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

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

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

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

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

54k Accesses

9 Citations

40 Altmetric

Metrics details

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

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.

figure 1

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.

figure 2

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.

figure 3

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

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

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

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

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

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

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

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

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

Obesity and underweightness

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

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

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

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

Sex hormones and gender

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

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

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

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

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

Circadian rhythm

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

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

Daytime nap

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

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

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

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

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

Other factors and associations

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

Sleep and mood: a bidirectional but unequal relationship

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

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

Summary and future direction

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

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

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

Limitations

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

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

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

Article   PubMed   PubMed Central   Google Scholar  

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

Article   Google Scholar  

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

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

Article   PubMed   Google Scholar  

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

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

Article   CAS   PubMed   Google Scholar  

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

Article   CAS   PubMed   PubMed Central   Google Scholar  

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

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

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

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

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

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

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

PubMed   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Article   ADS   CAS   PubMed   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

PubMed   PubMed Central   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Article   CAS   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Download references

Author information

Authors and affiliations.

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

Kosha J. Mehta

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualisation, composition, and writing: KJM.

Corresponding author

Correspondence to Kosha J. Mehta .

Ethics declarations

Competing interests.

The author declares no competing interests.

Informed consent

Not applicable.

Ethical approval

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

Additional information

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

Rights and permissions

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

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 24 June 2021

Accepted : 31 December 2021

Published : 11 January 2022

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

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Neurocognitive and mental health outcomes in children with tungiasis: a cross-sectional study in rural kenya and uganda.

  • Berrick Otieno
  • Lynne Elson
  • Amina Abubakar

Infectious Diseases of Poverty (2023)

The Role of School Connectedness and Friend Contact in Adolescent Loneliness, and Implications for Physical Health

  • Yixuan Zheng
  • Margarita Panayiotou
  • Joanna Inchley

Child Psychiatry & Human Development (2022)

Quick links

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

sleep deprivation on academic performance essay

  • Open access
  • 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

89k Accesses

12 Citations

3 Altmetric

Metrics details

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.

Matricciani L, Bin YS, Lallukka T, Kronholm E, Wake M, Paquet C, Dumuid D, Olds T. Rethinking the sleep-health link. Sleep Health. 2018;4(4):339–348. doi: https://doi.org/10.1016/j.sleh.2018.05.004 .

Article   PubMed   Google Scholar  

Zeek ML, Savoie MJ, Song M, Kennemur LM, Qian J, Jungnickel PW, Westrick SC. Sleep Duration and Academic Performance Among Student Pharmacists. Am J Pharm Educ. 2015;79(5):63. doi: https://doi.org/10.5688/ajpe79563 .

Article   PubMed   PubMed Central   Google Scholar  

Dijk DJ, Landolt HP. Sleep Physiology, Circadian Rhythms, Waking Performance and the Development of Sleep-Wake Therapeutics. Handb Exp Pharmacol. 2019;253:441–481. doi: https://doi.org/10.1007/164_2019_243 .

Article   CAS   PubMed   Google Scholar  

Zerbini G, Merrow M. Time to learn: How chronotype impacts education. Psych J. 2017;6(4):263–276. doi: https://doi.org/10.1002/pchj.178 .

Huang W, Ramsey KM, Marcheva B, Bass J. Circadian rhythms, sleep, and metabolism. J Clin Invest. 2011;121(6):2133–41. doi: https://doi.org/10.1172/JCI46043 .

Owens H, Christian B, Polivka B. Sleep behaviors in traditional-age college students: A state of the science review with implications for practice. J Am Assoc Nurse Pract. 2017; 29(11):695–703. doi: https://doi.org/10.1002/2327-6924.12520 .

Becerra MB, Bol BS, Granados R, Hassija C. Sleepless in school: The role of social determinants of sleep health among college students. J Am Coll Health. 2020; 68(2):185–191. doi: https://doi.org/10.1080/07448481.2018.1538148 .

Kozak AT, Pickett SM, Jarrett NL, Markarian SA, Lahar KI, Goldstick JE. Project STARLIT: protocol of a longitudinal study of habitual sleep trajectories, weight gain, and obesity risk behaviors in college students. BMC Public Health. 2019;19(1):1720. doi: https://doi.org/10.1186/s12889-019-7697-x .

El Hangouche AJ, Jniene A, Aboudrar S, Errguig L, Rkain H, Cherti M, Dakka T. Relationship between poor sleep quality, excessive daytime sleepiness and poor academic performance in medical students. Adv Med Educ Pract. 2018; 9: 631–638. doi: 10.2147 / AMEP.S162350.

Article   Google Scholar  

Makino K, Ikegaya Y. Learning Paradigms for the Promotion of Memory, and Their Underlying Principles. Brain Nerve. 2018;70(7):821–828. doi: https://doi.org/10.11477/mf.1416201083 .

Haile YG, Alemu SM, Habtewold TD. Insomnia and Its Temporal Association with Academic Performance among University Students: A Cross-Sectional Study. Biomed Res Int. 2017;2017:2542367. doi: https://doi.org/10.1155/2017/2542367 .

Gianfredi V, Nucci D, Tonzani A, Amodeo R, Benvenuti AL, Villarini M, Moretti M. Sleep disorder, Mediterranean Diet and learning performance among nursing students: inSOMNIA, a cross-sectional study. Ann Ig. 2018; 30(6):470–481. doi: https://doi.org/10.7416/ai.2018.2247 .

Zhao K, Zhang J, Wu Z, Shen X, Tong S, Li S. The relationship between insomnia symptoms and school performance among 4966 adolescents in Shanghai, China. Sleep Health. 2019;5(3):273–279. doi: https://doi.org/10.1016/j.sleh.2018.12.008 .

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

Adan, A.; Almirall, H. Horne & Östberg Morningnees-Eveningnees Questionnaire: a reduced scale. Pers Individ Dif. 1991, 12, 241–53. doi: https://doi.org/10.1016/0191-8869(91)90110-W

Randler C. German version of the reduced Morningness-Eveningness Questionnaire (rMEQ). Biological Rhythm Research. 2013;44(5):730–736. doi: https://doi.org/10.1080/09291016.2012.739930

Peach H, Gaultney JF. Charlotte Attitudes Towards Sleep (CATS) Scale: A validated measurement tool for college students. J Am Coll Health. 2017;65(1):22–31. doi: https://doi.org/10.1080/07448481.2016.1231688 .

Al-Kandari S, Alsalem A, Al-Mutairi S, Al-Lumai D, Dawoud A, Moussa M. Association between sleep hygiene awareness and practice with sleep quality among Kuwait Zhao University students. Sleep Health. 2017;3(5):342–347. doi: https://doi.org/10.1016/j.sleh.2017.06.004 .

Miró E, Iáñez MA, Cano-Lozano MC. Sleep and health patterns. Int J Clin Health Psychol. 2002;2:301–326.

Google Scholar  

Zohal MA, Yazdi Z, Kazemifar AM, Mahjoob P, Ziaeeha M. Sleep Quality and Quality of Life in COPD Patients with and without Suspected Obstructive Sleep Apnea. Sleep Disord. 2014;2014:508372. doi: https://doi.org/10.1155/2014/508372.21

Núñez P, Perillan C, Arguelles J, Diaz E. Comparison of sleep and chronotype between senior and undergraduate university students. Chronobiol Int. 2019;36(12):1626–1637. doi: https://doi.org/10.1080/07420528.2019.1660359 .

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

Niño García JA, Barragán Vergel MF, Ortiz Labrador JA, Ochoa Vera ME, González Olaya HL. Factors Associated with Excessive Daytime Sleepiness in Medical Students of a Higher Education Institution of Bucaramanga. Rev Colomb Psiquiatr. 2019;48(4):222–231. doi: https://doi.org/10.1016/j.rcp.2017.12.002 .

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

Yang G, Lai CS, Cichon J, Ma L, Li W, Gan WB. Sleep promotes branch-specific formation of dendritic spines after learning. Science. 2014;344(6188):1173–8. doi: https://doi.org/10.1126/science.1249098 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bruin EJ, van Run C, Staaks J, Meijer AM. Effects of sleep manipulation on cognitive functioning in adolescents: a systematic review. Sleep Med Rev. 2017; 32: 45–57. doi: https://doi.org/10.1016/j.smrv.2016.02.006 .

Arbabi T, Vollmer C, Dörfler T, Randler C The influence of timing and intelligence on academic performance in elementary school is mediated by awareness, sleep midpoint and motivation. Chronobiol Int. 2015;32(3):349–57. doi: https://doi.org/10.3109/07420528.2014.980508

Menon B, Karishma HP, Mamatha IV. Sleep quality and health complaints among nursing students. Ann Indian Acad Neurol. 2015;18(3):363–4. doi: https://doi.org/10.4103/0972-2327.157252 .

Simonelli-Muñoz AJ, Balanza S, Rivera-Caravaca JM, Vera-Catalán T, Lorente AM, Gallego-Gómez JI. Reliability and validity of the student stress inventory-stress manifestations questionnaire and its association with personal and academic factors in university students. Nurse Educ Today. 2018;64:156–160. doi: https://doi.org/10.1016/j.nedt.2018.02.019 .

Begdache L, Kianmehr H, Sabounchi N, Marszalek A, Dolma N. Principal component regression of academic performance, substance use and sleep quality in relation to risk of anxiety and depression in young adults. Trends Neurosci Educ. 2019;15:29–37. doi: https://doi.org/10.1016/j.tine.2019.03.002 .

LeBourgeois MK, Hale L, Chang AM, Akacem LD, Montgomery-Downs HE, Buxton OM. Digital Media and Sleep in Childhood and Adolescence. Pediatrics. 2017;140(Suppl 2):S92-S96. doi: https://doi.org/10.1542/peds.2016-1758J .

Talero-Gutiérrez C, Durán-Torres F, Pérez-Olmos I. Sleep: general characteristics Physiological and pathophysiological patterns in adolescence. Revista Ciencias de la Salud. 2013;11(3):333–348.

Crowley SJ, Wolfson AR, Tarokh L, Carskadon MA. An update on adolescent sleep: New evidence informing the perfect storm model. J Adolesc. 2018;67:55–65. doi: https://doi.org/10.1016/j.adolescence.2018.06.001 .

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

Download references

Acknowledgements

Not applicable.

Author information

Authors and affiliations.

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

Department of Pneumology, Alcalá de Henares, Hospital Universitario Príncipe de Asturias, 28805, Madrid, Spain

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

Agustín Javier Simonelli-Muñoz

Department of Cardiology, Hospital Clínico Universitario Virgen de la Arrixaca, Universidad de Murcia, Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), CIBERCV, 30120, Murcia, Spain

José Miguel Rivera-Caravaca

You can also search for this author in PubMed   Google Scholar

Contributions

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.

Corresponding author

Correspondence to Agustín Javier Simonelli-Muñoz .

Ethics declarations

Ethics approval and consent to participate.

The Research Ethics Committee of the Catholic University of Murcia, Spain, approved the current study (Reference: CE-6191). Along with the questionnaire, the researchers provided a letter stating the purpose and methods of the study, the voluntary nature of participation, and the confidentiality of responses. Participants signed an informed consent form.

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

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

Supplementary Information

Additional file 1:, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 28 February 2021

Accepted : 10 June 2021

Published : 17 June 2021

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

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

  • Sleep habits
  • Circadian rhythm
  • Sleep pattern
  • Nursing students

BMC Nursing

ISSN: 1472-6955

sleep deprivation on academic performance essay

sleep deprivation on academic performance essay

  • Proceedings

Information

Humanities and Social Sciences

sleep deprivation on academic performance essay

  • Processing Charges

sleep deprivation on academic performance essay

The Influence of Sleep Deprivation on Academic Performance Among College Students

Joel Matiku Joshua

Department of Educational Psychology, College of Education, Mwalimu Julius K. Nyerere University of Agriculture and Technology, Musoma-Mara, United Republic of Tanzania

Add to Mendeley

sleep deprivation on academic performance essay

Educators worldwide have been concerned with searching for the determinants of academic performance with the purpose of improving the same. This article discusses a study which intended to examine the influence of sleep deprivation on academic performance among college students. This included examining the extent to which students report sleep deprivation and whether or not they experienced sleep deprivation differently by sex and other selected demographic variables. A total of 116 first year Community Development college students responded to the James Maas’s sleep deprivation scale and to questions seeking information on demographic variables such as sex, age, marital status, past education experience, employment status, religion, fee payment status and birth order. Their semester General Average Performance (GPA) was then traced at the end of semester. Descriptive analysis identified three categories of sleep deprivation among students: normal range (43.9%), borderline (31.6%) and abnormal sleep deprivation (23.9%). It was further found that female than male students reported abnormal sleep deprivation [Ӽ 2 (2, n = 114) = 7.27, p = 0.03, Cramer’s V = 0.32]. Although Chi-square analysis found significant sex difference with large effect in sleep deprivation, there was no difference in GPA with sleep deprivation among both male and female students. It was concluded that sleep deprivation must not necessarily account for students’ difference in academic performance in terms of GPA where almost everyone in the sample is already sleep deprived.

Sleep Deprivation, College Students, Academic Performance, Abnormal Sleep Deprivation

Joel Matiku Joshua. (2022). The Influence of Sleep Deprivation on Academic Performance Among College Students. Humanities and Social Sciences , 10 (4), 241-249. https://doi.org/10.11648/j.hss.20221004.17

sleep deprivation on academic performance essay

Joel Matiku Joshua. The Influence of Sleep Deprivation on Academic Performance Among College Students. Humanit. Soc. Sci. 2022 , 10 (4), 241-249. doi: 10.11648/j.hss.20221004.17

Joel Matiku Joshua. The Influence of Sleep Deprivation on Academic Performance Among College Students. Humanit Soc Sci . 2022;10(4):241-249. doi: 10.11648/j.hss.20221004.17

Cite This Article

  • Author Information

Verification Code/Mostafa Zaman Chowdhury

sleep deprivation on academic performance essay

The verification code is required.

Verification code is not valid.

sleep deprivation on academic performance essay

Science Publishing Group (SciencePG) is an Open Access publisher, with more than 300 online, peer-reviewed journals covering a wide range of academic disciplines.

Learn More About SciencePG

sleep deprivation on academic performance essay

  • Special Issues
  • AcademicEvents
  • ScholarProfiles
  • For Authors
  • For Reviewers
  • For Editors
  • For Conference Organizers
  • For Librarians
  • Article Processing Charges
  • Special Issues Guidelines
  • Editorial Process
  • Peer Review at SciencePG
  • Open Access
  • Ethical Guidelines

Important Link

  • Manuscript Submission
  • Propose a Special Issue
  • Join the Editorial Board
  • Become a Reviewer

Home — Essay Samples — Nursing & Health — Sleep Deprivation — The Effect Of Sleep Deprivation On A Student’s Performance

test_template

The Effect of Sleep Deprivation on a Student’s Performance

  • Categories: Sleep Deprivation Sleep Disorders

About this sample

close

Words: 1080 |

Published: Feb 8, 2022

Words: 1080 | Pages: 2 | 6 min read

Introduction

Image of Alex Wood

Cite this Essay

Let us write you an essay from scratch

  • 450+ experts on 30 subjects ready to help
  • Custom essay delivered in as few as 3 hours

Get high-quality help

author

Dr. Karlyna PhD

Verified writer

  • Expert in: Nursing & Health

writer

+ 120 experts online

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

Related Essays

4 pages / 1991 words

3 pages / 1423 words

5 pages / 2474 words

3 pages / 1438 words

Remember! This is just a sample.

You can get your custom paper by one of our expert writers.

121 writers online

Still can’t find what you need?

Browse our vast selection of original essay samples, each expertly formatted and styled

Related Essays on Sleep Deprivation

Sleep deprivation is becoming an increasing challenge among people today. Most individuals have experienced sleep deprivation at least once, and the occurrence is becoming more widespread—especially among adults. Much like food [...]

Sleep deprivation is a significant issue that affects millions of people worldwide. It can have profound effects on physical health, mental well-being, and overall quality of life. Understanding the causes, effects, and [...]

Sleeping is a time where a person can get their time of relaxing and repairing both mind and body. It is related to having a healthy, steady life. The quality, duration, and regularity of sleep determines whether a person has [...]

Does sleep deprivation deteriorate school and daily performances? Due to a lack of sleep, school and daily performances will be affected in a negative way and start to deteriorate as students are unable to get the sufficient [...]

Sleep plays a pivotal role in recharging, refreshing and restoring our bodies. According to Suni and Callendar, sleep allows the brain and body to slow down and engage in processes of recovery, promoting better physical and [...]

The Curious Incident of the Dog in the Night-Time introduces fifteen-year-old Christopher Boone, whose counselor has suggested that he write a book. Christopher's book is about his quest to find out who murdered his neighbors' [...]

Related Topics

By clicking “Send”, you agree to our Terms of service and Privacy statement . We will occasionally send you account related emails.

Where do you want us to send this sample?

By clicking “Continue”, you agree to our terms of service and privacy policy.

Be careful. This essay is not unique

This essay was donated by a student and is likely to have been used and submitted before

Download this Sample

Free samples may contain mistakes and not unique parts

Sorry, we could not paraphrase this essay. Our professional writers can rewrite it and get you a unique paper.

Please check your inbox.

We can write you a custom essay that will follow your exact instructions and meet the deadlines. Let's fix your grades together!

Get Your Personalized Essay in 3 Hours or Less!

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

  • Instructions Followed To The Letter
  • Deadlines Met At Every Stage
  • Unique And Plagiarism Free

sleep deprivation on academic performance essay

89 Sleep Deprivation Essay Topic Ideas & Examples

🏆 best sleep deprivation topic ideas & essay examples, 📌 simple & easy sleep deprivation essay titles, 👍 good essay topics on sleep deprivation, ❓ sleep deprivation research questions.

