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

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

Conclusions

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

Introduction

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Panel deliberations and consensus voting

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Literature review

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Limitations

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

Supplementary material

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Article info

Publication history, identification.

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

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• Review Article
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• 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

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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.

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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.

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

Marie Skłodowska-Curie Senior Research Fellow, University of York

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

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

1. We know more about lucid dreaming

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

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

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

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

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

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

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

2. Our brain replays memories while we sleep

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

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

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

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

3. Sleep keeps our minds healthy

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

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

4. Sleep protects us against neurodegenerative diseases

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

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

5. We can engineer sleep

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

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

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

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

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The Role of Sleep in Cardiovascular Disease

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• Garred S. Greenberg 1 ,
• Siddhant Parihar 1 ,
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Purpose of Review

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

Recent Findings

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

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

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Introduction

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

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

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

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

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

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

Sleep Quality and Duration as a Risk Factor for CVD

Pathophysiology.

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

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

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

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

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

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

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

Sleep Duration and CV Health

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

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

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

Disparities in Sleep Health

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

Confounding and Mediating Factors

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

Sleep Quality and the Prevention of CVD

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

OSA as a Risk Factor for CVD

Acute physiological effects of osa.

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

OSA as a Risk Factor for Hypertension

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

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

OSA as a Risk Factor for Arrhythmias

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

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

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

OSA and CAD

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

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

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

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

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

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

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

OSA and Metabolic Syndrome

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

Impact of Treatment of OSA on CVD Outcomes

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

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

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

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

CVD as a Risk Factor for Poor Sleep

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

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

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

Data Availability

No datasets were generated or analysed during the current study.

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

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

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

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Effect of sleep on oral health: A scoping review

Affiliations.

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

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

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

Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.

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Is Household Chaos Ruining Sleep for Teens with ADHD?

May 31, 2024 | Age | 0 |

A new study explored the role of household structure and stability for healthy sleep in teens.

Summary: A study to be presented at SLEEP 2024 reveals that household chaos and poor sleep hygiene significantly impact the relationship between sleep quality and ADHD symptoms in teenagers. By analyzing data from 259 mother-adolescent pairs, researchers found that improving household routines and sleep hygiene could enhance sleep quality for adolescents with ADHD symptoms.

Key Takeaways:

• Impact of Household Chaos: The study identifies household chaos as a significant factor that negatively affects sleep quality in teens with ADHD. A disorganized home environment can exacerbate sleep difficulties associated with ADHD.
• Role of Sleep Hygiene: Poor sleep hygiene was found to be a mediator in the relationship between ADHD symptoms and poor sleep quality. Implementing better sleep habits and routines can potentially improve sleep for adolescents with ADHD.
• Importance of Structured Routines: Researchers suggest that stabilizing household routines and reducing chaos are crucial strategies for improving sleep quality in teens with ADHD. 

A new study found that household chaos and sleep hygiene are important factors in the relationship between sleep quality and attention deficit hyperactivity disorder (ADHD) symptoms in teens.

Results of structural equation modeling, to be presented at the SLEEP 2024 annual meeting, show that household chaos and sleep hygiene were significant mediators of the relationship between ADHD symptoms and poor sleep quality. 

Improving Household Stability

Researchers say results suggest that improving the daily routine and stability of the household is an important strategy to consider when seeking to improve sleep quality in adolescents with symptoms of ADHD.

“These results begin to explicate some contextual factors that may help explain the increase in sleep difficulties observed in youth with higher symptoms of ADHD,” says lead author and co-principal investigator Jamie Flannery, who is a doctoral candidate in developmental psychology at the University of Notre Dame, in a release. “It suggests that when ADHD symptoms are high, aspects of the individual—poor sleep hygiene—and the familial environment—household chaos—are associated with poor sleep quality in adolescents.”

Study Methodology and Data Collection

The researchers collected data from 259 pairs of mothers and adolescents from across the United States. Mothers used a scale to rate the severity of their adolescent’s ADHD symptoms, while adolescents completed three separate surveys about sleep quality, home environment and sleep hygiene.

Importance of Routine and Stability

Flannery notes in a release that it’s important for adolescents and their families to know that it is more than just individual characteristics that can impact their sleep.

“While improving sleep hygiene in youths with ADHD may be beneficial, a household characterized by a lack of structure, routine , and stability may undermine the adolescent’s sleep quality,” Flannery says in a release.

The research abstract was published recently in an online supplement of the journal Sleep and will be presented Monday, June 3, during SLEEP 2024 in Houston.

