effects of sleep deprivation on students research paper

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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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Effects of Sleep Deprivation on Performance: A Meta-Analysis

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June J. Pilcher, Allen I. Huffcutt, Effects of Sleep Deprivation on Performance: A Meta-Analysis, Sleep , Volume 19, Issue 4, June 1996, Pages 318–326, https://doi.org/10.1093/sleep/19.4.318

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To quantitatively describe the effects of sleep loss, we used meta-analysis, a technique relatively new to the sleep research field, to mathematically summarize data from 19 original research studies. Results of our analysis of 143 study coefficients and a total sample size of 1,932 suggest that overall sleep deprivation strongly impairs human functioning. Moreover, we found that mood is more affected by sleep deprivation than either cognitive or motor performance and that partial sleep deprivation has a more profound effect on functioning than either long-term or short-term sleep deprivation. In general, these results indicate that the effects of sleep deprivation may be underestimated in some narrative reviews, particularly those concerning the effects of partial sleep deprivation.

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Relationship between sleep habits and academic performance in university Nursing students

• Juana Inés Gallego-Gómez 1 ,
• María Teresa Rodríguez González-Moro 1 ,
• José Miguel Rodríguez González-Moro 2 ,
• Tomás Vera-Catalán 1 ,
• Serafín Balanza 1 ,
• Agustín Javier Simonelli-Muñoz 3 &
• José Miguel Rivera-Caravaca 4  

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Sleep disorders are composed of a group of diseases of increasing prevalence and with social-health implications to be considered a public health problem. Sleep habits and specific sleep behaviors have an influence on the academic success of students. However, the characteristics of sleep and sleep habits of university students as predictors of poor academic performance have been scarcely analyzed. In the present study, we aimed to investigate sleep habits and their influence on academic performance in a cohort of Nursing Degree students.

This was a cross-sectional and observational study. An anonymous and self-administered questionnaire was used, including different scales such as the ‘Morningness and Eveningness scale’, an author-generated sleep habit questionnaire, and certain variables aimed at studying the socio-familial and academic aspects of the Nursing students. The association of sleep habits and other variables with poor academic performance was investigated by logistic regression. The internal consistency and homogeneity of the ‘sleep habits questionnaire’ was assessed with the Cronbach’s alpha test.

Overall, 401 students (mean age of 22.1 ± 4.9 years, 74.8 % females) from the Nursing Degree were included. The homogeneity of the ‘sleep habits questionnaire’ was appropriate (Cronbach’s alpha = 0.710). Nursing students were characterized by an evening chronotype (20.2 %) and a short sleep pattern. 30.4 % of the Nursing students had bad sleep habits. Regarding the academic performance, 47.9 % of the students showed a poor one. On multivariate logistic regression analysis, a short sleep pattern (adjusted OR = 1.53, 95 % CI 1.01–2.34), bad sleep habits (aOR = 1.76, 95 % CI 1.11–2.79), and age < 25 years (aOR = 2.27, 95 % CI 1.30–3.98) were independently associated with a higher probability of poor academic performance.

Conclusions

Almost 1/3 of the Nursing students were identified as having bad sleep habits, and these students were characterized by an evening chronotype and a short sleep pattern. A short sleep pattern, bad sleep habits, and age < 25 years, were independently associated with a higher risk of poor academic performance. This requires multifactorial approaches and the involvement of all the associated actors: teachers, academic institutions, health institutions, and the people in charge in university residences, among others.

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Introduction

Sleep is a complex phenomenon resulting from the interaction between the neuroendocrine system, biological clock and biochemical processes, with environmental, social and cultural aspects that are very relevant in the life stages of adolescence and youth [ 1 ]. Indeed, the chronic lack of sleep is a recent worry among adolescents and young university students and it is associated with worse health and clinical outcomes [ 2 , 3 ].

Among biological factors determining sleep, there are “chronotypes” and sleep patterns. The first term refers to the personal preferences of scheduling the sleep-wake cycle, emphasizing three basic chronotypes: morning (early-risers), and evening (night-owls) and those who are intermediate, defined as those who do not have clear preferences towards any of the extreme schedules for the fulfilling of their activities [ 4 ]. The sleep pattern refers to the personal schedule of bedtime and wake-up time. In this sense, a circadian rhythm is a natural, internal process, driven by a circadian clock that repeats roughly every 24 h and regulates the sleep-wake cycle [ 5 ].

On the other hand, the sleep habits are in the intersection between biological and cultural values. Endogenous, exogenous or environmental factors are included here, as well as those activities that are developed by the population to induce or maintain sleep, with its study and care becoming a challenge for Nursing [ 6 ]. Currently, spontaneous abusive behaviors regarding sleep habits are becoming frequent, leading to a state of chronic sleep deprivation, which translates to fatigue and somnolence during the day [ 7 ]. Hence, there is a high prevalence of sleep disorders in university students, especially those that affect the wake-sleep rhythm [ 2 ]. For this reason,the interest in establishing relationships between sleep and cognitive processes such as memory, learning ability and motivation, has gained attention during the last years. However, studies that relate sleep with academic problems are scarce, despite previous authors have shown that the reduction of sleep time in teenagers and university students was associated with poor academic performance, accidents and obesity [ 8 , 9 ]. Since good-quality sleep does not only imply sleeping well at night but also an adequate level of attention during the day for performing different tasks, appropriate sleep has an influence in efficient learning processes in university students [ 10 , 11 , 12 ].

Although some scientific evidence has shown a relationship between sleep and low academic performance [ 13 , 14 ], so far, there are no questionnaires to specifically evaluate sleep habits in Nursing students. Considering that this population has special characteristics, they are mostly young, combine hospital training at the same time they attend classes at the university, they present lifestyles that can negatively influence the academic performance. To study the sleep habits using a specific tool, in addition to analyze the sleep pattern and chronotype, could help to identify students with inappropriate sleep habits for developing interventions to modify these habits. This might have a positive impact on their academic performance and avoid potentially serious negative consequences for their physical and mental health. In the present research, we aimed (a) to design a ‘sleep habits questionnaire’, (b) to analyze the sleep habits, sleep pattern and chronotype, and (c) to investigate sleep habits and their influence on academic performance, in a cohort of Nursing Degree students.

Design and study population

This was an observational, prospective and cross-sectional study involving Nursing students, all of them distributed among the 4 years of the Nursing Degree. There were no inclusion criteria, i.e. all Nursing students were suitable for the study, unless those who did not attend class on the day of data collection, or those who did not wish to participate (from 420 students, 19 refused to participate in the study). The study was fully carried out during the first semester of the 2019–2020 academic year.

Study Variables

Circadian rhythm: the reduced “horne & östberg morningness-eveningness questionnaire”.

