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The importance of sleep regularity: a consensus statement of the National Sleep Foundation sleep timing and variability panel

• Tracey L. Sletten, PhD 1 Author Footnotes 1 Tracey L. Sletten and Matthew D. Weaver contributed equally. Tracey L. Sletten Footnotes 1 Tracey L. Sletten and Matthew D. Weaver contributed equally. Affiliations Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia Search for articles by this author
• Russell G. Foster, PhD, FRS Russell G. Foster Affiliations Sleep & Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK Search for articles by this author
• David Gozal, MD, MBA, PhD (Hon) David Gozal Affiliations Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia, USA Search for articles by this author
• Till Roenneberg, PhD Till Roenneberg Affiliations Institutes for Occupational, Social, and Environmental Medicine and Medical Psychology, LMU Munich, Munich, Germany Search for articles by this author
• Fred W. Turek, PhD Fred W. Turek Affiliations Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, USA Search for articles by this author
• Michael V. Vitiello, PhD Michael V. Vitiello Affiliations Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA Search for articles by this author
• Michael W. Young, PhD Michael W. Young Affiliations Laboratory of Genetics, The Rockefeller University, New York City, New York, USA Search for articles by this author
• Show footnotes Hide footnotes Author Footnotes 1 Tracey L. Sletten and Matthew D. Weaver contributed equally.

Conclusions

• Sleep patterns
• Circadian misalignment
• Catch-up sleep
• Performance
• • • Daily regularity in sleep timing is important for health.
• • • Daily regularity in sleep timing is important for performance.
• • • When sleep is of insufficient duration during the week (or work days), catch-up sleep on weekends (or non-work days) is important for health.

Introduction

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Panel deliberations and consensus voting

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Literature review

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Limitations

Credit author statement, acknowledgments, declaration of conflict of interest, appendix a. supplementary material.

Supplementary material

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Article info

Publication history, identification.

DOI: https://doi.org/10.1016/j.sleh.2023.07.016

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‘Sleeping on it’ really does help and four other recent sleep research breakthroughs

Marie Skłodowska-Curie Senior Research Fellow, University of York

Disclosure statement

Dan Denis receives funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101028886.

University of York provides funding as a member of The Conversation UK.

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Twenty-six years. That is roughly how much of our lives are spent asleep. Scientists have been trying to explain why we spend so much time sleeping since at least the ancient Greeks , but pinning down the exact functions of sleep has proven to be difficult.

During the past decade, there has been a surge of interest from researchers in the nature and function of sleep. New experimental models coupled with advances in technology and analytical techniques are giving us a deeper look inside the sleeping brain. Here are some of the biggest recent breakthroughs in the science of sleep.

1. We know more about lucid dreaming

No longer on the fringes, the neuroscientific study of dreaming has now become mainstream.

US researchers in a 2017 study woke their participants up at regular intervals during the night and asked them what was going through their minds prior to the alarm call. Sometimes participants couldn’t recall any dreaming. The study team then looked at what was happening in the participant’s brain moments before waking.

Participants’ recall of dream content was associated with increased activity in the posterior hot zone, an area of the brain closely linked to conscious awareness . Researchers could predict the presence or absence of dream experiences by monitoring this zone in real time.

Another exciting development in the study of dreams is research into lucid dreams, in which you are aware that you are dreaming. A 2021 study established two-way communication between a dreamer and a researcher. In this experiment, participants signalled to the researcher that they were dreaming by moving their eyes in a pre-agreed pattern.

The researcher read out maths problems (what is eight minus six?). The dreamer could respond to this question with eye movements. The dreamers were accurate, indicating they had access to high level cognitive functions. The researchers used polysomnography , which monitors bodily functions such as breathing and brain activity during sleep, to confirm that participants were asleep.

These discoveries have dream researchers excited about the future of “interactive dreaming”, such as practising a skill or solving a problem in our dreams.

Read more: As we dream, we can listen in on the waking world – podcast

2. Our brain replays memories while we sleep

This year marks the centenary of the first demonstration that sleep improves our memory . However, a 2023 review of recent research has shown that memories formed during the day get reactivated while we are sleeping. Researchers discovered this using machine learning techniques to “decode” the contents of the sleeping brain.

A 2021 study found that training algorithms to distinguish between different memories while awake makes it possible to see the same neural patterns re-emerge in the sleeping brain. A different study, also in 2021, found that the more times these patterns re-emerge during sleep, the bigger the benefit to memory.

In other approaches, scientists have been able to reactivate certain memories by replaying sounds associated with the memory in question while the participant was asleep. A 2020 meta-analysis of 91 experiments found that when participants’ memory was tested after sleep they remembered more of the stimuli whose sounds were played back during sleep, compared with control stimuli whose sounds were not replayed.

Research has also shown that sleep strengthens memory for the most important aspects of an experience, restructures our memories to form more cohesive narratives and helps us come up with solutions to problems we are stuck on. Science is showing that sleeping on it really does help.

3. Sleep keeps our minds healthy

We all know that a lack of sleep makes us feel bad. Laboratory sleep deprivation studies, where researchers keep willing participants awake throughout the night, have been combined with functional MRI brain scans to paint a detailed picture of the sleep-deprived brain. These studies have shown that a lack of sleep severely disrupts the connectivity between different brain networks. These changes include a breakdown of connectivity between brain regions responsible for cognitive control , and an amplification of those involved in threat and emotional processing .

The consequence of this is that the sleep-deprived brain is worse at learning new information , poorer at regulating emotions , and unable to suppress intrusive thoughts . Sleep loss may even make you less likely to help other people . These findings may explain why poor sleep quality is so ubiquitous in poor mental health .

4. Sleep protects us against neurodegenerative diseases

Although we naturally sleep less as we age , mounting evidence suggests that sleep problems earlier in life increase the risk of dementia.

The build-up of β-amyloid, a metabolic waste product , is one of the mechanisms underlying Alzheimer’s disease. Recently, it has become apparent that deep, undisturbed sleep is good for flushing these toxins out of the brain. Sleep deprivation increases the the rate of build-up of β-amyloid in parts of the brain involved in memory, such as the hippocampus . A longitudinal study published in 2020 found that sleep problems were associated with a higher rate of β-amyloid accumulation at a follow-up four years later . In a different study, published in 2022, sleep parameters forecasted the rate of cognitive decline in participants over the following two years.

5. We can engineer sleep

The good news is that research is developing treatments to get a better night’s sleep and boost its benefits.

For example, the European Sleep Research Society and the American Academy of Sleep Medicine recommend cognitive behavioural therapy for insomnia (CBT-I). CBT-I works by identifying thoughts, feelings and behaviour that contribute to insomnia, which can then be modified to help promote sleep.

In 2022, a CBT-I app became the first digital therapy recommended by England’s National Institute for Health and Care Excellence for treatment on the NHS.

These interventions can improve other aspects of our lives as well. A 2021 meta-analysis of 65 clinical trials found that improving sleep via CBT-I reduced symptoms of depression, anxiety, rumination and stress.

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• Open access
• Published: 01 October 2019

Sleep quality, duration, and consistency are associated with better academic performance in college students

• Kana Okano 1 ,
• Jakub R. Kaczmarzyk 1 ,
• Neha Dave 2 ,
• John D. E. Gabrieli 1 &
• Jeffrey C. Grossman   ORCID: orcid.org/0000-0003-1281-2359 3  

npj Science of Learning volume  4 , Article number:  16 ( 2019 ) Cite this article

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Although numerous survey studies have reported connections between sleep and cognitive function, there remains a lack of quantitative data using objective measures to directly assess the association between sleep and academic performance. In this study, wearable activity trackers were distributed to 100 students in an introductory college chemistry class (88 of whom completed the study), allowing for multiple sleep measures to be correlated with in-class performance on quizzes and midterm examinations. Overall, better quality, longer duration, and greater consistency of sleep correlated with better grades. However, there was no relation between sleep measures on the single night before a test and test performance; instead, sleep duration and quality for the month and the week before a test correlated with better grades. Sleep measures accounted for nearly 25% of the variance in academic performance. These findings provide quantitative, objective evidence that better quality, longer duration, and greater consistency of sleep are strongly associated with better academic performance in college. Gender differences are discussed.

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Introduction.

The relationship between sleep and cognitive function has been a topic of interest for over a century. Well-controlled sleep studies conducted with healthy adults have shown that better sleep is associated with a myriad of superior cognitive functions, 1 , 2 , 3 , 4 , 5 , 6 including better learning and memory. 7 , 8 These effects have been found to extend beyond the laboratory setting such that self-reported sleep measures from students in the comfort of their own homes have also been found to be associated with academic performance. 9 , 10 , 11 , 12 , 13

Sleep is thought to play a crucial and specific role in memory consolidation. Although the exact mechanisms behind the relationship between sleep, memory, and neuro-plasticity are yet unknown, the general understanding is that specific synaptic connections that were active during awake-periods are strengthened during sleep, allowing for the consolidation of memory, and synaptic connections that were inactive are weakened. 5 , 14 , 15 Thus, sleep provides an essential function for memory consolidation (allowing us to remember what has been studied), which in turn is critical for successful academic performance.

