sleep deprivation research paper introduction

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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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Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem (2006)

Chapter: 1 introduction, 1 introduction.

“Sleep that knits up the ravelled sleave of care, The death of each day’s life, sore labour’s bath, Balm of hurt minds, great Nature’s second course, Chief nourisher in life’s feast.” Shakespeare, Macbeth

CHAPTER SUMMARY The public health burden of chronic sleep loss and sleep disorders is immense. Although clinical activities and scientific opportunities in the field are expanding, awareness among the general public and health care professionals is low, given the burden. The available workforce of health care providers is not sufficient to diagnose and treat individuals with sleep disorders. Therefore, the current situation necessitates a larger and more interdisciplinary workforce to meet health care demands as well as advance the field’s knowledge base. Further, there is a need to develop and reorganize public health and academic sleep programs to facilitate and improve the efficiency and effectiveness in public awareness, training, research, diagnosis, and treatment of sleep loss and sleep disorders. Finally, the fragmentation of research and clinical care currently present in most academic institutions requires the creation of accredited interdisciplinary sleep programs in academic institutions. The success of existing comprehensive academic Somnology and Sleep Medicine Programs offers evidence of the value of interdisciplinary approaches to patient care, education, research training, faculty development, and science. An interdisciplinary approach requires the coordinated and integrated effort of not only the major medical fields involved in sleep clinical care (internal medicine and its relevant subspecialties, pediatrics, neurology, psychiatry, psychology, and otolaryngology) but also other disciplines such as neuroscience, dentistry, nursing, and pharmacology.

MAGNITUDE AND COST OF THE PROBLEM

Fitful sleep, restless nights, hitting the alarm clock button for an additional 10 minutes of sleep—all are all too familiar manifestations of the interactions of life with one of the frontiers of science and clinical practice—somnology 1 and sleep medicine. It is estimated that 50 to 70 million Americans suffer from a chronic disorder of sleep and wakefulness (NHLBI, 2003), hindering daily functioning and adversely affecting health. The current capacity of America’s health system is not sufficient to diagnose and treat all individuals with sleep disorders. Further, awareness among health care professionals and the general public is low considering the size of the problem. Among those individuals with sleep disorders are 3 to 4 million individuals with moderate to severe obstructive sleep apnea (Young et al., 1993), a disorder characterized by brief periods of recurrent cessation of breathing caused by airway obstruction with morbid or fatal consequences. Chronic insomnia, which hampers a person’s ability to fall asleep, is observed in approximately 10 percent of the American population (Ford and Kamerow, 1989; Simon and VonKorff, 1997; Roth and Ancoli-Israel, 1999). Restless legs syndrome and periodic limb movement disorder are neurological conditions characterized by nocturnal limb movements and an irresistible urge to move the legs. These conditions affect approximately 5 percent of the general population (Lavigne and Montplaisir, 1994; Rothdach et al., 2000; NSF, 2000; Montplaisir et al., 2005), making it one of the most common movement disorders (Montplaisir et al., 2005).

The negative public health consequences of sleep loss and sleep-related disorders are enormous. Some of the most devastating human and environmental health disasters have been partially attributed to fatigue-related performance failures, 2 sleep loss, and night shift work-related performance failures, including the tragedy at the Union Carbide chemical plant in Bhopal, India; the nuclear reactor meltdowns at Three Mile Island and Chernobyl; and the grounding of the Exxon Valdez oil tanker (NCSDR, 1994; Moss and Sills, 1981; United States Senate Committee on Energy and National Resources, 1986; USNRC, 1987; Dinges et al., 1989). Each of these incidents not only cost millions of dollars but also had a disastrous impact on the environment and the health of local communities.

Over the past century, the average amount of time that Americans sleep has decreased by around 20 percent (NCSDR, 1994). Further, 1 out of every 5 workers in industrialized countries (well over 20 million Americans [OTA, 1991]) perform shift-work, which requires them to work at night and attempt to sleep during the daytime hours (AASM, 2005). These reversed sleep patterns cause maladjustment of circadian rhythms that often lead to sleep disruption. Americans are working more hours or multiple jobs and spending more time watching television and using the Internet, resulting in later sleep times and less sleep.