Preferences of schedule for the sleep-wake cycle and its influence on academic performance were assessed using the reduced version of the Horne & Östberg Morningness-Eveningness Questionnaire (rMEQ) proposed by Adan & Almirall [ 15 ], translated to Spanish, that is composed of 5 items. The score determines the following five types of schedule: clearly morning type (22–25 points), moderately morning type (18–21 points), no preference (12–17 points), moderately evening type (8–11 points), and clearly evening type (4–7 points). The internal consistency of the circadian rhythm scale assessed using the rMEQ by Adan & Almirall is good, as the scores from all the items are correlated among themselves [ 15 , 16 ].

Sleep habits questionnaire

For the initial design of the sleep habits questionnaire, a panel of 10 voluntary experts was included. This panel was composed of 5 registered nurses and 5 physicians, with a minimum of 5 years of experience in sleep. All of them were interviewed and informed individually about the study. Items composing of the questionnaire were obtained according to the scientific literature and the main factors influencing sleep habits as the discretion of the expert panel [ 14 , 17 , 18 ]. Eleven questions were finally included in a self-reported questionnaire, each ranging from 1 to 4 (never (1), sometimes (2), usually (3), always (4)) ( Supplementary file ). Sleep habits, including sleep routines, study schedule preference, and napping were also evaluated. The overall score of the questionnaire ranges from 11 to 44 points, with the highest scores indicating the worst sleep habits. As there is no specific cut-off point for this questionnaire, students over the fourth quartile (4Q, i.e. ≥25 points) were categorized as having inappropriate habits. Therefore, these Nursing students were included in the “bad sleeping habits” group.

• Academic performance

The academic performance was measured by the ratio “failed exams/performed exams” and checked in the student’s academic records. A good academic performance was considered if the final grade of every exam completed during the Nursing Degree was ≥ 5 (in a 0–10 range, where an exam is considered passed if the score is ≥ 5).

Other variables

Other variables such as gender, age and hours of sleep (sleep pattern), were analyzed. To describe the sleep pattern of the Nursing students, we used the classification described by Miró et al. (2002) [ 19 ]. This classification was composed of three categories as a function of the hours slept, so that we found subjects that had a short sleep pattern (< 6 h per day), subjects with a long sleep pattern (≥ 9 h per day), and subjects with an intermediate sleep pattern (6–9 h per day).

Ethical considerations

The study protocol was approved by an accredited Ethics Committee (Reference: CE-6191) and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. All students were informed and gave consent to participation in the study. The anonymity and confidentiality were guaranteed.

Statistical analysis

The sample size was calculated by a non-probabilistic sampling technique using Ene 2.0 (GlaxoSmithKline) with a precision ± 5 % and α error = 0.05. This calculation was based on the estimation that the prevalence of bad sleep habits in Nursing students of our university was 30.4 %, which resulted in a minimum sample of 229 subjects.

Categorical variables were expressed as frequencies and percentages. Continuous variables were presented as mean ± standard deviation (SD) or median and interquartile range (IQR), as appropriate.

The Pearson Chi-squared test was used to compare proportions whereas comparison of continuous variables was performed using the Student t test. Correlations between different scales were performed using the Pearson’s correlation test.

In order to investigate if sleep habits and other variables were independently associated with poor academic performance, a logistic regression model (with odds ratios [OR] and two-sided 95 % confidence intervals [CI]) was performed. To measure the internal consistency and homogeneity of the sleep habits questionnaire, the Cronbach’s alpha test was performed.

A p -value < 0.05 was accepted as statistically significant. Statistical analyses were performed using SPSS v. 21.0 (SPSS, Inc., Chicago, IL, USA).

We included 401 Nursing students (100 students from 1st year, 105 from 2nd year, 101 from 3rd year, and 95 from 4th year) in the study. The students were characterized for being predominantly females (300, 74.8 %), with a mean age of 22.1 ± 4.9 years, and the majority of them (88.5 %) were singles.

Sleep habits of the Nursing students were examined using our previously designed (as described in the Methods section) self-reported ‘sleep habits questionnaire’. The homogeneity of the questionnaire was appropriate, with a Cronbach’s alpha value of 0.710. The mean score in the questionnaire was 22.3 ± 3.9, and 30.4 % of the Nursing students had bad sleep habits (i.e. score > 4Q), which were characterized by a clear preference of studying at night, easily lose a night of sleep for work-related or academic tasks that imply staying up late, and showing difficulties in maintaining sleep routines.

Table  1 shows the summarized results for each question of the sleep habits questionnaire.

The Nursing students in our sample were characterized by an evening chronotype (20.2 %, 81) and a short sleep pattern (i.e. <6 h of sleep daily), with 51.1 % (205) of the students sleeping less than 6 h/day, 42.1 % (169) sleeping 6–9 h/day, and 6.7 % (27) sleeping more than 9 h/day. The mean duration of sleep found in the Nursing students was 6.52 ± 1.4 h.

Of note, most of the Nursing students that had an evening chronotype were < 25 years old (22.2 %, p  = 0.011). In addition, age showed a positive association with circadian rhythm and as age increased, the students tended to have a predominantly morning chronotype ( R  = 0.223, p  < 0.001). Nursing students < 25 years of age had also worse sleep habits according to the sleep habits questionnaire than those ≥ 25 years (22.61 ± 3.79 vs. 21.19 ± 4.37, p  = 0.005). A negative correlation was found between the overall sleep habits questionnaire score and age as a continuous variable ( R = -0.105, p  = 0.03).

In addition, 29.5 % of patients that had bad sleep habits ( p  = 0.001), and 23.9 % that had poor academic performance ( p  = 0.020), had also an evening chronotype (Table  2 ). A significant negative correlation was found between the sleep pattern and sleep habits ( R = -0.293, p  < 0.001), and between circadian rhythm and sleep habits, hence Nursing students with good sleep habits have predominantly a morning circadian rhythm ( R = -0.201, p  < 0.001).

Regarding the academic performance, 93 % (373) of the Nursing students attended all the exams planned, and 47.9 % (192) of the students showed poor academic performance. When we investigated specifically if the sleep habits, as assessed by the ‘sleep habits questionnaire’, influenced the academic performance, we found that 32 % (140) of the Nursing students that had bad sleep habits obtained poor academic results ( p  < 0.001). Those that had the worst academic results were the ones that did not have a regular hour for waking up and going to sleep (2.66 ± 1.03, p  = 0.031), presented difficulties to maintain the sleep during the night (1.73 ± 0.77, p  = 0.003), and preferred to study for an exam at night (1.33 ± 0.48, p  = 0.030), as well as going to bed late to obtain better results (1.46 ± 0.51, p  = 0.041). Also, those students with poorer academic results where those listening to music before going to bed (1.84 ± 1.10, p  = 0.007), and going out at night even if they had to get-up early the next day (1.58 ± 0.72, p  = 0.012). Overall, those Nursing students whose work or academic activities entailed going to bed late to attain their objectives, had the lowest academic performance (2.25 ± 1.01, p  = 0.001). Lastly, we can confirm that the Nursing students that had better academic performance were the ones who had the best sleep habits. Indeed, the overall ‘sleep habits questionnaire’ score was significantly lower compared to those Nursing students who had poor academic performance (21.91 ± 3.90 vs. 24.18 ± 3.55, p  < 0.001) (Table  3 ).