Beyond the effects of sleep on memory consolidation, lack of sleep has been linked to poor attention and cognition. Well-controlled sleep deprivation studies have shown that lack of sleep not only increases fatigue and sleepiness but also worsens cognitive performance. 2 , 3 , 16 , 17 In fact, the cognitive performance of an individual who has been awake for 17 h is equivalent to that exhibited by one who has a blood alcohol concentration of 0.05%. 1 Outside of a laboratory setting, studies examining sleep in the comfort of peoples’ own homes via self-report surveys have found that persistently poor sleepers experience significantly more daytime difficulties in regards to fatigue, sleepiness, and poor cognition compared with persistently good sleepers. 18

Generally, sleep is associated with academic performance in school. Sleep deficit has been associated with lack of concentration and attention during class. 19 While a few studies report null effects, 20 , 21 most studies looking at the effects of sleep quality and duration on academic performance have linked longer and better-quality sleep with better academic performance such as school grades and study effort. 4 , 6 , 9 , 10 , 11 , 12 , 13 , 22 , 23 , 24 , 25 , 26 , 27 Similarly, sleep inconsistency plays a part in academic performance. Sleep inconsistency (sometimes called “social jet lag”) is defined by inconsistency in sleep schedule and/or duration from day to day. It is typically seen in the form of sleep debt during weekdays followed by oversleep on weekends. Sleep inconsistency tends to be greatest in adolescents and young adults who stay up late but are constrained by strict morning schedules. Adolescents who experience greater sleep inconsistency perform worse in school. 28 , 29 , 30 , 31

Although numerous studies have investigated the relationship between sleep and students’ academic performance, these studies utilized subjective measures of sleep duration and/or quality, typically in the form of self-report surveys; very few to date have used objective measures to quantify sleep duration and quality in students. One exception is a pair of linked studies that examined short-term benefits of sleep on academic performance in college. Students were incentivized with offers of extra credit if they averaged eight or more hours of sleep during final exams week in a psychology class 32 or five days leading up to the completion of a graphics studio final assignment. 33 Students who averaged eight or more hours of sleep, as measured by a wearable activity tracker, performed significantly better on their final psychology exams than students who chose not to participate or who slept less than eight hours. In contrast, for the graphics studio final assignments no difference was found in performance between students who averaged eight or more hours of sleep and those who did not get as much sleep, although sleep consistency in that case was found to be a factor.

Our aim in this study was to explore how sleep affects university students’ academic performance by objectively and ecologically tracking their sleep throughout an entire semester using Fitbit—a wearable activity tracker. Fitbit uses a combination of the wearer’s movement and heart-rate patterns to estimate the duration and quality of sleep. For instance, to determine sleep duration, the device measures the time in which the wearer has not moved, in combination with signature sleep movements such as rolling over. To determine sleep quality, the Fitbit device measures the wearer’s heart-rate variability which fluctuates during transitions between different stages of sleep. Although the specific algorithms that calculate these values are proprietary to Fitbit, they have been found to accurately estimate sleep duration and quality in normal adult sleepers without the use of research-grade sleep staging equipment. 34 By collecting quantitative sleep data over the course of the semester on nearly 100 students, we aimed to relate objective measures of sleep duration, quality, and consistency to academic performance from test to test and overall in the context of a real, large university college course.

A secondary aim was to understand gender differences in sleep and academic performance. Women outperform men in collegiate academic performance in most subjects 35 , 36 , 37 , 38 and even in online college courses. 39 Most of the research conducted to understand this female advantage in school grades has examined gender differences in self-discipline, 40 , 41 , 42 and none to date have considered gender differences in sleep as a mediating factor on school grades. There are inconsistencies in the literature on gender differences in sleep in young adults. While some studies report that females get more quantity 43 but worse quality sleep compared with males, 43 , 44 other studies report that females get better quality sleep. 45 , 46 In the current study, we aim to see whether we would observe a female advantage in grades and clarify how sleep contributes to gender differences.

Bedtime and wake-up times

On average, students went to bed at 1:54 a.m. (Median = 1:47 a.m., Standard Deviation (SD) of all bedtime samples = 2 h 11 min, SD of mean bedtime per participant = 1 h) and woke up at 9:17 a.m. (Median = 9:12 a.m., SD of all wake-up time samples = 2 h 2 min; SD of mean wake-up time per participant = 54 min). The data were confirmed to have Gaussian distribution using the Shapiro–Wilks normality test. We conducted an ANOVA with the overall score (sum of all grade-relevant quizzes and exams—see “Procedure”) as the dependent variable and bedtime (before or after median) and wake-up time (before or after median) as the independent variables. We found a main effect of bedtime ( F (1, 82) = 6.45, p  = 0.01), such that participants who went to bed before median bedtime had significantly higher overall score ( X  = 77.25%, SD = 13.71%) compared with participants who went to bed after median bedtime ( X  = 70.68%, SD = 11.01%). We also found a main effect of wake-up time ( F (1, 82) = 6.43, p  = 0.01), such that participants who woke up before median wake-up time had significantly higher overall score ( X  = 78.28%, SD = 9.33%) compared with participants who woke up after median wake-up time ( X  = 69.63%, SD = 14.38%), but found no interaction between bedtime and wake-up time ( F (1, 82) = 0.66, p  = 0.42).

A Pearson’s product-moment correlation between average bedtime and overall score revealed a significant and negative correlation ( r (86) = −0.45, p  < 0.0001), such that earlier average bedtime was associated with a higher overall score. There was a significant and negative correlation between average wake-up time and overall score ( r (86) = −0.35, p  < 0.001), such that earlier average wake-up time was associated with a higher overall score. There was also a significant and positive correlation between average bedtime and average wake-up time (r (86) = 0.68, p  < 0.0001), such that students who went to bed earlier tended to also wake up earlier.

Sleep duration, quality, and consistency in relation to academic performance

Overall, the mean duration of sleep for participants throughout the entire semester was 7 h 8 min (SD of all sleep samples = 1 h 48 min, SD of mean sleep duration per participant = 41 min). There was a significant positive correlation between mean sleep duration throughout the semester (sleep duration) and overall score ( r (86) = 0.38, p  < 0.0005), indicating that a greater amount of sleep was associated with a higher overall score (Fig. 1a ). Similarly, there was a significant positive correlation between mean sleep quality throughout the semester (Sleep Quality) and Overall Score ( r (86) = 0.44, p  < 0.00005). Sleep inconsistency was defined for each participant as the standard deviation of the participant’s daily sleep duration in minutes so that a larger standard deviation indicated greater sleep inconsistency. There was a significant negative correlation between sleep inconsistency and overall score ( r (86) = −0.36, p   <  0.001), indicating that the greater inconsistency in sleep duration was associated with a lower overall score (Fig. 1b ).

Correlations between sleep measures and overall score. a Average daily hours slept (sleep duration) vs. overall score for the semester. b Standard deviation of average daily hours of sleep (sleep inconsistency) vs. overall score in class

Timing of sleep and its relation to academic performance

To understand sleep and its potential role in memory consolidation, we examined the timing of sleep as it related to specific assessments. All Pearson correlations with three or more comparisons were corrected for multiple comparisons using false discovery rate. 47

Night before assessments

We conducted a correlation between sleep quality the night before a midterm and respective midterm scores as well as sleep duration the night before a midterm and respective scores. There were no significant correlations with sleep duration or sleep quality for all three midterms (all r s < 0.20, all p s > 0.05). Similar analyses for sleep duration and sleep quality the night before respective quizzes revealed no correlations ( r s from 0.01 to 0.26, all p s > 0.05).

Week and month leading up to assessments

To understand the effect of sleep across the time period while course content was learned for an assessment, we examined average sleep measures during the 1 month leading up to the midterms. We found a significant positive correlation between average sleep duration over the month leading up to scores on each midterm ( r s from 0.25 to 0.34, all p s < 0.02). Similar analyses for average sleep duration over one week leading up to respective quizzes were largely consistent with those of midterms, with significant correlations on 3 of 8 quizzes (rs from 0.3 to 0.4, all p s < 0.05) and marginal correlations on an additional 3 quizzes (rs from 0.25 to 0.27, all p s < 0.08).

There was a significant and positive correlation between sleep quality scores averaged over the month leading up to each midterm for all three midterms ( r s from 0.21 to 0.38, all p s < 0.05). Similar analyses for average Sleep Quality over one week leading up to respective quizzes revealed a significant correlation on 1 of 8 quizzes ( r (86) = 0.42, p  < 0.005) and marginal correlations on 3 quizzes ( r s from 0.25 to 0.27, all p s < 0.08).