Photo  138233025  ©  Fizkes  |  Dreamstime.com

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The Importance of Sleep

According to U.S. Centers for Disease Control and Prevention , more than 80 million American adults are chronically sleep deprived, meaning they sleep less than the recommended minimum of seven hours a night. Anyone who regularly sleeps less than six hours a night has an elevated risk of depression, psychosis, and stroke. Lack of sleep is also directly tied to obesity – without enough sleep, the stomach and other organs overproduce the hunger hormone ghrelin , causing us to eat more than we need. Additionally, chronic sleep deprivation can lead to a worsened appearance and disrupted mood. Sleep-deprived individuals are likely to look older, with more visible wrinkles and dark circles around the eyes. Sleep deprivation can make us more irritable and impairs our ability to both communicate effectively and cope with workplace stressors.

So, How Many Hours of Sleep Do You Need?

The amount of  sleep  a person needs depends on many things, including their age. In general:

• Infants (ages 0-3 months) need 14-17 hours a day.
• Infants (ages 4-11 months) need 12-15 hours a day
• Toddlers (ages 1-2 years) need about 11-14 hours a day.
• Preschool children (ages 3-5) need 10-13 hours a day.
• School-age children (ages 6-13) need 9-11 hours a day.
• Teenagers (ages 14-17) need about 8-10 hours each day.
• Most adults need 7 to 9 hours, although some people may need as few as 6 hours or as many as 10 hours of sleep each day.
• Older adults (ages 65 and older) need 7-8 hours of sleep each day.
• Women in the first 3 months of pregnancy often need several more hours of sleep than usual.

But experts say that if you feel drowsy during the day, even during boring activities, you haven’t had enough sleep.

Here are simple changes you can make throughout the day so you can sleep more restfully at night:

• Stick to a sleep schedule . Go to bed and wake up the same time each day. Sleeping later on weekends won’t fully make up for the lack of sleep during the week and will make it harder to wake up early on Monday morning.

• Power down from digital devices . Using smartphones and computer screens late into the night can interfere with our ability to sleep because these devices emit blue light that decreases the body’s natural production and secretion of the sleep-inducing hormone melatonin.

• Have a bedtime routine. Try to establish a nightly wind-down routine, beginning about an hour before bedtime. This can include listening to soothing music or reading.

• Make your bedroom dark . Light is the single most important environmental factor affecting your ability to sleep. Consider blackout shades or curtains that block out all sunlight and outdoor electronic lights.

• Keep room temperature cool . If your room is warm, this may interrupt your sleep quality.

• Seek silence . Sleeping in noisy environments prevents us from falling asleep and staying in a state of deep, restorative slumber. Earplugs or white-noise machines can filter out noise distractions during sleep time.

• Sleep partners can be snooze stealers. A partner that snores loudly or moves around frequently can keep you awake. Sleeping in separate beds may be the solution. Children and/or pets on your bed can also be disruptive to restful sleep.

• Don’t lie in bed awake. If you find yourself still awake after staying in bed for more than 20 minutes or if you are starting to feel anxious or worried, get up and do some relaxing activity until you feel sleepy. The anxiety of not being able to sleep can make it harder to fall asleep.

• Limit caffeine consumption . Caffeine is a stimulant, and its effects can take as long as 8 hours to wear off fully. Most sleep experts recommend ending your caffeine consumption by 3 p.m.

• Avoid large meals and beverages late at night. A large meal may cause indigestion that can interfere with sleep. Drinking too many fluids at night can cause frequent trips to the bathroom.

• Don’t take naps after 3 p.m. When you nap too close to your bedtime you’re talking away the sleep drive that was building all day, making it harder to fall asleep at night.

• Be physically active. Physical activity can improve the quality and quantity of sleep by reducing stress and anxiety and increasing total sleep time and quality of sleep.

How to Adapt to Daylight Savings Time?

Divide the time change over the weekend.   Simply make small changes each day instead of the full hour on Sunday morning. Start going to bed and waking up at slightly different times so your body is not shocked by the full 1 hour change.

Eat Breakfast.  This might sound like a weird one, but eating breakfast can send signals to your body and start changing your circadian rhythm to match the new time. Eat a medium sized breakfast shortly after waking up so your digestion kicks in, telling your body it’s time to wake up and be alert.

Get Sunlight   This is one that is a good habit to get into even when it isn’t Daylight Savings. Getting sun on your skin and in your eyes in the morning is the best signal to your body that it is day time.

Be Active in the Morning   It doesn’t have to be anything too intense, just enough activity to get your blood flowing. You can see that all of these steps are signals to your brain that it is day time. When you do all of these things your brain and body will get the message and adjusting to the time change will be much easier.