Finally, the profile of Nursing students with more failed courses was characterized by an evening circadian rhythm ( R = -0.134, p  = 0.007), bad sleep habits ( R  = 0.216, p  < 0.001), and less hours of sleep daily ( R = -0.211, p  < 0.001).

To confirm these observations, a multivariate logistic regression analysis was performed. Therefore, a short sleep pattern (adjusted OR = 1.53, 95 % CI 1.01–2.34), bad sleep habits (adjusted OR = 1.76, 95 % CI 1.11–2.79), and age < 25 years (adjusted OR = 2.27, 95 % CI 1.30–3.98) were independently associated with a higher probability of poor academic performance (Table  4 ).

Sleep is an excellent indicator of the health status and an element that favors good quality of life [ 20 ], but entering university is a change that highly impacts the student in every dimension, including sleep habits [ 21 , 22 ]. A potential barrier for maximizing performance during the university stage is the irregular sleep schedule, which lead to sleep deficit and high prevalence of somnolence during the day [ 23 ]. A review by Shochat et al. (2014) [ 24 ] examined the consequences of lack of sleep among Nursing students, and confirmed the relationship between sleep disorders and changes in sleep patterns with a reduced academic performance. Other studies have established that sleep has an integral role in learning and memory consolidation [ 25 , 26 ]. Therefore, despite some scientific evidence has shown a relationship between sleep and low academic performance [ 13 , 14 ], the originality of our study was to examine the influence that sleep characteristics exert (chronotypes and sleep patterns), as well as sleep habits of the university population on academic performance.

Overall, the academic performance of our Nursing students was suboptimal. When analyzing how sleep pattern, sleep habits, and circadian rhythms influenced this academic performance, we observed that all of them may be determine factors for learning, as other studies have done [ 27 ].

Concerning the sleep pattern, it should be noted that most of the students enrolled in the Nursing Degree slept less than 6 h per day. Of note, our results seem to establish a relationship between the hours slept and the academic performance during the first semester, as gathered from the academic records. This finding is in accordance to observations by other authors in university students from Medicine [ 9 ], Pharmacy [ 2 ] or Nursing [ 28 ], which also showed evidence between the hours slept and the academic achievement. In a previous study, we already observed that university students from the Faculty of Nursing attributed the hours slept with academic performance [ 29 ]. Indeed, it should be highlighted that chronic lack of sleep is not only associated with alterations of attention and academic performance, but also to a series of adverse consequences for health such as risky behaviors, depression, anxiety, alterations in social relations, and obesity, among others [ 30 ].

In addition, our study has evidenced how the sleep habits directly influenced the academic performance of these Nursing students, and approximately 1/3 of the students with bad sleep habits obtained poor academic results. Certainly, the sleep pattern and inadequate sleep habits could be related. Good sleep hygiene includes aspects such as a regular sleep-wake schedule, adequate environment, avoiding stimulating activities before going to bed, and limiting the use of technology in bed or immediately before going to bed. In the present study, 30.4 % of the students had bad sleep habits, characterized by having a clear preference for studying at night, often losing a night of sleep for work or academic activities that imply go to bed late, and show difficulties in maintaining sleep routines. An important proportion of our Nursing degree students declared that they watched television, listened to music, worked or read academic documents during the last hour before going to bed. In this sense, LeBourgeois et al. (2017) [ 31 ] have described the university population as great consumers of technology, and have associated the frequent use of technology before going to bed with problems to sleep and daytime somnolence.

Finally, age was another factor that should be considered in the analysis of sleep habits. According to our results, the Nursing students that were < 25 years of age had the worst sleep habits and used to have more difficulties in maintaining sleep routines, modifying them on the weekends and holidays, preferring to stay up late to obtain better study results, and going out at night without considering that they had to get up early. As other studies [ 21 ], we observed that social activities were a priority in the life of the university adolescents and the substituting of hours of sleep for enjoying and sharing activities with friends and classmates did not constitute a problem for them. These behaviors were added to the physiological delay of the start of sleep that is typical in this stage of life and might unleash deprivation or a chronic deficit of sleep, maintained throughout the entire week. The students then tried to compensate for this lack of sleep by increasing their hours of sleep during the weekend. We agree with previous studies that this circumstance, far from minimizing or compensating the effects of sleep deprivation, aggravates them, worsening the pattern and the quality of sleep of the students [ 22 ].

Further, we found an association between age and circadian type. We observed that most of the university students with evening chronotypes were aged < 25, had bad sleep habits, and a poor academic performance. Physiologically, adolescents and adults tend to have delayed circadian preferences and are “lovers of the night” [ 23 ]. In our study, 20.2 % of students had an evening chronotype, which is lower than that reported in other studies, where 59 % of the students between 18 and 29 years of age described themselves as night owls [ 32 ]. Our results also showed a clear normalization of the evening behaviors of the students. These data are in agreement with other authors who highlighted the influence exerted by the aforementioned normalization of evening habits among the youth on the quality of sleep, leading to a medium to long-term sleep deficit [ 20 ]. As Crowley et al. (2018) [ 33 ], we think that evening behavior leads to asynchrony between the biological rhythm and the social life of the student, having negative consequences on the academic performance. However, how this really affects academic results requires extending researches, since the circadian rhythm was not significantly associated with academic performance.

The results of this study evidence the need to seriously take into consideration the sleep deficits that are associated with inadequate sleep habits, with the aim of developing preventative and educational initiatives to improve the sleep habits of the university population. The challenge ahead starts with the social awareness of the importance of having good-quality sleep since many times, adequate knowledge about sleep does not translate into a change of sleep habits [ 23 ].

Limitations

Some limitations should be noted. Due to the cross-sectional design of the study, we could not establish an exact causal relationship between sleep pattern and academic performance. In addition, it should be note that the ‘sleep habits questionnaire’ is a subjective questionnaire, and therefore the result could be biased if the student did not answer honestly. Another limitation is the difficulty in conceptualizing academic performance, due to its complex and multi-causal character, where many factors intervene. The factors include attitudes, habits, the character of the staff, methodologies, family environment, organization of the educational system, socio-economic condition, as well as other social, economic, and psychological aspects [ 34 ]. Finally, the study was conducted only in Nursing students, so our results must be prospectively validated in University students from a larger variety of academic sectors. Similarly, this study was conducted in a single University, so more studies involving other Universities are also necessary. Despite these circumstances, we believe that our hypothesis that the duration of sleep could lead to better academic performance is based on current scientific data.