Variance of assessment performance accounted for by sleep measures

In order to calculate how much of the variance on assessment performance was accounted for by the sleep measures, we conducted a stepwise regression on overall score using three regressors: sleep duration, sleep quality, and sleep inconsistency. The relative importance of each variable was calculated using the relaimpo package in R 48 to understand individual regressor’s contribution to the model, which is not always clear from the breakdown of model R 2 when regressors are correlated. We found a significant regression ( F (3,84) = 8.95, p  = .00003), with an R 2 of 0.24. Students’ predicted overall score was equal to 77.48 + 0.21 (sleep duration) + 19.59 (Sleep Quality) – 0.45 (sleep inconsistency). While sleep inconsistency was the only significant individual predictor of overall score ( p  = 0.03) in this analysis, we found that 24.44% of variance was explained by the three regressors. The relative importance of these metrics were 7.16% sleep duration, 9.68% sleep quality, and 7.6% sleep inconsistency.

Gender differences

Females had better Sleep Quality ( t (88) = 2.63, p  = 0.01), and less sleep inconsistency ( t (88) = 2.18, p  = 0.03) throughout the semester compared with males, but the two groups experienced no significant difference in sleep duration ( t (88) = 1.03, p  = 0.3). Sleep duration and sleep quality were significantly correlated in both males ( r (41) = 0.85, p  < 0.00001) and females ( r (43) = 0.64, p  < 0.00001), but this correlation was stronger in males ( Z  = −2.25, p  = 0.02) suggesting that it may be more important for males to get a long-duration sleep in order to get good quality sleep. In addition, sleep inconsistency and sleep quality were significantly negatively correlated in males ( r (41) = −0.51, p  = 0.0005) but not in females ( r (43) = 0.29, p  > 0.05), suggesting that it may be more important for males to stick to a regular daily sleep schedule in order to get good quality sleep.

Females scored higher on overall score compared with males ( t (88) = −2.48, p  = 0.01), but a one-way analysis of covariance (ANCOVA) revealed that females and males did not perform significantly different on overall score when controlling for Sleep Quality, F (1, 85) = 2.22, p  = 0.14. Sleep inconsistency and overall score were negatively correlated in males ( r (41) = −0.44, p  = 0.003) but not in females ( r (43) = −0.13, p  = 0.39), suggesting that it is important for males to stick to a regular sleep schedule in order to perform well in academic performance but less so for females. No other gender differences were detected between other sleep measures and overall score.

This study found that longer sleep duration, better sleep quality, and greater sleep consistency were associated with better academic performance. A multiple linear regression revealed that these three sleep measures accounted for 24.44% of the variance in overall grade performance. Thus, there was a substantial association between sleep and academic performance. The present results correlating overall sleep quality and duration with academic performance are well aligned with previous studies 6 , 11 , 12 , 24 , 25 on the role of sleep on cognitive performance. Similarly, this study compliments the two linked studies that found longer sleep duration during the week before final exams 47 and consistent sleep duration five days prior to a final assignment 48 enhanced students’ performance. The present study, however, significantly extends our understanding of the relation between sleep and academic performance by use of multiple objective measures of sleep throughout an entire semester and academic assessments completed along the way.

The present study also provides new insights about the timing of the relation between sleep and academic performance. Unlike a prior study, 23 we did not find that sleep duration the night before an exam was associated with better test performance. Instead we found that both longer sleep duration and better sleep quality over the full month before a midterm were more associated with better test performance. Rather than the night before a quiz or exam, it may be more important to sleep well for the duration of the time when the topics tested were taught. The implications of these findings are that, at least in the context of an academic assessment, the role of sleep is crucial during the time the content itself is learned, and simply getting good sleep the night before may not be as helpful. The outcome that better “content-relevant sleep” leads to improved performance is supported by previous controlled studies on the role of sleep in memory consolidation. 14 , 15

Consistent with some previous research 45 , 46 female students tended to experience better quality sleep and with more consistency than male students. In addition, we found that males required a longer and more regular daily sleep schedule in order to get good quality sleep. This female advantage in academic performance was eliminated once sleep patterns were statistically equated, suggesting that it may be especially important to encourage better sleep habits in male students (although such habits may be helpful for all students).

Several limitations of the present study may be noted. First, the sleep quality measures were made with proprietary algorithms. There is an evidence that the use of cardiac, respiratory, and movement information from Fitbit devices can accurately estimate sleep stages, 32 but there is no published evidence that Fitbit’s 1~10 sleep quality scores represent a valid assessment of sleep quality. Second, the relation between sleep and academic performance may be moderated by factors that can affect sleep, such as stress, anxiety, motivation, personality traits, and gender roles. Establishing a causal relation between sleep and academic performance will require experimental manipulations in randomized controlled trials, but these will be challenging to conduct in the context of real education in which students care about their grades. Third, these findings occurred for a particular student population at MIT enrolled in a particular course, and future studies will need to examine the generalizability of these findings to other types of student populations and other kinds of classes.

In sum, this study provides evidence for a strong relation between sleep and academic performance using a quantifiable and objective measures of sleep quality, duration, and consistency in the ecological context of a live classroom. Sleep quality, duration, and consistency together accounted for a substantial amount (about a quarter) of the overall variance in academic performance.

Participants

One hundred volunteers (47 females) were selected from a subset of students who volunteered among 370 students enrolled in Introduction to Solid State Chemistry at the Massachusetts Institute of Technology to participate in the study. Participants were informed of the study and gave written consent obtained in accordance with the guidelines of and approved by the MIT Committee on the Use of Humans as Experimental Subjects. Due to limitations in funding, we only had access to 100 Fitbit devices and could not enroll all students who volunteered; consequently, the first 100 participants to volunteer were selected. All participants were gifted a wearable activity tracker at the completion of the study in exchange for their participation. Seven participants were excluded from analysis because they failed to wear their activity tracker for more than 80% of the semester, three participants were excluded because they lost their wearable activity tracker, and another two participants were excluded because they completed less than 75% of the assessments in the class. Of the 88 participants who completed the study (45 females), 85 were freshmen, one was a junior and two were seniors (mean age = 18.19 years).

The Solid State Chemistry class is a single-semester class offered in the fall semester and geared toward freshmen students to satisfy MIT’s general chemistry requirement. The class consisted of weekly lectures by the professor and two weekly recitations led by 12 different teaching assistants (TAs). Each student was assigned to a specific recitation section that fit their schedule and was not allowed to attend other sections; therefore, each student had the same TA throughout the semester. Students took (1) weekly quizzes that tested knowledge on the content covered the week leading up to the quiz date, (2) three midterms that tested knowledge on the content covered in the 3–4 weeks leading up to the exam date, and (3) a final exam that tested content covered throughout the semester. Based on a one-way between subjects’ analysis of variance (ANOVA) to compare the effect of teaching assistants (TAs) on overall grade, we found no significant differences in overall grade across the TAs (F (10, 77) = 1.82, p  = 0.07. (One TA was removed from the analysis because he only had one student who was participating in this study).

Participants were asked to wear an activity tracker for the entire duration of the semester without going below 80% usage each week. If 80% or more usage was not maintained, warning emails were sent at the end of that respective week. Participants were asked to return the device if they dipped below 80% usage more than three out of the 14 weeks of the semester. The average usage rate at the end of the semester for the 88 participants who completed the study was 89.4% (SD = 5.5%). The missing data appeared to be at random and were deleted prior to data analysis. As part of a separate research question, 22 of the 88 participants joined an intense cardiovascular exercise class for which they received separate physical education credit. These students performed similarly to the other 67 participants in terms of final class grade ( t (88) = 1.57, p  = 0.12), exercise amount (total amount of moderately and very active minutes on the wearable device) (t (88) = 0.59, p  = 0.56), sleep amount ( t (88) = 0.3, p  = 0.77), and sleep quality ( t (88) = 0.14, p  = 0.9), so they were included in all of the analyses.

Participants’ activities were tracked using a Fitbit Charge HR. Data from the device were recorded as follows: heart rate every 5 min; steps taken, distance traveled, floors climbed, calories burned and activity level measurements every 15 min; resting heart rate daily; and sleep duration and quality for every instance of sleep throughout the day. Sleep quality was determined using Fitbit’s proprietary algorithm that produces a value from 0 (poor quality) to 10 (good quality).

Assessments

Nine quizzes, three midterm examinations, and one final examination were administered throughout the 14-week class to assess the students’ academic achievement. The students’ cumulative class grade was made up of 25% for all nine quizzes (lowest quiz grade was dropped from the average), 15% for each midterm exam, and 30% for the final exam for a total of 100%.

At MIT, freshmen are graded on a Pass or No Record basis in all classes taken during their first semester. Therefore, all freshmen in this class needed a C- level or better (≥50%, no grading on a curve) to pass the class. A failing grade (a D or F grade) did not go on their academic record. All upperclassmen were given letter grades; A (≥85%), B (70–84%), C (50–69%), D (45–49%), F (≤44%). Because a large portion of the class had already effectively “passed” the class before taking Quiz 9 and the final exam, we excluded these two assessments from our analyses due to concerns about students’ motivation to perform their best. We calculated for each student an overall score defined as the sum of the eight quizzes and three midterms to summarize academic performance in the course.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

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

Code availability

No custom codes were used in the analysis of this study

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Acknowledgements

This research was supported by a grant from the Horace A. Lubin Fund in the MIT Department of Materials Science and Engineering to J.C.G. and funding from MIT Integrated Learning Initiative to K.O. and J.R.K. The authors are grateful for many useful discussions with Carrie Moore and Matthew Breen at the Department of Athletics, Physical Education, and Recreation at MIT.

