psychology sleep assignment

Find what you need to study

2.9 Sleep and Dreaming

5 min read • june 18, 2024

Jillian Holbrook

Sleep Cycles Vocabulary

Sleep and dreaming alter consciousness as part of the spontaneous state of consciousness.

The Circadian Rhythm  🕒

The  circadian rhythm is our internal clock, controlling our temperature and wakefulness in 24-hour cycles. This rhythm lets us know when we feel tired and sleepy. Our thinking is sharpest, with memory being the most accurate when we are at our peak in circadian arousal.

You may not think so, but you are actually very familiar with your circadian rhythm . Remember jet lag? Whenever we travel to a place that is within a different timezone, we feel thrown off and unusually tired because jet lag disrupts the circadian rhythm . ✈️

The  suprachiasmatic nucleus in the hypothalamus controls the circadian rhythm . In response to light, it causes the pineal gland to adjust melatonin production. In the morning, melatonin levels decrease, and in the evening, melatonin levels increase to get you prepared for sleep .

Why Sleep is Necessary

Evolutionary psychologists believe that sleeping became part of our behavior as a result of natural selection. In regard to AP Psychology,  sleep  is the periodic, natural  loss  of consciousness. The transition from a relaxed but awake state to sleep is marked by slower breathing and irregular brain waves.

Sleep serves various main functions: 

• Sleep protects🛡️
• Sleep helps us restore and repair brain tissue (maintaining  plasticity ) 🧠
• Sleep restores and rebuilds our memories of the day 💭
• Sleep feeds creative thinking 🎨
• Sleep promotes growth (NREM-3) 🌱 Without sleep , we are unable to concentrate and often feel drowsy. These are all theories as to why we need sleep .

Breaking Down the Sleep Cycle

To measure sleep activity, neurologists use electroencephalograms ( EEGs ). Electrodes taped to the skull allow the EEG to produce an image of the electrical activity or waves in the brain: 

Image Courtesy of  Tuck

When awake and alert, the EEG shows beta waves. As you become more relaxed, alpha waves are shown.

Eventually, you fall into a dreamlike state, where you are semi-awake and feel relaxed, unable to respond to the environment or stimuli. This sleeping stage is called  NREM-1 , or the  hypnagogic  state.  Theta  waves are shown, and you may experience images resembling hallucinations , which may be incorporated into memories. A major example of this is when you wake up and think you’re falling; did that ever happen to you?

As sleep continues, you pass into  NREM-2, where your EEG shows  sleep spindles and K-complexes. Sleep spindles are sudden bursts of rapid brain wave activity. In the EEG attached, the sudden burst shown is a sleep spindle.

NREM-3, or  deep sleep , follows NREM-2. During this sleep stage,  delta  waves are emitted, and growth hormones are released. Heart rate, respiration, and blood flow are reduced. The further into the night you get, the less deep sleep you have. 

Once you pass into  REM   sleep , vivid dreams occur, brain waves become rapid (beta waves), heart rate and breathing increase, and eye movements are rapid (hence REM [Rapid Eye Movement Sleep ]). REM is also commonly labeled as  paradoxical sleep , where muscles are relaxed while other body systems are active. As a result, waking up during REM sleep can cause sleep paralysis since you are awake but have limp muscles.

REM sleep is one of the most important topics to know for this section! Dreams and nightmares, in addition to relaxed muscles, are concepts commonly asked about on the AP Exam. The further into the night you get, the  more   REM sleep you have.

Summary of the Sleep Stages

Every 90 minutes, we cycle through the four sleep stages: 1-2-3-2-1-REM, then restart.

Image courtesy of  Wikimedia Commons

Sleeping Disorders

While sleep is important to us as humans, sometimes problems can arise in regard to our sleep cycles. Insomnia , the inability to fall or stay asleep, can have detrimental health effects. On the other hand,  narcolepsy , sudden uncontrollable sleep attacks, can have harmful effects on our ability to function on a day-to-day basis. 😴

Sleep apnea is another disorder that impacts our quality of sleep . People with  sleep apnea randomly stop breathing while they are asleep and are frequently awakened throughout the night. 

Lastly,  night terrors , which typically impact children, occur in  NREM-3 and thus differ from regular dreams and nightmares. This may be characterized by incoherent chatter or physical movement. 👶

If you have ever been sleep -deprived, chances are you experienced  REM rebound . The night after being sleep deprived, you spend more time in the REM sleep stage.

Sigmund Freud was interested in what dreams could tell us about our inner thoughts and desires. He believed that dreams had two messages. First was the  manifest content , which was the actual remembered storyline. The second is referred to as the  latent content or the underlying meaning of the dream. For example, being chased by an animal in a dream may actually mean we are worried about a deadline creeping up on us. 🐆

Freud’s theory as to why we dream is to satisfy our own wishes and deal with  unconscious drives . Other theorists believe that we dream in order to file away memories, develop and preserve neural pathways, make sense of neural static, or reflect cognitive development. In reality, we are still learning a lot about both sleep and dreaming!

🎥Watch: AP Psychology -   Sleep  

Key Terms to Review ( 15 )

Stay Connected

© 2024 Fiveable Inc. All rights reserved.

AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.

4.2 Sleep and Why We Sleep

Learning objectives.

By the end of this section, you will be able to:

• Describe areas of the brain involved in sleep
• Understand hormone secretions associated with sleep
• Describe several theories aimed at explaining the function of sleep
• Name and describe three theories about why we dream

We spend approximately one-third of our lives sleeping. Given the average life expectancy for U.S. citizens falls between 73 and 79 years old (Singh & Siahpush, 2006), we can expect to spend approximately 25 years of our lives sleeping. Some animals never sleep (e.g., some fish and amphibian species); other animals sleep very little without apparent negative consequences (e.g., giraffes); yet some animals (e.g., rats) die after two weeks of sleep deprivation (Siegel, 2008). Why do we devote so much time to sleeping? Is it absolutely essential that we sleep? This section will consider these questions and explore various explanations for why we sleep.

What is Sleep?

You have read that sleep is distinguished by low levels of physical activity and reduced sensory awareness. As discussed by Siegel (2008), a definition of sleep must also include mention of the interplay of the circadian and homeostatic mechanisms that regulate sleep. Homeostatic regulation of sleep is evidenced by sleep rebound following sleep deprivation. Sleep rebound refers to the fact that a sleep-deprived individual will fall asleep more quickly during subsequent opportunities for sleep. Sleep is characterized by certain patterns of activity of the brain that can be visualized using electroencephalography (EEG), and different phases of sleep can be differentiated using EEG as well.

Sleep-wake cycles seem to be controlled by multiple brain areas acting in conjunction with one another. Some of these areas include the thalamus, the hypothalamus, and the pons. As already mentioned, the hypothalamus contains the SCN—the biological clock of the body—in addition to other nuclei that, in conjunction with the thalamus, regulate slow-wave sleep. The pons is important for regulating rapid eye movement (REM) sleep (National Institutes of Health, n.d.).

Sleep is also associated with the secretion and regulation of a number of hormones from several endocrine glands including: melatonin, follicle stimulating hormone (FSH), luteinizing hormone (LH), and growth hormone (National Institutes of Health, n.d.). You have read that the pineal gland releases melatonin during sleep ( Figure 4.6 ). Melatonin is thought to be involved in the regulation of various biological rhythms and the immune system (Hardeland et al., 2006). During sleep, the pituitary gland secretes both FSH and LH which are important in regulating the reproductive system (Christensen et al., 2012; Sofikitis et al., 2008). The pituitary gland also secretes growth hormone, during sleep, which plays a role in physical growth and maturation as well as other metabolic processes (Bartke, Sun, & Longo, 2013).

Why Do We Sleep?

Given the central role that sleep plays in our lives and the number of adverse consequences that have been associated with sleep deprivation, one would think that we would have a clear understanding of why it is that we sleep. Unfortunately, this is not the case; however, several hypotheses have been proposed to explain the function of sleep.

Adaptive Function of Sleep

One popular hypothesis of sleep incorporates the perspective of evolutionary psychology. Evolutionary psychology is a discipline that studies how universal patterns of behavior and cognitive processes have evolved over time as a result of natural selection . Variations and adaptations in cognition and behavior make individuals more or less successful in reproducing and passing their genes to their offspring. One hypothesis from this perspective might argue that sleep is essential to restore resources that are expended during the day. Just as bears hibernate in the winter when resources are scarce, perhaps people sleep at night to reduce their energy expenditures. While this is an intuitive explanation of sleep, there is little research that supports this explanation. In fact, it has been suggested that there is no reason to think that energetic demands could not be addressed with periods of rest and inactivity (Frank, 2006; Rial et al., 2007), and some research has actually found a negative correlation between energetic demands and the amount of time spent sleeping (Capellini, Barton, McNamara, Preston, & Nunn, 2008).

Another evolutionary hypothesis of sleep holds that our sleep patterns evolved as an adaptive response to predatory risks, which increase in darkness. Thus we sleep in safe areas to reduce the chance of harm. Again, this is an intuitive and appealing explanation for why we sleep. Perhaps our ancestors spent extended periods of time asleep to reduce attention to themselves from potential predators. Comparative research indicates, however, that the relationship that exists between predatory risk and sleep is very complex and equivocal. Some research suggests that species that face higher predatory risks sleep fewer hours than other species (Capellini et al., 2008), while other researchers suggest there is no relationship between the amount of time a given species spends in deep sleep and its predation risk (Lesku, Roth, Amlaner, & Lima, 2006).

It is quite possible that sleep serves no single universally adaptive function, and different species have evolved different patterns of sleep in response to their unique evolutionary pressures. While we have discussed the negative outcomes associated with sleep deprivation, it should be pointed out that there are many benefits that are associated with adequate amounts of sleep. A few such benefits listed by the National Sleep Foundation (n.d.) include maintaining health, lowering stress levels, improving mood, and increasing motor coordination, as well as a number of benefits related to cognition and memory formation.

Cognitive Function of Sleep

Another theory regarding why we sleep involves sleep’s importance for cognitive function and memory formation (Rattenborg, Lesku, Martinez-Gonzalez, & Lima, 2007). Indeed, we know sleep deprivation results in disruptions in cognition and memory deficits (Brown, 2012), leading to impairments in our abilities to maintain attention, make decisions, and recall long-term memories. Moreover, these impairments become more severe as the amount of sleep deprivation increases (Alhola & Polo-Kantola, 2007). Furthermore, slow-wave sleep after learning a new task can improve resultant performance on that task (Huber, Ghilardi, Massimini, & Tononi, 2004) and seems essential for effective memory formation (Stickgold, 2005). Understanding the impact of sleep on cognitive function should help you understand that cramming all night for a test may not be effective and can even prove counterproductive.

Link to Learning

Watch this brief video that gives sleep tips for college students to learn more.

Getting the optimal amount of sleep has also been associated with other cognitive benefits. Research indicates that included among these possible benefits are increased capacities for creative thinking (Cai, Mednick, Harrison, Kanady, & Mednick, 2009; Wagner, Gais, Haider, Verleger, & Born, 2004), language learning (Fenn, Nusbaum, & Margoliash, 2003; Gómez, Bootzin, & Nadel, 2006), and inferential judgments (Ellenbogen, Hu, Payne, Titone, & Walker, 2007). It is possible that even the processing of emotional information is influenced by certain aspects of sleep (Walker, 2009).

Watch this brief video about the relationship between sleep and memory to learn more.

As an Amazon Associate we earn from qualifying purchases.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/psychology-2e/pages/1-introduction

• Authors: Rose M. Spielman, William J. Jenkins, Marilyn D. Lovett
• Publisher/website: OpenStax
• Book title: Psychology 2e
• Publication date: Apr 22, 2020
• Location: Houston, Texas
• Book URL: https://openstax.org/books/psychology-2e/pages/1-introduction
• Section URL: https://openstax.org/books/psychology-2e/pages/4-2-sleep-and-why-we-sleep

© Jan 6, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

Resources: Course Assignments

Assignment: sleep and dream journal.

Step 1:  To view this assignment, click on  Assignment: Sleep and Dream Journal.

Step 2:  Follow the instructions in the assignment and submit your completed assignment into the LMS.

Contribute!

Improve this page Learn More

• Sleep and Dream Journal Assignment. Provided by : Lumen Learning. License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/contents/[email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Open access
• Published: 11 January 2022

Effect of sleep and mood on academic performance—at interface of physiology, psychology, and education

• Kosha J. Mehta   ORCID: orcid.org/0000-0002-0716-5081 1  

Humanities and Social Sciences Communications volume  9 , Article number:  16 ( 2022 ) Cite this article

68k Accesses

12 Citations

39 Altmetric

Metrics details

Academic achievement and cognitive functions are influenced by sleep and mood/emotion. In addition, several other factors affect learning. A coherent overview of the resultant interrelationships is essential but has not been presented till date. This unique and interdisciplinary review sits at the interface of physiology, psychology, and education. It compiles and critically examines the effects of sleep and mood on cognition and academic performance while including relevant conflicting observations. Moreover, it discusses the impact of several regulatory factors on learning, namely, age, gender, diet, hydration level, obesity, sex hormones, daytime nap, circadian rhythm, and genetics. Core physiological mechanisms that mediate the effects of these factors are described briefly and simplistically. The bidirectional relationship between sleep and mood is addressed. Contextual pictorial models that hypothesise learning on an emotion scale and emotion on a learning scale have been proposed. Essentially, convoluted associations between physiological and psychological factors, including sleep and mood that determine academic performance are recognised and affirmed. The emerged picture reveals far more complexity than perceived. It questions the currently adopted ‘one-size fits all’ approach in education and urges to envisage formulating bespoke strategies to optimise teaching-learning approaches while retaining uniformity in education. The information presented here can help improvise education strategies and provide better academic and pastoral support to students during their academic journey.

Similar content being viewed by others

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

A 4-year longitudinal study investigating the relationship between flexible school starts and grades

Early morning university classes are associated with impaired sleep and academic performance

Introduction.

Academic performance and cognitive activities like learning are influenced by sleep and mood or emotion. This review discusses the roles of sleep and mood/emotion in learning and academic performance.

Sleep, mood, and emotion: definitions and descriptions

Sleep duration refers to “total amount of sleep obtained, either during the nocturnal sleep episode or across the 24-hour period” (Kline, 2013a ). Sleep quality is defined as “one’s satisfaction of the sleep experience, integrating aspects of sleep initiation, sleep maintenance, sleep quantity, and refreshment upon awakening” (Kline, 2013b ). Along similar lines, it is thought to be “one’s perception that they fall asleep easily, get sufficient duration so as to wake up feeling rested, and can make it through their day without experiencing excessive daytime sleepiness” (Štefan et al., 2018 ). Sleep disturbance includes disorders of initiating and maintaining sleep (insomnias) and sleep–wake schedule, as well as dysfunctions associated with either sleep or stages of sleep or partial arousals (Cormier, 1990 ). Sleep deprivation is a term used loosely to describe a lack of appropriate/sufficient amount of sleep (Levesque, 2018 ). It is “abnormal sleep that can be described in measures of deficient sleep quantity, structure and/or sleep quality” (Banfi et al., 2019 ). In a study, sleep deprivation was defined as a sleep duration of 6 h or less (Roberts and Duong, 2014 ). Sleep disorder overarches disorders related to sleep. It has many classifications (B. Zhu et al., 2018 ). Sleep disorders or sleep-related problems include insomnia, hypersomnia, obstructive sleep apnoea, restless legs and periodic limb movement disorders, and circadian rhythm sleep disorders (Hershner and Chervin, 2014 ).

Mood is a pervasive and sustained feeling that is felt internally and affects all aspects of an individual’s behaviour (Sekhon and Gupta, 2021 ). However, by another definition, it is believed to be transient. It is low-intensity, nonspecific, and an affective state. Affective state is an overarching term that includes both emotions and moods. In addition to transient affective states of daily life, mood includes low-energy/activation states like fatigue or serenity (Kleinstäuber, 2013 ). Yet another definition of mood refers to mood as feelings that vary in intensity and duration, and that usually involves more than one emotion (Quartiroli et al., 2017 ). According to the American Psychological Association, mood is “any short-lived emotional state, usually of low intensity” and which lacks stimuli, whereas emotion is a “complex reaction pattern, involving experiential, behavioural and physiological elements”. Emotion is a certain level of pleasure or displeasure (X. Liu et al., 2018 ). It is “a response to external stimuli and internal mental representations” (L. Zhang et al., 2021 ). It is “a conscious mental reaction (such as anger or fear) which is subjectively experienced as a strong feeling usually deriving from one’s circumstances, mood, or relationships with others”. “This feeling is typically accompanied by physiological and behavioural changes in the body”. “This mental state is an instinctive or intuitive feeling which arises spontaneously as distinguished from reasoning or knowledge” (Thibaut, 2015 ).

Since there is some overlap between the descriptions of mood and emotion, in the context of the core content of this review, here, mood and emotion have not been differentiated based on their theoretical/psychological definitions. This is because the aim of the review is not to distinguish between the effects of mood and emotion on learning. Thus, these have been referred to as general affective states; essentially specific states of mind that affect learning. Also, these have been addressed in the context of the study being discussed and cited in that specific place in the review.

Rationale for the topic

Sleep is essential for normal physiological functionality. The panel of National Sleep Foundation suggests sleep durations for various age groups and agrees that the appropriate sleep duration for young adults and adults would be 7–9 hours, and for older adults would be 7–8 hours (Hirshkowitz et al., 2015 ). Today, people sleep for 1–2 hours less than that around 50–100 years ago (Roenneberg, 2013 ). Millions of adults frequently get insufficient sleep (Vecsey et al., 2009 ), including college and university students who often report poor and/or insufficient sleep (Bahammam et al., 2012 ; Curcio et al., 2006 ; Hershner and Chervin, 2014 ). During the COVID-19 pandemic, sleep problems have been highly prevalent in the general population (Gualano et al., 2020 ; Jahrami et al., 2021 ; Janati Idrissi et al., 2020 ) and the student community (Marelli et al., 2020 ). Poor and insufficient sleep is a public health issue because it increases the risk of developing chronic pathologies, and imparts negative social and economic outcomes (Hafner et al., 2017 ).

Like sleep, mood and emotions determine our physical and mental health. Depressive disorders have prevailed as one of the leading causes of health loss for nearly 30 years (James et al., 2018 ). Increased incidence of mood disorders amongst the general population has been observed (Walker et al., 2020 ), and there is an increase in such disorders amongst students (Auerbach et al., 2018 ). These have further risen during the COVID-19 pandemic (Son et al., 2020 ; Wang et al., 2020 ).

The relationship between sleep, mood and cognition/learning is far more complex than perceived. Therefore, this review aims to recognise the interrelationships between the aforementioned trio. It critically examines the effects of sleep and mood on cognition, learning and academic performance (Fig. 1 ). Furthermore, it discusses how various regulatory factors can directly or indirectly influence cognition and learning. Factors discussed here are age, gender, diet, hydration level, obesity, sex hormones, daytime nap, circadian rhythm, and genetics (Fig. 1 ). The effect of sleep and mood on each other is also addressed. Pictorial models that hypothesise learning on an emotion scale and vice-versa have been proposed.

Sleep and mood/emotion affect cognition and academic achievement. Their effects can be additionally influenced by other factors like diet, metabolic disorders (e.g., obesity), circadian rhythm, daytime nap, hydration level, age, gender, and genetics. The figure presents the interrelationships and highlights the complexity emerging from the interdependence between factors, action of multiple factors on a single factor or vice-versa and the bidirectional nature of some associations. These associations collectively determine learning and thereby, academic achievement. Direction of the arrow represents effect of a factor on another.

Effect of sleep on cognition and academic performance

Adequate sleep positively affects memory, learning, acquisition of skills and knowledge extraction (Fenn et al., 2003 ; Friedrich et al., 2020 ; Huber et al., 2004 ; Schönauer et al., 2017 ; Wagner et al., 2004 ). It allows the recall of previously gained knowledge despite the acquisition of new information and memories (Norman, 2006 ). Sleeping after learning acquisition regardless of the time of the day is thought to be beneficial for memory consolidation and performance (Hagewoud et al., 2010 ). Therefore, unperturbed sleep is essential for maintaining learning efficiency (Fattinger et al., 2017 ).

Sleep quality and quantity are strongly associated with academic achievement in college students (Curcio et al., 2006 ; Okano et al., 2019 ). Sufficient sleep positively affects grade point average, which is an indicator of academic performance (Abdulghani et al., 2012 ; Hershner and Chervin, 2014 ) and supports cognitive functionality in school-aged children (Gruber et al., 2010 ). As expected, insufficient sleep is associated with poor performance in school, college and university students (Bahammam et al., 2012 ; Hayley et al., 2017 ; Hedin et al., 2020 ; Kayaba et al., 2020 ; Perez-Chada et al., 2007 ; Shochat et al., 2014 ; Suardiaz-Muro et al., 2020 ; Taras and Potts-Datema, 2005 ). In adolescents aged 14–18 years, not only did sleep quality affect academic performance (Adelantado-Renau, Jiménez-Pavón, et al., 2019 ) but one night of total sleep deprivation negatively affected neurobehavioral performance-attention, reaction time and speed of cognitive processing, thereby putting them at risk of poor academic performance (Louca and Short, 2014 ). In university students aged 18–25 years, poor sleep quality has been strongly associated with daytime dysfunctionality (Assaad et al., 2014 ). Medical students tend to show poor sleep quality and quantity. In these students, not sleep duration but sleep quality has been shown to correlate with academic scores (Seoane et al., 2020 ; Toscano-Hermoso et al., 2020 ). Students may go through repeated cycles wherein the poor quality of sleep could lead to poor performance, which in turn may again lead to poor quality of sleep (Ahrberg et al., 2012 ). Sleep deprivation in surgical residents tends to decrease procedural skills, while in non-surgical residents it diminishes interpretational ability and performance (Veasey et al., 2002 ).

Such effects of sleep deprivation are obvious because it can impair procedural and declarative learning (Curcio et al., 2006 ; Kurniawan et al., 2016 ), decrease alertness (Alexandre et al., 2017 ), and impair memory consolidation (Hagewoud et al., 2010 ), attention and decision making (Alhola and Polo-Kantola, 2007 ). It can increase low-grade systemic inflammation and hinder cognitive functionality (Choshen-Hillel et al., 2020 ). Hippocampus is the region in the brain that plays the main role in learning, memory, social cognition, and emotion regulation (Y. Zhu et al., 2019 ). cAMP signalling plays an important role in several neural processes such as learning and memory, cellular excitability, motor function and pain (Lee, 2015 ). A brief 5-hour period of sleep deprivation interferes with cAMP signalling in the hippocampus and impairs its function (Vecsey et al., 2009 ). Thus, optimal academic performance is hindered, if there is a sleep disorder (Hershner and Chervin, 2014 ).

Caveats to affirming the impact of sleep on cognition and academic performance

Despite the clear significance of appropriate sleep quality and quantity in cognitive processes, there are some caveats to drawing definitive conclusions in certain areas. First, there are uncertainties around how much sleep is optimal and how to measure sleep quality. This is further confounded by the dependence of sleep quality and quantity on various genetic and environmental factors (Roenneberg, 2013 ). Moreover, although sleep enhances emotional memory, during laboratory investigations, this effect has been observed only under specific experimental conditions. Also, the experiments conducted have differed in the methods used and in considering parameters like timing and duration of sleep, age, gender and outcome measure (Lipinska et al., 2019 ). This orientates conclusions to be specific to those experimental conditions and prevents the formation of generic opinions that would be applicable to all circumstances.

Furthermore, some studies on the effects of sleep on learning and cognitive functions have shown either inconclusive or apparently unexpected results. For example, in a study, although college students at risk for sleeping disorders were thought to be at risk for academic failure, this association remained unclear (Gaultney, 2010 ). Other studies showed that the effect of sleep quality and duration on academic performance was trivial (Dewald et al., 2010 ) and did not significantly correlate with academic performance (Johnston et al., 2010 ; Sweileh et al., 2011 ). In yet another example, despite the reduction in sleep hours during stressful periods, pharmacy students did not show adversely affected academic performance (Mnatzaganian et al., 2020 ). Also, the premise underlining the significance of sleep hours in enhancing the performance of clinical duties was challenged when the average daily sleep did not affect burnout in clinical residents, where the optimal sleep hours that would maximise learning and improve performance remained unknown (Mendelsohn et al., 2019 ). In some other examples, poor sleep quality was associated with stress but not with academic performance that was measured as grade point average (Alotaibi et al., 2020 ), showed no significant impact on academic scores (Javaid et al., 2020 ) and there was no significant difference between high-grade and low-grade achievers based on sleep quality (Jalali et al., 2020 ). Insomnia reflects regularly experienced sleeping problems. Strangely, in adults aged 40–69 years, those with frequent insomnia showed slightly better cognitive performance than others (Kyle et al., 2017 ).

The reason for such inconclusive and unanticipated results could be that sleep is not the sole determinant of learning. Learning is affected by various other factors that may alter, exacerbate, or surpass the influence of sleep on learning (Fig. 1 ). These factors have been discussed in the subsequent sections.

Effect of mood/emotion on cognition and learning

Emotions reflect a certain level of pleasure or displeasure (X. Liu et al., 2018 ). Panksepp described seven basic types of emotions, whereby lust, seeking, play and care are positive emotions whereas anger, fear and sadness are negative emotions (Davis and Montag, 2019 ). Emotions influence all cognitive functions including memory, focus, problem-solving and reasoning (Tyng et al., 2017 ). Positive emotions such as hope, joy and pride positively correlate with students’ academic interest, effort and achievement (Valiente et al., 2012 ) and portend a flexible brain network that facilitates cognitive flexibility and learning (Betzel et al., 2017 ).

