannot be rehearsed intentionally26,27. Thus, converging human and animal research indicates that awake quiescence supports the offline consolidation of new memories.

It is not yet known how exactly this rest-related consolidation benefits new memories, and whether it affects the fine detail of new memories. Rest-related consolidation could increase the number of retained memories without necessarily supporting the fine detail of individual memories. Therefore, in the study reported here, we examined how a brief period of awake quiescence after encoding affects the fine detail of new memories. To this end, we applied the robust and sensitive 'Mnemonic Similarity Task' (MST) to probe the fine detail of new memories. The MST, originally designed to assess pattern separation, is sensitive to manipulations during memory encoding and consolidation30,31. In this task, participants are first sequentially presented a set of photos of everyday items (targets). Memory for the presented items is subsequently examined via a visual recognition test, where participants are sequentially presented a set of photos of everyday items again, but asked to respond "old", "similar", or "new" to indicate whether each presented item is (i) identical to a photo presented during encoding (target, correct response = "old"), (ii) visually similar, but subtly different, to a photo presented during encoding (lure, correct response = "similar"), or (iii) a new photo that was not presented during encoding (foil, correct response = "new"). An equal number of target, lure and foil photos are presented. Two memory measures are typically extracted from the MST: a standard recognition score, and a Lure Discrimination Index (LDI) score. The standard recognition score is calculated as the proportion of "old" responses to target items minus the proportion of "old" responses to foils, and thus reflects a person's ability to correctly endorse targets, while rejecting foils. A high standard recognition score can indicate good gist-based memory, but it need not imply that memories are fine in detail32,33. The LDI score is calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils, and thus reflects a person's ability to discriminate lures from targets, while controlling for any response biases28,34. Thus, if a participant holds detailed representations of encoded target photos, they should demonstrate superior ability to correctly identify subtle visual differences in subsequently presented lures, and thus discriminate them from targets (i.e. respond "similar" to lures, rather than "old"). However, if a participant holds less detailed representations of encoded target photos, they should be less likely to correctly identify subtle visual differences in subsequently presented lures, and thus incorrectly mistake lures for target items (i.e. respond "old" to lures, rather than "similar"). Thus, a higher LDI score should indicate memories of fine detail, where subtle visual differences between previously encoded targets and presented lures can be identified.

Using the MST, Borota et al.30 recently demonstrated a positive dose-dependent effect of post-encoding caffeine administration on LDI scores. They hypothesised that caffeine promotes the consolidation of encoded (target) items, thus supporting participants' ability to discriminate these viewed (target) items from similar (lure) items during subsequent testing30. Similarly, we anticipated that if rest-related consolidation protects the fine detail of newly formed memories, then the MST LDI score should reveal this, because people who experience post-encoding awake quiescence should be better able to discriminate targets from lure items.

In our between-subjects study (see Fig. 1), sixty young adults were exposed to a subset of 60 photos of everyday items from the MST within the context of an incidental encoding task (indoor/outdoor judgement making task), before experiencing either: (i) no delay between item encoding and the subsequent testing phase ('No delay group', N = 20), (ii) 10 minutes of awake quiescence, i.e. resting quietly in dimly-lit room ('Awake quiescence delay group', N = 20), or (iii) 10 minutes of a perceptual task, i.e. playing a spot-the-difference game ( 'Perceptual task delay group', N = 20). Participants' memory for the earlier presented items was then probed via the MST memory test (see above), during which participants were presented 90 photos of everyday items: 30 were old, i.e. were identical to those presented during encoding (targets), 30 were similar (visually similar items from the same semantic category) to the remaining photos presented during encoding (lures), and 30 were new to those presented during encoding (foils) - see Fig. 1 for examples. Using the responses in the MST memory test we calculated a standard recognition score - to measure gist memory - and an LDI score - to measure the fine detail of memories. We also measured response times during the MST memory test and during the encoding task. We hypothesised that, if post-encoding awake quiescence protects the fine detail of new memories, participants' LDI scores should be superior following the awake quiescence delay than the perceptual task delay.

Experimental paradigm. Participants underwent three phases: (i) encoding, (ii) delay, and (iii) testing. During encoding, participants were presented 60 photos of a range of unique everyday items from the Mnemonic Similarity Task database (e.g. Stark et al. 2013). Participants incidentally encoded these items via the performance of a judgment making task, where they were required to respond whether a presented item would typically be found indoors or outdoors. Each item was presented for 2000 ms and was followed by a 500 ms inter-stimulus crosshair (+). Following encoding, participants completed one of three delay conditions: ( A) no delay (N = 20), ( B) 10 minutes of awake quiescence (N = 20), or ( C) 10 minutes of an engaging perceptual task (spot-the-difference game) (N = 20). In the subsequent testing phase, participants were presented 30 of the 'old' items presented during encoding (targets), along with 30 'similar' items that were visually similar objects from the same semantic category to the remaining 30 items presented during encoding (lures), and 30 'new' items that were visually and semantically different to the items presented during encoding (foils). There was no limit on the time to respond during testing.

Results

Our three delay condition groups were well matched in their demographic information (see supplementary information). Participants' responses in the encoding task (indoor/outdoor judgment-making task) were also matched across groups (see supplementary information). Importantly, significant group differences emerged in the subsequent memory test (Mnemonic Similarity Task, MST). Participants in the delay groups differed significantly in their LDI scores, which was calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils28, (F(2,53) = 6.954, P = 0.002, ηρ² = 0.208) - see Fig. 2D. Specifically, the LDI score was significantly higher in the awake quiescence delay than the perceptual task delay (t(35) = 4.454, P < 0.001), and this finding survived the Bonferroni correction (α = 0.017; 0.05/3 comparisons). LDI scores did not differ significantly between the (i) no delay and awake quiescence delay groups (t(36) = −1.602, P = 0.118), or (ii) no delay and perceptual task delay groups (t(35) = 1.876, P = 0.069) - see Fig. 2D.

Mnemonic Similarity Test (MST) response data for the no delay (N = 19), 10 min awake quiescence delay (N = 19), and 10 min perceptual task delay (N = 18) groups showing the proportion of old, similar, and new, responses to ( A) 'old' targets, ( B) 'similar' lures, and ( C) 'new' foils. Lure Discrimination Index (LDI) scores for the three groups are also shown ( D) - this measure is calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils, and thus reflects a person's ability to discriminate similar lures from old targets, while controlling for any response biases. Standard recognition measure ( E) - this measure is calculated as the proportion of "old" responses to target items minus the proportion of "old" responses to foils, and thus reflects a person's ability to correctly endorse old targets, while rejecting new foils. The awake quiescence delay group's ability to discriminate between targets and lures was significantly better than that of the perceptual task delay group, as shown in the groups' ( D) LDI scores and ( B) specific responses to lures. The latter (see red box) shows that the perceptual task delay group provided a significantly higher proportion of (incorrect) old responses to lure items than those in the awake quiescence delay group. In contrast, the awake quiescence delay group provided a significantly higher proportion of (correct) similar responses to lure items than those in the perceptual task delay. In all cases, error bars show the standard error of the mean. Non-corrected significance levels: *p < 0.05, **p < 0.01, ***p < 0.001.

If the benefit of awake quiescence delay over perceptual task delay in the LDI measure reflects a difference in participants' ability to correctly discriminate lures from targets, an interaction should be observed between delay condition group (quiescence vs. task) and response type to lure items ("old" vs. "similar"). Specifically, a greater proportion of correct "similar" responses to lures (i.e. correct identification of lures) would be expected in the quiescence group and a greater proportion of incorrect "old" responses to lures (i.e. incorrectly mistaking lures for targets) would be expected in the task group. In line with this prediction a 2 × 2 ANOVA revealed a significant interaction between delay condition group and response type to lure items (F(1,35) = 9.030, P = 0.005, ηρ² = 0.205). Pairwise comparisons of the proportion of "old" and "similar" responses to lure items - see Fig. 2B - confirmed that participants in the perceptual task delay provided a significantly higher proportion of (incorrect) "old" responses to lure items than those in the awake quiescence delay (t(35) = −2.395, P = 0.022). In contrast, participants in the awake quiescence delay provided a significantly higher proportion of (correct) "similar" responses to lure items than those in the perceptual task delay (t(35) = 3.584, P = 0.001). Moreover, participants in the awake quiescence delay provided a significantly smaller proportion of (incorrect) "new" responses to lure items (t(35) = −3.141, P = 0.003) and (incorrect) "similar" responses to foil items (t(35) = −2.613, P = 0.013) than the perceptual task delay. No other comparisons reached significance (all P > 0.05). When using a Bonferroni-corrected alpha level of 0.006 (0.05/9 comparisons), only the differences in "similar" responses to lures and "new" responses to lure items remained significant.

Performance on standard recognition, which was calculated as the proportion of "old" responses to target items minus the proportion of "old" responses to foils, was near ceiling across groups (see Fig. 2E). This indicates that all participants encoded and retained (at least) gist memory of most presented target items. Although there was no significant main effect of delay group in this measure (F(2,53) = 2.600, P = 0.084, ηρ² = 0.089), those who experienced no delay between encoding and testing were significantly better at discriminating targets from foil items than those who experienced 10 mins of a perceptual task (t(35) = 2.145, P = 0.039), though this finding did not survive the Bonferroni correction (α = 0.017; 0.05/3 comparisons). There were no significant differences in standard recognition performance between (i) the no delay and awake quiescence delay groups (t(36) = 1.424, P = 0.163), or (ii) the awake quiescence delay and perceptual task delay groups (t(35) = 1.033, P = 0.309).

As reported above, the LDI and standard recognition scores of the MST rely on the subtraction of probabilities. While this method has been shown to be sensitive to various experimental manipulations28,29,30,34, concerns have been raised regarding the method by which these measures are calculated33. These concerns relate to the use of proportional values in measurement calculation due to inconsistencies across studies and an inability to directly compare against signal detection theory analyses33. Thus, we examined whether our results held when using signal detection theory by computing d' scores using the standard formula: z(hit rate)-z(false alarm rate). Specifically, we computed traditional memory sensitivity d' scores for the standard recognition measure [z(proportion of old responses to targets)-z(proportion of old responses to foils)] in order to show how well old items were discriminated from new items. We also computed target vs. lure discrimination scores [z(proportion of old responses to targets)-z(proportion of old responses to lures)] to show how well old targets were discriminated from similar lures. See Table 1 for group means of these two measures. Critically, analysis of these new signal detection theory scores replicated the findings of our proportional scores. All significant results in proportional analyses were significant in our signal detection theory analyses (see below).

Table 1 Signal detection theory scores for the no delay, 10 min quiescence and 10 min task delay condition groups.

For the traditional memory sensitivity d' scores, we found no significant main effect of delay condition group (F(2,53) = 2.656, P = 0.080, ηρ² = 0.091). Pairwise comparisons revealed no significant difference in d' scores between the quiescence and task groups (t(35) = 0.743, P = 0.463), or no delay and quiescence groups (t(36) = −1.627, P = 0.112). We did find a significant difference between the no delay and task groups (t(35) = −2.265, P = 0.030), though this finding did not survive the Bonferroni correction (α = 0.017; 0.05/3 comparisons).

For the target vs. lure discrimination scores, we found a significant main effect of delay group (F(2,53) = 3.249, P = 0.047, ηρ² = 0.109). Pairwise comparisons confirmed a significant difference between the quiescence and task delay groups (t(35) = 2.405, P = 0.022). There was no significant difference between the no delay and quiescence delay groups (t(36) = −0.249, P = 0.804), or no delay and task delay groups (t(35) = 1.850, P = 0.072). These findings are in keeping with our results when participants' ability to discriminate lures from targets was computed using proportional scores (Lure Discrimination Index, LDI; see above), though it is noted that, unlike when using the LDI measure, the significant difference between the quiescence and task delay groups did not survive Bonferroni correction (α = 0.017; 0.05/3 comparisons).

The groups' response times during the testing phase did not differ significantly (F(2,53) = 2.415, P = 0.099, ηρ² = 0.084) - see Fig. 3. However, we found that, overall, participants' responses differed significantly for item type (target vs. lure vs. foil) (F(2,106) = 33.914, P < 0.001, ηρ² = 0.390). As shown in Fig. 3, there was an overall increase in the time it took participants to respond between target and lure items (t(55) = −4.672, P < 0.001) and lure and foil items (t(55) = −3.940, P < 0.001). As a result, there was also a significant difference in response times between target and foil items (t(55) = −7.304, P < 0.001), where participants were slower to respond to the latter. All findings survived Bonferroni correction (α = 0.017; 0.05/3 comparisons). There was no significant interaction between item type and delay group (F(4,106) = 1.463, P = 0.219, ηρ² = 0.052).