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K.O. and J.C.G. conceived, designed, supervised, and analyzed the project. J.K. and N.D. helped analyze the data. The manuscript was written by K.O., J.D.E.G., and J.C.G.

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Okano, K., Kaczmarzyk, J.R., Dave, N. et al. Sleep quality, duration, and consistency are associated with better academic performance in college students. npj Sci. Learn. 4 , 16 (2019). https://doi.org/10.1038/s41539-019-0055-z

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The Role of Sleep in Cardiovascular Disease

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Purpose of Review

Sleep is an important component of cardiovascular (CV) health. This review summarizes the complex relationship between sleep and CV disease (CVD). Additionally, we describe the data supporting the treatment of sleep disturbances in preventing and treating CVD.

Recent Findings

Recent guidelines recommend screening for obstructive sleep apnea in patients with atrial fibrillation. New data continues to demonstrate the importance of sleep quality and duration for CV health.

There is a complex bidirectional relationship between sleep health and CVD. Sleep disturbances have systemic effects that contribute to the development of CVD, including hypertension, coronary artery disease, heart failure, and arrhythmias. Additionally, CVD contributes to the development of sleep disturbances. However, more data are needed to support the role of screening for and treatment of sleep disorders for the prevention of CVD.

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Insomnia and Cardiovascular Health: Exploring the Link Between Sleep Disorders and Cardiac Arrhythmias

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Introduction

Sleep is increasingly recognized as a key component of cardiovascular (CV) health. Humans spend approximately 30% of their lives sleeping [ 1 ]. Additionally, CV disease (CVD) is the leading cause of morbidity and mortality in the United States [ 2 ]; therefore, it is critical to understand the relationship between sleep and CV health and disease.

In 2022, the American Heart Association (AHA) expanded their “Life’s Simple 7,” which constitute important determinants of cardiovascular health, to “Life’s Essential 8,” by adding sleep as one of the eight core components that define optimal CV health [ 3 ••]. Healthy sleep was added to the list of well recognized components of good CV health, including: diet, exercise, avoidance of nicotine, maintenance of a healthy weight, healthy blood lipid levels, healthy blood glucose levels, and normal blood pressure, upon a foundation of psychological health and social determinants of health.

Epidemiological studies have demonstrated the important role of sleep duration in CV health [ 4 ]. Ultimately, the AHA decided to include sleep duration in their “Life’s Essential 8” due to the influence of sleep on each of the other seven components of CV health.

While the AHA specifically focuses on sleep duration, there is overwhelming evidence that sleep quality and the presence of primary sleep disorders are also important mediators of CV health. A prospective study of the MESA (Multi-Ethnic Study of Atherosclerosis) cohort revealed that CV health scores that incorporated aspects of sleep health, including sleep duration, daytime sleepiness, and obstructive sleep apnea (OSA) better predicted CV disease risk than those that merely incorporated the original “Life’s Simple 7” [ 5 •].

In this review of the literature, we summarize the data demonstrating how perturbations of normal sleep are associated with increased risk of CVD. Additionally, we demonstrate the links between OSA and CVD. Finally, we illustrate the bidirectional relationship between sleep quality and CVD (Fig.  1 ).

Central Illustration- Overview of the links between sleep health and cardiovascular health. OSA obstructive sleep apnea, CAD coronary artery disease, CV cardiovascular. Created with BioRender.com. Central illustration demonstrating the links between sleep health and cardiovascular health. OSA = obstructive sleep apnea, CAD = coronary artery disease, CV = cardiovascular

Sleep Quality and Duration as a Risk Factor for CVD

Pathophysiology.

Proper sleep, defined as 4–5 sleep cycles of light, deep, and rapid eye movement (REM) sleep, is essential to maintaining cardiometabolic homeostasis [ 6 ]. Disruptions in both sleep duration and quality have been implicated as risk factors for CVD [ 7 , 8 , 9 ]. This may be due to immune dysregulation, increased sympathetic tone, chronic endocrine stress response, and endothelial dysfunction [ 10 ].

The hypothalamic–pituitary–adrenal (HPA) axis, which is tightly linked to circadian rhythms, is a principal mediator of the neuroendocrine stress system and likely plays a key role in the propagation of cardiometabolic diseases [ 10 ]. Research has demonstrated that after just a few nights of sleeping only 3–4 h, subjects experienced a significant hormonal imbalance, with morning cortisol levels decreasing by approximately 30% and afternoon levels increasing by around 40% [ 11 , 12 ]. This observation was noted in those undergoing acute and chronic sleep restriction, defined as 3 or 4 h in bed, as well as sleep fragmentation, defined as being woken up multiple times overnight [ 13 , 14 , 15 ]. Ultimately, this stress response leads to increased heart rate, decreased heart rate variability, increased blood pressure, and increased secretion of catecholamines, all of which are risk factors for or associated with coronary artery disease (CAD) [ 16 , 17 , 18 ].

Several analyses demonstrated an association between sleep restriction and both increased heart rate and decreased heart rate variability, suggesting a decrease in cardiac parasympathetic and/or increase in sympathetic tone [ 19 , 20 , 21 , 22 , 23 ]. One cross-sectional study of 30 young males during university final exams demonstrated that sleep deprivation, defined as sleep duration less than 80% of baseline over 4 weeks, was associated with increased plasma norepinephrine levels (315 to 410 pg/ml, p < 0.05) [ 24 ]. Autonomic dysregulation leads to a perpetuation of sleep issues like insomnia and fragmented sleep, as well as obesity, insulin resistance, and ultimately, increased risk for CAD [ 10 , 25 ].

Chronic inflammation is likely a mediating factor in the connection between sleep quality and the development of CAD. Inflammation is a key factor in the development of CAD [ 26 ]. The physiologic circadian rhythm directly regulates immune cells and inflammatory cytokines, including tumor necrosis factor-α (TNF-α), and interleukins (IL): IL-1, IL-2, IL-6, and IL-10. Several of these inflammatory markers have been associated with sleep duration and have thus been implicated in CAD mediated by poor sleep [ 27 , 28 ]. Studies on the impact of sleep duration and TNF-α have shown that sleep restriction generally increases TNF-α levels [ 29 , 30 , 31 ]. The Cleveland Family Study, a population level evaluation, showed that each hour less of sleep on polysomnography was associated with an 8% increase in TNF-α. However, other studies have shown that sleep deprivation did not consistently increase TNF-α levels [ 32 , 33 ]. Sleep deprivation studies have also linked restricted sleep with increased inflammation through increased IL-6 levels [ 34 , 35 , 36 ].

High-sensitivity C-reactive protein (hs-CRP), an acute phase reactant that plays a critical role in in opsonizing low-density lipoprotein cholesterol by macrophages in atherosclerotic plaque, has been linked with sleep duration [ 28 , 37 ]. Epidemiological studies suggest that hs-CRP is a predictor of CVD events [ 38 , 39 ]. Several studies have demonstrated an association between decreased sleep and increased hs-CRP [ 40 , 41 ]. Additionally, large epidemiological studies including The Nurses’ Health Study, The Cleveland Family Study, Whitehall Study, and Study of Women’s Health Across the Nation, revealed significant associations between increased sleep duration and elevated hs-CRP levels, especially in women. This association persisted even after adjusting for demographic, socioeconomic, and health risk factors [ 35 , 42 , 43 , 44 ]. A meta-analysis of 72 studies, showed that sleep disturbances and longer sleep duration are associated with higher levels of hs-CRP (ES 0.12: 95% CI 0.05 – 0.19; and ES 0.17: 95% CI 0.01 – 0.34, respectively) and IL-6 (ES 0.20: 95% CI 0.08 – 0.31; and ES 0.11: 95% CI 0.02 – 0.20, respectively). However, short sleep duration was not associated with increased inflammatory markers [ 27 ].

Elevated fibrinogen levels have also been linked with CVD. Among 3,471 participants in the PESA (Progression of Early Subclinical Atherosclerosis) cohort study, lower fibrinogen levels were associated with regression of subclinical atherosclerosis [ 45 ]. Multiple large cohort studies, including one analysis of 3,942 post-menopausal women as part of the Women’s Health Initiative, revealed an association between prolonged sleep and elevated fibrinogen levels [ 36 , 46 ]. This study also implicated fibrinogen as a mediating factor between prolonged sleep duration and CVD.

Lastly, endothelial dysfunction is an independent predictor of CVD risk [ 10 , 47 ]. Randomized studies have shown significant impairment in both arterial and venous endothelial function after several days of sleep restriction [ 48 ]. Total sleep deficit also hindered arterial endothelial and microvascular function in healthy subjects [ 49 , 50 ].