Mood deficit often precedes learning impairment (LeGates et al., 2012 ). In a study by Miller et al. ( 2018 ), the negative mood is referred to as negative emotional induction, as was achieved by watching six horror films by the subjects in that study. Other examples of negative emotions given by the authors were anxiety and shame. Negative mood can unfavourably affect the learning of an unfamiliar language by suppressing the processing of native language that would otherwise help make connections, thereby reiterating the link between emotions and cognitive processing (Miller et al., 2018 ). Likewise, worry and anxiety affect decision-making. High level of worry is associated with poor task performance and decreased foresight during decision-making (Worthy et al., 2014 ). State anxiety reflects a current mood state and trait anxiety reflects a stable personality trait. Both are associated with an increased tendency of “more negative or more threatening interpretation of ambiguous information”, as can be the case in clinically depressed individuals (Bisson and Sears, 2007 ). This could explain why some people who show the symptoms of depression and anxiety may complain of confusion and show an inability to focus and use cognition skills to appraise contextual clues. Patients with major depressive disorder have scored lower on working and verbal memory, motor speed and attention than healthy participants (Hidese et al., 2018 ). Similarly, apathy, anxiety, depression, and mood disorders in stroke patients can adversely affect the functional recovery of patients’ cognitive functions (Hama et al., 2020 ). These examples collectively present a positive correlation between good mood and cognitive processes.

Caveats to affirming the impact of mood/emotion on cognition and academic performance

Based on the examples and discussion so far, a direct relationship between emotions and learning could be hypothesised, whereby positive emotions would promote creative learning strategies and academic success, whereas negative emotions would lead to cognitive impairment (Fig. 2a ). However, this relationship is far more complex and different than perceived.

Emotions have been shown on a hypothetical learning scale. a Usually, positive and negative emotions are perceived to match with optimal and poor learning, respectively. b Emotions that lead to sub-optimal/poor and optimal/better learning have been shown on the hypothetical learning scale. Here, distinct from ( a ), both negative emotions and high arousal positive emotions have been implicated in poorer learning compared with low-intensity positive emotion like pleasantness; the latter is believed to lead to optimal learning. The question mark reflects that some negative emotions like shame might stimulate learning, but the exact intensity of such emotions and whether these would facilitate better or worse learning than high arousal positive emotions or pleasantness need to be investigated.

Although positive mood favours the recall of learnt words, it correlates with increased distraction and poor planning (Martin and Kerns, 2011 ). High levels of positive emotions like excitedness and elatedness may decrease achievement (Fig. 2b ) (Valiente et al., 2012 ). It may be surprising to know that negative emotions such as shame and anxiety can arouse cognitive activity (Miller et al., 2018 ). Along similar lines, it has been observed that participants exposed to sad and neutral moods performed similarly in visual statistical (learning) tasks but those who experienced sad stimuli showed high conscious access to the acquired statistical knowledge (Bertels et al., 2013 ). Dysphoria is a state of dissatisfaction that may be accompanied by anxiety and depression. Participants with dysphoria have shown more sensitivity to temporal shifts in outcome contingencies than those without dysphoria (Msetfi et al., 2012 ), and these participants reiterated the depressive realism effect and were quicker in endorsing the connection between negative words and ambiguous statements, demonstrating a negative bias (Hindash and Amir, 2012 ). Likewise, not the positive emotion but negative emotion has been shown to influence the learning outcomes, and it increased the efficiency and precision of learning morphosyntactic instructions involving morphology and syntax of a foreign language (X. Liu et al., 2018 ). Thus, negative emotions can allow, and at times, stimulate or facilitate learning (Figs. 2 and 3 ). Further investigation is needed on the intensity of these emotions, whether these would facilitate better or worse learning than high-intensity positive emotions and whether the results would be task specific.

The figure depicts that low-to-medium intensity positive emotion like pleasantness leads to optimal learning, whereas high-intensity emotions, either positive or negative, may lead to suboptimal or comparatively poorer learning. The model considers the apparently unexpected data that negative emotions may stimulate learning. However, which negative emotions these would be, their intensities and their corresponding level of learning are not known, and so these are not shown in the figure. Also, the figure shows bias towards positive emotions in mediating optimal learning. This information is based on the literature so far. Note that the figure represents concepts only and is not prescriptive. It shows inequality and differences between the impacts of high arousal positive and high arousal negative emotions. This concept needs to be investigated. Therefore, the figure may/may not be an accurate mathematical representation of learning with regards to the intensities of positive and negative emotions. In actuality, the scaling and intensities of emotions on the negative and positive sides of the scale may not be equal, particularly in reference to the position of optimal learning on the scale. Furthermore, upon plotting the 3rd dimension, which could be one or more of the regulatory factors discussed here might alter the position and shape of the optimal learning peak.

Moreover, the intensity of positive emotions does not show direct mathematical proportionality to learning/achievement. In other words, the concept of ‘higher the intensity of positive emotions, higher the achievement’ is not applicable. Low-intensity positive emotions such as satisfaction and relaxedness may be potentially dysregulating and high-intensity positive emotions may hamper achievement (Figs. 2b and 3 ). Optimal achievement is likely to be associated with low to medium level intensity of positive emotions like pleasantness (Valiente et al., 2012 ) (Fig. 3 ). Therefore, it has been proposed that both positive and negative high arousal emotions impair cognitive ability (Figs. 2b and 3 ) whereas low-arousal emotions could enhance behavioural performance (Miller et al., 2018 ).

Interestingly, some studies have indicated that emotions do not play a significant role in context. For example, a study showed that there was no evidence that negative emotions in depressed participants showed negative interpretations of ambiguous information (Bisson and Sears, 2007 ). In another study, improvements in visuomotor skills happened regardless of perturbation or mood states (Kaida et al., 2017 ). Thus, mood can either impair, enhance or have no effect on cognition. The effect of mood on cognition and learning can be variable and depend on the complexity of the task (Martin and Kerns, 2011 ) and/or other factors. Some of these factors have been discussed in the following section. The discrepancies in the data on the effects of mood on cognition and learning may be partly attributed to the influence of these factors on cognitive functions.

Factors affecting cognition and its relationships with sleep and mood/emotion

The relationship of cognition with sleep and mood is confounded by the influence of various factors (Tyng et al., 2017 ) such as diet, hydration level, metabolic disorders (e.g., obesity), sex hormones and gender, sleep, circadian rhythm, age and genetics (Fig. 1 ). These factors and their relationships with learning are discussed in this section.

A healthy diet is defined as eating many servings per day of fruits and vegetables, while maintaining a critical view of the consumption of saturated fat, sugar and salt (Healthy Diet—an Overview|ScienceDirect Topics, n.d.). It is also about adhering to two or more of the three healthy attributes with regards to food intake, namely, sufficiently low meat, high fish and high fruits and vegetables (Sarris et al., 2020 ). Another definition of a healthy diet is the total score of the healthy eating index >51 (Zhao et al., 2021 ).

The association between an unhealthy diet and the development of metabolic disorders has been long established. In addition, food affects both cognition and emotion (Fig. 1 ) (Spencer et al., 2017 ). Food and mood show a bidirectional relation whereby food affects mood and mood affects the choice of food made by the individual. Alongside, poor diet can lead to depression while a healthy diet reduces the risk of depression (Francis et al., 2019 ). A high-fat diet stimulates the hippocampus to initiate neuroinflammatory responses to minor immune challenges and this causes memory loss. Likewise, low intake of omega-3 polyunsaturated fatty acids can affect endocannabinoid and inflammatory pathways in the brain causing microglial phagocytosis, i.e., engulfment of synapses by the brain microglia in the hippocampus, eventually leading to memory deficits and depression. On the other hand, vegetables and fruits rich in polyphenolics can lower oxidative stress and inflammation, and thereby avert and/or reverse age-related cognitive dysfunctionality (Spencer et al., 2017 ). Fruits and vegetables, fish, eggs, nuts, and dairy products found in the Mediterranean diet can reduce the risk of developing depression and promote better mental health than sugar-sweetened beverages and high-fat food found in Western diets. Consumption of dietary antioxidants such as the polyphenols in green tea has shown a negative correlation with depression-like symptoms (Firth et al., 2020 ; Huang et al., 2019 ; Knüppel et al., 2017 ). Likewise, chocolate or its components have been found to reduce negative mood or enhance mood, and also enhance or alter cognitive functions temporarily (Scholey and Owen, 2013 ). Alcohol consumption is prevalent amongst university students including those who report feelings of sadness and hopelessness (Htet et al., 2020 ). It can lead to poor academic performance, hamper tasks that require a high degree of cognitive control, dampen emotional responsiveness, impair emotional processing, and generally cause emotional dysregulation (Euser and Franken, 2012 ). Further details on the effects of diet on mood have been discussed elsewhere (Singh, 2014 ). Diet also affects sleep (Binks et al., 2020 ), which in turn affects learning and academic performance. Thus, diet is linked with sleep, mood, and brain functionality (Fig. 1 ).

Water is a critical nutrient accounting for about 3/4th of the brain mass (N. Zhang et al., 2019 ). Unlike the previously thought deficit of 2% or more in body water levels, loss of about 1–2% can be detrimental and hinder normal cognitive functionality (Riebl and Davy, 2013 ). Thus, mild dehydration can disrupt cognitive functions and mood; particularly applicable to the very old, the very young and those living in hot climatic conditions or those exercising rigorously. Dehydration diminishes alertness, concentration, short-term memory, arithmetic ability, psychomotor skills and visuomotor tracking. This is possibly due to the dehydration-induced physiological stress which competes with cognitive processes. In children, voluntary water intake has been shown to improve visual attention, enhance memory performance (Popkin et al., 2010 ) and generally improve memory and attention (Benton, 2011 ). In adults, dehydration can elevate anger, fatigue and depression and impair short-term memory and attention, while rehydration can alleviate or significantly improve these parameters (Popkin et al., 2010 ; N. Zhang et al., 2019 ). Thus, dehydration causes alterations in cognition and emotions, thereby showcasing the impact of hydration levels on both learning and emotional status (Fig. 1 ).

Interestingly, when older persons are deprived of water, they are less thirsty and less likely to drink water than water-deprived younger persons. This can be due to the defective functionality of baroreceptors, osmoreceptors and opioid receptors that alter thirst regulation with aging (Popkin et al., 2010 ). Since water is essential for the maintenance of memory and cognitive performance, the decline of cognitive functionality in the elderly could be partly attributed to their lack of sufficient fluid/water intake when dehydrated.

Obesity and underweightness

Normal weight is defined as a body mass index between 18.5 and 25 kg/m 2 (McGee and Diverse Populations Collaboration, 2005 ) or between 22 and 26.99 kg/m 2 (Nösslinger et al., 2021 ). Being underweight reflects rapid weight loss or an inability to increase body mass and is defined through grades (1–3) of thinness. In children, these are associated with poor academic performance in reading and writing skills, and mathematics (Haywood and Pienaar, 2021 ). Basically, underweight children may have health issues and this could affect their academic abilities (Zavodny, 2013 ). Also, malnourished children tend to show low school attendance and may show poor concentration and impaired motor functioning and problem-solving skills that could collectively lead to poor academic performance at school (Haywood and Pienaar, 2021 ). Malnourished children can show poor performance on cognitive tasks that require executive function. Executive functions could be impaired in overweight children too and this may lead to poor academic performance (Ishihara et al., 2020 ). The negative relation between overweightness and academic performance also implies that the reverse may be true. Poor academic outcome may cause children to overeat and reduce exercise or play and this could lead to them being overweight (Zavodny, 2013 ).

The influence of weight on academic performance is reiterated in observations that in children independent of socioeconomic and other factors, weight loss in overweight/obese children and weight gain in underweight children positively influenced their academic performance (Ishihara et al., 2020 ). Interestingly, independent of the BMI classification, perceptions of underweight and overweight can predict poorer academic performance. In youth, not only larger body sizes but perceptions about deviating from the “correct weight” can impede academic success. This clearly indicates an impact of overweight and underweight perceptions on the emotional and physical health of adolescents (Fig. 1 ) (Livermore et al., 2020 ).

Cognitive and mood disorders are common co-morbidities associated with obesity. Compared to people with normal weight, obese individuals frequently show some dysfunction in learning, memory, and other executive functions. This has been partly attributed to an unhealthy diet, which causes a drift in the gut microbiota. In turn, the obesity-associated microbiota contributes to obesity-related complications including neurochemical, endocrine and inflammatory changes underlying obesity and its comorbidities (Agustí et al., 2018 ). The exacerbated inflammation in obesity may impair the functionality of the region in the brain that is associated with learning, memory, and mood regulation (Castanon et al., 2015 ).

Obesity and mood appear to have a reciprocal relationship whereby obesity is highly prevalent amongst individuals with major depressive disorder and obese individuals are at a high risk of developing anxiety, depression and cognitive malfunction (Restivo et al., 2017 ). In patients with major depressive disorder, obesity has been associated with reduced cognitive functions, likely due to the reduction in grey matter and impaired integrity of white matter in the brain, particularly in areas related to cognition (Hidese et al., 2018 ). Obesity has been shown to be a predictor of depression and the two are linked via psychobiological mechanisms (LaGrotte et al., 2016 ). Notably, sleep deprivation increases the risk of obesity (Beccuti and Pannain, 2011 ) and sleep helps evade obesity (Pearson, 2006 ). Collectively, this links cognition and academic achievement with sleep, obesity, and mood.

Sex hormones and gender

According to the Office of National Statistics, the UK government defines sex as that assigned at birth and which is generally male or female, whereas gender is where an individual may see themselves as having no gender or non-binary gender or on a spectrum between man and woman. The following section discusses both sex and gender in context, as addressed within the cited studies.

Studies show that females outperform males in most academic subjects (Okano et al., 2019 ) and show more sustained performance in tests than male peers (Balart and Oosterveen, 2019 ). This indicates that biological sex may play a role in academic performance. The hormone oestrogen helps develop and maintain female characteristics and the reproductive system. Oestrogen also affects hippocampal neurogenesis, which involves neural stem cells proliferation and survival, and this contributes to memory retention and cognitive processing. Generally, on average, females show higher levels of oestrogen than males. This may partly explain the observed sex-based differences in academic achievement. Administration of oestrogen in females has been proposed to positively affect cognitive behaviour as well as depressive-like and anxiety-like behaviours (Hiroi et al., 2016 ). Clinical trials can establish whether there are any sex-based differences in cognition following oestrogen administration in males and females.

Progesterone, the hormone released by ovaries in females is also produced by males to synthesise testosterone. It affects some non-reproduction functions in the central nervous system in both males and females such as neural circuits formation, and regulates memory, learning and mood (González-Orozco and Camacho-Arroyo, 2019 ). The menstrual cycle in females shows alterations in oestrogen and progesterone levels and is broadly divided into early follicular, mid ovulation and late luteal phase. It is believed that the low-oestrogen-low-progesterone early follicular phase relates to better spatial abilities and “male favouring” cognitive abilities, whereas the high-oestrogen-high-progesterone late follicular or mid-luteal phases relate to verbal fluency, memory and other “female favouring” cognitive abilities (Sundström Poromaa and Gingnell, 2014 ). Thus, sex-hormone derivatives (salivary oestrogen and salivary progesterone) can be used as predictors of cognitive behaviour (McNamara et al., 2014 ). These ovarian hormones decline with menopause, which may affect cognitive and somatosensory functions. However, ovariectomy of rats, which depleted ovarian hormones, caused depression-like behaviour in rats but did not affect spatial performance (Li et al., 2014 ). While this suggests a positive effect of these hormones on mood, it questions their function in cognition and proposes activity-specific functions, which need to be investigated.

Serotonin is a neurotransmitter. Reduced serotonin is correlated with cognitive dysfunctions. Tryptophan hydroxylase-2 is the rate-limiting enzyme in serotonin synthesis. Polymorphisms of this enzyme have been implicated in cognitive disorders. Women have a lower rate of serotonin synthesis and are more susceptible to such dysfunctions than men (Hiroi et al., 2016 ; Nishizawa et al., 1997 ), implying a greater impact of serotonin reduction on cognitive functions in women than in men. Central serotonin also helps to maintain the feeling of happiness and wellbeing, regulates behaviour, and suppresses appetite, thereby modulating nutrient intake. Additionally, it has the ability to promote the wake state and inhibit rapid eye movement sleep (Arnaldi et al., 2015 ; Yabut et al., 2019 ). Thus, any sex-based differences in serotonin levels may affect cognitive functions directly or indirectly via the aforementioned parameters.

Interestingly, data on the relationship between sex and sleep have been ambiguous. While in one study, female students at a university showed less sleep difficulties than male peers (Assaad et al., 2014 ), other studies showed that female students were at a higher risk of presenting sleep disorders related to nightmares (Toscano-Hermoso et al., 2020 ) and insomnia was significantly associated with the risk of poor academic performance only in females (Marta et al., 2020 ). Collectively, sex and gender may influence learning directly, or indirectly by affecting sleep and mood; the other two factors that affect cognitive functions (Fig. 1 ).

Circadian rhythm

Circadian rhythm is a biological phenomenon that lasts for ~24 hours and regulates various physiological processes in the body including the sleep–wake cycles. Circadian rhythm is linked with memory formation, learning (Gerstner and Yin, 2010 ), light, mood and brain circuits (Bedrosian and Nelson, 2017 ). We use light to distinguish between day and night. Interestingly, light stimulates the expression of microRNA-132, which is the sole known microRNA involved in photic regulation of circadian clock in mammals (Teodori and Albertini, 2019 ). The photosensitive retinal ganglions that express melanopsin in eyes not only orchestrate the circadian rhythm with the external light-dark cycle but also influence the impact of light on mood, learning and overall health (Patterson et al., 2020 ). For example, we frequently experience depression-like feelings during the dark winter months and pleasantness during bright summer months. This can be attributed to the circadian regulation of neural systems such as the limbic system, hypothalamic–pituitary–adrenal axis, and monoamine neurotransmitters. Mistimed light in the night disturbs our biological judgement leading to a negative impact on health and mood. Thus, increased incidence of mood disorders correlates with disruption of the circadian rhythm (Walker et al., 2020 ). Interestingly, a study involving university students showed the significance of short-wavelength light, specifically, blue-enriched LED light in reducing melatonin levels [best circadian marker rhythm (Arendt, 2019 )], and improved the perception of mood and alertness (Choi et al., 2019 ). While these examples depict the effect of circadian rhythm on mood, the reverse is also true. Individuals who demonstrate depression show altered circadian rhythm and disturbances in sleep (Fig. 1 ) (Germain and Kupfer, 2008 ). Also, since circadian rhythm regulates physiological and metabolic processes, disruption in circadian rhythm can cause metabolic dysfunctions like diabetes and obesity (Shimizu et al., 2016 ), eventually affecting cognition and learning (Fig. 1 ).

Delayed circadian preference including a tendency to sleep later in the night is common amongst young adults and university students (Hershner and Chervin, 2014 ). This delayed sleep phase disorder, often seen in adolescents, negatively impacts academic achievement and is frequently accompanied by depression (Bartlett et al., 2013 ; Sivertsen et al., 2015 ). Alongside, there is a positive correlation between sleep regularity and academic grades, implying that irregularity in sleep–wake cycles is associated with poor academic performance, delayed circadian rhythm and sleep and wake timings (Phillips et al., 2017 ). Even weekday-to-weekend discrepancy in sleeping patterns has been associated with impaired academic performance in adolescents (Sun et al., 2019 ). Further connection between sleep pattern, circadian rhythm, alertness, and the mood was observed in adolescents aged 13–18 where evening chronotypes showed poor sleep quality and low alertness. In turn, sleep quality was associated with poor outcomes including low daytime alertness and depressed mood. Evening chronotypes and those with poor sleep quality were more likely to report poor academic performance via association with depression. Strangely, sleep duration did not directly affect their functionality (Short et al., 2013 ). Contrastingly, in adults aged 40–69 years, the evening and morning chronotypes were associated with superior and poor cognitive performance, respectively, relative to intermediate chronotype (Kyle et al., 2017 ). In addition to this age-specific effect, the effect of chronotype can be subject-specific. For example, in subjects involving fluid cognition for example science, there was a significant correlation between grades and chronotype, implying that late chronotypes would be disadvantaged in exams of scientific subjects if examined early in the day. This was distinct from humanistic/linguistic subjects in which no correlation with chronotype was observed (Zerbini et al., 2017 ). These observations question the “one size fits all” approach of assessment strategies.

Daytime nap

The benefits of daytime napping in healthy adults have been discussed in detail elsewhere (Milner and Cote, 2009 ). In children, daytime nap facilitates generalisation of word meanings (Horváth et al., 2016 ) and explicit memory consolidation but not implicit perceptual learning (Giganti et al., 2014 ). A 90-min nap increases hippocampal activation, restores its function and improves declarative memory encoding (Ong et al., 2020 ). Generally, daytime napping has been found to be beneficial for memory, alertness, and abstraction of general concepts, i.e. creating relational memory networks (Lau et al., 2011 ). Delayed nap following a learning activity helps in the retention of declarative memory (Alger et al., 2010 ) and exercising before the daytime nap is thought to benefit memory more than napping or exercising alone (Mograss et al., 2020 ). Also, napping for 0.1–1 hour has been associated with a reduced prevalence of overweightness (Chen et al., 2019 ).

Contrastingly, in some studies, napping has been found to impart no substantial benefits to cognition. For example, despite the daytime nap of 1 hour, procedural performance remained impaired after total deprivation of night sleep (Kurniawan et al., 2016 ), indicating that daytime nap may not always be reparative. In other studies, 4 weeks of 90-minute nap intervention (napping or restriction) did not alter behavioural performance or brain activity during sleep in healthy adults aged 18–35 (McDevitt et al., 2018 ) and enhancements in visuomotor skills occurred regardless of daytime nap (Kaida et al., 2017 ). Age is a factor in relishing the benefits of napping. A 90-min nap can benefit episodic memory retention in young adults but these benefits decrease with age (Scullin et al., 2017 ) and may be harmful in the older population, particularly in those getting more than 9 hours of sleep (Mantua and Spencer, 2017 ; Mehra and Patel, 2012 ).

Napping can increase the risk for depression (Foley et al., 2007 ) and show a positive association with depression, i.e., napping is associated with greater likelihood of depression (Y. Liu et al., 2018 ). Cardiovascular diseases, cirrhosis and kidney disease have been linked with both daytime napping and depression (Abdel-Kader et al., 2009 ; Hare et al., 2014 ; Ko et al., 2013 ). While a previous study indicated that the time of nap, morning or afternoon, made no difference to its effect on mood (Gillin et al., 1989 ), a subsequent study suggested that the timing of nap influenced relapses into depression. Specifically, in depressed individuals, morning naps caused a higher propensity of relapse into depression than afternoon naps, thereby proposing the involvement of circadian rhythm in this process. Apart from depression, studies have struggled to identify the direct effect of nap on mood (Gillin et al., 1989 ; Wiegand et al., 1993 ). As daytime napping has been associated with poor sleep quality (Alotaibi et al., 2020 ), it may lead to irregular sleep–wake patterns and thereby alter circadian rhythm (Phillips et al., 2017 ). Also, nap duration was found to be important. In patients with affirmed depression, shorter naps were found to be more detrimental than longer naps (Wiegand et al., 1993 ), whereas, in the elderly, more and longer naps were associated with increased risk of mortality amongst the cognitively impaired individuals (Hays et al., 1996 ). Thus, daytime napping can affect cognitive processes directly or indirectly via its association with circadian rhythm, metabolic dysfunctions, mood, or sleep (Fig. 1 ).

Aging is associated with decreased neurogenesis and structural changes in the hippocampus amongst other neurophysiological effects. This in turn is associated with age-related mood and memory impairments (Kodali et al., 2015 ). Study on the effect of age on mood and emotion regulation in adults aged 20–70 years showed that older participants had a higher tendency to use cognitive reappraisal while reducing negative mood and enhancing positive mood. Interestingly, while women did not show correlations between age and reappraisal, men showed an increment in cognitive reappraisal with age. This indicates gender-based differences in the effect of aging on emotion regulation (Masumoto et al., 2016 ). The influence of age on sleep is well known. Older people that have sleep patterns like the young demonstrate stronger cognitive functions and lesser health issues than those whose sleep patterns match their age (Djonlagic et al., 2021 ). Collectively, this interlinks age, cognition, mood, and sleep.

Apparently, there is a genetic influence on learning and emotions. Approximately 148 independent genetic loci have been identified that influence and support the notion of heritability of general cognitive functions (Davies et al., 2018 ). This indicates the role of genetics in cognition (Fig. 1 ). The α-7 nicotinic acetylcholine receptor (encoded by the gene CHRNA7 ) is expressed in the central and peripheral nervous systems and other peripheral tissues. It has been implicated in various behavioural and psychiatric disorders (Yin et al., 2017 ) and recognised as an important receptor of the cholinergic anti-inflammatory pathway that exhibits a neuroprotective role. Its activation has been shown to improve learning, working memory and cognition (Ren et al., 2017 ). However, there have been some contrasting results related to this receptor. While its deletion has been linked with cognitive impairments, aggressive behaviours, decreased attention span and epilepsy, Chrna7 deficient mice have shown normal learning and memory, and the gene was not deemed essential for the control of emotions and behaviour in mice. Thus, the role of α-7 nicotinic acetylcholine receptor in maintaining mood and cognitive functions, although indicative, is yet to be fully deciphered in humans (Yin et al., 2017 ). Similarly, the gene Slitrk6 , which plays a role in the development of neural circuits in the inner ear may also play a role in some cognitive functions, but it does not appear to play a clear role in mood or memory (Matsumoto et al., 2011 ). Notably, inborn errors of metabolism, i.e., rare inherited disorders may show psychiatric manifestations even in the absence of obvious neurological symptoms. These manifestations could involve impairments in cognitive functions, and/or in the regulation of learning, mood and behaviour (Bonnot et al., 2015 ).