Mean response times in the Mnemonic Similarity Test for the no delay (N = 19), 10 min awake quiescence delay (N = 19), and 10 min perceptual task delay (N = 18) groups, across all target, lure, and foil items ( left). Mean response times for each delay group broken down by responses to target, lure, and foil items ( right). In all cases, error bars show the standard error of the mean.

Discussion

Our results show that participants who rested quietly for 10 minutes (awake quiescence) after viewing photos of everyday items were subsequently better at discriminating these target items from visually similar lure items, relative to those who were engaged in an unrelated perceptual task for 10 minutes. This finding suggests that awake quiescence protects the fine detail of new memories, bestowing on them a higher resolution than they would otherwise have after a filled delay.

The effect of the awake quiescence delay, relative to the perceptual task delay, cannot be explained away by potential between-group differences in participants' background or encoding performance because the groups were well matched on these measures (see supplementary information for analyses). Furthermore, it is unlikely that this effect can be accounted for merely by intentional rehearsal of items during the 10-minute quiescence delay because the effect remained even when we excluded 4 participants who reported thinking about the items while resting (see supplementary information for analyses). In fact, the key group difference emerged in the LDI measure (see below), and it is unlikely that thinking about a presented item, e.g. a tree, during the rest period would have allowed participants to discriminate a similar lure tree from a previously encoded target tree (see Fig. 1). In addition, we observed no between-group differences in the time that it took participants to respond to items presented during testing. Therefore, it is also unlikely that the difference in memory performance between the awake quiescence and perceptual task delays can be accounted for by differences in the groups' ability to retrieve stored representations. Moreover, in light of concerns regarding the use of proportions in recognition memory measures33, results were replicated when analysing the scores computed via signal detection theory (see results for details).

We hypothesise that superior LDI scores in those who experienced awake quiescence, relative to the perceptual task, can be explained by better/increased consolidation during rest. In particular, our findings suggest that rest-related consolidation protects the fine detail of new memories. This, we propose, resulted in participants in the awake quiescence delay having a better subsequent ability to discriminate similar lures from encoded targets, relative to those in the task delay. Our consolidation hypothesis resonates with other recent findings that suggest that successful discrimination of target items and lures is dependent, not only on the successful encoding of memory representations, but also on the early-stage consolidation of these newly encoded representations30,31. Specifically, Borota et al. (2014) showed higher 24-hour MST LDI scores in participants who consumed caffeine immediately after encoding than in participants who consumed a placebo30. The superior LDI score could not be explained by a retrieval account because no superior LDI score was demonstrated in control participants who consumed the caffeine immediately prior to the 24-hour test only. Instead, Borota et al.30 hypothesise that caffeine enhances the consolidation of new memories, thus facilitating the discrimination of lures from target items. The mechanisms by which caffeine does so remain unknown30. Moreover, Doxey et al. (2018) recently reported higher MST LDI scores in participants who slept for an extended period after encoding than in participants who spent the equivalent time in active wakefulness (e.g. going about daily activities)35. They hypothesise that the benefit of sleep in the MST reflects superior consolidation during sleep than during active wakefulness35. In our study, it is possible that awake quiescence protected the fine detail of new memories by providing a state of reduced sensory input and task engagement. These factors are hypothesised to impact automatic consolidation-related neural reactivation, and their reduction might therefore be conducive to memory consolidation.

In contrast to the LDI measure, performance in the standard recognition measure, which probed participants' ability to endorse targets and reject foils, was not significantly different between delay groups (see Fig. 2E). We did find a significantly lower standard recognition score in the perceptual task delay than the no delay group, but this apparent forgetting did not survive corrections for multiple comparisons. Similar findings of a significant effect in the MST LDI, but not in standard recognition, have been reported in a range of studies applying the MST, including the aforementioned studies on the effects of post-encoding caffeine30 and sleep35, studies examining the effect of healthy and pathological ageing, hippocampal damage37, as well as acute exercise38. Together, these findings demonstrate that standard (old/new) recognition performance can be achieved via gist memory alone, in the absence of detailed target memories32,33, and that the various factors above do not affect gist memory substantially.

In our study, performance in the standard recognition measure was near ceiling across groups. Therefore, it cannot be inferred whether the conditions experienced during the delay period affected the consolidation of gist memory for target items. Nevertheless, this near-ceiling performance in the standard recognition measure provides greater insight into the scores observed in the LDI measure. It suggests that, irrespective of delay condition, participants retained gist memory for most presented targets. This was not the case for detail memory of the targets. Participants retained less detail memory (see Fig. 2B and D), and critically, our results indicate that the behavioural conditions experienced during the 10-minute delay significantly affected the consolidation of this detail memory of targets.

It is of note that whereas the awake quiescence delay group's LDI score was significantly higher than that of the perceptual task delay, neither groups' LDI scores differed significantly from that of the No delay group. However, Fig. 2D suggests tentatively that memory detail increased slightly during the awake quiescence delay and decreased more substantially during the perceptual task delay, albeit not significantly. It is possible that these effects would have been more robust and reached significance had we (i) imposed a longer delay between the encoding and test phase, and/or (ii) applied a within-subjects design, which would have provided more statistical power.

In summary, we provide the first evidence that rest-related consolidation protects the fine detail of new memories. This provides us with higher resolution memories and supports our ability to discriminate recently encoded memories from similar representations. Our findings also support the view that consolidation benefits from states of reduced sensory processing more generally, rather than being restricted to sleep1,16. Future work should examine the neurocognitive mechanisms of rest-related memory consolidation, with a focus on unveiling the factors implicated in the consolidation of detail memory.

Methods

Subjects

Sixty young adults (35 females, 25 males; mean age = 21.77 years, SD = 2.90, age range: 18-31 years) were recruited as participants. All participants had normal or corrected-to-normal visual acuity and no known premorbid psychiatric or neurological disorders that might affect performance. This research was approved by Heriot-Watt University's School of Life Sciences Research Ethics Committee (Ref: 2015-140) and all procedures adhered to the appropriate ethical principles for research in humans. All participants were briefed and provided their informed consent in writing prior to their participation. Twenty participants were pre-experimentally allocated pseudo-randomly to each of our three delay condition groups. Three participants (1 × awake quiescence, 2 × perceptual task) scored +2 SDs from their respective group means in our LDI measure, while one participant scored +2 SDs from their group mean in the standard recognition measure (1 × no delay), and were thus deemed as outliers and removed from our analyses. Thus, group Ns in the analyses reported below were as follows: no delay = 19; awake quiescence delay = 19; perceptual task delay = 18. It is however worth noting that no significant results changed when all recruited participants (N = 60) were included in our analyses.

Design

We employed a between-subjects design to examine retention of photographs of everyday items. The experiment took place in a single session, divided into an encoding phase, a delay phase, and a testing phase. Our experimental manipulation occurred during the delay phase (see Fig. 1).

Materials

Our paradigm used a modified (shortened) version of the Mnemonic Similarity Task28, which was combined with our sensitive memory paradigm that has been shown to detect positive rest-related effects in memory consolidation. The computerised task used to administer our procedure was developed and run using PsychoPy (version 1.83.01) via a Python-coded script39. The task was presented via a 22-inch wide screen computer monitor. Visual instructions were presented on the computer screen during the procedure. Stimuli (photos of everyday items) in the encoding and testing phases were presented in the centre of the screen on all occasions. Responses during encoding and testing phases were collected from participants via keyboard input.

Procedure

Encoding phase

Participants were pre-experimentally informed that they were taking part in a study investigating how humans make judgements about everyday items. They were not informed that they would experience one of three delay conditions or a subsequent memory test for the presented items. This incidental encoding procedure has been used previously28, and was particularly relevant in our study because it minimises the likelihood of mnemonic strategies, e.g. active rehearsal of items during the delay period, in particular the awake quiescence delay.

Participants were presented a total of 60 photos of a range of unique everyday items (see Fig. 1 for examples). Each item was presented as a standalone item on a white background. As in previous work28, each item was presented for a duration of 2 seconds, with an inter-stimuli crosshair (+) appearing in the centre of the screen for 0.5 seconds (total duration of encoding phase = 150 seconds). Thus, all participants received identical treatment and exposure to stimuli during encoding. When presented an item, participants were required to make a judgment as to whether they believed the item would typically be found indoors or outdoors. For example, if presented a photo of a sofa, this item would typically be found indoors, but if presented a photo of a tree, this item would typically be found outdoors. Participants input responses via the 'z' (indoors) and 'm' (outdoors) keys on the keyboard. Participants were instructed that some items may be ambiguous in their typical location (i.e. may be found indoors and outdoors), but that they should respond as quickly as possible and respond with their first instinct.

Delay phase

During the delay phase, participants experienced one of three delay conditions, where they either (i) moved immediately from the encoding phase to the testing phase (i.e. no delay) (N = 20), (ii) rested wakefully for 10 minutes between the encoding and testing phases (i.e. awake quiescence delay) (N = 20), or (iii) performed an unrelated perceptual task (a spot-the-difference game) for 10 minutes between the encoding and testing phases (i.e. perceptual task delay) (N = 20). Participants assigned to the awake quiescence condition were asked to sit quietly in the dimly-lit testing room and relax while the experimenter left the room to "set up the next section of the experiment"18. Care was taken to ensure that the testing room was devoid of any rich visual and/or audible sensory cues to minimise sensory information, and thus the disruption of consolidation. Participants assigned to the perceptual task delay were asked to play a visual spot the difference game18. Participants performed a total of 30 spot-the-difference trials in silence, each 20 second in duration. A trial consisted of the presentation of a pair of real-world photos on the computer screen (see Fig. 1 for examples). Photos were identical other than for two discrete differences. Participants were instructed to search for these two differences, and to silently point toward them if discovered. The experimenter sat behind the participant during this task and scored the number of differences that the participant correctly identified. There was no overlap between the contents of the spot-the-difference photos and the MST photos of everyday items. The no-delay condition was included as a baseline measure of recognition performance (i.e. immediate memory test).

Testing phase

Following the delay phase, all participants performed a modified (fewer items) version of the Mnemonic Similarity Task (MST)28, which probed the fine detail of memories for stimuli presented during the encoding phase. In this test, participants were sequentially visually presented 90 photos of everyday items in a random order which was the same across participants. Of these photos, 30 were old, i.e. were identical to those presented during encoding (targets), 30 were similar (visually similar items from the same semantic category) to the remaining photos presented during encoding (lures), and 30 were new to those presented during encoding (foils) - see Fig. 1 for examples. Participants were informed that, like in the first part of the study (Encoding phase), they would be visually presented a set of photos of everyday items in the centre of the computer screen. However, on this occasion, rather than performing an "indoor/outdoor" judgment-making task, their memory for the earlier presented items would be probed. They were instructed that presented items would be either (i) visually identical to those presented earlier (old targets), (ii) visually similar to those presented earlier (similar lures), or (ii) brand new and not presented earlier (new foils). Using the computer keyboard, they were asked to provide an 'old' (visually identical target item; 'z' key), 'similar' (visually similar lure item; 'v' key), or 'new' (new foil item; 'm' key) response. Participants were informed that there was no time limit to provide a response, and they should respond as accurately as possible. Onscreen instructions regarding which keys corresponded to which response (e.g. 'z' = old) were shown at all times during encoding and testing. To reduce the possibility of order presentation effects, target items were always the oddly numbered items presented during the encoding phase (i.e. 1, 3, 5, 7, 9...etc) and lure items were related to the evenly number items presented during the encoding phase (i.e. 2, 4, 6, 8, 10...etc). The same order of items was presented to all participants during encoding. These 30 targets and 30 lure items were then combined with 30 foil items and then ordered randomly using the random number generation tool in Microsoft Excel. The same random order of items was presented to all participants during testing.

Scoring

Performance in the MST was scored as in previous work28,30,34,40. Firstly, the total number of old, similar, and new responses for the 30 targets, 30 lures, and 30 target items was extracted and converted to proportion scores (each score/30). From these proportion scores, two key memory measures were calculated: (i) a standard recognition score, and a Lure Discrimination Index (LDI) score28. The standard recognition score is calculated as the proportion of "old" responses to targets minus the proportion of "old" responses to foils, and thus reflects a person's ability to correctly endorse targets, while rejecting foils. The LDI score is calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils, and thus reflects a person's ability to discriminate lures from targets, while controlling for any response biases28,36. In addition to these proportional scores, we examined performance in line with signal detection theory by computing d' scores using the standard formula: z(hit rate)-z(false alarm rate). Specifically, we computed traditional memory sensitivity d' scores for the standard recognition measure [z(proportion of old responses to targets)-z(proportion of old responses to foils)] in order to show how well old items were discriminated from new items. We also computed target vs. lure discrimination scores [z(proportion of old responses to targets)-z(proportion of old responses to lures)] to show how well old targets were discriminated from similar lures. Finally, we extracted the time that it took participants to respond during encoding and testing to check for any potential group differences in encoding and/or retrieval.