Sleep Duration and CV Health

Insomnia and sleep restriction are linked to poor CVD outcomes [ 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. A prospective Dutch cohort study of 20,432 men without CAD who slept less than or equal to 6 h per night and had poor sleep quality had a 79% higher risk of CAD (HR: 1.79 [1.24–2.58]) after adjusting for risk factors compared to those with > 7 h of sleep per night (Table  1 ) [ 58 ]. Similarly, an analysis of a Chinese cohort of 60,586 subjects showed that both short sleep duration and poor sleep quality were associated with an increased risk of CAD (HR 1.13, 95% CI: 1.04–1.23; and HR: 1.40, 95% CI: 1.25–1.56, respectively) [ 59 ].

While decreased sleep is associated with CVD, accumulating evidence suggests that increased sleep duration is also linked to poor CV health. A meta-analysis of 15 studies demonstrated that both shorter sleep duration (usually defined as ≤ 6 h per night) and longer sleep duration (usually > 8 h per night) were associated with significantly increased risk of CAD and stroke [ 60 •]. Subsequently, a large cohort study of 392,164 adults followed for 18 years found that those who slept less than 4 h/night and greater than 8 h/night had a 34% and 35% increased risk of dying from CAD, respectively, when compared with those that slept 6–8 h/night. A statistically significant U-shaped association between sleep duration and CVD mortality was only observed in female subjects and those aged 65 years and above [ 61 ]. A meta-analysis of 15 studies showed that both short and long sleep duration were associated with increased CVD mortality (RR 1.25, 95% CI 1.06–1.47 and 1.26 95% CI 1.11–1.42, respectively) [ 4 ]. Moreover, when stratified by sex, the negative effects of sleep duration on CVD mortality were only observed in women. Consistent with these findings, others have noted that the extremes of sleep duration increase the risk of CV death in patients with prior myocardial infarctions (MI) and are associated with prevalence of subclinical atherosclerosis as evidenced by coronary artery calcium scores (CAC) [ 8 , 62 , 63 ].

While the U-shaped relationship between sleep duration and CVD is mirrored by similar trends in inflammatory markers, the underlying mechanisms are not completely understood. Possible rationales include the effects of confounding factors such as depressive symptoms, socio-economic status, unemployment, and limited physical activity associated with longer sleep durations [ 64 , 65 ].

Disparities in Sleep Health

Many environmental factors impact sleep health, including exposure to stressors, tobacco, alcohol, pollutants, and allergens [ 66 ]. Therefore, certain communities may be more prone to poor sleep than others. Several studies have investigated racial and ethnic differences in sleep health. For example, an analysis of data from the National Health Interview Survey of 155,203 participants revealed that compared to White participants, Filipino individuals were less likely to get adequate sleep (> 7 h) [ 67 ]. Additionally, a retrospective analysis of a large United States cohort revealed that relative to White adults, Black adults were more likely to have short sleep duration, and that there were significant interactions with income, sex, and geographic location [ 68 ]. In addition to racial and ethnic disparities in sleep health, there are sex disparities in sleep. A meta-analysis of 31 studies including 1,265,015 participants revealed that women were more likely than men to experience insomnia [ 69 ]. Additionally, a randomized controlled crossover study of 4 h versus 8 to 9 h of sleep, short sleep was associated with increases in both daytime and nighttime BP, predominantly in women [ 70 ]. More studies are needed to determine how these differences in sleep health translate to disparities in CV health. This is especially important as sleep health seems to be deteriorating on a population level [ 71 ].

Confounding and Mediating Factors

While sleep health has been linked with cardiovascular health, there are several factors that may confound or mediate this relationship. Sleep disturbances frequently occur in conjunction with numerous psychiatric conditions, including major depressive disorder and acute stress disorder [ 72 ]. There is a bidirectional relationship between sleep health and mental health [ 73 ]. Thus, mental health may act as an important mediating factor or confounding variable when analyzing the relationship between sleep health and CV health. Additionally, there are complex multidirectional relationships between obesity, mental health, sleep health, and CV health [ 74 , 75 , 76 ], which could further confound or mediate the relationship between sleep health and CV health. Therefore, it is difficult to determine how much of the link between sleep and CV health is primarily due to the effects of sleep quality and duration versus due to the complex interplay among many interrelated factors.

Sleep Quality and the Prevention of CVD

While there is a plethora of evidence that poor sleep health is associated with CVD, there are significantly less data supporting the role of addressing sleep health for the primary prevention of CVD. A prospective analysis of the MESA Sleep Study revealed that participants with an increased CV health score, which included increased multidimensional sleep health, had lower incident CVD risk [ 5 •]. Additionally, a recent study of 6,251 participants concluded that low delta wave entropy, a marker of poor sleep quality, was associated with increased risk of CVD and CVD mortality [ 77 ]. This suggests that there may be a role for addressing sleep health for the primary prevention of CVD. Ultimately, the AHA determined that despite the paucity of evidence directly indicating that improved sleep duration reduces CVD incidence, there is enough evidence supporting the links between sleep duration and cardiometabolic health and health outcomes to include sleep duration in the formal definition of CV health [ 3 ••]. Notably, the AHA did not directly include sleep quality as part of this definition, though this may change in the future as more data becomes available.

OSA as a Risk Factor for CVD

Acute physiological effects of osa.

Obstructive sleep apnea (OSA) is characterized by repetitive upper airway closure during sleep, resulting in cycles of apnea and hypopnea associated with oxygen desaturations [ 78 ••]. These repetitive cycles of apnea and hypopnea have many direct physiologic consequences. For example, the intermittent hypoxia and reoxygenation results in oxidative stress through the production of reactive oxygen species, resulting in systemic inflammation and endothelial dysfunction [ 79 ]. Several inflammatory markers, including cytokine IL-6 and hs-CRP have been found to be elevated in patients with OSA compared with obese controls, with improvement after treatment with continuous positive airway pressure [ 79 , 80 ]. Recurrent arousals, along with intermittent hypoxia, are thought to result in increased sympathetic activation [ 79 ]. Additionally, inspiration against a closed upper airway results in large intrathoracic pressure swings, which contributes directly to shear stress on the aorta and other intrathoracic vessels [ 79 ]. Ultimately, intermittent hypoxia, intrathoracic pressure changes, and sympathetic activation have many implications for CVD, including links to hypertension, arrhythmias, heart failure (HF), and CAD.

OSA as a Risk Factor for Hypertension

Hypertension and OSA frequently co-occur in the same patients. More than 30% of patients with hypertension have concomitant OSA [ 81 ]. A prospective study of the Wisconsin Sleep Cohort of 709 participants revealed a dose–response association between apnea–hypopnea index (AHI) and the presence of hypertension [ 82 ]. There is a particularly strong association between resistant hypertension, defined as suboptimal blood pressure control despite the use of at least three antihypertensives including a diuretic, and OSA. A recent meta-analysis of 7 studies including 2,541 patients demonstrated that patients with OSA were at more than three times increased risk of resistant hypertension (OR 3.34 [2.44, 4.58]) even when adjusting for associated risk factors, including obesity, age, and smoking status [ 83 •].

Unfortunately, despite strong evidence that OSA is associated with hypertension, the impact of OSA treatment on blood pressure (BP) has been relatively modest. A randomized controlled trial (RCT) of patients with OSA without daytime sleepiness randomized to CPAP or no CPAP demonstrated no difference in incidence of hypertension or CVD [ 84 ]. Several studies have demonstrated a reduction in systolic BP of 3–5 mm Hg [ 85 , 86 ]. Interestingly, one meta-analysis revealed that reduction in BP was only seen in studies that had > 3 month follow-up, suggesting that perhaps the benefits of continuous positive airway pressure (CPAP) are more chronic and require longer follow-up time to appreciate improvements in hypertension [ 85 ]. Finally, the CRESCENT (Cardiosleep Research Program on Obstructive Sleep Apnoea, Blood Pressure Control and Maladaptive Myocardial Remodeling—Non-inferiority Trial) study, a recent RCT of patients with moderate to severe OSA and hypertension found that mandibular advancement devices were non-inferior to CPAP in reduction in BP, with a reduction in mean arterial pressure of 2.5 mmHg in 6 months [ 87 ]. As of 2021, the AHA recommends screening for OSA in patients with resistant or poorly controlled hypertension [ 78 ••]. Screening can be completed quickly, easily, and reliable with the STOP-BANG questionnaire [ 88 ].