If you want to find out more about the importance of sleep, check out the resources at our library here .

https://www.webmd.com/sleep-disorders/sleep-requirements

https://www.hill.af.mil/News/Article-Display/Article/1792818/the-importance-of-sleep/

Daylight Savings Time Infographic 

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Takeda’s TAK-861 Phase 2b Late-Breaking Data Presentations at SLEEP 2024 Demonstrate Clinically Meaningful Impact of Oral Orexin Agonist in Narcolepsy Type 1 Compared to Placebo

Phase 2b Trial Demonstrated Statistically Significant and Clinically Meaningful Improvements Across Primary and all Secondary Endpoints up to 8 Weeks

TAK-861 is the First Oral Orexin Receptor 2 Agonist to Potentially Address the Underlying Pathophysiology of NT1

Safety Results Indicated TAK-861 is Generally Safe and Well Tolerated

Phase 3 Trials of TAK-861 to be Initiated in 1H FY2024

OSAKA, Japan and CAMBRIDGE, Massachusetts, June 3, 2024 – Takeda ( TSE: 4502/NYSE:TAK ) will present today positive results from its Phase 2b trial of TAK-861 in narcolepsy type 1 (NT1) as late-breaking data presentations at SLEEP 2024, the 38th annual meeting of the American Academy of Sleep Medicine and the Sleep Research Society. TAK-861 is an investigational oral orexin receptor 2 (OX2R) agonist and, based on the results, has the potential to provide transformative efficacy in addressing the overall disease burden in people with NT1. The randomized, double-blind, placebo-controlled, multiple dose trial, TAK-861-2001 ( NCT05687903 Go to https://classic.clinicaltrials.gov/ct2/show/NCT05687903?term=TAK-861&draw=2&rank=3 ), in 112 patients with NT1 demonstrated statistically significant and clinically meaningful improvements across primary and secondary endpoints, with efficacy sustained over 8 weeks of treatment.*

NT1 is a chronic, rare neurological central disorder of hypersomnolence caused by a significant loss of orexin neurons, resulting in low levels of orexin neuropeptides in the brain and cerebrospinal fluid. No currently approved treatments target the underlying pathophysiology of NT1. People with NT1 suffer from excessive daytime sleepiness (EDS), cataplexy (sudden loss of muscle tone), disrupted nighttime sleep, hypnagogic and hypnopompic hallucinations and sleep paralysis. These debilitating symptoms lead to a markedly reduced quality of life and can severely impact job performance, academic achievement and personal relationships. TAK-861 is designed to address the orexin deficiency in NT1 by selectively stimulating the orexin receptor 2.

The presentation highlights results from the Phase 2b trial including:

The primary endpoint demonstrated statistically significant and clinically meaningful increased sleep latency on the Maintenance of Wakefulness Test (MWT) versus placebo across all doses (LS mean difference versus placebo all p ≤0.001). Improvements were sustained over 8 weeks.

Consistent results were achieved in the key secondary endpoints including the Epworth Sleepiness Scale (ESS) and Weekly Cataplexy Rate (WCR), demonstrating significantly improved subjective measures of sleepiness and cataplexy (sudden loss of muscle tone) frequency versus placebo that were also sustained over 8 weeks.

The majority of NT1 patients in the trial were found to be within normative ranges for MWT and ESS by the end of the 8-week treatment period as a result of these sustained improvements.

The majority of the participants who completed the trial enrolled in the long-term extension (LTE) study with some patients reaching one year of treatment.

The trial also included additional exploratory endpoints that showed meaningful improvements in narcolepsy symptoms and functioning according to most participants. These data will also be presented in poster presentations at SLEEP and at future scientific congresses.

The dataset showed that TAK-861 was generally safe and well tolerated during the study, with no treatment-related serious treatment-emergent adverse events (TEAEs) or discontinuations due to TEAEs.

No cases of hepatotoxicity or visual disturbances were reported in the Phase 2b trial or in the ongoing LTE study. The most common TEAEs were insomnia, urinary urgency and frequency, and salivary hypersecretion. Most TEAEs were mild to moderate in severity, and most started within 1-2 days of treatment and were transient.

“In this trial, TAK-861's profile balanced efficacy and safety with the potential to establish a new standard of care for people with NT1,” said Sarah Sheikh, M.D., M.Sc., B.M., B.Ch., MRCP, Head, Neuroscience Therapeutic Area Unit and Head, Global Development at Takeda. “We are dedicated to investigating the full potential of orexin biology and advancing TAK-861 to late-stage clinical trials, with the ultimate goal of delivering a potential first-in-class treatment that can make a meaningful difference for patients.”

Based on these results, and in consultation with global health authorities, Takeda plans to initiate global Phase 3 trials of TAK-861 in NT1 in the first half of its fiscal year 2024. The Phase 2b data also supported the recent Breakthrough Therapy designation for TAK-861 for the treatment of EDS in NT1 from the U.S. Food and Drug Administration (FDA). Breakthrough Therapy designation is a process designed to expedite the development and review of a drug that is intended to treat a serious or life-threatening condition, for which preliminary clinical evidence indicates that the drug may demonstrate substantial improvement over available therapies on at least one clinically significant endpoint.

Takeda will be hosting a call to discuss these data this evening, June 3, at 7:30 p.m. CT for investors and analysts. Presentation slides and a virtual meeting link will be available here .