The cumulative long-term effects of sleep loss and sleep disorders have been associated with a wide range of deleterious health consequences, including an increased risk of hypertension, diabetes, obesity, heart attack, and stroke. In addition, sleep loss and sleep disorders have a significant economic impact. Billions of dollars a year are spent on direct medical costs associated with doctor visits, hospital services, prescriptions, and over-the-counter medications (NCSDR, 1994). Compared to healthy individuals, individuals with chronic sleep loss are less productive, have health care needs greater than the norm, and have an increased likelihood of injury; for example, it is estimated that there are 110,000 sleep-related injuries and 5,000 fatalities each year in motor vehicle crashes involving commercial trucks (CNTS, 1996).

HISTORICAL BACKGROUND

For centuries, sleep and dreams have long been topics of immense interest; however, the modern scientific study of sleep began relatively recently. In 1937 an electroencephalograph was used for the first time to observe the electrical activity in the brain during nonrapid eye movement sleep (Loomis et al., 1937). This opened the field to further advances. Rapid eye movement (REM) was discovered in 1953 by Kleitman and colleagues, and its correlation with dreams was a major step forward in understanding sleep physiology (Aserinsky and Kleitman, 1953). The culmination of this work came in 1957 when Dement and Kleitman defined the stages of sleep (see Chapter 2 of this report) (Dement and Kleitman, 1957). Since the 1950s a convergence of findings from many fields (e.g., neurology, pulmonology, neuroscience, psychiatry, otolaryngology, anatomy, and physiology) have led to a greater understanding of sleep as a basic universal biological process that affects the functioning of many organ systems (Shepard et al., 2005). In 1989, a seminal study demonstrated that rats that were subjected to total sleep deprivation developed skin lesions, experienced weight loss in spite of increased food intake, developed bacterial infections, and died within 2 to 3 weeks (Rechtschaffen et al., 1989). Researchers in sleep and circadian biology continue to work toward a greater understanding of the

etiology and pathophysiology of sleep disorders. The field is maturing into an interdisciplinary field in which integration and coordination across the traditional medical specialties, other health care providers (e.g. nurses, dentists), and between basic and clinical science is vital.

GROWTH OF SOMNOLOGY AND SLEEP MEDICINE

The maturation of the study of sleep and the field of Somnology and Sleep Medicine ( Box 1-1 ) has seen the establishment of many organizations devoted to promoting public awareness, ensuring quality care for individuals who suffer from chronic sleep loss and sleep disorders, and supporting education and research endeavors. In addition to the National Center on Sleep Disorders Research (NCSDR) at the National Institutes of Health (NIH), professional societies and foundations have been established, including the American Academy of Sleep Medicine, the Sleep Research Society, the American Sleep Apnea Association, the Restless Legs Syndrome Foundation, and the National Sleep Foundation

The field of somnology and sleep medicine has been marked by a number of milestones over the last 35 years. Sleep laboratories dedicated to the evaluation and management of sleep disorders have been established. In 1970, sleep disorders were evaluated at only a handful of sleep laboratories in the world. In 2001, there were close to 1,300 sleep laboratories in the United States (Tachibana et al., 2005). Membership in the American Academy of Sleep Medicine and the Sleep Research Society and participation at the annual meeting of the American Professional Sleep Societies has continued to increase. In 2005 sleep medicine was recognized as a medical subspecialty by the Accreditation Council for Graduate Medical Education and the American Board of Medical Specialties.

CHALLENGES IN ADVANCING THE STUDY OF SLEEP DISORDERS

Coordinating research and research funding.