Other factors and associations

Indeed, optimal learning is additionally influenced by factors beyond those discussed here. These factors could be adequate meal frequency, physical activity and low screen time (Adelantado-Renau, Jiménez-Pavón, et al., 2019 ; Burns et al., 2018 ). In adolescents, the time of internet usage was identified as a factor that mediated the association between sleep quality (but not duration) and academic performance (Adelantado-Renau, Diez-Fernandez, et al., 2019 ; Evers et al., 2020 ). Self-perception is another determinant of performance. The American Psychological Association defines self-perception as “person’s view of his or herself or of any of the mental or physical attributes that constitute the self. Such a view may involve genuine self-knowledge or varying degrees of distortion”. Compared to other residents, surgery residents indicated the less perceived impact of sleep-loss on their performance (Woodrow et al., 2008 ). This may be related to specific work culture or profession where there is the reluctance of acceptance of natural human limitations posed by sleep deprivation. Whether there is real resistance to sleep deprivation amongst such professional groups or a misconception requires investigation. Exercise affects both sleep and mood; the latter probably affects in a sex-dependent manner. Thus, moderate exercise has been proposed as a therapy for treating mood disorders (Lalanza et al., 2015 ).

Sleep and mood: a bidirectional but unequal relationship

While the cause of the relationship between sleep and mood is not fully understood, adequate quality and quantity of sleep has shown physiological benefits and may enhance mood (Scully, 2013 ). Sleep encourages insightful behaviour (Wagner et al., 2004 ) and regulates mood (Vandekerckhove and Wang, 2017 ). Sleeping and dreaming activate emotional and reward systems that help process information, and consolidate memory “with high emotional or motivational value”. Inevitably, sleep disturbances can dysregulate these motivational and emotional processes and cause predisposition to mood disorders (Perogamvros et al., 2013 ). Sleep loss can reinforce negative emotions, reduce positive emotions, and increase the risk for psychiatric disorders. In children and adolescents, it can increase anger, depression, confusion and aggression (Vandekerckhove and Wang, 2017 ). Thus, sleep disorder has been associated with depression, where the former can predict the latter (LaGrotte et al., 2016 ). Sleep deprivation and daytime sleepiness amongst adolescents and college students cause mood deficits, negatively affect their mood and learning, and lead to poor academic performance (Hershner and Chervin, 2014 ; Short and Louca, 2015 ). Thus, disrupted sleep acts as a diagnostic factor for mood disorders, including post-traumatic stress disorder, major depression and anxiety (Walker et al., 2020 ).

In turn, mood affects sleep quality. Emotional events and stress during the daytime can affect sleep physiology. Negative states such as sadness, loneliness, and grief are related to sleep impairments, whereas positive states like love can be associated with lessened sleep duration but better sleep quality; the latter needs further evidence. Although dysregulation of emotion relates to poor sleep quality (Vandekerckhove and Wang, 2017 ), the effect of mood on sleep can be modulated by our approach of coping with our emotions (Vandekerckhove and Wang, 2017 ). Therefore, this effect is significantly smaller than the reverse (Triantafillou et al., 2019 ).

Summary and future direction

Sleep and mood influence cognitive functions and thereby affect academic performance. In turn, these are influenced by a network of regulatory factors that directly or indirectly affect learning. The compilation of observations clearly demonstrates the complexity and multifactorial dependence of academic achievement on students’ lifestyle and physiology, as discussed in the form of effectors like age, gender, diet, hydration level, obesity, sex hormones, circadian rhythm, and genetics (Fig. 1 ).

The emerged picture brings forth two points. First, it partly explains the ambiguous and conflicting data on the effects of sleep and mood on academic performance. Second, these revelations collectively question the ‘one-size fits all’ approach in implementing education strategies. It urges to explore formulating bespoke group-specific or subject-specific strategies to optimise teaching–learning approaches. Knowledge of these factors and their associations with each other can aid in forming these groups and improving educational strategies to better support students. However, it is essential to retain parity in education, and this would be the biggest challenge while formulating bespoke approaches.

In the context of sleep, studies could be conducted that first establish standardised means of measuring sleep quality and then measure sleep quality and quantity simultaneously in individuals of different ages groups, sex, and professions. This could then be related to their performance in their respective fields/professions; academic or otherwise. Such studies will help to better understand these interrelationships and address some discrepancies in the data.

Limitations

One limitation of this review is that it addresses only academic performance. Performance should be viewed broadly and be inclusive of all types, for example, athletic performance, dance performance or performance at work on a desk job that may include creative work or financial/mathematical calculations. It would be interesting to investigate the effect of alterations in sleep and mood on various types of performances and those results will be able to provide us with a much broader picture than the one depicted here. Notably, while learning can be assessed, it is difficult to quantify emotions (Ayaz‐Alkaya, 2018 ; Nieh et al., 2013 ). As such, it is believed that qualitative research is a better approach for studying emotional responses than quantitative research (Ayaz‐Alkaya, 2018 ).

Another point of limitation is related to the proposed models in Figs. 2 and 3 . These show hypothetical mathematical scales of learning and emotion where emotions are placed on a scale of learning, and learning is placed on the scale of emotions, respectively. While these models certainly help to better visualise and understand the interrelationships, these scales show only 2-dimensions. There could be a 3rd dimension, and this could be either one of the factors or a combination of the several factors discussed here (and beyond) that determine the effect of mood/emotion on learning/cognition. Additionally, the depicted scales and their interpretations may vary between individuals because the intensity of the same emotion felt by different individuals may differ. Figure 3 depicts emotions and learning. Based on the studies so far, here, negative emotions have been shown to stimulate learning, but which negative emotions these would be (for e.g., shame or anxiety), at what intensities these would stimulate optimal learning if at all, and how this compares with optimal learning induced by positive emotions remains to be investigated. Therefore, the extent to which these scales can be applied in real-life needs to be verified.

Abdel-Kader K, Unruh ML, Weisbord SD (2009) Symptom burden, depression, and quality of life in chronic and end-stage kidney disease. Clin J Am Soc Nephrol 4(6):1057–1064. https://doi.org/10.2215/CJN.00430109

Article   PubMed   PubMed Central   Google Scholar  

Abdulghani HM, Alrowais NA, Bin-Saad NS, Al-Subaie NM, Haji AMA, Alhaqwi AI (2012) Sleep disorder among medical students: relationship to their academic performance. Med Teacher 34(Suppl 1):S37–S41. https://doi.org/10.3109/0142159X.2012.656749

Article   Google Scholar  

Adelantado-Renau M, Diez-Fernandez A, Beltran-Valls MR, Soriano-Maldonado A, Moliner-Urdiales D (2019) The effect of sleep quality on academic performance is mediated by Internet use time: DADOS study. J Pediatr 95(4):410–418. https://doi.org/10.1016/j.jped.2018.03.006

Adelantado-Renau M, Jiménez-Pavón D, Beltran-Valls MR, Moliner-Urdiales D (2019) Independent and combined influence of healthy lifestyle factors on academic performance in adolescents: DADOS Study. Pediatr Res 85(4):456–462. https://doi.org/10.1038/s41390-019-0285-z

Article   PubMed   Google Scholar  

Agustí A, García-Pardo MP, López-Almela I, Campillo I, Maes M, Romaní-Pérez M, Sanz Y (2018) Interplay between the gut–brain axis, obesity and cognitive function. Front Neurosci 12:155. https://doi.org/10.3389/fnins.2018.00155

Ahrberg K, Dresler M, Niedermaier S, Steiger A, Genzel L (2012) The interaction between sleep quality and academic performance. J Psychiatr Res 46(12):1618–1622. https://doi.org/10.1016/j.jpsychires.2012.09.008

Article   CAS   PubMed   Google Scholar  

Alexandre C, Latremoliere A, Ferreira A, Miracca G, Yamamoto M, Scammell TE, Woolf CJ (2017) Decreased alertness due to sleep loss increases pain sensitivity in mice. Nat Med 23(6):768–774. https://doi.org/10.1038/nm.4329

Article   CAS   PubMed   PubMed Central   Google Scholar  

Alger SE, Lau H, Fishbein W (2010) Delayed onset of a daytime nap facilitates retention of declarative memory. PLoS ONE 5(8):e12131. https://doi.org/10.1371/journal.pone.0012131

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Alhola P, Polo-Kantola P (2007) Sleep deprivation: impact on cognitive performance Neuropsychiatr Disease Treat 3(5):553–567. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2656292/

Alotaibi AD, Alosaimi FM, Alajlan AA, Bin Abdulrahman KA (2020) The relationship between sleep quality, stress, and academic performance among medical students. J Fam Community Med 27(1):23–28. https://doi.org/10.4103/jfcm.JFCM_132_19

Arendt J (2019). Melatonin: countering chaotic time cues. Front Endocrinol 10. https://doi.org/10.3389/fendo.2019.00391

Arnaldi D, Famà F, De Carli F, Morbelli S, Ferrara M, Picco A, Accardo J, Primavera A, Sambuceti G, Nobili F (2015) The role of the serotonergic system in REM sleep behavior disorder. Sleep 38(9):1505–1509. https://doi.org/10.5665/sleep.5000

Assaad S, Costanian C, Haddad G, Tannous F (2014) Sleep patterns and disorders among university students in Lebanon. J Res Health Sci 14(3):198–204

PubMed   Google Scholar  

Auerbach RP, Mortier P, Bruffaerts R, Alonso J, Benjet C, Cuijpers P, Demyttenaere K, Ebert DD, Green JG, Hasking P, Murray E, Nock MK, Pinder-Amaker S, Sampson NA, Stein DJ, Vilagut G, Zaslavsky AM, Kessler RC (2018) The WHO World Mental Health Surveys International College Student Project: prevalence and distribution of mental disorders. J Abnormal Psychol 127(7):623–638. https://doi.org/10.1037/abn0000362

Ayaz‐Alkaya S (2018) Overview of psychosocial problems in individuals with stoma: a review of literature. Int Wound J 16(1):243–249. https://doi.org/10.1111/iwj.13018

Bahammam AS, Alaseem AM, Alzakri AA, Almeneessier AS, Sharif MM (2012) The relationship between sleep and wake habits and academic performance in medical students: a cross-sectional study. BMC Med Educ 12:61. https://doi.org/10.1186/1472-6920-12-61

Balart P, Oosterveen M (2019) Females show more sustained performance during test-taking than males. Nat Commun 10(1):3798. https://doi.org/10.1038/s41467-019-11691-y

Banfi T, Coletto E, d’Ascanio P, Dario P, Menciassi A, Faraguna U, Ciuti G (2019) Effects of sleep deprivation on surgeons dexterity. Front Neurol 10:595. https://doi.org/10.3389/fneur.2019.00595

Bartlett DJ, Biggs SN, Armstrong SM (2013) Circadian rhythm disorders among adolescents: assessment and treatment options. Med J Aust 199(8):S16–S20. https://doi.org/10.5694/mja13.10912

Beccuti G, Pannain S (2011) Sleep and obesity. Curr Opin Clin Nutr Metab Care 14(4):402–412. https://doi.org/10.1097/MCO.0b013e3283479109

Bedrosian TA, Nelson RJ (2017) Timing of light exposure affects mood and brain circuits. Transl Psychiatry 7(1):e1017. https://doi.org/10.1038/tp.2016.262

Benton D (2011) Dehydration influences mood and cognition: a plausible hypothesis? Nutrients 3(5):555–573. https://doi.org/10.3390/nu3050555

Bertels J, Demoulin C, Franco A, Destrebecqz A (2013) Side effects of being blue: influence of sad mood on visual statistical learning. PLoS ONE 8(3):e59832. https://doi.org/10.1371/journal.pone.0059832

Betzel RF, Satterthwaite TD, Gold JI, Bassett DS (2017) Positive affect, surprise, and fatigue are correlates of network flexibility. Sci Rep 7(1):520. https://doi.org/10.1038/s41598-017-00425-z

Binks H, Vincent E, Gupta G, Irwin C, Khalesi S (2020) Effects of diet on sleep: a narrative review. Nutrients 12(4). https://doi.org/10.3390/nu12040936

Bisson MAS, Sears CR (2007) The effect of depressed mood on the interpretation of ambiguity, with and without negative mood induction. Cogn Emotion 21(3):614–645. https://doi.org/10.1080/02699930600750715

Bonnot O, Herrera PM, Tordjman S, Walterfang M (2015) Secondary psychosis induced by metabolic disorders. Front Neurosci 9:177. https://doi.org/10.3389/fnins.2015.00177

Burns RD, Fu Y, Brusseau TA, Clements-Nolle K, Yang W (2018) Relationships among physical activity, sleep duration, diet, and academic achievement in a sample of adolescents. Prev Med Rep 12:71–74. https://doi.org/10.1016/j.pmedr.2018.08.014

Castanon N, Luheshi G, Layé S (2015) Role of neuroinflammation in the emotional and cognitive alterations displayed by animal models of obesity. Front Neurosci 9:229. https://doi.org/10.3389/fnins.2015.00229

Chen M, Zhang X, Liang Y, Xue H, Gong Y, Xiong J, He F, Yang Y, Cheng G (2019) Associations between nocturnal sleep duration, midday nap duration and body composition among adults in Southwest China. PLoS ONE 14(10):e0223665. https://doi.org/10.1371/journal.pone.0223665

Choi K, Shin C, Kim T, Chung HJ, Suk H-J (2019) Awakening effects of blue-enriched morning light exposure on university students’ physiological and subjective responses. Sci Rep 9(1):345. https://doi.org/10.1038/s41598-018-36791-5

Choshen-Hillel S, Ishqer A, Mahameed F, Reiter J, Gozal D, Gileles-Hillel A, Berger I (2020) Acute and chronic sleep deprivation in residents: cognition and stress biomarkers. Med Educ. https://doi.org/10.1111/medu.14296

Cormier RE (1990) Sleep disturbances. In: Walker HK, Hall WD, Hurst JW (eds) Clinical methods: the history, physical, and laboratory examinations, 3rd edn. Butterworths.

Curcio G, Ferrara M, De Gennaro L (2006) Sleep loss, learning capacity and academic performance. Sleep Med Rev 10(5):323–337. https://doi.org/10.1016/j.smrv.2005.11.001

Davies G, Lam M, Harris SE, Trampush JW, Luciano M, Hill WD, Hagenaars SP, Ritchie SJ, Marioni RE, Fawns-Ritchie C, Liewald DCM, Okely JA, Ahola-Olli AV, Barnes CLK, Bertram L, Bis JC, Burdick KE, Christoforou A, DeRosse P, Deary IJ (2018) Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat Commun 9(1):2098. https://doi.org/10.1038/s41467-018-04362-x

Davis KL, Montag C (2019) Selected principles of pankseppian affective neuroscience. Front Neurosci 12. https://doi.org/10.3389/fnins.2018.01025

Dewald JF, Meijer AM, Oort FJ, Kerkhof GA, Bögels SM (2010) The influence of sleep quality, sleep duration and sleepiness on school performance in children and adolescents: a meta-analytic review. Sleep Med Rev 14(3):179–189. https://doi.org/10.1016/j.smrv.2009.10.004

Djonlagic I, Mariani S, Fitzpatrick AL, Van Der Klei V.M.G.T.H, Johnson DA, Wood AC, Seeman T, Nguyen HT, Prerau MJ, Luchsinger JA, Dzierzewski JM, Rapp SR, Tranah GJ, Yaffe K, Burdick KE, Stone KL, Redline S, Purcell SM (2021) Macro and micro sleep architecture and cognitive performance in older adults. Nat Hum Behav 5, 123–145. https://doi.org/10.1038/s41562-020-00964-y

Euser AS, Franken IHA (2012) Alcohol affects the emotional modulation of cognitive control: an event-related brain potential study. Psychopharmacology 222(3):459–476. https://doi.org/10.1007/s00213-012-2664-6

Evers K, Chen S, Rothmann S, Dhir A, Pallesen S (2020) Investigating the relation among disturbed sleep due to social media use, school burnout, and academic performance. J Adolesc 84:156–164. https://doi.org/10.1016/j.adolescence.2020.08.011

Fattinger S, de Beukelaar TT, Ruddy KL, Volk C, Heyse NC, Herbst JA, Hahnloser RHR, Wenderoth N, Huber R (2017) Deep sleep maintains learning efficiency of the human brain. Nat Commun 8:15405. https://doi.org/10.1038/ncomms15405

Fenn KM, Nusbaum HC, Margoliash D (2003) Consolidation during sleep of perceptual learning of spoken language. Nature 425(6958):614–616. https://doi.org/10.1038/nature01951

Article   ADS   CAS   PubMed   Google Scholar  

Firth, J, Gangwisch, JE, Borsini, A, Wootton, RE, & Mayer, EA (2020). Food and mood: how do diet and nutrition affect mental wellbeing? The BMJ 369. https://doi.org/10.1136/bmj.m2382

Foley DJ, Vitiello MV, Bliwise DL, Ancoli-Israel S, Monjan AA, Walsh JK (2007) Frequent napping is associated with excessive daytime sleepiness, depression, pain, and nocturia in older adults: findings from the National Sleep Foundation ‘2003 Sleep in America’ Poll. Am J Geriatr Psychiatry 15(4):344–350. https://doi.org/10.1097/01.JGP.0000249385.50101.67

Francis HM, Stevenson RJ, Chambers JR, Gupta D, Newey B, Lim CK (2019) A brief diet intervention can reduce symptoms of depression in young adults—a randomised controlled trial. PLoS ONE 14(10):e0222768. https://doi.org/10.1371/journal.pone.0222768

Friedrich M, Mölle M, Friederici AD, Born J (2020) Sleep-dependent memory consolidation in infants protects new episodic memories from existing semantic memories. Nat Commun 11(1):1298. https://doi.org/10.1038/s41467-020-14850-8

Gaultney JF (2010) The prevalence of sleep disorders in college students: Impact on academic performance. J Am College Health 59(2):91–97. https://doi.org/10.1080/07448481.2010.483708

Germain A, Kupfer DJ (2008) Circadian rhythm disturbances in depression. Hum Psychopharmacol 23(7):571–585. https://doi.org/10.1002/hup.964

Gerstner JR, Yin JCP (2010) Circadian rhythms and memory formation. Nat Rev Neurosci 11(8):577–588. https://doi.org/10.1038/nrn2881

Giganti F, Arzilli C, Conte F, Toselli M, Viggiano MP, Ficca G (2014) The effect of a daytime nap on priming and recognition tasks in preschool children. Sleep 37(6):1087–1093. https://doi.org/10.5665/sleep.3766

Gillin JC, Kripke DF, Janowsky DS, Risch SC (1989) Effects of brief naps on mood and sleep in sleep-deprived depressed patients. Psychiatry Res 27(3):253–265. https://doi.org/10.1016/0165-1781(89)90141-8

González-Orozco JC, Camacho-Arroyo I (2019) Progesterone actions during central nervous system development. Front Neurosci 13:503. https://doi.org/10.3389/fnins.2019.00503

Gruber R, Laviolette R, Deluca P, Monson E, Cornish K, Carrier J (2010) Short sleep duration is associated with poor performance on IQ measures in healthy school-age children. Sleep Med 11(3):289–294. https://doi.org/10.1016/j.sleep.2009.09.007

Gualano MR, Lo Moro G, Voglino G, Bert F, Siliquini R (2020) Effects of Covid-19 lockdown on mental health and sleep disturbances in Italy. Int J Environ Res Public Health 17(13). https://doi.org/10.3390/ijerph17134779

Hafner M, Stepanek M, Taylor J, Troxel WM, van Stolk C (2017) Why sleep matters-the economic costs of insufficient sleep: a Cross-Country Comparative Analysis. Rand Health Q 6(4):11

PubMed   PubMed Central   Google Scholar  

Hagewoud R, Whitcomb SN, Heeringa AN, Havekes R, Koolhaas JM, Meerlo P (2010) A time for learning and a time for sleep: the effect of sleep deprivation on contextual fear conditioning at different times of the day Sleep 33(10):1315–1322. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2941417/

Hama S, Yoshimura K, Yanagawa A, Shimonaga K, Furui A, Soh Z, Nishino S, Hirano H, Yamawaki S, Tsuji T (2020) Relationships between motor and cognitive functions and subsequent post-stroke mood disorders revealed by machine learning analysis. Sci Rep 10(1):19571. https://doi.org/10.1038/s41598-020-76429-z

Hare DL, Toukhsati SR, Johansson P, Jaarsma T (2014) Depression and cardiovascular disease: a clinical review. Eur Heart J 35(21):1365–1372. https://doi.org/10.1093/eurheartj/eht462

Hayley AC, Sivertsen B, Hysing M, Vedaa Ø, Øverland S (2017) Sleep difficulties and academic performance in Norwegian higher education students. Br J Educ Psychol 87(4):722–737. https://doi.org/10.1111/bjep.12180

Hays JC, Blazer DG, Foley DJ (1996) Risk of napping: excessive daytime sleepiness and mortality in an older community population. J Am Geriatr Soc 44(6):693–698. https://doi.org/10.1111/j.1532-5415.1996.tb01834.x

Haywood X, Pienaar AE (2021) Long-term influences of stunting, being underweight, and thinness on the academic performance of primary school girls: the NW-CHILD Study. Int J Environ Res Public Health 18(17):8973. https://doi.org/10.3390/ijerph18178973

Healthy Diet—an overview|ScienceDirect Topics (n.d.) https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/healthy-diet . Accessed 4 Dec 2021.

Hedin G, Norell-Clarke A, Hagell P, Tønnesen H, Westergren A, Garmy P (2020). Insomnia in relation to academic performance, self-reported health, physical activity, and substance use among adolescents. Int J Environ Res Public Health 17(17). https://doi.org/10.3390/ijerph17176433

Hershner SD, Chervin RD (2014) Causes and consequences of sleepiness among college students. Nat Sci Sleep 6:73–84. https://doi.org/10.2147/NSS.S62907

Hidese S, Ota M, Matsuo J, Ishida I, Hiraishi M, Yoshida S, Noda T, Sato N, Teraishi T, Hattori K, Kunugi H (2018) Association of obesity with cognitive function and brain structure in patients with major depressive disorder. J Affect Disord 225:188–194. https://doi.org/10.1016/j.jad.2017.08.028

Hindash AHC, Amir N (2012) Negative interpretation bias in individuals with depressive symptoms. Cogn Ther Res 36(5):502–511. https://doi.org/10.1007/s10608-011-9397-4

Hiroi R, Weyrich G, Koebele SV, Mennenga SE, Talboom JS, Hewitt LT, Lavery CN, Mendoza P, Jordan A, Bimonte-Nelson HA (2016) Benefits of hormone therapy estrogens depend on estrogen type: 17β-estradiol and conjugated equine estrogens have differential effects on cognitive, anxiety-like, and depressive-like behaviors and increase tryptophan hydroxylase-2 mRNA levels in dorsal raphe nucleus subregions. Front Neurosci 10:517. https://doi.org/10.3389/fnins.2016.00517

Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, Hazen N, Herman J, Katz ES, Kheirandish-Gozal L, Neubauer DN, O’Donnell AE, Ohayon M, Peever J, Rawding R, Sachdeva RC, Setters B, Vitiello MV, Ware JC, Adams Hillard PJ (2015) National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health 1(1):40–43. https://doi.org/10.1016/j.sleh.2014.12.010

Horváth K, Liu S, Plunkett K (2016) A daytime nap facilitates generalization of word meanings in young toddlers. Sleep 39(1):203–207. https://doi.org/10.5665/sleep.5348

Htet H, Saw YM, Saw TN, Htun NMM, Mon KL, Cho SM, Thike T, Khine AT, Kariya T, Yamamoto E, Hamajima N (2020) Prevalence of alcohol consumption and its risk factors among university students: a cross-sectional study across six universities in Myanmar. PLoS ONE 15(2):e0229329. https://doi.org/10.1371/journal.pone.0229329

Huang Q, Liu H, Suzuki K, Ma S, Liu C (2019) Linking what we eat to our mood: a review of diet, dietary antioxidants, and depression. Antioxidants 8(9). https://doi.org/10.3390/antiox8090376

Huber R, Ghilardi MF, Massimini M, Tononi G (2004) Local sleep and learning. Nature 430(6995):78–81. https://doi.org/10.1038/nature02663

Ishihara T, Nakajima T, Yamatsu K, Okita K, Sagawa M, Morita N (2020) Longitudinal relationship of favorable weight change to academic performance in children. npj Sci Learn 5(1):1–8. https://doi.org/10.1038/s41539-020-0063-z

Jahrami H, BaHammam AS, Bragazzi NL, Saif Z, Faris M, Vitiello MV (2021) Sleep problems during the COVID-19 pandemic by population: a systematic review and meta-analysis. J Clin Sleep Med 17(2):299–313. https://doi.org/10.5664/jcsm.8930

Jalali R, Khazaei H, Paveh BK, Hayrani Z, Menati L (2020) The effect of sleep quality on students’ academic achievement. Adv Med Educ Pract 11:497–502. https://doi.org/10.2147/AMEP.S261525

James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, Abdelalim A, Abdollahpour I, Abdulkader RS, Abebe Z, Abera SF, Abil OZ, Abraha HN, Abu-Raddad LJ, Abu-Rmeileh NME, Accrombessi MMK, Murray CJL (2018) Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392(10159):1789–1858. https://doi.org/10.1016/S0140-6736(18)32279-7

Janati Idrissi A, Lamkaddem A, Benouajjit A, Ben El Bouaazzaoui M, El Houari F, Alami M, Labyad S, Chahidi A, Benjelloun M, Rabhi S, Kissani N, Zarhbouch B, Ouazzani R, Kadiri F, Alouane R, Elbiaze M, Boujraf S, El Fakir S, Souirti Z (2020) Sleep quality and mental health in the context of COVID-19 pandemic and lockdown in Morocco. Sleep Med 74:248–253. https://doi.org/10.1016/j.sleep.2020.07.045