Statistical analyses

Analyses were performed using SPSS Statistics 19 (copyright IBM Corp., NY, USA), with the alpha level set to 0.05. ANOVAs with between-subject factor delay condition (no delay vs. awake quiescence delay vs. perceptual task delay) were performed to examine group differences in encoding, LDI and standard recognition scores and test response times. Two-tailed independent t-tests were conducted to examine group differences between pairs of groups. Paired t-tests were conducted to examine within-subject differences in responses to different item types. Bonferroni corrections for multiple comparisons were applied post-hoc to avoid Type 1 error accumulation.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

Additional information

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Acknowledgements

Thank you to Georgina Ottaway who assisted with data collection as a volunteer research assistant. Thanks go to all participants who gave their time to take part in our research and to three anonymous reviewers who provided valuable comments on an earlier version of this paper. Thanks to Alzheimer's Research UK Scotland Network and Alzheimer's Society, UK, who part-funded this project. Dr Michael Craig was supported by a Fellowship award from Alzheimer's Society, UK (grant number 316).

Author information

Affiliations

Department of Psychology, School of Social Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom

Michael Craig

& Michaela Dewar

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Contributions

Michael Craig and Michaela Dewar conceptualised the study. Michael Craig developed the computerised PsychoPy task. Michael Craig performed data collection. Michael Craig performed data analyses. Both authors contributed to writing the paper and approved the paper before submission.

Competing Interests

The authors declare no competing interests.

Corresponding author

Correspondence to Michael Craig.

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# data[].attributes.attachment_link.title
Rest-related consolidation protects the fine detail of new memories

# data[].attributes.attachment_link.description
Newly encoded memories are labile and consolidate over time. The importance of sleep in memory consolidation has been well known for almost a decade. However, recent research has shown that awake quiescence, too, can support consolidation: people remember more new memories if they quietly rest after encoding than if they engage in a task.

# data[].attributes.attachment_link.readable_text
Nobody goes to graduate school expecting a walk in the park - long hours spent in the lab, in the the library, and sitting in front of a computer screen are just an accepted part of daily life. For many students, it is the most energetically demanding time of their lives, not just for the hours put in, but the cognitive resources required to think critically and absorb complex material. While such an assiduous lifestyle should perhaps warrant more rest than normal, sleep often takes a backseat to projects, reading, and deadlines.

Sure, every student knows sleep is important. But how important is it, really?

According to Northern Illinois University professor and Industrial-Organizational psychologist Dr. Larissa Barber, sleep is the single most important health behavior we engage in. In a 2010 study, she and her colleague found that healthy sleep patterns play a critical role in self-regulation - an executive function in the brain that controls our behaviors ( Stress and Health, 2010). It helps us to override urges, initiate behaviors, and persist. It tells us to get out of bed and get to class on time, to start working on that thesis/dissertation, and to keep trudging through that dense journal article, despite the impulse to incessantly check e-mail and Facebook. Without adequate sleep, our brains are less equipped to control our behaviors and direct them towards our goals, which can spell trouble for burdened graduate students.

"I would say that graduate school is probably the ultimate self-regulatory exercise," Barber said. "Almost every critical performance outcome in graduate school requires self-regulatory resources, or energy."

Not only is self-regulation important for knowing when to buckle down and focus, or get yourself out of bed in the morning, it's also critical for managing emotions and stress. Sleeping less can cause people to feel more threatened by stressful events, which in response, can lead to dysfunctional coping.

"Things that usually rate as minor stressors all of a sudden seem like a big deal," Barber said. "So, sleep can often mean the difference between you crying after receiving negative feedback from your advisor on a paper, or calmly evaluating the information as constructive or motivating."

How much sleep is enough? While most people think of the standard as 8 hours, studies show that performance decrements don't appear until you hit below 7 hours, with less-than-6 hours showing a large effect. But personality and individual differences do play a role in a person's "magic number." On average, people need between 6 and 8 hours of sleep. However, people who score high on extraversion (talkative, out-going, assertive) tend to recharge a bit faster than others. Same goes for older people, and people with high metabolisms.

But 7 hours a night may not get you off the hook. It's not just the amount of sleep that matters for positive well-being and performance outcomes; sleep routines are just as important. Barber's study found that in order to see stress levels decline and self-control improve, people need to get enough sleep consistently over the course of the work week.

Further evidence for the importance of sleep routines comes from a study of the effect of staying up late and sleeping late on the weekends, which is a common practice for young adults. CUNY researchers found that a delayed sleep schedule on the weekend can seriously hurt cognitive performance and overall mood in the beginning of the week, through "Sunday night insomnia" and the "Monday morning blues" (Psychology and Health, 2001). Scientists suggest that a later wake-up time prevents exposure to morning light which throws our circadian rhythm off kilter, and subsequently shifts our body's sleep schedule.

Barber finds it troubling that people commonly overlook routines in favor of just catching up on sleep at a later date.

"People often think of sleep in compensatory terms, like a credit card - I'll just pay it back later!" she said. "But they forget what I call the 'hidden interest rates,' which is circadian rhythm disruption. Poor Monday always gets a bad reputation, but the reality is that we set ourselves up for Monday failure via our weekend sleep habits."

In addition to getting enough sleep and getting it routinely, high-quality rest is also an important factor to consider. Studies indicate that sleep hygiene is one of the greatest predictors of overall sleep quality (Hong Kong Med J, 2010). This refers to general behavioral practices and environmental factors that allow for a sound sleep, like diet, exercise, light, temperature, noise, as well as sleep schedule and pre-sleep activities. On the flip side, poor sleep quality is associated with significant psychological distress, depression, confusion, and generally lower life satisfaction. Poor sleep hygiene, in specific, is linked to a higher prevalence of insomnia and chronic sleep difficulties.

In a 2010 study, Chinese researchers found that despite its importance in sleep quality, the level of sleep hygiene knowledge among university students was "relatively inadequate" (Hong Kong Med J, 2010). A more recent study found that although sleep disturbances are often comorbid with most psychiatric disorders, and can exacerbate symptoms, graduate clinical psychology students receive little training in the assessment of sleep and treatment of sleep disorders (Training and Education in Professional Psychology, 2012).

So give your sleep hygiene the attention it deserves, and don't treat it like an afterthought. It has a far more profound effect on your well-being than your probably even realize.

Take a moment to educate yourself on sleep hygiene with these research-based tips.

- Don't take naps

- Avoid sleep medications (prescription or over-the-counter)

- Use the bed only for sleeping, and lie down only when sleepy. If you can't fall asleep within 10 minutes, do something else not work or school-related

- Avoid caffeinated drinks at least 4 hours before bedtime. This includes the obvious culprits like coffee, but also includes chocolate milk, certain teas, and some vitamin drinks

- Avoid nicotine and alcohol 4 hours before bedtime

- Avoid glowing screens before bedtime: TV, phone, tablets, computer, etc. The light interferes with with your body's ability to wind down

- Don't stay up late to do work. Research shows that staying up 17-19 consecutive hours is similar to having a blood alcohol limit over the legal limit. So, working late at night is like writing a paper while drunk. You're better off waking up early and starting on it first thing in the morning

- Find out your sleep need and stick to it

- Don't sleep with your phone on (or in the same room). Remove the temptation to check it altogether

- Put a notebook next to your bed to write down things you think of before bedtime (including brilliant thesis/dissertation ideas) to prevent you from worrying about remembering it the next day or getting up to check into it

- Enlist "sleep allies" to help you stick to your schedule, such as a spouse or roommate. Anyone who can tell you (or text you), "Shut it down," at a certain time of night

- Put your sleep time in your calendar and protect it like you would class time or your dissertation defense day. It's non-negotiable (only in case of emergency)

- Manage your time well overall. If you find yourself sacrificing sleep, then you have too many things on your high-priority list (that are not sleep) and you need to re-evaluate (a) what all you are trying to do and (b) how you are doing it

TAKE THE QUIZ: Sleep Hygiene Index
(Assessment of Sleep Hygiene Using the Sleep Hygiene Index, Journal of Behavioral Medicine, 2006)Always (5), frequently (4), sometimes (3), rarely (2), never (1).
Sum the items for your score. The higher your score, the more maladaptive your sleep hygiene status is.1. I take daytime naps lasting two or more hours.
2. I go to bed at different times from day to day.
3. I get out of bed at different times from day to day.
4. I exercise to the point of sweating within 1 hour of going to bed.
5. I stay in bed longer than I should two or three times a week.
6. I use alcohol, tobacco, or caffeine within 4 hours of going to bed or after going to bed.
7. I do something that may wake me up before bedtime (for example: play video games, use the internet, or clean).
8. I go to bed feeling stressed, angry, upset, or nervous.
9. I use my bed for things other than sleeping or sex (for example: watch television, read, eat, or study).
10. I sleep on an uncomfortable bed (for example: poor mattress or pillow, too much or not enough blankets).
11. I sleep in an uncomfortable bedroom (for example: too bright, too stuffy, too hot, too cold, or too noisy).
12. I do important work before bedtime (for example: pay bills, schedule, or study).
13. I think, plan, or worry when I am in bed.

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Sleep and Grad School: How Important Is It For Students?

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Graduate school is energetically taxing - you're spending long hours in the lab, sprinting across campus, synthesizing ideas, and absorbing complex material. With routines like this, sleep often takes a backseat. But how important is sleep for graduate students, and how can it be improved?

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One of the first studies to suggest that insufficient sleep can disrupt metabolism came out 2 decades ago. And yet, despite growing evidence that inadequate slumber is a risk factor for obesity and diabetes, approximately a third of US adults sleep fewer than the 7 hours a night recommended by the American Academy of Sleep Medicine and the Sleep Research Society. Many people think they can repay sleep debt incurred during the week by catching extra z's on the weekend. But, unfortunately, it's not that easy to mitigate the metabolic dysregulation associated with recurrent insufficient sleep, suggests a recent study in Current Biology.

The researchers brought 36 healthy normal-weight adults, aged 18 to 39 years, into their sleep lab and randomly assigned them to 1 of 3 groups:

Sufficient sleep (control group), consisting of up to 9 hours of sleep per night for 9 nights.

Sleep restriction without weekend recovery sleep, consisting of up to 5 hours of sleep per night for 9 nights.

Sleep restriction with weekend recovery sleep, consisting of up to 5 hours of sleep per weeknight, unrestricted sleep on the weekend, followed by another 2 weeknights of sleep restriction.

Prior to randomization, baseline sleep was assessed in the lab over 3 nights; participants slept on average about 8 hours each night. Over the next 9 days, beginning on a Monday, participants slept restricted or sufficient hours, depending on their group assignment, and the scientists assessed their eating patterns, weight gain, and changes in insulin sensitivity.

Those assigned to sleep restriction with weekend sleep recovery slept a total of only 3 extra hours Friday and Saturday night, even though they had missed out on more than 12 hours of sleep the previous 5 nights. On top of that, sleeping in on the weekend disrupted their circadian clock, making it difficult for them to fall asleep Sunday night, even though they had to awake early Monday morning.

Sleep restriction was associated with rapid diet and metabolic changes:

Insulin sensitivity declined within only 3 days of sleep restriction.

The decline in insulin sensitivity was more pronounced among participants allowed weekend recovery sleep. By the end of the study, insulin sensitivity decreased about 13% among those not allowed recovery sleep but fell as much as 27% among those allowed weekend recovery sleep.

Participants not allowed weekend recovery sleep consumed an excess of around 500 calories each night after dinner compared with baseline calorie intake.

Participants allowed weekend recovery sleep consumed as much as 641 calories more after dinner on weeknights relative to baseline, but their after-dinner calorie consumption on the weekend was not significantly different from that at baseline.

On average, participants in the sleep restricted groups each gained 3 pounds by the end of the study.

"When we don't get enough sleep, we also are expending more energy," said coauthor Kenneth Wright Jr, PhD, director of the Sleep and Chronobiology Laboratory at the University of Colorado. "That's a signal for us to eat more. The problem is we don't have to forage for food. We can go right to the kitchen." In an earlier study, Wright found that insufficient sleep increased total daily energy expenditure by around 5%, but energy intake, especially at night after dinner, more than made up for that.

The study does not answer a couple of big questions: Could sleeping in on the weekend make up for only a night or 2 of sleep loss during the week? And might the metabolic changes be even worse in people who aren't young or healthy?

"These were super-healthy people," Wright said. "That's what makes this even scarier."

Research is needed into the effects of sleep restriction and weekend makeup sleep on other populations, he said.

Wright said he and his coauthors were surprised to see that in the weeknight sleep-restricted group, men slept more on the weekend than women. While both men and women slept longer on Friday night than they had at baseline, only men did on Saturday night. "What we do know as a general rule is that women are at a higher risk for insomnia than men," he said.

