research paper on sleep

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The impact of stress on sleep: Pathogenic sleep reactivity as a vulnerability to insomnia and circadian disorders

Affiliations.

1 Sleep Disorders and Research Center, Henry Ford Hospital, Detroit, Michigan.
2 Department of Psychological Sciences, Kent State University, Kent, Ohio.
PMID: 29797753
PMCID: PMC7045300
DOI: 10.1111/jsr.12710

Sleep reactivity is the trait-like degree to which stress exposure disrupts sleep, resulting in difficulty falling and staying asleep. Individuals with highly reactive sleep systems experience drastic deterioration of sleep when stressed, whereas those with low sleep reactivity proceed largely unperturbed during stress. Research shows that genetics, familial history of insomnia, female gender and environmental stress influence how the sleep system responds to stress. Further work has identified neurobiological underpinnings for sleep reactivity involving disrupted cortical networks and dysregulation in the autonomic nervous system and hypothalamic-pituitary-adrenal axis. Sleep reactivity is most pathologically and clinically pertinent when in excess, such that high sleep reactivity predicts risk for future insomnia disorder, with early evidence suggesting high sleep reactivity corresponds to severe insomnia phenotypes (sleep onset insomnia and short sleep insomnia). High sleep reactivity is also linked to risk of shift-work disorder, depression and anxiety. Importantly, stress-related worry and rumination may exploit sensitive sleep systems, thereby augmenting the pathogenicity of sleep reactivity. With the development of cost-effective assessment of sleep reactivity, we can now identify individuals at risk of future insomnia, shift-work disorder and mental illness, thus identifying a target population for preventive intervention. Given that insomniacs with high sleep reactivity tend to present with severe insomnia phenotypes, patient sleep reactivity may inform triaging to different levels of treatment. Future research on sleep reactivity is needed to clarify its neurobiology, characterize its long-term prospective associations with insomnia and shift-work disorder phenotypes, and establish its prognostic value for mental illness and other non-sleep disorders.

Keywords: Ford insomnia response to stress test; mental health; preventive treatment.

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Conflict of interest statement

CONFLICT OF INTEREST

CLD has received research support from Merck & Co., Eisai Co., Aladdin Dreamer, Jazz, Actelion and Teva, and has served on speakers’ bureau for Merck & Co. DAK and JRA report no conflicts of interest.

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Conceptual diagram depicting moderators of the effect of stress on sleep disturbance. Negative…

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Theorized relation of sleep reactivity to other components of stress reactivity

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The role of sleep reactivity in the relationship between cognitive intrusion (i.e. rumination)…

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Depiction of the cyclical impact of sleep reactivity in sleep disturbance. Sleep reactivity…

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Research Update on Diet
Research Update on Sleep

Research Update 1

By Marie Conley Smith

I n a world full of opportunities, stressors, inequalities, and distractions, maintaining a healthy lifestyle can be challenging, and sleep is often the first habit to suffer. Good sleep hygiene is a huge commitment: it takes up about a third of the day, every day, and works best when kept on a consistent schedule. It does not help that the primary short-term symptoms of insufficient sleep can be self-medicated away with caffeine. However, the effects of sleep loss can range from inconvenient to downright dangerous; people have trouble learning and being productive, take risks more readily, and are more likely to get into accidents. These effects also last longer than it takes to get them, as recovering from each night of poor sleep takes multiple days. When it comes to sleep, every night counts. In this update, we will discuss what Stanford researchers have to say about sleep and why we need it, who is getting too little of it, and some of the latest findings that may help us sleep better.

We have not cracked the code on sleep

Despite this progress, scientists have not been able to crack the code of why sleep is critical to brain function. There is also little consensus about how sleep stages actually affect quality of sleep and how they affect us when we are awake.

Part of the challenge of cracking the code on sleep is how difficult it is to study. The gold standard of sleep study, polysomnography, developed by Dement in the 1960s, 1 is the most reliable tool for measuring many sleep characteristics and detecting sleep disorders such as obstructive sleep apnea and narcolepsy. However, it is expensive and time-consuming to run, which means that usually only a night or two is recorded. This snapshot of sleep may not reflect what normally occurs for a given person, and makes it difficult to draw conclusions about their behavior and performance in the days surrounding the sleep measurement.

The recent explosion in consumer wearable devices is a promising trend for researchers because of their potential to measure thousands of people’s sleep in their natural environments. They have not yet been widely adopted as measurement tools by scientists, however, as it is unclear if they provide the level of precision and measurement consistency required for a scientific study. Researchers at Stanford have called for these devices to be cleared by the FDA before using them to assign a diagnosis. 2 The “holy grail” would be a wearable device that could track sleep accurately while also providing performance information about the rest of the day, which would allow researchers to recognize more nuanced relationships between how people sleep and how it affects their lives.

The short- and long-term effects of insufficient sleep

We all know anecdotally what it is like to get too little sleep; it might be described with words and phrases like “tired,” “cranky,” “sluggish,” and “need caffeine.” Review of the scientific literature reveals how wide-ranging these effects can be. With too little sleep, people have a harder time learning 3 and concentrating, and are more likely to take risks. 4,5 The likelihood of getting into an auto accident increases. 6 Sleep deprivation has a bidirectional relationship with depression, 7,8 in that insomnia often both precedes and follows a depressive episode. Short sleep also interferes with other Healthy Living behaviors: people are more likely to crave sweet and fatty foods 9 and to choose foods that are calorically dense, 10 are more prone to injury during exercise, 11 and have an increased risk of obesity. 12

Sleep deprivation can even affect mundane daily activities. In 2017, then Stanford PhD candidate Tim Althoff and Professor Jamie Zeitzer of the Stanford Center for Sleep Sciences and Medicine took up the sleep measurement challenge by collaborating with Microsoft Research to examine the effects of sleep deprivation through a common daily activity: using an online search engine. 13 They paired users’ Microsoft Band sleep data with their Bing searches among users who had agreed to share their activity for study. By linking quantity and timing of sleep with typing speed during the searches, they were able to draw a number of conclusions about how sleep quality affects performance.

In this study, the researchers captured the sleep duration and search engine interactions of over 31,000 people. The researchers measured the amount of time between keystrokes as people typed their search engine entries, and used this as a measure of daily performance (that is, how well people did after a night of sleep). They were able to track the people who had multiple nights of insufficient sleep (defined as 6 hours of sleep or fewer) to see if their typing speed changed. They found that, on average, one night of insufficient sleep resulted in worse performance for three days, and two nights of insufficient sleep negatively impacted performance for six days. In other words, it took people almost an entire week to recover their performance after two consecutive nights of insufficient sleep. The implication is that the impact of sleep loss can persist for days.

Recent Stanford solutions for better sleep

Ongoing research at Stanford has led both to treatments for sleep disorders and to recommendations for best sleep practices for the public.

There are a few clinics and organizations that offer CBTI remotely in an effort to give more people access. There are apps such as SleepRate , which features content designed by Stanford researchers, Somryst , which was recently approved by the FDA, and Sleepio , which is offered by several large employers as an employee benefit. The Cleveland Sleep Clinic offers a 6-week online program called “ Go! to Sleep ,” and the U.S. Department of Veterans Affairs offers one of the same duration called “ Path to Better Sleep .” A physician should be consulted before starting any of these programs to ensure there are not any underlying disorders that need to be addressed.

Ultrashort light flash therapy Professor Jamie Zeitzer was interested in helping people who had a hard time sleeping because their circadian rhythm was not in sync with their desired sleep schedule. He discovered that ultrashort bursts of light directed into a person’s closed eyes while they were sleeping was very effective at shifting the time a person starts getting sleepy. Sleep doctors had already been using continuous light to help people reset their internal clock while they were awake; this new short-flash method shows great promise not only because of its effectiveness, but because it can be administered passively while people are sleeping. The approach involves wearing a sleep mask that emits the bright flashes and has been shown to only wake individuals who are particularly sensitive to light.

Lumos Sleep Mask

Professor Zeitzer and his team administered these ultrashort light flashes to teenagers, whose natural circadian systems have shifted so that their sleep and wake times are considerably later than children or adults. The time structure of our society, and schools in particular, does not take this into account. Professor Zeitzer administered the light flashes to see if it would help teens go to bed earlier. 20 They found that, while the teenagers were getting sleepy earlier, the light flashes alone were not enough to get the teenagers to bed earlier. With a second group of teens, they combined the light therapy with cognitive behavioral therapy (CBT) sessions. The CBT sessions served to inform the teens about sleep health and hygiene and helped them schedule their activities to allow for their desired sleep hours. After this combined therapy trial, the teens went to bed an average of 50 minutes earlier, getting an average of 43 more minutes of sleep per night. The researchers found the CBT component to be integral to behavior change – without the added education and support, the teens were not motivated enough to change their behavior and would simply push past their sleepiness.

This ultrashort light flash therapy can be used by anyone who may want to shift their sleep schedule; for example, to rebound from jet lag or to cope with a consistent graveyard shift at work. There is no evidence that other groups would require accompanying CBT like the teens, as long as they are self-motivated to change their sleep schedule. Zeitzer plans to test this technology next with older adults who wish to push their sleep time later. A company has spun out of this work, which Zeitzer advises but in which he has no financial interest, called Lumos . They are currently developing their product, and are hoping to make this intervention widely available.