Additional presentations on TAK-861 will be shared during the SLEEP 2024 poster presentation session on Tuesday, June 4, from 10:00 to 11:45 a.m. CT, assessing function and health-related quality of life in individuals with NT1, as well as patient satisfaction with TAK-861 treatment. There is no change in Takeda’s full year consolidated forecast for the fiscal year ending March 31, 2025 (FY2024), announced on May 9, 2024.

About Takeda’s Orexin Franchise

Takeda is advancing the field of orexin therapeutics with a multi-asset franchise offering tailored treatments to unlock the full potential of orexin. Orexin is a key regulator of the sleep-wake cycle and is involved in other essential functions, including respiration and metabolism. TAK-861 is the leading program in this franchise. The company is also progressing multiple orexin agonists in patient populations with normal levels of orexin neuropeptides and other indications where orexin biology is implicated. This includes TAK-360, an oral OX2R agonist being investigated for narcolepsy type 2 and idiopathic hypersomnia, which recently initiated a Phase 1 trial and received Fast Track designation from the U.S. FDA, and danavorexton (TAK-925), an intravenously administered OX2R agonist being investigated in a Phase 2 trial in patients with moderate to severe obstructive sleep apnea undergoing general anesthesia.

About Takeda

Takeda is focused on creating better health for people and a brighter future for the world. We aim to discover and deliver life-transforming treatments in our core therapeutic and business areas, including gastrointestinal and inflammation, rare diseases, plasma-derived therapies, oncology, neuroscience and vaccines. Together with our partners, we aim to improve the patient experience and advance a new frontier of treatment options through our dynamic and diverse pipeline. As a leading values-based, R&D-driven biopharmaceutical company headquartered in Japan, we are guided by our commitment to patients, our people and the planet. Our employees in approximately 80 countries and regions are driven by our purpose and are grounded in the values that have defined us for more than two centuries. For more information, visit www.takeda.com .

* The topline results were announced on February 8, 2024, via a press release, “Takeda Intends to Rapidly Initiate the First Global Phase 3 Trials of TAK-861, an Oral Orexin Agonist, in Narcolepsy Type 1 in First Half of Fiscal Year 2024."

Media Contacts:

Japanese media.

Yuko Yoneyama

[email protected]

+81 70-2610-6609

U.S. and International Media

Rachel Wallace

Important Notice

For the purposes of this notice, “press release” means this document, any oral presentation, any question-and-answer session and any written or oral material discussed or distributed by Takeda Pharmaceutical Company Limited (“Takeda”) regarding this release. This press release (including any oral briefing and any question-and-answer in connection with it) is not intended to, and does not constitute, represent or form part of any offer, invitation or solicitation of any offer to purchase, otherwise acquire, subscribe for, exchange, sell or otherwise dispose of, any securities or the solicitation of any vote or approval in any jurisdiction. No shares or other securities are being offered to the public by means of this press release. No offering of securities shall be made in the United States except pursuant to registration under the U.S. Securities Act of 1933, as amended, or an exemption therefrom. This press release is being given (together with any further information which may be provided to the recipient) on the condition that it is for use by the recipient for information purposes only (and not for the evaluation of any investment, acquisition, disposal or any other transaction). Any failure to comply with these restrictions may constitute a violation of applicable securities laws. The companies in which Takeda directly and indirectly owns investments are separate entities. In this press release, “Takeda” is sometimes used for convenience where references are made to Takeda and its subsidiaries in general. Likewise, the words “we”, “us” and “our” are also used to refer to subsidiaries in general or to those who work for them. These expressions are also used where no useful purpose is served by identifying the particular company or companies.

Forward-Looking Statements

This press release and any materials distributed in connection with this press release may contain forward-looking statements, beliefs or opinions regarding Takeda’s future business, future position and results of operations, including estimates, forecasts, targets and plans for Takeda. Without limitation, forward-looking statements often include words such as “targets”, “plans”, “believes”, “hopes”, “continues”, “expects”, “aims”, “intends”, “ensures”, “will”, “may”, “should”, “would”, “could”, “anticipates”, “estimates”, “projects” or similar expressions or the negative thereof. These forward-looking statements are based on assumptions about many important factors, including the following, which could cause actual results to differ materially from those expressed or implied by the forward-looking statements: the economic circumstances surrounding Takeda’s global business, including general economic conditions in Japan and the United States; competitive pressures and developments; changes to applicable laws and regulations, including global health care reforms; challenges inherent in new product development, including uncertainty of clinical success and decisions of regulatory authorities and the timing thereof; uncertainty of commercial success for new and existing products; manufacturing difficulties or delays; fluctuations in interest and currency exchange rates; claims or concerns regarding the safety or efficacy of marketed products or product candidates; the impact of health crises, like the novel coronavirus pandemic, on Takeda and its customers and suppliers, including foreign governments in countries in which Takeda operates, or on other facets of its business; the timing and impact of post-merger integration efforts with acquired companies; the ability to divest assets that are not core to Takeda’s operations and the timing of any such divestment(s); and other factors identified in Takeda’s most recent Annual Report on Form 20-F and Takeda’s other reports filed with the U.S. Securities and Exchange Commission, available on Takeda’s website at: https://www.takeda.com/investors/sec-filings-and-security-reports/ or at www.sec.gov Go to https://www.sec.gov . Takeda does not undertake to update any of the forward-looking statements contained in this press release or any other forward-looking statements it may make, except as required by law or stock exchange rule. Past performance is not an indicator of future results and the results or statements of Takeda in this press release may not be indicative of, and are not an estimate, forecast, guarantee or projection of Takeda’s future results.