OSA as a Risk Factor for Arrhythmias

OSA contributes to rhythm disturbances at the level of the sinus node, atria, and ventricles [ 89 ]. Atrial fibrillation (AF) is the most common arrhythmia associated with OSA, with a prevalence of approximately 35% [ 90 •]. Animal models suggest that this is likely a result of atrial oxidative stress [ 91 ]. Additionally, increased vagal tone during apneic events results in a shortened effective refractory period, which promotes atrial fibrillation in a porcine model [ 91 ]. A meta-analysis of 16 studies demonstrated increased likelihood of developing AF with increased AHI [ 90 •]. A separate meta-analysis of nine studies including 14,812 patients concluded that CPAP reduced the risk of AF recurrence or progression by 63% in patients with OSA compared to patients with OSA not on CPAP [ 92 ]. Screening for OSA is recommended in patients with recurrent AF after cardioversion or ablation [ 78 ••], though two RCTs concluded that there was no evidence that CPAP treatment of OSA after cardioversion [ 93 ] or ablation [ 94 ] resulted in reduced AF recurrence. The 2023 American College of Cardiology/AHA/American College of Chest Physicians/Heart Rhythm Society Guidelines for the Diagnosis and Management of AF provide a grade 2b recommendation of screening for OSA in patients with AF, though they note that the role of treatment of OSA to maintain sinus rhythm is uncertain [ 95 ••].

In addition to atrial arrhythmias, patients with OSA are prone to sick sinus syndrome, sino-atrial block, and tachycardia-bradycardia syndrome [ 96 ]. Among patients with OSA, bradycardia was present in 25% during the daytime and 70% during the night [ 97 ]. This has significant clinical implications, as the European Multicenter Polysomnographic Study showed an excessively high prevalence of undiagnosed OSA (59%) in patients who required pacing [ 98 ]. There are insufficient data to assess whether treatment of the underlying OSA would have obviated the need for pacing in these patients.

Finally, patients with OSA are predisposed to ventricular arrhythmias. This is thought to be related to the imbalance of sympathetic and parasympathetic tone [ 96 ]. Patients with OSA are more likely to experience sudden cardiac death overnight, which is a stark contrast from the general population, which has a nadir from midnight to 6 a.m. [ 99 ], suggesting a role of OSA in the development of ventricular arrhythmias.

OSA and CAD

OSA is thought to be a risk factor for the development of CAD due to oxidative stress and systemic inflammation. Interestingly, OSA may also have protective effects against the development of CAD as cycles of hypoxia could promote the generation of increased coronary collateral blood flow. A recent study of the UK Biobank suggests a gene-environment interaction mediating the risk of CAD in patients with OSA [ 100 ]. This study suggested involvement of various pathways including vascular endothelial growth factor and TNF in the gene-by-environment interaction in the development of CAD in patients with OSA.

One study of 124 participants undergoing coronary artery computed tomography angiography for clinical indications revealed that OSA with an AHI > 14.9 was a predictor of a high CAC score (> 400 Agatston Units) with a sensitivity of 62% and specificity of 80% [ 101 ]. Prior observational studies have shown increased CAD events in patients with OSA [ 102 , 103 , 104 ].

There is controversy whether treatment of OSA reduces the risk of CAD. The Sleep Apnea Cardiovascular Endpoints (SAVE) trial, a RCT of 2,717 patients with moderate-to-severe OSA with CAD or cerebrovascular disease with a mean follow up of 3.7 years, demonstrated no benefit of CPAP in reducing CVD events [ 105 ]. Additionally, a separate RCT of patients with OSA and newly revascularized CAD showed no significant difference in rates of repeat revascularization, MI, stroke, or CVD mortality in those who did versus did not receive treatment with CPAP [ 106 ]. Further analysis of the same study population found that those with CPAP use for > 4 h per day had significant risk reduction in repeat revascularization, MI, stroke, or cardiovascular mortality during a median 4.7-year follow up (HR 0.17, 95% CI 0.03–0.81; p = 0.03) [ 107 ]. Ultimately, more data is needed to better understand the importance of CPAP on the development and progression of CAD in patients with OSA.

OSA is quite common among HF patients, with 48% of HF with reduced ejection fraction (HFrEF) and 36% of HF with preserved ejection fraction (HFpEF) patients having an AHI of at least 15 per hour in a German registry [ 108 ]. In this registry, OSA comprises 69% of these cases in HFrEF patients, and 81% in HFpEF patients, with central sleep apnea (CSA) comprising the remaining cases.

There are several mechanisms by which OSA causes adverse hemodynamic consequences for HF patients. An occluded airway reduces intrathoracic pressure with inspiration, increasing venous return and right ventricular distension, while reducing left ventricular (LV) filling, increasing LV transmural pressure, and increasing afterload [ 109 , 110 ]. Afterload and myocardial oxygen demand also increase due to the sympathetic stimulus and hypertension induced by recurrent hypoxia, which can result in LV remodeling and hypertrophy over time [ 111 , 112 ]. There is evidence of a bidirectional relationship, as fluid accumulation in the neck is thought to be a contributor to the development of OSA in HF patients [ 113 ].

OSA has been shown to be a risk factor for mortality in patients with HF, and the mortality rate for patients with HF and sleep-disordered breathing (SDB) in the United States has been rising over the last decade [ 114 ]. A small RCT of 24 patients with OSA and an ejection fraction (EF) less than 45% tested the addition of CPAP to optimal medical therapy, and after one month, showed a significant improvement in mean systolic BP (-10 mmHg, p = 0.02), reduction in LV end-systolic diameter (-2.8 mm, p = 0.009), and recovery of LVEF (+ 8.8%, p < 0.001) as assessed by echocardiography [ 115 ]. While there are small studies testing intermediate outcomes, there are no RCTs to date assessing CPAP therapy in HF patients with OSA [ 116 ].

Three major RCTs tested positive airway pressure for the treatment of CSA in HF patients, and neither showed a mortality benefit. The Canadian CPAP for Patients with CSA and HF (CANPAP) trial, which randomized 258 patients with both CSA and HFrEF on optimal medical therapy for the time period, with an average EF of 24.5%, to CPAP and no CPAP [ 117 ]. While there were small but statistically significant increases in EF and the six-minute walk test, there were no differences in hospitalizations, quality of life, death, or heart transplantation, and the trial was stopped prematurely. The Treatment of Predominant CSA by Adaptive Servo Ventilation in Patients With Heart Failure (SERVE-HF) trial was an RCT that randomized 1325 patients with an LVEF of 45% or less to adaptive servo-ventilation, a non-invasive ventilatory therapy that delivers dynamically adjusted air pressure, compared to medical therapy alone [ 118 ]. The composite primary endpoint of all-cause mortality, lifesaving CV intervention, or unplanned HF hospitalization was not significant; however, adaptive servo-ventilation (ASV) was associated with a significant increase in all-cause and CVD mortality. Finally, the ASV for SDB in Patients with HFrEF (ADVENT-HF) trial, an RCT that randomized patients with HFrEF and SDB to ASV versus standard care demonstrated that while ASV was safe and effective for treatment of SDB, it did not result in a reduction in all cause mortality or a composite of CV outcomes [ 119 ].

OSA and Metabolic Syndrome

OSA has long been investigated as a potential independent contributor to the CVD risk associated with the metabolic syndrome [ 120 ]. Patients with OSA have significantly higher BP, serum glucose, triglycerides, cholesterol, and low-density lipoprotein cholesterol [ 121 ]. Sleep-disordered breathing was independently associated with glucose intolerance, insulin resistance, and diabetes in population based studies [ 122 , 123 , 124 ]. Additionally, treatment of OSA is associated with improvement in cardiometabolic and inflammatory parameters, including reduced BP, total cholesterol, apolipoprotein B, insulin resistance index, malondialdehyde, and TNF-α [ 125 ]. Animal models and clinical studies provide evidence that OSA contributes to the metabolic syndrome via metabolic, sympathetic, and inflammatory pathways [ 126 ].

Impact of Treatment of OSA on CVD Outcomes

There are multiple device, lifestyle, and procedural interventions that have been shown to successfully treat OSA, but there is limited evidence to support an improvement in CVD outcomes [ 78 ••, 127 ]. CPAP is the mainstay of therapy for OSA, and it is associated with a large improvement in the AHI, sleepiness, quality of life, and cognitive measures, and it is associated with a small reduction in systolic blood pressure [ 128 , 129 , 130 ]. As discussed above, the CANPAP and SAVE trials did not demonstrate a reduction in cardiovascular events or mortality with CPAP. Mandibular advancement devices are oral appliances that can reduce OSA symptom severity, reduce systolic BP, and improve quality of life, but they are not as efficacious at reducing the AHI compared to CPAP [ 95 ••, 131 ].

Guidelines support weight loss to a body mass index (BMI) less than 25 in obese patients, in addition to other lifestyle interventions including exercise, and positional therapy [ 132 ]. The Sleep Action for Health in Diabetes (AHEAD) compared an intensive lifestyle intervention to routine education in obese diabetics with OSA, which resulted in a 10.2 kg weight loss (P < 0.001) and an improvement in the AHI by 9.7 events per hour (P < 0.001) [ 133 ]. While very few of these patients were receiving CPAP therapy, the positive effect of weight loss on OSA severity among patients on CPAP was shown in a subsequent RCT [ 134 ].