  • Effects of Sleep Deprivation While scientists are at a loss explaining the varying sleeping habits of different animals, they do concede that sleep is crucial and a sleeping disorder may be detrimental to the health and productivity of a […]
  • Problem of Sleep Deprivation This is due to disruption of the sleep cycle. Based on the negative effects of sleep deprivation, there is need to manage this disorder among Americans. We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
  • How Sleep Deprivation Affects College Students’ Academic Performance The study seeks to confirm the position of the hypothesis that sleep deprivation leads to poor academic performance in college students.
  • Sleep Deprivation Impacts on College Students Additional research in this field should involve the use of diverse categories of students to determine the effects that sleep deprivation would have on them.
  • Sleep Deprivation and Specific Emotions The purpose of this study is to develop an understanding of the relationship between sleep deprivation and emotional behaviors. The study looks to create a link between the findings of past researches on the emotional […]
  • Sleep Deprivation: Research Methods The purpose of the research will be to determine sleep deprivation, what causes it, the effect, and why sleep is important.
  • Sleep Deprivation: Personal Experiment As I had been perplexed, I did not take a step of reporting the matter to the police neither did I inform my neighbors.
  • Sleep Disorders: Sleep Deprivation of the Public Safety Officers The effects of sleep disorders and fatigue on public safety officers is a social issue that needs to be addressed with more vigor and urgency so that the key issues and factors that are salient […]
  • Sleep Deprivation: Biopsychology and Health Psychology Another theory that has been proposed in relation to sleep is the Circadian theory which suggests that sleep evolved as a mechanism to fit organisms into the light dark cycle of the world.
  • Sleep Deprivation and Learning at University It is a widely known fact that numerous people face the problem of lack of sleep. Second, sleeping is essential for increasing the productivity of students in the context of learning.
  • “Childbirth Fear and Sleep Deprivation in Pregnant Women” by Hall To further show that the information used is current, the authors have used the APA style of referencing which demand the naming of the author as well as the year of publication of the article/book […]
  • Neurocognitive Consequences of Sleep Deprivation The CNS consists of the brain and the spinal cord while the PNS consists of all the endings of the nerve extensions in all organs forming the web that extends throughout the entire organ.
  • Sleep Deprivation and Insomnia: Study Sources The topic of this audio record is a variety of problems with sleep and their impact on an organism. They proved the aforementioned conclusion and also paid attention to the impact of sleep deprivation on […]
  • The Influence of Sleep Deprivation on Human Body It contradicts living in harmony with God, as when the person is irritated and moody, it is more difficult to be virtuous and to be a source of joy for others.
  • The Issue of Chronic Sleep Deprivation The quality of sleep significantly impacts the health and performance of the human body. These findings point to significant promise for the use of exercise in the treatment of sleep disorders, but a broader body […]
  • What Are The Effects Of Sleep Deprivation For Paramedics
  • The Innate Immune System During Sleep Deprivation
  • Sleep Deprivation Negatively Influences Driving Performance
  • What Effect Does Sleep Deprivation Have on Physiological and Cognition
  • Sleep Deprivation And Its Effects On The Lives And Culture Of Different
  • The Correlation Between Sleep Deprivation And Academic Performance
  • The Importance of Sleep and the Health Impact of Sleep Deprivation in Humans
  • Effects of Sleep Deprivation on the Academic Performance of DLSL Account
  • The Effects Of Sleep Deprivation Among College Students
  • The Dangers and Effects of Sleep Deprivation Among Nurses and the Ways to Prevent the Sleep-Related Problem
  • Sleep Deprivation and its Affects on Daily Performances
  • The Body Of Knowledge Regarding Adolescent Sleep Deprivation
  • Poor Performance in School/Work as a Consequence of Sleep Deprivation
  • The Fascinating World of Sleep and the Effects of Sleep Deprivation
  • Symptoms And Treatment Of Sleep Deprivation
  • Sleep Deprivation And Aggression Among College Students
  • The Effects Of Sleep Deprivation On Academic Performance
  • Sleep Deprivation On Eating And Activity Behaviors
  • Sleep Deprivation: What Causes The Sleeplessness And How Long It Lasts
  • The Relationship Between Sleep Deprivation And The Human Body
  • Students And Chronic Sleep Deprivation: How School Start Times Can Impact This
  • What is Sleep and the Effects of Sleep Deprivation
  • Several Health and Behavioral Symptoms of Sleep Deprivation
  • Sleep Deprivation, Nightmares, And Sleepwalking
  • The Factors That Contribute to Sleep Deprivation and Its Effects on the Sleep Cycle
  • The Dangers Of Teen Sleep Deprivation: Benefits Of Adopting Later Start Times For High Schools
  • The Issue of Sleep Deprivation, Its Results and Associated Risks
  • The Negative Effects of Sleep Deprivation in Human Beings
  • The Stages of Sleep and the Effects of Sleep Deprivation
  • The Negative Effects of Sleep Deprivation to Mental and Physical Health
  • Effects Of Sleep Deprivation On One’s Performance And Function
  • How Sleep Deprivation Can Effect Weightlifting Performance
  • The Causes of Sleep Deprivation in America: a Nation of Walking Zombies
  • The Sleep Deprivation Epidemic Is Affecting Teenagers
  • Sleep Matters: The Human Condition in the Midst of Sleep Deprivation
  • Sleep Deprivation : The Brain Function And Physical Body
  • Sleep Deprivation And Reduction, Sleep Disorders, And The Drugs Used To Treat Them
  • The Effects of Total Sleep Deprivation on Bayesian Updating
  • The Negative Effects of Sleep Deprivation Among Teens and the Solutions to the Problem
  • Light Pollution, Sleep Deprivation, and Infant Health at Birth
  • The Effects Of Food And Sleep Deprivation During Civilian
  • The Study of Rechtschaffen (1983) on Sleep Deprivation
  • How Sleep Deprivation Affects Psychological Variables Related to College Students Cognitive Performance
  • Sleep Deprivation : Sleep And The Adverse Effects Of Sleep Disorders
  • How Does Sleep Deprivation Affect Psychological Health?
  • What Effect Does Sleep Deprivation Have on Physiology and Cognition?
  • How Does Lack of Sleep Affect Physical Health?
  • Does Sleep Deprivation Significantly Interfere With Driving?
  • How Does Sleep Deprivation Affect Psychological Variables Related to College Students’ Cognitive Performance?
  • Are the Brains’ Motor Function Affected by Sleep Deprivation?
  • How Does Sleep Deprivation Affect Work Performance?
  • Does Sleep Deprivation Effect College Students’ Academic Performance?
  • How Does Sleep Deprivation Affect Cognitive Functions?
  • Does Too Much Homework Cause Sleep Deprivation?
  • How Can Sleep Deprivation Effect Weightlifting Performance?
  • What Are the Effects of Sleep Deprivation for Paramedics?
  • How Does Sleep Deprivation Lead to Cardiovascular Disease?
  • What Are the Symptoms of Sleep Deprivation?
  • How Does Sleep Deprivation Affect Health?
  • Can Sleep Problems in Patients With Parkinson’s Disease Be About Serotonin?
  • How Common Are Sleep Problems in Teenagers?
  • What Are the Criteria to Classify Mild, Moderate, and Severe Sleep Deprivation in Humans?
  • How to Measure Sleep and Insomnia in Adult Video Gamers?
  • What Are the Physiological and Psychological Effects on Sleep of Electronics in the Bedroom?
  • Is Bipolar Disease a Sleep Regulation Disorder?
  • What Is the Scale on Sleep Deprivation?
  • How Does Lack Sleep Affect Physical Health?
  • Does Sleep Deprivation Induce by Reward Rather Than Punishment Result in Different Effects?
  • How Does Lack of Sleep Affect the Ability to Concentrate, Think and Learn?
  • What Are the Main Types of Sleep Disorders?
  • Can a Person either Become Sick or Die After Complete Sleep Deprivation?
  • What Are Problems Can Sleep Deprivation Lead To?
  • Does Sleep Deprivation Cause Permanent Brain Damage?
  • How Long Does It Take to Reverse Sleep Deprivation?
  • Chicago (A-D)
  • Chicago (N-B)

IvyPanda. (2024, February 29). 89 Sleep Deprivation Essay Topic Ideas & Examples. https://ivypanda.com/essays/topic/sleep-deprivation-essay-topics/

"89 Sleep Deprivation Essay Topic Ideas & Examples." IvyPanda , 29 Feb. 2024, ivypanda.com/essays/topic/sleep-deprivation-essay-topics/.

IvyPanda . (2024) '89 Sleep Deprivation Essay Topic Ideas & Examples'. 29 February.

IvyPanda . 2024. "89 Sleep Deprivation Essay Topic Ideas & Examples." February 29, 2024. https://ivypanda.com/essays/topic/sleep-deprivation-essay-topics/.

1. IvyPanda . "89 Sleep Deprivation Essay Topic Ideas & Examples." February 29, 2024. https://ivypanda.com/essays/topic/sleep-deprivation-essay-topics/.

Bibliography

IvyPanda . "89 Sleep Deprivation Essay Topic Ideas & Examples." February 29, 2024. https://ivypanda.com/essays/topic/sleep-deprivation-essay-topics/.

  • Sleep Research Topics
  • Insomnia Questions
  • Dreaming Essay Titles
  • Sleep Disorders Research Topics
  • Postpartum Depression Paper Topics
  • Caffeine Paper Topics
  • Mental Disorder Essay Topics
  • Disability Essay Topics
  • Bipolar Disorder Research Ideas
  • Disease Questions
  • Disorders Ideas
  • Biomedicine Essay Topics
  • Chronic Pain Research Ideas
  • Hyperactivity Disorder Research Ideas
  • Dementia Research Ideas

U.S. flag

An official website of the United States government

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

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

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • HHS Author Manuscripts

Logo of nihpa

A Systematic Review of Sleep Deprivation and Neurobehavioral Function in Young Adults

Stephanie griggs.

Case Western Reserve University, Frances Payne Bolton School of Nursing, Cleveland, Ohio, USA 44106

Alison Harper

Case Western Reserve University, Frances Payne Bolton School of Nursing, Department of Anthropology, Cleveland, Ohio, USA 44106

Ronald L. Hickman, Jr

Ruth M. Anderson Endowed Professor of Nursing and Associate Dean for Research Case Western Reserve University, Frances Payne Bolton School of Nursing, Cleveland, OH, USA 44106

To examine the effect of sleep deprivation (total and partial) on neurobehavioral function compared to a healthy sleep opportunity (7–9 hours) in young adults 18–30 years.

Background:

More than one-third of young adults are sleep deprived, which negatively affects a range of neurobehavioral functions, including psychomotor vigilance performance (cognitive), affect, and daytime sleepiness.

A systematic review of randomized controlled trials (RCTs) on sleep deprivation and neurobehavioral function. Multiple electronic databases (Cochrane Central Registry of Controlled Trials [CENTRAL], PubMed, PsycINFO, CINAHL, and Web of Science) were searched for relevant RCTs published in English from the establishment of each database to December 31, 2020.

Nineteen RCTs were selected (N = 766, mean age = 23.7 ± 3.1 years; 44.8% female). Seven were between-person (5 were parallel-group designs and 2 had multiple arms), and 12 were within-person designs (9 were cross over and 3 used a Latin square approach). Total sleep deprivation had the strongest detrimental effect on psychomotor vigilance performance, with the largest effects on vigilance tasks in young adults in the included studies.

Conclusion:

Acute sleep deprivation degrades multiple dimensions of neurobehavioral function including psychomotor vigilance performance, affect, and daytime sleepiness in young adults. The effect of chronic sleep deprivation on the developing brain and associated neurobehavioral functions in young adults remains unclear.

1. Introduction

Sleep loss has a negative effect on multiple neurobehavioral functions, such as psychomotor vigilance performance (cognitive), daytime sleepiness, and affect ( Franzen et al., 2011 ; Van Dongen et al., 2003 ). Degradation of vigilance following sleep deprivation is one of the most robust alterations in healthy young adults aged 18–30 years ( Lim & Dinges, 2010 ). Multiple dimensions of neurobehavioral impairment are differentially affected by sleep deprivation ( Van Dongen et al., 2004 ). Sleep deprivation affects regions of the prefrontal cortex ( Chee & Choo, 2004 ), which continues to mature up to the late ‘20s ( Johnson et al., 2009 ), leading to executive dysfunctions with the prefrontal cortex ( Dinges et al., 1997 ; Nilsson et al., 2005 ). The prefrontal cortex is most vulnerable to the effects between states of sleep and wake due to the metabolic change associated with sleep deprivation ( Muzur et al., 2002 ).

Biological, social, and environmental factors converge, resulting in sleep deprivation in more than one-third (32.3%) of young adults ( Peltzer & Pengpid, 2016 ). Sleep deprivation contributes to a negative interaction between homeostatic and circadian processes. In young adulthood, there is reduced homeostatic sleep pressure (adenosine) accumulation during wakefulness, a delay in sleep timing, and a delay in releasing the onset of melatonin that peaks in the mid-’20s ( Crowley & Carskadon, 2010 ; Fischer et al., 2017 ). Motor vehicular accident risk increases at the circadian cycle nadir following total sleep deprivation which, correlates with slowing of psychomotor vigilance performance ( Patanaik, Zagorodnov, Kwoh, et al., 2014 ).

The broad effect of sleep manipulation (sleep deprivation, sleep restriction, and sleep improvement) on cognitive functioning in adolescents aged 10 – 19 years was addressed in one previous systematic review ( de Bruin et al., 2017 ). In the systematic review, the effect of total sleep deprivation was examined in 4 studies, partial sleep deprivation in 10 studies, sleep extension in one study, and cognitive behavioral therapy for insomnia in one study and 45 unique cognitive tests were reported where a vast array of cognition was assessed ( de Bruin et al., 2017 ). In the review, partial sleep deprivation had a small or no effect on cognitive functioning, total sleep deprivation negatively affected psychomotor vigilance performance, and sleep extension improved working memory in the adolescents studied ( de Bruin et al., 2017 ). However, conclusions could not be made about the specific domains affected by sleep manipulation due to the differences and quantity of tests ( de Bruin et al., 2017 ). The extent of the associations between total and partial sleep deprivation and neurobehavioral impairment (e.g., decrements in psychomotor vigilance performance – cognitive performance impairment, affect, and daytime sleepiness) remains unclear.

The primary aim of this research was to determine the effect of sleep deprivation compared to healthy sleep opportunity (sleep duration 7–9 hours) on psychomotor vigilance performance as measured by psychomotor vigilance testing (PVT) only. PVT-related outcomes may include mean and median response time, reciprocal response time slowest 10%, mean reaction time fastest 10%, number of lapses (No. of times RT is > 500 ms lapses). The secondary aim of this research was to determine the effect of sleep deprivation on affect or daytime sleepiness compared to a healthy sleep opportunity. Secondary outcomes were change in affect or daytime sleepiness outcomes measured by diagnostic criteria or self-reported questionnaires.

Our focus is on young adults aged 18 to 30 years who are at a key developmental stage at a great risk of sleep deprivation and sleep deprivation-related neurobehavioral impairment. This focus addresses a significant gap in the existing literature. Additionally, the focus on sleep deprivation with a primary outcome of psychomotor vigilance performance to assess cognitive performance via psychomotor vigilance testing, a proven assay for evaluating vigilance ( Dinges et al., 2004 ), will allow a common outcome to be synthesized across studies.

2.1. Design

The Preferred Reporting Items for Systematic Reviews and Meta-analyses Statement guidelines were followed for this systematic review ( Nagendrababu et al., 2019 ). We registered our protocol with the PROSPERO registry before implementing the search in the International Prospective Register of Systematic Reviews (Prospero; registration number CRD42021225200).

2.2. Search methods

Studies with participants between the ages of 18 to 30 years were included. Sampling adults across the lifespan has a great potential to underestimate the effects of sleep deprivation in young adults. The following studies were included in this systematic review: (1) randomized controlled trials (RCTs) of young adults published in English; (2) data collected for both the intervention and control group(s); (3) sample mean age from 18 to 30 years; and (4) one or more objectively measured neurobehavioral-related outcomes (e.g., mean reaction time, median reaction time, reciprocal response time slowest 10%, mean reaction time fastest 10%, number of lapses (No. of times RT is > 500 ms lapses) by psychomotor vigilance testing only. Additionally, affect or daytime sleepiness outcomes were also extracted if available. We excluded studies of people with: (1) known sleep disorders; (2) chronic medical; (3) severe psychiatric illness (e.g., bipolar disorder, schizophrenia); (4) Body Mass Index (BMI) > 35 kg/m 2 in addition to (5) night shift workers.

The following databases were searched with controlled vocabulary and keywords: Cochrane Central Registry of Controlled Trials (CENTRAL), PubMed, PsycINFO, CINAHL, and Web of Science. Articles published in English from the establishment of each database to December 13, 2020 were searched. We provide the PubMed search terms in Table 1 . We adjusted the syntax for the search strategies for each database as appropriate.

Database: PubMed ALL Search Strategy

The search was conducted under the guidance of a health science librarian with input from the primary and senior investigator. Also, an ancestry/bibliographic search was conducted to identify additional articles until the end of December 2020.

2.3. Search outcome

All 4,149 references were imported to Covidence ™ (Veritas Health Information) and duplicates were removed. A total of 3,110 were screened through Covidence ™ . Two reviewers independently screened all titles and abstracts with 93% agreement. Next, the two reviewers independently assessed full texts. A third reviewer resolved any disagreements regarding eligibility when consensus was not reached among the first two reviewers. The largest study was included when more than one article included the same trial and/or participants.

2.4. Quality appraisal

The risk of bias in the included studies was assessed independently by two reviewers using the Cochrane risk of bias tool through Covidence ™ ( Jørgensen et al., 2016 ). Sequence generation, concealment of allocation, blinding of outcome assessment blinding, >80% incomplete outcome data (< 80%), selective reporting of outcomes, and ‘other issues’ were the components of the risk of bias tool. The blinding domain was omitted as the intervention was sleep deprivation, and thus it would not be possible to blind participants.

2.5. Data abstraction and synthesis

A customized spreadsheet was used to extract and record data from the papers. Study characteristics, total or partial sleep deprivation with hours and length of time, age, measures used, the sample size (intervention and control groups), along with means and standard deviations of data were extracted. We contacted corresponding authors when insufficient or unclear data were reported. Extracted data were compared between the two reviewers, and disagreements were resolved by consultation with data in original papers and discussion.

We followed guidance on the conduct of a narrative synthesis described by Popay et al. (2006) . Three standardized data tables were used to organize the data which included (1) all studies, (2) between-persons designs, and (3) within-person designs. We started with a preliminary synthesis to organize findings from the studies to describe patterns along with direction and size of the effect when effects were reported. Next, we explored relationships considering factors that might explain any differences in significance or direction/size of the effect if applicable. Lastly, we assessed the robustness of the synthesis to draw conclusions and assess generalizability/reproducibility of the findings. Significant PVT outcomes and the effect size if applicable are presented in Table 2 . The between-person and within-person designs were considered and described separately as within-person comparisons have the advantage of a smaller within-person variation and possibility of a carryover effect ( Jones & Kenward, 2014 ).

Characteristics of studies

Note: ACT, actigraphy; PSG, polysomnography; TSD, total sleep deprivation; PSD, partial sleep deprivation; Lab, controlled setting; 1:1 parallel group design; multi-arm, more than two experimental conditions - only the TSD condition is listed on the table when the study has multiple arms; NR: not reported. All studies were randomized controlled trials. Data from two studies are presented in one article.

3.1. Study selection

We identified 19 RCTs and present results below. We contacted seven corresponding authors; two responded, one shared additional data, and one provided additional clarification on their data. The study selection process is illustrated in Figure 1 .

An external file that holds a picture, illustration, etc.
Object name is nihms-1765872-f0001.jpg

PRISMA Flow Diagram

3.2. Characteristics of the included studies

A summary of the details of the 19 RCTs included in this systematic review is presented in Table 2 . A total of 766 young adults with mean ages ranging from 20.2 to 27.5 years (mean age, 23.7 ± 3.09 years; 55.2% male) were included in these RCTs. BMI was only reported in one trial, and the mean was 20.0 ± 1.9 kg/m 2 . Seven were between-person (5 were parallel-group designs and 2 had multiple arms), and 12 were within-person designs (9 were cross over and 3 used a Latin square approach).

Sleep was measured via polysomnography in 9 studies and with actigraphy in eight studies ( Table 2 ). The setting for a majority of these studies was a controlled laboratory (e.g., temperature, sound, avoidance of alcohol and caffeine) except for four studies ( Kaida & Niki, 2014 ; Rossa et al., 2014 ; Schwarz et al., 2016 ; Schwarz et al., 2013 ). The RCTs were conducted in the following countries: the United States (8), Italy (2), Finland (1), Australia (1), Japan (1), South China (1), Singapore (2), Canada (1), and Germany (2). All RCTs had a sleep deprivation experimental condition (15 were total sleep deprivation ranging from 24 hours to 72 hours and four were partial sleep deprivation of 4-hours per night ranging from one night to four nights) and a healthy sleep opportunity (duration of 7–9 hours) comparison condition.

The dose-response effect of total and partial sleep deprivation on psychomotor vigilance performance was examined in three different RCTs ( Drake et al., 2001 ; Jewett et al., 1999 ; Van Dongen et al., 2004 ). Acute sleep deprivation was assessed in two trials ( Drake et al., 2001 ; Jewett et al., 1999 ) and chronic sleep deprivation in the other trial ( Van Dongen et al., 2004 ). All trials had one 8-hour condition and one total sleep deprivation condition, but total sleep deprivation varied in each of the trials and was for one night in one trial ( Jewett et al., 1999 ), two nights in the second trial ( Drake et al., 2001 ), and three nights in the third trial ( Van Dongen et al., 2004 ). The comparison groups also varied in dose and length with 8-hours, 5-hours, or 2-hours for one night ( Jewett et al., 1999 ); 8-hours for four nights, 6-hours for four nights, and 4-hours for two nights ( Drake et al., 2001 ); and 8-hours, 6-hours, or 4-hours per night for 14 nights ( Van Dongen et al., 2004 ).

The daytime sleepiness measures used in the trials included a 9-item self-report Karolinska Sleepiness Scale ( Akerstedt & Gillberg, 1990 ), 7-item self-report Stanford Sleepiness Scale ( Babkoff et al., 1991 ), a visual analogue scale ( Monk, 1989 ), and objective pupillography as a physiological daytime sleepiness indicator ( Lüdtke et al., 1998 ). The affect measures included the 10-item positive and negative affect schedule (PANAS) ( Watson et al., 1988 ), 100mm visual analogue profile of mood states (POMS) ( McNair et al., 1971 ), and visual analogue scale ( Tempesta et al., 2014 ).

3.3. Risk of bias

A graph summarizing the risk of bias of the included studies is presented in Table 3 and Figure 2 . We determined that a majority of the studies were of high quality, with an overall low risk of bias ( n = 8). Sequence generation was judged six times to be both low and high risk, as allocation of the participants was low risk, but the time in between the sleep deprivation trial and the control condition for cross-over studies was only a week; therefore, there was a high likelihood of carryover effects from sleep deprivation. Incomplete outcome data was unclear in 6 trials, and selective outcome reporting was unclear in one. Selective outcome reporting was determined to be both low risk and high risk as it was low risk for objective measures but high risk for self-reported measures like affect and daytime sleepiness. Other source of bias was high risk in four studies due to the trials being held outside of a controlled laboratory setting.