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This press release contains information about products that may not be available in all countries, or may be available under different trademarks, for different indications, in different dosages, or in different strengths. Nothing contained herein should be considered a solicitation, promotion or advertisement for any prescription drugs including the ones under development.

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

1 Uludag University Faculty of Health Sciences, Bursa, Turkey

F. TANRIKULU

2 Sakarya University Faculty of Health Sciences, Sakarya, Turkey

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

Introduction

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

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

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

Aim of Study

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

Material and Method

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

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

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

Statistical Analysis

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

Ethical Concerns

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

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

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

PSQI total and internal component score averages of the sample

PSQIscore averages of the sample

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

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

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

*Mann Whitney U Analysis

**Correlation Analysis

***Kruskal Wallis Analysis

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

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

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

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

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

Acknowledgments

All authors had equal contribution

The Best Mattress For Athletes To Help Aid Recovery While You Sleep

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Whether you’re an athlete, exercise enthusiast or work a physically demanding job, a good night’s sleep is essential to helping your body recover from strenuous activity. The best mattress for athletes should meet the needs of active individuals, provide ample support and promote muscle recovery with special materials woven into its cover. As such, our top recommendation is the Bear Elite Hybrid mattress, which offers ergonomic zoned support, a Celliant-infused cover and is available in three firmness levels to accommodate a wide range of sleeper types.

The Nectar Premier Copper and Bear Elite Hybrid are two of the best mattresses for athletes, and can ... [+] help promote better rest.

To find the best pick, select a mattress firmness based on your sleep position and body type. Some should consider a cooling mattress for temperature regulation, or a product with features tailored for active individuals. We’ve put together a list of the best mattresses for athletes, all of which offer comfort and support after a long day of exercise.

• Best Mattress For Athletes Overall: Bear Elite Hybrid
• Best Firm Mattress For Athletes: Plank Firm
• Best Mattress For Heavy Athletes: Titan Plus Luxe
• Best Hybrid Mattress For Athletes: Zoma Hybrid
• Best Mattress For Athletes With Back Pain: Saatva Classic Mattress
• Best Cooling Mattress For Athletes: Nectar Premier Copper
• Best Mattress For Athletes Who Sleep On Their Side: Helix Midnight Luxe
• Best Mattress For Athlete Recovery: Brooklyn Spartan
• Best Massaging Mattress For Athletes: GhostBed Massage Mattress

Best Mattress For Athletes Overall

A top-rated pick you can customize to your needs, bear elite hybrid.

Type: Hybrid | Firmness: Soft, medium or firm | Delivery: Free in a box or $175 for white glove delivery and mattress removal | Trial: 120 nights | Warranty: Lifetime

• Available in three firmness levels
• Coil system provides support in strategic areas
• Celliant technology in the cover costs extra

No two people—or athletes—are the same, so it’s ideal to have options when shopping for a mattress. This premium hybrid option from Bear comes in soft, medium and firm comfort levels, allowing you to pick the one that best suits your sleep style. And you can choose to add a special Celliant-infused cover, which is designed to help promote muscle recovery during the night. The mattress has a zoned coil system that provides more support in key areas like your lower back, and it’s even endorsed by the American Chiropractic Association. Like many beds on this list, it’s also GREENGUARD Gold certified to be free from chemical emissions. 

What the reviews say: “[It’s] hands-down the best mattress I’ve ever owned at the best cost and payment option,” writes one reviewer. “My back hasn’t felt this good in years—I’ve been an active competitive athlete and sleep is the easiest version of recovery.”

Best Firm Mattress For Athletes

Firm and extra-firm options, all in one mattress.

Type: Memory foam | Firmness: Firm | Delivery: Free (in a box) | Trial: 120 nights | Warranty: 10 years

• Flippable design for firm or extra-firm feel
• Extended size range
• Optional cooling cover 
• It’s all-foam design may not be as supportive for heavy sleepers

For those who sleep on their stomachs or simply prefer a firm sleep surface, this mattress from Plank has a flippable design that offers two firmness levels in one bed. One side has a medium-firm feel (rated around an 8 out of 10), while the reverse side is extra firm—the brand describes it as a 10 out of 10 on the firmness scale, so it’s one of the stiffest options you can find. The mattress is crafted from high-density memory foam, and it’s available in a wide range of sizes, including unconventional options like Olympic queen, short king and RV king. Plus, if you sleep warm, you can add a temperature-regulating cover that stays cool to the touch. 