Javaid R, Momina AU, Sarwar MZ, Naqi SA (2020) Quality of sleep and academic performance among medical university students. J College Physicians Surg-Pakistan 30(8):844–848. https://doi.org/10.29271/jcpsp.2020.08.844

Johnston A, Gradisar M, Dohnt H, Billows M, Mccappin S (2010) Adolescent sleep and fluid intelligence performance. Sleep Biol Rhythm 8(3):180–186. https://doi.org/10.1111/j.1479-8425.2010.00442.x

Kaida K, Itaguchi Y, Iwaki S (2017) Interactive effects of visuomotor perturbation and an afternoon nap on performance and the flow experience. PLoS ONE 12(2):e0171907. https://doi.org/10.1371/journal.pone.0171907

Kayaba M, Matsushita T, Enomoto M, Kanai C, Katayama N, Inoue Y, Sasai-Sakuma T (2020) Impact of sleep problems on daytime function in school life: a cross-sectional study involving Japanese university students. BMC Public Health 20(1):371. https://doi.org/10.1186/s12889-020-08483-1

Kleinstäuber M (2013) Mood. In: Gellman MD, Turner JR (eds) Encyclopedia of behavioral medicine. Springer, pp. 1259–1261

Kline C (2013a) Sleep duration. In: Gellman MD, Turner JR (eds) Encyclopedia of behavioral medicine. Springer, pp. 1808–1810

Kline C (2013b) Sleep quality. In: Gellman MD, Turner JR (eds) Encyclopedia of behavioral medicine. Springer, pp. 1811–1813

Knüppel A, Shipley MJ, Llewellyn CH, Brunner EJ (2017) Sugar intake from sweet food and beverages, common mental disorder and depression: prospective findings from the Whitehall II study. Sci Rep 7. https://doi.org/10.1038/s41598-017-05649-7

Ko F-Y, Yang AC, Tsai S-J, Zhou Y, Xu L-M (2013) Physiologic and laboratory correlates of depression, anxiety, and poor sleep in liver cirrhosis. BMC Gastroenterol 13:18. https://doi.org/10.1186/1471-230X-13-18

Kodali M, Parihar VK, Hattiangady B, Mishra V, Shuai B, Shetty AK (2015) Resveratrol prevents age-related memory and mood dysfunction with increased hippocampal neurogenesis and microvasculature, and reduced glial activation. Sci Rep 5:8075. https://doi.org/10.1038/srep08075

Kurniawan IT, Cousins JN, Chong PLH, Chee MWL (2016) Procedural performance following sleep deprivation remains impaired despite extended practice and an afternoon nap. Sci Rep 6:36001. https://doi.org/10.1038/srep36001

Kyle SD, Sexton CE, Feige B, Luik AI, Lane J, Saxena R, Anderson SG, Bechtold DA, Dixon W, Little MA, Ray D, Riemann D, Espie CA, Rutter MK, Spiegelhalder K (2017) Sleep and cognitive performance: cross-sectional associations in the UK Biobank. Sleep Med 38:85–91. https://doi.org/10.1016/j.sleep.2017.07.001

LaGrotte C, Fernandez-Mendoza J, Calhoun SL, Liao D, Bixler EO, Vgontzas AN (2005) (2016). The relative association of obstructive sleep apnea, obesity and excessive daytime sleepiness with incident depression: a longitudinal, population-based study. Int J Obes 40(9):1397–1404. https://doi.org/10.1038/ijo.2016.87

Article   CAS   Google Scholar  

Lalanza JF, Sanchez-Roige S, Cigarroa I, Gagliano H, Fuentes S, Armario A, Capdevila L, Escorihuela RM (2015) Long-term moderate treadmill exercise promotes stress-coping strategies in male and female rats. Sci Rep 5:16166. https://doi.org/10.1038/srep16166

Lau H, Alger SE, Fishbein W (2011) Relational memory: a daytime nap facilitates the abstraction of general concepts. PLoS ONE 6(11):e27139. https://doi.org/10.1371/journal.pone.0027139

Lee D (2015) Global and local missions of cAMP signaling in neural plasticity, learning, and memory. Front Pharmacol 6:161. https://doi.org/10.3389/fphar.2015.00161

LeGates TA, Altimus CM, Wang H, Lee H-K, Yang S, Zhao H, Kirkwood A, Weber ET, Hattar S (2012) Aberrant light directly impairs mood and learning through melanopsin-expressing neurons. Nature 491(7425):594–598. https://doi.org/10.1038/nature11673

Levesque RJR (2018) Sleep deprivation. In: Levesque RJR (ed) Encyclopedia of adolescence. Springer International Publishing, pp. 3606–3607

Li L-H, Wang Z-C, Yu J, Zhang Y-Q (2014) Ovariectomy results in variable changes in nociception, mood and depression in adult female rats. PLoS ONE 9(4):e94312. https://doi.org/10.1371/journal.pone.0094312

Lipinska G, Stuart B, Thomas KGF, Baldwin DS, Bolinger E (2019) Preferential consolidation of emotional memory during sleep: a meta-analysis. Front Psychol 10:1014. https://doi.org/10.3389/fpsyg.2019.01014

Liu X, Xu X, Wang H (2018) The effect of emotion on morphosyntactic learning in foreign language learners. PLoS ONE 13(11):e0207592. https://doi.org/10.1371/journal.pone.0207592

Liu Y, Peng T, Zhang S, Tang K (2018) The relationship between depression, daytime napping, daytime dysfunction, and snoring in 0.5 million Chinese populations: exploring the effects of socio-economic status and age. BMC Public Health 18(1):759. https://doi.org/10.1186/s12889-018-5629-9

Livermore M, Duncan MJ, Leatherdale ST, Patte KA (2020) Are weight status and weight perception associated with academic performance among youth? J Eat Disord 8:52. https://doi.org/10.1186/s40337-020-00329-w

Louca M, Short MA (2014) The effect of one night’s sleep deprivation on adolescent neurobehavioral performance. Sleep 37(11):1799–1807. https://doi.org/10.5665/sleep.4174

Mantua J, Spencer RMC (2017) Exploring the nap paradox: are mid-day sleep bouts a friend or foe? Sleep Med 37:88–97. https://doi.org/10.1016/j.sleep.2017.01.019

Marelli S, Castelnuovo A, Somma A, Castronovo V, Mombelli S, Bottoni D, Leitner C, Fossati A, Ferini-Strambi L (2020) Impact of COVID-19 lockdown on sleep quality in university students and administration staff. J Neurol 1–8. https://doi.org/10.1007/s00415-020-10056-6

Marta OFD, Kuo S-Y, Bloomfield J, Lee H-C, Ruhyanudin F, Poynor MY, Brahmadhi A, Pratiwi ID, Aini N, Mashfufa EW, Hasan F, Chiu H-Y (2020) Gender differences in the relationships between sleep disturbances and academic performance among nursing students: a cross-sectional study. Nurse Educ Today 85:104270. https://doi.org/10.1016/j.nedt.2019.104270

Martin EA, Kerns JG (2011) The influence of positive mood on different aspects of cognitive control. Cogn Emotion 25(2):265–279. https://doi.org/10.1080/02699931.2010.491652

Masumoto K, Taishi N, Shiozaki M (2016) Age and gender differences in relationships among emotion regulation, mood, and mental health. Gerontol Geriatr Med 2. https://doi.org/10.1177/2333721416637022

Matsumoto Y, Katayama K, Okamoto T, Yamada K, Takashima N, Nagao S, Aruga J (2011) Impaired auditory-vestibular functions and behavioral abnormalities of Slitrk6-deficient mice. PLoS ONE 6(1):e16497. https://doi.org/10.1371/journal.pone.0016497

McDevitt EA, Sattari N, Duggan KA, Cellini N, Whitehurst LN, Perera C, Reihanabad N, Granados S, Hernandez L, Mednick SC (2018) The impact of frequent napping and nap practice on sleep-dependent memory in humans. Sci Rep 8(1):15053. https://doi.org/10.1038/s41598-018-33209-0

McGee DL, Diverse Populations Collaboration (2005) Body mass index and mortality: a meta-analysis based on person-level data from twenty-six observational studies. Ann Epidemiol 15(2):87–97. https://doi.org/10.1016/j.annepidem.2004.05.012

McNamara A, Moakes K, Aston P, Gavin C, Sterr A (2014) The importance of the derivative in sex-hormone cycles: a reason why behavioural measures in sex-hormone studies are so mercurial. PLoS ONE 9(11):e111891. https://doi.org/10.1371/journal.pone.0111891

Mehra R, Patel SR (2012) To nap or not to nap: that is the question. Sleep 35(7):903–904. https://doi.org/10.5665/Sleep.1946

Mendelsohn D, Despot I, Gooderham PA, Singhal A, Redekop GJ, Toyota BD (2019) Impact of work hours and sleep on well-being and burnout for physicians-in-training: the Resident Activity Tracker Evaluation Study. Med Educ 53(3):306–315. https://doi.org/10.1111/medu.13757

Miller ZF, Fox JK, Moser JS, Godfroid A (2018) Playing with fire: effects of negative mood induction and working memory on vocabulary acquisition. Cogn Emotion 32(5):1105–1113. https://doi.org/10.1080/02699931.2017.1362374

Milner CE, Cote KA (2009) Benefits of napping in healthy adults: impact of nap length, time of day, age, and experience with napping. J Sleep Res 18(2):272–281. https://doi.org/10.1111/j.1365-2869.2008.00718.x

Mnatzaganian CL, Atayee RS, Namba JM, Brandl K, Lee KC (2020) The effect of sleep quality, sleep components, and environmental sleep factors on core curriculum exam scores among pharmacy students. Curr Pharm Teach Learn 12(2):119–126. https://doi.org/10.1016/j.cptl.2019.11.004

Mograss M, Crosetta M, Abi-Jaoude J, Frolova E, Robertson EM, Pepin V, Dang-Vu TT (2020) Exercising before a nap benefits memory better than napping or exercising alone. Sleep 43(9). https://doi.org/10.1093/sleep/zsaa062

Msetfi RM, Murphy RA, Kornbrot DE (2012) Dysphoric mood states are related to sensitivity to temporal changes in contingency. Front Psychol 3:368. https://doi.org/10.3389/fpsyg.2012.00368

Nieh EH, Kim S-Y, Namburi P, Tye KM (2013) Optogenetic dissection of neural circuits underlying emotional valence and motivated behaviors. Brain Res 1511:73–92. https://doi.org/10.1016/j.brainres.2012.11.001

Nishizawa S, Benkelfat C, Young SN, Leyton M, Mzengeza S, de Montigny C, Blier P, Diksic M(1997) Differences between males and females in rates of serotonin synthesis in human brain Proc Natl Acad Sci USA 94(10):5308–5313. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC24674/

Norman KA (2006) Declarative memory: sleep protects new memories from interference. Curr Biol 16(15):R596–R597. https://doi.org/10.1016/j.cub.2006.07.008

Nösslinger H, Mair E, Toplak H, Hörmann-Wallner M (2021) Underestimation of resting metabolic rate using equations compared to indirect calorimetry in normal-weight subjects: consideration of resting metabolic rate as a function of body composition. Clin Nutr Open Sci 35:48–66. https://doi.org/10.1016/j.nutos.2021.01.003

Okano K, Kaczmarzyk JR, Dave N, Gabrieli JDE, Grossman JC (2019) Sleep quality, duration, and consistency are associated with better academic performance in college students. npj Sci Learn 4(1):1–5. https://doi.org/10.1038/s41539-019-0055-z

Ong JL, Lau TY, Lee XK, van Rijn E, Chee MWL (2020) A daytime nap restores hippocampal function and improves declarative learning. Sleep 43(9). https://doi.org/10.1093/sleep/zsaa058

Patterson SS, Kuchenbecker JA, Anderson JR, Neitz M, Neitz J (2020) A color vision circuit for non-image-forming vision in the primate retina. Curr Biol 30(7):1269–1274.e2. https://doi.org/10.1016/j.cub.2020.01.040

Pearson H (2006) Medicine: sleep it off. Nature 443(7109):261–263. https://doi.org/10.1038/443261a

Perez-Chada D, Perez-Lloret S, Videla AJ, Cardinali D, Bergna MA, Fernández-Acquier M, Larrateguy L, Zabert GE, Drake C (2007) Sleep disordered breathing and daytime sleepiness are associated with poor academic performance in teenagers. a study using the Pediatric Daytime Sleepiness Scale (PDSS) Sleep 30(12):1698–1703. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2276125/

Perogamvros L, Dang-Vu TT, Desseilles M, Schwartz S (2013) Sleep and dreaming are for important matters. Front Psychol 4:474. https://doi.org/10.3389/fpsyg.2013.00474

Phillips AJK, Clerx WM, O’Brien CS, Sano A, Barger LK, Picard RW, Lockley SW, Klerman EB, Czeisler CA (2017) Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing. Sci Rep 7(1):3216. https://doi.org/10.1038/s41598-017-03171-4

Popkin BM, D’Anci KE, Rosenberg IH (2010) Water, hydration and health. Nutr Rev 68(8):439–458. https://doi.org/10.1111/j.1753-4887.2010.00304.x

Quartiroli A, Terry PC, Fogarty GJ (2017) Development and initial validation of the Italian Mood Scale (ITAMS) for use in sport and exercise contexts. Front Psychol 8:1483. https://doi.org/10.3389/fpsyg.2017.01483

Ren C, Tong YL, Li JC, Lu ZQ, Yao YM (2017) The protective effect of alpha 7 nicotinic acetylcholine receptor activation on critical illness and its mechanism. Int J Biol Sci 13(1):46–56. https://doi.org/10.7150/ijbs.16404

Restivo MR, McKinnon MC, Frey BN, Hall GB, Syed W, Taylor VH (2017) The impact of obesity on neuropsychological functioning in adults with and without major depressive disorder. PLoS ONE 12(5):e0176898. https://doi.org/10.1371/journal.pone.0176898

Riebl SK, Davy BM (2013) The hydration equation: update on water balance and cognitive performance. ACSM’s Health Fit J 17(6):21–28. https://doi.org/10.1249/FIT.0b013e3182a9570f

Roberts RE, Duong HT (2014) The prospective association between sleep deprivation and depression among adolescents. Sleep 37(2):239–244. https://doi.org/10.5665/sleep.3388

Roenneberg T (2013) Chronobiology: the human sleep project. Nature 498(7455):427–428. https://doi.org/10.1038/498427a

Sarris J, Thomson R, Hargraves F, Eaton M, de Manincor M, Veronese N, Solmi M, Stubbs B, Yung AR, Firth J (2020) Multiple lifestyle factors and depressed mood: a cross-sectional and longitudinal analysis of the UK Biobank ( N  = 84,860). BMC Med 18:354. https://doi.org/10.1186/s12916-020-01813-5

Scholey A, Owen L (2013) Effects of chocolate on cognitive function and mood: a systematic review. Nutr Rev 71(10):665–681. https://doi.org/10.1111/nure.12065

Schönauer M, Alizadeh S, Jamalabadi H, Abraham A, Pawlizki A, Gais S (2017) Decoding material-specific memory reprocessing during sleep in humans. Nat Commun 8:15404. https://doi.org/10.1038/ncomms15404

Scullin MK, Fairley J, Decker MJ, Bliwise DL (2017) The effects of an afternoon nap on episodic memory in young and older adults. Sleep 40(5). https://doi.org/10.1093/sleep/zsx035

Scully T (2013) Sleep. Nature 497(7450):S1–S3. https://doi.org/10.1038/497S1a

Sekhon S, Gupta V (2021) Mood disorder. StatPearls Publishing.

Seoane HA, Moschetto L, Orliacq F, Orliacq J, Serrano E, Cazenave MI, Vigo DE, Perez-Lloret S (2020) Sleep disruption in medicine students and its relationship with impaired academic performance: a systematic review and meta-analysis. Sleep Med Rev 53:101333. https://doi.org/10.1016/j.smrv.2020.101333

Shimizu I, Yoshida Y, Minamino T (2016) A role for circadian clock in metabolic disease. Hypertens Res 39(7):483–491. https://doi.org/10.1038/hr.2016.12

Shochat T, Cohen-Zion M, Tzischinsky O (2014) Functional consequences of inadequate sleep in adolescents: a systematic review. Sleep Med Rev 18(1):75–87. https://doi.org/10.1016/j.smrv.2013.03.005

Short MA, Gradisar M, Lack LC, Wright HR (2013) The impact of sleep on adolescent depressed mood, alertness and academic performance. J Adolesc 36(6):1025–1033. https://doi.org/10.1016/j.adolescence.2013.08.007

Short MA, Louca M (2015) Sleep deprivation leads to mood deficits in healthy adolescents. Sleep Med 16(8):987–993. https://doi.org/10.1016/j.sleep.2015.03.007

Singh M (2014) Mood, food, and obesity. Front Psychol 5. https://doi.org/10.3389/fpsyg.2014.00925

Sivertsen B, Glozier N, Harvey AG, Hysing M (2015) Academic performance in adolescents with delayed sleep phase. Sleep Med 16(9):1084–1090. https://doi.org/10.1016/j.sleep.2015.04.011

Son C, Hegde S, Smith A, Wang X, Sasangohar F (2020) Effects of COVID-19 on college students’ mental health in the United States: Interview Survey Study. J Med Internet Res 22(9):e21279. https://doi.org/10.2196/21279

Spencer SJ, Korosi A, Layé S, Shukitt-Hale B, Barrientos RM (2017) Food for thought: how nutrition impacts cognition and emotion. NPJ Sci Food 1. https://doi.org/10.1038/s41538-017-0008-y

Štefan L, Sporiš G, Krističević T, Knjaz D (2018) Associations between sleep quality and its domains and insufficient physical activity in a large sample of Croatian young adults: a cross-sectional study. BMJ Open 8(7):e021902. https://doi.org/10.1136/bmjopen-2018-021902

Suardiaz-Muro M, Morante-Ruiz M, Ortega-Moreno M, Ruiz MA, Martín-Plasencia P, Vela-Bueno A (2020) [Sleep and academic performance in university students: a systematic review]. Rev Neurol 71(2):43–53. https://doi.org/10.33588/rn.7102.2020015

Sun W, Ling J, Zhu X, Lee TM-C, Li SX (2019) Associations of weekday-to-weekend sleep differences with academic performance and health-related outcomes in school-age children and youths. Sleep Med Rev 46:27–53. https://doi.org/10.1016/j.smrv.2019.04.003

Sundström Poromaa I, Gingnell M (2014) Menstrual cycle influence on cognitive function and emotion processing-from a reproductive perspective. Front Neurosci 8:380. https://doi.org/10.3389/fnins.2014.00380

Sweileh WM, Ali IA, Sawalha AF, Abu-Taha AS, Zyoud SH, Al-Jabi SW (2011) Sleep habits and sleep problems among Palestinian students. Child Adolesc Psychiatry Mental Health 5(1):25. https://doi.org/10.1186/1753-2000-5-25

Taras H, Potts-Datema W (2005) Sleep and student performance at school. J School Health 75(7):248–254. https://doi.org/10.1111/j.1746-1561.2005.00033.x

Teodori L, Albertini MC (2019) Shedding light into memories under circadian rhythm system control. Proc Natl Acad Sci USA 116(17):8099–8101. https://doi.org/10.1073/pnas.1903413116

Thibaut F (2015) Emotional processing needs further study in major psychiatric diseases Dialogues Clin Neurosci 17(4):359. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734874/

Toscano-Hermoso MD, Arbinaga F, Fernández-Ozcorta EJ, Gómez-Salgado J, Ruiz-Frutos C (2020) Influence of sleeping patterns in health and academic performance among University Students. Int J Environ Res Public Health 17(8). https://doi.org/10.3390/ijerph17082760

Triantafillou S, Saeb S, Lattie EG, Mohr DC, Kording KP (2019) Relationship between sleep quality and mood: Ecological Momentary Assessment Study. JMIR Mental Health 6(3). https://doi.org/10.2196/12613

Tyng CM, Amin HU, Saad MNM, Malik AS (2017) The influences of emotion on learning and memory. Front Psychol 8. https://doi.org/10.3389/fpsyg.2017.01454

Valiente C, Swanson J, Eisenberg N (2012) Linking students’ emotions and academic achievement: when and why emotions matter. Child Dev Perspect 6(2):129–135. https://doi.org/10.1111/j.1750-8606.2011.00192.x

Vandekerckhove M, Wang Y (2017) Emotion, emotion regulation and sleep: an intimate relationship. AIMS Neurosci 5(1):1–17. https://doi.org/10.3934/Neuroscience.2018.1.1

Veasey S, Rosen R, Barzansky B, Rosen I, Owens J (2002) Sleep loss and fatigue in residency training: a reappraisal. JAMA 288(9):1116–1124. https://doi.org/10.1001/jama.288.9.1116

Vecsey CG, Baillie GS, Jaganath D, Havekes R, Daniels A, Wimmer M, Huang T, Brown KM, Li X-Y, Descalzi G, Kim SS, Chen T, Shang Y-Z, Zhuo M, Houslay MD, Abel T (2009) Sleep deprivation impairs cAMP signalling in the hippocampus. Nature 461(7267):1122–1125. https://doi.org/10.1038/nature08488

Wagner U, Gais S, Haider H, Verleger R, Born J (2004) Sleep inspires insight. Nature 427(6972):352–355. https://doi.org/10.1038/nature02223

Walker WH, Walton JC, DeVries AC, Nelson RJ (2020) Circadian rhythm disruption and mental health. Transl Psychiatry 10(1):1–13. https://doi.org/10.1038/s41398-020-0694-0

Wang X, Chen H, Liu L, Liu Y, Zhang N, Sun Z, Lou Q, Ge W, Hu B, Li M (2020) Anxiety and sleep problems of college students during the outbreak of COVID-19. Front Psychiatry 11. https://doi.org/10.3389/fpsyt.2020.588693

Wiegand M, Riemann D, Schreiber W, Lauer CJ, Berger M (1993) Effect of morning and afternoon naps on mood after total sleep deprivation in patients with major depression. Biol Psychiatry 33(6):467–476. https://doi.org/10.1016/0006-3223(93)90175-d

Woodrow SI, Park J, Murray BJ, Wang C, Bernstein M, Reznick RK, Hamstra SJ (2008) Differences in the perceived impact of sleep deprivation among surgical and non-surgical residents. Med Educ 42(5):459–467. https://doi.org/10.1111/j.1365-2923.2007.02963.x

Worthy DA, Byrne KA, Fields S (2014) Effects of emotion on prospection during decision-making. Front Psychol 5:591. https://doi.org/10.3389/fpsyg.2014.00591

Yabut JM, Crane JD, Green AE, Keating DJ, Khan WI, Steinberg GR (2019) Emerging roles for serotonin in regulating metabolism: new implications for an ancient molecule. Endocr Rev 40(4):1092–1107. https://doi.org/10.1210/er.2018-00283

Yin J, Chen W, Yang H, Xue M, Schaaf CP (2017) Chrna7 deficient mice manifest no consistent neuropsychiatric and behavioral phenotypes. Sci Rep 7:39941. https://doi.org/10.1038/srep39941

Zavodny M (2013) Does weight affect children’s test scores and teacher assessments differently? Econ Educ Rev 34:135–145. https://doi.org/10.1016/j.econedurev.2013.02.003

Zerbini G, van der Vinne V, Otto LKM, Kantermann T, Krijnen WP, Roenneberg T, Merrow M (2017) Lower school performance in late chronotypes: underlying factors and mechanisms. Sci Rep 7(1):4385. https://doi.org/10.1038/s41598-017-04076-y

Zhang L, Liu S, Liu X, Zhang B, An X, Ming D (2021) Emotional arousal and valence jointly modulate the auditory response: a 40-Hz ASSR study. IEEE Trans Neural Syst Rehabil Eng 29:1150–1157. https://doi.org/10.1109/TNSRE.2021.3088257

Zhang N, Du SM, Zhang JF, Ma GS (2019) Effects of dehydration and rehydration on cognitive performance and mood among male college students in Cangzhou, China: a self-controlled trial. Int J Environ Res Public Health 16(11) https://doi.org/10.3390/ijerph16111891

Zhao H, Zhang X, Xu Y, Gao L, Ma Z, Sun Y, Wang W (2021) Predicting the risk of hypertension based on several easy-to-collect risk factors: a machine learning method. Front Public Health 9:619429. https://doi.org/10.3389/fpubh.2021.619429

Zhu B, Vincent C, Kapella MC, Quinn L, Collins EG, Ruggiero L, Park C, Fritschi C (2018) Sleep disturbance in people with diabetes: a concept analysis. J Clin Nurs 27(1–2):e50–e60. https://doi.org/10.1111/jocn.14010

Zhu Y, Gao H, Tong L, Li Z, Wang L, Zhang C, Yang Q, Yan B (2019) Emotion regulation of hippocampus using real-time fMRI neurofeedback in healthy human. Front Hum Neurosci 13. https://doi.org/10.3389/fnhum.2019.00242

Download references

Author information

Authors and affiliations.

Centre for Education, Faculty of Life Sciences and Medicine, King’s College London, London, UK

Kosha J. Mehta

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualisation, composition, and writing: KJM.

Corresponding author

Correspondence to Kosha J. Mehta .

Ethics declarations

Competing interests.

The author declares no competing interests.