An external file that holds a picture, illustration, etc.
Object name is nihms-1765872-f0002.jpg

Cochrane Risk of Bias Assessment Across Studies (Higgins et al., 2011)

Cochrane Risk of Bias Assessment

3.4. Effect of sleep deprivation by outcome

3.4.1. effect of sleep deprivation on cognitive performance.

The effect of total sleep deprivation on cognitive performance was tested in 6 RCT’s using a between-person comparison ( n = 272); four were parallel-group ( Esposito et al., 2015 ; Franzen et al., 2008 ; Tucker et al., 2009 ; Whitney et al., 2015 ) and two had multiple-arms ( Jewett et al., 1999 ; Van Dongen et al., 2004 ). In these RCTs, the total sleep deprivation condition ranged from 24 hours to 72 hours, and all trials had a healthy sleep opportunity condition for comparison. Significant declines in psychomotor vigilance performance were observed in all trials using a between-person comparison with a slower mean reaction time in three trials ( Drake et al., 2001 ; Esposito et al., 2015 ; Tucker et al., 2009 ; Van Dongen et al., 2004 ), increased slowest 10% in one trial ( Esposito et al., 2015 ), and a higher number of lapses in four trials ( Esposito et al., 2015 ; Franzen et al., 2008 ; Haavisto et al., 2010 ; Whitney et al., 2015 ). The effect sizes ranged from small ( Franzen et al., 2008 ) to medium ( Whitney et al., 2015 ) and were not reported in four between-person comparison trials ( Esposito et al., 2015 ; Haavisto et al., 2010 ; Jewett et al., 1999 ; Tucker et al., 2009 ). In Haavisto’s trial of 20 young adults comparing 4 hours of partial sleep deprivation ( n = 13) to healthy sleep opportunity ( n = 7), lapses increased significantly for the partial sleep deprivation group compared to the healthy sleep opportunity group (0.92 ± 0.73 to 3.54 ± 0.73 vs. 0.62 ± 1.00 to 0.90 ± 1.00, p = .0321, respectively) and there was a tendency that the slowest 10% of all responses were slower in the partial sleep deprivation group, but the group difference was not significant ( p = .16) ( Haavisto et al., 2010 ).

The effect of total sleep deprivation on psychomotor vigilance performance was tested in nine RCT’s using a within-person comparison ( n = 375) ( Kaida & Niki, 2014 ; Lin et al., 2020 ; Patanaik, Zagorodnov, Kwoh, et al., 2014 ; Robillard et al., 2011 ; Rossa et al., 2014 ; Schwarz et al., 2016 ; Schwarz et al., 2013 ; Tempesta et al., 2014 ; Yeo et al., 2015 ), three of which used a Latin square approach ( Drake et al., 2001 ; Honn et al., 2020 ). Total sleep deprivation ranged from 32 to 62 hours, and the cross-over between the sleep deprivation and healthy sleep opportunity conditions ranged from one week to one month. One night of total sleep deprivation resulted in significant decrements in psychomotor vigilance performance in four of the cross-over trials ( Drake et al., 2001 ; Kaida & Niki, 2014 ; Patanaik, Zagorodnov, Kwoh, et al., 2014 ; Robillard et al., 2011 ) with a slower mean reaction time in four trials ( Adler et al., 2017 ; Drake et al., 2001 ; Kaida & Niki, 2014 ; Patanaik, Zagorodnov, Kwoh, et al., 2014 ; Robillard et al., 2011 ), slower median reaction time in two of the trials ( Kaida & Niki, 2014 ; Patanaik, Zagorodnov, Kwoh, et al., 2014 ), and a higher number of lapses in two of the trials ( Lin et al., 2020 ; Patanaik, Zagorodnov, Kwoh, et al., 2014 ).

The difference was not significant between the total sleep deprivation and healthy sleep opportunity condition in Tempesta et al. 2014 ’s cross-over trial of 25 young adults (mean age 23.8 ± 2.4 years). In this trial, a 5-minute PVT on a computer was used when a 10-minute PVT was used in most studies which may have affected these outcomes ( Tempesta et al., 2014 ). The reaction time was slower in the sleep deprivation condition in one trial; however, whether the difference between the two conditions was significant was not reported as the focus of the analysis was not on change in PVT performance ( Honn et al., 2020 ). In the cross-over trials where significant decrements in psychomotor vigilance performance from total sleep deprivation were reported, effect sizes ranged from medium ( Rossa et al., 2014 ) to large ( Lin et al., 2020 ). The effect size was not reported in four trials ( Drake et al., 2001 ; Kaida & Niki, 2014 ; Patanaik, Zagorodnov, Kwoh, et al., 2014 ; Robillard et al., 2011 ). Differences in age and sex were not discussed in all but two studies reported in one paper ( Honn et al., 2020 ), where no significant group differences in age or sex were found (p = 0.24 and 0.26 respectively).

3.4.2. Dose-response effects on cognitive performance from sleep deprivation

The dose-response effect of sleep deprivation on psychomotor vigilance performance was tested in 3 RCTs ( n = 121) ( Drake et al., 2001 ; Jewett et al., 1999 ; Van Dongen et al., 2004 ). Greater psychomotor vigilance performance impairment was observed in all three trials with larger doses of sleep deprivation ( Drake et al., 2001 ; Jewett et al., 1999 ). In Jewett’s trial of 61 young adults (0-hours, 2-hours, 5-hours, or 8-hours for one night), all PVT metrics improved as sleep duration increased ( p < .0002), particularly between the 0-hour and 2-hour sleep conditions; however, only a slight improvement was observed between the 5-hour and 8-hour sleep conditions with a 2.14-hour decay mean rate for all PVT metrics. Chronic sleep deprivation (8-hours, 6-hours, 4-hours – time in bed (TIB) per night for 14 nights) resulted in cumulative dose-dependent deficits in psychomotor vigilance performance, and daytime sleepiness showed an acute response but did not differentiate between the 6-hour and 4-hour conditions in Van Dongen’s trial of 48 young adults (mean age 26 ± 3.6 y). In this same trial, deficits in cognitive performance were equivalent between the chronic sleep deprivation of sleep to 6-hours or less per night over 10 nights and up to 2-nights of total sleep deprivation conditions ( Van Dongen et al., 2004 ). In Drake’s trial of 12 young adults using a Latin square design (no sleep loss-8 hours TIB for 4-nights; slow: 6-hours TIB hours for 4 nights; intermediate: 4-hours TIB for two nights; and rapid: 0-hours TIB for one night), higher impairment of cognitive performance impairment with rapid loss of sleep loss as opposed to when loss of sleep occurred or accumulated over time ( Drake et al., 2001 ). Also, alertness levels were lower in the 6-hour per night condition relative to the 8-hour condition in the same trial ( Drake et al., 2001 ). We present a dose response graph comparing pooled baseline to partial sleep deprivation conditions (6- and 4-hour sleep duration) and total sleep deprivation (0-hour sleep duration) mean reaction time as measured by the PVT over the days of monitoring in Figure 3 .

An external file that holds a picture, illustration, etc.
Object name is nihms-1765872-f0003.jpg

Dose Response graph Note: 1 day = 24 hours; 0-hour time in bed is total sleep deprivation; 4 and 6-hour time in bed is partial sleep deprivation; and 8-hour time in bed is a healthy sleep opportunity.

3.4.3. Effect of sleep deprivation on daytime sleepiness

The effect of sleep deprivation on self-reported daytime sleepiness was assessed in 5 trials ( n = 135) using a between-person comparison ( Esposito et al., 2015 ; Franzen et al., 2008 ; Haavisto et al., 2010 ; Jewett et al., 1999 ; Van Dongen et al., 2004 ) and objective daytime sleepiness was additionally assessed in one of the trials ( Franzen et al., 2008 ). Trials of total sleep deprivation ( Esposito et al., 2015 ; Franzen et al., 2008 ; Jewett et al., 1999 ; Van Dongen et al., 2004 ) and partial sleep deprivation ( Haavisto et al., 2010 ) resulted in significantly higher daytime sleepiness ratings in the sleep deprivation as opposed to the healthy sleep opportunity conditions. In comparison to the PVT, the largest magnitude of effects were seen in all measures of daytime sleepiness (2 objective and 1 self-report) in Franzen et al. 2008 ’s trial of 29 young adults following one night of total sleep deprivation ( n = 15) compared to a healthy sleep opportunity condition ( n = 14) (mean sleep latency test F = 25.08, p < .001, n 2 = 0.501, pupillary unrest test F = 11.58, p = .002, n 2 = 0.317, visual analogue scale F = 42.80, p <.001, n 2 = 0.631).

The effect of total sleep deprivation on self-reported daytime sleepiness was assessed in 4 cross-over trials ( Lin et al., 2020 ; Patanaik, Zagorodnov, Kwoh, et al., 2014 ; Tempesta et al., 2014 ; Yeo et al., 2015 ). Results were not reported in 3 trials ( Patanaik, Zagorodnov, & Kwoh, 2014 ; Tempesta et al., 2014 ; Yeo et al., 2015 ). The effect of one night of total sleep deprivation on self-reported daytime sleepiness was only significant in one of the cross-over trials (F 1,28.95 = 103.09; p < 0.01) ( Tempesta et al., 2014 ); whereas a marginal increase in daytime sleepiness was noted in the other cross-over trial, but the effect was not significant ( t = −1.890, p = 0.071, Cohen’s d = −0.39) ( Lin et al., 2020 ). On the other hand, the effect of partial sleep deprivation (4-hours for one night) on self-reported daytime sleepiness relative to healthy sleep opportunity was significant in 3 cross-over trials with a medium effect size ( Rossa et al., 2014 ; Schwarz et al., 2016 ; Schwarz et al., 2013 ). Also, the partial sleep deprivation as opposed to the healthy sleep opportunity condition displayed higher objective daytime sleepiness via the pupillary unrest test (5.7 ± 2.1 vs. 4.5 ± 2.1 mm/min, p = .002) with a medium effect size (Cohen’s d = 0.55) ( Schwarz et al., 2016 ).

3.4.4. Effect of sleep deprivation on affect

The effect of sleep deprivation on affect was only assessed in one trial using a between persons comparison ( Franzen et al., 2008 ). Those in the total sleep deprivation condition (n = 14) as opposed to the healthy sleep opportunity condition (n = 15) had a higher negative mood ( F = 4.76, p = .039), lower positive affect ( F = 4.78, p = .038), but the change in negative affect was not significant ( F = 1.74, p = .20) ( Franzen et al., 2008 ).

The effect of sleep deprivation on affect was assessed in 5 RCTs using a within-person comparison ( n = 178) ( Drake et al., 2001 ; Kaida & Niki, 2014 ; Lin et al., 2020 ; Rossa et al., 2014 ; Tempesta et al., 2014 ). The effect of one night of total sleep deprivation resulted in a significant negative effect on affect in 3 trials relative to the healthy sleep opportunity condition ( Drake et al., 2001 ; Kaida & Niki, 2014 ; Lin et al., 2020 ). Compared to a healthy sleep opportunity, both positive affect and negative affect were significantly reduced when participants were totally sleep deprived in one cross-over trial ( Lin et al., 2020 ) and partially sleep-deprived (4-hours one night) in another cross over trial ( Rossa et al., 2014 ). The effect size was small in the partial-sleep deprivation cross over trial ( Rossa et al., 2014 ), medium in one of the total sleep deprivation cross-over trials (Cohen’s d = 0.51) ( Lin et al., 2020 ), and not reported in the other two trials ( Drake et al., 2001 ; Kaida & Niki, 2014 ). Lastly, there was a significant interaction between sleep loss and negative affect in working memory performance, but not with PVT performance in Tempesta et al. (2014) ‘s cross-over trial of 25 young adults.

4. Discussion

In this systematic review, the effect of sleep deprivation on neurobehavioral functioning (psychomotor vigilance performance, affect, and daytime sleepiness) in young adults was examined. The primary aim of this study was to examine the effect of sleep deprivation on psychomotor vigilance performance. The largest effects with significant decrements on the most PVT metrics were found in total sleep deprivation studies ( Drake et al., 2001 ; Esposito et al., 2015 ; Franzen et al., 2008 ; Honn et al., 2020 ; Jewett et al., 1999 ; Kaida & Niki, 2014 ; Lin et al., 2020 ; Patanaik, Zagorodnov, Kwoh, et al., 2014 ; Robillard et al., 2011 ; Tempesta et al., 2014 ; Tucker et al., 2009 ; Van Dongen et al., 2004 ). There was a dose-response relationship between the rate of sleep loss and psychomotor vigilance performance measured via PVT. Also, adaptation occurred with a slower accumulation of sleep loss ( Drake et al., 2001 ; Jewett et al., 1999 ; Van Dongen et al., 2004 ). The short time constant that was observed in one of the trials (0h to 2h conditions) ( Jewett et al., 1999 ) indicates that the first few hours of sleep may serve to restore psychomotor vigilance decrements following sleep deprivation. This may partially explain why a nap affords recovery disproportionate to its duration ( Jewett et al., 1999 ).

The second aim of this systematic review was to determine how sleep deprivation affected daytime sleepiness. Daytime sleepiness was measured via self-report in a majority of the trials with the Karolinska Sleepiness Test or Stanford Sleepiness Test and objectively with the Multiple Sleep Latency Test and Pupillary Unrest Index ( Lüdtke et al., 1998 ) in two trials ( Franzen et al., 2008 ; Schwarz et al., 2016 ). Most of the trials included acute sleep deprivation, however in the trial where partial sleep deprivation was examined over 14-days ( Van Dongen et al., 2004 ), chronic partial sleep deprivation of 4 – 6 hours resulted in an initial elevation of self-report ratings on both the Stanford Sleepiness Scale and Karolinska Sleepiness Scale, but as the study progressed only minor further increases in self-report daytime sleepiness that did not mirror the decrements in PVT performance were observed. Even at the end of the 14 days, participants only reported feeling slightly sleepy ( Van Dongen et al., 2004 ). This suggests that there is an adaptation to chronic partial sleep deprivation especially considering the chronic partial sleep deprivation condition was compared to a total sleep deprivation condition ruling out the potential for a ceiling effect as the total sleep deprivation condition showed considerably greater levels of daytime sleepiness after two nights ( Van Dongen et al., 2004 ). Another consideration when assessing daytime sleepiness is that it might be intertwined with affect and related to the same latent construct making it difficult to differentiate perceptions of daytime sleepiness from mood; therefore, it is warranted to include physiologic measures more sensitive than self-report measures as suggested by Franzen et al, 2008 .

Regarding our final aim to determine the effect of sleep deprivation on affect, it must be highlighted that affect was only assessed in one-third of the studies. Also, the designs and instruments to measure affect varied, making it difficult to draw conclusions. Nonetheless, both partial and total sleep deprivation conditions resulted in worsened affect in the young adults in the selected studies, which is consistent with other young adult and adolescent studies ( Baum et al., 2014 ; Franzen et al., 2008 ; Haavisto et al., 2010 ). Studies where objective physiological and/or neural measures of affect were assessed provide additional verification of the emotional dysregulation following sleep deprivation. This was demonstrated in two of the trials in the current review with additional measures of pupillary affective response ( Franzen et al., 2008 ; Schwarz et al., 2016 ). In previous research, a 60% amplification in reactivity of the amygdala assessed using functional MRI (fMRI) was observed following one night of total sleep deprivation (n = 14) in response to negative pictures triggering emotions, when compared to a healthy sleep opportunity condition ( n = 12) ( Yoo et al., 2007 ).

Limitations

There are some limitations of this systematic review that should be considered. First, regarding sample characteristics, we included individuals free of medical, psychiatric, and sleep disorders with previous healthy weight and sleep schedules, limiting the generalizability of these findings. Second, although psychomotor vigilance performance was a common outcome across studies, only 6 used a parallel-group design, and with a lack of baseline and outcome data reporting, we could not conduct a meta-analysis. Baseline and some post-intervention values were not available to calculate mean change in these studies, so our results are fully based on a narrative review. Third, although outcomes were common via the PVT, the heterogeneity across designs, analyses, and objectives made the synthesis and analysis difficult. We recommend more transparent data reporting in the future, particularly through the inclusion of baseline data. This would allow for meta-analyses to be performed in the future, allowing the effects to be pooled to advance the science. Also, because of the different designs and analyses, a determination about reproducibility could not be made.

Objective assessments and physiologic measures (e.g., the Multiple Sleep Latency test and Pupillary Unrest Index) were more precise and sensitive, which may have affected the self-reported daytime sleepiness and affective outcomes. A larger effect size was reported for the physiologic measures (daytime sleepiness and affect regulation) as opposed to the self-report mood and PVT outcomes in one of the trials ( Franzen et al., 2008 ).

5. Conclusions

We determined that sleep deprivation degrades young adults’ neurobehavioral functioning. These results are congruent with adult and adolescent studies, where total sleep deprivation (as opposed to partial sleep deprivation) has a substantial detrimental effect on psychomotor vigilance performance, with the largest effects for vigilance tasks ( de Bruin et al., 2017 ; Lim & Dinges, 2010 ). The studies were all based on acute sleep deprivation, so it was not possible to determine if psychomotor vigilance deficits accumulate over time during chronic sleep deprivation, which is most consistent with real-world settings ( Goel et al., 2009 ). This is important as young adult brains are sensitive to sleep loss, as indicated by imaging studies examining the prefrontal cortex ( Chee & Choo, 2004 ). There is considerable evidence that the prefrontal cortex continues to develop into early adulthood which may affect speed of performance on psychomotor vigilance tasks, although this association has not been examined longitudinally ( Chee & Choo, 2004 ; Gied et al., 1999; Muzur et al., 2002 ). Thus, the effects of chronic sleep deprivation on the psychomotor vigilance performance of the developing brain remain unclear. Also, though our primary intention was to assess the effect of sleep deprivation on psychomotor vigilance performance via PVT, daytime sleepiness was only assessed in 10 and affect in 6 of the studies limiting the ability to comprehensively assess neurobehavioral function among young adults in the included studies.

The findings presented underscore the importance of measuring different neurobehavioral function metrics (e.g., psychomotor vigilance - cognitive performance via PVT, daytime sleepiness via self-report and objective measures, and affect) when studying their response to sleep and wakefulness. Larger RCTs that include an objective to examine the effect of sleep deprivation on neurobehavioral function under controlled conditions are needed to reveal predictors and negative effects of acute and chronic sleep deprivation in this high-risk group. Researchers should also consider including moderators (e.g., age, sex, dose) when these larger studies are available for meta-analysis. Nurses working across tertiary care and the community are well-positioned to take the lead on advocating for policies and practices promoting a healthy sleep opportunity and sleep education to optimize brain development in this age group.

  • Total and partial sleep deprivation lead to significant decrements in neurobehavioral function (cognitive performance, affect, and sleepiness) in young adults.
  • Adaptation to sleep loss can occur when it accumulates over time.
  • The focus of the current literature is on short term sleep loss limiting the ability to draw inference to real world settings where sleep loss occurs at a more stable state over time (e.g., chronic partial sleep deprivation).
  • The prefrontal cortex continues to develop until the late 20’s, thus the effects of sleep loss over time in the developing brain remain unclear.

Acknowledgements:

The authors would like to acknowledge the contributions of DG in screening for inclusion and assisting with quality assessment.

Funding Statement:

This work was supported by American Academy of Sleep Medicine Foundation (AASM), 220-BS-19 and the National Institute for Nursing Research (NINR), K99NR018886. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the AASM Foundation or NIH.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

CRediT authorship contribution statement: Stephanie Griggs: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing – original draft, Project administration, Funding acquisition. Alison Harper: Validation, Formal analysis, Investigation, Data Curation, Writing – original draft. Ronald L. Hickman: Supervision, Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – review and editing, Project administration.

Declaration of competing interests: No conflict of interest has been declared by the authors.

Contributor Information

Stephanie Griggs, Case Western Reserve University, Frances Payne Bolton School of Nursing, Cleveland, Ohio, USA 44106.

Alison Harper, Case Western Reserve University, Frances Payne Bolton School of Nursing, Department of Anthropology, Cleveland, Ohio, USA 44106.

Ronald L. Hickman, Jr, Ruth M. Anderson Endowed Professor of Nursing and Associate Dean for Research Case Western Reserve University, Frances Payne Bolton School of Nursing, Cleveland, OH, USA 44106.