What the reviews say: “I am an athletic 185 pounds. My wife is athletic as well. I have lower back pain and we both work out at least five times a week,” explains one reviewer. “I decided to buy a Plank after reading a lot of reviews. What really sold me was the ability to flip it over and have my choice of either firm or extra firm. It is nice and solid. It doesn’t feel like memory foam. I can’t speak for the extra firm side, but the firm side has just enough cushion to be comfortable while being firm.”

Best Mattress For Heavy Athletes

A durable mattress that supports up to 1,000 pounds, titan plus luxe.

Type: Hybrid | Firmness: Medium | Delivery: Free (in a box) | Trial: 120 nights | Warranty: 10 years

• Specifically esigned for heavier individuals
• Supports up to 1,000 pounds
• Optional cooling cover
• May be too soft for some heavy stomach sleepers

The Titan Plus Luxe was specially designed for heavier individuals, and its hybrid design can accommodate up to 1,000 pounds of weight. It features an 8-inch steel coil core for support and is topped with multiple layers of comfort foam for a medium-plush feel. Keep your sleep position in mind, though, as this may be too soft for those who sleep on their stomachs. The mattress is designed to resist sagging, and you can have it outfitted with an optional cooling cover to help prevent overheating during the night. 

What the reviews say: “I am a bigger guy and 300 pounds of muscle and my wife is tall, 215 pounds and athletic,” says one buyer. “I am a side sleeper, and get a solid eight hours [of sleep], and my wife is a stomach sleeper. We both love it.” He also notes that the customer service is great: “Super fast response, super nice, very accommodating and gives you more information than you ask for.”

Best Hybrid Mattress For Athletes

An athlete-approved option with both coils and foam, zoma hybrid.

Type: Hybrid | Firmness: Medium | Delivery: Free (in a box) | Trial: 100 nights | Warranty: 10 years

• Promotes muscle recovery and deeper sleep
• Ventilated knit cover wicks away moisture and heat
• May be too soft for stomach sleepers and some back sleepers

The Zoma Hybrid Mattress is designed to provide support for your body, promote recovery and keep you cool during the night. To show it’s the real deal, it’s also been endorsed by professional athletes. On the product page, the brand includes a list of pros that sleep on a Zoma Mattress, including Gavin Lux of the Los Angeles Dodgers and Josiah Gray of the Washington Nationals. The hybrid design includes a layer of individually-cased coils and multiple layers of comfort foam, and together, it creates a medium feel that’s plush yet supportive. The brand claims the design can help promote muscle recovery and encourage deeper sleep, and the whole thing is wrapped in a moisture-wicking fabric to keep you cool at night. 

What the reviews say: “I purchased one for my daughter who plays D1 field hockey in college and is a goalie,” says one reviewer. “Zoma was our choice because of the reviews of the great recovery after workouts or games, and she loves it. She says it's the best mattress ever!”

Best Mattress For Athletes With Back Pain

A luxury pick with a lumbar support zone, saatva classic mattress.

Type: Innerspring | Firmness: Plush soft, luxury firm or firm | Delivery: Free white glove delivery and mattress removal | Trial: 365 nights | Warranty: Lifetime

• Multiple firmness level options
• Added lumbar support 
• Free in-home delivery and mattress removal 
• Firmness levels are firmer than advertised, might be too firm for side sleepers

If you wake up with back pain or other body aches, it might be worth upgrading to the supportive Saatva innerspring mattress. This luxury mattress offers superior lumbar support thanks to a special strip of high-density memory foam, and its two layers of steel coils, one of which are firmer in the center of the mattress where your body weight is concentrated. This design features help to keep your spine in proper alignment throughout the night, and you can choose from three firmness levels to cater to your sleep style. The mattress also comes in two height options, and the brand offers free in-home delivery and mattress/box spring removal, if needed. 

What the reviews say: “As someone who has led an athletic life-style, I have had knee surgeries, serious spinal/neck injuries and, most recently, I was recovering from shoulder surgery,” explains one reviewer. “No longer do I toss and turn all night with all my various damaged and surgically-repaired joints screaming at me. Sleeping on our new Saatva mattress is like sleeping on a cloud—the moment you stretch out, all the pressure points on all the old aching joints and injuries just disappear.”

Best Cooling Mattress For Athletes

A breathable design infused with gel and copper, nectar premier copper.