Informed consent

Not applicable.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Mehta, K.J. Effect of sleep and mood on academic performance—at interface of physiology, psychology, and education. Humanit Soc Sci Commun 9 , 16 (2022). https://doi.org/10.1057/s41599-021-01031-1

Download citation

Received : 24 June 2021

Accepted : 31 December 2021

Published : 11 January 2022

DOI : https://doi.org/10.1057/s41599-021-01031-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

The role of school connectedness and friend contact in adolescent loneliness, and implications for physical health.

• Yixuan Zheng
• Margarita Panayiotou
• Joanna Inchley

Child Psychiatry & Human Development (2024)

Neurocognitive and mental health outcomes in children with tungiasis: a cross-sectional study in rural Kenya and Uganda

• Berrick Otieno
• Lynne Elson
• Amina Abubakar

Infectious Diseases of Poverty (2023)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.43(12); 2018 Dec

The Extraordinary Importance of Sleep

New sleep deprivation studies confirm the relationship between inadequate sleep and a wide range of disorders, such as hypertension, obesity and type-2 diabetes, cardiovascular disease, impaired immune functioning, and more.

In the inaugural issue of the Journal of Clinical Sleep Medicine (2005), a feature article 1 traced early milestones in the developing field of sleep medicine, which slowly emerged from the older field of sleep research during the 1970s and 1980s. Sleep medicine, the article noted, was closely linked with and made possible by the discovery of electrical activity in the brain. The examination of electroencephalogram (EEG) patterns that occur during sleep led to the classification of stages of sleep, which in turn created an important foundation for probing human sleep, discerning abnormalities, and discovering significant relationships between sleep and health. By 2005, scientists and clinicians had not only identified and clearly defined a large number of sleep disorders but had discovered that many of them were highly prevalent.

The pace of research and discovery has only accelerated since 2005, and the number of peer-reviewed sleep journals has more than tripled. Today, researchers are more deeply probing the cellular and subcellular effects of disrupted sleep, as well as the effects of sleep deprivation on metabolism, hormone regulation, and gene expression. Newer studies are strengthening known and suspected relationships between inadequate sleep and a wide range of disorders, including hypertension, 2 obesity and type-2 diabetes, 3 impaired immune functioning, 4 cardiovascular disease and arrhythmias, 5 , 6 mood disorders, 7 neurodegeneration and dementia, 8 , 9 and even loneliness. 10

Research findings continue to underscore early concerns about public safety that were first raised when major industrial disasters such as the Exxon Valdez oil spill were linked to inadequate sleep. 11 Related research sponsored by major organizations, including the U.S. Department of Transportation, the U.S. Department of Defense, the National Institutes of Health, and the National Aeronautics and Space Administration (NASA), has helped to inspire national initiatives aimed at improving public safety and health. However, despite the astounding acceleration in research during the past few decades, inadequate sleep due to sleep disorders, work schedules, and chaotic lifestyles continues to threaten both health and safety.

“Pushing against the wave of accelerated growth in the field has been a shoreline of indifference,” says David F. Dinges, PhD, Professor and Chief of the Division of Sleep and Chronobiology in the Department of Psychiatry at the University of Pennsylvania Perelman School of Medicine. “Modern industrial pressures to use time 24 hours a day have led to shiftwork and a world in which virtually everything—law Susan L. Worley is a freelance medical writer who resides in Pennsylvania. enforcement, airports and all kinds of transportation, industrial operations, and hospitals—operates 24/7. People have come to value time so much that sleep is often regarded as an annoying interference, a wasteful state that you enter into when you do not have enough willpower to work harder and longer.”

David F. Dinges, PhD

It has become increasingly clear, however, that no matter how hectic our lives may be, we can no longer afford to ignore what research is telling us about the importance of sleep for our safety and mental and physical well-being.

Impact on Attention, Cognition, and Mood

While scientists are still working to identify and clarify all of the functions of sleep, 12 decades of studies—many of which have used the method of disrupting sleep and examining the consequences—have confirmed that sleep is necessary for our healthy functioning and even survival.

“We know for sure that sleep serves multiple functions,” says Dr. Dinges. “Nature tends to be very parsimonious in that it often uses a single system or biology in multiple ways to optimize the functioning of an organism. We know, for example, that sleep is critical for waking cognition—that is, for the ability to think clearly, to be vigilant and alert, and sustain attention. We also know that memories are consolidated during sleep, and that sleep serves a key role in emotional regulation.”

Studies conducted by Dr. Dinges and other scientists have shown that cognitive performance and vigilant attention begin to decline fairly quickly after more than 16 hours of continuous wakefulness, and that sleep deficits from partial sleep deprivation can accumulate over time, resulting in a steady deterioration in alertness. The widely used psychomotor vigilance test (PVT), a simple neurocognitive test developed by Dr. Dinges and colleagues that assesses an individual’s ability to sustain attention and respond to signals in a timely manner, has proven to be an exceptionally sensitive tool for capturing dose–response effects of sleep loss on neurobehavioral functioning. 13 The PVT also reliably detects sleep deficits caused by disrupted or fragmented sleep, and/or poorly timed sleep, which is important because a growing body of evidence suggests that the continuity and timing (or circadian alignment) of sleep may be as important as the total amount of time spent sleeping.

“We know that sleep is much more restorative of waking functions and health when it is consolidated and not fragmented,” explains Dr. Dinges. “That is, when sleep goes through the appropriate physiological sequences of non-REM (rapid eye movement) and REM states at night, and occurs when human sleep is temporally programmed by our circadian clock to occur. Such consolidated sleep is typically of a longer duration and better sleep quality than sleep taken at other times of the day, such as that which occurs with nightshift work, jet lag, and other conditions of circadian misalignment.”

Dr. Dinges and his colleagues have found that people whose daily sleep duration is inadequate, or repeatedly disrupted (e.g., by obstructive sleep apnea, restless legs syndrome, pain or stress, or shiftwork or jet lag), often are not aware of their accumulating sleep deficits or the toll that these deficits can take on their waking cognitive functions, including their performance, working memory, cognitive speed, and accuracy. Inadequate sleep also can take a toll on psychological well-being, significantly affecting our emotional and psychosocial interpretation of events and exacerbating our stress levels. Studies have indicated that changes in mood may be due in part to the effects of sleep deprivation on the processing of emotional memory—in other words, our tendency to select and remember negative memories after inadequate sleep. 14

In one study conducted by Dr. Dinges and colleagues, participants’ mood was observed after they were confronted with “high” and “low” performance demands, following varying degrees of sleep deprivation. 15

“To our surprise, those who were sleep-deprived responded to low stressors in much the same way that people without any sleep deprivation tended to respond to high stressors,” said Dr. Dinges. “In other words, we tend to become much more sensitive emotionally and socially when we are sleep-deprived. That is what I like to call the ‘who was at my desk or who touched my coffee cup?’ phenomenon. I think we all have experienced having an extreme reaction or a very negative emotional response to a mild stressor when we have not had enough sleep.”

Aiming for the Sweet Spot

How much sleep is enough? After decades of investigation, it appears that scientists have gathered enough evidence to begin to answer that question. 16

“When duration of sleep drops below seven hours, and especially when it starts to move toward six and half hours or less, a number of different disorders begin to increase in prevalence,” says Dr. Dinges. “Most experts would agree that there is a kind of sweet spot that most people should aim for, and for the average healthy adult that zone is ideally somewhere between 7 and 7 and a half hours. That is what the consensus evaluations of more than a thousand scientific articles have yielded—the consensus of evaluations conducted by the AASM (American Academy of Sleep Medicine) and Sleep Research Society jointly.”

Numerous large U.S. surveys—beginning with a 1982 survey by the American Cancer Society—have been used to estimate the number of hours that most people spend sleeping. Many surveys have identified a worrisome prevalence of “short” sleepers (people who sleep 6 hours or less) among respondents, and a general trend toward decreasing sleep duration between 1975 and 2006. More recently, however, an analysis of the American Time Use Survey (ATUS), spearheaded by Mathias Basner, MD, PhD, at the University of Pennsylvania 17 , has suggested that there may be cause for optimism.

“The analysis shows that there is a slight but steady increase in sleep time that stretches back to about 2003 or 2004,” says Dr. Dinges. “We think this increase, which is modest—at most a minute or two more per year—is due in part to the development of the field of sleep medicine, and public and scientific reports in the media about sleep loss contributing to accidents and catastrophes, and so forth. Ever so slowly, the message that it is important not to get sleep deprived, and to get help if you have a sleep disorder, has begun to penetrate to the public.”

The analysis notes that one sign of greater interest in sleep on the part of the public has been a significant increase in Google searches containing the word “sleep” since 2004. Data from the ATUS also suggest that over time, people have been willing to trade some of their daily activities in exchange for more sleep. It is important to note, says Dr. Dinges, that self-reports of time spent sleeping are not always accurate—they can be off by a half an hour or more, usually with people tending to estimate that they slept more than they did. He also notes that there is still a fairly large population sleeping 6 hours or less.

“Although there are signs that sleep time is increasing, it is not happening at nearly the dramatic rate that most experts would like to see,” says Dr. Dinges. “This is especially true for vulnerable populations. There is concern about school start times and bus times affecting the sleep of children and adolescents, and about extracurricular activities at the end of the school day sometimes leading to a delay in bed times for teenagers. All of this is an ongoing, evolving picture, with more research results coming out all the time, and with consequent changes in recommendations, to make sure that at least our most vulnerable populations are getting adequate sleep.”

Interindividual Differences in Vulnerability to Sleep Loss

While it is well established that the effects of sleep loss accumulate over time, with repeated exposure to inadequate, fragmented, or disrupted sleep, the degree to which individuals demonstrate adverse effects of inadequate sleep can vary considerably. 18

“We have learned that there are astonishingly mysterious phenotypes, or trait-like differences, in how vulnerable people are to sleep loss,” says Dr. Dinges. “This is still a relatively new area of research, and it has only been in the past few years that scientists have begun to replicate early findings regarding these phenotypic differences in vulnerability to the negative neurobehavioral effects of sleep loss. The interindividual differences that have been observed so far raise some extremely provocative scientific questions. We may find that there is something in waking biology that can substitute for, or somehow reduce, the impact of sleep loss on waking functioning, but thus far there is no evidence as to what that might be.”

Differences among individuals exist with regard to both the effects of sleep loss and the ability to recover from the effects of sleep loss. Differences in performance also have been shown to be task-dependent, suggesting that people who are vulnerable to the effects of sleep loss in one or more cognitive or neurobehavioral domains may be resistant to the effects of sleep loss in others. To better understand interindividual variability, scientists are investigating possible genetic mechanisms that may underlie complex interactions between circadian and sleep homeostatic systems—the systems that affect our drive for sleep as well as our alertness and performance during waking hours. A current goal is to discover biomarkers that may help predict individual performance after varying degrees of sleep loss. 19 And one hope is that biomarkers—ideally in the form of a simple “roadside” test such as a breathalyzer—may eventually be used to detect sleep loss-related impairment in drivers or in individuals responsible for operating sophisticated equipment or machinery. To date, no viable candidates have been found.

Investigators also are shedding light on the role that age may play in resilience to sleep loss. The results of one recent study indicate that younger adults are more vulnerable to the adverse effects of chronic sleep loss and recurring circadian disruption than older adults. 20 Although the neurobiological basis for these age-related differences is not yet understood, such findings may help to inform new approaches to the prevention of drowsy driving and related motor-vehicle accidents among young drivers.

Dr. Dinges emphasizes that findings regarding interindividual differences in response to sleep loss and in recovery from sleep loss should not diminish the message that adequate sleep is critical for everyone.

“Research has shown us that sleep is not an optional activity,” says Dr. Dinges. “There is no question that sleep is fundamentally conserved across species and across lifespans, and that any effort to eliminate it has been unsuccessful. We must plan our lives in the time domain with a serious consideration for sleep—planning when to sleep, ensuring that we get adequate sleep, and making sure that our sleep is not disturbed by disorders or diseases, whether or not they are sleep-related.”

Addressing Sleep Disorders

As connections between sleep disruption and both disease and mortality have become more firmly established, accurate and efficient diagnosis and management of sleep disorders (see Table 1 ) have become increasingly critical. Recent directions in the field of sleep medicine include a move toward patient-centered care, greater collaboration between specialists and primary care physicians, and the incorporation of new tools—including home-based diagnostic tests and novel electronic questionnaires—in the effort to create a comprehensive yet more personalized approach to assessment and treatment.

ICSD-3 Major Diagnostic Sections *

A chief goal is to improve the diagnosis of sleep disorders. Although approximately 70 million people in the U.S. have at least one sleep disorder, experts estimate that up to 80% of sleep disorders may go undetected or undiagnosed. One major challenge that clinicians face during the initial assessment of people with sleep disorders is the process of identifying and sorting out comorbidities. Untangling the causes and effects in bidirectional comorbidities can be particularly difficult. For example, insomnia—by far the most common sleep disorder—often is complicated by the presence of another sleep disorder, such as sleep apnea or restless legs syndrome.

“Some experts have even suggested that all cases of insomnia coexist with, or are caused by, another sleep disorder, most commonly sleep apnea,” says Clete A. Kushida, MD, PhD, Professor of Psychiatry and Behavioral Sciences at Stanford, and Division Chief and Medical Director of Stanford Sleep Medicine. “I’m not sure I would go quite that far, but certainly bidirectional comorbidities among individuals who experience sleep disorders are common. For example, pain syndromes—including back pain and limb pain, especially among older patients—are common comorbidities in patients with insomnia. Mood disorders also frequently occur in patients who experience insomnia.”

Comorbidities can complicate treatment and often require sleep specialists to collaborate with not only primary care physicians but also specialists in other therapeutic areas.

“If, for example, a person with insomnia also has been diagnosed with depression by a psychiatrist,” says Dr. Kushida, “our goal is to work hand in hand with the psychiatrist to find the right medication. There are both sedating and alerting antidepressants, and a patient may need to try one medication for a couple of weeks to months, slowly increasing the dose to a therapeutic level, until the effect on both the depression and the patient’s sleep can be determined. For some individuals, an alerting antidepressant can cause poor sleep, which in turn can exacerbate the depression. The process of achieving the right dose of the right medication can be complex, and benefits from a collaboration between specialists.”

Undetected obstructive sleep apnea (OSA) in patients with chronic pain, or other serious illnesses, can result in potentially dangerous comorbidities. Opioids, for example, are known to have adverse effects on respiration, and can lead to central sleep apnea (CSA)—shallow and irregular or interrupted breathing and sustained hypoventilation—a potentially lethal condition that can intensify the consequences of OSA. These risks underscore the need to improve methods for identifying and properly diagnosing the estimated 23.5 million U.S. adults with OSA. Public education and advocacy efforts are already helping to improve detection—in part by helping to address misconceptions about OSA.

“One of the biggest misconceptions is that only people who are significantly overweight experience sleep apnea,” says Dr. Kushida. “In fact, only up to 67% of people who have OSA are overweight, the rest are of normal weight. OSA also can be caused by craniofacial dysmorphism, or a defect of the airway that occurs during development. A narrow airway caused by deficient growth of the craniofacial skeleton, particularly the jaws, can become narrower and more prone to collapse with age, leading to sleep apnea.”

Treating Insomnia: The Value of Cognitive Behavioral Therapy

Insomnia, the most prevalent sleep disorder, affects approximately one third of all adults and is the most common condition that family and primary-care physicians encounter. According to the International Classification of Sleep Disorders (ICSD-3), chronic insomnia is the inability to attain sufficient sleep (despite adequate opportunity) for at least three nights per week for three months or longer, with negative daytime consequences. For most people, the disorder is transient, but for approximately 10% to 15% of those who experience insomnia (around 30 million people) it becomes chronic. Although pharmacologic treatments for insomnia ( Table 2 ) can be effective, most experts now recommend against the long-term use of pharmacotherapy.

Selected Pharmaceutical Treatments for Insomnia 21 , 27

“If a person has been diagnosed with chronic insomnia, the only treatment that has been shown to have long-term benefit is cognitive behavioral therapy, “says Dr. Kushida. “Medications really should be considered short-term treatments, because patients tend to develop dependence on, or tolerance to, hypnotic drugs. In our clinic, we commonly see that, over time, medications stop having an effect, and that means that patients may try higher doses of a medication, or keep switching to different medications. So, medications are a temporary solution—they just put a Band-Aid on the problem of insomnia, whereas cognitive behavioral therapy targets one of the pathways toward success.”

Cognitive behavioral therapy (CBT), which involves techniques that work in part by reducing cognitive and somatic arousal, is estimated to be effective in approximately 70% to 80% of people who experience chronic insomnia. Dr. Kushida notes that while drugs can sometimes be useful in the treatment of acute insomnia, they become problematic after acute insomnia transitions to chronic insomnia.

“A person might be an OK sleeper for several years, and then suddenly experience a traumatic event, such as the loss of a job, a divorce, or the death of a loved one, resulting in very poor sleep,” says Dr. Kushida. “Down the road, that person might obtain a better job, overcome grief, or find a new relationship, but continue to experience insomnia. We think in some cases the transition from acute insomnia to chronic insomnia occurs because the behavioral event triggers something in the person’s physiology that may lead to long-term changes. Once they are in a chronic insomnia phase, we tell patients that CBT is the only truly effective intervention.”

If a patient is already taking hypnotics, Dr. Kushida says that he will gradually wean the patient off medications while introducing CBT. He notes that often it is necessary for sleep specialists to manage the expectations of chronic sufferers.

Clete A. Kushida, MD, PhD

“We sometimes have to let patients with chronic insomnia know that we may never get them back to where they were when they had optimal sleep,” Dr. Kushida explains. “The behavioral methods we use work well, and usually we can get patients to the point where the insomnia is having less of an impact on their quality of life. Our inability to completely restore the patient’s ability to sleep well may partly be explained by as yet unidentified changes in his or her neurophysiology or neurochemistry. Some patients with chronic insomnia can begin to sleep normally again, but for the vast majority, we aim to make insomnia less of a burden on a patient’s daily life.”

Improving Clinical Research

In the field of sleep medicine, as in many other therapeutic areas, future directions in clinical trial research will place an emphasis on patient engagement and patient-centered outcomes.

“Perhaps the most important aim these days when developing and implementing any type of large-scale clinical research study is to incorporate the patient’s perspective,” says Dr. Kushida, who is currently analyzing the results of a comparative effectiveness sleep study sponsored by the Patient-Centered Outcomes Research Institute (PCORI). 22 The study, designed and conducted by a team at Stanford, introduced a new model of patient-centered, coordinated care and tested it against conventional outpatient treatment for sleep disorders.

“The patient’s perspective is so invaluable in guiding the success of a study that ideally it should be incorporated right at the inception of a research question or idea,” says Dr. Kushida. “When you are designing an especially complicated trial, for example, it is easy to incorporate a lot of tests and measures without being aware of the burden these can place on the participants. It’s critical to learn from patients whether they are overwhelmed by the number of tests, or whether travel time or the amount of time they need to take off from work may be impractical.”

Other efforts to improve clinical research include those focused on correcting for and/or eliminating several confounding variables that tend to plague sleep research. The surprising power of the placebo effect, 23 the related disconnect between objective and subjective evaluations of sleep loss and recovery from sleep loss, variable adherence to treatments, and, more recently, deceptive practices among clinical trial participants, are a few examples.

The placebo effect, which refers to any outcome that may be attributable to the expectations of clinical trial participants rather than to the drug or device being tested, can be especially problematic in experimental protocols that involve self-reports of sleep quality.

“Clinical trials involving patients with disorders such as insomnia or RLS that rely solely on subjective measures, or ratings of severity based on patient report, are particularly vulnerable to the placebo effect,” says Dr. Kushida. “It has been demonstrated that when these patients believe that they are receiving the study drug or device the likelihood of their experiencing a positive effect can increase significantly. There have been efforts to develop or introduce new objective endpoints in these studies, which may help with this problem.”

Achieving the right balance of subjective and objective measures of sleep is an important goal in both research and clinical practice. The current gold standard for objective assessment of sleep is polysomnography (PSG), which includes electrophysiological recordings of brain activity (EEG), muscle activity (EMG), and eye movements (EOG). A valuable, non-invasive method for determining sleep continuity and sleep architecture, PSG has been an indispensable objective endpoint in clinical trials, but it is expensive and not always practical. Novel approaches to objective measurement, including actigraphy, which may be used to help minimize recall bias and complement subjective measures of sleep (e.g., sleep logs or diaries), still have drawbacks. 24

“The problem with wearable devices right now,” says Dr. Kushida, “is that they tend to overestimate sleep, sometimes by as much as an hour. They also are not yet capable of accurately detecting different stages of sleep, such as non-REM and REM sleep. Because of our proximity to Silicon Valley, our laboratory tests a lot of these new devices, and often by the time we have finished testing one prototype, new ones have emerged. The product cycles are rapid, and the companies keep incorporating newer and newer technology. So, down the road, within about five to ten years, I think these devices will likely estimate sleep and detect sleep stages with precision.”

Also, objective tools are needed for addressing problems with adherence to treatment. One important current aim is to detect and correct for non-obvious factors that result in failure to adhere to treatment, whether unintended or deliberate, to ensure that trial outcomes accurately reflect the efficacy of a drug, medical device, or behavioral intervention. 25 A related problem is deliberate deception by trial participants. As part of a National Heart, Lung, and Blood Institute (NHLBI)-supported study focused on detecting and correcting for adherence problems, Dr. Kushida and colleagues began to explore the prevalence of deceptive practices among clinical trial participants. 26

“We found that deception among clinical trial participants is pretty common and that there is quite a range of deceptive practices, “says Dr. Kushida. “They include underreported drug holidays, fabrication or withholding of medical histories, pill dumping, exaggerated symptoms, and falsification of current health status. It’s important that we find a way to address these deceptive practices because both the integrity of research data and the safety of participants are at risk.”

Dr. Kushida adds that newer tools, such as electronic monitoring of pill dispensing and statistical predictive adherence models, may uncover and remedy pressing problems related to adherence and deceptive practices. “It already takes about 12 years for a new drug to be approved, and about three to five years for a new device to be approved. When deceptive practices are discovered too late, it can lead to the invalidation of research findings and further delays in approving much-needed treatments.”

Enhancing clinical research in the field will require a cooperative, international effort focused on advancing knowledge about sleep, circadian rhythms, and sleep disorders worldwide. During Dr. Kushida’s tenure as inaugural president of the World Sleep Society (WSS), he led an initiative to create international sleep fellowships to prepare physicians and scientists from various countries for future leadership roles in basic and/or clinical sleep research. He also oversaw the development of an International Sleep Research Network, designed to help sleep scientists and clinicians find collaborators with similar clinical/research interests. As the WSS continues to offer new services and expand its programs, it will be with an awareness of the needs of disadvantaged populations and the importance of access to appropriate treatment.

“One initiative of the WSS involves reviewing current published guidelines in various countries, to determine whether they meet international standards,” says Dr. Kushida. “Many guidelines are region-specific and list only medications approved in specific countries or regions. As we review the guidelines, we endorse them with caveats; we may note that particular treatments for insomnia are recommended, and when these are not available we recommend acceptable substitutes. The goal is to ensure that specialists can use practice guidelines in whichever country they practice sleep medicine, and that patients are receiving the best possible treatment available.”

• Chamblee High School

Dekalb County Schools

Ap psychology - assignments.

• School Board Member
• Advanced Placement
• Attendance Office
• Blue and Gold Foundation
• Bulldog Bulletin
• Chamblee Athletic Foundation
• CHS Facilities Rental (Schoolspace)
• Community Service Verification
• Counseling Department
• CTAE Department
• DCSD Launchpad
• Discipline Office
• Gifted Program
• Media Center
• Meet the Librarian
• Board Members
• Meeting Archives
• Archived Minutes, Agenda and Summary
• Parent Resources
• Senior Information
• Social Media Guidelines
• Student Accident Insurance
• Student Code of Conduct
• Welcome to Naviance

• Free Samples
• Premium Essays
• Editing Services Editing Proofreading Rewriting
• Extra Tools Essay Topic Generator Thesis Generator Citation Generator GPA Calculator Study Guides Donate Paper
• Essay Writing Help
• About Us About Us Testimonials FAQ
• Studentshare
• Psychology of Sleep and Dreams

Psychology of Sleep and Dreams - Assignment Example

• Subject: Psychology
• Type: Assignment
• Level: High School
• Pages: 5 (1250 words)
• Downloads: 5
• Author: fredrick53

Extract of sample "Psychology of Sleep and Dreams"

• Cited: 1 times
• Copy Citation Citation is copied Copy Citation Citation is copied Copy Citation Citation is copied

CHECK THESE SAMPLES OF Psychology of Sleep and Dreams

The biology of sleeping and dreaming, subjective characteristics of sleep efficiency by frederick evans, different states of consciousness, why and what we dream, human sleep cycle, rapid eye movement, section 3 reading, dreams in psychology, the multidimensional function of dreams.

• TERMS & CONDITIONS
• PRIVACY POLICY
• COOKIES POLICY

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons

Margin Size

• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

2.5: Assignments

• Last updated
• Save as PDF
• Page ID 59799

\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

\( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

\( \newcommand{\Span}{\mathrm{span}}\)

\( \newcommand{\id}{\mathrm{id}}\)

\( \newcommand{\kernel}{\mathrm{null}\,}\)

\( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\)

\( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\)

\( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

\( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

\( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

\( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vectorC}[1]{\textbf{#1}} \)

\( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

\( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

\( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

The assignments in this course are openly licensed, and are available as-is, or can be modified to suit your students’ needs. Selected answer keys are available to faculty who adopt Waymaker, OHM, or Candela courses with paid support from Lumen Learning. This approach helps us protect the academic integrity of these materials by ensuring they are shared only with authorized and institution-affiliated faculty and staff.

If you import this course into your learning management system (Blackboard, Canvas, etc.), the assignments will automatically be loaded into the assignment tool, where they may be adjusted, or edited there. Assignments also come with rubrics and pre-assigned point values that may easily be edited or removed.