"Sleep is pretty fundamental, and when we don't get enough, it will disturb a lot of our physiology," Wright said. "It's kind of like smoking: The consequences are long term."

And, he said, his study suggests that yoyo sleeping-trying to compensate on the weekends for too-little slumber during the week-is probably not going to prevent those long-term consequences and might even make them worse.

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Sleeping in Doesn't Mitigate Metabolic Changes Linked to Sleep Deficit

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One of the first studies to suggest that insufficient sleep can disrupt metabolism came out 2 decades ago. And yet, despite growing evidence that inadequate slumber is a risk factor for obesity and diabetes, approximately a third of US adults sleep fewer than the 7 hours a night recommended by the American Academy of Sleep Medicine and the Sleep Research Society.

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Calm Sleep Stories | Stephen Fry's 'Blue Gold'

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Sleep and performance research

Papers

Podcasts

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Sleep health: can we define it? Does it matter? - PubMed - NCBI

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Three online modules over 10 weeks

Synchronous weeks, asynchronous lectures most weeks

Guided and Semi-Self-paced

Completion within one Drexel University quarter

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The Mini-course will be hosted on Blackboard

Students encouraged to follow sleep thought leaders on Twitter

Podcast resourses

Zoom

Vocaroo

iPhone Voice Memos

Smartphone camera apps

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Whether you're coming from the original CourseSites, from Educator Preview, or you're brand new to CourseSites - this is your destination to try out Blackboard's Ultra experience and teach courses online, for free.

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"Nature" Journal Article (2018): Rest to Learn! Awake Quiescence Aids Memory: 

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"Nature" Journal Article (2018): Rest to Learn! Awake Quiescence Aids Memory: 

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Abstract

Newly encoded memories are labile and consolidate over time. The importance of sleep in memory consolidation has been well known for almost a decade. However, recent research has shown that awake quiescence, too, can support consolidation: people remember more new memories if they quietly rest after encoding than if they engage in a task. It is not yet known how exactly this rest-related consolidation benefits new memories, and whether it affects the fine detail of new memories. Using a sensitive picture recognition task, we show that awake quiescence aids the fine detail of new memories. Young adults were significantly better at discriminating recently encoded target pictures from similar lure pictures when the initial encoding of target pictures had been followed immediately by 10 minutes of awake quiescence than an unrelated perceptual task. This novel finding indicates that, in addition to influencing how much we remember, our behavioural state during wakeful consolidation determines, at least in part, the level of fine detail of our new memories. Thus, our results suggest that rest-related consolidation protects the fine detail of new memories, allowing us to retain detailed memories.

Introduction

In order for labile new memories to be remembered over the longer term they must be consolidated, i.e. strengthened and stabilised, over time1,2. Research in humans and non-human animals suggests that an important mechanism of consolidation is the automatic neural reactivation of newly encoded memories in the minutes that follow their formation3,4,5,6,7,8,9,10,11,12,13,14,15. Memory reactivation is observed especially during periods of post-encoding sleep and awake quiescence (quiet resting), where the magnitude of reactivation predicts subsequent memory3,4,6,9,16, and the electrophysiological disruption of reactivation results in reduced memory11. Sleep and awake quiescence may be especially conducive to memory reactivation due to reductions in sensory input and task engagement, which could otherwise impact this process.

Behavioural research in humans supports this hypothesis, demonstrating better memory retention after post-encoding sleep19,20,21,22 and post-encoding awake quiescence18,23,24,25, relative to a filled period of wakefulness. While the effect of sleep in memory is well established, research into the effects of awake quiescence is still in its infancy. To date the latter research has shown that humans retain more words and associative information after a 15-30 minute delay and a 7-day delay26,27 when they rest quietly in the minutes immediately after memory encoding than when they engage in an unrelated perceptual task. This effect cannot be explained away by intentional rehearsal during the rest period, because people remember more memories after awake quiescence than task conditions, even when the memories cannot be rehearsed intentionally26,27. Thus, converging human and animal research indicates that awake quiescence supports the offline consolidation of new memories.

It is not yet known how exactly this rest-related consolidation benefits new memories, and whether it affects the fine detail of new memories. Rest-related consolidation could increase the number of retained memories without necessarily supporting the fine detail of individual memories. Therefore, in the study reported here, we examined how a brief period of awake quiescence after encoding affects the fine detail of new memories. To this end, we applied the robust and sensitive 'Mnemonic Similarity Task' (MST) to probe the fine detail of new memories. The MST, originally designed to assess pattern separation, is sensitive to manipulations during memory encoding and consolidation30,31. In this task, participants are first sequentially presented a set of photos of everyday items (targets). Memory for the presented items is subsequently examined via a visual recognition test, where participants are sequentially presented a set of photos of everyday items again, but asked to respond "old", "similar", or "new" to indicate whether each presented item is (i) identical to a photo presented during encoding (target, correct response = "old"), (ii) visually similar, but subtly different, to a photo presented during encoding (lure, correct response = "similar"), or (iii) a new photo that was not presented during encoding (foil, correct response = "new"). An equal number of target, lure and foil photos are presented. Two memory measures are typically extracted from the MST: a standard recognition score, and a Lure Discrimination Index (LDI) score. The standard recognition score is calculated as the proportion of "old" responses to target items minus the proportion of "old" responses to foils, and thus reflects a person's ability to correctly endorse targets, while rejecting foils. A high standard recognition score can indicate good gist-based memory, but it need not imply that memories are fine in detail32,33. The LDI score is calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils, and thus reflects a person's ability to discriminate lures from targets, while controlling for any response biases28,34. Thus, if a participant holds detailed representations of encoded target photos, they should demonstrate superior ability to correctly identify subtle visual differences in subsequently presented lures, and thus discriminate them from targets (i.e. respond "similar" to lures, rather than "old"). However, if a participant holds less detailed representations of encoded target photos, they should be less likely to correctly identify subtle visual differences in subsequently presented lures, and thus incorrectly mistake lures for target items (i.e. respond "old" to lures, rather than "similar"). Thus, a higher LDI score should indicate memories of fine detail, where subtle visual differences between previously encoded targets and presented lures can be identified.

Using the MST, Borota et al.30 recently demonstrated a positive dose-dependent effect of post-encoding caffeine administration on LDI scores. They hypothesised that caffeine promotes the consolidation of encoded (target) items, thus supporting participants' ability to discriminate these viewed (target) items from similar (lure) items during subsequent testing30. Similarly, we anticipated that if rest-related consolidation protects the fine detail of newly formed memories, then the MST LDI score should reveal this, because people who experience post-encoding awake quiescence should be better able to discriminate targets from lure items.

In our between-subjects study (see Fig. 1), sixty young adults were exposed to a subset of 60 photos of everyday items from the MST within the context of an incidental encoding task (indoor/outdoor judgement making task), before experiencing either: (i) no delay between item encoding and the subsequent testing phase ('No delay group', N = 20), (ii) 10 minutes of awake quiescence, i.e. resting quietly in dimly-lit room ('Awake quiescence delay group', N = 20), or (iii) 10 minutes of a perceptual task, i.e. playing a spot-the-difference game ( 'Perceptual task delay group', N = 20). Participants' memory for the earlier presented items was then probed via the MST memory test (see above), during which participants were presented 90 photos of everyday items: 30 were old, i.e. were identical to those presented during encoding (targets), 30 were similar (visually similar items from the same semantic category) to the remaining photos presented during encoding (lures), and 30 were new to those presented during encoding (foils) - see Fig. 1 for examples. Using the responses in the MST memory test we calculated a standard recognition score - to measure gist memory - and an LDI score - to measure the fine detail of memories. We also measured response times during the MST memory test and during the encoding task. We hypothesised that, if post-encoding awake quiescence protects the fine detail of new memories, participants' LDI scores should be superior following the awake quiescence delay than the perceptual task delay.

Experimental paradigm. Participants underwent three phases: (i) encoding, (ii) delay, and (iii) testing. During encoding, participants were presented 60 photos of a range of unique everyday items from the Mnemonic Similarity Task database (e.g. Stark et al. 2013). Participants incidentally encoded these items via the performance of a judgment making task, where they were required to respond whether a presented item would typically be found indoors or outdoors. Each item was presented for 2000 ms and was followed by a 500 ms inter-stimulus crosshair (+). Following encoding, participants completed one of three delay conditions: ( A) no delay (N = 20), ( B) 10 minutes of awake quiescence (N = 20), or ( C) 10 minutes of an engaging perceptual task (spot-the-difference game) (N = 20). In the subsequent testing phase, participants were presented 30 of the 'old' items presented during encoding (targets), along with 30 'similar' items that were visually similar objects from the same semantic category to the remaining 30 items presented during encoding (lures), and 30 'new' items that were visually and semantically different to the items presented during encoding (foils). There was no limit on the time to respond during testing.

Results

Our three delay condition groups were well matched in their demographic information (see supplementary information). Participants' responses in the encoding task (indoor/outdoor judgment-making task) were also matched across groups (see supplementary information). Importantly, significant group differences emerged in the subsequent memory test (Mnemonic Similarity Task, MST). Participants in the delay groups differed significantly in their LDI scores, which was calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils28, (F(2,53) = 6.954, P = 0.002, ηρ² = 0.208) - see Fig. 2D. Specifically, the LDI score was significantly higher in the awake quiescence delay than the perceptual task delay (t(35) = 4.454, P < 0.001), and this finding survived the Bonferroni correction (α = 0.017; 0.05/3 comparisons). LDI scores did not differ significantly between the (i) no delay and awake quiescence delay groups (t(36) = −1.602, P = 0.118), or (ii) no delay and perceptual task delay groups (t(35) = 1.876, P = 0.069) - see Fig. 2D.

Mnemonic Similarity Test (MST) response data for the no delay (N = 19), 10 min awake quiescence delay (N = 19), and 10 min perceptual task delay (N = 18) groups showing the proportion of old, similar, and new, responses to ( A) 'old' targets, ( B) 'similar' lures, and ( C) 'new' foils. Lure Discrimination Index (LDI) scores for the three groups are also shown ( D) - this measure is calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils, and thus reflects a person's ability to discriminate similar lures from old targets, while controlling for any response biases. Standard recognition measure ( E) - this measure is calculated as the proportion of "old" responses to target items minus the proportion of "old" responses to foils, and thus reflects a person's ability to correctly endorse old targets, while rejecting new foils. The awake quiescence delay group's ability to discriminate between targets and lures was significantly better than that of the perceptual task delay group, as shown in the groups' ( D) LDI scores and ( B) specific responses to lures. The latter (see red box) shows that the perceptual task delay group provided a significantly higher proportion of (incorrect) old responses to lure items than those in the awake quiescence delay group. In contrast, the awake quiescence delay group provided a significantly higher proportion of (correct) similar responses to lure items than those in the perceptual task delay. In all cases, error bars show the standard error of the mean. Non-corrected significance levels: *p < 0.05, **p < 0.01, ***p < 0.001.

If the benefit of awake quiescence delay over perceptual task delay in the LDI measure reflects a difference in participants' ability to correctly discriminate lures from targets, an interaction should be observed between delay condition group (quiescence vs. task) and response type to lure items ("old" vs. "similar"). Specifically, a greater proportion of correct "similar" responses to lures (i.e. correct identification of lures) would be expected in the quiescence group and a greater proportion of incorrect "old" responses to lures (i.e. incorrectly mistaking lures for targets) would be expected in the task group. In line with this prediction a 2 × 2 ANOVA revealed a significant interaction between delay condition group and response type to lure items (F(1,35) = 9.030, P = 0.005, ηρ² = 0.205). Pairwise comparisons of the proportion of "old" and "similar" responses to lure items - see Fig. 2B - confirmed that participants in the perceptual task delay provided a significantly higher proportion of (incorrect) "old" responses to lure items than those in the awake quiescence delay (t(35) = −2.395, P = 0.022). In contrast, participants in the awake quiescence delay provided a significantly higher proportion of (correct) "similar" responses to lure items than those in the perceptual task delay (t(35) = 3.584, P = 0.001). Moreover, participants in the awake quiescence delay provided a significantly smaller proportion of (incorrect) "new" responses to lure items (t(35) = −3.141, P = 0.003) and (incorrect) "similar" responses to foil items (t(35) = −2.613, P = 0.013) than the perceptual task delay. No other comparisons reached significance (all P > 0.05). When using a Bonferroni-corrected alpha level of 0.006 (0.05/9 comparisons), only the differences in "similar" responses to lures and "new" responses to lure items remained significant.