Data Spotlight on: Black Americans

While most Americans have seen improvements in sleep over the past decade, Black Americans continue to sleep significantly less than other groups. This trend has been examined both by researchers and the popular press. 21,22 Researchers have found that Black Americans, in addition to getting shorter sleep, are also more likely to get poor quality sleep – spending less time in the most restorative stages of sleep 23,24 – and to develop obstructive sleep apnea. 25 Black Americans are also disproportionately affected by diseases that have been associated with poor sleep, such as obesity, diabetes, 26 and cardiovascular disease. 25

The exact reason(s) for Black Americans’ poor sleep is still unclear, though researchers have proposed potential contributing factors, largely related to the social inequality Black Americans face in the U.S.:

Experiences of discrimination : the stress of racial discrimination has been associated with spending lesstime in deep sleep and more time in light sleep among Black Americans. 24

Living environment : neighborhood quality has been linked to sleep quality, 27 and Stanford researchersfound that racial and income disparities persist in neighborhoods. 28 They found that while middle-income white families are more likely to live in resource-rich neighborhoods with other middle-income families, middle-income black families tend to live in markedly lower-income, resource-poorneighborhoods.

Work and income inequality : for example, shift work can cause irregular working hours. This leadspeople to suffer “social jetlag,”; a discrepancy in sleep hours between work and free days, 29 leading tosymptoms of sleep deprivation.

Lack of access to resources : particularly sleep-related healthcare and education.

Some of these factors are being addressed directly. Professor Girardin Jean-Louis from New York University and his team have devoted themselves to addressing the access to healthcare and education issue among local black communities in New York by tailoring online materials about obstructive sleep apnea to the culture, language, and barriers of specific communities. 30 Professor Jamie Zeitzer and his team at Stanford recently completed an initial clinical trial of a drug (suvorexant), which was found to help people who work at night get three more hours of sleep during the day. 31 Professor Zeitzer’s ultrashort light flash therapy (discussed above) may also help with shift work. These interventions could help to improve sleep for Black Americans, but they may not make up the whole picture; it could be that the underlying social inequality needs to be addressed in order to fully close the sleep gap.

Thanks to Jamie Zeitzer and Ken Smith for their insights and edits on this report.

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National Center on Sleep Disorders Research

The National Center on Sleep Disorders Research (NCSDR) supports research, technology innovation, training, health education, and other activities that advance scientific knowledge of sleep disorders and circadian biology, and that promote sleep health. The NCSDR also coordinates sleep and circadian biology research throughout the National Institutes of Health (NIH) and other Federal agencies. Located within the National Heart, Lung, and Blood Institute, the NCSDR was established by Congress as part of the NIH Revitalization Act of 1993 (42 USC Sec. 285b-7) .

Under that act, the Sleep Disorders Research Advisory Board (SDRAB) makes recommendations and assists with the development of a comprehensive NIH plan that identifies sleep and circadian research priorities. Read the 2021 NIH Sleep Research Plan .

NIH funds sleep and circadian research through investigator-initiated and Institute-initiated programs.

Learn more about funding opportunities for research on sleep and the circadian biology of sleep disorders.
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Mission - National Center on Sleep Disorders Research

The National Center on Sleep Disorders Research (NCSDR) promotes sleep and circadian scientific advances, from laboratory research to clinical practice, to improve scientific knowledge, transform health care, and advance public health and safety and the well-being of the nation.

Infographic on the National Center of Sleep Disorders Mission. See content below image for details.

The NCSDR works toward achieving its mission in the following ways:

Advancing scientific knowledge in essential research areas including chronobiology, neurophysiology, gene expression, epigenetics, and proteostatis.
Transforming health care using chronomedicine, clinical interventions, and patient-centered strategies, including measuring epidemiological risks and risk stratification.
Advancing well-being by considering the impact of social determinants of health, health disparities, and individual and community-based interventions on health outcomes.
Improving public health and safety by promoting sleep health awareness and related risk prevention at worksites, schools, housing and in transportation through technologies such as solid-state lighting.

One enduring strategy the NCSDR uses to achieve this mission is by fostering a strong and diverse workforce.

What We Do - National Center on Sleep Disorders Research

Chronobiology and Ventilatory Control

The chronobiology and ventilatory control portfolio supports basic and clinical research that explores how circadian science applies to the development, diagnosis, and treatment of heart, lung, blood disorders. The portfolio also includes research that describes neural and peripheral mechanisms of breathing control, chemoreception, and the integration of breathing with other functions (e.g., coughing and swallowing). Research in this portfolio is performed using a range of approaches, from studying genes (genomics) to conducting clinical trials. The research addresses current public health concerns, such as the role of sleep, circadian regulation, and ventilatory control in maternal morbidity and mortality, opioid use disorder, and obesity.

Contact: Aaron D. Laposky, Ph.D., [email protected]

Neurobiology of Sleep

The sleep and neurobiology portfolio supports studies on the neurobiological mechanisms of sleep and its disorders, as well as analyses of the diverse influences on these processes. Investigations include explorations of the roles of identified physiological and pathological mechanisms in sleep and the use of genetic and genomic approaches to uncover novel factors affecting sleep. The research also explores the connections between sleep disorders, health disparities, and social determinants of health. This portfolio includes computational models, animal models, and clinical investigations of the mechanisms of sleep disorders and their consequences. It also includes a variety of training grant mechanisms , particularly institutional training grants (T32) focused on sleep and circadian rhythms.

Contact: Lawrence Baizer, Ph.D., [email protected]

Prevention and Sleep Health

The prevention and sleep health portfolio supports research focused on the relationship between healthy sleep and physical health and well-being, and chronic disease prevention. Studies include investigations into modifying sleep-wake patterns and sleep behaviors to improve health across the life span, and social determinants that contribute to sleep health disparities and impact special populations. This portfolio also includes training and career development grant mechanisms, and Small Business Innovation Research and Small Business Technology Transfer (SBIR/STTR) grant mechanisms for innovative research to develop novel devices and interventions that improve healthy sleep and adherence to treatment.

Contact: Shilpy Dixit, Ph.D., [email protected]

Sleep Disorders Medicine

The sleep disorders medicine portfolio supports research on the genetic predisposition, risk factors, pathogenesis, epidemiology, diagnosis, and treatment of sleep and circadian rhythm sleep-wake disorders. This includes research examining the role of sleep in inflammation, cardiometabolic disorders, vascular contributions to cognitive impairment and dementia, and people living with Down syndrome as part of the NIH INCLUDE program. Additionally, this program supports studies using big data analytics, artificial intelligence, and clinical trials to explore sleep and circadian disorders.

Contact: Alfonso Alfini, Ph.D., [email protected]

NIH and Inter-Agency Coordination - National Center on Sleep Disorders Research

Sleep disorders, circadian biology, and sleep health relate to a multitude of biological systems, health conditions, and medical disciplines. That is why a coordinated and collaborative approach must be used to successfully advance sleep and circadian science, foster technology transfer, expand training, transform health care, and increase dissemination of health information to advance well-being and improve public health and safety.

The NCSDR serves as the point-of-contact for researchers, professional societies, non-governmental stakeholders (public, private, and nonprofit groups), and other Federal agencies interested in NIH sleep research activities.

The NCSDR director serves as the Executive Secretary of the SDRAB , an Advisory Board composed of Members and Ex Officio Members and led by a member-elected chairperson.

The NCSDR leads the NIH-wide Sleep Research Coordinating Committee , a forum to discuss and consider potential opportunities for programmatic coordination of biological and circadian rhythms research and other sleep-related research, training, and health information dissemination.

The NCSDR fosters the coordination of sleep and circadian research among other Federal agencies, including:

Bureau of the Census
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The NCSDR director and staff meet regularly with sleep and circadian researchers, professional societies, and other stakeholders to gather feedback and offer updates.

Healthcare Provider Resources - National Center on Sleep Disorders Research

NCSDR works to turn research findings into health information that benefits the public. It does this, in part, by educating healthcare professionals about the results of sleep disorders research and offering resources they can share with their patients and families.

The NHLBI Online Catalog lists all NHLBI publications and resources for healthcare professionals, including summaries of the latest sleep science and clinical care best practices. It also lists educational materials that help answer patients’ questions about sleep and sleep disorders.

Patient and Public Education Resources - National Center on Sleep Disorders Research

The NHLBI website features health information on sleep-related health topics for patients, caregivers, and the public. To find links to topics such as insomnia, sleep deprivation and deficiency, sleep apnea, narcolepsy, and sleep studies, go to the Sleep Health webpage and click on the Health Topics icon. You can also find there a link to Your Guide to Healthy Sleep , as well as information about participating in a clinical trial.

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Findings highlight insufficient sleep as a modifiable risk factor for type 2 diabetes

Chronic insufficient sleep can increase insulin resistance in otherwise healthy women, with more marked effects in postmenopausal women, according to a study funded by the National Institutes of Health. The findings, published in Diabetes Care, highlight the...

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National Center on Sleep Disorders Research [email protected]

Introduction
Conclusions
Article Information

Participants continued to live in their home environment without any prescribed diet or physical activity during the 28 consecutive days of the study. Error bars are SEs of the mean. The vertical dashed line separates the two 2-week sleep periods.

A-D, Data are in ascending order of change in sleep duration for the control group and sleep extension group. E, Data were from 74 participants. All available data were used. The line represents the line of best fit from the linear regression model. One participant in the control group and 3 participants in the sleep extension group had missing data in change in sleep duration (ie, missing mean data in at least 1 of 2 study periods). One participant in the control group and 4 participants in the sleep extension group had missing data in change in energy intake. Overall, 1 participant in the control group and 5 participants in the sleep extension group had missing data in either change in sleep duration or change in energy intake.