Type: Memory foam or hybrid | Firmness: Medium-firm | Delivery: Free in a box or $199 for in-home setup | Trial: 365 nights | Warranty: Lifetime

• Available in both memory foam and hybrid options
• Copper and gel memory foam for heat regulation
• Minimal motion transfer
• Memory foam version may not offer enough support for athletes

For those who get hot during the night, this mattress from Nectar has multiple cooling features to help regulate your body temperature. Its comfort layer is gel-infused memory foam that doesn’t retain as much heat, and the mattress cover is infused with copper to help dispel heat. As a result, it’s much cooler to sleep on than traditional memory foam. If you want more support—often beneficial for athletes—the mattress comes in a hybrid option that has a layer of individually wrapped steel coils alongside several layers of comfort memory foam. 

What the reviews say: “This mattress has been incredibly comfortable, so consistently,” writes one buyer. “It's very good at making sure not to trap too much heat as well, and as someone who always runs hot and likes it cold, this is a big plus.” Other reviewers say it’s great for recovery after exercise: “As a professional athlete who needs a good sleep for recovery, I can attest that you wake up feeling refreshed.”

Best Mattress For Athletes Who Sleep On Their Side

Zoned support design offers targeted relief, helix midnight luxe.

Type: Hybrid | Firmness: Medium | Delivery: Free (in a box) | Trial: 100 nights | Warranty: 15 years

• Zoned support layer
• Ideal for side and combination sleepers
• Likely too soft for stomach sleepers and some back sleepers

The Helix Midnight Luxe is a top choice for athletes who prefer sleeping on their side. This hybrid mattress has special zoned support that lets your hips and shoulders sink in while keeping your back and head supported, and its premium quilted pillow top offers a plush feel that’s comfortable for back or combination sleepers. You can upgrade the mattress with a cooling cover, if needed, and the design is GREENGUARD Gold certified, so you don’t have to worry about chemical emissions in your bedroom. 

What the reviews say: “At first I thought it was maybe a little firmer than I thought it would be, but after sleeping on it, I truly have not had any back pain,” writes one side sleeper. “I can tell that it truly is designed for side sleepers and it clearly provides excellent support.” The mattress also gets the seal of approval from athletes: “We are both professional athletes and have some injuries that make it hard to sleep on just any mattress. Finally my man and I are getting a good night's sleep with the Helix Midnight Luxe.”

Best Mattress For Athlete Recovery

Special tech promotes muscle recovery while you sleep.

Brooklyn Bedding

Brooklyn Spartan

Type: Hybrid | Firmness: Soft, medium or firm | Delivery: Free (in a box) | Trial: 120 nights | Warranty: 10 years

• Cover fabric promotes muscle recovery 
• Copper and gel-infused foam for heat regulation
• Reviews say it doesn’t have the best edge support

The Spartan mattress is designed with athletes in mind, and the brand claims the cover, which uses far infrared rays (FIR), is able to create waves of energy that encourage better blood flow, more restful sleep and even faster muscle recovery during the night. The hybrid design comes in three firmness levels for different sleep positions, and it has several features for heat regulation, as well, including layers of copper and gel-infused memory foam. Side sleepers should opt for the soft model, while back and stomach sleeper should consider medium or firm.

What the reviews say: “My boyfriend and I frequently have sore muscles from daily workouts. This bed has helped reduce muscle soreness and provided better rest/recovery for both of us.”

Best Massaging Mattress For Athletes

Air massage helps soothe sore muscles at night, ghostbed massage mattress.

Type: Memory foam or hybrid | Firmness: Medium | Delivery: Free (in a box) | Trial: 101 nights | Warranty: 25 years

• Memory foam or hybrid options
• Built-in air pressure massage 
• Cover stays cool to the touch
• Brand recommends special sheets for the mattress
• May be too soft for heavy back and stomach sleepers

After a tough workout, you can relax your muscles with a soothing massage, right from the comfort of your bed. Most massage systems are built into adjustable bed frames , but the GhostBed Massage Mattress has its own unique, built-in air pressure massage system that inflates and deflates, stretching your joints and muscles. There are five zones you can target with the massager, and larger mattresses come with individual controls for each side of the bed. The GhostBed Massage Bed comes in both memory foam and hybrid options, and it’s wrapped in a special cover that stays cool to the touch, which is great for warm sleepers. 

What the reviews say: “Being active has always been part of my life,” explains one reviewer. “The GhostBed massage mattress seemed like the perfect solution to soothe my aching muscles. Not only does my body feel great, rejuvenated and refreshed, but at night, when my mind goes to disturbing places, the motion from the full body massage quiets me and allows me to drift off to sleep peacefully.”

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The best daypacks of 2024, based on months and miles of testing, why trust forbes vetted.

The Forbes Vetted team has years of experience researching, testing and writing about mattresses and sleep products . We use our firsthand experience and thorough research to recommend the right mattresses for your needs.