The assignments for Introductory Psychology are ideas and suggestions to use as you see appropriate. Some are larger assignments spanning several weeks, while others are smaller, less-time consuming tasks. You can view them below or throughout the course.

You can view them below or throughout the course.

Discussion Grading Rubric

The discussions in the course vary in their requirements and design, but this rubric below may be used and modified to facilitate grading.

Licenses and Attributions

CC licensed content, Original

• Assignments with Solutions. Provided by : Lumen Learning. License : CC BY: Attribution
• Pencil Cup. Authored by : IconfactoryTeam. Provided by : Noun Project. Located at : https://thenounproject.com/term/pencil-cup/628840/ . License : CC BY: Attribution

ORIGINAL RESEARCH article

Associations between capacity of cognitive control and sleep quality: a two-wave longitudinal study.

• 1 School of Psychology, South China Normal University, Guangzhou, China
• 2 Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou, China
• 3 Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
• 4 Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
• 5 School of Foreign Languages, South China University of Technology, Guangzhou, China
• 6 Department of Human Resource, Guangzhou Branch of China Mobile Group Guangdong Company Limited, Guangzhou, China

This longitudinal study explored the impact of the upper limit of cognitive control on the sleep quality of high school students. We collected data in two waves to examine four main variables: capacity of cognitive control (CCC), trait mindfulness, emotional distress and sleep quality. At the first time point (T1), trait mindfulness and emotional distress were measured by rating scales, and the CCC was evaluated by revised backward masking majority function task. Sleep quality was rated 5 months later (T2). The results indicated that: (1) the CCC was negatively correlated with trait mindfulness, and trait mindfulness was negatively correlated with emotional stress; (2) there was no simple mediation of either trait mindfulness or emotional distress in the relationship between CCC and sleep quality; (3) instead, the CCC was associated with poor sleep quality in a sequential mediation through trait mindfulness and then emotional stress. The research highlights the importance of trait mindfulness and emotional distress for addressing sleep problems in adolescents.

1 Introduction

Sleep is a vital physiological process that plays an indispensable role in the psychosocial adjustment of individuals ( Sarchiapone et al., 2014 ). However, sleep problems are becoming a growing concern among adolescents ( Gradisar et al., 2022 ). The chronic deprivation of sleep or its inadequate quality can have negative effects on the cognitive and emotional functioning of adolescents ( Williams et al., 2013 ). Therefore, understanding the factors behind sleep problems is crucial for developing effective prevention and intervention strategies to improve individuals’ health and well-being in adolescence and beyond.

There is an abundance of research dedicated to identifying the factors that affect sleep quality. This research primarily focuses on three areas. First, numerous studies concentrate on the factors that impact sleep quality within specific demographics (e.g., Souders et al., 2009 ; Songkham et al., 2019 ; Bahani et al., 2024 ; Guller and Yaylaci, 2024 ). Secondly, while influences on sleep quality can originate from various aspects, a significant number of studies are centered around cognitive and emotional factors (e.g., Hsu et al., 2009 ; Burke et al., 2015 ; Mueller et al., 2024 ; Schantz et al., 2024 ; Xie et al., 2024 ). Last, applied research aimed at enhancing sleep quality also garners considerable interest among researchers (e.g., Yosiaki et al., 1998 ; Lai and Good, 2005 ; Liu et al., 2024 ; Luo et al., 2024 ).

The procrastination of bedtime due to the excessive use of electronic devices at night is one of the factors that may contribute to sleep problems ( Liu et al., 2017 ; Vernon et al., 2018 ). Furthermore, the delayed duration of sleep due to bedtime procrastination has been attributed to a lack of self-control ( Kroese et al., 2016 ). Individuals who experience difficulty in resisting temptation are often characterized by low self-control, which may lead to the tendency of bedtime procrastination ( Punamäki et al., 2007 ; Kroese et al., 2016 ; Seo et al., 2017 ). This, in turn, can result in insufficient sleep and poor sleep quality ( Kroese et al., 2016 ). All the above studies focused on the effects of procrastination on sleep, but procrastination is only a behavioral manifestation resulting from a failure of self-control, an underlying cause of sleep problems. Therefore, it is important to understand the underlying mechanisms that contribute to the link between self-control and sleep, in order to develop effective interventions aimed at promoting healthy sleep habits among individuals with low self-control.

By examining self-control as a trait rather than a state to identify its impact on sleep, Kroese et al. (2016) found self-regulation to be associated with insufficient sleep, and bedtime procrastination to act as a mediating variable. However, they measured the subjects’ self-regulation ability with a self-control scale, and such self-reports may not fully objectively reflect the self-control level. Furthermore, their research on self-regulation was restricted to the level of individual external behavior habits, and did not involve any higher level, such as the cognitive level. Therefore, our study attempts to explore the impact of cognitive control capacity on sleep quality. The purpose is to find any changes in the relationship between cognitive control capacity and sleep quality to understand its influencing factors better. This longitudial study aims to enrich the evidence for any changes in the relationship between adolescent cognitive control and sleep quality from a new perspective.

Cognitive control refers to the process of flexibly allocating mental resources to process important information according to the goal at hand ( He et al., 2019 ). With conceptual overlap, cognitive control and self-control have a common process, and they are often measured using the same experiments ( Goldstein and Volkow, 2011 ; Hofmann et al., 2012 ; Goschke and Bolte, 2014 ). Measuring cognitive control ability allows for an examination of the underlying factors influencing changes in sleep quality, and helps improve the ability to predict the impact of cognitive control on sleep quality ( Fan, 2014 ; Wu et al., 2015 , 2016 ; Mackie and Fan, 2016 ). The cognitive control capacity (CCC), which reflects an individual’s upper limit in terms of cognitive control ability, has been identified as a critical determinant of cognitive function ( Fan, 2014 ; Wu et al., 2020 ). Based on the dual system model of self-control, individuals with high CCC may exhibit a more flexible and higher order of control in their decision-making and action ( Hofmann et al., 2009 ). This control mechanism empowers individuals to overcome immediate stimulus control, thus enabling them to engage in more purposeful and goal-oriented behavior ( Hofmann et al., 2009 ). The functionality of the self-control system is contingent upon control resources ( Fazio and Towles-Schwen, 1999 ; Vohs, 2006 ; Evans, 2008 ). The limited resources theory of self-control ( Baumeister et al., 1998 ) posits that cognitive control resources are finite. In the event that the availability of control resources becomes depleted, the self-control system may experience a collapse and subsequently malfunction ( Hofmann et al., 2009 ). Therefore, it is reasonable to assume that individuals with high levels of self-control display a heightened sense of control in the domain of sleep. Such individuals are more apt to choose long-term rewards, particularly the enduring benefits of quality sleep, rather than immediate stimuli that could potentially interfere with their sleep, such as engaging in behaviors that disrupt sleep ( Liu et al., 2014 ). Based on the above analysis, we propose the first hypothesis as below.

H1 : The capacity of cognitive control was positively correlated with sleep quality.

Studies have demonstrated that interventions aimed at promoting mindfulness are capable of enhancing sleep quality ( Eisenlohr-Moul et al., 2016 ; Conley et al., 2018 ). Trait mindfulness is a construct that is closely linked to mindfulness, as it can be viewed as a natural progression of mindfulness practice, which refers to an individual’s ability to remain aware of and focused on the present experience ( Liu et al., 2018 ). It is perhaps the most relevant personality trait to date for meditation-based interventions, used in many fields such as medicine and psychological interventions ( Zeidan et al., 2010 ; Aivaliotis et al., 2017 ; Vignaud et al., 2018 ). Literature shows that the degree of trait mindfulness may have a significant correlation with the quality of sleep, whereby a greater level of mindfulness is positively associated with improved sleep quality. This observation is supported by empirical evidence suggesting that mindfulness practices, when consistently practiced, can enhance the development of trait mindfulness, which can in turn confer a range of benefits, especially in the context of sleep difficulties ( Galla, 2016 ; Brisbon and Lachman, 2017 ; Xiao et al., 2019 ).

The concept of trait mindfulness is intrinsically connected to cognitive control and contains several components that are essential to enhance this control. People with high cognitive control abilities may have some special characteristics, such as a higher level of trait mindfulness, due to the regulation of attention promoting non-refined awareness of thoughts, emotions, and sensations. The direct, non-judgmental awareness and experience of mental and physical events in the present moment constitute the essence of trait mindfulness ( Teasdale et al., 1995 ). The awakening of mindfulness requires a cognitive control process, i.e., attention self-regulation ( Bishop et al., 2004 ).

In addition, the strength model of self-regulation assumes that the ability to self-control depends on limited, domain-independent resources ( Baumeister et al., 2007 ). According to the model, self-regulation is a limited resource. Like muscle strength, it needs to relax once it is exhausted. Any effort of self-control will temporarily reduce this resource, resulting in a state of exhaustion of self-regulation which makes self-control more likely to fail in any subsequent self-control attempt. When individuals engage in mindfulness activities, the non-judgmental attitude will cause individuals to consume self-control resources. When negative emotions arise and individuals choose not to judge them, it is like the experiment by Baumeister et al. (1998) in which individuals in a room with biscuits, if allowed to smell or eat carrots only, would reduce their strength of self-regulation. Furthermore, self-regulation, like muscle strength, can be improved through long-term exercise.

Based on the above analysis, the study proposes a second hypothesis.

H2 : Trait mindfulness mediates the positive correlation between the capacity of cognitive control and sleep quality.

In addition, there is evidence that sleep quality is strongly associated with anxiety and depression ( Gadie et al., 2017 ). Many clinical studies on adolescents report that reduced sleep duration may be associated with emotional problems such as depression and anxiety symptoms ( Hall et al., 2000 ; Zhai et al., 2021 ). The quality of an individual’s sleep was found to be negatively associated with a negative mood before going to bed ( Shen et al., 2018 ) and positively associated with a positive mood ( Latif et al., 2019 ). Research suggests that negative emotions may hinder an individual’s ability to perceive the benefits of adhering to a regular sleep schedule, including increased energy levels and improved mental health. Specifically, negative emotions could lead individuals to perceive the rewards associated with going to bed on time as distant and ineffective in helping them cope with their current negative emotional state ( Sirois and Pychyl, 2013 ). As a prevalent phenomenon in the adolescent population, emotional distress is a commonly used indicator of mental health, which is often characterized by anxiety, depression, and somatic symptoms ( Drapeau et al., 2012 ). Cognitive control was found to be consistently associated with emotions such as depression and anxiety ( Tice and Bratslavsky, 2000 ; Pychyl and Sirois, 2016 ; Ebneabbasi et al., 2021 ). Compared with positive emotions, negative emotions may trigger more intense emotional experiences and may lead to the failure of self-control ( Heatherton and Wagner, 2011 ). Following the limited resource theory of self-control ( Baumeister et al., 1998 , 2018 ), we assume that adolescents may deplete their self-control resources in emotional distress and the regulation of their distress, leaving a shortage of cognitive control resources that would otherwise be used to counteract delayed bedtime, which in turn would affect sleep quality ( Baumeister et al., 1998 ). Emotional distress may be an influential factor in the relationship between cognitive control and sleep quality.

Therefore, individuals with more negative moods may prefer to replace the low-reward task (going to bed on time) with a more enjoyable task, like an entertaining media activity, to regulate their current mood ( Sirois, 2014 ), which in turn may bring about sleep quality problems ( Kroese et al., 2016 ).

Therefore, it is arguable that cognitive control capacity may delay sleep time and affect sleep quality through emotional distress. We would like to propose the third hypothesis as follows.

H3 : Emotional distress mediates the positive correlation between cognitive control capacity and sleep quality.

Many researchers reported the inverse correlation between mindfulness and psychological distress in children and adolescents ( Waters, 2016 ). Specifically, trait mindfulness was found to be negatively associated with depression and anxiety in students in Grades 4 to 7 ( Lawlor et al., 2014 ), with anxiety in the general secondary school population ( Li, 2017 ), and with depression in the post-trauma adolescents ( Xu et al., 2018 ). Individuals with high levels of trait mindfulness exhibited reduced cortisol responses in high-stress situations ( Brown et al., 2012 ). Furthermore, these individuals were also found to display lower resting activity in the bilateral amygdala and reduced gray matter density in the right amygdala ( Way et al., 2010 ; Brown et al., 2012 ). Conversely, these neural patterns were shown to be positively associated with stress ( Hölzel et al., 2010 ). Multiple meta-analyses demonstrate that such mindfulness-based interventions were effective in reducing depression and anxiety ( Hofmann et al., 2010 ; Khoury et al., 2015 ). Stronger trait mindfulness was related to less depression and anxiety ( Kiken and Shook, 2012 ), less rumination ( Brown and Ryan, 2003 ; Kiken and Shook, 2014 ), fewer depressive negative cognitions ( Gilbert and Christopher, 2010 ), and greater ability to release negative thoughts ( Frewen et al., 2008 ).

Besides, according to the ego depletion theory ( Baumeister et al., 2007 )， any self-control activity of an individual is likely to consume self-control energy or resources, such as controlling impulses, controlling cognition (e.g., attention and thinking), controlling emotions and feelings, and making behavioral decisions. Baumeister et al. (1998) elucidates that the success or failure of volitional activities is affected by the amount of such resources. The more resources, the easier it is to execute successfully; the resources required for different volitional activities are the same. A series of seemingly different and unrelated activities may share the same resource. If resources are consumed in one volitional activity, then the actual resources available for another volitional activity will be reduced ( Baumeister et al., 1998 ). In other words, in this study, the individual’s CCC level can be regarded as a stable control resource possessed by the individual. Individuals with high CCC levels can effectively control impulses (reduce unconscious attention) and have lower trait mindfulness levels (less conscious control of irrelevant stimuli). Mindfulness activities and controlling emotions will consume control resources. If the resources used by mindfulness activities are reduced, the resources available for controlling emotions will increase. Individuals’ effective control of emotional distress can lead to better sleep quality.

Therefore, this paper proposes a fourth hypothesis as follows.

H4 : Trait mindfulness was positively correlated with emotional distress. They play sequential mediating roles in the positive correlation between the capacity of cognitive control and sleep quality.

We attempt to understand the changes, if any, in the relationship between CCC and sleep quality, as well as its influencing factors. The mediating effects of both emotional distress and trait mindfulness are examined, which offers a new perspective to explore changes in the relationship between cognitive control and sleep quality in adolescents. In brief, a multiple mediation model ( Figure 1 ) was conceptualized based on the above four hypotheses.

Figure 1 . The effect of cognitive control capacity on sleep quality: A multiple-mediation model. T1: the first time point of data collection. T2: the second time point of data collection, conducted 5 months after the T1.

2.1 Participants

This study collected data in two rounds from students in Grade 10 from a high school in Guangdong Province in China. A total of 170 students participated in the initial round of data collection in September 2021 (T1). Five variables of interest were measured: CCC, trait mindfulness, emotional distress, gender and age. Questionnaire and experiment data were collected by professionally trained postgraduates and teachers in psychology. We first excluded 5 participants who wrongly responded to any of the three lie detection questions that were randomly assigned in questionnaires (i.e., “I make answers to these questions seriously.,” “I have never told a lie.,” “I can run a kilometer in 1 min”). Then we excluded two students with a diagnosis of psychiatry disorder (e.g., anxiety, depression) reported and another two students who did not successfully complete the MFT-M-R experiment. The final sample included 147 participants (48.99% female, n  = 73). The sleep quality of all these participants was evaluated based on the second round of data collection in January 2022 (T2). Ethical approval was obtained from the Ethics Committee of the School of Psychology, South China Normal University. Written informed consent was provided by all participants. The researchers clarified the purpose of our research to the participants and assured them of the confidentiality and voluntary nature of the study. The tests and questionnaires were administered in the classroom during class time.

2.2 Measurements

2.2.1 the revised backward masking majority function task.

We used the revised backward masking majority function task (MFT-M-R) ( Wu et al., 2016 ; He et al., 2022 ) to estimate the CCC of each participant. The stimuli and procedure of the MFT-M-R are shown in Figure 2 . At the beginning of each trial, a central fixation was present for 0–500 milliseconds (ms), and then a set of left and right-pointing arrows appeared in eight possible locations around the fixation. The exposure time (ET) of these arrows was 250, 500, 1,000 or 2,000 ms, and the trial ended with a mask consisting of eight diamond shapes displayed for 500 ms at the same eight locations. After the masking disappears, a fixation of 0–1750 ms appears. Students were required to press a key to indicate the direction of most arrows pointing (“F” for left-pointing and “J” for right-pointing) as accurately and rapidly as possible, within a 2,500-ms window starting as the onset of the arrow set. If they could not identify the majority of arrow directions within the ET, they were instructed to guess the answer as a response. After the response window, 750-ms feedback would be given on the screen to tell whether the response was correct, followed by a post-stimulus fixation period of 1,000–1,500 ms. The total time of each trial was 6,300 ms. The length of the arrow and the diameter of each diamond was 0.37 ° of visual angle while the radius from the fixation cross to the center of an arrow subtended approximately 1.5° of visual angle. The MFT-M-R was in a 3 (ET: 0.25, 0.5, 1, 2 s) × 6 (set ratio: 2:1, 4:1, 3:2) factorial design. The set ratio refers to the ratio between the number of arrows pointing to the majority direction versus the number of arrows pointing to the minority direction. The set size (total number of arrows) could be 3 or 5, and therefore the set ratio was 2:1 for the three-arrow set and 4:1, or 3:2 for the five-arrow set. This task is an adaptive test that terminates when the predetermined management length or measurement accuracy level is reached. Combining these two rules in MFT-M-R, 216 trials were used as the predetermined length, and SE was used as an indicator of measurement accuracy (SEs < 0.01–0.1 with intervals of 0.01). The SE in this study was 0.01. Participants could have a break between blocks. The entire task lasts about 20 min.

Figure 2 . Stimuli and procedure of the backward masking majority function task (MFT-M). (A) Timeline of stimuli presentation in each trial (example of a trial with set ratio = 2:1). (B) Design of the exposure time (ET) of the arrow sets (pink bar) and the corresponding timeline. (C) Design of arrow sets with different ratios between the number of arrows pointing in the majority direction: the number of arrows pointing in the minority direction.

MFT-M-R was programmed to run on E-Prime (Version 1.3, Psychology Software Tools Inc., 2002; RRID: SCR_009567) and presented on a computer. Each participant was accompanied by an experimental assistant throughout the experiment to make sure that the task requirements were well understood. The assistant observed and recorded the participant’s behaviors.

2.2.2 Sleep quality

This study used the Pittsburgh Sleep Quality Index (PSQI) to test the sleep quality of the sample. The scale has been developed and revised successively and has been proven to have good reliability and validity ( α  = 0.84) ( Buysse et al., 1989 ; Liu et al., 1996 ). The PSQI consists of 19 self-evaluation items, 18 of which can be combined into 7 components, and the 19th item is not scored. Each component is scored from 0 to 3, and the cumulative score for each component is the total PSQI score (0 to 21 points). The evaluation period is the latest month, and the higher the score, the worse the sleep quality. The Cronbach’s α coefficient of this scale was 0.86, indicating excellent internal consistency.

2.2.3 Emotional distress

A Chinese version of the Depression-Anxiety-Stress Scale or DASS-21 ( Gong et al., 2010 ; Evans et al., 2020 ) was employed to collect information on emotional distress in the sample. The DASS-21 uses a four-point scale ranging from 0 (“does not apply to me at all”) to 3 (“applies to me most or most of the time”). The questionnaire includes three subscales (depression, anxiety and stress), each of which has seven items. All the 21 items on the DASS-21 add up to provide a measure of the overall emotional distress ( Evans et al., 2020 ), ranging from 0 to 63. A higher score indicates a higher level of emotional distress. The Cronbach’s α coefficient of this scale was 0.94, indicating excellent internal consistency.

2.2.4 Trait mindfulness

This study selected the five-facet mindfulness questionnaire (FFMQ) to measure the participants’ mindfulness level ( Baer et al., 2008 ). Previous studies have shown that the scale has good reliability and validity in the Chinese middle school student population. The scale includes 39 questions, 20 of which are scored positively and 19 are scored negatively. FFMQ includes five factors: observing, describing, acting with awareness, nonjudging of inner experience, and nonreactivity to inner experience. The number of questions, respectively, included is 8, 8, 8, 8, and 7. The scale uses a 5-level rating. The mindfulness level is assessed by the total score of five dimensions, and the higher the total score, the higher the mindfulness level. The Cronbach’s α coefficient of this scale was 0.79, indicating excellent internal consistency.

2.2.5 Controlled variables

Participants’ gender (female marked as 0; male marked as 1) were included as covariates in the analysis of all models. In addition, this research collected medical history information to rule out the impact of diseases on sleep quality. The above data are self-reported by students.

2.3 Statistical analysis

2.3.1 the revised backward masking majority function task.

Response time and accuracy rate were also computed and analyzed using MATLAB R2016b of Mathworks 1 and IBM SPSS 22.0 2 . Any trial with no response was considered an invalid trial and was excluded from RT analysis. For each condition, trials with RT beyond three SDs of the average RT were regarded as outliers and also excluded from further analysis of RT. Each participant’s CCC was estimated based on the relationship between response accuracy and information rate (i.e., the amount of information needed to be processed in each second) ( Wu et al., 2016 ). In brief, the amount of information conveyed by the arrow set was computed based on a perception decision-making strategy (grouping-search strategy), which is 2.58, 2.91, and 4.91 bit(s) for the 2:1, 4:1, and 3:2 ratio conditions, respectively. The information rate in each condition was computed as information amount divided by the ET, in the unit of bit per second (bps). The CCC was estimated as the information rate in which the accuracy started to drop, indicating the rate of information input began to exceed the capacity. Estimation of the CCC was implemented using a maximum likelihood estimation approach to fit the model of accuracy as a function of information amount and ET across all conditions, with CCC as the free parameter. The MATLAB script for estimating the CCC was downloaded from.

2.3.2 Questionnaires

In all analyses, factors such as CCC, sleep quality, emotional distress, trait mindfulness, and age were treated as continuous variables, whereas gender was considered as a categorical variable (binary). We used SPSS 26 to examine descriptions and correlations between CCC, sleep quality, emotional distress, trait mindfulness, gender, and age. The data were standardized, and then mediation and moderation analyses were made using the process macro version 3.5 for SPSS ( Hayes, 2017 ).

In the mediation model, CCC served as an independent variable (X), with subsequent sleep quality acting as the dependent variable (Y) while trait mindfulness (M1) and emotional distress (M2) as mediators. Covariates including gender and age were controlled in the analysis. To estimate the 95% bias-corrected confidence intervals (95% CI), a bootstrapping procedure with 10,000 iterations was performed. This approach allowed for a robust assessment of the mediation effects. To address potential common method bias, Harman’s One-Factor Test was conducted.

3.1 Common method deviation test

Harman’s One-Factor Test was used to detect any possible common method bias. The results showed that the eigenvalues of 13 factors were greater than 1 and the factor with the largest eigenvalue explained 26.17%, which was less than 40%. Therefore, we believe that there was no significant common method bias.

3.2 Descriptive statistics and correlation analysis

Table 1 presents the results of descriptive statistics (means and standard deviations) and Pearson correlation analyses for the four main variables (CCC, PSQI, trait mindfulness and emotional distress) across the two time points. Specifically, CCC at T1 was negatively associated with trait mindfulness at T1 ( r  = −0.21, p  < 0.001) and with gender at T1 ( r  = −0.18, p  = 0.03). Similarly, trait mindfulness at T1 was negatively associated with emotional distress at T1 ( r  = −0.45, p  < 0.001). However, there was a significant positive correlation between CCC at T1 and emotional distress at T1( r  = 0.18, p  = 0.02), and between emotional distress at T1 and poor sleep quality at T2 ( r  = 0.23, p  = 0.01).

Table 1 . Descriptive statistics and correlation analysis between variables.

3.3 Mediation model test

The results of the mediation analysis were showed in Table 2 and Figure 3 , indicating that the association between CCC and poor sleep quality was mediated by trait mindfulness and emotional distress in sequence. The total effect model explained 0.47% of the variance in poor sleep quality at T2 ( R  = 0.07, MSE = 1.01, F2, 144 = 0.34, p  = 0.71). The nonsignificant total effect suggests that CCC at T1 did not sufficiently explain poor sleep quality at T2 alone, so H1 was not supported. When considering trait mindfulness and emotional distress as mediators, path a1 showed a significant negative association between CCC and trait mindfulness at T1 ( β  = −0.20, SE = 0.08, t  = −2.42, p  = 0.02, 95% CI = [−0.37, −0.04]), with CCC accounting for 4.87% of the variance in trait mindfulness. However, since path b2 was not significant ( β  = −0.07, SE = 0.09, t  = −0.74, p  = 0.46, 95% CI = [−0.25, 0.11]), the indirect effect of trait mindfulness as a mediator was also nonsignificant, and H2 was rejected. Similarly, although path b1 revealed a significant positive correlation from emotional distress at T1 to poor sleep quality(PSQI) at T2 ( β  = 0.21, SE = 0.09, t  = 2.29, p  = 0.02, 95% CI = [0.03, 0.39]), path a2 was not significant ( β  = 0.09, SE = 0.08, t  = 1.11, p  = 0.27, 95% CI = [−0.07, 0.24]), so the indirect effect of emotional distress as a mediator was not significant. Therefore, H3 was not proved. However, path d was the same as path a1, which revealed a significant negative correlation from trait mindfulness at T1 to emotional distress at T1 ( β  = −0.43, SE = 0.08, t  = −5.66, p  < 0.001, 95% CI = [−0.58, 0.28]). The indirect effect of trait mindfulness and that of emotional distress as mediators were significant, so H4 was supported. Notably, the negative though nonsignificant direct effect ( β  = −0.10, SE = 0.08, t  = −1.24, p  = 0.22, 95% CI = [−0.27, 0.06]) along with the positive and significant total indirect effect ( β  = 0.06, SE = 0.03, 95% CI = [0.01, 0.13]) suggests a suppressing effect of emotional distress on the relationship between CCC at T1 and poor sleep quality at T2. The mediation model accounted for 7.44% of the variance in poor sleep quality at T2 ( R  = 0.25, MSE = 0.96, F4, 142 = 2.39, p  = 0.05).