Trial Protocol

eMethods. Participants, Inclusion and Exclusion Criteria

eReferences

eTable 1. Effect of Treatment on Actigraphy-Based Time in Bed and Sleep Duration on All Days, Workdays and Free Days

eTable 2. Effect of Treatment on Actigraphy-Based Outcomes

eTable 3. Baseline Characteristics of Participants With Complete vs Incomplete Data

eTable 4. Self-Reported Outcomes by Visual Analog Scales

Data Sharing Statement

Good Sleep, Better Life—Enhancing Health and Safety With Optimal Sleep JAMA Internal Medicine Invited Commentary April 1, 2022 Mark R. Rosekind, PhD; Rafael Pelayo, MD; Debra A. Babcock, MD

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Tasali E , Wroblewski K , Kahn E , Kilkus J , Schoeller DA. Effect of Sleep Extension on Objectively Assessed Energy Intake Among Adults With Overweight in Real-life Settings : A Randomized Clinical Trial . JAMA Intern Med. 2022;182(4):365–374. doi:10.1001/jamainternmed.2021.8098

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Effect of Sleep Extension on Objectively Assessed Energy Intake Among Adults With Overweight in Real-life Settings : A Randomized Clinical Trial

1 Department of Medicine, The University of Chicago, Chicago, Illinois
2 Department of Public Health Sciences, The University of Chicago, Chicago, Illinois
3 Biotechnology Center, Department of Nutritional Sciences, University of Wisconsin–Madison, Madison
Invited Commentary Good Sleep, Better Life—Enhancing Health and Safety With Optimal Sleep Mark R. Rosekind, PhD; Rafael Pelayo, MD; Debra A. Babcock, MD JAMA Internal Medicine

Question What is the effect of sleep extension on objectively assessed energy intake in adults with overweight in their usual home environment?

Findings In this randomized clinical trial of 80 adults with overweight and habitual sleep less than 6.5 hours per night, those randomized to a 2-week sleep extension intervention significantly reduced their daily energy intake by approximately 270 kcal compared with the control group. Total energy expenditure did not significantly differ between the sleep extension and control groups, resulting in a negative energy balance with sleep extension.

Meaning The findings suggest that improving and maintaining adequate sleep duration could reduce weight and be a viable intervention for obesity prevention and weight loss programs.

Importance Short sleep duration has been recognized as a risk factor for obesity. Whether extending sleep duration may mitigate this risk remains unknown.

Objective To determine the effects of a sleep extension intervention on objectively assessed energy intake, energy expenditure, and body weight in real-life settings among adults with overweight who habitually curtailed their sleep duration.

Design, Setting, and Participants This single-center, randomized clinical trial was conducted from November 1, 2014, to October 30, 2020. Participants were adults aged 21 to 40 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) between 25.0 and 29.9 and had habitual sleep duration of less than 6.5 hours per night. Data were analyzed according to the intention-to-treat principle.

Interventions After a 2-week habitual sleep period at baseline, participants were randomized to either an individualized sleep hygiene counseling session that was intended to extend their bedtime to 8.5 hours (sleep extension group) or to continue their habitual sleep (control group). All participants were instructed to continue daily routine activities at home without any prescribed diet or physical activity.

Main Outcomes and Measures The primary outcome was change in energy intake from baseline, which was objectively assessed as the sum of total energy expenditure and change in body energy stores. Total energy expenditure was measured by the doubly labeled water method. Change in body energy stores was computed using regression of daily home weights and body composition changes from dual-energy x-ray absorptiometry. Sleep duration was monitored by actigraphy. Changes from baseline were compared between the 2 groups using intention-to-treat analysis.

Results Data from 80 randomized participants (mean [SD] age, 29.8 [5.1] years; 41 men [51.3%]) were analyzed. Sleep duration was increased by approximately 1.2 hours per night (95% CI, 1.0 to 1.4 hours; P < .001) in the sleep extension group vs the control group. The sleep extension group had a significant decrease in energy intake compared with the control group (−270 kcal/d; 95% CI, −393 to −147 kcal/d; P < .001). The change in sleep duration was inversely correlated with the change in energy intake ( r = −0.41; 95% CI, −0.59 to −0.20; P < .001). No significant treatment effect in total energy expenditure was found, resulting in weight reduction in the sleep extension group vs the control group.

Conclusions and Relevance This trial found that sleep extension reduced energy intake and resulted in a negative energy balance in real-life settings among adults with overweight who habitually curtailed their sleep duration. Improving and maintaining healthy sleep duration over longer periods could be part of obesity prevention and weight loss programs.

Trial Registration ClinicalTrials.gov Identifier: NCT02253368

Obesity is a major public health concern. 1 The obesity epidemic appears to coincide with a pattern of sleeping less that has been observed in society over the past several decades. For example, one-third of the US population reported not getting the recommended 7 to 9 hours of sleep per night. 2 - 4 Substantial evidence suggests that sleeping less than 7 hours per night on a regular basis is associated with adverse health consequences. 5 Particularly, insufficient sleep duration has been increasingly recognized as an important risk factor for obesity. 6 , 7 Prospective epidemiologic studies suggest that short sleep duration is an important risk factor for weight gain. 8 - 10 However, it remains unknown whether extending sleep duration can be an effective strategy for preventing or reversing obesity. Although sleep hygiene education is encouraged by obesity experts, 11 most health professionals and patients do not implement obtaining adequate sleep duration as part of the strategies to combat the obesity epidemic. 12

At the population level, the association between energy flux and body weight implicates that increased energy intake is the main factor in higher body weights in modern society. 13 According to dynamic prediction models, a sustained increase in energy intake of even 100 kcal/d would result in a weight gain of about 4.5 kg over 3 years. 14 , 15 Factors that underlie the observed persistent increase in energy intake and mean weight gain at the population level need to be better understood. One such factor is insufficient sleep duration. Short-term experimental laboratory studies have found that sleep restriction in healthy individuals is associated with an increased mean energy intake of about 250 to 350 kcal/d with minimal to no change in energy expenditure. 16 - 19 However, these laboratory studies do not represent real life. The magnitude of sleep restriction was extreme in most cases, and energy intake was ascertained from a single or a few meals. In a real-life setting in which participants continue their normal daily activities, multiple interacting factors (eg, social interactions and free-living physical activity) can influence energy intake or expenditure and weight.

To date, it remains unknown whether and to what extent an intervention that is intended to increase sleep duration in a real-life setting affects energy balance and body weight. We conducted a randomized clinical trial (RCT) to determine the effects of a sleep extension intervention on objectively assessed energy intake, energy expenditure, and body weight in real-life settings among adults with overweight who habitually curtailed their sleep duration.

This single-center, parallel-group RCT was conducted from November 1, 2014, to October 30, 2020. The protocol was approved by The University of Chicago Institutional Review Board, and participants provided written informed consent. The study protocol is available in Supplement 1 . We followed the Consolidated Standards of Reporting Trials ( CONSORT ) reporting guideline.

Adult men and women aged 21 to 40 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) between 25.0 and 29.9 and a mean habitual sleep duration of less than 6.5 hours per night were eligible. Individuals were required to have stable self-reported sleep habits for the past 6 months. They were recruited from the community and completed an initial online survey followed by a face-to-face interview. Race and ethnicity data were self-reported at this time and included the following race and ethnicity categories: Asian, Black or African American, Hispanic, and White. Those who met the inclusion criteria underwent laboratory screening (polysomnography, oral glucose tolerance test, and blood tests) to determine eligibility. Habitual sleep duration was confirmed by a 1-week screening wrist actigraphy at home. Those who had obstructive sleep apnea confirmed by laboratory polysomnography (apnea-hypopnea index >5), insomnia or history of any other sleep disorder, or night shift and rotating shift work (current or in the past 2 years) were excluded. Detailed eligibility criteria are provided in the eMethods in Supplement 2 .

After a 2-week habitual sleep period at baseline, participants were randomized to either 2-week sleep extension (sleep extension group) or 2-week continued habitual sleep (control group) ( Figure 1 ). Participants continued their daily routine activities at home without any prescribed diet or physical activity.

To blind participants to the sleep extension intervention, we described the study in the recruitment materials as follows: “we will collect information about sleep habits and metabolism.” The sleep extension group was blinded to randomization until after the 2-week baseline assessments, and the control group was blinded until the end of the 4-week study. This approach allowed us to capture habitual sleep-wake patterns without influencing participants' usual behavior or creating selection bias with only participants interested in improving sleep habits. After study completion, all participants were provided with information about the health benefits of optimal sleep duration. Block randomization, stratified by sex, was performed using computer-generated random numbers. Before the trial, randomization assignments were prepared by a biostatistician (K.W.) using opaque, sealed, and numbered envelopes and were given to the research coordinator (E.K.).

Sleep-wake patterns were continuously monitored at home by wrist actigraphy throughout the 4-week study. Participants were asked to wear an accelerometer (motion)-based monitor (Actiwatch Spectrum Plus; Philips) and to press a built-in event marker button when they went to bed to sleep each night and when they got out of bed each morning. Sleep was automatically scored (Actiware, version 6.0.9; Philips) using validated algorithms as the sum of all epochs that were scored as sleep during the total time spent in bed. 20 , 21

During the 2-week baseline, all participants were instructed to continue their habitual sleep patterns at home. On the morning of day 15, participants met with study investigators (E.T. and E.K.) in the research center. Those who were randomized to the sleep extension group received individualized sleep hygiene counseling through a structured interview (E.T.) (eMethods in Supplement 2 ). 22 At the end of the interview, participants were provided with individualized recommendations to follow at home for 2 weeks, with the aim of extending their bedtime duration to 8.5 hours. On day 22, participants returned for a brief follow-up visit. Actigraphy data from the first intervention week were reviewed, and further sleep counseling was provided as needed.