Integrating and coordinating the efforts of the many relevant institutes and centers at the NIH presents many challenges related to funding and advancing somnology research. For example, it has recently been recognized that restless legs syndrome (National Institute of Neurological Disorders and Stroke) and sleep apnea (National Heart, Lung, and Blood Institute) may be a major cause of attention deficit hyperactivity disorder (National Institute of Child Health and Human Development, National Institute of Mental Health) and other behavioral problems (Chervin et al., 2002). The National Institute on Aging is interested in the increase in sleep

and wake disruption during senescence. Insomnia is typically treated using behavioral therapy techniques (Office of Behavioral and Social Sciences Research) and is often comorbid with depression, eating disorders, and other mental disorders (National Institute of Mental Health). Drugs of abuse, including alcohol and stimulants (National Institute on Drug Abuse, National Institute on Alcohol Abuse and Alcoholism), have major effects on sleep and are often used to treat underlying sleep problems such as insomnia or narcolepsy. Sleep apnea research and therapy cuts across a number of disciplines, including nursing (National Institute of Nursing Research), dentistry and otolaryngology (National Institute of Dental and Craniofacial Research), surgery, neurology (National Institute of Neurological Disorders and Stroke), cardiology, and pulmonary medicine (National Heart, Lung, and Blood Institute). At the basic research level, somnology research often involves multiple disciplines such as genetics (National Human Genome Research Institute), environmental sciences (National Institute of Environmental Health Sciences), epidemiology, immunology (National Institute of Allergy and Infectious Diseases), endocrinology (National Institute of Diabetes and Digestive and Kidney Diseases), neurosciences (National

Institute of Neurological Disorders and Stroke, National Institute of Mental Health, National Eye Institute), and otolaryngology (National Institute on Deafness and Other Communication Disorders).

Trans-NIH Sleep Research Coordinating Committee

To facilitate an interchange of information on somnology research the Trans-NIH Sleep Research Coordinating Committee was formed in 1986. The coordinating committee consists of representatives from 13 NIH institutes and centers and meets quarterly to discuss current sleep-related activities in the NIH and to develop new programs.

National Center on Sleep Disorders Research

In 1993 the National Heart, Lung, and Blood Institute established the NCSDR. As described in the congressional language, the mission of the NCSDR is the “conduct and support of biomedical and related research and research training, the dissemination of health information, and the conduct of other programs with respect to various sleep disorders, the basic understanding of sleep, biological and circadian rhythm research, chronobiology, and other sleep related research” 3 (see Appendix D ).

The function of the NCSDR and the Trans-NIH Sleep Research Coordinating Committee are intertwined. The director of the NCSDR serves as Chair of the Coordinating Committee. Further, the NCSDR is responsible for coordinating the information collected by individual institutions for the Coordinating Committee’s annual report; including sleep related activities, initiatives, and funding of sleep-related activities.

NIH funding for somnology research has increased by more than 150 percent since the NCSDR became fully operational in 1996, reaching a total of $196.2 million (0.07 percent of the NIH budget) in fiscal year 2004 (NHLBI, 2003). However, this growth occurred during the same period that the overall budget to the NIH doubled, and currently NIH funding for sleep-related activities is reaching a plateau. In 2004, for the first time since the NCSDR was established, there was a decrease in annual NIH expenditures for sleep-related projects; there were fewer research project grants funded in 2004, and the number of new grants awarded also decreased (see Appendix G ). Consequently, the future outlook for somnology and sleep medicine is unclear. This presents an even greater challenge for a field that requires growth in its scientific workforce and technology.

Increasing the Numbers of Trained Researchers and Clinicians

New investigators and clinicians knowledgeable about sleep-related research and clinical care are needed. The growth of the discipline in terms of clinical volume has not been reflected in a corresponding increase in the number of clinical and basic sleep researchers. In the spring of 2005 there were 781 American members of the Sleep Research Society, a number representing the majority of individuals performing sleep-related research. There are only 253 principal investigators who work on sleep-related research. There are 151 researchers involved primarily in clinical sleep research, and 126 focus primarily on basic research projects. In 2004, of the top 30 academic institutions that received the greatest number of grants from the NIH, less than half had career development and training awards in somnology and sleep medicine, and only 17 had NIH-sponsored fellowships that were sleep related. Between the years 2000 and 2004, the NIH increased its support of sleep-related training and fellowship grants; however, during this same period there was a decrease in the number of career development awards. Over the same period, the number of academic institutions receiving sleep-related career development awards also decreased. Therefore, creating an infrastructure to develop a workforce capable of meeting the clinical and scientific demand remains a major challenge.