Pharmacologic or surgically supported weight loss can also improve outcomes in OSA. The Satiety and Clinical Adiposity Liraglutide Evidence (SCALE) Sleep Apnea RCT tested liraglutide 3.0 in a randomized, double-blind trial of non-diabetics and showed a statistically significant improvement in weight and AHI [ 135 ]. Another RCT compared traditional weight loss to bariatric surgery in 60 obese patients with OSA, and despite a weight loss of 27.8 kg in the surgery group (compared to 5.1 kg with lifestyle intervention, P < 0.001), the improvement in the AHI was not statistically significant [ 136 ]. This suggests that the relationship between OSA severity and obesity is non-linear, and that there are other factors at play, such as the anatomy of the upper airway. However, as obesity is associated with poor cardiovascular health, weight loss is likely helpful for both OSA and CVD outcomes [ 137 ].

The main surgical procedures used in management of OSA include uvulopalatopharyngoplasty and other soft tissue reduction procedures, maxillomandibular advancement, and hypoglossal nerve stimulation [ 127 ]. However, these are invasive procedures and there is limited evidence that they improve CVD outcomes.

CVD as a Risk Factor for Poor Sleep

Finally, while poor sleep is associated with CVD, CVD is also associated with poor sleep quality. Patients with HF are prone to the development of CSA due to the effect of pulmonary venous congestion on vagal irritation receptors, resulting in reflex hyperventilation and dysregulation in the ventilatory control system due to high hypercapnic responsiveness [ 138 , 139 , 140 ]. This then leads to oscillating breathing patterns with periods of central apnea and/or hypopnea followed by periods of hyperventilation. This waxing-waning breathing pattern is commonly referred to as “Cheyne-Stokes respiration” (CSR) [ 141 , 142 ]. Prior studies have reported a prevalence of 33–40% among patients with HF [ 143 , 144 ]. CSA and CSR cause disrupted sleep with frequent arousals and overall reduced time spent in REM and slow wave sleep [ 142 ]. This manifests as symptoms of daytime sleepiness, paroxysmal nocturnal dyspnea, and fatigue [ 141 ]. HF patients with CSA have higher mortality and morbidity compared to those without CSA. CSA was found to be an independent risk factor for overall mortality, with studies showing the cumulative survival and transplant free progression was significantly lower in HF patients with CSA compared to HF patients without CSA [ 145 , 146 ]. There was also a higher predisposition for fatal arrhythmias, possibly via sympathetic nerve activation that can be exacerbated by the frequent arousals during the periodic breathing patterns in CSA [ 141 , 142 ].

Additionally, CVD is associated with poor sleep health indirectly through impacts on mental health. Depression, which is significantly more common in patients with CVD, is associated with poor sleep. The relationship between depression and CVD is complex and bidirectional, with biological, environmental, and behavioral links [ 147 ].

Sleep is increasingly recognized as an important component of CV health. There is a complex bidirectional relationship between sleep and CVD. Perturbations to normal sleep as well as primary sleep disorders have systemic effects, including changes in autonomic tone and inflammation, which contribute to the development of a wide range of CV disorders, including hypertension, rhythm disturbances, metabolic syndrome, and coronary artery disease. There is also an interplay with sleep quality and mental health, which has implications for cardiovascular disease. Finally, CV diseases can also impact sleep quality, both directly through the development of CSA, and indirectly mediated by effects on mental health. Recent guidelines are beginning to incorporate screening and treatment of sleep disorders for the treatment of cardiovascular disease. More data is necessary to determine the role of screening and addressing sleep disturbances for the prevention of cardiovascular disease.

Data Availability

No datasets were generated or analysed during the current study.

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V.J., G.G., S.P., C.P., and L.S. wrote the main manuscript. V.J. prepared Table  1 and Fig.  1 . All authors reviewed the manuscript and made critical revisions to the work.

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Leandro Slipczuk is supported by institutional grants from Amgen and Philips. Salim Virani is supported by research grants from the NIH, UK NIHR, US Department of Veterans Affairs and research endowments from the Tahir and Jooma Family and Asharia Family. Additionally, Dr. Virani serves as a section editor for Current Atherosclerosis Reports. Michael D. Shapiro is supported by institutional grants from Amgen, Boehringer Ingelheim, 89Bio, Esperion, Genentech, Novartis, Ionis, Merck, and New Amsterdam. He has participated in Scientific Advisory Boards with Amgen, Agepha, Ionis, Novartis, New Amsterdam, and Merck. He has served as a consultant for Ionis, Novartis, Regeneron, Aidoc, Shanghai Pharma Biotherapeutics, Kaneka, and Novo Nordisk. Virend K Somers is supported by NIH HL65176 and NIH HL160619. He is a consultant for Jazz Pharma, Axsome, Know Labs, Lilly and ApniMed and serves on the Sleep Number Scientific Advisory Board. The remaining authors have nothing to disclose.

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Jaspan, V.N., Greenberg, G.S., Parihar, S. et al. The Role of Sleep in Cardiovascular Disease. Curr Atheroscler Rep (2024). https://doi.org/10.1007/s11883-024-01207-5

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Effect of sleep on oral health: A scoping review

Affiliations.

• 1 Australian Research Centre for Population Oral Health, Adelaide Dental School, University of Adelaide, Adelaide, South Australia, Australia.
• 2 Australian Research Centre for Population Oral Health, Adelaide Dental School, University of Adelaide, Adelaide, South Australia, Australia. Electronic address: [email protected].
• PMID: 38781809
• DOI: 10.1016/j.smrv.2024.101939

Sleep is a vital biological process that facilitates numerous vital functions integral to mental and physical restoration of the body. Sleep deprivation or poor sleep quality not only affects physical health but may also affect oral health. This scoping review aims to collate existing evidence related to the impact of sleep duration and/or quality on oral health. A systematic search strategy using PubMed, Embase, Scopus and CINAHL databases was performed to identify studies that assessed the association between sleep quality or duration and oral health or hygiene. Two researchers independently screened and extracted the data. Eligible studies were critically appraised using the NIH quality assessment tool for observational cohort and cross-sectional studies checklist. The search identified 18,398 studies, from which 14 fulfilled the inclusion criteria. Of the 14 papers, four papers were associated with effect of sleep on caries, 8 papers described the effect of sleep on gingival and periodontal health, and two papers described the effect of sleep on general oral health and oral disease symptoms. This review found a direct link between sleep and dental decay in children, and short sleep duration was associated with an increased risk of periodontitis adults.

Keywords: Dental decay; Oral health; Periodontal disease; Sleep.

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Research on Sleep Quality and the Factors Affecting the Sleep Quality of the Nursing Students

1 Uludag University Faculty of Health Sciences, Bursa, Turkey

F. TANRIKULU

2 Sakarya University Faculty of Health Sciences, Sakarya, Turkey

Purpose: This research has been conducted in order to examine the quality of sleep and the factors affecting the sleep quality.Material/Methods: The sample of this descriptive research is comprised of 223 volunteer students studying at Uludağ University Faculty of Health Sciences Department of Nursing. Research datas have been collected through personal features survey and Pittsburg Sleep Quality Index(PSQI). Results: The average result derived from the sample is 6.52±3.17. To briefly explain the average of the component scores: subjective sleep quality 1.29±0.76, sleep latency 1,55±0.94, sleep duration 0.78±0.99, habitual sleep activity 0.47±0.90, sleep disturbances 0.99±0.09, use of sleeping medication 0.12±0.48, daytime dysfunction 1.29±0.90. It has been observed that there is a meaningful discrepancies between average PSQI results and smoking habits of the students, total daily sleeping hours, efficient waking up times, average daily coffee consumption(p<0.05). According to the analyses there is no meaningful discrepancies between the age,gender, where the students live,snoozing during the morning classes, the existence of chronic diseases and daily average tea consumption.(p>0.05)Conclusions: According to the findings in the light of this research; nursing students have low sleep quality.

Introduction

Sleep, which is directly related to health and quality of life, is a basic need for a human being to continue his bio-psycho-social and cultural functions [ 1 ]. Sleep affects the quality of life and health,which is also perceived as an important variable[ 2 , 3 ]. Feeling energetic and fit after sleeping is descriped as the sleep quality [ 4 ]. The fact that, nowadays the complaints about sleep disorder being prevalent, low sleep quality being an indicator of many medical diseases and there is strong relationship between physical ,psychological wellness and sleep; sleep quality is an important concept in the clinic practices and related researches on sleep [ 5 ].

Sleeping disorders is a common health problem among adolescants and young adults [ 6 ]. There is a general belief that university students do not sleep enough [ 7 ]. It has been reported that the the amount and the quality of the sleep of university students has been changed in past few decades and the sleep disorders has been inclined [ 8 ]. In the related researches is found that sleeping disorder among university students in various frequencies and amounts [ 9 , 10 , 11 ]. Low quality of sleep harms not only the academic success but also behavioral and emotional problems [ 12 ], negative emotional status, increase in alcohol and smoking habits[ 13 , 14 ]. In another research, it has been found that, there is a link between sleep quality and pschological wellbeing; more psychological diseases are observed among university students with low sleep quality [ 15 ]. Additionally it is recorded in the medical literature that, sleep quality is affected from the external factors such as gender, academic success, academic background, general health, socio-economic status and the stress level of the person [ 1 , 4 , 7 , 16 ].