Performance on standard recognition, which was calculated as the proportion of "old" responses to target items minus the proportion of "old" responses to foils, was near ceiling across groups (see Fig. 2E). This indicates that all participants encoded and retained (at least) gist memory of most presented target items. Although there was no significant main effect of delay group in this measure (F(2,53) = 2.600, P = 0.084, ηρ² = 0.089), those who experienced no delay between encoding and testing were significantly better at discriminating targets from foil items than those who experienced 10 mins of a perceptual task (t(35) = 2.145, P = 0.039), though this finding did not survive the Bonferroni correction (α = 0.017; 0.05/3 comparisons). There were no significant differences in standard recognition performance between (i) the no delay and awake quiescence delay groups (t(36) = 1.424, P = 0.163), or (ii) the awake quiescence delay and perceptual task delay groups (t(35) = 1.033, P = 0.309).

As reported above, the LDI and standard recognition scores of the MST rely on the subtraction of probabilities. While this method has been shown to be sensitive to various experimental manipulations28,29,30,34, concerns have been raised regarding the method by which these measures are calculated33. These concerns relate to the use of proportional values in measurement calculation due to inconsistencies across studies and an inability to directly compare against signal detection theory analyses33. Thus, we examined whether our results held when using signal detection theory by computing d' scores using the standard formula: z(hit rate)-z(false alarm rate). Specifically, we computed traditional memory sensitivity d' scores for the standard recognition measure [z(proportion of old responses to targets)-z(proportion of old responses to foils)] in order to show how well old items were discriminated from new items. We also computed target vs. lure discrimination scores [z(proportion of old responses to targets)-z(proportion of old responses to lures)] to show how well old targets were discriminated from similar lures. See Table 1 for group means of these two measures. Critically, analysis of these new signal detection theory scores replicated the findings of our proportional scores. All significant results in proportional analyses were significant in our signal detection theory analyses (see below).

Table 1 Signal detection theory scores for the no delay, 10 min quiescence and 10 min task delay condition groups.

For the traditional memory sensitivity d' scores, we found no significant main effect of delay condition group (F(2,53) = 2.656, P = 0.080, ηρ² = 0.091). Pairwise comparisons revealed no significant difference in d' scores between the quiescence and task groups (t(35) = 0.743, P = 0.463), or no delay and quiescence groups (t(36) = −1.627, P = 0.112). We did find a significant difference between the no delay and task groups (t(35) = −2.265, P = 0.030), though this finding did not survive the Bonferroni correction (α = 0.017; 0.05/3 comparisons).

For the target vs. lure discrimination scores, we found a significant main effect of delay group (F(2,53) = 3.249, P = 0.047, ηρ² = 0.109). Pairwise comparisons confirmed a significant difference between the quiescence and task delay groups (t(35) = 2.405, P = 0.022). There was no significant difference between the no delay and quiescence delay groups (t(36) = −0.249, P = 0.804), or no delay and task delay groups (t(35) = 1.850, P = 0.072). These findings are in keeping with our results when participants' ability to discriminate lures from targets was computed using proportional scores (Lure Discrimination Index, LDI; see above), though it is noted that, unlike when using the LDI measure, the significant difference between the quiescence and task delay groups did not survive Bonferroni correction (α = 0.017; 0.05/3 comparisons).

The groups' response times during the testing phase did not differ significantly (F(2,53) = 2.415, P = 0.099, ηρ² = 0.084) - see Fig. 3. However, we found that, overall, participants' responses differed significantly for item type (target vs. lure vs. foil) (F(2,106) = 33.914, P < 0.001, ηρ² = 0.390). As shown in Fig. 3, there was an overall increase in the time it took participants to respond between target and lure items (t(55) = −4.672, P < 0.001) and lure and foil items (t(55) = −3.940, P < 0.001). As a result, there was also a significant difference in response times between target and foil items (t(55) = −7.304, P < 0.001), where participants were slower to respond to the latter. All findings survived Bonferroni correction (α = 0.017; 0.05/3 comparisons). There was no significant interaction between item type and delay group (F(4,106) = 1.463, P = 0.219, ηρ² = 0.052).

Mean response times in the Mnemonic Similarity Test for the no delay (N = 19), 10 min awake quiescence delay (N = 19), and 10 min perceptual task delay (N = 18) groups, across all target, lure, and foil items ( left). Mean response times for each delay group broken down by responses to target, lure, and foil items ( right). In all cases, error bars show the standard error of the mean.

Discussion

Our results show that participants who rested quietly for 10 minutes (awake quiescence) after viewing photos of everyday items were subsequently better at discriminating these target items from visually similar lure items, relative to those who were engaged in an unrelated perceptual task for 10 minutes. This finding suggests that awake quiescence protects the fine detail of new memories, bestowing on them a higher resolution than they would otherwise have after a filled delay.

The effect of the awake quiescence delay, relative to the perceptual task delay, cannot be explained away by potential between-group differences in participants' background or encoding performance because the groups were well matched on these measures (see supplementary information for analyses). Furthermore, it is unlikely that this effect can be accounted for merely by intentional rehearsal of items during the 10-minute quiescence delay because the effect remained even when we excluded 4 participants who reported thinking about the items while resting (see supplementary information for analyses). In fact, the key group difference emerged in the LDI measure (see below), and it is unlikely that thinking about a presented item, e.g. a tree, during the rest period would have allowed participants to discriminate a similar lure tree from a previously encoded target tree (see Fig. 1). In addition, we observed no between-group differences in the time that it took participants to respond to items presented during testing. Therefore, it is also unlikely that the difference in memory performance between the awake quiescence and perceptual task delays can be accounted for by differences in the groups' ability to retrieve stored representations. Moreover, in light of concerns regarding the use of proportions in recognition memory measures33, results were replicated when analysing the scores computed via signal detection theory (see results for details).

We hypothesise that superior LDI scores in those who experienced awake quiescence, relative to the perceptual task, can be explained by better/increased consolidation during rest. In particular, our findings suggest that rest-related consolidation protects the fine detail of new memories. This, we propose, resulted in participants in the awake quiescence delay having a better subsequent ability to discriminate similar lures from encoded targets, relative to those in the task delay. Our consolidation hypothesis resonates with other recent findings that suggest that successful discrimination of target items and lures is dependent, not only on the successful encoding of memory representations, but also on the early-stage consolidation of these newly encoded representations30,31. Specifically, Borota et al. (2014) showed higher 24-hour MST LDI scores in participants who consumed caffeine immediately after encoding than in participants who consumed a placebo30. The superior LDI score could not be explained by a retrieval account because no superior LDI score was demonstrated in control participants who consumed the caffeine immediately prior to the 24-hour test only. Instead, Borota et al.30 hypothesise that caffeine enhances the consolidation of new memories, thus facilitating the discrimination of lures from target items. The mechanisms by which caffeine does so remain unknown30. Moreover, Doxey et al. (2018) recently reported higher MST LDI scores in participants who slept for an extended period after encoding than in participants who spent the equivalent time in active wakefulness (e.g. going about daily activities)35. They hypothesise that the benefit of sleep in the MST reflects superior consolidation during sleep than during active wakefulness35. In our study, it is possible that awake quiescence protected the fine detail of new memories by providing a state of reduced sensory input and task engagement. These factors are hypothesised to impact automatic consolidation-related neural reactivation, and their reduction might therefore be conducive to memory consolidation.

In contrast to the LDI measure, performance in the standard recognition measure, which probed participants' ability to endorse targets and reject foils, was not significantly different between delay groups (see Fig. 2E). We did find a significantly lower standard recognition score in the perceptual task delay than the no delay group, but this apparent forgetting did not survive corrections for multiple comparisons. Similar findings of a significant effect in the MST LDI, but not in standard recognition, have been reported in a range of studies applying the MST, including the aforementioned studies on the effects of post-encoding caffeine30 and sleep35, studies examining the effect of healthy and pathological ageing, hippocampal damage37, as well as acute exercise38. Together, these findings demonstrate that standard (old/new) recognition performance can be achieved via gist memory alone, in the absence of detailed target memories32,33, and that the various factors above do not affect gist memory substantially.

In our study, performance in the standard recognition measure was near ceiling across groups. Therefore, it cannot be inferred whether the conditions experienced during the delay period affected the consolidation of gist memory for target items. Nevertheless, this near-ceiling performance in the standard recognition measure provides greater insight into the scores observed in the LDI measure. It suggests that, irrespective of delay condition, participants retained gist memory for most presented targets. This was not the case for detail memory of the targets. Participants retained less detail memory (see Fig. 2B and D), and critically, our results indicate that the behavioural conditions experienced during the 10-minute delay significantly affected the consolidation of this detail memory of targets.

It is of note that whereas the awake quiescence delay group's LDI score was significantly higher than that of the perceptual task delay, neither groups' LDI scores differed significantly from that of the No delay group. However, Fig. 2D suggests tentatively that memory detail increased slightly during the awake quiescence delay and decreased more substantially during the perceptual task delay, albeit not significantly. It is possible that these effects would have been more robust and reached significance had we (i) imposed a longer delay between the encoding and test phase, and/or (ii) applied a within-subjects design, which would have provided more statistical power.

In summary, we provide the first evidence that rest-related consolidation protects the fine detail of new memories. This provides us with higher resolution memories and supports our ability to discriminate recently encoded memories from similar representations. Our findings also support the view that consolidation benefits from states of reduced sensory processing more generally, rather than being restricted to sleep1,16. Future work should examine the neurocognitive mechanisms of rest-related memory consolidation, with a focus on unveiling the factors implicated in the consolidation of detail memory.

Methods

Subjects

Sixty young adults (35 females, 25 males; mean age = 21.77 years, SD = 2.90, age range: 18-31 years) were recruited as participants. All participants had normal or corrected-to-normal visual acuity and no known premorbid psychiatric or neurological disorders that might affect performance. This research was approved by Heriot-Watt University's School of Life Sciences Research Ethics Committee (Ref: 2015-140) and all procedures adhered to the appropriate ethical principles for research in humans. All participants were briefed and provided their informed consent in writing prior to their participation. Twenty participants were pre-experimentally allocated pseudo-randomly to each of our three delay condition groups. Three participants (1 × awake quiescence, 2 × perceptual task) scored +2 SDs from their respective group means in our LDI measure, while one participant scored +2 SDs from their group mean in the standard recognition measure (1 × no delay), and were thus deemed as outliers and removed from our analyses. Thus, group Ns in the analyses reported below were as follows: no delay = 19; awake quiescence delay = 19; perceptual task delay = 18. It is however worth noting that no significant results changed when all recruited participants (N = 60) were included in our analyses.

Design

We employed a between-subjects design to examine retention of photographs of everyday items. The experiment took place in a single session, divided into an encoding phase, a delay phase, and a testing phase. Our experimental manipulation occurred during the delay phase (see Fig. 1).

Materials

Our paradigm used a modified (shortened) version of the Mnemonic Similarity Task28, which was combined with our sensitive memory paradigm that has been shown to detect positive rest-related effects in memory consolidation. The computerised task used to administer our procedure was developed and run using PsychoPy (version 1.83.01) via a Python-coded script39. The task was presented via a 22-inch wide screen computer monitor. Visual instructions were presented on the computer screen during the procedure. Stimuli (photos of everyday items) in the encoding and testing phases were presented in the centre of the screen on all occasions. Responses during encoding and testing phases were collected from participants via keyboard input.

Procedure

Encoding phase

Participants were pre-experimentally informed that they were taking part in a study investigating how humans make judgements about everyday items. They were not informed that they would experience one of three delay conditions or a subsequent memory test for the presented items. This incidental encoding procedure has been used previously28, and was particularly relevant in our study because it minimises the likelihood of mnemonic strategies, e.g. active rehearsal of items during the delay period, in particular the awake quiescence delay.

Participants were presented a total of 60 photos of a range of unique everyday items (see Fig. 1 for examples). Each item was presented as a standalone item on a white background. As in previous work28, each item was presented for a duration of 2 seconds, with an inter-stimuli crosshair (+) appearing in the centre of the screen for 0.5 seconds (total duration of encoding phase = 150 seconds). Thus, all participants received identical treatment and exposure to stimuli during encoding. When presented an item, participants were required to make a judgment as to whether they believed the item would typically be found indoors or outdoors. For example, if presented a photo of a sofa, this item would typically be found indoors, but if presented a photo of a tree, this item would typically be found outdoors. Participants input responses via the 'z' (indoors) and 'm' (outdoors) keys on the keyboard. Participants were instructed that some items may be ambiguous in their typical location (i.e. may be found indoors and outdoors), but that they should respond as quickly as possible and respond with their first instinct.