Time devoted in medical school curriculum to sleep medicine is limited. The percentage of medical schools that include sleep disorders in their curriculums has risen modestly from 54 percent in 1978 (Orr et al., 1980) to 63 percent in 1993, but the time devoted averages only 2.11 hours (Rosen et al., 1998). Similar analysis has not recently been performed, but there is no evidence to suggest that medical schools are placing increased emphasis on sleep-related content in their curriculums. Clearly, the educational effort is still inadequate given the magnitude of the morbid effects that sleep loss and sleep disorders have on the most common diseases (e.g., obesity, hypertension, heart attack, and diabetes). In response to this perceived shortcoming in sleep education, the National Heart, Lung, and Blood Institute supported a series of grants (K07 funding mechanism) to develop model medical school curricula. This resulted in the establishment of MEDSleep, a collection of over 75 sleep education tools and products (AASM, 2005). Although this program generated a large number of resources, it is unclear how many of them have been used and implemented. Despite these advances, physician education regarding the recognition, diagnosis, management, and treatment of sleep disorders is still inadequate (Strohl et al., 2003; Owens, 2005).

To strengthen the interdisciplinary aspects of the field it is important to attract new investigators to the field and expand the number of trained somnology scientists in other relevant and related disciplines. These areas

include, but are not limited to, biology and health informatics, health service research, nursing, epidemiology and genetic epidemiology, clinical trials, functional imaging, genetics, pathology, neurosciences, and molecular biology.

Distribution of Resources and Technology Development

Today, the capacity needed to serve the population seeking diagnosis and treatment is inadequate. Analysis commissioned on behalf of the committee indicated that in many health care systems and communities, the waiting time for a polysomnogram, the procedure used to diagnose many sleep disorders, may be as much as 10 weeks (see Chapter 9 ). This shortfall will worsen as awareness of the clinical consequences and public health burden of sleep disorders increases. A substantial investment is needed to enlarge the clinical and research workforce and improve the technology for diagnosis and treatment. Ambulatory diagnostic technologies currently available need to be validated. Further, there is a need for improved treatments for individuals with chronic sleep loss and sleep disorders. For example, the most common treatment for sleep apnea, continuous positive airway pressure therapy, which requires an individual to wear a mask over the face while sleeping, has a low rate of compliance, between 45 to 70 percent (Kribbs et al., 1993).

There are approximately 1,300 sleep laboratories in the United States, 39 percent of which are accredited by the American Academy of Sleep Medicine (Tachibana et al., 2005). However, millions of individuals suffering from sleep disorders remain undiagnosed and untreated (Young et al., 1997; Kapur et al., 2002). The utilization and capacity of sleep laboratories is not distributed based on the prevalence of sleep disorders (Tachibana et al., 2005). Apart from creating new sleep centers and laboratories, developing and validating reliable portable diagnostic technologies is required to meet the demand that will arise from greater awareness among the general public (see Chapter 6 ).

SOMNOLOGY AND SLEEP MEDICINE RESEARCH IN ACADEMIC INSTITUTIONS

The division of a university and medical school into academic departments is based upon distinct clinical and graduate training programs. Many of the most promising new lines of academic research and the most effective clinical services depend on strong, interdisciplinary programs that emerge from the knowledge base of the more traditional disciplines (CFAT, 2001). Unfortunately, the organization of academic disciplines among the schools and colleges does not effectively support existing interdisciplinary programs

or those that could be created (Ehrenberg and Epifantseva, 2001; Thursby and Thursby, 2002).

Somnology and Sleep Medicine Is an Interdisciplinary Field

The field of Somnology and Sleep Medicine is an emerging interdisciplinary field that is being forged from several existing sciences and medical specialties. However, the current organization of academic health centers houses clinicians and scientists in discrete departments that do not favor interdisciplinary research efforts. Although the scientific enterprise of the field requires interdisciplinary strategies, the clinical service of patients is multidisciplinary and requires linkages to other medical specialties.

As described in the National Academy of Sciences (2004) report Facilitating Interdisciplinary Research:

Interdisciplinary research is a mode of research performed by teams or individuals that integrates information, data, techniques, tools, perspectives, concepts, and/or theories from two or more disciplines or bodies of specialized knowledge to advance fundamental understanding or to solve problems whose solutions are beyond the scope of a single discipline or field of research practice ( Figure 1-1A ).