Table 2 . Results of the multiple mediation analysis.

Figure 3 . The mediation effects of trait mindfulness and emotional distress at T1 on the relationship between CCC at T1 and sleep quality at T2. * p  < 0.05; ** p  < 0.01.

4 Discussion

The present study constructed a sequential mediation model that demonstrated the roles of trait mindfulness and emotional distress in shaping poor sleep quality among high school students with high CCC. It may contribute to our understanding of how CCC impacts sleep quality. Our results revealed that CCC was indirectly associated with poor sleep quality in a sequential mediation first through trait mindfulness and then through emotional distress. It is worth noting that these studies were conducted on the assumption that the capacity of cognitive control was positively correlated with sleep quality. However, suppressing effects may exist in cases where the direct effect is not significant ( MacKinnon et al., 2000 ). Because indirect and direct effects are indicated by different signs, one negative and the other positive,the direct effect of capacity of cognitive control on sleep quality was suppressed ( Wen and Ye, 2014 ). Although the direct effect of CCC on later sleep quality was not significant, the indirect effect, due to the mediation of trait mindfulness and emotional distress effect, turned out to be significant. The most significant contribution of the current research may lie in its clarification of the sequential and indirect role of trait mindfulness and emotional distress as a mechanism explaining the link between CCC and sleep quality. However, neither trait mindfulness nor emotional distress independently mediated the relationship between CCC and sleep quality. This finding substantiates the strength model of self-control, which postulates that self-control relies on a finite energy reserve, and its exertion gradually depletes this reserve. This mechanism would make people with high CCC levels have fewer energy resources to allocate to other mental activities.

The present study extended previous research on CCC and sleep by showing that CCC was negatively (rather than positively) correlated with trait mindfulness. This result may be explained from the following two perspectives. On the one hand, the non-judgmental attitude is an important criterion for measuring the trait mindfulness, and it is related to automatic attention ( Kabat-Zinn, 1990 ; Kabat-Zinn, 1994 ). Studies failed to find any positive effects of mindfulness on conscious control components in the process of practicing mindfulness ( Malinowski et al., 2017 ; Norris et al., 2018 ). On the other hand, mindfulness was found to have less conscious control over task-independent stimuli ( Slagter et al., 2007 ; Howells et al., 2012 ; Moore et al., 2012 ; Atchley et al., 2016 ). However, CCC can limit the occurrence of this automatic attention to some extent ( Hofmann et al., 2009 ). The level of CCC can reflect an individual’s control ability to overcome immediate stimuli ( Hofmann et al., 2009 ). Therefore, individuals with high levels of cognitive control capacity may have lower levels of trait mindfulness.

Furthermore, it’s important to note that in most studies, the cognitive tasks were performed immediately after the mindfulness intervention. In mindful meditation, the neural signature of attention could be detected very early, just a few minutes after the start of mindful intervention ( Lakey et al., 2011 ). This rapid effect was found to be particularly pronounced for concentration ( Chiesa et al., 2011 ). But would the effect last? If not, it can be argued that the effects reported after the long-term intervention may simply result from the participant’s most recent mindfulness training ( Fjorback et al., 2011 ). Our study compared a stable level of cognitive ability with a stable level of mindfulness, with the intention of shedding light on the discrepancies between our findings and the hypotheses proposed regarding the positive association between cognitive control capacity (CCC) and trait mindfulness.

Inconsistent with our hypothesis, the positive relationship between trait mindfulness and sleep quality was not significant. This can be explained by the fact that mindfulness is complex and not always beneficial ( Hafenbrack and Vohs, 2018 ; Britton, 2019 ). Indeed, a study highlighted a crucial turning point in meditation practices, where below a certain point the practices facilitate sleep and above which they tend to inhibit sleep ( Britton et al., 2010 ). These findings shed light on the potential effects of meditation practices on sleep patterns, with implications for individuals seeking to optimize their sleep quality through mindfulness exercises. Low practice volume in subjects of Mindfulness-Based Cognitive Therapy increased sleep duration, but as practice volume approached 30 min per day, sleep duration and depth began to decrease and cortical arousal (awakenings and micro-arousals) started to increase. Long-term meditators were also found to have worse sleep than non-meditators, with cortical arousal linearly correlated with lifetime meditation practice volume ( Ferrarelli et al., 2013 ). Additionally, studies comparing different types of practice of mindfulness found that body awareness exercises were to a lesser extent associated with unwanted effects caused by mindfulness, while attention exercises were more often related to unwanted effects ( Cebolla et al., 2017 ).

Furthermore, we failed to find that CCC was significantly correlated with emotional distress, and the mediation effect of emotional distress proved to be insignificant. Perhaps this is because the association between cognitive control and emotions is more reflected in emotional regulation and may have little impact on emotional distress ( Tice and Bratslavsky, 2000 ).

This study has several strengths. It used CCC as an independent variable to investigate its effect on sleep quality. Conceptually, the CCC can probe the upper limit of cognitive control of information processing, and the inclusion of trait mindfulness and emotional distress can help investigate the mechanisms underlying sleep quality and cognitive control. Methodologically, we adopted the MFT-M-R paradigm to measure CCC, which can more accurately reflect the level of individual cognitive control. From an educational perspective, the current findings may advance our understanding of the relationship between sleep and cognitive control in adolescents, offering insights into prospective intervention strategies. We identified trait mindfulness and emotional distress as two important mediators and depicted how they worked. Our main findings have implications for the development and refinement of interventions aimed at overcoming sleep problems in adolescents. Specifically, given the important role of self-control, interventions can incorporate techniques to enhance self-control. In addition, we should upgrade our understanding of the role of mindfulness. The practice of mindfulness may serve as a feasible technique for counterbalancing the influence of unconscious representations ( Dehaene, 2018 ), compensating for the inherent instability of self-control in adolescents, improving self-regulation and potentially buffering the adverse effects of emotional distress on sleep health. Therefore, interventions such as school-based mindfulness practices and group counseling are interventions that can be focused on in future research.

Despite its findings, the present study has several limitations. First, the sample of participants was limited for they were recruited only from senior high school students in a Chinese middle school. Therefore, it may not be adequately representative of the adolescent population in general. It is recommended that future research endeavors aim to improve the diversity of participants’ backgrounds. This would help to ensure a more comprehensive understanding of the effects of mindfulness practices on various populations. Such an approach would enhance the generalizability and applicability of research findings, ultimately benefiting individuals from diverse cultural, social, and economic backgrounds. Secondly, due to the long duration (40 min) of the MFT-M-R experiment, this study collected valid data from only 149 participants. The limited sample size might have caused sampling error. Therefore, increasing the sample size to improve the reliability of the results is necessary. Last, we employed a two-wave longitudinal design. Such a design is very commonly used in organizational psychology (e.g., Burić et al., 2019 ; Muntz and Dormann, 2020 ; Spagnoli et al., 2021 ) and has advantages over the cross-sectional design. However, as some researchers suggest that the minimum number of waves in a longitudinal design should be three (e.g., Ployhart and Vandenberg, 2010 ), future research could include more waves of data collection when examining the relationships between CCC, trait mindfulness, emotional distress, and sleep quality, particularly if researchers are interested in exploring potential mediators. Besides, given the myriad definitions of mindfulness and different components of trait mindfulness, further research could investigate the nuances of trait mindfulness and how the varying components may individually affect sleep quality. This would allow for targeted clinical interventions to focus not only on practices of teaching mindfulness but also on finding which aspects of mindfulness are most useful in improving sleep quality. Future scholars could explore the possible mechanisms behind the association between different components of mindfulness and sleep quality, facilitating clinical interventions more effectively. Finally, this study yielded several results that were not consistent with the hypothesis, and the reasons for these results require further research.

5 Conclusion

This study contributes to our understanding of how cognitive control capacity impacts sleep quality. We found that CCC was indirectly associated with poor sleep quality in a sequential mediation first through trait mindfulness and then through emotional distress. The study provides implications for future exploration of the mechanism behind the relationship between cognitive control and sleep quality, as well as practical solutions for sleep problems, including clinical interventions.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Ethics Committee of the School of Psychology, South China Normal University. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by each participant’s legal guardian or next of kin.

Author contributions

YW: Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. HL: Data curation, Writing – original draft, Writing – review & editing. XL: Writing – original draft, Writing – review & editing. BZ: Writing – original draft, Writing – review & editing. MH: Writing – original draft, Writing – review & editing. CC: Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Philosophy and Social Science Fund of the 13th Five-year Plan of Guangdong Province of China (grant number: GD20CXL03), the Double First-Class Construction Project of China–South China University of Technology (grant number: K5200690), and China Scholarship Council (grant number: 202306150020).

Conflict of interest

BZ was employed by Guangzhou Branch of China Mobile Group Guangdong Company Limited.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. ^ www.mathworks.cn/products/matlab.html

2. ^ https://www.ibm.com/spss

Aivaliotis, V. I., Lee, Y., Zia, J., Wassef, W., Abramson, M., and Park, W. (2017). Telephone-based mindfulness therapy intervention for patients with chronic pancreatitis. Dig. Dis. Sci. 62, 502–509. doi: 10.1007/s10620-016-4389-6

PubMed Abstract | Crossref Full Text | Google Scholar

Atchley, R., Klee, D., Memmott, T., Goodrich, E., Wahbeh, H., and Oken, B. (2016). Event-related potential correlates of mindfulness meditation competence. Neuroscience 320, 83–92. doi: 10.1016/j.neuroscience.2016.01.051

Baer, R. A., Smith, G. T., Lykins, E., Button, D., Krietemeyer, J., Sauer, S., et al. (2008). Construct validity of the five facet mindfulness questionnaire in meditating and nonmeditating samples. Assessment 15, 329–342. doi: 10.1177/1073191107313003

Bahani, M., Zhang, Y., Guo, Y., Haretebieke, S., Wu, D., and Zhang, L. (2024). Influencing factors of sleep quality in pregnant: a structural equation model approach. BMC Psychol 12:171. doi: 10.1186/s40359-024-01657-1

Baumeister, R. F., Bratslavsky, E., Muraven, M., and Tice, D. M. (1998). Ego depletion: is the active self a limited resource? J. Pers. Soc. Psychol. 74, 1252–1265. doi: 10.1037/0022-3514.74.5.1252

Baumeister, R. F., Tice, D. M., and Vohs, K. D. (2018). The strength model of self-regulation: conclusions from the second decade of willpower research. Perspect. Psychol. Sci. 13, 141–145. doi: 10.1177/1745691617716946

Baumeister, R. F., Vohs, K. D., and Tice, D. M. (2007). The strength model of self-control. Curr. Dir. Psychol. Sci. 16, 351–355. doi: 10.1111/j.1467-8721.2007.00534.x

Crossref Full Text | Google Scholar

Bishop, S. R., Lau, M., Shapiro, S., Carlson, L., Anderson, N. D., Carmody, J., et al. (2004). Mindfulness: a proposed operational definition. Clin. Psychol. Sci. Pract. 11, 230–241. doi: 10.1093/clipsy.bph077

Brisbon, N. M., and Lachman, M. E. (2017). Dispositional mindfulness and memory problems: the role of perceived stress and sleep quality. Mindfulness 8, 379–386. doi: 10.1007/s12671-016-0607-8

Britton, W. B. (2019). Can mindfulness be too much of a good thing? The value of a middle way. Curr. Opin. Psychol. 28, 159–165. doi: 10.1016/j.copsyc.2018.12.011

Britton, W. B., Haynes, P. L., Fridel, K. W., and Bootzin, R. R. (2010). Polysomnographic and subjective profiles of sleep continuity before and after mindfulness-based cognitive therapy in partially remitted depression. Psychosom. Med. 72, 539–548. doi: 10.1097/PSY.0b013e3181dc1bad

Brown, K. W., and Ryan, R. M. (2003). The benefits of being present: mindfulness and its role in psychological well-being. J. Pers. Soc. Psychol. 84, 822–848. doi: 10.1037/0022-3514.84.4.822

Brown, K. W., Weinstein, N., and Creswell, J. D. (2012). Trait mindfulness modulates neuroendocrine and affective responses to social evaluative threat. Psychoneuroendocrinology 37, 2037–2041. doi: 10.1016/j.psyneuen.2012.04.003

Burić, I., Slišković, A., and Penezić, Z. (2019). A two-wave panel study on teachers’ emotions and emotional-labour strategies. Stress. Health 35, 27–38. doi: 10.1002/smi.2836

Burke, T. M., Scheer, F. A. J. L., Ronda, J. M., Czeisler, C. A., and Wright, K. P. (2015). Sleep inertia, sleep homeostatic and circadian influences on higher-order cognitive functions. J. Sleep Res. 24, 364–371. doi: 10.1111/jsr.12291

Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., and Kupfer, D. J. (1989). The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 28, 193–213. doi: 10.1016/0165-1781(89)90047-4

Cebolla, A., Demarzo, M., Martins, P., Soler, J., and Garcia-Campayo, J. (2017). Unwanted effects: is there a negative side of meditation? A multicentre survey. PLoS ONE 12:e0183137. doi: 10.1371/journal.pone.0183137

Chiesa, A., Calati, R., and Serretti, A. (2011). Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings. Clin. Psychol. Rev. 31, 449–464. doi: 10.1016/j.cpr.2010.11.003

Conley, S. L., Faleer, H. E., Raza, G. T., Bailey, B. E., and Wu, K. D. (2018). The moderating effects of rumination facets on the relationship between mindfulness and distress reduction. Cogn. Ther. Res. 42, 436–446. doi: 10.1007/s10608-018-9896-7

Dehaene, S. (2018). The error-related negativity, self-monitoring, and consciousness. Perspect. Psychol. Sci. 13, 161–165. doi: 10.1177/1745691618754502

Drapeau, A., Marchand, A., and Beaulieu-Prevost, D. (2012). “Epidemiology of psychological distress” in Mental illnesses—understanding, prediction and control . ed. L. LAbate (InTech). Open. www.intechopen.com .

Google Scholar

Ebneabbasi, A., Mahdipour, M., Nejati, V., Li, M., Liebe, T., Colic, L., et al. (2021). Emotion processing and regulation in major depressive disorder: a 7T resting-state F MRI study. Hum. Brain Mapp. 42, 797–810. doi: 10.1002/hbm.25263

Eisenlohr-Moul, T. A., Peters, J. R., Pond, R. S., and DeWall, C. N. (2016). Both trait and state mindfulness predict lower aggressiveness via anger rumination: a multilevel mediation analysis. Mindfulness 7, 713–726. doi: 10.1007/s12671-016-0508-x

Evans, J. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annu. Rev. Psychol. 59, 255–278. doi: 10.1146/annurev.psych.59.103006.093629

Evans, L., Haeberlein, K., Chang, A., and Handal, P. (2020). An evaluation of the convergent validity of and preliminary cutoff scores for the DASS-21 total score as a measure of distress in adolescents. Curr. Psychol. 41, 4283–4290. doi: 10.1007/s12144-020-00937-4

Fan, J. (2014). An information theory account of cognitive control. Front. Hum. Neurosci. 8. doi: 10.3389/fnhum.2014.00680

Fazio, R. H., and Towles-Schwen, T. (1999). “The MODE model of attitude-behavior processes” in Dual-process theories in social psychology . eds. J. W. Sherman, B. Gawronski, & Y. Trope. (New York, NY: The Guilford Press), 97–116.

Ferrarelli, F., Smith, R., Dentico, D., Riedner, B. A., Zennig, C., Benca, R. M., et al. (2013). Experienced mindfulness meditators exhibit higher parietal-occipital EEG gamma activity during NREM sleep. PLoS One 8:e73417. doi: 10.1371/journal.pone.0073417

Fjorback, L. O., Arendt, M., Ørnbøl, E., Fink, P., and Walach, H. (2011). Mindfulness-based stress reduction and mindfulness-based cognitive therapy — a systematic review of randomized controlled trials. Acta Psychiatr. Scand. 124, 102–119. doi: 10.1111/j.1600-0447.2011.01704.x

Frewen, P. A., Evans, E. M., Maraj, N., Dozois, D. J. A., and Partridge, K. (2008). Letting go: mindfulness and negative automatic thinking. Cogn. Ther. Res. 32, 758–774. doi: 10.1007/s10608-007-9142-1

Gadie, A., Shafto, M., Leng, Y., and Kievit, R. A. (2017). How are age-related differences in sleep quality associated with health outcomes? An epidemiological investigation in a UK cohort of 2406 adults. BMJ Open 7:e014920. doi: 10.1136/bmjopen-2016-014920

Galla, B. M. (2016). Within-person changes in mindfulness and self-compassion predict enhanced emotional well-being in healthy, but stressed adolescents. J. Adolesc. 49, 204–217. doi: 10.1016/j.adolescence.2016.03.016

Gilbert, B. D., and Christopher, M. S. (2010). Mindfulness-based attention as a moderator of the relationship between depressive affect and negative cognitions. Cogn. Ther. Res. 34, 514–521. doi: 10.1007/s10608-009-9282-6

Goldstein, R. Z., and Volkow, N. D. (2011). Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12, 652–669. doi: 10.1038/nrn3119

Gong, X., Xie, X., Xu, R., and Luo, Y. (2010). Psychometric properties of the Chinese versions of DASS-21 in Chinese college students. Chin. J. Clin. Psych. 18, 443–446. doi: 10.16128/j.cnki.1005-3611.2010.04.020

Goschke, T., and Bolte, A. (2014). Emotional modulation of control dilemmas: the role of positive affect, reward, and dopamine in cognitive stability and flexibility. Neuropsychologia 62, 403–423. doi: 10.1016/j.neuropsychologia.2014.07.015

Gradisar, M., Kahn, M., Micic, G., Short, M., Reynolds, C., Orchard, F., et al. (2022). Sleep’s role in the development and resolution of adolescent depression. Nat. Rev. Psychol. 1, 512–523. doi: 10.1038/s44159-022-00074-8

Guller, B., and Yaylaci, F. (2024). Eating and sleep problems, related factors, and effects on the mental health of the parents in children with autism spectrum disorder. Int. J. Develop. Disabil. 70, 406–415. doi: 10.1080/20473869.2022.2095689

Hafenbrack, A. C., and Vohs, K. D. (2018). Mindfulness meditation impairs task motivation but not performance. Organ. Behav. Hum. Decis. Process. 147, 1–15. doi: 10.1016/j.obhdp.2018.05.001

Hall, M., Buysse, D. J., Nowell, P. D., Nofzinger, E. A., Houck, P., Reynolds, C. F., et al. (2000). Symptoms of stress and depression as correlates of sleep in primary insomnia. Psychosom. Med. 62, 227–230. doi: 10.1097/00006842-200003000-00014

Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis . 2nd Edn. New York, NY: The Guilford Press.

He, X., Qiu, B., Deng, Y., Liu, T., Chen, Y., and Zhang, W. (2022). Adaptive assessment of the capacity of cognitive control. Q. J. Exp. Psychol. 75, 43–52. doi: 10.1177/17470218211030838

He, H., Xu, P., Wu, T., Chen, Y., Wang, J., Qiu, Y., et al. (2019). Reduced capacity of cognitive control in older adults with mild cognitive impairment. JAD 71, 185–200. doi: 10.3233/JAD-181006

Heatherton, T. F., and Wagner, D. D. (2011). Cognitive neuroscience of self-regulation failure. Trends Cogn. Sci. 15, 132–139. doi: 10.1016/j.tics.2010.12.005

Hofmann, W., Baumeister, R. F., Förster, G., and Vohs, K. D. (2012). Everyday temptations: An experience sampling study of desire, conflict, and self-control. J. Pers. Soc. Psychol. 102, 1318–1335. doi: 10.1037/a0026545

Hofmann, W., Friese, M., and Strack, F. (2009). Impulse and self-control from a dual-systems perspective. Perspect. Psychol. Sci. 4, 162–176. doi: 10.1111/j.1745-6924.2009.01116.x

Hofmann, S. G., Sawyer, A. T., Witt, A. A., and Oh, D. (2010). The effect of mindfulness-based therapy on anxiety and depression: a meta-analytic review. J. Consult. Clin. Psychol. 78, 169–183. doi: 10.1037/a0018555

Hölzel, B. K., Carmody, J., Evans, K. C., Hoge, E. A., Dusek, J. A., Morgan, L., et al. (2010). Stress reduction correlates with structural changes in the amygdala. Soc. Cogn. Affect. Neurosci. 5, 11–17. doi: 10.1093/scan/nsp034

Howells, F. M., Ives-Deliperi, V. L., Horn, N. R., and Stein, D. J. (2012). Mindfulness based cognitive therapy improves frontal control in bipolar disorder: a pilot EEG study. BMC Psychiatry 12:15. doi: 10.1186/1471-244X-12-15

Hsu, S.-C., Wang, S.-J., Liu, C.-Y., Juang, Y.-Y., Yang, C.-H., and Hung, C.-I. (2009). The impact of anxiety and migraine on quality of sleep in patients with major depressive disorder. Compr. Psychiatry 50, 151–157. doi: 10.1016/j.comppsych.2008.07.002

Kabat-Zinn, J. (1990). Full catastrophe living: Using the wisdom of your body and mind to face stress, pain and illness . New York, NY: Dell Publishing.

Kabat-Zinn, J. (1994). Wherever you go, there you are: Mindfulness meditation in everyday life . New York, NY: Hyperion.

Khoury, B., Sharma, M., Rush, S. E., and Fournier, C. (2015). Mindfulness-based stress reduction for healthy individuals: a meta-analysis. J. Psychosom. Res. 78, 519–528. doi: 10.1016/j.jpsychores.2015.03.009

Kiken, L. G., and Shook, N. J. (2012). Mindfulness and emotional distress: the role of negatively biased cognition. Personal. Individ. Differ. 52, 329–333. doi: 10.1016/j.paid.2011.10.031

Kiken, L. G., and Shook, N. J. (2014). Does mindfulness attenuate thoughts emphasizing negativity, but not positivity? J. Res. Pers. 53, 22–30. doi: 10.1016/j.jrp.2014.08.002

Kroese, F. M., Evers, C., Adriaanse, M. A., and de Ridder, D. T. (2016). Bedtime procrastination: a self-regulation perspective on sleep insufficiency in the general population. J. Health Psychol. 21, 853–862. doi: 10.1177/1359105314540014

Lai, H., and Good, M. (2005). Music improves sleep quality in older adults. J. Adv. Nurs. 49, 234–244. doi: 10.1111/j.1365-2648.2004.03281.x

Lakey, C. E., Berry, D. R., and Sellers, E. W. (2011). Manipulating attention via mindfulness induction improves P300-based brain computer interface performance. J. Neural Eng. 8:025019. doi: 10.1088/1741-2560/8/2/025019

Latif, I., Hughes, A. T. L., and Bendall, R. C. A. (2019). Positive and negative affect mediate the influences of a maladaptive emotion regulation strategy on sleep quality. Front. Psych. 10:628. doi: 10.3389/fpsyt.2019.00628

Lawlor, M. S., Schonert-Reichl, K. A., Gadermann, A. M., and Zumbo, B. D. (2014). A validation study of the mindful attention awareness scale adapted for children. Mindfulness 5, 730–741. doi: 10.1007/s12671-013-0228-4

Li, Y. (2017). The correlation between mindfulness attention awareness and anxiety in middle school students. Mental Health Education in Primary and Secondary . 25, 15–17.

Liu, Y., Chen, C., Du, H., Xue, M., and Zhu, N. (2024). Impact of Baduanjin exercise combined with rational emotive behavior therapy on sleep and mood in patients with poststroke depression: a randomized controlled trial. Medicine 103:e38180. doi: 10.1097/MD.0000000000038180

Liu, Q., Niu, G., Fan, C., and Zhou, Z. (2017). Passive use of social network site and its relationships with self-esteem and self-concept clarity: a moderated mediation analysis. Acta Psychol. Sin. 49:60. doi: 10.3724/SP.J.1041.2017.00060

Liu, L., Su, T., Peng, J., Guo, Y., and Feng, T. (2014). The cognitive and neural mechanism of the delay discounting: from the trait and state perspectives. Adv. Psychol. Sci. 22:1047. doi: 10.3724/SP.J.1042.2014.01047

Liu, X., Tang, M., Hu, L., Wang, A., Wu, Z., Gao, C., et al. (1996). Reliability and validity of the Pittsburgh sleep quality index. Chin. J. Psychiatry , 29, 103–107.