Using the 11-item ‘sleep habits questionnaire’, 30.4 % of the Nursing students were identified as having bad sleep habits. In addition, Nursing students included in this research were characterized by an evening chronotype and a short sleep pattern. Regarding academic performance, half of the Nursing students showed a poor one. A short sleep pattern, bad sleep habits, and younger age, were independently associated with a higher risk of poor academic performance. This requires multifactorial approaches and the involvement of all the associated actors: teachers, academic institutions, health institutions, and the people in charge in university residences, among others.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Gallego-Gómez, J.I., González-Moro, M.T.R., González-Moro, J.M.R. et al. Relationship between sleep habits and academic performance in university Nursing students. BMC Nurs 20 , 100 (2021). https://doi.org/10.1186/s12912-021-00635-x

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Total sleep deprivation effects on the attentional blink

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• Carlos Gallegos   ORCID: orcid.org/0000-0002-1513-0321 1 ,
• Candelaria Ramírez 1 ,
• Aída García 1 ,
• Jorge Borrani 1 &
• Pablo Valdez 1  

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The Attentional Blink (AB) is a phenomenon that reflects difficulty in detecting or identifying the second of two successive targets (T1 and T2) that are presented in rapid succession, between 200-500ms apart. The AB involves indicators of attentional and temporal integration mechanisms related to the early stages of visual processing. The aim of this study was to identify the effects of 24-h of sleep deprivation (total sleep deprivation, TSD) on the attentional and temporal integration mechanisms of the AB. Twenty-two undergraduate students were recorded during five successive days, in these three conditions: baseline (two days), TSD (one day), and recovery (two days). Each day, at around 12:00 h, participants responded to a Rapid Serial Visual Presentation task (RSVP) that presented two targets separated by random intervals from 100 to 1000ms. The attentional mechanisms were assessed by the AB presence, the AB magnitude, and the AB interval, while the temporal integration mechanisms were evaluated by lag-1 sparing and order reversal responses. TSD negatively affected the attentional mechanisms, which is expressed by an overall reduction in performance, an extended AB interval, and a reduced AB magnitude. TSD also negatively affected the temporal integration mechanisms, manifested by an absence of lag-1 sparing and an increase in order reversals. These results suggest that people are still able to respond to two successive stimuli after 24 h without sleep. However, it becomes more difficult to respond to both stimuli because the attentional and temporal integration mechanisms of the AB are impaired.

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The datasets analyzed during the current study are available from the corresponding author upon reasonable request.

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by all authors. The first draft of the manuscript was written by Carlos Gallegos. The preliminary versions of the manuscript were commented on by all authors. The final manuscript was read and approved by all authors.

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Gallegos, C., Ramírez, C., García, A. et al. Total sleep deprivation effects on the attentional blink. Exp Brain Res (2024). https://doi.org/10.1007/s00221-024-06826-7

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Short- and long-term health consequences of sleep disruption

Goran medic.

1 Market Access, Horizon Pharma B.V., Utrecht

2 Unit of Pharmacoepidemiology & Pharmacoeconomics, Department of Pharmacy, University of Groningen, Groningen, The Netherlands

Micheline Wille

Michiel eh hemels.

Sleep plays a vital role in brain function and systemic physiology across many body systems. Problems with sleep are widely prevalent and include deficits in quantity and quality of sleep; sleep problems that impact the continuity of sleep are collectively referred to as sleep disruptions. Numerous factors contribute to sleep disruption, ranging from lifestyle and environmental factors to sleep disorders and other medical conditions. Sleep disruptions have substantial adverse short- and long-term health consequences. A literature search was conducted to provide a nonsystematic review of these health consequences (this review was designed to be nonsystematic to better focus on the topics of interest due to the myriad parameters affected by sleep). Sleep disruption is associated with increased activity of the sympathetic nervous system and hypothalamic–pituitary–adrenal axis, metabolic effects, changes in circadian rhythms, and proinflammatory responses. In otherwise healthy adults, short-term consequences of sleep disruption include increased stress responsivity, somatic pain, reduced quality of life, emotional distress and mood disorders, and cognitive, memory, and performance deficits. For adolescents, psychosocial health, school performance, and risk-taking behaviors are impacted by sleep disruption. Behavioral problems and cognitive functioning are associated with sleep disruption in children. Long-term consequences of sleep disruption in otherwise healthy individuals include hypertension, dyslipidemia, cardiovascular disease, weight-related issues, metabolic syndrome, type 2 diabetes mellitus, and colorectal cancer. All-cause mortality is also increased in men with sleep disturbances. For those with underlying medical conditions, sleep disruption may diminish the health-related quality of life of children and adolescents and may worsen the severity of common gastrointestinal disorders. As a result of the potential consequences of sleep disruption, health care professionals should be cognizant of how managing underlying medical conditions may help to optimize sleep continuity and consider prescribing interventions that minimize sleep disruption.

Introduction

Sleep is a biologic process that is essential for life and optimal health. Sleep plays a critical role in brain function and systemic physiology, including metabolism, appetite regulation, and the functioning of immune, hormonal, and cardiovascular systems. 1 , 2 Normal healthy sleep is characterized by sufficient duration, good quality, appropriate timing and regularity, and the absence of sleep disturbances and disorders. 3 Despite the importance of sleep, up to 70 million people in the US and ~45 million people in Europe have a chronic sleep disorder that impacts daily functioning and health. 2 , 4 For example, ~20% of the serious injuries that result from car accidents can be associated with driver sleepiness, independent of the effects of alcohol. 2 Lifestyle and environmental factors, psychosocial issues, and medical conditions all contribute to sleep problems. 2 There are ~100 sleep disorder classifications; however, they are typically manifested in one of the following three ways: failure to obtain the necessary amount or quality of sleep (sleep deprivation), an inability to maintain sleep continuity (disrupted sleep, also called sleep fragmentation, difficulty maintaining sleep, and middle insomnia), and events that occur during sleep (eg, sleep apnea, restless legs syndrome). 2 The effects of sleep disorders on the body are numerous and widely varied across multiple body systems. This review focuses on the clinical consequences, both short term and long term, that result from disrupted sleep (not including short sleep duration) in adults, adolescents, and children who are otherwise healthy and in those who have an underlying medical condition. Information on basic science and mechanisms of these effects are included to provide background for the clinical outcomes, but are not thoroughly reviewed. Several recent reviews provide detailed information on the science and mechanisms of sleep disruption. 5 – 7