  • Adler A, Gavan MY, Tauman R, Phillip M, & Shalitin S (2017). Do children, adolescents, and young adults with type 1 diabetes have increased prevalence of sleep disorders? Pediatric Diabetes , 18 ( 6 ), 450–458. 10.1111/pedi.12419 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Akerstedt T, & Gillberg M (1990). Subjective and objective sleepiness in the active individual . International Journal of Neuroscience , 52 ( 1–2 ), 29–37. 10.3109/00207459008994241 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Babkoff H, Caspy T, & Mikulincer M (1991). Subjective sleepiness ratings: the effects of sleep deprivation, circadian rhythmicity and cognitive performance . Sleep , 14 ( 6 ), 534–539. 10.1093/sleep/14.6.534 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Baum KT, Desai A, Field J, Miller LE, Rausch J, & Beebe DW (2014). Sleep restriction worsens mood and emotion regulation in adolescents . The Journal of Child Psychology and Psychiatry , 55 ( 2 ), 180–190. 10.1111/jcpp.12125 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chee MWL, & Choo WC (2004). Functional imaging of working memory after 24 hr of total sleep deprivation . Journal of Neuroscience , 24 ( 19 ), 4560–4567. 10.1523/jneurosci.0007-04.2004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Crowley SJ, & Carskadon MA (2010). Modifications to weekend recovery sleep delay circadian phase in older adolescents . Chronobiology International , 27 ( 7 ), 1469–1492. https://dx.doi.org/10.3109%2F07420528.2010.503293 [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • de Bruin EJ, van Run C, Staaks J, & Meijer AM (2017). Effects of sleep manipulation on cognitive functioning of adolescents: A systematic review . Sleep Medicine Reviews , 32 , 45–57. 10.1016/j.smrv.2016.02.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Dinges DF, Pack F, Williams K, Gillen KA, Powell JW, Ott GE, Aptowicz C, & Pack AI (1997). Cumulative sleepiness, mood disturbance and psychomotor vigilance performance decrements during a week of sleep restricted to 4–5 hours per night . Sleep: Journal of Sleep Research & Sleep Medicine , 20 ( 4 ), 267–277. [ PubMed ] [ Google Scholar ]
  • Dinges DF, Rogers NL, & Dorrian J (2004). Psychomotor vigilance performance: Neurocognitive assay sensitive to sleep loss. In Sleep deprivation (pp. 67–98). CRC Press. [ Google Scholar ]
  • Drake CL, Roehrs TA, Burduvali E, Bonahoom A, Rosekind M, & Roth T (2001). Effects of rapid versus slow accumulation of eight hours of sleep loss . Psychophysiology , 38 ( 6 ), 979–987. 10.1111/1469-8986.3860979 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Esposito MJ, Occhionero M, & Cicogna P (2015). Sleep deprivation and time-based prospective memory . Sleep , 38 ( 11 ), 1823–1826. 10.5665/sleep.5172 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Fischer D, Lombardi DA, Marucci-Wellman H, & Roenneberg T (2017). Chronotypes in the US–influence of age and sex . PLoS One , 12 ( 6 ), e0178782. 10.1371/journal.pone.0178782 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Franzen PL, Gianaros PJ, Marsland AL, Hall MH, Siegle GJ, Dahl RE, & Buysse DJ (2011). Cardiovascular reactivity to acute psychological stress following sleep deprivation . Psychosomatic Medicine , 73 ( 8 ), 679–682. 10.1097/PSY.0b013e31822ff440 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Franzen PL, Siegle GJ, & Buysse DJ (2008). Relationships between affect, vigilance, and sleepiness following sleep deprivation . Journal of Sleep Research , 17 ( 1 ), 34–41. 10.1111/j.1365-2869.2008.00635.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Giedd JN, Snell J, Lange N, Rajapakse J Casey BJ, Kozuch P, et al. (1996). Quantitative magnetic imaging of human brain development: Ages 4–18 . Cerebral Cortex , 6 , 551–560. 10.1093/cercor/6.4.551 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Goel N, Rao H, Durmer JS, & Dinges DF (2009). Neurocognitive consequences of sleep deprivation . Seminars in Neurology , 29 ( 4 ), 320–339. 10.1055/s-0029-1237117 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Haavisto ML, Porkka-Heiskanen T, Hublin C, Härmä M, Mutanen P, Müller K, Virkkala J, & Sallinen M (2010). Sleep restriction for the duration of a work week impairs multitasking performance . Journal of Sleep Research , 19 ( 3 ), 444–454. 10.1111/j.1365-2869.2010.00823.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Honn KA, Halverson T, Jackson ML, Krusmark M, Chavali VP, Gunzelmann G, & Van Dongen HPA (2020). New insights into the cognitive effects of sleep deprivation by decomposition of a cognitive throughput task . Sleep: Journal of Sleep and Sleep Disorders Research , 43 ( 7 ), 1–14. 10.1093/sleep/zsz319 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Jewett ME, Dijk D-J, Kronauer RE, & Dinges DF (1999). Dose-response relationship between sleep duration and human psychomotor vigilance and subjective alertness . Sleep: Journal of Sleep Research & Sleep Medicine , 22 ( 2 ), 171–179. 10.1093/sleep/22.2.171 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Johnson SB, Blum RW, & Giedd JN (2009). Adolescent maturity and the brain: the promise and pitfalls of neuroscience research in adolescent health policy . Journal of Adolescent Health , 45 ( 3 ), 216–221. 10.1016/j.jadohealth.2009.05.016 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Jones B, & Kenward MG (2014). Design and analysis of cross-over trials (Third edition. ed.). CRC Press/Taylor & Francis. [ Google Scholar ]
  • Jørgensen L, Paludan-Müller AS, Laursen DR, Savović J, Boutron I, Sterne JA, Higgins JP, & Hróbjartsson A (2016). Evaluation of the Cochrane tool for assessing risk of bias in randomized clinical trials: overview of published comments and analysis of user practice in Cochrane and non-Cochrane reviews . Systematic Reviews , 5 , 80. 10.1186/s13643-016-0259-8 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kaida K, & Niki K (2014). Total sleep deprivation decreases flow experience and mood status . Neuropsychiatric Disease and Treatment , 10 , 19–25. 10.2147/ndt.s53633 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lim J, & Dinges DF (2010). A meta-analysis of the impact of short-term sleep deprivation on cognitive variables . Psychological Bulletin , 136 ( 3 ), 375–389. 10.1037/a0018883 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lin Y, Hu P, Mai ZF, Jiang TX, Mo L, & Ma N (2020). Sleep deprivation impairs cooperative behavior selectively: Evidence from prisoner’s and chicken dilemmas . Nature and Science of Sleep , 12 , 29–37. 10.2147/nss.s237402 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lüdtke H, Wilhelm B, Adler M, Schaeffel F, & Wilhelm H (1998). Mathematical procedures in data recording and processing of pupillary fatigue waves . Vision Research , 38 ( 19 ), 2889–2896. 10.1016/s0042-6989(98)00081-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • McNair D, Lorr M, & Droppleman L (1971). Manual for the profile of mood states (POMS) . San Diego: Educational and Industrial Testing Service. [ Google Scholar ]
  • Monk TH (1989). A Visual Analogue Scale technique to measure global vigor and affect . Psychiatry Research , 27 ( 1 ), 89–99. 10.1016/0165-1781(89)90013-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Muzur A, Pace-Schott EF, & Hobson JA (2002). The prefrontal cortex in sleep . Trends in Cognitive Sciences , 6 ( 11 ), 475–481. 10.1016/s1364-6613(02)01992-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Nagendrababu V, Duncan H, Tsesis I, Sathorn C, Pulikkotil S, Dharmarajan L, & Dummer P (2019). Preferred reporting items for systematic reviews and meta- analyses for abstracts: best practice for reporting abstracts of systematic reviews in Endodontology . International Endodontic Journal , 52 ( 8 ), 1096–1107. https://onlinelibrary.wiley.com/doi/pdf/ 10.1111/iej.13118 [ PubMed ] [ Google Scholar ]
  • Nilsson JP, Söderström M, Karlsson AU, Lekander M, Akerstedt T, Lindroth NE, & Axelsson J (2005). Less effective executive functioning after one night’s sleep deprivation . Journal of Sleep Research , 14 ( 1 ), 1–6. 10.1111/j.1365-2869.2005.00442.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Patanaik A, Zagorodnov V, & Kwoh C (2014). Parameter estimation and simulation for one-choice Ratcliff diffusion model. Proceedings of the 29th Annual ACM Symposium on Applied Computing . [ Google Scholar ]
  • Patanaik A, Zagorodnov V, Kwoh CK, & Chee MWL (2014). Predicting vulnerability to sleep deprivation using diffusion model parameters . Journal of Sleep Research , 23 ( 5 ), 576–584. 10.1111/jsr.12166 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Peltzer K, & Pengpid S (2016). Sleep duration and health correlates among university students in 26 countries . Psychology, Health, and Medicine , 21 ( 2 ), 208–220. 10.1080/13548506.2014.998687 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Popay J, Roberts H, Sowden A, Petticrew M, Arai L, Rodgers M, … & Duffy S (2006). Guidance on the conduct of narrative synthesis in systematic reviews . A Product from the ESRC Methods Programme Version , 1 , b92 [ Google Scholar ]
  • Robillard R, Prince F, Boissonneault M, Filipini D, & Carrier J (2011). Effects of increased homeostatic sleep pressure on postural control and their modulation by attentional resources . Clinical Neurophysiology , 122 ( 9 ), 1771–1778. 10.1016/j.clinph.2011.02.010 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Rossa KR, Smith SS, Allan AC, & Sullivan KA (2014). The effects of sleep restriction on executive inhibitory control and affect in young adults . Journal of Adolescent Health , 55 ( 2 ), 287–292. 10.1016/j.jadohealth.2013.12.034 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Schwarz JFA, Geisler P, Hajak G, Zulley J, Rupprecht R, Wetter TC, & Popp RFJ (2016). The effect of partial sleep deprivation on computer-based measures of fitness to drive . Sleep and Breathing , 20 ( 1 ), 285–292. 10.1007/s11325-015-1220-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Schwarz JFA, Popp R, Haas J, Zulley J, Geisler P, Alpers GW, Osterheider M, & Eisenbarth H (2013). Shortened night sleep impairs facial responsiveness to emotional stimuli . Biological Psychology , 93 ( 1 ), 41–44. 10.1016/j.biopsycho.2013.01.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Tempesta D, De Gennaro L, Presaghi F, & Ferrara M (2014). Emotional working memory during sustained wakefulness . Journal of Sleep Research , 23 ( 6 ), 646–656. 10.1111/jsr.12170 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Tucker AM, Basner RC, Stern Y, & Rakitin BC (2009). The variable response-stimulus interval effect and sleep deprivation: An unexplored aspect of psychomotor vigilance task performance . Sleep: Journal of Sleep and Sleep Disorders Research , 32 ( 10 ), 1393–1395. 10.1093/sleep/32.10.1393 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Van Dongen HPA, Baynard MD, Maislin G, & Dinges DF (2004). Systematic interindividual differences in neurobehavioral impairment from sleep loss: Evidence of trait-like differential vulnerability . Sleep , 27 ( 3 ), 423–433. [ PubMed ] [ Google Scholar ]
  • Van Dongen HPA, Maislin G, Mullington JM, & Dinges DF (2003). The cumulative cost of additional wakefulness: Dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation . Sleep , 26 ( 2 ), 117–126. 10.1093/sleep/26.2.117 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Watson D, Clark LA, & Tellegen A (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales . Journal of Personality and Social Psychology , 54 ( 6 ), 1063–1070. 10.1037//0022-3514.54.6.1063 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Whitney P, Hinson JM, Jackson ML, & Van Dongen HPA (2015). Feedback blunting: Total sleep deprivation impairs decision making that requires updating based on feedback . Sleep: Journal of Sleep and Sleep Disorders Research , 38 ( 5 ), 745–754. 10.5665/sleep.4668 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Yeo BTT, Tandi J, & Chee MWL (2015). Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation . Neuroimage , 111 , 147–158. 10.1016/j.neuroimage.2015.02.018 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Yoo SS, Gujar N, Hu P, Jolesz FA, & Walker MP (2007). The human emotional brain without sleep--a prefrontal amygdala disconnect . Current Biology , 17 ( 20 ), R877–878. 10.1016/j.cub.2007.08.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]

The Worldwide Prevalence of Sleep Problems Among Medical Students by Problem, Country, and COVID-19 Status: a Systematic Review, Meta-analysis, and Meta-regression of 109 Studies Involving 59427 Participants

  • Published: 03 June 2023
  • Volume 9 , pages 161–179, ( 2023 )

Cite this article

  • Mohammed A. Binjabr 1 ,
  • Idrees S. Alalawi 1 ,
  • Rayan A. Alzahrani 1 ,
  • Othub S. Albalawi 1 ,
  • Rakan H. Hamzah 1 ,
  • Yazed S. Ibrahim 1 ,
  • Fatima Buali 1 ,
  • Mariwan Husni 1 ,
  • Ahmed S. BaHammam 2 , 3 ,
  • Michael V. Vitiello 4 &
  • Haitham Jahrami   ORCID: orcid.org/0000-0001-8990-1320 1 , 5  

4546 Accesses

4 Citations

Explore all metrics

Purpose of Review

Several studies have found that medical students have a significant prevalence of sleep issues, such as poor sleep quality, excessive daytime sleepiness, and inadequate sleep duration. The purpose of this review is to carefully evaluate the current research on sleep problems among medical students and, as a result, estimate the prevalence of these disturbances. The EMBASE, PsychINFO, PubMed/MEDLINE, ScienceDirect, Scopus, and Web of Science and retrieved article reference lists were rigorously searched and rated for quality. Random effects meta-analysis was performed to compute estimates.

Recent Findings

The current meta-analysis revealed an alarming estimated pooled prevalence of poor sleep quality (K = 95, N = 54894) of 55.64% [95%CI 51.45%; 59.74%]. A total of 33.32% [95%CI 26.52%; 40.91%] of the students (K = 28, N = 10122) experienced excessive sleepiness during the day. The average sleep duration for medical students (K = 35, N = 18052) is only 6.5 h per night [95%CI 6.24; 6.64], which suggests that at least 30% of them get less sleep than the recommended 7–9 h per night.

Sleep issues are common among medical students, making them a genuine problem. Future research should focus on prevention and intervention initiatives aimed at these groups.

Similar content being viewed by others

sleep deprivation on academic performance essay

Prevalence of sleep problems among medical students: a systematic review and meta-analysis

Haitham Jahrami, Julia Dewald-Kaufmann, … Noor AlAnsari

sleep deprivation on academic performance essay

Sleep quality in medical students: a comprehensive meta-analysis of observational studies

Wen-Wang Rao, Wen Li, … Yu-Tao Xiang

sleep deprivation on academic performance essay

Sleep disorders and associated factors among medical students in the Middle East and North Africa: a systematic review and meta-analysis

Sonia Chaabane, Karima Chaabna, … Sohaila Cheema

Avoid common mistakes on your manuscript.

Introduction

Sleep is, without question, one of the most important physiological activities for the human body to function correctly and is essential to maintaining the human body’s health and well-being. Insufficient sleep has negative effects on cardiovascular diseases [ 1 , 2 , 3 ] neurocognitive function [ 4 , 5 , 6 , 7 ], psychological disorders [ 8 , 9 , 10 ], metabolic abnormalities [ 11 , 12 , 13 ] immunological response [ 14 , 15 , 16 ], and academic performance [ 17 , 18 ].

According to both the National Sleep Foundation and the American Academy of Sleep Medicine, it is recommended that adults obtain 7–9 h of sleep every night, while the recommendation for school-aged children and teens is get up to 11 h [ 19 , 20 , 21 ]. Despite this, several studies have demonstrated that sleep disturbances are more frequent than we realize. For example, a 2020 research study in Australia of 836 participants revealed that 41% of females and 42% of males have sleep problems. Another study in Turkey with 5021 participants found that more than half (53%) of the individuals had sleep disturbances [ 22 ].

Because admission to medical school requires high academic and professional achievement, it is regarded as one of the most demanding professions. As a result, stress and psychological state are important factors that might impair sleep quality and quantity [ 10 , 23 ], and medical students as a group are particularly stressed. It is expected that they are prone to numerous forms of sleep problems. Sleep disruption is described as a pandemic in the population of medical students compared to the general population [ 24 ], with particular reports of falling asleep late and having difficulty initiating sleep, as well as sleeping fewer hours [ 20 , 24 , 25 ]. It has also been found that using mobile phones and watching television are highly linked to sleep-related difficulties in medical students [ 26 , 27 , 28 ].

Due to the demanding nature of medical school and the possible consequences of poor sleep on outcomes in academics, clinical care, and mental health, sleep quality is a crucial concern for medical students. High academic demands, long study and clinical hours, and other factors that can cause sleep disturbances and sleep disorders are faced by medical students [ 29 , 30 ].

Poor sleep hygiene can have an adverse effect on patient care and safety by lowering cognitive function, judgment, and clinical abilities [ 29 , 30 ]. Additionally, sleep disorders and disturbances can worsen mental health conditions like depression, anxiety, and burnout as well as raise the risk of developing chronic illnesses like diabetes and cardiovascular disease [ 29 , 30 ]. The importance of sleep health in medical students can enhance their performance in the classroom and in the clinic, enhance general wellbeing, and have a beneficial impact on future healthcare outcomes [ 29 , 30 ].

The ongoing global COVID-19 pandemic, which the WHO proclaimed in March 2020, has had a profound impact on many facets of everyday life. Wearing masks, social distancing, travel limitations, shift to online instruction, and quarantine were all undertaken to minimize the virus’ spread. In addition, students encountered a radically new daily schedule after switching to online learning, which altered their learning experience, sleep patterns, and social connections.

There are several meta-analyses being conducted on sleep difficulties in the medical student community [ 24 , 29 , 30 , 31 ]. However, there has been limited data on the influence of COVID-19 on sleep problems among medical students. As a result, this study aims to determine the prevalence of sleep problems among medical students during the COVID-19 pandemic using data from international English language studies.

It is crucial to examine sleep issues among medical students before and after COVID-19 for a number of reasons. First, it is widely recognized that medical students are more susceptible to sleep disorders and poor sleep quality, both of which can have a detrimental effect on their academic performance, clinical abilities, and general well-being [ 32 , 33 , 34 ]. Second, because of adjustments in their academic and clinical training, medical students have been profoundly impacted by the COVID-19 pandemic [ 29 , 35 , 36 , 37 ]. Online learning and virtual clinical encounters have required medical students to adjust, which may have affected their sleep habits and quality. Additionally, medical students have been involved in the care of COVID-19 patients, which might have contributed to their increased stress levels and poorer quality sleep [ 36 ]. Researchers can learn more about the possible effects of these changes on medical students' academic and clinical performance by examining how the epidemic has affected their sleep habits and sleep disorders.

Methodology

PRISMA 2020 (Preferred Reporting Items for Systematic Review and Meta-analysis) criteria were followed for this systematic review and meta-analysis [ 38 ]. The project was registered on the open science framework (OSF), identifier: DOI 10.17605/OSF.IO/UVH5C.

Information Sources and Search Strategy

From the inception to January 15, 2023, three authors (MBJ, ISA, RAA) independently conducted a systematic literature search utilizing five electronic databases (EMBASE, PsychINFO, PubMed/MEDLINE, ScienceDirect, Scopus, and Web of Science).

We broadened our search by consulting additional sources (i.e., backward, and forward citation tracking of all included articles). After removing duplicates, two authors (any two of MBJ, ISA, RAA, YSI) independently examined titles, abstracts, tables, and graphs in the first screening stage, and completed texts in the second eligibility step to determine whether publications satisfied eligibility requirements. Consensus was used to settle disagreements between any two judges.

The following keywords were used in the search strategy: 'medical student' AND 'sleep dis*' OR 'sleep issue(s)' OR 'sleep quality' OR 'sleep length**' OR 'excessive daytime sleepiness' OR 'sleep disorder' OR 'sleep habit' OR 'sleep hygiene'. The * included disruption and disturbance; and the ** included variants of the keyword length including duration, sufficient, and insufficient. Only English-language research publications were considered. However, the characteristics of the subjects were not restricted.

Data Collection Process and Eligibility Criteria

Two authors (any two of MBJ, ISA, RAA, YSI) screened the title and abstract of all studies found in the systematic search to identify studies that met our criteria for inclusion in the meta-analysis. The inclusion criteria were as follows: (1) research published in the English language, (2) date of publication from the inception of the database until the second week of January 2023, (3) medical students as the targeted population, (4) reported data on the prevalence of sleep disturbance using a validated, commonly utilized measurement tool.