To minimize any imbalance in contact with the investigators between the 2 groups, we asked participants in the control group to meet with the study investigators on days 15 and 22. Actigraphy data of these participants were downloaded, but the participants did not receive any specific sleep recommendations and were instructed to continue their daily routine and habitual sleep behaviors until the end of the study.

For each 2-week period, the energy intake was calculated from the sum of total energy expenditure and change in body energy stores using the principle of energy balance. 14 , 23 , 24 Total energy expenditure was measured by the doubly labeled water method. 25 - 29 For each 2-week period, the change in body energy stores was computed from the regression (slope, grams per day) of daily home weights and change in body composition (ie, fat mass and fat-free mass) using dual-energy x-ray absorptiometry. Participants were provided a cellular-enabled weight scale (BodyTrace; BodyTrace Inc) and instructed to take their nude weights twice every morning after awakening before eating or drinking. Weight values were hidden from the participants to minimize potential influence on behavior. Changes in body composition were converted to changes in energy stores using 9.5 kcal/g as the energy coefficient of fat mass and 1.0 kcal/g as the energy coefficient of fat-free mass. 30 Resting metabolic rate was measured by indirect calorimetry for 30 minutes after fasting and for 4 hours after eating a standardized breakfast. Thermic effect of the meal was calculated, which was previously described elsewhere. 31 Activity energy expenditure was calculated by subtracting the resting metabolic rate and thermic effect of the meal from the total energy expenditure. 31 , 32 Additional details are provided in the eMethods in Supplement 2 .

The primary outcome was change in energy intake from baseline. A total final sample size of 80 participants (40 per group) was originally planned and provided 80% power to detect a true difference in energy intake between groups of 207 kcal/d using a 2-sided α = .05 significance threshold (trial protocol in Supplement 1 ). An intention-to-treat analysis was conducted in Stata, version 16 (StataCorp LLC) using 2-tailed tests with statistical significance set at P < .05. Categorical data are presented as counts and percentages. Continuous data are presented as means and SDs. Linear mixed-effects models were fit to determine the treatment differences between the groups. 33 Models included the randomization group, 2-week baseline period (period 1) vs 2-week intervention (period 2) and their interaction, and random effects for each participant. The treatment effect (95% CI) was estimated by the treatment group and period interaction, which is equivalent to testing the difference in change from baseline (period 2 minus period 1) in the sleep extension group vs the control group. To confirm the robustness of primary findings, we fit additional models using the analysis of covariance approach with the period 2 value as the dependent variable, treatment group as the independent variable, and period 1 value as covariates.

In secondary analyses, mixed models that adjusted for sex or menstrual cycle were also fit; these covariates were chosen because of the known influence of menstrual cycle on short-term changes in weight. A Pearson correlation coefficient was calculated to assess the relationships between the changes from baseline in sleep duration and the changes from baseline in energy intake. No adjustments were made to P values or CIs for multiple comparisons. Baseline characteristics of participants with complete data were compared with those of participants with incomplete data using unpaired, 2-tailed t tests and Fisher exact tests. No imputation for missing values was performed.

Of the 210 adults who provided consent and were assessed for eligibility, 81 were randomized (41 to the control group and 40 to the sleep extension group) initially ( Figure 1 ). One participant in the control group revealed adhering to a weight loss regimen and thus did not meet the study inclusion criteria and was deemed ineligible after randomization. 34 The 80 participants had a mean (SD) age of 29.8 (5.1) years and consisted of 41 men (51.3%) and 39 women (48.7%). Baseline characteristics of participants were similar between randomization groups ( Table 1 ). None of the participants were using any antihypertensive or lipid-lowering agents or any prescription medication that can affect sleep or metabolism.

Figure 2 illustrates the mean nightly sleep duration by actigraphy in each group throughout the 4-week study. Participants in the sleep extension group had a significant increase from baseline in mean sleep duration by actigraphy compared with those in the control group (1.2 hours; 95% CI, 1.0-1.4 hours; P < .001). The findings were similar with regard to change in sleep duration when only participants' workdays (1.3 hours; 95% CI, 1.0-1.5 hours; P < .001) or free days (1.1 hours; 95% CI, 0.7-1.5 hours; P < .001) were considered (eTable 1 in Supplement 2 ). No difference was found in change in sleep efficiency (percentage of time spent asleep during time in bed) between the 2 groups (–0.6 hours; 95% CI, –2.1 to 1.0 hours; P = .48), confirming the success of the intervention (eTable 2 in Supplement 2 ).

Energy intake was statistically significantly decreased in the sleep extension group compared with the control group (−270.4 kcal/d; 95% CI, −393.4 to −147.4 kcal/d; P < .001). Figure 3 A through D illustrates the changes from baseline in energy intake and the changes from baseline in sleep duration in individual participants. There was a significant increase in energy intake from baseline in the control group (114.9 kcal/d; 95% CI, 29.6 to 200.2 kcal/d) and a significant decrease in energy intake from baseline in the sleep extension group (−155.5 kcal/d; 95% CI, −244.1 to −66.9 kcal/d) ( Table 2 ). Considering all participants, the change in sleep duration was inversely correlated with the change in energy intake ( r = −0.41; 95% CI, −0.59 to −0.20; P < .001) ( Figure 3 E). Each 1-hour increase in sleep duration was associated with a decrease in energy intake of approximately 162 kcal/d (−162.3 kcal/d; 95% CI, −246.8 to −77.7 kcal/d; P < .001).

No statistically significant treatment effect was found in total energy expenditure or other measures of energy expenditure ( Table 2 ). Participants in the sleep extension group had a statistically significant reduction in weight compared with those in the control group (−0.87 kg; 95% CI, −1.39 to −0.35 kg; P = .001). There was weight gain from baseline in the control group (0.39 kg; 95% CI, 0.02 to 0.76 kg) and weight reduction from baseline in the sleep extension group (−0.48 kg; 95% CI, −0.85 to −0.11 kg) ( Table 2 ).

The findings on energy intake, energy expenditure, and weight were similar after adjustment for the effects of sex or menstrual cycle. No statistically significant differences in baseline characteristics were found between the 75 participants (93.8%) who had complete data on energy intake (primary outcome) vs participants with missing data on energy intake. The proportion of participants with complete data on energy intake was not significantly different between the sleep extension and control groups (90.0% vs 97.5%; P = .36). When all reported outcomes were considered, no significant differences (except for depressive symptoms) in baseline characteristics were found between participants with complete data and participants with incomplete or missing data (eTable 3 in Supplement 2 ). The proportion of participants with complete data on all reported outcomes was similar between the sleep extension and control groups (82.5% vs 85.0%; P > .99).

In this RCT of adults with overweight who habitually curtailed their sleep duration, sleep extension reduced energy intake and resulted in a negative energy balance (ie, energy intake that is less than energy expenditure) in real-life settings. To our knowledge, this study provides the first evidence of the beneficial effects of extending sleep to a healthy duration on objectively assessed energy intake and body weight in participants who continued to live in their home environment. Modest lifestyle changes in energy intake or expenditure are increasingly promoted as viable interventions to reverse obesity.

According to the Hall dynamic prediction model, a decrease in energy intake of approximately 270 kcal/d, which we observed after short-term sleep extension, would predict an approximately 12-kg weight loss over 3 years if the effects were sustained over a long term. 14 , 15 However, this study cannot infer how long healthy sleep habits may be sustained. Nevertheless, these modeling predictions on weight change suggest that continued adequate sleep duration and beneficial effect on energy intake could translate into clinically meaningful weight loss and help reverse or prevent obesity. Thus, the findings of this study may have important public health implications for weight management and policy recommendations.

The findings of decreased energy intake, negative energy balance, and weight reduction resulting from sleep extension are in agreement with the findings of short-term laboratory sleep-restriction studies showing increased energy intake and weight gain 17 as well as the findings of prospective epidemiologic studies linking sleep restriction to obesity risk. 8 A recent meta-analysis of randomized controlled laboratory studies found that short-term sleep restriction over 1 to 14 days of duration in healthy individuals was associated with increases of mean energy intake by approximately 253 kcal/d, as assessed during a single meal. 17 Another meta-analysis of prospective cohort studies found that the risk of obesity increased by 9% for each 1-hour decrease in sleep duration. 8 We did not observe a statistically significant change in total energy expenditure by doubly labeled water method or mean daytime activity counts by actigraphy (eTable 2 in Supplement 2 ). Although some laboratory sleep-restriction studies reported an increase in total energy expenditure of approximately 92 to 111 kcal/d, using a whole-room calorimeter, 35 , 36 other studies observed no change. 16 , 37 We found a modest reduction in weight after sleep extension, and the composition of weight change was primarily in fat-free mass, which is consistent with the short-term changes in body composition. 38 , 39 If sleep is extended over longer periods, weight loss in the form of fat mass would likely increase over time. A few observations suggest that sleeping 7 to 8 hours per night is associated with greater success in weight loss interventions. 40 - 43

In this RCT, we found an overall increase in objective sleep duration of approximately 1.2 hours in participants who habitually slept less than 6.5 hours per night. The change in sleep duration from baseline varied between participants and from night to night in the real-life setting. Overall, the sleep extension group compared with the control group had significantly higher subjective scores in obtaining sufficient sleep, with more daytime energy and alertness and better mood (eTable 4 in Supplement 2 ). Similar to a previous study of sleep extension, 22 the present RCT used an individualized counseling approach. Another study used bedtime extension in habitual short sleepers in real-life conditions but obtained variable benefits on sleep, likely because of a lack of an individualized approach or appropriate blinding. 44 None of these previous studies objectively measured energy intake.