Methodology

In order to better focus on the topics of interest among the myriad parameters affected by sleep, this review of the literature was designed to be nonsystematic. A search of English-language publications in the PubMed database was conducted in March and April 2016. Search terms were “caregiver AND sleep”, “caregiver AND drug administration”, “insomnia”, “middle insomnia”, “restless leg[s] syndrome”, “sleep AND drug administration”, “sleep apnea”, “sleep continuity”, “sleep deprivation”, “sleep disorder”, “sleep disruption”, “sleep disturbance”, “sleep fragmentation”, and “sleep maintenance”. Together, these search terms generated over 60,000 hits. For each individual search, we reviewed the most recent articles to identify those that specifically discussed the consequences of disrupted sleep, rather than those of short sleep duration or other sleep problems. For topics that were not adequately covered by recent literature (previous ~5–10 years), we looked slightly further back in the literature. Other publications were identified by examining the reference lists of publications included in the literature searches. The websites of the American Academy of Sleep Medicine, Sleep Research Society, and the European Sleep Research Society were also searched for additional publications. This nonsystematic review pulled information from a total of 97 references.

Characteristics of normal sleep

The stages of sleep have historically been divided into one stage of rapid eye movement (REM) sleep and four stages (Stages 1–4) of non-rapid eye movement (NREM) sleep that are characterized by increasing sleep depth. 2 , 8 The deeper sleep stages (Stages 3 and 4) are collectively referred to as slow-wave sleep (SWS), which is believed to be the most restorative type of sleep and typically occurs during the first one-third of the night. 2 , 8 , 9 In contrast, REM sleep increases as the night progresses and is longest in the last one-third of a sleep episode. 2 REM and NREM sleep are characterized by numerous, yet different, physiologic changes, including brain activity, heart rate, blood pressure (BP), sympathetic nervous system activity, muscle tone, blood flow to the brain, respiration, airway resistance, renal function, endocrine function, body temperature, and sexual arousal. 2 For example, during NREM sleep, heart rate, BP, blood flow to the brain, and respiration are decreased compared with wakeful periods. During REM sleep, these processes are increased compared with NREM sleep. Brain activity decreases from wakefulness during NREM sleep; activity levels are similar during REM sleep, except for increases in motor and sensory areas. 2

A newer sleep classification system developed by the American Academy of Sleep Medicine has only three stages of NREM sleep: lighter sleep (Stages N1 and N2) and deeper sleep (or SWS; Stage N3). 10 The major changes with the newer classification system are focused on electroencephalogram (EEG) derivations and the merging of Stages 3 and 4 into Stage N3. 11 In a comparison of the two sleep classifications, only minor differences were noted for total sleep time, sleep efficiency, and REM sleep, but the choice of classification impacted the measurement of wake after sleep onset and the distribution of NREM sleep stages. 11

The two-process model describes the interplay between the sleep-promoting process (process S) and the maintenance of wakefulness system (process C). 2 The balance between these processes shifts throughout the course of the day, leading to regulation of the sleep–wake cycle. This sleep–wake cycle is controlled by daily rhythms of physiology and behavior, called circadian rhythms. 2 Circadian rhythms also control metabolic activity through physical activity and food consumption, as well as body temperature, heart rate, muscle tone, and hormone secretion. 2 The sleep process is regulated by neurons in the hypothalamus, which turn off the arousal systems in order to allow sleep to occur. 2 Insomnia results from the loss of these neurons. Other brain regions are also involved in sleep disruption, including the brain stem and cognitive areas of the forebrain. Over the course of the night, neurons in the pons switch between NREM and REM sleep by sending outputs to the brain stem and spinal cord, causing muscle atonia and chaotic autonomic activity; to the forebrain; and to the thalamus via cholinergic pathways. 2

The circadian rhythms work to synchronize sleep with the external day–night cycle, via the suprachiasmatic nucleus (SCN) that receives direct input from nerve cells in the retina acting as brightness detectors. 2 , 12 Light travels from the retina to the SCN, which signals the pineal gland to control the secretion of melatonin. This neurohormone acts to synchronize the circadian rhythms with the environment and the body through melatonin receptors in nearly all tissues. The SCN also works with a series of clock genes to synchronize the peripheral tissues, giving rise to daily patterns of activity.

Overview of sleep disruption

Disruption of sleep is widespread. A 2014 survey conducted by the National Sleep Foundation reported that 35% of American adults rated their sleep quality as “poor” or “only fair”. 13 Trouble falling asleep at least one night per week was reported by 45% of respondents. 13 In addition, 53% of respondents had trouble staying asleep on at least one night of the previous week, and 23% of respondents had trouble staying asleep on five or more nights. 13 Snoring was reported by 40% of respondents, 13 and 17% of respondents had been told by a physician that they have a sleep disorder, the majority (68%) of which was sleep apnea. 13 Relatively few studies have looked at sleep disruption in children. In a study that included a random sample of Chinese children aged 5–12 years, the overall prevalence of chronic sleep disruption was 9.8% (boys, 10.0%; girls, 8.9%). 14

Risk factors for sleep disruption are vast and involve a combination of biologic, psychologic, genetic, and social factors ( Table 1 ). 2 , 6 , 15 – 39 Lifestyle factors include consuming excessive amounts of caffeine 15 and drinking alcohol. 16 Performing shift work 20 or being a college student 2 is also a risk factor for sleep disruption. Exposure to excessive nighttime light pollution and underexposure to daytime sunlight can lead to disruption of circadian rhythms. 19 Stressful life circumstances, such as being the parent of a young infant 21 or serving as a caregiver for a family member with a chronic, life-threatening, or terminal illness, 22 – 25 are also contributors to sleep problems. In addition to the stress and worry associated with caregiving, caregivers of patients with complex medication schedules may experience sleep disruption due to the requirement to wake themselves during the night to administer medication. 25

Risk factors contributing to sleep deprivation and disruption

Note: Data from the following references. 2 , 6 , 15 – 19

Sleep disruption is frequently attributable to a sleep disorder, such as obstructive sleep apnea 26 , 27 and restless legs syndrome, which is related to altered dopamine and iron metabolism; >50% of idiopathic cases of restless leg syndrome have a positive family history. 28 , 29 Many major medical conditions have been associated with sleep disruption, particularly those that require nighttime medical monitoring (eg, continuous glucose monitoring for individuals with diabetes) 38 or hospitalization, especially in an intensive or critical care unit. 39 , 40