Our exclusion criteria included the following: (1) case reports and case series; (2) studies that reported results for medical students with non-medical students in the same group but did not provide a subgroup analysis; (3) lack of study availability and inability to obtain the full text after contacting the authors; and (4) studies that concentrated on particular sleep disorders (e.g., sleep apnea, insomnia, etc.) among medical students.

Outcomes Measures

The population, intervention, comparison, and outcome design (PICO) [ 39 ] method dictated the following inclusion criteria: population; (1) medical students; (2)intervention/exposure; sleep issues; (3) comparison; none;( 4) outcomes; poor sleep quality, increased daytime drowsiness, and sleep duration.

The predicted results from this systematic review and meta-analysis were to conduct the prevalence of sleep disturbance among medical students during the COVID-19 pandemic. Thus, we used the following specific measure: (1) the Pittsburgh Sleep Quality Index (PSQI) [ 40 ] to determine the score and the corresponding prevalence of poor sleep quality as measured by the index, subjects with PSQI overall score greater than five are considered poor sleepers [ 40 ]; (2) the Epworth Sleepiness Scale (ESS) to determine the prevalence of excessive[ 41 ]; (3) the reported mean duration of sleep per night. Finally, (4) age, gender, country, and the COVID-19 pandemic were covariates/factors of sleep quality and excessive daytime sleepiness among medical students.

The Pittsburgh Sleep Quality Index (PSQI) [ 40 ]. The PSQI assesses sleep quality by examining seven core areas over the preceding month: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction [ 40 ]. After assessing these components, the PSQI provides a composite score on a 0 to 21-point scale to evaluate sleep quality, a score of 5 or higher indicates poor overall sleep quality [ 40 ].

The Epworth Sleepiness Scale (ESS) assesses the level of daytime sleepiness by asking patients to rate their likely sleepiness on a four-point scale in eight different situations [ 41 ]. This results in a “sleepiness score” between 0 and 24, with higher numbers indicating greater sleepiness [ 41 ]. The ESS is a sensitive tool that can provide insight into how a patient’s sleepiness is affecting their daily life, a score of 11 or higher indicating excessive daytime sleepiness (EDS) [ 41 ].

Data Extraction

Following the research selection procedure, two reviewers (any two of MBJ, ISA, RAA, YSI) retrieved data from the original studies separately. Extracted data comprised basic features such as the date of publication and the geographical location of the study. The following demographic information was extracted: age and sex (proportion of males); data on sleep quality, sleep duration, and daytime sleepiness, as well as the evaluation technique utilized. Disagreements between reviewers were solved by consulting a third reviewer (HJ).

Quality Evaluation (Risk of Bias Assessment)

The study quality was assessed using the Newcastle-Ottawa scale (NOS) [ 42 ]. Each study received a quality score based on the groups included in the study, comparability, and assessment of the result and exposure. Overall scores varied from 0 to 9, with 0–4, 5–7, and more than 8 indicating low, moderate, and high-risk of bias studies, respectively. Based on the quality ranking, no studies were omitted. Two authors (any two of MBJ, ISA, RAA, YSI) rated the risk of bias separately, and differences between the two judges were addressed by discussion with HJ.

Data Analysis

Using the meta [ 43 ] and metafor [ 44 ] packages in R, version 4.2.2, the analysis was conducted [ 45 ]. A random-effects model was used for estimating poor sleep quality, EDS, and average sleep duration pooled prevalence rates. We reported point estimates and the corresponding 95% confidence intervals (95% CI) [ 46 ]. We calculated the pooled results using the inverse variance method with DerSimonian-Laird estimator to calculate the heterogeneity variance τ2 and Jackson method for confidence intervals of τ2 and τ. The Hartung-Knapp adjustment was applied to address uncertainty in estimating the between-study variance. To facilitate the presentation of the results, we presented results visually in forest plot format using the package forester [ 47 ]. We used the sensitivity analysis termed as “one study eliminated,” which examines what impact does each included study have on the total effect estimate [ 48 ].

Egger’s test [ 49 ] for funnel plot [ 49 ] asymmetry and Begg’s rank correlation [ 50 ] were used to determine publication bias. Statistically significant publication bias was adjusted for by using the trim-and-fill method [ 51 ]. Cochran's Q [ 52 ] and I 2 [ 53 , 54 ] statistics were used to test for between-study heterogeneity, with I 2 values of 25%, 50%, and 75% reflecting low, moderate, and high levels of heterogeneity, respectively [ 53 , 54 ]. To further aid interpreting heterogeneity, we computer predicted intervals (PI). A prediction interval is a group of values that is likely to include the value of a single new observation given the predictors’ preset parameters. For example, we can be 95% certain that the next new (i.e., future) observation will fall inside a 95% prediction interval (95% PI) [ 55 ].

We performed subgroup analyses using random-effects models to identify possible sources of heterogeneity based on study location (i.e., country) and COVID-19 status (pre- vs. during- COVID-19) [ 55 ]. To determine whether subgroup differences can be explained solely by sampling error, Q tests were conducted. The mean age and sex (proportion of males) of each estimate were corrected using meta-regressions under random-effects models [ 55 , 56 ].

Descriptive Results

The electronic database search identified a total of 862 studies after removing duplicates and automated screening. The selection process described in Fig. 1 resulted in 109 qualified studies for this meta-analysis. All the studies were published after the year 2000 (i.e., 2001–2023), with a total of 59,427 participants from 31 countries. Detailed results are shown in Table 1 .

figure 1

PRISMA 2020 flow diagram for study selection

The countries included Brazil (K = 7), China (K = 11), Egypt (K = 4), Ethiopia (K = 1), France (K = 1), Georgia (K = 1), Germany (K = 1), Ghana (K = 1), Greece (K = 1), India (K = 18), Indonesia (K = 1), Iran (K = 12), Israel (K = 1), Italy (K = 2), Kingdom of Saudi Arabia (KSA, K = 16), Malaysia (K = 1), Morocco (K = 1), Multiple countries (K = 1), Nepal (K = 2), Nigeria (K = 1), Pakistan (K = 8), Peru (K = 2), Poland (K = 1), Rwanda (K = 1), Sudan (K = 2), Thailand (K = 1), Tunisia (K = 1), Turkey (K = 2), UAE (K = 1), USA (K = 5), and Yemen (K = 1). K denotes the number of studies per country. The four countries providing the most studies were India (K = 18, 16.5%), KSA (K = 16, 14.67%), Iran (K = 12, 11.01%), and China (K = 11, 10.1%). Figure 2 shows the distribution of the studies worldwide.

figure 2

Distribution of studies worldwide

The mean number of participants per study was 545 (range 27–6085), and their mean age was 21.6 (range 18.8–27.8 years); 44.2% of the participants were males. The Newcastle-Ottawa Scale was used to assess the quality of the assessment and the risk of bias (NOS). Eighty-five percent of the studies were of high or moderate quality. Figure 3 shows that the selection dimension, specifically the sample size and representativeness, exhibits the greatest risk bias. Supplemental Fig. 1 presents a thorough analysis of the quality rating for each study analyzed in the meta-analysis.

figure 3

Summary of the included studies

Narrative Summary of the Literature About Sleep Problems Among Medical Students

Research has repeatedly shown that sleep issues are quite common among medical students [ 32 , 35 ]. It was noted that about 50–60% of them had poor sleep, with female students more likely to have it. This rate is higher than that of the general population and other college students [ 29 ].

The high prevalence of sleep difficulties among medical students is the result of several factors. These factors include increased academic workload, shift work and unpredictable schedules, lifestyle factors, and mental health issues. According to previous research, among these factors are as follows: (1) Increased academic workload: Medical students must complete a demanding academic program that necessitates extended study sessions and clinical responsibilities [ 57 , 58 , 59 , 60 , 61 ]. Increased stress and anxiety can have a negative impact on sleep quality because of the pressure to perform well in school and the fear of failing [ 17 , 62 , 63 , 64 , 65 , 66 , 67 ]. (2) Shift work and unpredictable schedules: Clinical rotations and on-call responsibilities are frequent among medical students, which might disturb their sleep cycles [ 68 ]. Their unpredictable schedules may cause circadian rhythm abnormalities and poor sleep as a result [ 35 , 41 , 69 ]. (3) Lifestyle factors: Medical students may develop harmful behaviors to deal with their demanding schedules, such as excessive caffeine use and inconsistent eating times [ 70 , 71 ]. (4) Mental health: High levels of stress, burnout, and depression are regularly reported by medical students, which can make it difficult to fall asleep [ 32 , 72 , 73 ]. Poor sleep can exacerbate mental health problems and vice versa due to the bidirectional association between sleep and mental health [ 32 , 72 , 73 , 74 ].

The effects of sleep disturbances on medical students can be severe, with a variety of potential repercussions. Academic performance can be harmed by sleep deprivation because it has been demonstrated to impede cognitive function, memory consolidation, and learning ability [ 17 , 57 , 60 , 62 , 63 , 64 , 65 , 66 , 67 , 75 ]. Mental health can suffer as sleep disturbances are linked to an increased risk of anxiety, depression, and burnout. This vicious cycle can make sleep issues worse by increasing the risk of these mental health issues [ 60 , 76 , 77 ]. Physical health can be impaired as chronic sleep loss has been linked to a number of physical health issues, including as obesity, diabetes, and heart disease [ 29 , 78 ]. Finally, the ability to deliver patient care can be compromised as medical students who have sleep issues may be more likely to make mistakes with patient safety and care delivery [ 32 , 72 , 73 , 74 ].

Prevalence of Poor Sleep Quality

A random effects meta-analysis of all the available studies evaluated sleep quality in medical students (K = 95, N = 54894). The overall pooled prevalence rate of sleep quality was 55.64% 95% CI [51.45%; 59.74%], with statistically significant evidence of between-study heterogeneity τ 2 = 0.69 [0.47; 0.93]; τ = 0.83 [0.69; 0.90]; I 2 = 98.8% [98.7%; 98.9%]; H = 9.08 [8.74; 9.45]; 95% PI [19.26%; 86.83%]. Neither age nor sex explained heterogeneity in sleep quality. Detailed results are shown in Table 2 .

Using the PSQI to measure sleep quality in medical students, the raw prevalence estimates for poor sleep quality varied from 12.6 to 92%. The forest plot of the meta-analysis of sleep disturbances in all populations using PSQI is shown in Fig. 4 .

figure 4

Meta-analysis of the prevalence of poor sleep quality in medical students

According to a (leave-one-out) sensitivity analysis, no study influenced the global prevalence estimate of more than 1%. Visual inspection of the funnel plot (Supplemental Fig. 2 ) and radial plot (Supplemental Fig. 3 ) indicates a modest publication bias; however, Begg’s test (z = 1.14, p -value = 0.26) was not significant, suggesting that there was no significant publication bias.

A subgroup analysis of the pooled prevalence of poor sleep quality by country was performed, highlighting countries with (K > 3). Results show that the highest prevalence was in the USA (K= 4) with a pooled prevalence of 61.57% [18.83%; 91.71%]; τ2 = 1.51; τ = 1.23. India, Brazil, and Pakistan followed sharing the same estimated pooled prevalence of 56% with India (K = 14) 95% CI [45.49%; 66.17%], τ2 = 0.4326, τ = 0.6578, Brazil (K = 7) 95% CI [41.77%; 70.45%], τ2 = 0.27; τ = 0.53, and Pakistan (K = 8) 95% CI [41.15%; 71.08%]; τ2 = 0.71; τ = 0.84, respectively. China (K = 11) demonstrated the lowest estimated pooled prevalence of 41.25% [31.55%; 51.68%], τ2 = 0.33, τ = 0.57. Iran (K = 11) and KSA (K = 13) fell in the middle with a pooled prevalence of 55.26% CI [47.01%; 63.23%]; τ2 = 0.21 τ = 0.45, and 54.94% [37.31%; 71.42%]; τ2 = 1.28, τ = 1.13, respectively. A statistically significant difference between countries was observed ( P -value <0.001).

A subgroup analysis of the pooled prevalence of poor sleep quality by COVID-19 was conducted. Results before the pandemic being (K = 75) 53.83% [49.05%; 58.55%], τ2 = 0.57, τ = 0.76. In contrast, the pooled prevalence of poor sleep quality during the pandemic (K = 20) was 62.11% [53.51%; 70.01%], τ2 = 0.57, τ = 0.76, revealing an increased pooled prevalence of poor sleep quality after the pandemic; however, this difference was statistically not significant ( P -value = 0.08), see (Supplemental Fig. 4 ).

Excessive Daytime Sleepiness

The random effects meta-analytical pooling of the estimate of EDS (K = 28, N = 10122) yielded a crude prevalence rate of 33.32% 95% CI [26.52%; 40.91%] with statistically significant evidence of between-study heterogeneity τ 2 = 0.35 [0.30; 1.02]; τ = 0.59 [0.54; 1.01] I 2 = 96.2% [95.3%; 96.9%]; H = 5.13 [4.61; 5.70]; 95% PI [12.66%; 63.27%]. Using ESS to measure EDS, the raw prevalence estimates of EDS reported among medical students using the ESS ranged from 10.3 to 100%, as illustrated in Fig. 5 . Neither age nor sex explained heterogeneity for EDS. Detailed results are shown in Table 2 .

figure 5

Meta-analysis of the prevalence of excessive daytime sleepiness in medical students

According to a (leave-one-out) sensitivity analysis, no study influenced the global EDS prevalence estimate of more than 1%. Visual inspection of the funnel plot (Supplemental Fig. 5 ) and radial plot (Supplemental Fig. 6 ) indicates no publication bias; this was supported by a non-significant Begg’s test (z = -0.91, p -value = 0.36).

A subgroup analysis of EDS by country was conducted. Highlighting countries with (K > 3), results yielded the highest prevalence rate of EDS was in Brazil (K = 3) with 49.88% [25.54%; 74.27%]; τ2 = 0.09, τ = 0.30. India holding the lowest prevalence rate (K = 5) of 28.56% [12.16%; 53.60%]; τ2 = 0.79; τ = 0.89. KSA with 40.64% [29.91%; 52.35%]; τ2 = 0.16; τ = 0.40. A statistically significant difference between countries was observed ( P -value <0.001).

A subgroup analysis of ESS by COVID-19 was also conducted. Results show a total of (K = 27) studies of EDS were done before the pandemic with a pooled prevalence rate of 32.62% [25.79%; 40.27%]; τ2 = 0.34; τ = 0.59. Only one study was found to measure the EDS using ESS among medical students during COVID-19 and revealed a result of 54.46% [44.7%; 63.88%], see (Supplemental Fig. 7 ).

Sleep Duration

The meta-analytic pooling of the point estimates (K = 35, N = 18052) of nightly sleep duration revealed that on average medical students sleep about 6.5 h per night 95% CI [6.24; 6.64], with statistically significant evidence of between-study heterogeneity τ 2 = 0.35 [0.22; 0.73]; τ = 0.59 [0.46; 0.85]; I 2 = 96.2% [95.3%; 96.9%]; H = 5.13 [4.61; 5.70]; 95% PI [5.21; 7.68]. The raw mean of sleep duration reported among medical students ranged from 5.3 to 7.9, as illustrated in Fig. 6 . Detailed results are shown in Table 2 .

figure 6

Meta-analysis of the mean sleep duration in medical students

A leave-one-out sensitivity analysis indicated that no study influenced the results by more than 0.25 h (i.e., 15 min) of sleep per night. Publication bias was assessed by visual inspection of the funnel plot (Supplemental Fig. 8 ) and radial plot (Supplemental Fig. 9 ), which indicated a slight publication bias; however, Begg’s test (z = 0.16, p -value = 0.87) was not significant.

A subgroup analysis by country was obtained. Highlighting countries with (K >3). Results revealed the highest mean of sleep duration was in China (K = 3) with 7.00 h of sleep per night [95% CI 6.67; 7.45]; τ2 = 0.11; τ = 0.34. The lowest was in KSA (K = 9), with a mean of 5.8 h of sleep per night [95% CI 5.60; 6.09]; τ2 = 0.12; τ = 0.35. Iran with 6.5 h of sleep per night [95% CI 5.93; 7.11]; τ2 = 0.25; τ = 0.50. There was a statistically significant difference between countries ( P -value <0.001).

A subgroup analysis by COVID-19 was also conducted. Results yielded that pre-COVID-19 era (K = 29) medical students, on average, got about 6.3 h of sleep per night [95% CI 6.14; 6.52]; τ2 = 0.26; τ = 0.51. In contrast, the average sleep duration during COVID-19 (K = 6) was 7.00 h of sleep per night [95% CI 6.17; 7.76]; τ2 = 0.98; τ = 0.99. The difference in sleep duration was not statistically significant between pre-COVID-19 and during COVID-19 ( P -value = 0.87), see (Supplemental Fig. 10 ).

This meta-analysis found a worldwide estimated pooled prevalence of poor sleep quality of 57% and an EDS prevalence of 33% in medical students, who also were found to be short sleepers, averaging 6.5 h per night, which suggests that at least 30% of the students were sleeping less than the recommended 7–9 h per night.

Insufficient sleep among medical students is of growing concern, with serious consequences for their health, academic performance, and career [ 29 , 30 ]. Recent studies have found that medical students are more likely to experience sleep deprivation than their peers in other fields, due to the intense academic and clinical demands of medical school [ 61 , 79 ]. This lack of sleep can profoundly impact medical students’ physical and mental health, leading to various negative effects [ 80 ]. The most obvious consequence of insufficient sleep is decreased alertness and focus [ 58 , 81 ]. Sleep is crucial for learning and consolidation of memory [ 82 ]; without adequate rest, medical students may find it difficult to concentrate in lectures and clinical rotations [ 81 ]. This can lead to poor academic performance and a greater risk of making mistakes in the clinical setting [ 63 ]. Furthermore, insufficient sleep can lead to impaired decision-making, which can have serious implications for patient care [ 29 ]. Unfortunately, it has been shown that sleep-deprived students who struggle academically are unaware of the extent to which their sleep loss can affect their capacity to perform cognitive tasks [ 63 ]. Pilcher and Walters exposed 44 college students to complete sleep deprivation for one night. They discovered that the sleep-deprived students considerably underperformed on cognitive tasks compared to the normal-sleep group [ 66 ]. However, the students who performed poorly due to lack of sleep also reported greater levels of estimated performance and incorrectly judged their performance as being higher than those who were not sleep deprived [ 66 ].

In addition to the cognitive effects of insufficient sleep, medical students may also experience physical health problems [ 14 , 16 , 83 ]. It can also lead to an increased risk of motor vehicle accidents, as well as an increased risk of depression and anxiety [ 14 , 84 , 85 ]. Finally, sleep deprivation can significantly impact medical students’ professional development. Studies have found that medical students who experience insufficient sleep are more likely to experience burnout and lack motivation [ 29 , 32 ]. This can lead to decreased job satisfaction and a greater risk of medical school dropout [ 29 , 32 ].

Considering these potential consequences, it is essential that medical students take steps to ensure they are getting adequate rest. College students need to be taught about good sleep behaviors, which may include establishing a consistent sleep schedule, avoiding caffeine and alcohol before bed and avoiding screens before bed [ 18 , 86 , 87 ]. Additionally, educators and college administrators must actively consider sleep habits and disturbances in the context of students' health and academic achievement [ 63 ]. Active measures should include providing students with resources to help them manage their sleep habits, e.g., lifestyle counseling and intervention techniques [ 23 ].

University students usually have poorer sleep quality than the overall population [ 29 , 30 ]. According to a recent meta-analysis, this can be explained by the challenging nature of the academic subject, the test season, side jobs, the fear of missing out, and irregular daytime schedules [ 60 , 88 , 89 ]. Due to their rigorous academic schedule, the competitive nature of the medical field, exposure to death and illness situations, and on-call and night shifts, medical students seem more susceptible to sleep issues than their academic peers [ 90 ].

Academic performance in medical students has been shown to be severely impacted by lower nocturnal sleep time, later bedtimes during weekdays and weekends, catching up on sleep on the weekends, and increased daytime sleepiness [ 63 , 91 ]. Moreover, a recent study demonstrated a significant negative association between sleep quality and grade point average (GPA), supporting the idea that poor sleep quality is linked to subpar academic performance [ 18 ].