Delay phase

During the delay phase, participants experienced one of three delay conditions, where they either (i) moved immediately from the encoding phase to the testing phase (i.e. no delay) (N = 20), (ii) rested wakefully for 10 minutes between the encoding and testing phases (i.e. awake quiescence delay) (N = 20), or (iii) performed an unrelated perceptual task (a spot-the-difference game) for 10 minutes between the encoding and testing phases (i.e. perceptual task delay) (N = 20). Participants assigned to the awake quiescence condition were asked to sit quietly in the dimly-lit testing room and relax while the experimenter left the room to "set up the next section of the experiment"18. Care was taken to ensure that the testing room was devoid of any rich visual and/or audible sensory cues to minimise sensory information, and thus the disruption of consolidation. Participants assigned to the perceptual task delay were asked to play a visual spot the difference game18. Participants performed a total of 30 spot-the-difference trials in silence, each 20 second in duration. A trial consisted of the presentation of a pair of real-world photos on the computer screen (see Fig. 1 for examples). Photos were identical other than for two discrete differences. Participants were instructed to search for these two differences, and to silently point toward them if discovered. The experimenter sat behind the participant during this task and scored the number of differences that the participant correctly identified. There was no overlap between the contents of the spot-the-difference photos and the MST photos of everyday items. The no-delay condition was included as a baseline measure of recognition performance (i.e. immediate memory test).

Testing phase

Following the delay phase, all participants performed a modified (fewer items) version of the Mnemonic Similarity Task (MST)28, which probed the fine detail of memories for stimuli presented during the encoding phase. In this test, participants were sequentially visually presented 90 photos of everyday items in a random order which was the same across participants. Of these photos, 30 were old, i.e. were identical to those presented during encoding (targets), 30 were similar (visually similar items from the same semantic category) to the remaining photos presented during encoding (lures), and 30 were new to those presented during encoding (foils) - see Fig. 1 for examples. Participants were informed that, like in the first part of the study (Encoding phase), they would be visually presented a set of photos of everyday items in the centre of the computer screen. However, on this occasion, rather than performing an "indoor/outdoor" judgment-making task, their memory for the earlier presented items would be probed. They were instructed that presented items would be either (i) visually identical to those presented earlier (old targets), (ii) visually similar to those presented earlier (similar lures), or (ii) brand new and not presented earlier (new foils). Using the computer keyboard, they were asked to provide an 'old' (visually identical target item; 'z' key), 'similar' (visually similar lure item; 'v' key), or 'new' (new foil item; 'm' key) response. Participants were informed that there was no time limit to provide a response, and they should respond as accurately as possible. Onscreen instructions regarding which keys corresponded to which response (e.g. 'z' = old) were shown at all times during encoding and testing. To reduce the possibility of order presentation effects, target items were always the oddly numbered items presented during the encoding phase (i.e. 1, 3, 5, 7, 9...etc) and lure items were related to the evenly number items presented during the encoding phase (i.e. 2, 4, 6, 8, 10...etc). The same order of items was presented to all participants during encoding. These 30 targets and 30 lure items were then combined with 30 foil items and then ordered randomly using the random number generation tool in Microsoft Excel. The same random order of items was presented to all participants during testing.

Scoring

Performance in the MST was scored as in previous work28,30,34,40. Firstly, the total number of old, similar, and new responses for the 30 targets, 30 lures, and 30 target items was extracted and converted to proportion scores (each score/30). From these proportion scores, two key memory measures were calculated: (i) a standard recognition score, and a Lure Discrimination Index (LDI) score28. The standard recognition score is calculated as the proportion of "old" responses to targets minus the proportion of "old" responses to foils, and thus reflects a person's ability to correctly endorse targets, while rejecting foils. The LDI score is calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils, and thus reflects a person's ability to discriminate lures from targets, while controlling for any response biases28,36. In addition to these proportional scores, we examined performance in line with signal detection theory by computing d' scores using the standard formula: z(hit rate)-z(false alarm rate). Specifically, we computed traditional memory sensitivity d' scores for the standard recognition measure [z(proportion of old responses to targets)-z(proportion of old responses to foils)] in order to show how well old items were discriminated from new items. We also computed target vs. lure discrimination scores [z(proportion of old responses to targets)-z(proportion of old responses to lures)] to show how well old targets were discriminated from similar lures. Finally, we extracted the time that it took participants to respond during encoding and testing to check for any potential group differences in encoding and/or retrieval.

Statistical analyses

Analyses were performed using SPSS Statistics 19 (copyright IBM Corp., NY, USA), with the alpha level set to 0.05. ANOVAs with between-subject factor delay condition (no delay vs. awake quiescence delay vs. perceptual task delay) were performed to examine group differences in encoding, LDI and standard recognition scores and test response times. Two-tailed independent t-tests were conducted to examine group differences between pairs of groups. Paired t-tests were conducted to examine within-subject differences in responses to different item types. Bonferroni corrections for multiple comparisons were applied post-hoc to avoid Type 1 error accumulation.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Download references

Acknowledgements

Thank you to Georgina Ottaway who assisted with data collection as a volunteer research assistant. Thanks go to all participants who gave their time to take part in our research and to three anonymous reviewers who provided valuable comments on an earlier version of this paper. Thanks to Alzheimer's Research UK Scotland Network and Alzheimer's Society, UK, who part-funded this project. Dr Michael Craig was supported by a Fellowship award from Alzheimer's Society, UK (grant number 316).

Author information

Affiliations

Department of Psychology, School of Social Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom

Michael Craig

& Michaela Dewar

Authors

1. Search for Michael Craig in:

2. Search for Michaela Dewar in:

Contributions

Michael Craig and Michaela Dewar conceptualised the study. Michael Craig developed the computerised PsychoPy task. Michael Craig performed data collection. Michael Craig performed data analyses. Both authors contributed to writing the paper and approved the paper before submission.

Competing Interests

The authors declare no competing interests.

Corresponding author

Correspondence to Michael Craig.

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# data[].attributes.attachment_link.title
Rest-related consolidation protects the fine detail of new memories

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Newly encoded memories are labile and consolidate over time. The importance of sleep in memory consolidation has been well known for almost a decade. However, recent research has shown that awake quiescence, too, can support consolidation: people remember more new memories if they quietly rest after encoding than if they engage in a task.

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Hidden Brain Podcast: Eyes Wide Open Part 1

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Hidden Brain Podcast: Eyes Wide Open Part 1

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Staying up until the early hours of the morning with friends. Skipping sleep to prepare for an exam. These are fairly standard aspects of student life for many young people.

But these sorts of all-nighters pale in comparison to the stunt Randy Gardner pulled when he was 17.

"You don't need sleep. That was the thinking in the 1960s, and that was the thinking I had," said Randy Gardner.

In 1963, Randy stayed awake for eleven days - breaking the world record for going without sleep. Today, at 71, he offers his wisdom about staying up past your bedtime - and describes how his teenage stunt came back to haunt him decades later.
Hidden Brain is hosted by Shankar Vedantam and produced by Maggie Penman, Jennifer Schmidt, Rhaina Cohen, Parth Shah, and Renee Klahr. Our supervising producer is Tara Boyle. You can also follow us on Twitter @hiddenbrain, and listen for Hidden Brain stories each week on your local public radio station.

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Eleven Days Without Sleep: The Haunting Effects Of A Record-Breaking Stunt

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subscribe to Hidden Brain podcast Staying up until the early hours of the morning with friends. Skipping sleep to prepare for an exam. These are fairly standard aspects of student life for many young people. But these sorts of all-nighters pale in comparison to the stunt Randy Gardner pulled when he was 17.

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Retrieval Practice

Each module integrates multimedia and starts with asking students to consider what they already know about the topic before proceeding.

There are questions in the middle of each module for reflective pause.

A short quiz ends each module, asking the audience to assess what they learned.

Elaboration

A comments section will prompt encourage “think, pair, share” with a partner after each module.

The discussion board can remain open so that users may asynchronously benefit from each other’s thoughts and perceptions.

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Hidden Brain PodcastEyes Wide Open Part 2

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Hidden Brain PodcastEyes Wide Open Part 2

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What does the song "Satisfaction" by The Rolling Stones have in common with the periodic table of elements? Both are the product of dreams.

Creative reawakening is just one of many boons provided by a full nights rest, argues neuroscientist Matthew Walker.

"Sleep is the Swiss army knife of health. When sleep is deficient, there is sickness and disease. And when sleep is abundant, there is vitality and health."

On this week's episode of Hidden Brain, the mind and body benefits of eight full hours of sleep - and yes, Matthew Walker says that's how many hours of rest you should be getting.

"If we didn't need eight hours of sleep and could survive on six, Mother Nature would have done away with 25 percent of our sleep time millions of years ago. Because when you think about it, sleep is an idiotic thing to do. If sleep does not provide a remarkable set of benefits, then it's the biggest mistake the evolutionary process has ever made."
Hidden Brain is hosted by Shankar Vedantam and produced by Maggie Penman, Jennifer Schmidt, Rhaina Cohen, Parth Shah, and Renee Klahr. Our supervising producer is Tara Boyle. You can also follow us on Twitter @hiddenbrain, and listen for Hidden Brain stories each week on your local public radio station.

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The "Swiss Army Knife" Of Health: A Good Night's Sleep

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subscribe to Hidden Brain podcast What does the song "Satisfaction" by The Rolling Stones have in common with the periodic table of elements? Both are the product of dreams. Creative reawakening is just one of many boons provided by a full nights rest, argues neuroscientist Matthew Walker.

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It's no secret that most of us don't get enough sleep - or that sleep deprivation can hurt our logical reasoning, focus, and mood. But do you know how to get the seven to nine hours you need? To start, you should go to bed when you're just starting to feel drowsy. For many people, that's usually between 10 PM and 11 PM, when melatonin, a natural hormone that makes you relax and ultimately fall asleep, often kicks in. And you probably already know to avoid screens at night, since their blue light rays can inhibit the production of melatonin, but you should also stay away from any activity that requires a lot of thinking. Making your brain work hard can keep you awake, even if you're simply reading. Before bed, try doing the dishes, going for a walk, or listening to music instead. Getting a good night's sleep is not a random event - it's a learnable skill.

Source: Adapted from "Senior Executives Get More Sleep Than Everyone Else," by Rasmus Hougaard and Jacqueline Carter

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It's no secret that most of us don't get enough sleep - or that sleep deprivation can hurt our logical reasoning, focus, and mood. But do you know how to get the seven to nine hours you need? To start, you should go to bed when you're just starting to feel drowsy.

# data[].attributes.attachment_link.readable_text
Nobody goes to graduate school expecting a walk in the park - long hours spent in the lab, in the the library, and sitting in front of a computer screen are just an accepted part of daily life. For many students, it is the most energetically demanding time of their lives, not just for the hours put in, but the cognitive resources required to think critically and absorb complex material. While such an assiduous lifestyle should perhaps warrant more rest than normal, sleep often takes a backseat to projects, reading, and deadlines.

Sure, every student knows sleep is important. But how important is it, really?

According to Northern Illinois University professor and Industrial-Organizational psychologist Dr. Larissa Barber, sleep is the single most important health behavior we engage in. In a 2010 study, she and her colleague found that healthy sleep patterns play a critical role in self-regulation - an executive function in the brain that controls our behaviors ( Stress and Health, 2010). It helps us to override urges, initiate behaviors, and persist. It tells us to get out of bed and get to class on time, to start working on that thesis/dissertation, and to keep trudging through that dense journal article, despite the impulse to incessantly check e-mail and Facebook. Without adequate sleep, our brains are less equipped to control our behaviors and direct them towards our goals, which can spell trouble for burdened graduate students.

"I would say that graduate school is probably the ultimate self-regulatory exercise," Barber said. "Almost every critical performance outcome in graduate school requires self-regulatory resources, or energy."

Not only is self-regulation important for knowing when to buckle down and focus, or get yourself out of bed in the morning, it's also critical for managing emotions and stress. Sleeping less can cause people to feel more threatened by stressful events, which in response, can lead to dysfunctional coping.

"Things that usually rate as minor stressors all of a sudden seem like a big deal," Barber said. "So, sleep can often mean the difference between you crying after receiving negative feedback from your advisor on a paper, or calmly evaluating the information as constructive or motivating."

How much sleep is enough? While most people think of the standard as 8 hours, studies show that performance decrements don't appear until you hit below 7 hours, with less-than-6 hours showing a large effect. But personality and individual differences do play a role in a person's "magic number." On average, people need between 6 and 8 hours of sleep. However, people who score high on extraversion (talkative, out-going, assertive) tend to recharge a bit faster than others. Same goes for older people, and people with high metabolisms.

But 7 hours a night may not get you off the hook. It's not just the amount of sleep that matters for positive well-being and performance outcomes; sleep routines are just as important. Barber's study found that in order to see stress levels decline and self-control improve, people need to get enough sleep consistently over the course of the work week.

Further evidence for the importance of sleep routines comes from a study of the effect of staying up late and sleeping late on the weekends, which is a common practice for young adults. CUNY researchers found that a delayed sleep schedule on the weekend can seriously hurt cognitive performance and overall mood in the beginning of the week, through "Sunday night insomnia" and the "Monday morning blues" (Psychology and Health, 2001). Scientists suggest that a later wake-up time prevents exposure to morning light which throws our circadian rhythm off kilter, and subsequently shifts our body's sleep schedule.