Nursing students may have sleep issues due to their program being though, time and effort-requiring [ 3 , 11 ]. Because of this matter, students who cannot sleep enough may have various physical,social, psychological problems. Therefore, it is much more important to indicate the sleep quality of the students and the factors affecting. There is a demand for this kind of research since there is only limited amount of related research

Aim of Study

This research is conducted in order to examine the sleep quality of the Nursing students and the factors affecting it.

Material and Method

The research sample of this descriptive and cross-sectional research is derived from the population of students studying at Uludag University Faculty of Health Sciences Department of Nursing in the Spring Semester of 2016-2017 academic year (N=450). The sample of the research is 223 volunteer students.

In the research data collection process, personal features survey and Pittsburg Sleep Quality Index(PSQI) has been used. Survey,which is prepared by the researchers scanning the related medical literature, comprises of 11 survey questions. These questions are aimed to indicate the introductory information of the students and the varibles affecting the sleep quality(age, gender, semester, aree of residence, existence of chronic diseases, caffeine consumption level, smoking habits).

Pittsburg Sleep Quality Index(PSQI) usef for examination of the sleep quality of the students; is a scale which assesses the sleep quality and the sleeping disorder in the last one month. Pittsburg Sleep Quality Index (PSQI) is devised by the Buysee et al. [ 17 ] is adapted to Turkish by the Agargun et al. [ 18 ] and internal consistency coefficient is calculated as 0.80. In the examination process of PSQI,19 issues are scored. PSQI has 7 internal components such as subjective sleep quality, duration of sleep, habitual sleeping activity, sleep disturbance, sleep delay, use of sleeping drugs and daytime dysfunctions. Each component is scored between 0-3. Total score varies between 0-21, total PSQI score being <5 shows high sleep quality, >5 indicates low sleep quality [ 18 ].

Statistical Analysis

In the data assessment process; frequency, percentage, arithmetic average and Cronbach’s alpha is measured. The total score average of the sample was calculated and the normality test was applied to determine the normal distribution of the sample scores According to this analysis, it is observed that the sample scores does not comply with the normal distribution(Kolmogorov-Smirnov Z=0.143, p<0.05);nonparametric tests such as Mann-Whitney U and Kruskall Wallis were used to examine the difference between the independent variables and sample averages.Scores are provided as average±standard deviation and p<0.05 is considered as statistically meaningful results

Ethical Concerns

For the use of the assessment, written permissions are taken via e-mail. For the purpose of the conduct of the survey, written approval from the research commission of the related institution is taken(Decision no: 2017/7). Before application and the approval was obtained from them, students were informed about the research and data collection tools.

According to the research, average age of the stundets is 20.03±1,73, 68,6% of them are women. 50.2% of the students are in I. year, 19.7% are in II. year, 18.4% in III. year,%11.7 of them are in IV. year. 17% of the students have smoking habits, 56.5% of the sleep 6-7 hours per day. 26% of the students consumes 4-7 cups of tea per day, 19.3% of them uses 2-3 cups of coffee, 46.6% of them wake up energetic after sleep, 19.9% of them have no chronic disease, 41.3% of them snooze during morning lectures.

The total PSQI average of the students is calculated as 6.52±3.17 and the ratio of the students with sleep quality average higher than 5 is 56.1%.(Table ​ 56.1%.(Table1, 1 , Table ​ Table2) 2 ) The students internal component score averages are given below: subjective sleep quality 1.29±0.76, sleep latency 1,55±0,94, sleep duration 0.78±0.99, habitual sleep activity 0.47±0.9, sleep disturbances 0.99±0.09, sleeping drug use 0.12±0.48 and daytime dysfunctions 1.29±0.9(Table 1 )

PSQI total and internal component score averages of the sample

PSQIscore averages of the sample

Although total PSQI score average being above 5, only 56.1% of the students' PSQI averages were above 5.According to this result nearly half of the students’ sleep quality can be considered as low sleep quality (Table ​ (Table2 2 ).

In Table ​ Table3 3 personal features of the nursing stdents, the relationship between these features and PSQI scores. According to the table,a statistically meaningful relationship between PSQI score averages amd smoking habit, total daily sleeping hours, waking up energetic and daily average coffee consumption(p<0.05); no meaningful relationship is found between PSQI scores and age, gender, semester level, area of residence, preexistence of chronic diseases, snoozing during morning lectures, daily average tea consumption(p>0.05)

>Table 3. Personal feature distribution of the sample students and the relationship between personal features and PSQI scores (n:223)

*Mann Whitney U Analysis

**Correlation Analysis

***Kruskal Wallis Analysis

According to the results of this research which we conducted in order examine the affecting nursing students’ sleep quality and the factors affecting; 56.1% of the students have PSQI average of 5 and lower. In the light of this research, we can infer that more than half of the students have low sleep quality.In a similar research in the United States of America, it is observed than 71% of the students have at least one sleeping disorder [ 19 ]. According to a similar research conducted by Karatay and colleagues [ 4 ] 56% of the nursing students have low sleep. According to Aysan and colleagues’ research [ 3 ] students with sleep quality scores higher than 5 comprises 59% of the sample. Similar research in the medical literature points out that university students have low quality of sleep [ 10 , 16 , 20 , 21 , 22 , 23 ]. Our research results justifies the results of researches given above. It is understood from the results of our research that low sleep quality is an important issue for the nursing students. Extraordinarly apart from our research, according to some similar researches conducted in Turkey less than half of the university students studying in Turkey have sleeping disorders [ 14 , 16 ]. We interpret that, this difference may be caused by the choice of a different sample of students.

According to the results of the study, there was a significant difference between students' sleep quality and smoking habits, total sleep hours, resting status in the morning and average daily coffee consumption (Table ​ (Table3). 3 ). It is reported that sleeping is important in terms of the health of young adults [ 3 ] and it is said that young people need sleep for an average of 9-10 hours per [ 4 , 24 ]. In this study, students who wake up well-rested and sleeping 6-7 hours per day have higher sleep quality.These findings also supports the medical literature.According to Karatay et al. [ 4 ], Sari et al. [ 14 ] and Vail-Smith and colleagues’ [ 8 ] studies,smoking students have lower sleep quality compared to non-smokers.It is known that cigarette contains nicotine which has stimulant effect and it is known that smoking before sleep especially makes it difficult to fall asleep and affects sleep quality negatively. On the other side according to Shcao et al. [ 25 caffeine containing drinks harms sleep quality. Our study also show parallelism with these findings.

According to the results of this research, it is found that there was no relation between the sleep quality and the age, sex, class level, area of residence, sleepiness in morning classes, presence of chronic diseases and average daily tea consumption (Table ​ (Table3). 3 ). Age and gender have been found to be among the factors that may affect sleep quality of individuals, though some studies have shown that some factors such as age, gender, class level and place of residence do not affect sleep quality [ 3 , 16 ]. In this study, it is interpreted that the age factor to be ineffective in sleep quality may be caused by the are in a similar age group.According to researches examining the correlation between gender and sleep quality, females have lower sleep quality than males [ 3 , 5 , 7 ]. Additionally, first year students’ sleep quality may be harmed by these factors; such as their first year curriculum being though, being deprived of family attention, adaptation efforts for a new social environment.Furthermore, considering that the environmental factor on sleep quality is also very effective, it can be assumed that the students living in dormitory stay more crowded rooms and the sleep quality is lower than the other students.Consequently, our research does not justify the medical literature.

Lund and colleagues[ 26 ] pointed out that physical and psychological problems have negative effects of sleep quality.In our study, it is observed that preexistence of chronic diseases does not effect sleep quality. In Saygili and colleagues’ research [ 16 ] students with chronic diseases have lower sleep quality. Sari and colleagues [ 14 ] showed that students confirming to have chronic illnesses have lower sleep quality but this result does not reflect a statistically meaningful relationship between sleep quality and existence of a chronic disease.It is known that chronic diseases related to the respiratory system, especially asthma, are frequently caused by sleep problems and affect sleep quality negatively [ 16 ]. The results are not consistent with the literature due to the fact that students who included in the study have declared illnesses which have ambiguous relationship with the sleep quality; since the variety of the chronic diseases are not questioned in this research.

According to the findings in the light of this research; nursing students have low sleep quality. Additionally, students who do not smoke, sleeps 6-7 hours per day and consuming beverages with caffeine less have a better quality of sleep.To raise awaeness among university students and about the concept of sleep quality and the factors affecting the sleep quality and to increase the quality of sleep quality; panel discussions,seminars and conferences focusing on the relationship between alcohol/caffeine consumption, smoking and the quality of sleep are suggested.

Acknowledgments

All authors had equal contribution