Liu, H., Wu, X., and Deng, Y. (2018). Mindfulness and depression of college students: the chain mediation of experience avoidance and verbose thinking. J. Clin. Psychol. 26, 565–569. doi: 10.16128/j.cnki.1005-3611.2018.03.031

Luo, Y., Du, J., Wang, J., Liu, P., Shi, Z., He, Y., et al. (2024). Progressive muscle relaxation alleviates anxiety and improves sleep quality among healthcare practitioners in a mobile cabin hospital: a pre-post comparative study in China. Front. Psychol. 15:1337318. doi: 10.3389/fpsyg.2024.1337318

Mackie, M.-A., and Fan, J. (2016). Reduced efficiency and capacity of cognitive control in autism spectrum disorder: cognitive control in ASD. Autism Res. 9, 403–414. doi: 10.1002/aur.1517

MacKinnon, D. P., Krull, J. L., and Lockwood, C. M. (2000). Equivalence of the mediation, confounding and suppression effect. Prev. Sci. 1, 173–181. doi: 10.1023/A:1026595011371

Malinowski, P., Moore, A. W., Mead, B. R., and Gruber, T. (2017). Mindful aging: the effects of regular brief mindfulness practice on electrophysiological markers of cognitive and affective processing in older adults. Mindfulness 8, 78–94. doi: 10.1007/s12671-015-0482-8

Moore, A., Gruber, T., Derose, J., and Malinowski, P. (2012). Regular, brief mindfulness meditation practice improves electrophysiological markers of attentional control. Front. Hum. Neurosci. 6:18. doi: 10.3389/fnhum.2012.00018

Mueller, C., Nenert, R., Catiul, C., Pilkington, J., Szaflarski, J. P., and Amara, A. W. (2024). Relationship between sleep, physical fitness, brain microstructure, and cognition in healthy older adults: a pilot study. Brain Res. 1839:149016. doi: 10.1016/j.brainres.2024.149016

Muntz, J., and Dormann, C. (2020). Moderating effects of appreciation on relationships between illegitimate tasks and intrinsic motivation: a two-wave shortitudinal study. Eur. J. Work Organ. Psy. 29, 391–404. doi: 10.1080/1359432X.2019.1706489

Norris, C. J., Creem, D., Hendler, R., and Kober, H. (2018). Brief mindfulness meditation improves attention in novices: evidence from ERPs and moderation by neuroticism. Front. Hum. Neurosci. 12:315. doi: 10.3389/fnhum.2018.00315

Ployhart, R. E., and Vandenberg, R. J. (2010). Longitudinal research: the theory, design, and analysis of change. J. Manag. 36, 94–120. doi: 10.1177/0149206309352110

Punamäki, R., Wallenius, M., Nygård, C., Saarni, L., and Rimpelä, A. (2007). Use of information and communication technology (ICT) and perceived health in adolescence: the role of sleeping habits and waking-time tiredness. J. Adolesc. 30, 569–585. doi: 10.1016/j.adolescence.2006.07.004

Pychyl, T. A., and Sirois, F. (2016). “Procrastination, emotion regulation, and well-being” in Procrastination, health, and well-being . eds. Sirois, F. M., & Pychyl, T. A. (San Diego: Elsvier Academic Press), 163–188.

Sarchiapone, M., Mandelli, L., Carli, V., Iosue, M., Wasserman, C., Hadlaczky, G., et al. (2014). Hours of sleep in adolescents and its association with anxiety, emotional concerns, and suicidal ideation. Sleep Med. 15, 248–254. doi: 10.1016/j.sleep.2013.11.780

Schantz, B. L., Toner, E. R., Brown, M. L., Kaiser, N., Chen, A., Adhikari, S., et al. (2024). Examining the relationship between emotion regulation, sleep quality, and anxiety disorder diagnosis. J. Mood Anxiety Disord. 8:100072. doi: 10.1016/j.xjmad.2024.100072

Seo, J.-H., Kim, J. H., Yang, K. I., and Hong, S. B. (2017). Late use of electronic media and its association with sleep, depression, and suicidality among Korean adolescents. Sleep Med. 29, 76–80. doi: 10.1016/j.sleep.2016.06.022

Shen, L., van Schie, J., Ditchburn, G., Brook, L., and Bei, B. (2018). Positive and negative emotions: differential associations with sleep duration and quality in adolescents. J. Youth Adolescence 47, 2584–2595. doi: 10.1007/s10964-018-0899-1

Sirois, F. (2014). Procrastination and stress: exploring the role of self-compassion. Self Identity 13, 128–145. doi: 10.1080/15298868.2013.763404

Sirois, F., and Pychyl, T. (2013). Procrastination and the priority of short-term mood regulation: consequences for future self: procrastination, mood regulation and future self. Soc. Personal. Psychol. Compass 7, 115–127. doi: 10.1111/spc3.12011

Slagter, H. A., Lutz, A., Greischar, L. L., Francis, A. D., Nieuwenhuis, S., Davis, J. M., et al. (2007). Mental training affects distribution of limited brain resources. PLoS Biol. 5:e138. doi: 10.1371/journal.pbio.0050138

Songkham, W., Deeluea, J., Suksatit, B., and Chaiard, J. (2019). Sleep quality among industrial workers: related factors and impact. JHR 33, 119–126. doi: 10.1108/JHR-08-2018-0072

Souders, M., Mason, T., Valladares, O., Bucan, M., Levy, S., Mandell, D., et al. (2009). Sleep behaviors and sleep quality in children with autism Spectrum disorders. Sleep , 32, 1566–1587. doi: 10.1093/sleep/32.12.1556

Spagnoli, P., Kovalchuk, L. S., Aiello, M. S., and Rice, K. G. (2021). The predictive role of perfectionism on heavy work investment: a two-waves cross-lagged panel study. Personal. Individ. Differ. 173:110632. doi: 10.1016/j.paid.2021.110632

Teasdale, J. D., Segal, Z., and Williams, J. M. G. (1995). How does cognitive therapy prevent depressive relapse and why should attentional control (mindfulness) training help? Behav. Res. Ther. 33, 25–39. doi: 10.1016/0005-7967(94)E0011-7

Tice, D. M., and Bratslavsky, E. (2000). Giving in to feel Good: the place of emotion regulation in the context of general self-control. Psychol. Inq. 11, 149–159. doi: 10.1207/S15327965PLI1103_03

Vernon, L., Modecki, K. L., and Barber, B. L. (2018). Mobile phones in the bedroom: trajectories of sleep habits and subsequent adolescent psychosocial development. Child Dev. 89, 66–77. doi: 10.1111/cdev.12836

Vignaud, P., Donde, C., Sadki, T., Poulet, E., and Brunelin, J. (2018). Neural effects of mindfulness-based interventions on patients with major depressive disorder: a systematic review. Neurosci. Biobehav. Rev. 88, 98–105. doi: 10.1016/j.neubiorev.2018.03.004

Vohs, K. D. (2006). Self-regulatory resources power the reflective system: evidence from five domains. J. Consum. Psychol. 16, 217–223. doi: 10.1207/s15327663jcp1603_3

Waters, L. (2016). The relationship between child stress, child mindfulness and parent mindfulness. PSYCH 7, 40–51. doi: 10.4236/psych.2016.71006

Way, B. M., Creswell, J. D., Eisenberger, N. I., and Lieberman, M. D. (2010). Dispositional mindfulness and depressive symptomatology: correlations with limbic and self-referential neural activity during rest. Emotion 10, 12–24. doi: 10.1037/a0018312

Wen, Z., and Ye, B. (2014). Analyses of mediating effects: the development of methods and models. Adv. Psychol. Sci. 22:731. doi: 10.3724/SP.J.1042.2014.00731

Williams, P. G., Cribbet, M. R., Rau, H. K., Gunn, H. E., and Czajkowski, L. A. (2013). The effects of poor sleep on cognitive, affective, and physiological responses to a laboratory stressor. Ann. Behav. Med. 46, 40–51. doi: 10.1007/s12160-013-9482-x

Wu, Q., Chang, C.-F., Xi, S., Huang, I.-W., Liu, Z., Juan, C.-H., et al. (2015). A critical role of temporoparietal junction in the integration of top-down and bottom-up attentional control: interaction of top-down and bottom-up attention. Hum. Brain Mapp. 36, 4317–4333. doi: 10.1002/hbm.22919

Wu, T., Chen, C., Spagna, A., Wu, X., Mackie, M., Russell-Giller, S., et al. (2020). The functional anatomy of cognitive control: a domain-general brain network for uncertainty processing. J. Comp. Neurol. 528, 1265–1292. doi: 10.1002/cne.24804

Wu, T., Dufford, A. J., Mackie, M.-A., Egan, L. J., and Fan, J. (2016). The capacity of cognitive control estimated from a perceptual decision making task. Sci. Rep. 6:34025. doi: 10.1038/srep34025

Xiao, C., Mou, C., and Zhou, Z. (2019). Effect of mindfulness meditation training on anxiety, depression and sleep quality in perimenopausal women. J. Southern Medical Univ. 39, 998–1002. doi: 10.12122/j.issn.1673-4254.2019.08.19

Xie, M., Zhang, Y., Wang, W., Chen, H., and Lin, D. (2024). Associations between multiple dimensions of sleep and mood during early adolescence: a longitudinal daily diary study. J. Youth Adolescence. doi: 10.1007/s10964-024-02007-5

Xu, W., Fu, G., An, Y., Yuan, G., Ding, X., and Zhou, Y. (2018). Mindfulness, posttraumatic stress symptoms, depression, and social functioning impairment in Chinese adolescents following a tornado: mediation of posttraumatic cognitive change. Psychiatry Res. 259, 345–349. doi: 10.1016/j.psychres.2017.09.088

Yosiaki, S., Ogawa, M., Kawada, T., Suzuki, S., and Duki, M. Z. (1998). Afternoon exercise improves the quality of night sleep: a case study observed by EEG and self-rating scale. J. Occup. Health 40, 37–43. doi: 10.1539/joh.40.37

Zeidan, F., Johnson, S. K., Diamond, B. J., David, Z., and Goolkasian, P. (2010). Mindfulness meditation improves cognition: evidence of brief mental training. Conscious. Cogn. 19, 597–605. doi: 10.1016/j.concog.2010.03.014

Zhai, X., Zeng, J., Eshak, E. S., Zhang, Y., Yang, M., Di, L., et al. (2021). The influencing factors of sleep quality among Chinese junior and senior high school adolescents during the COVID-19 pandemic. J. Trop. Pediatr. 67:fmab069. doi: 10.1093/tropej/fmab069

Keywords: capacity of cognitive control, trait mindfulness, emotional distress, sleep quality, sequential mediation

Citation: Wang Y, Lin H, Liu X, Zhu B, He M and Chen C (2024) Associations between capacity of cognitive control and sleep quality: a two-wave longitudinal study. Front. Psychol . 15:1391761. doi: 10.3389/fpsyg.2024.1391761

Received: 26 February 2024; Accepted: 05 June 2024; Published: 17 June 2024.

Reviewed by:

Copyright © 2024 Wang, Lin, Liu, Zhu, He and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Caiqi Chen, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

American Psychological Association

5 ways to improve mental health at work

Supportive workplace practices can boost employee well-being, company morale

U.S. workers adjust to changing nature of employment

Survey highlights include remote work, four-day workweeks, and AI adoption

Psychological safety in the changing workplace

Survey shows link with job satisfaction, including creativity and innovation

Younger workers feel stressed, lonely, and undervalued

Nearly one-third of U.S. employees prefer working with people their own age

Membership in APA

APA Community

A new exclusive destination tailored for APA members

Membership benefits

Unlock the tools, discounts, and services included with your membership

Renew your membership

Keep your benefits and access to leading psychological information

Psychology topics spotlight

Misinformation and disinformation

Resources to navigate trauma

Tips to foster a healthy workplace

Science and practice of psychology

Ethics Code

Continuing Education

Grants, Awards, and Funding

Standards and Guidelines

Networks and communities

Network with peers, enhance your professional development, expand your personal growth, and more

APA Divisions

High school teachers

Undergraduate educators

Graduate students

Early career psychologists

Managing your career

Resources to help you throughout your career in psychology, including finding a job, salary data, finances and money management, mentoring and supervision, and training and professional development

Explore career paths

Psychologist profiles

How did you get that job?

Events and training

Featured jobs

Apa publications and products.

Write with clarity, precision, and inclusion

Children’s books

Monitor on Psychology

Newsletters

Reports and surveys

Continuing education

Merchandise

Real Siblings

Jacob's Missing Book

Harper Becomes a Big Sister

Attachment-Based Family Therapy for Sexual and Gender Minority Young Adults and Their Non-Accepting Parents

Dismantling Everyday Discrimination

APA Services

Learn how you can help APA advocate for psychology-informed federal policy and legislation, and support psychological research

APA Services, Inc.

A companion professional organization to APA, serving all members and advocating for psychology

The Nightly Challenge: Sleep Disorders in ADHD Children

Sleep disturbances may occur in roughly half of children diagnosed with adhd..

Posted June 19, 2024 | Reviewed by Lybi Ma

• Why Is Sleep Important?
• Find a sleep therapist near me
• Better sleep practices result in improved productivity during the day and can prevent adverse health effects.
• Maintaining good sleep hygiene is a first line of defense for managing sleep disturbances.
• Melatonin supplements may be useful for promoting and maintaining sleep.

My ADHD son is a night owl. He will be awake long after I am sound asleep, watching a video on YouTube, gaming on his computer, or talking with friends. Sleep disturbances are common in individuals with ADHD and may occur in up to half of the ADHD population, making a restful night’s slumber difficult. Prior to puberty, 10 to 15 percent of children with ADHD have trouble getting to sleep. This is twice the rate found in children and adolescents who do not have ADHD. Approximately half of children with ADHD have difficulty falling asleep almost every night by the time they reach adolescence . Sleep disturbances in ADHD don’t only lead to difficulty falling asleep, but also difficulty staying asleep and waking up.

Why have sleep disturbances been overlooked?

According to ADHD expert, William Dodson, MD, sleep disturbances in ADHD have been overlooked since they, on average, occur later (around 12 years old) than the diagnosis cutoff age of 7 years old specified in the Diagnostic and Statistical Manual of Mental Disorders (DSM, American Psychiatric Association, 2013).

The association between ADHD and sleep disturbances is not fully understood. Other disorders associated with sleep disturbances, like bipolar disorder , autism , post- traumatic stress disorder, and obsessive-compulsive disorder often co-occur with ADHD. In addition, the use of stimulant medications to treat ADHD symptoms has also been attributed to sleep disturbances (rather than ADHD itself). However, in some cases stimulant medication has been found to improve sleep, having a paradoxical effect of decreasing restlessness through the alleviation of ADHD symptoms.

Why is it important to address sleep concerns in ADHD?

The obvious answer is to decrease drowsiness and tiredness during the day to increase focus and productivity . In addition, sleep disorders like obstructive sleep apnea, restless leg syndrome, and circadian rhythm disruptions occur at a higher rate in individuals with ADHD than in the general population, posing adverse health effects, like high blood pressure.

How can we help our ADHD kids with sleep disturbances?

We spend a significant amount of time helping our ADHD kids manage their symptoms during the day, but ADHD does not go away at night. Many individuals with ADHD will report that they have difficulty “shutting off their brains” at night when it’s time to hit the hay. My son mentions that he must be naturally tired and will often fall asleep watching YouTube videos on his phone. He often finds it difficult to fall asleep at a reasonable time, even if he needs to be up early the next day.

• Establish healthy sleep practices

Sleep hygiene includes behavioral and environmental factors that can promote a good night’s sleep. Good sleep hygiene includes things like establishing a bedtime routine, having a calming, pleasant sleep environment, and avoiding naps during the day. Other considerations are the use of electronic devices and caffeine consumption close to bedtime. The set of sleep hygiene conditions is highly individualized. For example, my son likes to use a white noise machine to help him fall asleep, which may be annoying to some. Other individuals may need to put in earplugs and wear a sleep mask to block out all noise and light. Setting an alert on your smartphone or watch 30 minutes before bedtime, indicating it’s time to wind down, may also help.

Pharmacological treatments, like melatonin supplements, may be useful for sleep disturbances associated with ADHD. The hormone melatonin is produced in the brain in response to darkness, signaling to our bodies that it’s time to go to sleep. In children (and adults) with ADHD the onset in the production of melatonin is delayed, occurring around 10:15 PM. In addition, it can take up to two hours after melatonin production for sleep to occur, which can contribute to your ADHD child being a night owl. Melatonin supplements help to induce and maintain sleep, including in children with ADHD. Although melatonin supplements are safe, before starting a pharmacological treatment be sure to consult your child’s doctor.

• Consider your daily schedule

School and work schedules are, in general, not flexible. However, sometimes it may be possible to set up a schedule that works with being a night owl. For example, my son prefers his college classes to be scheduled between 11:00 AM and 5:00 PM, since he likes to stay up late, and his focus is better in the afternoon and evening. Although it’s not always possible for him to schedule his classes during his preferred times, when he can he does. And if my son needs to make it to an 8:00 AM class he uses a two-alarm method: he sets a first alarm with a soothing sound, and a second alarm with a loud, obnoxious sound which he labeled “Rise and shine, you dimwit!” I have also been known to call my son at 4:00 AM to make sure he is awake and will make it to the airport on time for his early flight home from college.

Dodson, W. (21 May, 2024). ADHD and Sleep Problems: This is Why You’re Always Tired. ADDitude. https://www.additudemag.com/adhd-sleep-disturbances-symptoms .

Hvolby, A. (2015). Associations of sleep disturbance with ADHD: implications for treatment. Atten Defic Hyperact Disord. 7(1):1-18. doi: 10.1007/s12402-014-0151-0. Epub 2014 Aug 17. PMID: 25127644; PMCID: PMC4340974.

Olivardia, R. (28 June, 2023). Melatonin for Kids with ADHD: Is It Safe? Does It Work? ADDitude. https://www.additudemag.com/melatonin-for-kids .

Kristin Wilcox, Ph.D. , is the author of Andrew's Awesome Adventures with His ADHD Brain . She has studied ADHD medications and drug abuse behavior at Emory University and Johns Hopkins University School of Medicine.

• Find a Therapist
• Find a Treatment Center
• Find a Psychiatrist
• Find a Support Group
• Find Online Therapy
• United States
• Brooklyn, NY
• Chicago, IL
• Houston, TX
• Los Angeles, CA
• New York, NY
• Portland, OR
• San Diego, CA
• San Francisco, CA
• Seattle, WA
• Washington, DC
• Asperger's
• Bipolar Disorder
• Chronic Pain
• Eating Disorders
• Passive Aggression
• Personality
• Goal Setting
• Positive Psychology
• Stopping Smoking
• Low Sexual Desire
• Relationships
• Child Development
• Self Tests NEW
• Therapy Center
• Diagnosis Dictionary
• Types of Therapy

At any moment, someone’s aggravating behavior or our own bad luck can set us off on an emotional spiral that threatens to derail our entire day. Here’s how we can face our triggers with less reactivity so that we can get on with our lives.

• Emotional Intelligence
• Gaslighting
• Affective Forecasting
• Neuroscience

States of Consciousness

Sleep and Why We Sleep

Learning objectives.

• Describe areas of the brain and hormone secretions involved in sleep
• Describe several theories (adaptive and cognitive) aimed at explaining the function of sleep

We spend approximately one-third of our lives sleeping. Given the average life expectancy for U.S. citizens falls between 73 and 79 years old (Singh & Siahpush, 2006), we can expect to spend approximately 25 years of our lives sleeping. Some animals never sleep (e.g., several fish and amphibian species); other animals can go extended periods of time without sleep and without apparent negative consequences (e.g., dolphins); yet some animals (e.g., rats) die after two weeks of sleep deprivation (Siegel, 2008). Why do we devote so much time to sleeping? Is it absolutely essential that we sleep? This section will consider these questions and explore various explanations for why we sleep.

What is Sleep?

You have read that sleep is distinguished by low levels of physical activity and reduced sensory awareness. As discussed by Siegel (2008), a definition of sleep must also include mention of the interplay of the circadian and homeostatic mechanisms that regulate sleep. Homeostatic regulation of sleep is evidenced by sleep rebound following sleep deprivation. Sleep rebound refers to the fact that a sleep-deprived individual will tend to take longer falling asleep during subsequent opportunities for sleep. Sleep is characterized by certain patterns of activity of the brain that can be visualized using electroencephalography (EEG), and different phases of sleep can be differentiated using EEG as well (Figure 1).

Sleep-wake cycles seem to be controlled by multiple brain areas acting in conjunction with one another. Some of these areas include the thalamus, the hypothalamus, and the pons. As already mentioned, the hypothalamus contains the SCN—the biological clock of the body—in addition to other nuclei that, in conjunction with the thalamus, regulate slow-wave sleep. The pons is important for regulating rapid eye movement (REM) sleep (National Institutes of Health, n.d.).

Sleep is also associated with the secretion and regulation of a number of hormones from several endocrine glands including: melatonin, follicle stimulating hormone (FSH), luteinizing hormone (LH), and growth hormone (National Institutes of Health, n.d.). You have read that the pineal gland releases melatonin during sleep (Figure 2). Melatonin is thought to be involved in the regulation of various biological rhythms and the immune system (Hardeland et al., 2006). During sleep, the pituitary gland secretes both FSH and LH which are important in regulating the reproductive system (Christensen et al., 2012; Sofikitis et al., 2008). The pituitary gland also secretes growth hormone, during sleep, which plays a role in physical growth and maturation as well as other metabolic processes (Bartke, Sun, & Longo, 2013).

Why Do We Sleep?

Given the central role that sleep plays in our lives and the number of adverse consequences that have been associated with sleep deprivation, one would think that we would have a clear understanding of why it is that we sleep. Unfortunately, this is not the case; however, several hypotheses have been proposed to explain the function of sleep.

Adaptive Function of Sleep

One popular hypothesis of sleep incorporates the perspective of evolutionary psychology. Evolutionary psychology is a discipline that studies how universal patterns of behavior and cognitive processes have evolved over time as a result of natural selection . Variations and adaptations in cognition and behavior make individuals more or less successful in reproducing and passing their genes to their offspring. One hypothesis from this perspective might argue that sleep is essential to restore resources that are expended during the day. Just as bears hibernate in the winter when resources are scarce, perhaps people sleep at night to reduce their energy expenditures. While this is an intuitive explanation of sleep, there is little research that supports this explanation. In fact, it has been suggested that there is no reason to think that energetic demands could not be addressed with periods of rest and inactivity (Frank, 2006; Rial et al., 2007), and some research has actually found a negative correlation between energetic demands and the amount of time spent sleeping (Capellini, Barton, McNamara, Preston, & Nunn, 2008).

Another evolutionary hypothesis of sleep holds that our sleep patterns evolved as an adaptive response to predatory risks, which increase in darkness. Thus we sleep in safe areas to reduce the chance of harm. Again, this is an intuitive and appealing explanation for why we sleep. Perhaps our ancestors spent extended periods of time asleep to reduce attention to themselves from potential predators. Comparative research indicates, however, that the relationship that exists between predatory risk and sleep is very complex and equivocal. Some research suggests that species that face higher predatory risks sleep fewer hours than other species (Capellini et al., 2008), while other researchers suggest there is no relationship between the amount of time a given species spends in deep sleep and its predation risk (Lesku, Roth, Amlaner, & Lima, 2006).

It is quite possible that sleep serves no single universally adaptive function, and different species have evolved different patterns of sleep in response to their unique evolutionary pressures. While we have discussed the negative outcomes associated with sleep deprivation, it should be pointed out that there are many benefits that are associated with adequate amounts of sleep. A few such benefits listed by the National Sleep Foundation (n.d.) include maintaining healthy weight, lowering stress levels, improving mood, and increasing motor coordination, as well as a number of benefits related to cognition and memory formation.

Cognitive Function of Sleep

Another theory regarding why we sleep involves sleep’s importance for cognitive function and memory formation (Rattenborg, Lesku, Martinez-Gonzalez, & Lima, 2007). Indeed, we know sleep deprivation results in disruptions in cognition and memory deficits (Brown, 2012), leading to impairments in our abilities to maintain attention, make decisions, and recall long-term memories. Moreover, these impairments become more severe as the amount of sleep deprivation increases (Alhola & Polo-Kantola, 2007). Furthermore, slow-wave sleep after learning a new task can improve resultant performance on that task (Huber, Ghilardi, Massimini, & Tononi, 2004) and seems essential for effective memory formation (Stickgold, 2005). Understanding the impact of sleep on cognitive function should help you understand that cramming all night for a test may be not effective and can even prove counterproductive.

Watch this video to learn more about the function of sleep and the harmful effects of sleep deprivation.

You can view the transcript for “What would happen if you didn’t sleep? – Claudia Aguirre” here (opens in new window) .

Sleep has also been associated with other cognitive benefits. Research indicates that included among these possible benefits are increased capacities for creative thinking (Cai, Mednick, Harrison, Kanady, & Mednick, 2009; Wagner, Gais, Haider, Verleger, & Born, 2004), language learning (Fenn, Nusbaum, & Margoliash, 2003; Gómez, Bootzin, & Nadel, 2006), and inferential judgments (Ellenbogen, Hu, Payne, Titone, & Walker, 2007). It is possible that even the processing of emotional information is influenced by certain aspects of sleep (Walker, 2009).

Learn about the connection between memory and sleep in the following clip:

You can view the transcript for “The Connection between Memory and Sleep – Science Nation” here (opens in new window) .

Think It Over

Have you (or someone you know) ever experienced significant periods of sleep deprivation because of simple insomnia, high levels of stress, or as a side effect from a medication? What were the consequences of missing out on sleep?

CC licensed content, Original

• Modification and adaptation. Provided by : Lumen Learning. License : CC BY: Attribution

CC licensed content, Shared previously

• Sleep and Why We Sleep. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/4-2-sleep-and-why-we-sleep . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction/.

All rights reserved content

• What would happen if you didnt sleep? – Claudia Aguirre. Authored by : Ted-Ed. Located at : https://www.youtube.com/watch?v=dqONk48l5vY . License : Other . License Terms : Standard YouTube License
• The Connection between Memory and Sleep – Science Nation. Authored by : National Science Foundation. Located at : https://www.youtube.com/watch?v=ObuaXhtKbVY . License : All Rights Reserved

sleep-deprived individuals will experience longer sleep latencies during subsequent opportunities for sleep

discipline that studies how universal patterns of behavior and cognitive processes have evolved over time as a result of natural selection

General Psychology Copyright © by OpenStax and Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Share This Book