Multidisciplinary research is taken to mean research that involves more than a single discipline in which each discipline makes a separate contribution. Investigators may share facilities and research approaches while working separately on distinct aspects of a problem ( Figure 1-1B ) (NAS, 2004).

There are a wide range of programs in Somnology and Sleep Medicine. Some are solely clinical in nature; others are clinical programs that include training of physicians and some research. There are also a limited number of comprehensive programs that emphasize clinical care education and training, as well as basic and clinical research. With few exceptions most programs continue to be not integrated and embedded in medical departments. This organization has many adverse implications for the field; including:

Clinical training in sleep loss and sleep disorders is often limited to those in the department where the program is housed to the exclusion of others.

The absence of interdisciplinary clinical teams hinders patient care.

A limited sense of identity with, or focus on the field, and an absence of an established career path for faculty makes it difficult to attract new students, researchers, and clinicians into the field.

FIGURE 1-1 Interdisciplinary and multidisciplinary research.

SOURCE: National Academy of Sciences, 2004.

Research or clinical funds generated from sleep-related activities are not generally reinvested to enhance sleep programs.

Collaboration can be more difficult because researchers and clinicians are geographically dispersed.

Sleep Loss and Sleep Disorders Require Long-Term Patient Care and Chronic Disease Management

Sleep disorders are chronic conditions necessitating complex treatments. They are frequently comorbid with other sleep disorders and other conditions (e.g., cardiovascular disease, depression, or diabetes), which, by themselves, are complex to treat. Despite the importance of early recognition and treatment, the primary focus of most existing sleep centers is on diagnosis, rather than on comprehensive care of sleep loss and sleep disorders as chronic conditions. The narrow focus of sleep centers may largely be the unintended result of accreditation criteria, which emphasize diagnostic standards and reimbursement for the diagnostic testing (see Chapter 9 ).

SCOPE AND ORGANIZATION OF THIS REPORT

Increased public education and greater awareness of the burden of sleep loss and sleep disorders as well as scientific advances have poised the field of somnology and sleep medicine for great strides. In 2003 the NCSDR published a set of research priorities for the field. However, advances will require an organized strategy to increase and coordinate efforts in training and educating the public, researchers, and clinicians, as well as improved infrastructure and funding for this endeavor.

Recognizing the need to develop a new coordinated strategy to improve public awareness and strengthen the field of Somnology and Sleep Medicine, the NCSDR at the NIH, along with the American Academy of Sleep Medicine, the National Sleep Foundation, and the Sleep Research Society, requested that the Institute of Medicine (IOM) conduct a study that would examine: (1) the public health significance of sleep, sleep loss, and sleep disorders, (2) gaps in the public health system and adequacy of the current resources and infrastructures for addressing the gaps, (3) barriers and opportunities for improving interdisciplinary research and medical education and training in the area of sleep and sleep medicine, and (4) develop a comprehensive plan for enhancing sleep medicine and sleep research ( Box 1-2 ).

The IOM appointed a 14-member committee with expertise in academic and medical administration, adolescent medicine, cardiology, epidemiology, geriatrics, health sciences research, neurology, nursing, otolaryngology, pediatrics, psychiatry, and pulmonology. The committee met five times during the course of its work and held two workshops that provided input on the current public health burden of sleep loss and chronic sleep disorders and the organization and operation of various types of academic sleep programs.

Chapter 2 of this report describes the basic biology and physiology of sleep and circadian rhythms. Chapter 3 introduces the primary sleep disorders and their associated health burdens, and Chapter 4 describes their impact on an individual’s performance and associated economic impact. Chapter 5 provides an overview of the barriers to providing optimal patient care, including the lack of public and professional education. Chapter 6 highlights the need for greater capacity to diagnose and treat individuals with sleep loss and sleep disorders. In Chapter 7 , the committee examines the education and training programs for students, scientists, and health care professionals. Chapter 8 discusses the current investment by the NIH and the NCSDR and the potential role of a national somnology and sleep medicine research network for advancing therapeutic interventions for sleep loss and sleep disorders. Chapter 9 highlights the infrastructure of the field and proposes recommendations for developing academic programs in somnology and sleep medicine.