• Our team of sleep experts has tested several of the mattresses on this list, including the Helix Midnight Luxe , Nectar Premier Copper and Saatva Original . We also have firsthand experience with the majority of brands included.
• Senior editor Bridget Chapman and editor McKenzie Dillon , who oversaw this article, have tested and written about dozens of mattresses over the years in order to understand how they compare in quality, firmness, support and more.
• For insights on the specific sleep needs of athletes, we spoke with John Gallucci Jr., sports medicine consultant, physical therapist and CEO of JAG Physical Therapy , who offered tips on which mattresses work best for active individuals.
• To ensure the information is this article stays fresh and accurate, it’s regularly updated. It was last updated in May 2024.

How We Chose The Best Mattress For Athletes

To find the best mattresses for athletes, we used guidance from our experts and our own research to select supportive, yet comfortable options that promote recovery after strenuous physical activity.

• We looked for top-rated mattresses from brands we’ve tested and trust, prioritizing hybrid models that offer zoned support to provide pressure relief and support your spine during the night, or Celliant-infused covers
• We evaluated each mattress on factors like its firmness level(s), material quality and height, and we looked at factors like delivery options, sleep trials and return policies.
• We included several mattresses that offer special features for athletes, such as cooling mattress covers, special recovery fabrics and built-in massage features.
• All of the mattresses we included have an overall rating of at least 4 stars and receive positive reviews from athletes and other active individuals.

What To Look For In The Best Mattress For Athletes

Construction.

You typically have the option between an all-foam mattress or a hybrid one that combines coils and foam. However, experts say that hybrid mattresses are generally the best option for athletes: “A hybrid mattress would be the ideal mattress for athletes and individuals with an active lifestyle,” recommends Gallucci. “Hybrid mattresses combine coils for proper back support and foam or latex for comfort, and these are both strategically layered together to enhance cooling and breathability.”

Mattress firmness, which ranges from soft to firm with options in between, plays a role in the comfort and support level of your bed, and the best choice depends on your preferred sleep position.

“Any athlete’s mattress should be firm enough to provide sufficient support for proper spine alignment, however, the range of firmness may vary due to sleeping positions,” explains Gallucci. “While medium firmness is best-suited for athletes sleeping on their back, as it provides a balance of support and comfort, those who sleep on their hips and shoulders should look for a softer mattress to relieve pain from those areas. On the other hand, those who sleep on their stomach should look for a firmer mattress to prevent back pain.”

Some mattresses are available in multiple firmness levels, allowing you to pick the option that works best for your sleep preferences, while others are only available in one option.

Pressure Relief

For those who live an active lifestyle, pressure relief is another important feature to look for in a mattress. “Athletes should be looking for mattresses with zoned center support, as back support is key in an athlete's mattress selection,” says Gallucci.

Mattresses like the Helix Midnight Luxe have zoned support to help promote proper ergonomic alignment of your spine. Because this option is designed for side sleepers, the bed is softer around the shoulder and hip areas and more supportive in the center, providing pressure relief in key areas.

Many people sleep warm at night, and if this is the case, you may want to look for a mattress with cooling properties. Some models, such as the Nectar Premier Copper mattress, use gel or copper-infused memory foam to help dispel heat during the night, while others have optional cooling covers that you can add to your purchase.

Enhanced Features

Mattresses that are specifically designed for athletes often have advanced features that can promote recovery. For instance, the Brooklyn Spartan mattress is covered in a unique heat-absorbing fabric that the brand claims encourages better blood flow and faster muscle recovery during the night. Or, the GhostBed Massage Mattress has a built-in air massage system that can help soothe sore muscles before bed.

Just keep in mind that these special features often drive up the price of the mattress and their effectiveness may vary from person to person.

You also want to keep your budget in mind as you shop. Many of the best mattresses for athletes cost between $1,000 and $2,000 for a queen size, but there are also more affordable options, such as the Plank Firm . You can also lower costs by opting out of add-ons like special covers and advanced features.

What Mattress Is Most Used By Athletes?

There are several mattresses on this list that are commonly used by professional athletes. Bear, which makes our top overall pick, the Bear Elite Hybrid , has a section of its website dedicated to Bear Athletes that use its mattresses. It included professional baseball, football and soccer players, as well as Olympians that compete in a wide range of sports.

Zoma is another popular brand among athletes—the brand’s website showcases professionals who use its mattresses, including many professional football and baseball players.

What Is The Best Mattress For Working Out?

If you work out frequently, you’ll likely want to look for a hybrid mattress that includes both innersprings for support and memory foam for comfort. The Bear Elite Hybrid is a top choice for active individuals, as it offers zoned support and comes in three firmness levels to suit any sleeping position. The mattress can also be wrapped in a special Celliant-infused cover, which stays cooler to the touch to prevent overheating at night.

What Beds Do Football Players Use?

There are a few mattresses that are endorsed by professional football players. The Zoma Hybrid is used by several players on teams like the New York Giants, Los Angeles Rams and Indianapolis Colts. Sleep Number , which makes high-end beds with air chambers, also has a partnership with the NFL, making it popular among professional football players.

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