Poor sleep quality can be caused by a variety of factors, such as stress, long hours of studying, and lack of time management [ 60 , 92 ]. In order to prevent and improve poor sleep quality, several solutions can be implemented [ 20 , 93 , 94 ]. One solution is to create a healthy sleep schedule and stick to it. By having a consistent bedtime and wake time, the body’s internal clock will become used to the routine and help with sleep quality. Additionally, by avoiding caffeine and other stimulants close to bedtime, the body will be more relaxed and ready for sleep [ 70 , 95 ].

Another solution is to reduce stress levels. Stress can be a major factor in poor sleep quality, and medical students often experience high levels of stress due to the demanding nature of their studies [ 29 , 30 ]. Finding ways to reduce stress, such as exercise, relaxation techniques, yoga, or mindfulness, can be beneficial in improving sleep quality [ 30 ]. Finally, medical students should practice time management. By breaking down tasks into smaller, more manageable pieces and setting realistic goals, medical students can avoid feeling overwhelmed and reduce the amount of stress they experience [ 96 , 97 , 98 ]. By implementing these solutions, medical students can improve their sleep quality and, ultimately, their well-being and academic performance [ 67 , 99 , 100 ].

Understanding how the epidemic affects medical students’ sleep could have wider effects on healthcare. It is well recognized that sleep issues are linked to a number of detrimental health effects, such as a higher chance of medical errors, burnout, and poor patient safety [ 32 , 33 , 34 , 101 ]. Researchers can learn more about the possible long-term health effects of the COVID-19 pandemic on this high-risk demographic by examining changes in the quality and amount of sleep among medical students before and after the epidemic. Utilizing this knowledge can help create interventions that enhance medical students' sleep health, ultimately enhancing patient safety and healthcare outcomes [ 68 , 102 , 103 ].

When it comes to sleep issues, medical students are particularly susceptible, especially during a pandemic. It is crucial for medical students to regulate their sleep health to lessen the possible deleterious effects of interrupted sleep patterns and sleep disorders during future pandemics. Setting sleep hygiene as a priority is a crucial first step [ 32 , 33 , 34 , 84 ]. This entails keeping a regular sleep schedule, avoiding stimulating activities right before bed, and establishing a relaxing sleeping environment. To control stress and enhance sleep, medical students may also find it helpful to practice relaxation techniques like meditation or deep breathing [ 32 , 33 , 34 , 84 ].

Maintaining physical activity and exercise, which has been demonstrated to enhance sleep quality and lower stress, is another crucial measure. Medical students may need to come up with novel ways to exercise during a pandemic, such as working out at home or going for walks or runs in empty spaces [ 32 , 33 , 34 ]. It is crucial for medical students to stay socially connected and ask for help when they need it. During a pandemic, social isolation and stress are frequent, and these elements might impair sleep quality. By using technology to stay in touch with loved ones, medical students can also gain from consulting mental health professionals when needed [ 32 , 33 , 34 ].

Self-reported assessments and cross-sectional study designs are frequent drawbacks in many studies. The impact of the pandemic on pupils who are known to have medical or mental health issues is still another crucial factor in addition to these restrictions.

Students who already have physical or mental health issues may be more susceptible to the pandemic's effects on their sleep health. For instance, students who already struggle with anxiety or depression may become even more stressed and anxious because of the pandemic, which may have a detrimental effect on how well they sleep. Like this, students who suffer from medical conditions like sleep apnea or chronic pain may find that changes in their daily routines and elevated stress levels exacerbate their symptoms.

Future research can follow students over time and evaluate the pandemic’s effects on their sleep health using more rigorous study methods, such as longitudinal studies, to address these shortcomings. Studies can also reduce the impact of self-report bias by using objective measurements of sleep quality, such as actigraphy or polysomnography. Additionally, research can examine potential interventions to lessen the pandemic’s detrimental effects on students who already have medical conditions or mental health issues, as well as the impact of the pandemic on those students.

To improve the sleep health of medical students, several treatment approaches could be utilized. The incorporation of sleep education and counseling programs into medical school curricula is one potential remedy. Students could learn through these programs the value of good sleep hygiene as well as techniques for increasing both the quantity and quality of sleep [ 37 , 104 ]. Programs for sleep education and counseling may be organized as group sessions or one-on-one counseling sessions, and they may be provided by qualified individuals like sleep specialists or mental health professionals [ 32 , 105 ].

The inclusion of sleep hygiene in wellness programs for medical schools is another potential strategy. These programs could contain elements aimed at enhancing sleep quality, such stress-reduction strategies or exercise regimens. To help medical students monitor and enhance their sleep quality, medical institutions may also think about offering them tools like sleep aids or sleep tracking applications [ 32 ].

Medical schools could implement policies to promote sleep health among medical students, such as limiting the number of consecutive hours medical students are required to work or providing accommodations for medical students with sleep problems [ 35 ]. These policies could help to promote a culture of sleep health within the medical school system and prioritize the well-being of medical students [ 30 ].

The current review has several merits: First, we included three important sleep issues, i.e., sleep duration, sleep quality, and excessive daytime sleepiness, as outcomes facing students. Second robust statistical modeling was applied, correcting for bias, outliers, and moderators; thus, the results of the present review are anticipated to be highly generalizable.

Nevertheless, this review has a few drawbacks: First, we only incorporated research written in English. Second, epidemiological meta-analyses will inevitably have substantial heterogeneity [ 29 , 30 , 106 ]. In our meta-analysis, the heterogeneity remained considerable even after undertaking subgroup analyses or moderator analyses using meta-regression approaches. To deal with this, we reported 95% prediction intervals to generalize easier. Additional sources of variability, like lifestyle factors, sleep disorders (like obstructive sleep apnea), and stress, could not be investigated because of the studies’ low availability of common information. Third, every study that was part of this review was a cross-sectional survey. To understand the causes linked to sleep issues in this cohort, longitudinal studies examining changes in sleep quality during medical education are required. Finally, the prevalence rates examined here were based on self-report measures. While the PSQI and the ESS are validated and widely used clinical and research instruments, the components of sleep quality and EDS they measure are limited. Future research is encouraged to look at additional aspects of sleep, such as objective sleep quality measurement, which includes polysomnography.

The current meta-analysis revealed that the worldwide estimated pooled prevalence of poor sleep quality is about 57% and of excessive daytime sleepiness is about 33%. The average sleep duration for medical students is only 6.5 h per night, which suggests that at least 30% of them get less sleep than the recommended 7–9 h per night. These are alarming figures as they indicate that a third to more than half of all medical students are sleeping insufficiently and are subject to all the consequences of this lack of adequate sleep. Due to the detrimental effects on their health, regular screening for poor sleep and proposed remedies are required for medical students. Sleep problems are frequent among medical students, making them a priority problem. Future studies should concentrate on preventative and intervention programs geared at these populations.

Medical students should pay careful attention to the amount of rest that they are getting and take steps to ensure that they are getting adequate amounts of sleep. This includes establishing a good sleep routine and avoiding technology for a few hours before bed. Additionally, medical students should seek help from professors and other resources on campus if they are having trouble managing their workload or are feeling overwhelmed.

Newman AB, Enright PL, Manolio TA, Haponik EF, Wahl PW, Group CHSR. Sleep disturbance, psychosocial correlates, and cardiovascular disease in 5201 older adults: the Cardiovascular Health Study. J Am Geriatr Soc. 1997;45(1):1–7.

Article   CAS   PubMed   Google Scholar  

Wang Q, Xi B, Liu M, Zhang Y, Fu M. Short sleep duration is associated with hypertension risk among adults: a systematic review and meta-analysis. Hypertens Res. 2012;35(10):1012–8.

Article   PubMed   Google Scholar  

Meier-Ewert HK, Ridker PM, Rifai N, Regan MM, Price NJ, Dinges DF, et al. Effect of sleep loss on C-reactive protein, an inflammatory marker of cardiovascular risk. J Am Coll Cardiol. 2004;43(4):678–83.

Palma J-A, Urrestarazu E, Iriarte J. Sleep loss as risk factor for neurologic disorders: a review. Sleep Med. 2013;14(3):229–36.

Hanke JM, Schindler KA, Seiler A. On the relationships between epilepsy, sleep, and Alzheimer’s disease: a narrative review. Epilepsy Behav. 2022;129:108609.

Scott LD, Arslanian-Engoren C, Engoren MC. Association of sleep and fatigue with decision regret among critical care nurses. Am J Crit Care. 2014;23(1):13–23.

Basner M, Rao H, Goel N, Dinges DF. Sleep deprivation and neurobehavioral dynamics. Curr Opin Neurobiol. 2013;23(5):854–63.

Article   CAS   PubMed   PubMed Central   Google Scholar  

López-Muciño LA, García-García F, Cueto-Escobedo J, Acosta-Hernández M, Venebra-Muñoz A, Rodríguez-Alba JC. Sleep loss and addiction. Neurosci Biobehav Rev. 2022;10(141) https://doi.org/10.1016/j.neubiorev.2022.104832 .

Lim J, Dinges DF. A meta-analysis of the impact of short-term sleep deprivation on cognitive variables. Psychological bulletin. 2010;136(3):375.

Article   PubMed   PubMed Central   Google Scholar  

Freeman D, Sheaves B, Waite F, Harvey AG, Harrison PJ. Sleep disturbance and psychiatric disorders. Lancet Psychiatry. 2020;7(7):628–37.

Cappuccio FP, D'Elia L, Strazzullo P, Miller MA. Quantity and quality of sleep and incidence of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2010;33(2):414–20.

Spaeth AM, Dinges DF, Goel N. Effects of experimental sleep restriction on weight gain, caloric intake, and meal timing in healthy adults. Sleep. 2013;36(7):981–90.

Liu M, Ahmed WL, Zhuo L, Yuan H, Wang S, Zhou F. Association of sleep patterns with type 2 diabetes mellitus: a cross-sectional study based on latent class analysis. Int J Environ Res Public Health. 2022;20(1):393.

Spiegel K, Sheridan JF, Van Cauter E. Effect of sleep deprivation on response to immunizaton. Jama. 2002;288(12):1471–2.

Prather AA, Hall M, Fury JM, Ross DC, Muldoon MF, Cohen S, et al. Sleep and antibody response to hepatitis B vaccination. Sleep. 2012;35(8):1063–9.

PubMed   PubMed Central   Google Scholar  

Irwin MR, Olmstead R, Carroll JE. Sleep disturbance, sleep duration, and inflammation: a systematic review and meta-analysis of cohort studies and experimental sleep deprivation. Biol Psychiatry. 2016;80(1):40–52.

Curcio G, Ferrara M, De Gennaro L. Sleep loss, learning capacity and academic performance. Sleep Med Rev. 2006;10(5):323–37.

Asarnow LD, McGlinchey E, Harvey AG. The effects of bedtime and sleep duration on academic and emotional outcomes in a nationally representative sample of adolescents. J Adolesc Health. 2014;54(3):350–6.

Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, et al. National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health. 2015;1(1):40–3.

Yassin A, Al-Mistarehi A-H, Yonis OB, Aleshawi AJ, Momany SM, Khassawneh BY. Prevalence of sleep disorders among medical students and their association with poor academic performance: a cross-sectional study. Ann Med Surg. 2020;58:124–9.

Article   Google Scholar  

Alfawaz RA, Aljuraiban GS, AlMarzooqi MA, Alghannam AF, BaHammam AS, Dobia AM, et al. The recommended amount of physical activity, sedentary behavior, and sleep duration for healthy Saudis: a joint consensus statement of the Saudi Public Health Authority. Ann Thorac Med. 2021;16(3):239–44. https://doi.org/10.4103/atm.atm_33_21 .

McArdle N, Ward SV, Bucks RS, Maddison K, Smith A, Huang R-C, et al. The prevalence of common sleep disorders in young adults: a descriptive population-based study. Sleep. 2020;43(10):zsaa072.

López-Muciño LA, García-García F, Cueto-Escobedo J, Acosta-Hernández M, Venebra-Muñoz A, Rodríguez-Alba JC. Sleep loss and addiction. NeurosciBiobehav Rev. 2022;141:104832.

Google Scholar  

Seoane HA, Moschetto L, Orliacq F, Orliacq J, Serrano E, Cazenave MI, et al. Sleep disruption in medicine students and its relationship with impaired academic performance: a systematic review and meta-analysis. Sleep Med Rev. 2020;53:101333.

Meer H, Jeyaseelan L, Sultan MA. Sleep quality and emotional state of medical students in Dubai. Sleep Disord. 2022;2022

Abdelmoaty Goweda R, Hassan-Hussein A, Ali Alqahtani M, Janaini MM, Alzahrani AH, Sindy BM, et al. Prevalence of sleep disorders among medical students of umm Al-Qura University, Makkah, Kingdom of Saudi Arabia. J Public Health Res. 2020;9(1_suppl) jphr. 2020.1921

Janatmakan Amiri A, Morovatdar N, Soltanifar A, Rezaee R. Prevalence of sleep disturbance and potential associated factors among medical students from Mashhad, Iran. Sleep Disord. 2020;2020

Lawson HJ, Wellens-Mensah JT, Attah NS. Evaluation of sleep patterns and self-reported academic performance among medical students at the University of Ghana School of Medicine and Dentistry. Sleep Disord. 2019;2019

Jahrami H, Dewald-Kaufmann J, MeA-I F, AlAnsari AM, Taha M, AlAnsari N. Prevalence of sleep problems among medical students: a systematic review and meta-analysis. J Public Health. 2020;28:605–22.

Jahrami H, Alshomili H, Almannai N, Althani N, Aloffi A, Algahtani H, et al. Predictors of excessive daytime sleepiness in medical students: a meta-regression. Clocks Sleep. 2019;1(2):209–19. https://doi.org/10.3390/clockssleep1020018 .

Rao W-W, Li W, Qi H, Hong L, Chen C, Li C-Y, et al. Sleep quality in medical students: a comprehensive meta-analysis of observational studies. Sleep Breath. 2020;24(3):1151–65.

Almutairi H, Alsubaiei A, Abduljawad S, Alshatti A, Fekih-Romdhane F, Husni M, et al. Prevalence of burnout in medical students: a systematic review and meta-analysis. Int J Soc Psychiatry. 2022;68(6):1157–70.

Pandi-Perumal SR, Zaki NFW, Qasim M, Elsayed Morsy N, Manzar MD, BaHammam AS, et al. Neuropsychiatric consequences of COVID-19 pandemic: a synthetic review from a global perspective. Alpha Psychiatry. 2022;23(4):144–54. https://doi.org/10.5152/alphapsychiatry.2022.21783 .

Habbash F, Ben Salah A, Almarabheh A, Jahrami H. Insomnia and related factors during the delta wave of the COVID-19 pandemic in the Kingdom of Bahrain: a cross-sectional study. Nat Sci Sleep. 2022;14:1963–75. https://doi.org/10.2147/nss.S380141 .

Jahrami HA, Alhaj OA, Humood AM, Alenezi AF, Fekih-Romdhane F, AlRasheed MM, et al. Sleep disturbances during the COVID-19 pandemic: a systematic review, meta-analysis, and meta-regression. Sleep Med Rev. 2022;62:101591. https://doi.org/10.1016/j.smrv.2022.101591 .

da Silva ML, Rocha RSB, Buheji M, Jahrami H, Cunha KDC. A systematic review of the prevalence of anxiety symptoms during coronavirus epidemics. J Health Psychol. 2021;26(1):115–25. https://doi.org/10.1177/1359105320951620 .

Alsalman A, Jahrami H, Mubarak H, Aljabal M, Abdulnabi M, Yusuf A, et al. The psychological impact of COVID-19 pandemic on the population of Bahrain. Acta Biomed. 2020;91(4):e2020131. https://doi.org/10.23750/abm.v91i4.10336 .

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg. 2021;88:105906.

Schardt C, Adams MB, Owens T, Keitz S, Fontelo P. Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med Inform Decis Mak. 2007;7:1–6.

Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213. https://doi.org/10.1016/0165-1781(89)90047-4 .

Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep. 1991;14(6):540–5. https://doi.org/10.1093/sleep/14.6.540 .

Lo CK-L, Mertz D, Loeb M. Newcastle-Ottawa Scale: comparing reviewers’ to authors’ assessments. BMC Med Res Methodol. 2014;14:1–5.

Schwarzer G, Schwarzer MG. Package ‘meta’. R Found Stat Comput. 2012;9:27.

Viechtbauer W, Viechtbauer MW. Package ‘metafor’. The Comprehensive R Archive Network Package ‘metafor’ http://cran r-project org/web/packages/metafor/metafor pdf. 2015.

Team RC. R: A language and environment for statistical computing. Published online 2023. 2023.

Sidik K, Jonkman JN. A simple confidence interval for meta-analysis. Stat Med. 2002;21(21):3153–9. https://doi.org/10.1002/sim.1262 .

Boyes R. Forester: an R package for creating publication-ready forest plots. R Package Version 0. 032021.

Patsopoulos NA, Evangelou E, Ioannidis JP. Sensitivity of between-study heterogeneity in meta-analysis: proposed metrics and empirical evaluation. Int J Epidemiol. 2008;37(5):1148–57. https://doi.org/10.1093/ije/dyn065 .

Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34. https://doi.org/10.1136/bmj.315.7109.629 .

Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;40:1088–101.

Duval S, Tweedie R. Trim and Fill: A simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455-463. doi: https://doi.org/ https://doi.org/10.1111/j.0006-341X.2000.00455.x .

Cochran WG. Some methods for strengthening the common χ 2 tests. Biometrics. 1954;10(4):417–51.

Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58. https://doi.org/10.1002/sim.1186 .

Higgins J, Li T, Deeks J, Thomas J, Chandler J, Cumpston M, et al. Obtaining standard errors from confidence intervals and P values: absolute (difference) measures. In: Cochrane Handbook for Systematic Reviews of Interventions.; 2017.

Harrer M, Cuijpers P, Furukawa TA, Ebert DD. Doing meta-analysis with R: A hands-on guide. Chapman and Hall/CRC; 2021.

Book   Google Scholar  

Knapp G, Hartung J. Improved tests for a random effects meta-regression with a single covariate. Stat Med. 2003;22(17):2693-2710. doi: https://doi.org/ https://doi.org/10.1002/sim.1482 .

Gladius Jennifer H, Sowmiya K, Vidya D, Archana Lakshmi P, William RF. A study of mobile phone usage on sleep disturbance, stress and academic performance among medical students in Tamil Nadu. Int J Commun Med Publ Health. 2018;5(1):365.

Fawzy M, Hamed SA. Prevalence of psychological stress, depression and anxiety among medical students in Egypt. Psychiatry Res. 2017;255:186–94.

Almojali AI, Almalki SA, Alothman AS, Masuadi EM, Alaqeel MK. The prevalence and association of stress with sleep quality among medical students. J Epidemiol Glob Health. 2017;7(3):169–74.

Alsaggaf MA, Wali SO, Merdad RA, Merdad LA. Sleep quantity, quality, and insomnia symptoms of medical students during clinical years: relationship with stress and academic performance. Saudi Med J. 2016;37(2):173.

Waqas A, Khan S, Sharif W, Khalid U, Ali A. Association of academic stress with sleeping difficulties in medical students of a Pakistani medical school: a cross sectional survey. PeerJ. 2015;3:e840.

ElArab HE, Rabie MA, Ali DH. Sleep behavior and sleep problems among a medical student sample in relation to academic performance: a cross-sectional questionnaire-based study. Middle East Curr Psychiatry. 2014;21(2):72–80.

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

Abdulghani HM, Alrowais NA, Bin-Saad NS, Al-Subaie NM, Haji AM, Alhaqwi AI. Sleep disorder among medical students: relationship to their academic performance. Med Teach. 2012;34(sup1):S37–41.

Medeiros ALD, Mendes DB, Lima PF, Araujo JF. The relationships between sleep-wake cycle and academic performance in medical students. Biol Rhythm Res. 2001;32(2):263–70.

Pilcher JJ, Walters AS. How sleep deprivation affects psychological variables related to college students' cognitive performance. J Am Coll Health. 1997;46(3):121–6. https://doi.org/10.1080/07448489709595597 .

Rasekhi S, Ashouri FP, Pirouzan A. Effects of sleep quality on the academic performance of undergraduate medical students. Health Scope. 1970;5(3)

Nair M, Moss N, Bashir A, Garate D, Thomas D, Fu S, et al. Mental health trends among medical students. Proc (Bayl Univ Med Cent). 2023;36(3):408–10. https://doi.org/10.1080/08998280.2023.2187207 .

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

Jahrami H, Al-Mutarid M, Penson PE, Al-Islam Faris M, Saif Z, Hammad L. Intake of caffeine and its association with physical and mental health status among university students in Bahrain. Foods. 2020;9(4) https://doi.org/10.3390/foods9040473 .