Barber finds it troubling that people commonly overlook routines in favor of just catching up on sleep at a later date.

"People often think of sleep in compensatory terms, like a credit card - I'll just pay it back later!" she said. "But they forget what I call the 'hidden interest rates,' which is circadian rhythm disruption. Poor Monday always gets a bad reputation, but the reality is that we set ourselves up for Monday failure via our weekend sleep habits."

In addition to getting enough sleep and getting it routinely, high-quality rest is also an important factor to consider. Studies indicate that sleep hygiene is one of the greatest predictors of overall sleep quality (Hong Kong Med J, 2010). This refers to general behavioral practices and environmental factors that allow for a sound sleep, like diet, exercise, light, temperature, noise, as well as sleep schedule and pre-sleep activities. On the flip side, poor sleep quality is associated with significant psychological distress, depression, confusion, and generally lower life satisfaction. Poor sleep hygiene, in specific, is linked to a higher prevalence of insomnia and chronic sleep difficulties.

In a 2010 study, Chinese researchers found that despite its importance in sleep quality, the level of sleep hygiene knowledge among university students was "relatively inadequate" (Hong Kong Med J, 2010). A more recent study found that although sleep disturbances are often comorbid with most psychiatric disorders, and can exacerbate symptoms, graduate clinical psychology students receive little training in the assessment of sleep and treatment of sleep disorders (Training and Education in Professional Psychology, 2012).

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Take a moment to educate yourself on sleep hygiene with these research-based tips.

- Don't take naps

- Avoid sleep medications (prescription or over-the-counter)

- Use the bed only for sleeping, and lie down only when sleepy. If you can't fall asleep within 10 minutes, do something else not work or school-related

- Avoid caffeinated drinks at least 4 hours before bedtime. This includes the obvious culprits like coffee, but also includes chocolate milk, certain teas, and some vitamin drinks

- Avoid nicotine and alcohol 4 hours before bedtime

- Avoid glowing screens before bedtime: TV, phone, tablets, computer, etc. The light interferes with with your body's ability to wind down

- Don't stay up late to do work. Research shows that staying up 17-19 consecutive hours is similar to having a blood alcohol limit over the legal limit. So, working late at night is like writing a paper while drunk. You're better off waking up early and starting on it first thing in the morning

- Find out your sleep need and stick to it

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- Put a notebook next to your bed to write down things you think of before bedtime (including brilliant thesis/dissertation ideas) to prevent you from worrying about remembering it the next day or getting up to check into it

- Enlist "sleep allies" to help you stick to your schedule, such as a spouse or roommate. Anyone who can tell you (or text you), "Shut it down," at a certain time of night

- Put your sleep time in your calendar and protect it like you would class time or your dissertation defense day. It's non-negotiable (only in case of emergency)

- Manage your time well overall. If you find yourself sacrificing sleep, then you have too many things on your high-priority list (that are not sleep) and you need to re-evaluate (a) what all you are trying to do and (b) how you are doing it

TAKE THE QUIZ: Sleep Hygiene Index
(Assessment of Sleep Hygiene Using the Sleep Hygiene Index, Journal of Behavioral Medicine, 2006)Always (5), frequently (4), sometimes (3), rarely (2), never (1).
Sum the items for your score. The higher your score, the more maladaptive your sleep hygiene status is.1. I take daytime naps lasting two or more hours.
2. I go to bed at different times from day to day.
3. I get out of bed at different times from day to day.
4. I exercise to the point of sweating within 1 hour of going to bed.
5. I stay in bed longer than I should two or three times a week.
6. I use alcohol, tobacco, or caffeine within 4 hours of going to bed or after going to bed.
7. I do something that may wake me up before bedtime (for example: play video games, use the internet, or clean).
8. I go to bed feeling stressed, angry, upset, or nervous.
9. I use my bed for things other than sleeping or sex (for example: watch television, read, eat, or study).
10. I sleep on an uncomfortable bed (for example: poor mattress or pillow, too much or not enough blankets).
11. I sleep in an uncomfortable bedroom (for example: too bright, too stuffy, too hot, too cold, or too noisy).
12. I do important work before bedtime (for example: pay bills, schedule, or study).
13. I think, plan, or worry when I am in bed.

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Module 3: Encouraging Sleep-Techniques, Resources and Classroom Tips to Support Quality Student Rest

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Constructivism: assess what learners know prior to the module and build from there

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Applied science: Recent research suggests 10 minutes of cognitive rest can increase memory retrieval of information encoded just before the rest (Craig & Dewar, 2018). A 10-minute timer can be set as the last slide of module PowerPoints to encourage quiet brain rest.

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Universal design using audio and visual delivery will be used to promote inclusitivity. Posted written resources will be available in PDF form for ease of prinitng or can be read from the screen

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To teach effectively, you have to reach your students, students who come to the classroom with varied backgrounds, expectations, abilities, and learning styles. This understanding is at the heart of the philosophy of practice known as universal design for learning (UDL). UDL is part of a larger movement of universal design, which works to increase access for all through designs that-from the beginning-consider needs of diverse people, not just adapting when a situation requires it. For instructors, UDL means designing your course with accessibility in mind-regardless of whether you've been approached by a student who may "require" such adjustments.

Why Consider UDL?

According to the Journal of Postsecondary Education and Disability Winter 2011 edition, eleven percent of undergraduates report some type of disability - and the proportions have swapped such that the majority of disabilities are now non-apparent (e.g., mental, emotional, psychiatric condition/depression, ADHD, specific learning disabilities/dyslexia), compared to more apparent disabilities (e.g. mobility impairment). Meanwhile, between sixty and eighty percent of students with disabilities don't actually contact campus university services for "official" accommodation requests/reporting - which means many students in your classrooms may fall in this category without your knowledge.

This is also an issue of social justice. By putting disability and access considerations at the center of our teaching, we join efforts to make higher education accessible to all of our students.

Key Principles of UDL UDL in Your Course Additional Resources

Key Principles of UDL

Many of you have heard about the increase in classroom technologies, but in their publication Universal Design for Learning in Postsecondary Education (PDF) Rose, et al. remind us that it's important to distinguish UDL from use of "assistive technologies" that are available (from "low-tech" wheelchairs, eyeglasses, or ASL interpreters to more "high tech" computerized support-spell-check, text-to-speech). UDL is about making material that is physically accessible to all, but also about accessible pedagogies.

Based in neuroscientific research and primary brain networks, UDL is grounded in three key principles that are core to learning:

Provide Multiple Means of Representation

Provide Multiple Means of Action and Expression

Provide Multiple Means of Engagement

Principle #1: Provide multiple means of representation

Principle #2: Provide multiple means of action and expression

Principle #3: Provide multiple means of engagement

Brain Networks Activated: Recognition

Brain Networks Activated: Strategic

Brain Networks Activated: Affective

Key word: what. The what of learning

Key word: how. The how of learning

Key word: why. The why of learning

Description: How we gather facts and categorize what we see, hear, and read. Identifying letters, words, or an author's style are recognition tasks.

Description: Planning and performing tasks. How we organize and express our ideas. Writing an essay or solving a math problem are strategic tasks.

Description: How learners get engaged and stay motivated. How they are challenged, excited, or interested. These are affective dimensions.

The principle put another way: Principle #1: Present information and content in different ways.

The principle put another way: Principle #2: Differentiate the ways that students can express what they know.

The principle put another way: Principle #3: Stimulate interest and motivation for learning.

Adapted from the National UDL Center
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UDL In Your Course

While several starters and tips for incorporating UDL into your course(s) are listed below, an especially helpful resource for checklists and recommendations on UDL is the Access Project, a project of Colorado State University's Department of Occupational Therapy. On their website, the Project hosts many UDL materials, including A Concise Introduction to UDL, How Do You Teach? (a UDL Checklist for professors), and a more in-depth UDL Quick Tips chart.

Your Syllabus-Consider how you address principles of UDL and access in your syllabus:

Do you include a disability or accessibility statement (your approach to disability and accommodations in the classroom)? Do you point this out to students in your in-class syllabus review?

Consider what tone you are setting with including such a statement. Since it is your classroom, perhaps create your own, tailored statement. Even if it's an addition to your institution's existing statement, this can help you to set the tone of your course.
For Georgetown's recommended statement and other examples, see the Teaching Commons Georgetown Policies page, and select "Disabilities Accommodations." Consider the placement of such statement(s) on the syllabus, and how you might highlight these during class.

Additional suggested practices for UDL & syllabus design

Assessment - Consider how UDL might impact your class assessment approach(es):

How can you best assess students' mastery? Have you considered combinations of assessments that are balanced to provide opportunities for students to use their different strengths (e.g., written assignments, creative submissions, electronic portfolios, real-time data)?

Within each assignment, are instructions clear? Are the layout and format of the assignment easy to navigate?

Check out our Designing Assessment page for more ideas and suggestions.

And if you'd like help with some self-assessment of how well you're doing with these ideas and issues, feel free to email CNDLS to set up a conversation or a Mid-Semester Teaching Feedback session.

Course Materials-Consider the accessibility of your course content and information:

Providing "multiple means of representation" involves preparing your course materials (whether print, digital, etc.) to be accessible to all students.

Creation of accessible materials:
The Access Project (also referenced earlier) has many guides that provide information on how to design materials that are in line with universal design principles for Microsoft products (Word, PowerPoint) and other applications (e.g., Adobe).

Class Time-Consider how you vary your approaches within the classroom, from methods of content delivery to student participation:

Present information in multiple formats, including text, graphics, audio, and video.

Try having your lectures supported by handouts or a few PowerPoint slides.

Davies et al. of Colorado State University, in Measuring the Effectiveness of Universal Design for Learning Intervention in Postsecondary Education, recommend the use of video clips, as long as you are sure to both introduce the video and summarize/debrief main points with the class.

Group discussions: There are many ways to use and tailor discussion activities to increase student engagement:

Providing time for individual reflection and writing prior to the group discussion may allow more students to be involved and gather their thoughts.

Using small groups: a "Think-Pair-Share" exercise with many pairs or small groups discussing ideas can also encourage more low-stakes participation.

Offering online formats: a forum for online discussion or blogging provides another space for students to participate and demonstrate comprehension.

Use classroom technologies such as clickers (Clickers - CNDLS technology page) and Poll Everywhere (Poll Everywhere - CNDLS technology page) for engagement and/or real-time interaction and feedback.

Use classroom technologies such as clickers and Poll Everywhere for engagement and/or real-time interaction and feedback.

Provide a bit of structure at the beginning of each class: outline key points, connect them to course objectives, and give a preview of the material to be covered in that day.

Check out our Planning and Leading Class, Teaching with Technologies, and Active Learning pages for more ideas.

Remember, key principles of UDL include providing multiple means of expression and of engagement-offer students a variety of ways to express themselves and to interact with course material.
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Additional Resources

SoTL Note: These resources are helpful for learning more about UDL, but much of the current literature focuses on K-12 education, so there's a need for additional empirical, peer-reviewed research. If you're considering Scholarship of Teaching and Learning (SoTL), learn more at our SoTL Teaching Commons page.
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Please reach out to us at cndls@georgetown.edu if you'd like to have a conversation with someone at CNDLS about these or other teaching issues.

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To teach effectively, you have to reach your students, students who come to the classroom with varied backgrounds, expectations, abilities, and learning styles. This understanding is at the heart of the philosophy of practice known as universal design for learning (UDL).

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Making and online course accessible to everyone 

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Making and online course accessible to everyone 

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I taught the first online learning course at the University of Washington in 1995. My co‑instructor was Dr. Norm Coombs, at the time a professor at the Rochester Institute of Technology. We designed the course to be accessible to anyone, including students who were blind, were deaf, had physical disabilities, or had multiple learning preferences. Norm himself is blind. He uses a screen reader and speech synthesizer to read text presented on the screen. We employed the latest technology of the time-email, discussion list, Gopher, file transfer protocol, and telnet (no World Wide Web yet!). All online materials were in a text-based format, and videos, presented in VHS format with captions and audio description, were mailed to the students. When asked if any of our students had disabilities, we were proud to say that we did not know. Why? Because no one needed to disclose a disability since all of the course materials and teaching methods were accessibly designed.

Technology has changed dramatically since I first taught online, but the basic principles that can guide the design of accessible courses have not. The term UD was coined by Ronald Mace, an architect, product designer, and wheelchair user whose work led to the creation of the Center for Universal Design (CUD) at North Carolina State University and its seven principles of UD. UD is defined as "the design of products and environments to be usable by all people, to the greatest extent possible, without the need for adaptation or specialized design." The UD definition, principles, and guidelines were created to make any application accessible, usable, and inclusive and, thus, are a logical choice to underpin practices that ensure that online courses meet the needs of potential students with a wide variety of characteristics that include those related to gender identity, race, ethnicity, culture, marital status, age, abilities, interests, values, learning preferences, socioeconomic status, and religious beliefs.