Sleep deprivation studies and studies of insomniacs have identified the primary mechanisms by which sleep disruption is believed to exert its detrimental short- and long-term health effects ( Figure 1 ). 41 – 44 During both brief and extended arousals during sleep, increased metabolism is evidenced by increased oxygen consumption and carbon dioxide production. 43 Levels of catecholamine, norepinephrine, and epinephrine have been correlated with fragmented sleep. 44 In addition, chronic persistent insomnia is associated with increased secretion of adrenocorticotropic hormone and cortisol, which is present throughout a 24-hour sleep–wake cycle. 42 These findings suggest that activations of the sympathetic nervous system, the sympathoadrenal system, and the hypothalamic–pituitary–adrenal axis are involved in the health consequences of sleep disruption. 41 – 44 In addition, suppression of SWS was associated with decreased insulin sensitivity that did not result in an increase in insulin release; these findings may explain the increased risk of type 2 diabetes mellitus (T2DM) in patients with poor sleep quality. 9 Other metabolic changes include decreased leptin and increased ghrelin that may contribute to increased appetite. 45 Sleep abnormalities affect immune function in a reciprocal manner, leading to changes in proinflammatory cytokines, such as tumor necrosis factor, interleukins 1 and 6, and C-reactive protein. 12 , 46 The multitude of systems that react to sleep loss suggest effects beyond the central nervous system and include total body functioning. 5

Proposed mechanisms by which sleep disruption is thought to exert its detrimental short- and long-term effects.

Notes: ↑, increase; ↓, decrease. Data from the following references. 9 , 12 , 41 – 45

Abbreviations: ACTH, adrenocorticotropic hormone; CO 2 , carbon dioxide; TNF, tumor necrosis factor; IL, interleukin; CRP, C-reactive protein; T2DM, type 2 diabetes mellitus.

These wide-ranging effects of sleep disruption are often interrelated and bidirectional. For example, the distress associated with sleep loss can create additional stress to maximize sleep, which, in turn, contributes to worsening (rather than improving) sleep disruption. 23 The current research suggests that the mechanisms of short- and long-term health consequences are similar but are affected by time. In chronic sleep deprivation, the body’s ability to compensate for physiologic changes is diminished, leading to gradually accumulating effects and basal changes. 47 Insomniacs have been shown to have increased EEG activity, abnormal hormone secretion, increased metabolic activity, and increased sympathetic nervous system activity throughout the day and night. Over time, this heightened and abnormal activity, resulting from the lack of appropriate body rest, can lead to the development of disease and chronic conditions. 45 Further, insufficient sleep may contribute to alterations in the neuroendocrine stress response system, ultimately leading to stress-related disorders such as mood disorders and depression. 47

Short-term health consequences of sleep disruption

As a result of the physiologic changes associated with sleep disruption, numerous health consequences have been reported. Short-term consequences of sleep disruption include increased stress responsivity; somatic problems; reduced quality of life (QoL); emotional distress; mood disorders and other mental health problems; cognition, memory, and performance deficits; and behavior problems in otherwise healthy individuals. Sleep disruption may also diminish the health-related quality of life (HRQoL) of children and adolescents with underlying medical conditions.

Short-term consequences in otherwise healthy individuals

Increased stress responsivity.

Increased autonomic sympathetic activation is a consequence of fragmented and interrupted sleep. 47 Results of experimental studies suggest that the extent of increased sympathetic activation was related more to the disruption and discontinuity of sleep than to the duration of sleep deprivation or the amount of sleep that was lost. 44 , 48 , 49 Sympathoadrenal activation produces a combination of transient hemodynamic, vasoconstrictive, and prothrombotic processes associated with a stress response. 48 These effects of sleep disruption on nocturnal regulation of sympathetic activity may offer a connection between sleep disruption and cardiovascular disease (CVD) as well as psychiatric conditions. 48 By affecting stress hormones, sleep disruption may directly affect functionality, including cognition and mood.

Somatic problems

A study of adolescents in two Finnish communities found that the 6-month prevalence of weekly sleep problems was 27% and that sleep problems were strongly associated with weekly headache and abdominal pain. 50 Girls had more symptoms than boys, and an increasing frequency of pain and sleep problems was associated with psychosocial difficulties, such as psychiatric symptoms and substance use. Bidirectional associations between somatic problems and sleep disorders are expected, and these associations may be related to common background factors, such as personality and adverse life events. 50 During clinical examination, when one symptom is reported, screening for related symptoms should be considered.

Psychosocial issues

Studies have identified a range of psychosocial issues associated with sleep disruption in adults, from emotional distress and mood disorders to cognitive, memory, and performance deficits.

In a qualitative interview-based study by Neu et al, 23 mothers of children who were receiving maintenance treatment for acute lymphoblastic leukemia routinely experienced sleep disruption because their children awoke and needed assistance or because of worries related to the child’s illness. The mothers reported being irritable, impatient, and less productive than before the illness. In a longitudinal, community-based study of midlife women who had a history of depression and/or anxiety but were not currently ill, sleep disturbance was significantly associated with reduced HRQoL, as measured by the 36-item Short Form Health Survey (SF-36). Odds ratios (ORs) ranged from 2.04 to 2.96, with P < 0.05 across all HRQoL domains. 51 A study of 61 maternal caregivers of young children with bronchopulmonary dysplasia showed that 80% of mothers had clinically disturbed sleep (based on self-report using the Pittsburgh Sleep Quality Index [PSQI]). 25 This sleep disturbance may be due to the need to administer medication and provide other care during the night, as well as worry about the child’s condition. Disrupted sleep was associated with diminished QoL in this study, as assessed using the World Health Organization’s Quality of Life Brief. Sleep quality emerged as the only independent variable to significantly predict QoL.

A recent review by Meerlo et al 52 surveyed the evidence that showed that disrupted sleep is a major causal factor in the development of depression. An experimental study that compared the effects of forced nocturnal awakenings with restricted sleep opportunity and uninterrupted sleep showed that partial sleep loss from sleep continuity disruption was more detrimental to positive mood than partial sleep loss from delaying bedtime. 53 Adult subjects experiencing forced awakenings had significantly less SWS after the first night of sleep deprivation than other participants. Furthermore, in adults who completed the Personality Assessment Inventory, self-reports of recurring sleep problems were associated with symptoms of depression and anxiety. 54 The reported frequency of sleep disturbance was closely linked with the severity of the self-reported symptoms. Among primary care physicians, disrupted sleep was associated with high burnout levels. 55

Sleep disruption alters cognition and performance in many domains, including attention/vigilance, executive function, emotional reactivity, memory formation, decision-making, risk-taking behavior, and judgment. 56 An experimental study showed that SWS disruption resulted in slower or impaired information processing, impaired sustained attention, less precise motor control, and erroneous implementation of well-practiced actions. 57 Younger, middle-aged, and older adults were similarly affected by SWS disruption. In another study, poor sleep quality negatively affected the emotional valence of memories. 58

Across these various studies, the interrelationships between sleep disruption, life events (such as illness of a child), and increased stress responsivity confound the physiologic response. These associations are bidirectional, as anxiety and depression are associated with sleep disruption, and thus make it challenging to separate cause from consequence. 47 Despite this difficulty, sleep disruption impacts psychosocial functioning in adults and may contribute to psychological conditions that require appropriate intervention.