Jahrami H, Sater M, Abdulla A, MeA-I F, AlAnsari A. Eating disorders risk among medical students: a global systematic review and meta-analysis. Eat Weight Disord-Stud Anorexia Bulimia Obesity. 2019;24:397–410.

Pagnin D, de Queiroz V, Carvalho YTMS, Dutra ASS, Amaral MB, Queiroz TT. The relation between burnout and sleep disorders in medical students. Acad Psychiatry. 2014;38:438–44.

PubMed   Google Scholar  

Mazurkiewicz R, Korenstein D, Fallar R, Ripp J. The prevalence and correlations of medical student burnout in the pre-clinical years: a cross-sectional study. Psychol Health Med. 2012;17(2):188–95.

Brubaker JR, Swan A, Beverly EA. A brief intervention to reduce burnout and improve sleep quality in medical students. BMC Med Educ. 2020;20(1):1–9.

Mirghani HO, Mohammed OS, Almurtadha YM, Ahmed MS. Good sleep quality is associated with better academic performance among Sudanese medical students. BMC Res Notes. 2015;8(1):1–5.

Mokros Ł, Witusik A, Michalska J, Łężak W, Panek M, Nowakowska-Domagała K, et al. Sleep quality, chronotype, temperament and bipolar features as predictors of depressive symptoms among medical students. Chronobiol Int. 2017;34(6):708–20.

Awasthi AATN, Maheshwari S, Gupta T. Prevalence of internet addiction, poor sleep quality, and depressive symptoms among medical students: a cross-sectional study. Osong Public Health Res Perspect. 2020;10(11)

Giri PA, Baviskar MP, Phalke DB. Study of sleep habits and sleep problems among medical students of Pravara Institute of Medical Sciences Loni, Western Maharashtra, India. Ann Med Health Sci Res. 2013;3(1):51–4.

Nadeem A, Cheema MK, Naseer M, Javed H. Assessment of sleep quality and patterns suggestive of somniopathies among students of Army Medical College, Rawalpindi. Pakistan Armed Forces Med J. 2018;68(1):143–8.

Chen J, Tuersun Y, Yang J, Xiong M, Wang Y, Rao X, et al. Association of depression symptoms and sleep quality with state-trait anxiety in medical university students in Anhui Province, China: a mediation analysis. BMC Med Educ. 2022;22(1):1–10.

Guo XST, Xiao H, Xiao R, Xiao Z. Using 24-h heart rate variability to investigate the sleep quality and depression symptoms of medical students. Front Psychiatry. 2022;12:781673.

Machado A, Ricardo L, Wendt A, Wehrmeister C. Association between sleep duration and academic, cognitive and socioeconomic outcomes: a systematic literature review of population-based studies. Sleep Epidemiol. 2022;2:100034. https://doi.org/10.1016/j.sleepe.2022 .

Perotta B, Arantes-Costa FM, Enns SC, Figueiro-Filho EA, Paro H, Santos IS, et al. Sleepiness, sleep deprivation, quality of life, mental symptoms and perception of academic environment in medical students. BMC Med Educ. 2021;21(1):1–13.

Zailinawati AH, Teng CL, Chung YC, Teow TL, Lee PN, Jagmohni KS. Daytime sleepiness and sleep quality among Malaysian medical students. Med J Malaysia. 2009;64(2):108–10.

CAS   PubMed   Google Scholar  

Al MM. Anxiety and depression during the COVID-19 pandemic and their impact on sleep. COVID-19 and Sleep: A Global Outlook. Springer; 2023. p. 41–59.

Kang J-H, Chen S-C. Effects of an irregular bedtime schedule on sleep quality, daytime sleepiness, and fatigue among university students in Taiwan. BMC Public Health. 2009;9(1):1–6.

Boukhris O, Jahrami H, Trabelsi K, Glenn JM, Bragazzi NL. Impact of screen time during the pandemic of COVID-19 on sleep habits. COVID-19 and Sleep: A Global Outlook. Springer; 2023. p. 281–94.

Sahraian A, Javadpour A. Sleep disruption and its correlation to psychological distress among medical students. Shiraz E-Medical Journal. 2010;11(1):12–7.

Soakin B, Maharaj N, Sakhelashvili I. Sleep disturbances and stress among foreign medical students at European University, Georgia. MedEdPublish. 2019;8(235):235.

Teimouri A, Amra B. Association between sleep quality and gastroesophageal reflux in medical students. Middle East J Digest Dis. 2021;13(2):139.

Falloon K, Bhoopatkar H, Moir F, Nakatsuji M, Wearn A. Sleep well to perform well: the association between sleep quality and medical student performance in a high-stakes clinical assessment. SLEEP. Advances. 2022;3(1) https://doi.org/10.1093/sleepadvances/zpac019 .

Alotaibi AD, Alosaimi FM, Alajlan AA, Abdulrahman KAB. The relationship between sleep quality, stress, and academic performance among medical students. J Fam Community Med. 2020;27(1):23.

Lamoria M, Sharma S, Poorey K, Bishnoi S. Effect of late-night mobile use on sleep quantity and quality in medical students. 2020.

Siddiqui AF, Al-Musa H, Al-Amri H, Al-Qahtani A, Al-Shahrani M, Al-Qahtani M. Sleep patterns and predictors of poor sleep quality among medical students in King Khalid University, Saudi Arabia. Malaysian J Med Sci: MJMS. 2016;23(6):94.

Bogati SST, Paudel S, Adhikari B, Baral D. Association of the pattern and quality of sleep with consumption of stimulant beverages, cigarette and alcohol among medical students. J Nepal Health Res Coun. 2020;13:379–85.

Shafique ZSF, Naz S, Urooj S, Khan S, Javed S. Assessment of factors affecting the sleep hygiene of medical students in Bahawalpur, Pakistan: a cross-sectional study. Sleep Sci. 2021;14(3):273–80.

Ding P, Li J, Chen H, Zhong C, Ye X, Shi H. Independent and joint effects of sleep duration and sleep quality on suboptimal self-rated health in medical students: a cross-sectional study. Front Public Health. 2022;10:957409. https://doi.org/10.3389/fpubh.2022.957409 .

Abdelghyoum Mahgoub A, Mustafa SS. P05-08 correlation between physical activity, sleep componants and quality: in the context of type and intensity: a cross-sectional study among sudanese medical students. Eur J Public Health. 2022;32(Supplement_2) ckac095:75.

Satti MZ, Khan TM, Azhar MJ, Javed H, Yaseen M, Raja MT, et al. Association of physical activity and sleep quality with academic performance among fourth-year MBBS students of Rawalpindi Medical University. Cureus. 2019;11(7)

Al-Khani AMSM, Zaghloul MS, Ewid M, Saquib N. A cross-sectional survey on sleep quality, mental health, and academic performance among medical students in Saudi Arabia. Biomed Res Notes. 2019;12(1) https://doi.org/10.1186/s13104-019-4713-2 .

AlRasheed MM, Fekih-Romdhane F, Jahrami H, Pires GN, Saif Z, Alenezi AF, et al. The prevalence and severity of insomnia symptoms during COVID-19: a global systematic review and individual participant data meta-analysis. Sleep Med. 2022;100:7–23. https://doi.org/10.1016/j.sleep.2022.06.020 .

Liu X, Peng L, Wang Z, Zeng P, Mi Y, Xu H. Effects of interpersonal sensitivity on depressive symptoms in postgraduate students during the COVID-19 pandemic: Psychological capital and sleep quality as mediators. Front Psychiatry. 2023;14:1100355. https://doi.org/10.3389/fpsyt.2023.1100355 .

Śliż D, Wiecha S, Gąsior JS, Kasiak PS, Ulaszewska K, Lewandowski M, et al. Impact of COVID-19 infection on cardiorespiratory fitness, sleep, and psychology of endurance Athletes-CAESAR study. J Clin Med. 2023;12(8) https://doi.org/10.3390/jcm12083002 .

Carpi M, Vestri A. The mediating role of sleep quality in the relationship between negative emotional states and health-related quality of life among Italian medical students. Int J Environ Res Public Health. 2023;20(1):26.

Jahrami H, Haji EA, Saif ZQ, Aljeeran NO, Aljawder AI, Shehabdin FN, et al. Sleep quality worsens while perceived stress improves in healthcare workers over two years during the COVID-19 pandemic: results of a longitudinal study. Healthcare (Basel). 2022;10(8) https://doi.org/10.3390/healthcare10081588 .

Sedov ID, Cameron EE, Madigan S, Tomfohr-Madsen LM. Sleep quality during pregnancy: a meta-analysis. Sleep Med Rev. 2018;38:168–76. https://doi.org/10.1016/j.smrv.2017.06.005 .

Abdali NNM, Ghorbani R. Evaluation of emotional intelligence, sleep quality, and fatigue among Iranian medical, nursing, and paramedical students: a cross-sectional study. Qatar Med J. 2019;3 https://doi.org/10.5339/qmj.2019 .

Al Shammari MA, Al Amer NA, Al Mulhim SN, Al Mohammedsaleh HN, AlOmar RS. The quality of sleep and daytime sleepiness and their association with academic achievement of medical students in the eastern province of Saudi Arabia. J Fam Commun Med. 2020;27(2):97.

Alsulami A, Bakhsh D, Baik M, Merdad M, Aboalfaraj N. Assessment of sleep quality and its relationship to social media use among medical students. Med Sci Educ. 2019;29:157–61.

Shelian AN, Khalid AB, Omar AA, Waled AB, Wedyan AB, Hyder M. The chronotype [eveningness-morningness] effects on academic achievement among medical students in Tabuk City, Saudi Arabia. Egyp J Hospital Med. 2018;71:3504–7.

AlQahtani MS, Alkhaldi TM, Al-Sultan AM, Bin Shihah AS, Aleid AS, Alzahrani ZK, et al. Sleeping disorders among medical students in Saudi Arabia; in relation to anti-insomnia medications. Egyp J Hospital Med. 2017;69(7):2750–3.

Alshahrani MATY. Sleep hygiene awareness: its relation to sleep quality among medical students in King Saud University, Riyadh, Saudi Arabia. J Family Med Prim Care. 2019;8(8)

Asiri AK, Almetrek MA, Alsamghan AS, Mustafa O, Alshehri SF. Impact of Twitter and WhatsApp on sleep quality among medical students in King Khalid University, Saudi Arabia. Sleep Hypnosis (Online). 2018;20(4):247–52.

Atlam SAEH. Sleep habits and their association with daytime sleepiness among medical students of Tanta University, Egypt. Epidemiol Methods. 2020;1(1):1–10.

Attal BABM, Abdulqader A. Quality of sleep and its correlates among Yemeni medical students: a cross-sectional study. Sleep Disord. 2021;1(18)

Bahammam AS, Al-Khairy OK, Al-Taweel AA. Sleep habits and patterns among medical students. Neurosci J. 2005;10(2):159–62.

Belingheri MPA, Facchetti R, De Vito G, Cesana G, Riva MA. Self-reported prevalence of sleep disorders among medical and nursing students. Occupational Med. 2020;3(2):127–30.

Brick CA, Seely DL, Palermo TM. Association between sleep hygiene and sleep quality in medical students. Behav Sleep Med. 2010;8(2):113–21.

Carpi MVA. The mediating role of sleep quality in the relationship between negative emotional states and health-related quality of life among Italian medical students. Int J Environ ResPublic Health. 2022;20(1):26–33.

Chatterjee S, Kar SK. Smartphone addiction and quality of sleep among Indian medical students. Psychiatry. 2021;84(2):182–91.

Christodoulou N, Maruani J, d’Ortho M-P, Lejoyeux M, Geoffroy P. Sleep quality of medical students and relationships with academic performances. L’encephale. 2021;49

Copaja-Corzo CM-CB, Vizcarra-Jiménez D, Hueda-Zavaleta M, Rivarola-Hidalgo M, Parihuana-Travezaño EG, Taype-Rondan A. Sleep disorders and their associated factors during the COVID-19 pandemic: data from Peruvian medical students. Medicina. 2022;58(10):1325.

Corrêa CC, Oliveira FK, Pizzamiglio DS, Ortolan EVP, Weber SAT. Sleep quality in medical students: a comparison across the various phases of the medical course. J Brasileiro de Pneumol. 2017;43:285–9.

Dhamija. Prevalence of smartphone addiction and its relation with sleep disturbance and low self- esteem among medical college students. Ind Psychiatry J. 2021;30(1):S189.

Dudo KEE, Fuchs S, Herget S, Watzke S, Unverzagt S, Frese T. The association of sleep patterns and depressive symptoms in medical students: a cross-sectional study. BMC Res Notes. 2022;15(1):1–6.

Eleftheriou ARA, Arvaniti A, Nena E, Steiropoulos P. Sleep quality and mental health of medical students in Greece during the COVID-19 pandemic. Front Public Health. 2021;9:1823.

Elwasify M, Barakat DH, Fawzy M, Elwasify M, Rashed I, Radwan DN. Quality of sleep in a sample of Egyptian medical students. Middle East Curr Psychiatry. 2016;23(4):200–7.

Ergin N, Kılıç BB, Ergin A, Varlı S. Sleep quality and related factors including restless leg syndrome in medical students and residents in a Turkish university. Sleep Breath. 2022;26(3):1299–307.

Feng Z, Diao Y, Ma H, Liu M, Long M, Zhao S, et al. Mobile phone addiction and depression among Chinese medical students: the mediating role of sleep quality and the moderating role of peer relationships. BMC Psychiatry. 2022;22(1):567.

Fernandes ACPD, de Moura AC, de Aquino CE, de Araújo Lima IB, Mota-Rolim SA. COVID-19 pandemic decreased sleep quality of medical students. Sleep Sci. 2022;15(4):436.

Fowler LAKN. The effect of COVID-19 pandemic stay-at-home orders on sleep deprivation in medical students: a retrospective study. SN. Soc Sci. 2022;2(3):29.

Gui ZSL, Zhou C. Self-reported sleep quality and mental health mediate the relationship between chronic diseases and suicidal ideation among Chinese medical students. Sci Rep. 2022;12(1):18835.

Gupta R, Taneja N, Anand T, Gupta A, Gupta R, Jha D, et al. Internet addiction, sleep quality and depressive symptoms amongst medical students in Delhi, India. Commun Mental Health J. 2021;57:771–6.

Ibrahim N, Badawi F, Mansouri Y, Ainousa A, Jambi S, Fatani A. Sleep quality among medical students at King Abdulaziz University: a cross-sectional study. J Community Med Health Educ. 2017;7(561):2161–711.

James BO, Omoaregba JO, Igberase OO. Prevalence and correlates of poor sleep quality among medical students at a Nigerian university. Ann Nigerian Med. 2011;5(1):1.

Javaid R, Momina A, Sarwar MZ, Naqi SA. Quality of sleep and academic performance among medical university students. Medical Educ. 2020;

EL JA, El Hangouche AJ, Rkain H, Aboudrar S, El Ftouh M, Dakka T. Perception of sleep disturbances due to bedtime use of blue light-emitting devices and its impact on habits and sleep quality among young medical students. BioMed Res Int. 2019;2019:12.

Johnson KM, Simon N, Wicks M, Barr K, O’Connor K, Schaad D. Amount of sleep, daytime sleepiness, hazardous driving, and quality of life of second year medical students. Acad Psychiatry. 2017;41:669–73.

Najafi Kalyani M, Jamshidi N, Salami J, Pourjam E. Investigation of the relationship between psychological variables and sleep quality in students of medical sciences. Depression Res Treat. 2017;2017

Kawyannejad R, Mirzaei M, Valinejadi A, Hemmatpour B, Karimpour HA, AminiSaman J, et al. General health of students of medical sciences and its relation to sleep quality, cell phone overuse, social networks and internet addiction. BioPsychoSoc Med. 2019;13(1):1–7.

Khero M, Fatima M, Shah MAA, Tahir A. Comparison of the status of sleep quality in basic and clinical medical students. Cureus. 2019;11(3)

Kumar VA, Chandrasekaran V, Brahadeeswari H. Prevalence of smartphone addiction and its effects on sleep quality: a cross-sectional study among medical students. Ind Psychiatry J. 2019;28(1):82.

Kumar A, Aslami AN. Analgesics self-medication and its association with sleep quality among medical undergraduates. J Clin Diagn Res: JCDR. 2016;10(12):FC07.

Li M, Han Q, Pan Z, Wang K, Xie J, Zheng B, Lv J. Effectiveness of multidomain dormitory environment and roommate intervention for improving sleep quality of medical college students: a cluster randomised controlled trial in China. Int J Environ Res Public Health. 2022;19(22):15337.

Lima P, Medeiros A, Araujo J. Sleep-wake pattern of medical students: early versus late class starting time. Braz J Med Biol Res. 2002;35:1373–7.

Mahadule. Sleep quality and sleep hygiene in preclinical medical students of tertiary care center amidst COVID-19 pandemic: a cross-sectional observational study. Naational Library of Medicine2022.

Maheshwari G, Shaukat F. Impact of poor sleep quality on the academic performance of medical students. Cureus. 2019;11(4)

Mazar D, Gileles-Hillel A, Reiter J. Sleep education improves knowledge but not sleep quality among medical students. J Clin Sleep Med. 2021;17(6):1211–5.

Meo SA, Alkhalifah JM, Alshammari NF, Alnufaie WS, Algoblan AF. Impact of COVID-19 pandemic on sleep quality among medical and general science students: King Saud University Experience. Pakistan J Med Sci. 2022;38(3Part-I):639.

Mirghani HO. The effect of chronotype (morningness/eveningness) on medical students' academic achievement in Sudan. J Taibah Univ Med Sci. 2017;12(6):512–6.

Mishra J, Panigrahi A, Samanta P, Dash K, Mahapatra P, Behera MR. Sleep quality and associated factors among undergraduate medical students during Covid-19 confinement. Clin Epidemiol Global Health. 2022;15:101004.

Article   CAS   Google Scholar  

Mohammadbeigi A, Absari R, Valizadeh F, Saadati M, Sharifimoghadam S, Ahmadi A, et al. Sleep quality in medical students; the impact of over-use of mobile cellphone and social networks. J Res Health Sci. 2016;16(1):46.

Nsengimana A, Mugabo E, Niyonsenga J, Hategekimana JC, Biracyaza E, Mutarambirwa R, et al. Sleep quality among undergraduate medical students in Rwanda: a comparative study. Sci Rep. 2023;13(1):265.

Olarte-Durand M, Roque-Aycachi JB, Rojas-Humpire R, Canaza-Apaza JF, Laureano S, Rojas-Humpire A, et al. Mood and sleep quality in Peruvian medical students during COVID-19 pandemic. Revista Colombiana de Psiquiatria. 2021;

Patil A, Chaudhury S, Srivastava S. Eyeing computer vision syndrome: awareness, knowledge, and its impact on sleep quality among medical students. Ind Psychiatry J. 2019;28(1):68.

Prashanth S, Kavyashree H, Krishnamurthy L, Deshpande D, Kalasuramath RPADS. Quality of sleep in medical students. J Public Health Med Res. 2015;3:8–10.

Priya J, Singh J, Kumari S, Res A. Study of the factors associated with poor sleep among medical students. Indian J Basic Appl Med Res. 2017;6(3):422–9.

Ramamoorthy S, Mohandas M, Sembulingam P, Swaminathan VR. Prevalence of excessive daytime sleepiness (EDS) among medical students. World. J Pharm Res. 2014;3(4)

Rathi A, Ransing RS, Mishra KK, Narula N. Quality of sleep among medical students: relationship with personality traits. J Clin Diagn Res. 2018;12(9)

Rique GLN, Fernandes Filho GMC, Ferreira ADC, de Sousa-Munoz RL. Relationship between chronotype and quality of sleep in medical students at the Federal University of Paraiba, Brazil. Sleep Sci. 2014;7(2):96–102.

Riskawati YK, Alfarabyn SU, Soeroso DA, Rakhmatiar R. The physical activity level of medical students does not correlate with their sleep quality and excessive daytime sleepiness (EDS). BIO Web of Conf: EDP Sci; 2022. p. 03003.

Safhi MA, Alafif RA, Alamoudi NM, Alamoudi MM, Alghamdi WA, Albishri SF, et al. The association of stress with sleep quality among medical students at King Abdulaziz University. J Fam Med Prim Care. 2020;9(3):1662.

Saguem B, Nakhli J, Romdhane I, Nasr S. Predictors of sleep quality in medical students during COVID-19 confinement. L'encephale. 2022;48(1):3–12.