Future similarly rigorous intervention studies of longer duration and using objective assessments of energy balance under real-life conditions are warranted to elucidate the underlying mechanisms and to investigate whether sleep extension could be an effective, scalable strategy for reversing obesity in diverse populations. Along with a healthy diet and regular physical activity, healthy sleep habits should be integrated into public messages to help reduce the risk of obesity and related comorbidities.

This study has several strengths. The major strengths are the randomized design and the objective tracking of energy intake and sleep in real-life settings. Most epidemiologic studies linking short sleep duration to body weight relied on self-reported dietary intake. 45 We did not collect self-reported dietary data because this method is subject to bias and has been shown to be inaccurate compared with the doubly labeled water method. 46 , 47 Most experimental studies that measured energy intake used a single meal under unnatural laboratory conditions. We used a validated method to objectively track energy intake by the doubly labeled water method and change in energy stores. 23 , 48 , 49 In this trial, we objectively quantified energy intake after sleep extension while individuals continued their daily routine in their usual environment. Participant blinding and use of actigraphy allowed us to capture true habitual sleep patterns at baseline. 22 , 50 In addition, we excluded insomnia and sleep apnea.

This study also has several limitations. We enrolled adults with overweight and used selective eligibility criteria, which may limit generalizability to more diverse populations. The increase in energy intake and weight from baseline that we observed in the control group may have contributed to the significant treatment effects. However, in RCTs, performing a between-group comparison, rather than separate tests against baseline within the groups, is strongly recommended. 51 The study did not provide information on how long healthy sleep habits could be maintained over longer periods. 44 We did not systematically assess the factors that may have influenced sleep behavior, but limiting the use of electronic devices appeared to be a key intervention among the participants (eTable 4 in Supplement 2 ). The doubly labeled water method has a precision of 5%, which may translate into some degree of uncertainty in the energy intake calculations. Although whole-room calorimeters can measure energy expenditure with a higher precision of approximately 1% to 2%, they do not represent real-life measurement and are not feasible over longer periods. We did not assess the underlying biological mechanisms of food frequency and the circadian timing of food intake. Multiple interrelated factors could contribute to the finding of decreased energy intake after sleep extension. 6 , 52 Evidence from laboratory sleep restriction studies suggests that increased hunger, alterations in appetite-regulating hormones, and changes in brain regions related to reward-seeking behavior are potential mechanisms that promote overeating after sleep restriction. 6 , 45

This RCT found that short-term sleep extension reduced objectively measured energy intake and resulted in a negative energy balance in real-life settings in adults with overweight who habitually curtailed their sleep duration. The findings highlighted the importance of improving and maintaining adequate sleep duration as a public health target for obesity prevention and increasing awareness about the benefits of adequate sleep duration for healthy weight maintenance.

Accepted for Publication: November 14, 2021.

Published Online: February 7, 2022. doi:10.1001/jamainternmed.2021.8098

Corresponding Author: Esra Tasali, MD, Department of Medicine, The University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637 ( [email protected] ).

Author Contributions: Author Dr Tasali and Ms Wroblewski had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Tasali, Schoeller.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Tasali, Schoeller.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Tasali, Wroblewski.

Obtained funding: Tasali.

Administrative, technical, or material support: Tasali, Kahn, Kilkus, Schoeller.

Supervision: Tasali.

Other - research coordination duties: Kahn.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was funded by grants R01DK100426, CTSA-UL1 TR0002389, and UL1TR002389 from the National Institutes of Health and by the Diabetes Research and Training Center at The University of Chicago.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement : See Supplement 3 .

Additional Contributions: Timothy Shriver, MS, University of Wisconsin–Madison, assisted with doubly labeled water measurements. Maureen Costello, MS, The University of Chicago, assisted with dual-energy x-ray absorptiometry scans. Becky Tucker, BA, Harry Whitmore, RPSGT, and Kristin Hoddy, PhD, RD, The University of Chicago, assisted with data collection. We thank the nurses, dieticians, and technicians at the Clinical Research Center at The University of Chicago for their expert assistance in data collection. We also thank the staff of the Sleep Research Center at The University of Chicago for their support. These individuals received no additional compensation, outside of their usual salary, for their contributions. We thank the volunteers for participating in this study.

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Study reveals strong associations between sleep problems, substance abuse, and suicidal ideation in teens

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Rebecca Robbins, PhD, of the Division of Sleep and Circadian Disorders at Brigham and Women's Hospital, is the senior author of a paper published in Psychiatry Research , "Exploring sleep difficulties, alcohol, illicit drugs, and suicidal ideation among adolescents with a history of depression."

How would you summarize your study for a lay audience?

Suicide is one of the leading causes of death for adolescents in the U.S. We know, due to previous research, that difficulty falling asleep or waking up too early as well as abuse of prescription drugs, sedative and opioids is associated with thinking, planning or attempting suicide -; otherwise known as, suicide ideation.

Using responses from the 2015 to 2020 National Surveys of Drug Use and Health (NSDUH), our team analyzed and quantified the associations between sleep difficulties and suicidal ideation among adolescents with a history of depression and how these associations were amplified by illicit drug and/or alcohol abuse/dependence. We found significant associations between sleeping difficulties and suicide ideation among adolescents with a history of depression, and a more robust association between sleep difficulties when the person reported alcohol abuse/dependence and those that reported illicit drug abuse/dependence in the past year

What knowledge gaps does your study help to fill?

Through our analysis, we quantified the connection between sleep difficulty and substance use among adolescents with a history of depression. Our work suggests significant associations between sleep difficulties, illicit drug use and suicidal ideation in adolescents with symptoms of major depressive episodes.

How did you conduct your study?

We analyzed NSDUH survey results from 38,418 respondents between the ages of 12 to 17 over a five-year period. Questions in the surveys asked respondents about their sleep difficulties, suicide ideation symptoms, illicit drug use and depression symptoms.

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Exploring the role of nicotine and smoking in sleep behaviours: A multivariable Mendelian Randomisation study

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Research has shown bidirectional relationships between smoking and adverse sleep behaviours, including late chronotype and insomnia, but the underlying mechanisms are not understood. One potential driver is nicotine, but its role in sleep is unclear. For this study, we estimated the direct effect of nicotine on six sleep behaviours measured in UK Biobank (chronotype, ease of getting up in the morning, insomnia symptoms, napping, daytime sleepiness and sleep duration). We conducted a Mendelian randomisation (MR) study to explore whether nicotine metabolism has a causal effect on these sleep behaviours. We explored whether the effects could be explained by regular nicotine exposure using genetic proxies of the nicotine metabolite ratio (NMR) and cigarettes per day (CPD) in a multivariable MR design. We found a higher NMR (indicating lower levels of circulating nicotine per cigarette smoked) decreased the likelihood of being an evening person when accounting for CPD in current (β = -0.04, 95%CI -0.06 to -0.02, p < 0.001) and ever smokers (β = -0.03 95%CI -0.04 to -0.01, p = 0.003). A higher NMR also increased the ease of getting up (β = 0.02, 95%CI 0.01 to 0.04, p = 0.015) and likelihood of napping (β = 0.02, 95%CI CI 0.002 to 0.03, p = 0.029) in current smokers. Increased nicotine exposure may directly affect sleep and could underlie relationships between smoking and sleep behaviours identified previously. Sleep could also be impacted in individuals using nicotine delivery systems or using nicotine replacement therapies. Further research is warranted to strengthen this conclusion.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study was funded indirectly by the Wellcome Trust (as part of a PhD studentship). The funder played no other role in study.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This research has been conducted using the UK Biobank Resource under Application Number 9142. Further details about the ethics approval sought for data collection in UK Biobank can be found online (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics). The authors of the NMR GWAS (Buchwald et al., 2021) provide details of ethics approval for these studies in their paper supplementary materials. The GSCAN GWAS (Liu et al., 2019) received ethics approval from the University of Minnesota Institutional Review Board and all participants provided informed consent.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

All data produced in the present work are contained in the manuscript.

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Research On Sleep And Why It's So Important

Outside of the research-based effects of sleep, individuals may understand that sleep is important. Adverse emotional and physical symptoms can occur when people don’t get adequate sleep. Those who have worked a night shift, pulled an all-nighter, or experienced a sleep disorder like obstructive sleep apnea may know the impacts of going a night or more without getting restorative sleep. In addition, individuals may understand that being well-rested can lead to a sense of being ready to face the day.

Scientists have spent significant resources researching sleep and have discovered that it plays a vital role in many body functions, including memory, immune function, learning, and mental health. Exploring these statistics can help individuals understand even further how sleep’s role in life can impact them personally, as well as ways to improve sleep and find support.

A man in a white shirt lays asleep in a chair in his home with headphones in.

Theories about why people sleep

Scientists have come up with multiple theories about why people sleep , including the following.