For a history of UD, the basic principles of UD and those that later evolved to address issues specific to the design of learning activities and IT, consult my book Creating Inclusive Learning Environments in Higher Education: A Universal Design Toolkit and other resources presented in the Center for Universal Design in Education, which is hosted by DO-IT Center at the University of Washington-where DO-IT stands for Disabilities, Opportunities, Internetworking, and Technology. For resources specific to applications of UD to online learning, including accessibility checkers, legal issues, technical details, and promising practices, consult .

A statement about how students can request disability-related accommodations should be included in the syllabus. Then instructors can apply the 20 tips I list below, as they begin to work toward making their online courses more inclusive. The complementary video, 20 Tips for Instructors about Making Online Learning Courses Accessible, may be viewed online, along with a for further background and directions for implementing these tips.

Tips

Nine tips for course materials follow. Consult Accessible Technology at uw.edu/accessibility for details on the design, selection, and use of accessible IT as well as accessibility checkers that help you identify accessibility problems in materials you use or create:

1. Use clear, consistent layouts, navigation, and organization schemes to present content. Keep paragraphs short and avoid flashing content.

2. Use descriptive wording for hyperlink text (e.g., "DO-IT website" rather than "click here").

3. Use a text-based format and structure headings, lists, and tables using style and formatting features within your Learning Management System (LMS) and content creation software, such as Microsoft Word, and PowerPoint and Adobe InDesign and Acrobat; use built-in page layouts where applicable.

4. Avoid creating PDF documents. Post most instructor-created content within LMS content pages (i.e., in HTML) and, if a PDF is desired, link to it only as a secondary source of the information.

5. Provide concise text descriptions of content presented within images (text descriptions web resource).

6. Use large, bold, sans serif fonts on uncluttered pages with plain backgrounds.

7. Use color combinations that are high contrast and can be distinguished by those who are colorblind (color contrast web resource). Do not use color alone to convey meaning.

8. Caption videos and transcribe audio content.

9. Don't overburden students with learning to operate a large number of technology products unless they are related to the topic of the course; use asynchronous tools; make sure IT used requires the use of the keyboard alone and otherwise employs accessible design practices.

Eleven tips for inclusive pedagogy follow; many are particularly beneficial for students who are neurodiverse (e.g., those on the autism spectrum or who have learning disabilities). Consult Equal Access: Universal Design of Instruction for more guidance.

1. Recommend videos and written materials to students where they can gain technical skills needed for course participation.

2. Provide multiple ways for students to learn (e.g., use a combination of text, video, audio, and/or image; speak aloud all content presented on slides in synchronous presentations and then record them for later viewing).

3. Provide multiple ways to communicate and collaborate that are accessible to individuals with a variety of disabilities.

4. Provide multiple ways for students to demonstrate what they have learned (e.g., different types of test items, portfolios, presentations, single-topic discussions).

5. Address a wide range of language skills as you write content (e.g., use plain English, spell out acronyms, define terms, avoid or define jargon).

6. Make instructions and expectations clear for activities, projects, discussions and readings.

7. Make examples and assignments relevant to learners with a wide variety of interests and backgrounds.

8. Offer outlines and other scaffolding tools and share tips that might help students learn.

9. Provide adequate opportunities to practice.

10. Allow adequate time for activities, projects, and tests (e.g., give details of all project assignments at the beginning of the course).

11. Provide feedback on project parts and offer corrective opportunities.

These tips apply to both synchronous and asynchronous teaching. Additional tips for synchronous presentations (e.g., speak all content presented visually, turn on the caption feature of your conferencing software, do not require students to have their cameras on) can be found in Equal Access: Universal Design of Your Presentation.

Acknowledgments

DO-IT (Disabilities, Opportunities, Internetworking, and Technology) serves to increase the success of individuals with disabilities. This publication was partially funded through DO-IT's AccessCyberlearning project that is supported by the National Science Foundation (NSF Grant #1550477). Any questions, findings, and conclusions or recommendations expressed in this publication are those of the author and do not necessarily reflect the views of the NSF. More information about DO-IT can be found at uw.edu/doit.

Copyright © 2022, 2021, 2020, 2019, 2018, 2015, University of Washington. Permission is granted to copy these materials for educational, noncommercial purposes provided the source is acknowledged.

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20 Tips for Teaching an Accessible Online Course

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scald=5808:sdl_editor_representation {"link":"","linkTarget":""}] I taught the first online learning course at the University of Washington in 1995. My co‑instructor was Dr. Norm Coombs, at the time a professor at the Rochester Institute of Technology.

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Guided Sleep

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Guided Meditation for Sleep | Get a Good Night’s Rest

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Try a sleep sound: "Forgotten Shapes" in Sleep by Headspace

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IPS 553:

Week 1: https://www.studyblue.com/?src_url=https://www.studyblue.com/?site#flashcard/view/21213935

Week 2: https://www.studyblue.com/?src_url=https://www.studyblue.com/?site#flashcard/view/21236208

Week 3: https://www.studyblue.com/?src_url=https://www.studyblue.com/?site#flashcard/view/21275338

Week 4: https://www.studyblue.com/?src_url=https://www.studyblue.com/?site#flashcard/view/21299920

Week 5 (Sleep Theme!): https://www.studyblue.com/?src_url=https://www.studyblue.com/?site#flashcard/view/21328298

Week 6: https://www.studyblue.com/?src_url=https://www.studyblue.com/?site#flashcard/view/21343022

Week 7:    https://s.tudy.it/cv8x5

Week 8:  https://s.tudy.it/4dbv9

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No-Stakes Quizzes

End of module, low-stakes, repeatable quizzes

Explaination provided of why correct answer is preferred and what opportunities for learning exist for incorrect answers

Reflection

Learnes will also be asked to share end of module self-reflection corresponding to the module content that will only be shared with the instructor.

The learners will be asked to examine their own attitudes regarding the value of sleep in medical education and share them in a dedicated discussion board section in written, graphic/drawn, audio or audio/visual format.

Technologies for recording reflections include recording a Zoom video conference (where the learner is the only attendee), Vacaroo, the Voice Memo "Extras" app provided as standard iPhone software, and smartphone cameras for taking photos of drawings

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End of course comprehensive quiz

Must pass with 90% or better.

Can retake until 90% is achieved.

Based on weekly quizzes

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Peer Modeling
To aid self-efficacy of the audience through peer modeling, learners will be encourged to interact with others who are using the modules at the same time or those who have shared contact information in the discussion boards.
Learners can meet each other and create (potentially interprofessional) working relationships after meeting in the course. Discussion boards offer a place to voluntarily share contact information for future discussion or collaboration.

Self-Assessment
Self-efficacy will be supported through self-assessment.
Modules are self-paced and encourage self-efficacy by providing opportunities for reflection and self-assessment before, during and after each module.

Feedback
Self-efficacy will be supported through feedback. Learners will receive course communication from the instructor(s) through email, Blackboard course announcements and discussion boards.
Instructor contact information will be shared with students as well as specificed virtual offices hours so that studetns can contact the min-course instructor.

Moving outside of the course itself, the self-efficacy benefits of community, social constructivism and peer-modeling will be encouraged by incoporating social media.
The social media accounts of sleep experts will be shared as a way to see what experts have to say on the course topic on social media and how others respond to them and each other.
Specifically, Twitter hashtags that focus on sleep hygiene, medical education, and sleep and performance  (see below) will be shared as resources in each of the modules as appropriate to the theme of the module.

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1. Describe the relationship        between sleep and memory

2. Explain the connection between sleep quality, academic performance  and clinical decision-making

3. Identify opportunities in a    health professions' education to support student sleep hygiene

4. Restructure education culture to support appropriate amounts of sleep

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We are cognitive psychological scientists interested in research on education. Our main research focus is on the science of learning. (Hence, "The Learning Scientists"!). Our Vision is to make scientific research on learning more accessible to students, teachers, and other educators.

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Get YOUR score! See what behaviors you could improve on!

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David F. Mastin

Jeff Bryson

Robert Corwyn

The Sleep Hygiene Index was developed to assess the practice of sleep hygiene behaviors. The Sleep Hygiene Index was delivered to 632 subjects and a subset of the subjects participated in a readministration of the instrument. Test-retest reliability analyses suggested that sleep hygiene behaviors are relatively stable over time for a nonclinical population. Results confirmed that sleep hygiene is strongly related to sleep quality and modestly related to perceptions of daytime sleepiness. As predicted, support of the sleep hygiene construct was also provided by strong correlations with the associated features of a diagnosis of inadequate sleep hygiene. The Sleep Hygiene Index, a much shorter sleep hygiene instrument than previously published, demonstrated comparable psychometric properties with additional evidence of validity and a clear item selection rationale.

KEY WORDS:

This is a preview of subscription content, log in to check access.

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Assessment of Sleep Hygiene Using the Sleep Hygiene Index

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David F. Mastin Jeff Bryson Robert Corwyn The Sleep Hygiene Index was developed to assess the practice of sleep hygiene behaviors. The Sleep Hygiene Index was delivered to 632 subjects and a subset of the subjects participated in a readministration of the instrument.

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Craig, M., & Dewar, M. (2018). Rest- related consolidation protects the fine detail of new memories. Scientific Reports, 8(1), 6857. doi:10.1038/s41598-018-25313-y

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Calm Sleep Stories | Stephen Fry's 'Blue Gold'

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It's no secret that most of us don't get enough sleep - or that sleep deprivation can hurt our logical reasoning, focus, and mood. But do you know how to get the seven to nine hours you need? To start, you should go to bed when you're just starting to feel drowsy. For many people, that's usually between 10 PM and 11 PM, when melatonin, a natural hormone that makes you relax and ultimately fall asleep, often kicks in. And you probably already know to avoid screens at night, since their blue light rays can inhibit the production of melatonin, but you should also stay away from any activity that requires a lot of thinking. Making your brain work hard can keep you awake, even if you're simply reading. Before bed, try doing the dishes, going for a walk, or listening to music instead. Getting a good night's sleep is not a random event - it's a learnable skill.

Source: Adapted from "Senior Executives Get More Sleep Than Everyone Else," by Rasmus Hougaard and Jacqueline Carter

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It's no secret that most of us don't get enough sleep - or that sleep deprivation can hurt our logical reasoning, focus, and mood. But do you know how to get the seven to nine hours you need? To start, you should go to bed when you're just starting to feel drowsy.

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Guided Sleep

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Guided Meditation for Sleep | Get a Good Night’s Rest

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Walker, M. (2017). Why we sleep: Unlocking the power of sleep and dreams. New York, NY: Scribner.

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Walker, M. (2017). Why we sleep: Unlocking the power of sleep and dreams. New York, NY: Scribner.

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How To Improve Your Sleep | Matthew Walker

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IPS 600 Capstone Accessible Sleep Syllabus Banning

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Vocaroo is a quick and easy way to share voice messages over the interwebs.

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Modernize workflows with Zoom's trusted collaboration tools: including video meetings, team chat, VoIP phone, webinars, whiteboard, contact center, and events.

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Students will evaluate the course with online, anonymous AEFIS assessment surveys distributed by the college.

I will also use the Open SUNY Couse Quality Review Rubrice (OSCQR) to assess the course before the course begins and then again after it ends to confirm what went well and what is an opportunity for development.

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To help campuses ensure that their online courses are learner centered and well designed, a team of Open SUNY staff and campus stakeholders has designed the OSCQR rubric, a customizable and flexible tool for online course quality review.

The OSCQR rubric specifically targets online course design. The OSCQR rubric is unique and differs from other online course quality rubrics in several ways. It is not restricted to mature online courses. The rubric can be used formatively with new online faculty to help guide, inform, and influence the design of their new online courses, and, it is non-evaluative.

Conceptually, the rubric and the online course review and refresh process are implemented as a professional development exercise designed to guide online faculty to use research-based effective practices and standards to improve the quality, effectiveness, and efficiency of their online course design, rather than as an online course evaluation, or quality assurance procedure.

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OSCQR - Open SUNY Course Quality Review Rubric

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To help campuses ensure that their online courses are learner centered and well designed, a team of Open SUNY staff and campus stakeholders has designed the OSCQR rubric, a customizable and flexible tool for online course quality review. The OSCQR rubric specifically targets online course design.

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Accessible if Word Doc does not open (but with no images)

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IPS 555: Sleep in Medicine Syllabus in Google Doc

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TedEd

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How does caffeine keep us awake? - Hanan Qasim

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Made with Adobe Spark-including Infographic

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Example of presentation with closed captioning

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Example of presentation with closed captioning

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Share your videos with friends, family, and the world.

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Adrian Banning IPS 600 Capstone: Sleep in Medical Education