Sarbazvatan H, Amini A, Aminisani N, Shamshirgaran SM. Sleep quality and academic progression among students of Tabriz University of Medical Sciences, Northwest of Iran. Res Dev Med Educ. 2017;6(1):29–33.

Saygın M, Öztürk Ö, Gonca T, Has M, Hayri UB, Kurt Y, et al. Investigation of sleep quality and sleep disorders in students of medicine. Turk Thorac J. 2016;17(4):132.

Shadzi MR, Salehi A, Vardanjani HM. Problematic internet use, mental health, and sleep quality among medical students: a path-analytic model. Ind J Psychol Med. 2020;42(2):128–35.

Shrestha D, Adhikari SP, Rawal N, Budhathoki P, Pokharel S, Adhikari Y, et al. Sleep quality among undergraduate students of a medical college in Nepal during COVID-19 pandemic: an online survey. F1000Research. 2021;10(505):505.

Sun J, Chen M, Cai W, Wang Z, Wu S, Sun X, et al. Chronotype: implications for sleep quality in medical students. Chronobiol Int. 2019;36(8):1115–23.

Sundas NGS, Bhusal S, Pandey R, Rana K, Dixit H. Sleep quality among medical students of a tertiary care hospital: a descriptive cross-sectional study. JNMA: J Nepal Med Assoc. 2020;58(222):76.

Surani AA, Zahid S, Surani A, Ali S, Mubeen M, Khan RH. Sleep quality among medical students of Karachi, Pakistan. J Pak Med Assoc. 2015;65(4):380–2.

Tahir MJ, Malik NI, Ullah I, Khan HR, Perveen S, Ramalho R, Siddiqi AR, Waheed S, Shalaby MMM, De Berardis D, Jain S. Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey. PLOS One. 2021;16(11):e0259594.

Thaipisuttikul P, Theansukont T, Boonmueng R, Wisajun P. Sleep quality problems in Thai medical students. Sleep Sci. 2022;15(Spec 1):244.

Wang Y, Zhao Y, Liu L, Chen Y, Ai D, Yao Y, et al. The current situation of internet addiction and its impact on sleep quality and self-injury behavior in Chinese medical students. Psychiatry Investig. 2020;17(3):237.

Wang L, Qin P, Zhao Y, Duan S, Zhang Q, Liu Y, et al. Prevalence and risk factors of poor sleep quality among Inner Mongolia Medical University students: a cross-sectional survey. Psychiatry Res. 2016;244:243–8.

Wondie T, Molla A, Mulat H, Damene W, Bekele M, Madoro D, et al. Magnitude and correlates of sleep quality among undergraduate medical students in Ethiopia: cross–sectional study. Sleep Sci Pract. 2021;5(1):1–8.

Xie J, Li X, Luo H, He L, Bai Y, Zheng F, et al. Depressive symptoms, sleep quality and diet during the 2019 novel coronavirus epidemic in China: a survey of medical students. Front Public Health. 2021;8:588578.

Yazdi Z, Loukzadeh Z, Moghaddam P, Jalilolghadr S. Sleep hygiene practices and their relation to sleep quality in medical students of Qazvin University of Medical Sciences. J Car Sci. 2016;5(2):153.

Yeluri. Electronic gadget screen-time, perceived sleep quality & quantity and academic performance in medical students. J Assoc Phys India. 2021;69(11):11–2.

Download references

Author information

Authors and affiliations.

Department of Psychiatry, College of Medicine and Medical Sciences, Arabian Gulf University, Manama, Bahrain

Mohammed A. Binjabr, Idrees S. Alalawi, Rayan A. Alzahrani, Othub S. Albalawi, Rakan H. Hamzah, Yazed S. Ibrahim, Fatima Buali, Mariwan Husni & Haitham Jahrami

Department of Medicine, University Sleep Disorders Center and Pulmonary Service, King Saud University, KSA, Riyadh, Saudi Arabia

Ahmed S. BaHammam

The Strategic Technologies Program of the National Plan for Sciences and Technology and Innovation in the Kingdom of Saudi Arabia, Riyadh, Saudi Arabia

Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, USA

Michael V. Vitiello

Government Hospitals, Manama, Bahrain

Haitham Jahrami

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Haitham Jahrami .

Ethics declarations

Conflict of interest.

The authors declare no competing interests.

Human and Animal Rights and Informed Consent

All reported studies/experiments with human or animal subjects performed by the original authors have been previously published and complied with all applicable ethical standards (including the Helsinki Declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).

Additional information

Publisher’s note.

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

Supplementary Information

Supplemental Figure 1: Traffic light of the included studies.

Supplemental Figure 2: Funnel plot of poor sleep quality in medical students.

Supplemental Figure 3: Radial plot of poor sleep quality in medical students.

Supplemental Figure 4: Subgroup meta-analysis of poor sleep quality in medical students by COVID-19 status.

Supplemental Figure 5: Funnel plot of excessive daytime sleepiness in medical students.

Supplemental Figure 6: Radial plot of excessive daytime sleepiness in medical students.

Supplemental Figure 7: Subgroup meta-analysis of excessive daytime sleepiness in medical students by COVID-19 status.

Supplemental Figure 8: Funnel plot of the mean sleep duration in medical students.

Supplemental Figure 9: Radial plot of the mean sleep duration in medical students.

Supplemental Figure 10: Subgroup meta-analysis of the mean sleep duration in medical students by COVID-19 status.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Binjabr, M.A., Alalawi, I.S., Alzahrani, R.A. et al. The Worldwide Prevalence of Sleep Problems Among Medical Students by Problem, Country, and COVID-19 Status: a Systematic Review, Meta-analysis, and Meta-regression of 109 Studies Involving 59427 Participants. Curr Sleep Medicine Rep 9 , 161–179 (2023). https://doi.org/10.1007/s40675-023-00258-5

Download citation

Accepted : 16 May 2023

Published : 03 June 2023

Issue Date : September 2023

DOI : https://doi.org/10.1007/s40675-023-00258-5

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

  • Excessive daytime sleepiness
  • Medical students
  • Pittsburgh sleep quality index
  • Project registration: Open Science Framework Identifier: DOI 10.17605/OSF.IO/UVH5C

Advertisement

  • Find a journal
  • Publish with us
  • Track your research
  • Mobile Site
  • Staff Directory
  • Advertise with Ars

Filter by topic

  • Biz & IT
  • Gaming & Culture

Front page layout

Sun up, sun down —

The science behind why people hate daylight saving time so much, can we use research and policy to change (or not change) the clocks for the last time.

Teresa Carr, Undark Magazine - Mar 13, 2024 2:56 pm UTC

Permanent DST meant that the sun also rose and set later in the winter. Results published in 2017 associated year-round DST with a greater likelihood of feeling down in the winter as well as sleeping later on weekends, a phenomenon known as social jet lag. Chronobiologist Till Roenneberg and colleagues coined the term nearly two decades ago to describe the chronic sleep deprivation that people experience when they have to get up for school or work before they would awaken naturally.

“Social jet lag is the umbrella term for not being able to live in sync with one’s biological time,” said Roenneberg. He likens wakening with an alarm to stopping the washing machine before the cycle is complete: “All we get is wet and dirty laundry,” he said. “And that’s what we get in our body.”

Social jet lag is an artifact of our modern world. Nearly half of US adults sleep at least an hour later when they have the chance, according to a study published in JAMA Network Open in 2022. And research suggests that the phenomenon is especially pronounced in adolescents due to both biology—melatonin release tends to be delayed in that age group, for example—and environmental factors such as late nights on electronics and early school-start times.

Social jet lag, a term for when people experience chronic sleep deprivation because they have to get up for school or work before they would awaken naturally, is more pronounced in adolescents. It is associated with a host of health risks, and linked to worse academic performance.

Research by Roenneberg and others have associated social jet lag—and the sleep deprivation it reflects—with smoking and consuming higher amounts of alcohol and caffeine as well as a range of ill health effects including obesity , metabolic syndrome (a group of health conditions that increase the risk of heart disease, stroke, and type 2 diabetes), risk factors for heart disease , and depression . Studies have also linked social jet lag to worse academic performance for high school and college students.

In a thorough review , Roenneberg and colleagues argue that by pushing sunrise and sunset an hour later, permanent DST is bound to worsen social jet lag. But the Russian study is the only direct evidence of that link, and it’s uncertain whether those effects, which the Russian researchers characterize as “small or very small,” apply to older age groups or people living where the cycles of light and dark are less extreme. In Vorkuta, one of three cities in the study, for example, the sun never rises for a time in the winter and never sets for six weeks in the summer.

Like all of the researchers I spoke with for this story, Derk-Jan Dijk, a sleep and physiology professor at the University of Surrey in England, sees potential harm in permanently setting our clocks an hour ahead because in the winter many people would have to start their day in darkness. “Any schedule that implies that you have to get up before sunrise may cause problems,” said Dijk. But he also doesn’t like to overstate the case against DST, especially when we observe it seasonally.

“The entire discussion about Daylight Saving Time and how bad it is upsets me a little bit,” he told me. The slight effects seen during the transition to DST in the spring and then back to ST in the autumn, quickly disappear he noted. “There is no good evidence that during the entire summer, when we are on Daylight Saving Time, everything is worse,” he said. “I don’t think the evidence is there.”

Changing the clocks is irritating

Polls show that we generally dislike mucking with time twice a year. Nearly two-thirds of Americans want to eliminate the changing of clocks, according to a nationally representative survey of 1,500 US adults conducted by The Economist magazine and market research company YouGov in 2021.

Permanent DST enjoys bipartisan support among many political leaders in the US. In a document supporting the Sunshine Protection Act, Sen. Marco Rubio, Republican of Florida, cites evidence that DST promotes health, safety, recreation, commerce, and energy savings. However, some of that research focuses on the harms of switching back and forth, so one could also use it to support year-around ST.

In other cases, Rubio cherry picks studies showing benefits to DST while ignoring contradictory research. A 2020 report from the Congressional Research Service prepared for members of the US Congress did not find substantial evidence that DST improves health and safety or that it reduces energy consumption by much—if at all.

And in drumming up supportive evidence, the permanent DST camp hits the same wall as the eliminate DST camp: Researchers haven’t sufficiently studied the effects of year-around DST.

In a controversial 2020 perspective for the journal Clocks & Sleep, sleep scientists Christina Blume and Manuel Schabus call on the scientific establishment to own up to uncertainties in the existing data and to do the research needed to fill those holes. Still, even Blume acknowledges that taken as a whole, the available data makes a decent case that changing clocks to shift light from the morning to the evening could be bad for our health and safety.

“We all agree as researchers that the safer option is to go for perennial Standard Time,” said Blume, a postdoctoral researcher at the University of Basel in Switzerland.

The nonprofit organization Save Standard Time lists endorsements from more than 30 sleep-science and medical organizations—including the American Academy of Sleep Medicine, the American Medical Association, and the American Academy of Neurology among others—in addition to individual scientists and researchers.

Here, I feel compelled to note that the last time we tried permanent DST, it didn’t go well. In attempt to conserve energy, Congress established a trial period of year-round DST in late 1973. But public approval dropped precipitously as Americans faced the reality of dark winter mornings. By October 1974, the country had reverted to four months of yearly ST.

The disconnect between the perception and reality arises because of how we think and talk about the seasons and time change, said neurologist Malow, who testified before the US Congress about the benefits of permanent ST. “People have associated being on standard time, with it being cold and winter and dark,” she said. Meanwhile “springing forward” coincides with the return of warmer, longer days.

But, of course, DST doesn’t buy you more light. Winter days are short and summer days are long regardless of how you mark time.

reader comments

Channel ars technica.

TOI logo

Sleep deprivation leading to neurological disorders in teens

Sleep deprivation leading to neurological disorders in teens

Good sleep is a fundamental need for good health. But in an age in which digital media has taken over our lives, many teens are addicted to its lures and suffer from lack of sleep. Schools and parents should be proactive in making kids aware of the downside of digital overload and find ways to make them move away from it. The danger is bigger than it seems.

Visual Stories

sleep deprivation on academic performance essay

IMAGES

  1. Cause and extent of sleep deprivation (600 Words)

    sleep deprivation on academic performance essay

  2. (PDF) The effect of sleep quality on academic performance

    sleep deprivation on academic performance essay

  3. Understanding of Sleep Deprivation Cause And Effect Essay on Samploon.com

    sleep deprivation on academic performance essay

  4. [PDF] The Effects of Sleep Deprivation on the Academic Performance of

    sleep deprivation on academic performance essay

  5. (DOC) Thesis Effects of Sleep Deprivation in the Academic Performance

    sleep deprivation on academic performance essay

  6. (PDF) The impact of sleep deprivation on sleepiness, risk factors and

    sleep deprivation on academic performance essay

VIDEO

  1. Why it's important to get enough sleep

  2. Comparison: You at Different Levels of Sleep Deprivation

  3. Rest & Recharge: Prioritize Sleep for Optimal Wellness! #sleepbenefits #health #wellness #rest

  4. Sleep deprivation is harmful to your health!

  5. Sleep Deprivation Expeiment

  6. Shift work, sleep deprivation, unpredictability, cultural habits, stress.. Be Proactive. #health

COMMENTS

  1. Effect of sleep and mood on academic performance—at ...

    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 ...

  2. The Effect of Sleep Quality on Students' Academic Achievement

    Background. Sleep is an inseparable part of human health and life, and is pivotal to learning and practice as well as physical and mental health. 1 Studies have suggested that insufficient sleep, increased frequency of short-term sleep, and going to sleep late and getting up early affect the learning capacity, academic performance, and neurobehavioral functions. 2, 3 Previous studies have ...

  3. The Effects of Sleep Deprivation on College Students

    Sleep deprivation can result in greater procedural errors, which places the clients at risk. Insufficient sleep negatively affects the nervous system, resulting in poor brain function. Because of the cognitive decline that is associated with sleep deprivation, academic performance is often decreased.

  4. Problem of Sleep Deprivation

    Effects of Sleep Deprivation. Sleep deprivation has a host of negative effects which affect people of all ages. The commonest effect is stress. Most people who suffer from sleep deficiency are likely to experience depression frequently as compared to their counterparts who enjoy quality sleep (Conroy et al. 188).

  5. Sleep quality and sleep deprivation: relationship with academic

    The beginning of the university brings together maturational, psychosocial and academic changes that make university students more prone to suffer from insufficient or poor quality sleep, which can negatively influence their academic performance. The period of taking exams is a key part of the academic year. However, there are few studies that analyze sleep during this period of time. Our aim ...

  6. (PDF) The Effects Of Sleep Deprivation Towards The Academic Performance

    Abstract. This study determined the effects of sleep deprivation on the academic performance of 2nd-year education students of the University of Science and Technology of Southern Philippines ...

  7. How Sleep Deprivation Affects College Students' Academic Performance

    This study analyses the effect of sleep deprivation on the performance of college students. Students usually neglect sleep for the purpose of excelling in their academic performance. They sacrifice sleep so as to accomplish school projects and assignments. Their lack of enough sleep makes them dysfunctional in other areas well.

  8. The Correlation of Sleep and Academic Performance

    While this study reveals that sleep deprivation may affect academic performance, the study obtains 95% confidence that respondents show a mean between 6.85 hours and 7.40 hours 6. This indicates ...

  9. Causes and consequences of sleepiness among college students

    The consequences of sleep deprivation and daytime sleepiness are especially problematic to college students and can result in lower grade point averages, increased risk of academic failure, compromised learning, impaired mood, and increased risk of motor vehicle accidents. This article reviews the current prevalence of sleepiness and sleep ...

  10. Can School Performance Be Improved With Good Sleep?

    A direct way that sleep and school performance are connected is through effects on mental function. Some known problems associated with lack of sleep include: Decreased attention. The ability to concentrate is vital to learning and academic achievement, but insufficient sleep reduces attention and focus. Impaired memory.

  11. Sleep deprivation: Impact on cognitive performance

    Sleep deprivation of 24 h impaired performance in one study (Wright and Badia 1999), whereas in two others, performance was maintained after 25-35 h of SD (Drummond et al 2001; Alhola et al 2005). The divergent findings in these studies may be explained by the uneven loads between different subtests as well as by uncontrolled practice effect.

  12. Sleep Deprivation and Learning at University Essay

    Nowadays, sleep deprivation is considered one of the most common problems among students of different educational establishments. In fact, it frequently results in poor academic performance and physical health issues. In terms of learning, sleep plays a prominent role, as this process consolidates memory and improves concentration.

  13. Relationship between sleep habits and academic performance in

    Background 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 ...

  14. The Influence of Sleep Deprivation on Academic Performance Among

    Educators worldwide have been concerned with searching for the determinants of academic performance with the purpose of improving the same. This article discusses a study which intended to examine the influence of sleep deprivation on academic performance among college students. This included examining the extent to which students report sleep deprivation and whether or not they experienced ...

  15. Sleep Deprivation Effects On Academic Performance

    Sleeping is a necessity for human survival and crucial to our health. Not getting enough sleep can result in hallucinations, irritability, depressive behaviour, diabetes, poor dieting, interference with daily activities, lack of alertness and motivation as well as poor academic performance among students. The majority of the population realizes ...

  16. Sleep Deprivation in Teens: Its Affect on Academic Performance

    Memory and thinking problems. Headaches. Eye bags. Slowed reaction times. How Sleep Deprivation Affects Academic Performance. School can sometimes feel overwhelming, so some students sacrifice sleep for their grades by cramming the night before to complete assignments and study for tests. However, not getting enough sleep can make your brain ...

  17. The Effects Of Sleep Deprivation On Academic Performance

    A study by Central Michigan University (2008) found that sleep deprivation can lead to poor academic performance, impaired driving, depression, and behavioral problems. There are several variables that may affect sleeping patterns among college students. One is genetics or biological issues. It is highly.

  18. The Effect of Sleep Deprivation on a Student's Performance

    In conclusion, Sleep deprivation does have a direct correlation with attention and ability to focus and memory, but the effects are ultimately different for different people, but the relative trend for the populus can be seen and a relative professional conclusion that the less sleep results in worse performance and loss of attention has been made.

  19. 89 Sleep Deprivation Essay Topic Ideas & Examples

    Problem of Sleep Deprivation. This is due to disruption of the sleep cycle. Based on the negative effects of sleep deprivation, there is need to manage this disorder among Americans. We will write. a custom essay specifically for you by our professional experts. 809 writers online.

  20. Sleep Deprivation and Academic Performance Essay

    Sleep deprivation continues to be a growing issue regarding a student's academic performance. Many students in all types of education are experiencing inadequate sleep as they are obtaining about one and a half hours less than the recommended sleep duration of eight and a half hours. (Lund, Reider, Whiting and Prichard, 2010).

  21. Sleep Deprivation And Academic Performance

    1430 Words 6 Pages. Sleep Deprivation and Academic Performance in Adolescents. Sleep deprivation is an increasing issue with adolescents. Statistics show that 60% of high schoolers report extreme daytime sleepiness. 20% to 33% of those high schools report falling asleep in class at least once during the week. Daytime sleepiness is only a small ...

  22. A Systematic Review of Sleep Deprivation and Neurobehavioral Function

    1. Introduction. Sleep loss has a negative effect on multiple neurobehavioral functions, such as psychomotor vigilance performance (cognitive), daytime sleepiness, and affect (Franzen et al., 2011; Van Dongen et al., 2003).Degradation of vigilance following sleep deprivation is one of the most robust alterations in healthy young adults aged 18-30 years (Lim & Dinges, 2010).

  23. Sleep Deprivation And Academic Performance Essay

    Whether positive or negative, sleep deprivation has a significant relationship with regards to academic performance. The authors of this research focus on the effects of sleep deprivation on the academic performance of DLSL Accountancy students. This research also contains supplementary information about the nature of sleep deprivation.

  24. The Worldwide Prevalence of Sleep Problems Among Medical ...

    The effects of sleep disturbances on medical students can be severe, with a variety of potential repercussions. Academic performance can be harmed by sleep deprivation because it has been demonstrated to impede cognitive function, memory consolidation, and learning ability [17, 57, 60, 62,63,64,65,66,67, 75]. Mental health can suffer as sleep ...

  25. The science behind why people hate Daylight Saving Time so much

    Research by Roenneberg and others have associated social jet lag—and the sleep deprivation it reflects—with smoking and consuming higher amounts of alcohol and caffeine as well as a range of ...

  26. Sleep deprivation leading to neurological disorders in teens

    An average teen, aged between 14-17 years, needs to sleep for at least 8-10 hours daily. However, doctors say, there are many children who do not get enough sleep, either due to academic pressure ...