Inactive theory

The inactive theory, also called evolutionary or adaptive theory, is one of the earliest theories about sleep. It suggests that being inactive at night served a survival function by keeping living creatures safe when they were particularly vulnerable. Animals that can stay still and quiet at night have an advantage over animals that are active. For example, in the past, predators were more likely to attack animals that were awake, active, and drawing attention to themselves than those who were still and quiet. The theory suggests that, through natural selection, this behavioral strategy evolved into what people now know as sleep.

This theory has been debunked, with some counterarguments pointing out that there is no advantage to being unconscious and asleep if safety is the goal because it prevents organisms from being able to react in an emergency.

Energy conservation theory

The energy conversation theory about why people sleep suggests that sleep’s primary purpose is to reduce energy demand for part of the day. While this factor may not seem applicable in the modern age, when human ancestors were hunting, gathering, and farming for all their food, conserving energy was a way to utilize limited resources effectively. Some scientists believe this theory to be tied to the inactive theory as both can be connected to survival on a fundamental level.

Restorative theories

Restorative theories are based on the long-held idea that sleep restores what the body loses when people are awake, allowing the body to repair and rejuvenate. Sleep research has supported this idea over the years. For example, studies have found that animals that are deprived of sleep lose autoimmune function and die in a matter of weeks.

Studies have found that sleep factors into muscle growth , protein synthesis, tissue repair, and growth hormone release, with some aspects of these functions happening only during sleep instead of when awake.

Synaptic homeostasis theory

Another theory about why people sleep is the synaptic homeostasis theory, which proposes that sleep is essential to reducing synapses in the brain. Based on this theory, daily activity increases the number of synapses in the brain, and the brain might become overcrowded if allowed to accumulate synapses continuously. Sleeping allows the brain to prune away unnecessary synapses and strengthen essential ones.

Brain plasticity theory

The brain plasticity theory is a more recent theory that suggests that sleep is correlated with changes in the organization and structure of the brain. The concept of brain plasticity is not well understood. Still, sleep is understood as crucial for brain development in infants and young children, and sleep deprivation can affect an adult’s ability to learn and perform tasks.

A woman lays asleep on her back on the couch with a book resting on her stomach.

What happens during sleep

The above theories remain unproven, but scientists have been interested in sleep for years, wondering why humans sleep and how sleep affects the body. Their insights have led to understanding the processes that occur during sleep. Sleep affects multiple systems in the body , including the heart and circulatory system, immune system, metabolism, and hormones.

Circulatory system

When you enter non-rapid eye movement or restorative sleep, your heart rate and blood pressure drop, and your heart does not work as hard as when awake. During REM sleep and when you wake up, your blood pressure and heart rate increase to the usual levels when awake but relaxed.

When people get insufficient sleep at night, their hearts don’t get these periods of rest. People who do not get enough sleep or who wake often throughout the night may have a higher risk of cardiovascular disease, high blood pressure, stroke, obesity, and coronary artery disease.

Immune system

A lack of sleep can affect the immune system . Studies show that people with short sleep duration or poor-quality sleep are more likely to get sick, and a lack of sleep can affect how long it takes to recover after an illness.

The immune system releases cytokines during sleep. These proteins have multiple functions and increase during periods of illness or stress. When someone doesn’t get enough sleep, they may not produce enough protective cytokines or other cells and antibodies that fight infection.

How the body processes fat may be related to sleep and the circadian rhythm. Eating and sleeping at irregular times can affect how the body handles fat. Studies have shown that people who do not get enough quality sleep may experience increased hunger, decreased physical activity, metabolic syndrome, and increased consumption of sweet, fatty, or salty foods.

The body makes different hormones at different times of day, and when they are produced, they may be related to the circadian rhythm. In the morning, your body releases hormones like cortisol, which promote alertness. A lack of sleep can interrupt the secretion of ghrelin and leptin, the hunger hormones, which can cause people to binge eat during waking hours. Growth hormones surge during sleep , which is essential for muscle growth, maintaining standard body structure, and supporting metabolism.

Researchers believe that sleep's rejuvenating aspects are specific to cognitive function and the brain. The glymphatic system , a drainage pathway in the brain, clears substances and toxins from the brain and performs more efficiently during sleep. Sleep maintains certain pathways in the brain that allow individuals to create new memories and learn new skills or topics.

Mental health

Not getting enough sleep or getting poor sleep can increase the risk for some mental health disorders . Insomnia can be a factor in some psychiatric disorders, like depression and anxiety, and a lack of sleep can elicit symptoms of these conditions in otherwise healthy people. People with mental health disorders may be more likely to experience chronic sleep problems, and those sleep problems can exacerbate symptoms, possibly increasing suicide risk.

How to improve sleep quality

There are many steps individuals can take to improve sleep quality , including but not limited to the following:

Instead of lying in bed for a long time, try getting up and partaking in a calming activity like reading for some time and then returning to bed.
Stay away from bright lights while you are awake, as it can incite you to stay awake. Turn off the lights in your room. Consider a dim lamp or a night light if you don't like the dark.
Don’t nap during the day to ensure you’re tired at bedtime.
Maintain a bedtime routine where you go to bed and wake up at the same time every day, even on the weekends.
Refrain from excessive exercise for at least four hours before bedtime.
Establish sleep rituals to remind your body that it is time to get ready to fall asleep.
Use your bed only for sleeping and sex. Don’t watch TV, eat, or work in bed.
Avoid nicotine, substances, caffeine, and alcohol for four to six hours before bed.
Take a hot shower or bath before bed.
Keep your bedroom comfortable and quiet. If the light from outside bothers you, try blackout curtains or use white noise to drown out other sounds that may prevent you from falling asleep.
Avoid blue light from devices. Do not use screens with blue light for at least two hours before bed. If you want to use your devices, you might use a blue-light-blocking filter or glasses.

A man in a white shirt leans forward while sitting on the couch across from his therapist.

Support options

If you are having trouble getting enough sleep, you might consider working with a therapist. Cognitive-behavioral therapy (CBT) can effectively treat long-term sleep problems like insomnia and other sleep disorders. This modality can help you identify and manage negative thoughts and worries that may keep you awake and teach positive sleep habits and hygiene techniques.

Online therapy may be a convenient and flexible option if you face barriers to in-person therapy, such as time, distance, or finances. With an online platform like BetterHelp , you can work with a qualified, licensed professional from the comfort of your home at a time that suits your schedule. In addition, online platforms allow you to use journaling prompts, worksheets, and support groups, which you might not have access to in a traditional face-to-face setting.

Research has found that online therapy is also effective. One review of 17 studies found that online CBT may be more effective than in-person treatment in some cases and that study participants were equally as satisfied with either type of CBT. This review also found that online CBT was a more cost-effective option.

Sleep research has uncovered many ways that sleep benefits physical and mental health. If you’re sleep-deprived or experiencing chronic sleep loss, working with a therapist may help you challenge the thoughts and behaviors that are keeping you up at night so you can get a good night’s sleep.

How To Fall Asleep Fast Naturally Medically reviewed by Laura Angers Maddox , NCC, LPC
Sleep Statistics And The Importance Of Getting Enough Rest Medically reviewed by Laura Angers Maddox , NCC, LPC
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Open access
Published: 12 July 2024

Sleep profiles of different psychiatric traits

John Axelsson 1 , 2 ,
Eus J. W. van Someren ORCID: orcid.org/0000-0002-9970-8791 3 , 4 &
Leonie J. T. Balter ORCID: orcid.org/0000-0002-3083-7456 1 , 2

Translational Psychiatry volume 14 , Article number: 284 ( 2024 ) Cite this article

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Human behaviour
Predictive markers

Disturbed sleep comes in many forms. While the key role of sleep in mental health is undisputed, our understanding of the type of sleeping problems that manifest in the early stages of psychiatric disorders is limited. A sample without psychiatric diagnoses ( N = 440, 341 women, 97 men, 2 non-binaries; M age = 32.1, SD = 9.4, range 18–77) underwent a comprehensive assessment, evaluating eight sleep features and 13 questionnaires on common psychiatric complaints. Results revealed that traits of affect disorders, generalized anxiety, and ADHD had the worst sleep profiles, while autism disorder, eating disorder, and impulsivity traits showed milder sleep issues. Mania was the only trait associated with an overall better sleep profile. Across traits, insomnia and fatigue dominated and sleep variability was least prominent. These findings provide support for both transdiagnostic and disorder-specific targets for prevention and treatment.

Vulnerability to bipolar disorder is linked to sleep and sleepiness

Sleep complaints are associated with increased suicide risk independently of psychiatric disorders: results from a national 3-year prospective study

Shared and distinct abnormalities in sleep-wake patterns and their relationship with the negative symptoms of Schizophrenia Spectrum Disorder patients

Introduction.

Sleep disturbances are common among individuals with subclinical and clinical psychiatric disorders, contributing to the development or worsening of symptoms [ 1 , 2 ]. Sleep disturbances can manifest in various forms, including insomnia, hypersomnia, and alterations in sleep duration and rhythms [ 2 , 3 ]. Insomnia or hypersomnia is explicitly listed as diagnostic criterion in disorders such as major depressive disorder (MDD), generalized anxiety disorder (GAD), and posttraumatic stress disorder (PTSD), while a decreased sleep need is typical in the manic phase of bipolar disorder [ 4 ]. Sleep disturbances are also prevailing across a spectrum of other disorders from attention-deficit hyperactivity disorder (ADHD) [ 5 ] to schizophrenia [ 6 ]. Moreover, they are frequently present in at-risk populations and subthreshold phenotypes, and correlate with psychiatric symptoms in otherwise healthy individuals [ 7 , 8 , 9 ]. Other sleep-related features that have been associated with mental problems include being an evening chronotype or having a delayed sleep phase (i.e., late sleep and wake times), and social jetlag (i.e., misalignment between an individual’s circadian rhythm and their external social or work schedules) [ 10 , 11 , 12 ]. Given the potential for various effective treatments [ 2 , 13 , 14 ], identifying the most prominent sleep features in different mental health presentations represents a fruitful research target.