Adolescents

Later bedtimes and an inadequate amount of sleep are well-documented changes in sleep patterns associated with adolescence. 59 A systematic review including 76 studies of the functional consequences of sleep problems in adolescents showed that sleep disruption had a negative effect on psychosocial health, school performance, and risk-taking behaviors, particularly use of nicotine and marijuana. 59 Studies assessing the relationships between sleep and psychosocial health measures found that sleep disruption was associated with new onset of poor mental health status, 60 loneliness, 61 worry, 62 anxiety, 61 , 63 and depression. 63 In a study of 1,629 adolescents, those with excellent academic performance had earlier bedtimes and longer sleep on weekdays with less severe daytime sleepiness than those with poor grades. 64 Other studies showed an association between sleep quality and sleep deprivation with poor academic performance. 65 , 66 Adolescent risk behaviors associated with sleep disruption included cigarette smoking, 67 , 68 drinking alcohol, 68 , 69 illicit drug use, 68 and aggressive behaviors, including driving while intoxicated, considering suicide, and having unprotected sex. 59 , 62 , 68

Psychosocial outcomes such as depression and mood disturbances, risk-taking behavior, and academic performance appear to be the primary factors affected by sleep disruption in adolescents. Taken together, causal bidirectional relationships are apparent between sleep and psychosocial health as noted earlier for adults. 59 These findings must be interpreted with caution, however, as many studies of sleep disturbance in adolescents group together the effects of short sleep duration (a common complaint among adolescents) and sleep disruption.

In a real-world study of 135 healthy children, diminished performance on neurobehavioral functioning measures (particularly those associated with more complex tasks, such as a continuous performance test and a symbol-digit substitution test) were found in children with fragmented sleep. 70 Parents of these children also rated them as having more behavioral problems than those with continuous sleep. Other reported issues include psychiatric symptoms, 71 social problems, 72 externalizing symptoms, 71 and self-harm behaviors. 73

Short-term consequences in individuals with underlying medical conditions

Reduced qol.

Of 159 children and adolescents with chronic kidney disease (pre-dialysis, dialysis, and transplant patients), 58.5% had symptoms of sleep disturbance, as measured by the Epworth Sleepiness Scale. 34 The presence of a sleep disturbance was most frequent in the dialysis group compared to the other groups, 34 while sleep disturbance was associated with a significant decrease in the overall total QoL score on the Pediatric Quality of Life Inventory (PedsQL) Version 4.0 Generic Core Scales for pre-dialysis and transplant subjects ( P = 0.002 and P = 0.001, respectively). A study of 47 pediatric liver transplant recipients investigated the impact of sleep problems (as assessed by the Pediatric Sleep Questionnaire) on HRQoL, as measured using the PedsQL. 74 Sleep-related breathing disorders and excessive daytime sleepiness were prevalent, affectinĝ23% and 40% of children in the study, respectively. 74 According to the parent proxy and child self-report, ~40% of participants had a substandard HRQoL. The physical manifestations of chronic diseases, such as chemical imbalances in dialysis patients, along with medications that may adversely affect sleep, play a role in sleep disruption and require comprehensive management to allow for effective sleep. 34 , 74

Long-term health consequences of sleep disruption

Long-term consequences of sleep disruption in otherwise healthy individuals include hypertension, dyslipidemia, CVD, weight-related issues, metabolic syndrome, and T2DM. Evidence suggests that sleep disruption may increase the risk of certain cancers and death. Sleep disruption may also worsen the symptoms of some gastrointestinal disorders.

Long-term consequences in otherwise healthy individuals

Cardiovascular.

The increased activity of the sympathetic nervous system that is associated with sleep deprivation has substantial long-term consequences for adults and adolescents. 45 , 47 , 75 – 79 Adults who experienced sleep disruption had elevated BP 70 and an increased risk of developing hypertension. 76 – 78 A meta-analysis of data from four prospective cohort studies found that the relative risk of incident hypertension was 1.20 (95% confidence interval [CI], 1.06–1.36) in adults with sleep continuity disturbance, with equal effects in men and women. 45 In adolescents, higher sleep disturbance scores on the PSQI were associated with higher cholesterol, higher body mass index (BMI), higher systolic BP, and an increased risk of hypertension. 79 Two large, population-based studies assessed the association between CVD and sleep disruption. 76 , 80 In the prospective, population-based Atherosclerosis Risk in Communities (ARIC) Study, incident CVD was observed in patients who experienced sleep continuity disturbance in combination with difficulty falling asleep and nonrestorative sleep (OR, 1.5; 95% CI, 1.1–2.0). 76 An association between difficulty maintaining sleep or short sleep duration and incident myocardial infarction was observed in middle-aged women who participated in the MONICA/KORA Augsburg Cohort Study. 80 Despite differences in study design and populations enrolled, these studies extend the literature to suggest that the effects of sleep disruption on sympathetic activity, glucose metabolism, and possibly inflammation may lead to adverse cardiovascular effects. 80

A recent review by Cedernaes et al 81 described a variety of molecular and behavioral factors that may lead to an association between sleep disruption and metabolic disorders, including obesity and T2DM. Sleep loss appears to affect energy metabolism primarily by impairing insulin sensitivity and increasing food intake. 81 Disrupted sleep has been associated with weight gain and other weight-related issues in both adults 82 , 83 and adolescents. 79 A 5-year ancillary study nested within the Coronary Artery Risk Development in Young Adults (CARDIA) study showed that sleep fragmentation was strongly associated with increases in BMI. 82 A common cause of sleep disruption is shift work, which has been implicated in high BP and increased stress. 20 A 14-year longitudinal study in male Japanese workers showed that alternating shift work increased the rate of everyday drinking, smoking, and absence of habitual exercise and also heightened the risk of increasing BMI. 83 In adolescents, sleep disruption was associated with a high BMI z -score, being overweight, and having a high waist circumference percentile. 79

The results of experimental studies in healthy volunteers suggest that, independent of sleep duration, sleep fragmentation can alter glucose homeostasis. 9 In an experimental study in healthy young adults, sleep disruption (characterized by three nights of SWS suppression) resulted in decreased insulin sensitivity, which was similar to that reported for populations at high risk of T2DM, and reduced glucose tolerance. 9 Other experimental studies showed that sleep fragmentation resulted in reduced insulin sensitivity, reduced glucose effectiveness (defined as the ability of glucose to mobilize itself independent of an insulin response), and increased cortisol levels. 84 , 85 Large longitudinal studies have shown that sleep disruption is associated with an increased risk of developing T2DM. 78 , 86 – 89 A meta-analysis of four of these studies 86 – 89 found that the overall relative risk of developing T2DM was 1.84 (95% CI, 1.39–2.43; P < 0.0001) in adults who experienced difficulty maintaining sleep. 90

The coexistence of obesity, elevated BP and glucose levels, and low levels of high-density lipoprotein cholesterol defines the metabolic syndrome. 91 An observational, cross-sectional study compared global scores on the PSQI with concurrently collected measures of metabolic syndrome components. 91 Poor global sleep-quality scores on the PSQI were related significantly to the presence of metabolic syndrome, and the PSQI global sleep-quality score was significantly related to waist circumference, BMI, percentage of body fat, serum levels of insulin and glucose, and estimated insulin resistance.