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Clinical practice related to sleep problems and sleep disorders has been expanding rapidly in the last few years, but scientific research is not keeping pace. Sleep apnea, insomnia, and restless legs syndrome are three examples of very common disorders for which we have little biological information. This new book cuts across a variety of medical disciplines such as neurology, pulmonology, pediatrics, internal medicine, psychiatry, psychology, otolaryngology, and nursing, as well as other medical practices with an interest in the management of sleep pathology. This area of research is not limited to very young and old patients—sleep disorders reach across all ages and ethnicities. Sleep Disorders and Sleep Deprivation presents a structured analysis that explores the following:

• Improving awareness among the general public and health care professionals.
• Increasing investment in interdisciplinary somnology and sleep medicine research training and mentoring activities.
• Validating and developing new and existing technologies for diagnosis and treatment.

This book will be of interest to those looking to learn more about the enormous public health burden of sleep disorders and sleep deprivation and the strikingly limited capacity of the health care enterprise to identify and treat the majority of individuals suffering from sleep problems.

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The effects of sleep deprivation on cognitive performance

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• Supplementary Data

A Costa, T Pereira, The effects of sleep deprivation on cognitive performance, European Journal of Public Health , Volume 29, Issue Supplement_1, April 2019, ckz034.096, https://doi.org/10.1093/eurpub/ckz034.096

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Introduction: Sleep is a crucial physiological process for maintaining homeostasis, as well as being an important mechanism for cell repair. Several epidemiological studies indicate that the umber of hours of sleep to maintain a good quality of life varies individually. Total sleep deprivation has harmful effects on brain function, especially the functions associated with the frontal lobe (involved in alertness, attention, decision making and cognitive processes) and the thalamus. The effects of sleep deprivation on frontal lobe functions include, mainly, decreased inhibition of response, decision making, divergent thinking and working memory.

Objectives: To evaluate the acute effects of partial sleep deprivation on cognitive performance in young students of higher education.

Methods: Fifteen young men and women, aged between 19 and 23, were evaluated. They were submitted to 2 assessment periods (normal sleep profile - 7/8 hours - and partial sleep deprivation - 3/4 hours), and in each of these periods, the individual was evaluated by performing an evaluation test of cognitive function (GMLT - Groton Maze Learning Test). The study began only after the participants had been given informed consent, which was accepted with awareness.

Results: In the Groton Maze Learning Test, there were no statistically significant differences between moment 1 (profile without sleep deprivation) and moment 2 (partial sleep deprivation). However, several associations were found after a correlation analysis of the various variables obtained with demographic parameters, with personality parameters, daily study load and coffee intake.

Conclusions: After acute partial sleep deprivation the results obtained in the Groton Maze Learning Test didn’t present statistically significant differences when compared to the normal sleep profile.

• sleep deprivation
• cognitive ability

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• Published: 17 June 2021

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  

BMC Nursing volume  20 , Article number:  100 ( 2021 ) Cite this article

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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.

Peer Review reports

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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Faculty of Health Sciences, Catholic University of Murcia, 30107, Murcia, Spain

Juana Inés Gallego-Gómez, María Teresa Rodríguez González-Moro, Tomás Vera-Catalán & Serafín Balanza

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José Miguel Rodríguez González-Moro

Department of Nursing, Physiotherapy and Medicine, Faculty of Health Sciences,, University of Almería, Ctra. Sacramento, s/n 04120 La Cañada de San Urbano, 04007, Almería, Spain

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JIGG, AJSM, MTRGM, TVC, and JMRGM conceptualized and designed the current study, and were major contributors in the data collection, and reviewing of the manuscript. JIGG and AJSM performed data curation, formal analysis, data interpretation, and writing of the original draft manuscript. JMRC and SB were major contributors in the writing and statistical analysis. All authors read and approved the final manuscript.

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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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Received : 28 February 2021

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Published : 17 June 2021

DOI : https://doi.org/10.1186/s12912-021-00635-x

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