Important steps in guiding prevention strategies include a better understanding of the type of sleep features that characterize populations with psychiatric symptoms but lacking formal diagnoses. Constructing sleep feature profiles can indicate which sleep features may contribute to symptomatology and can identify potential risk factors and prevention targets before disorder onset. Analyzing the same individuals across all sleep and psychiatric dimensions enables us to gauge the magnitude of associations relative to each other.

Dimensional approaches involve studying a spectrum of underlying causes and mental health issues rather than focusing on clinical populations [ 15 ]. Instead of focusing on mental health conditions as discrete categories, dimensional approaches recognize that mental health exists on a continuum or spectrum, with varying degrees of severity. This approach can offer valuable insights into the mechanisms involved in the early stage of psychiatric disorders and facilitate a better understanding of the complex interplay between sleep and psychiatric problems, potentially leading to more effective preventative strategies [ 16 ].

Using a dimensional approach, we aimed to identify the most central sleep features seen in different psychiatric dimensions in a cohort free of formal psychiatric diagnoses. Based on literature on the presence of sleep symptoms in disorders and literature on the predictive value of sleep and circadian features in longitudinal risk studies, we hypothesized that insomnia, fatigue, and the evening chronotype would be the most prominent, particularly in traits relating to mood disorders. A better understanding of the specific sleep features associated with mental health can aid prevention and tailoring treatment strategies to address central sleep issues associated with psychiatric problems.

Participants

A total of 440 participants (341 women, 97 men, 2 non-binary; M age = 32.1, SD = 9.4, range 18–77) were included in the final analyses after excluding 75 individuals with a psychiatric diagnosis, taking psychotropic medication, or incorrectly answering the data quality checks. Among the 440 participants, 273 (62.0%) worked part-time, full-time, or were self-employed, 77 (17.5%) were part-time or full-time students, 6 (1.4%) did voluntary work, 47 (10.7%) were unemployed, 2 (0.5%) were retired, and 18 (4.1%) did not fall into any of these categories (e.g., homemaker/parent, in between jobs, sick leave) (the total can add up to > 100% due to an individual fitting multiple categories).

Recruitment strategy

Participants were recruited via an online recruitment platform (Prolific.co). Individuals can sign up for studies that are listed on this platform. Researchers can specify the eligibility criteria for their studies. Participants qualified if: residing in United Kingdom; fluent in English; ≥18 years; ≥99% approval of previous participations on Prolific.co. Individuals with a psychiatric diagnosis or taking psychotropic medication were excluded (see also under “Participants”). No other stringent exclusion criteria applied in order to recruit participants with a diverse range of psychiatric trait levels, ranging from low level to high level symptoms. Participants received financial compensation. See Supplement for further information on recruitment.

Procedures and measures

The study is part of a larger two-day study on diurnal variation in psychiatric symptoms (Balter et al. 17 it also includes the chronotype-psychiatric trait associations but including a larger sample than the one used here) and cognitive functioning. The data analyzed in the present study were collected during the baseline session. The baseline session was conducted on a weekday between 09:00-21:00. Participants completed questionnaires on sleep, psychiatric traits and risk factors, and performed brief cognitive tests (results not reported here). Data collection took place in October 2021, during the COVID-19 pandemic.

Ethics approval

All participants provided online informed consent at the start of the study. The study was approved by the Swedish Ethical Review Authority (dnr:2021-01695) and performed in accordance with ethical principles of the Declaration of Helsinki.

Sleep features

The following sleep features were assessed: sleep duration deviation (the participant’s deviation from the sample’s mean sleep duration was calculated for the last night’s sleep, represented as either shorter or longer than the mean of 7h39min); fatigue (one item of the Sickness Questionnaire [ 18 ]); social jetlag (Munich Chronotype Questionnaire [ 19 ]); non-restorative sleep; poor sleep quality; perceiving sleep as insufficient (Karolinska Sleep Questionnaire [ 20 ]); evening chronotype (reduced Morningness Eveningness Questionnaire [ 21 ]); insomnia (Insomnia Severity Index [ 22 ]). The KSQ and the ISI were not administered in a subset of the participants. See Supplement for further information. These features were selected as they represent central dimensions of sleep and encompass a spectrum of sleep features commonly encountered to deviate in psychiatric disorders [ 2 , 23 , 24 ]. The inclusion of chronotype and social jetlag was deemed relevant due to their potential impact on circadian rhythms and its association with certain psychiatric symptoms [ 10 , 23 , 25 ].

Psychiatric trait questionnaires

Thirteen validated questionnaires on common psychiatric traits and risk factors were included (referred to as “psychiatric traits” for the remainder of the text) assessing: depression; generalized anxiety; mania; delusional ideation; emotion dysregulation; autism; impulsivity; emotional instability; ADHD; obsessive compulsive disorder (OCD); eating disorder; apathy; social anxiety. See Table 1 for an overview of all questionnaires and the sample sizes per measure. The questionnaires were completed in the order as listed in Table 1 unless otherwise stated. Demographic information, as well as details pertaining to psychiatric diagnoses and medication intake were also collected. See Supplement for further information about the sample, including sample distribution plots and heatmaps of the questionnaires.

Data quality checks

For data quality purposes, two attention checks and one honesty check were included [ 26 ]: “Please rate the response alternative ‘agree (9)’ for this question”; “Please answer 100”; “Have you been completely honest in your answers?”. Data of participants who failed more than one quality check ( n = 6) were excluded.

Statistical analysis

Univariate fixed effect regression models were fitted to assess the relationship between sleep features and psychiatric traits, using the lm function in R. All variables were Z-transformed before analysis to allow comparison of coefficients. For all sleep measures, datapoints > 4 SD above or below the means were removed. This resulted in excluding eight out of 3,705 datapoints (0.22%): four datapoints for social jetlag; one for last night’s sleep duration; an additional three for sleep duration deviation. No datapoints were excluded for other sleep measures.

Insomnia was among the most dominant sleep problem, associated positively with all traits except for mania, which showed a negative association. See Fig. 1 and Tables S2 – 14 for results. Traits relating to affect disorders, such as depression, generalized anxiety, emotional instability, and emotion dysregulation, and ADHD were most strongly associated with insomnia. Autism disorder, eating disorder, impulsivity, and delusional ideation traits showed the weakest association with insomnia.

Standardized coefficient plots illustrating the relationships between sleep features and psychiatric traits. Error bars represent 95% confidence intervals. A coefficient of 0 indicates no relationship (range −1 to +1). Lighter color intensities indicate a stronger coefficient. Higher values of sleep duration deviation indicate either a shorter or longer sleep duration than the mean of 7h39min. The rMEQ score was rescored such that a higher score is interpretated as a tendency towards being an evening-type.

All traits were associated with fatigue. Fatigue was particularly prominent among affect disorder-related traits. Impulsivity and autism exhibited a weak association with fatigue, and mania was associated with reduced fatigue.

Non-restorative sleep

Among the sleep features experienced in delusion ideation and OCD, non-restorative sleep was most prominent.

Poor sleep quality

Poor sleep quality was most prominent in traits of depression and generalized anxiety, followed by emotion dysregulation, social anxiety, emotional instability, ADHD, and apathy. Poor sleep quality was less pronounced in OCD, impulsivity, and eating disorder, and non-significant in delusional ideation, autism, and mania.

Perceived too little sleep

Mania was associated with a reduced perception of too little sleep. Except for autism, delusional ideation, and impulsivity, all traits were associated with perceiving sleep as insufficient.

Sleep duration deviation

OCD, depression, delusional ideation, emotional instability, generalized anxiety, and ADHD were the only traits associated with a deviation in sleep duration (i.e., either shorter or longer sleep duration than the sample mean of 7h39min).

Evening type

Mania was associated with morningness while all other traits (except for eating disorder) were associated with eveningness. See also [ 17 ]. In autism disorder, eveningness was the most prominent sleep feature.

Social jetlag

Modest relationships were apparent between psychiatric traits and the degree of social jetlag, significantly so for OCD, delusional ideation, and emotional instability.

In the present study we characterized the sleep features manifesting in a range of psychiatric aspects in a sample of individuals without formal psychiatric diagnoses. Insomnia was not only strongly related to levels of depression and generalized anxiety, but also to many other psychiatric traits, ranging from features of affect disorders, including apathy, emotion dysregulation, emotional instability, and social anxiety, to ADHD, delusional ideation, and OCD. This mirrors observations in patients diagnosed with a psychiatric disorder, where insomnia disorder is highly common [ 27 ]. Given that these associations exist in a population without formal psychiatric diagnoses, it is crucial to evaluate presence of insomnia symptoms in relation to most psychiatric indications, recognizing it as both a risk factor and potential early intervention target. Notably, insomnia treatment yields more favorable outcomes than depression treatment in patients with comorbid depression and insomnia [ 28 ]. Moreover, addressing insomnia has shown promise in ameliorating a range of psychiatric symptoms [ 29 ], indicating that insomnia interventions could be fruitful for various psychiatric indications.