The accumulating evidence points to the importance of regular sleep for normal metabolic functioning and prevention of the metabolic syndrome. 81 The metabolic effects of sleep disruption appear to manifest in both the brain and peripheral organs. The effects of sleep disruption on appetite, glucose metabolism, and diabetes risk are critical to understanding the epidemic of obesity and metabolic disease. It has even been suggested that sleep may be an appropriate therapeutic target for treatment and prevention of obesity and diabetes. 81

Disruption of circadian rhythm and sleep deprivation have been shown to accelerate tumor formation 12 and may increase the risk of cancer. 12 , 92 Exposure to light at night decreases production of melatonin, which may lead to increased production of reproductive hormones. 93 Melatonin has other important properties, including DNA repair, inhibition of tumor growth, and acting as a potent free-radical scavenger. 92 , 94 A study in mice subjected to suprachiasmatic nuclei destruction showed that disruption of circadian coordination accelerated malignant growth, which suggests that the host circadian clock controls tumor progression 95 and provides a potential mechanistic reason for this association.

With regard to clinical data, night shift work has been associated with an increased risk of cancer. In the Nurses’ Health Study, 602 incident cases of colorectal cancer were documented among 78,586 women who were followed over 10 years. 93 Compared with women who never worked rotating night shifts, women who worked 1–14 years or ≥15 years on rotating night shifts had multivariate relative risks of colorectal cancer of 1.00 (95% CI, 0.84–1.19) and 1.35 (95% CI, 1.03–1.77), respectively ( P trend = 0.04). These data suggest that extended night shift work may increase the risk of colorectal cancer. Moreover, men who suffered from severe problems of falling and staying asleep were about twice as likely to develop prostate cancer as those without insomnia. 92

A recent large nested case–control study from Taiwan determined an increased risk of cancer among patients with sleep disorders compared with those without sleep disorders. 96 In this study, sleep disorders were separated into three categories: insomnia, parasomnia, and obstructive sleep apnea, all of which can contribute to sleep disruption. The risk of breast cancer was increased for patients with each of these types of disorder (adjusted hazard ratio 1.73 [95% CI, 1.57–1.90] for insomnia, 2.76 [95% CI, 1.53–5.00] for parasomnia, 2.10 [95% CI, 1.16–3.80] for obstructive sleep apnea). There was also a higher risk of nasal cancer and prostate cancer in patients with obstructive sleep apnea compared with those without sleep disruptions.

The mechanisms responsible for carcinogenesis in sleep-disrupted individuals are not clear, and much of the work is focused on nighttime light exposure and decreased melatonin levels. 92 , 93 Additional research is required to determine the effect and etiology of sleep disruption on cancer risk.

In the GAZEL cohort study that assessed sleep disturbances using the 5-item sleep dimension from the Nottingham Health Profile, sleep disturbance was associated with a higher all-cause risk of mortality in men ( P = 0.005), but not in women ( P = 0.33). In particular, men who reported sleep disruption on the Nottingham Health Profile (“I sleep badly at night”) had a higher all-cause mortality risk compared with those who did not report sleep disruption (hazard ratio, 1.69; 95% CI, 1.25–2.31). 78 In a study in which the family and friends of adolescent suicide completers reported sleep disturbances for the deceased, history of sleep disturbances, including middle insomnia, was significantly associated with suicide compared with matched community controls. 97 The effect remained significant when controlling for current affective disorders and severity of depressive symptoms.

The high correlation between sleep disturbances, depression, and suicidal ideation may play a role in identifying an increased risk of mortality in these studies. Other studies have linked sleep disorders to mortality through an increase in cardiovascular deaths, which have also been related to sleep disruption. Additional studies are needed in larger cohorts and controlling for confounding factors. Importantly, hypertension and diabetes may not explain death in younger individuals with sleep disruption, but the association of sleep disruption with these factors is a risk factor for mortality in later life. 78

Long-term consequences in individuals with underlying medical conditions

The interdependent relationship between sleep and the immune system may be a factor in the effect of sleep abnormalities on common gastrointestinal disorders. Sleep disruption may worsen symptoms of inflammatory bowel disease, irritable bowel syndrome, and gastroesophageal reflux disease. 12 Conversely, these same gastrointestinal disorders can also contribute to sleep disruption. As seen with many other consequences of sleep disruption, the bidirectional interplay between sleep disruption and gastrointestinal disorders provides the opportunity for clinicians to treat both conditions for improved patient outcomes.

Disrupted sleep is a pervasive problem, with numerous contributing factors from lifestyle and environmental factors to psychosocial issues and iatrogenic effects. Sleep is vital to most major physiologic processes, and, as such, sleep disruption has vast potential for adverse short- and long-term health consequences in otherwise healthy individuals as well as those with underlying medical conditions. In healthy individuals, short-term consequences include a heightened stress response; pain; depression; anxiety; and cognition, memory, and performance deficits. In adolescents and children, disrupted sleep can lead to poor school performance and behavior problems. Reduced QoL may be a short-term consequence of sleep disruption in otherwise healthy individuals and those with an underlying medical condition. Long-term consequences for otherwise healthy individuals include hypertension, dyslipidemia, CVD, weight gain, metabolic syndrome, and T2DM. There is also evidence that sleep disruption may increase the risk of certain cancers and death in males and suicidal adolescents. Long-term sleep disruption may also worsen the symptoms of a variety of gastrointestinal disorders.

Ultimately, it has been suggested that the physiologic consequences of disrupted sleep may be just as damaging as those of short sleep duration. 5 Given the detrimental impact of disrupted sleep, it is important for health care professionals to effectively treat symptoms of underlying medical conditions to optimize sleep continuity. In addition, when possible, health care providers should consider prescribing interventions that minimize disruptions to sleep continuity, 25 such as medications with a long dosing interval.

Acknowledgments

Medical writing assistance for this manuscript was provided by Katie Gersh, PhD, of MedErgy and was funded by Horizon Pharma.

All authors are employees of Horizon Pharma, which funded medical writing assistance for this manuscript. The authors report no other conflicts of interest in this work.