Traits relating to affect disorders, generalized anxiety, and ADHD showed the worst sleep profiles. Individuals with higher levels of these traits suffered from insomnia, fatigue, non-restorative sleep, poor sleep quality, and insufficient sleep and have an evening chronotype. Mania was the only trait associated with an overall better sleep profile, e.g., lower levels of insomnia, less fatigue, less non-restorative sleep and perceiving sleep as less insufficient, as well as having a morning chronotype. Indeed, a decreased sleep need is among the diagnostic criteria for the manic phase of bipolar disorder [ 4 ], which appears to manifest even at subclinical mania levels, as shown in the current study. Autism, eating disorder, and impulsivity showed the least severe sleep profiles, with insomnia, fatigue, or the evening chronotype being their most prominent feature. Delusional ideation and OCD showed a moderately bad sleep profile, with non-restorative sleep being their main sleep complaint. Across all traits, social jetlag was the least significant problem. Despite the eveningness being common among most psychiatric traits (11 out of 13 psychiatric traits), it did not rank among the three most common characteristics in most psychiatric traits (with exceptions noted for autism and apathy). This further highlights the relevance of evaluating multiple sleep characteristics in order to understand the magnitude of their associations relative to each other.

While insomnia is associated with most traits, delusional ideation, eating disorder, and OCD traits showed stronger associations with fatigue and non-restorative sleep. This indicates that various psychiatric vulnerabilities may be best targeted by different sleep intervention strategies. Early identification and management of sleep problems have the potential to mitigate the development or worsening of mental health issues. Tailored sleep intervention strategies, based on the specific sleep profiles, hold significant promise to guide the choice of interventions an individual may benefit from most, although further investigation is necessary. Furthermore, certain combinations of sleep features may indicate a common underlying disturbance. For instance, individuals with elevated OCD symptoms, that was associated with both social jetlag and evening chronotype, may benefit from interventions addressing circadian misalignment - referring to a discrepancy between the body’s internal clock and environmental cues - such as optimal scheduling of light exposure [ 30 ]. Conversely, insomnia and fatigue emerged as primary complaints in depression, suggesting these individuals may derive greater benefits from cognitive behavioral therapy for insomnia (CBT-I) treatment [ 31 ].

A limitation of this study is the use of self-report measures, which can introduce response bias and require recognition and communication of symptoms. However, many psychiatric symptoms are first and foremost subjective experiences and diagnosis of psychiatric disorders primarily rely on self-reported symptoms and observations. The use of validated measures in this study ensures a standardized and reliable assessment of traits relating to psychiatric disorders. Another limitation is that the cross-sectional design does not allow for conclusions on causality. Furthermore, data were collected during the COVID-19 pandemic, which may have altered sleep patterns [ 32 , 33 ]. Future research may therefore aim to replicate the findings in a post-pandemic context. Despite these limitations, the thorough comparison of a large range of sleep features and key psychiatric dimensions offers valuable insights into the relationship between sleep characteristics and psychiatric traits within the non-diagnosed range. Analyzing the same individuals across all sleep and psychiatric dimensions allowed for the estimation and illustration of the magnitude of associations with sleep features relative to each other. This provides important information that may help the reader to interpret other research findings in the field by highlighting the relative importance of different sleep health problems. The findings underscore that in populations free of formal psychiatric diagnoses, discernible sleep problems are already noticeable, with insomnia symptoms and fatigue being the prominent problem. The data also show that, in many cases, more than one sleep health problem exists with some differences in the primary, most taxing, problem. Besides the strong relationships seen between insomnia and features of affect disorders, our study highlights that individuals with subclinical, or undiagnosed, delusional ideation and autism display distinct sleep problem profiles, with stronger associations with fatigue, non-restorative sleep, and having an evening chronotype. The findings hold promise for identifying early indicators and potential risk factors for the onset of psychiatric disorders.

Data availability

A markdown file containing the analysis is available on the Open Science Framework (OSF) at https://osf.io/82d9b .

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This work was supported by grants from: SU – Region Stockholm (nr FoUI-980356, LB, JA) and Rut and Arvid Wolff Memorial Foundation (nr FS-2021:0008, LB). EVS is funded by the European Union (ERC AdG_2021_101055383, OVERNIGHT). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Open access funding provided by Karolinska Institute.

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Axelsson, J., van Someren, E.J.W. & Balter, L.J.T. Sleep profiles of different psychiatric traits. Transl Psychiatry 14 , 284 (2024). https://doi.org/10.1038/s41398-024-03009-4

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Curr Health Sci J
v.43(1); Jan-Mar 2017

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

PSQI Components	X ±SS
Subjective Sleep Quality	1.29 ±0.76
Sleep Latency	1.55 ± 0.94
Sleep Duration	0.78 ± 0.99
Habitual Sleeping Activity	0.47 ± 0.90
Sleep Disturbances	0.99 ± 0.09
Sleeping Drug Usage	0.12 ± 0.48
Daytime Dysfunctions	1.29 ± 0.90
Total PSQI	6.52 ±3.17

PSQIscore averages of the sample

	n	%
5 and below	98	43.9
above 5	125	56.1

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)

Personal Features	n	%	Test Results
Gender			U*=1.36
Male	70	31.4	p=0.174
Female	153	68.6
Age (Gender)	20.03±1.73		r **=0.094
Age (Gender)	20.03±1.73		p=0.160
Semester Year
1.Year	112	50.2
2.Year	44	19.7	KW***=6.050
3.Year	41	18.4	p=0.109
4.Year	26	11.7
Area of Residence
With Family	65	29.1	KW***=3.58
İn Dormitory	120	53.8	p=0.310
Alone at Home	10	4.5
Sharing flat	28	12.6
Preexistence of Chronic Diseases
Yes	18	8.1	U*=1.21
No	205	91.9	p=0.226
Smoking Habits
Smoking	38	17	U*=2.54
Non-smoking	185	83	p=0.011
Snoozing during the Lecture Hours
Yes	92	41.3	KW***=1.59
No	35	15.7	p=0.45
Sometimes	96	43
Waking Up Energetic
Yes	29	13	KW***=26.43
No	90	40.4	p=0.00
Sometimes	104	46.6
Total Sleeping Hours
4-5 hours	29	13	KW***=40.06
6-7 hours	126	56.5	p=0.000
8-9 hours	57	25.6
9 hours and above	11	4.9
Tea Consumption			KW***=2.92
0-3 cups	151	67.7	p=0.231
4-7 cups	58	26
8 cups and above	14	6.3
Coffee consumption			KW***=10.75
0-1 Cup	172	77.1	p=0.005
2-3 Cup	43	19.3
4 Cups and above	83	3.6

*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

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In a paper published today in Current Biology, researchers show that just hours of sleep deprivation reduce how many different types of synapses—the places where neurons meet—there are in brain regions associated with learning and memory. The findings hint at a novel way sleep might help keep us sharp, the team says.
Improving sleep quality leads to better mental health: A meta-analysis
Evidence on the relationship between sleep and mental health. The association between sleep and mental health is well documented [9,13,, , , , , [23]∗].For example, people with insomnia are 10 and 17 times more likely than those without insomnia to experience clinically significant levels of depression and anxiety, respectively [].Furthermore, a meta-analysis of 21 longitudinal studies ...
Study reveals strong associations between sleep problems, substance
Rebecca Robbins, PhD, of the Division of Sleep and Circadian Disorders at Brigham and Women's Hospital, is the senior author of a paper published in Psychiatry Research, "Exploring sleep ...
Exploring the role of nicotine and smoking in sleep behaviours: A
Research has shown bidirectional relationships between smoking and adverse sleep behaviours, including late chronotype and insomnia, but the underlying mechanisms are not understood. One potential driver is nicotine, but its role in sleep is unclear. For this study, we estimated the direct effect of nicotine on six sleep behaviours measured in UK Biobank (chronotype, ease of getting up in the ...
Research On Sleep And Why It's So Important
Outside of the research-based effects of sleep, individuals may understand that sleep is important. Adverse emotional and physical symptoms can occur when people don't get adequate sleep. Those who have worked a night shift, pulled an all-nighter, or experienced a sleep disorder like obstructive sleep apnea may know the impacts of going a ...
The Effects of Sleep Quality on Dream and Waking Emotions
1. Introduction. The interaction between sleep and affective brain function has received attention only in the last couple of decades. As pointed out by Walker and van der Helm [], this delay appears surprising in light of two observations.On one hand, there is significant overlap between sleep physiology and the brain networks and neurochemical processes involved in affective modulation; in ...
Sleep profiles of different psychiatric traits
Sleep disturbances are common among individuals with subclinical and clinical psychiatric disorders, contributing to the development or worsening of symptoms [1, 2].Sleep disturbances can manifest ...
LIVE PRESS CONFERENCE: Boise State coach Spencer Danielson previews
Boise State football coach Spencer Danielson previews the start of fall camp with the media on Monday, July 29th, 2024.
Research on Sleep Quality and the Factors Affecting the Sleep Quality
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 ...

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Exploring the role of nicotine and smoking in sleep behaviours: A multivariable Mendelian Randomisation study

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Research On Sleep And Why It's So Important

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Sleep complaints are associated with increased suicide risk independently of psychiatric disorders: results from a national 3-year prospective study

Shared and distinct abnormalities in sleep-wake patterns and their relationship with the negative symptoms of Schizophrenia Spectrum Disorder patients

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