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The effects of insufficient sleep and adequate sleep on cognitive function in healthy adults

Molly E. Zimmerman, PhD Molly E. Zimmerman Correspondence Corresponding author: Molly E. Zimmerman, PhD, Fordham University, Department of Psychology, Dealy Hall, 441 East Fordham Rd., Bronx, NY 10458, USA. Tel.: 718-817-3815. Contact Affiliations Fordham University, Department of Psychology, Bronx, New York, USA Search for articles by this author
Giada Benasi, PhD Giada Benasi Affiliations Division of General Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA Search for articles by this author
Christiane Hale, MS Christiane Hale Affiliations Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA Search for articles by this author
Lok-Kin Yeung, PhD Lok-Kin Yeung Affiliations Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA Search for articles by this author
Justin Cochran, BS Justin Cochran Affiliations Center of Excellence for Sleep & Circadian Research and Division of General Medicine, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA Search for articles by this author

Study objectives

Conclusions, clinical trial registration.

Insufficient sleep
Sleep restriction
Adequate sleep
Stable sleep
Working memory

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This work was performed at Columbia University Irving Medical Center.

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DOI: https://doi.org/10.1016/j.sleh.2023.11.011

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The effect of sleep deprivation on objective and subjective measures of facial appearance

Affiliations.

1 Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
2 Department of Psychology, New York University, New York, NY.
3 Stress Research Institute, Stockholm University, Stockholm, Sweden.
4 School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK.
PMID: 31006920
DOI: 10.1111/jsr.12860

The faces of people who are sleep deprived are perceived by others as looking paler, less healthy and less attractive compared to when well rested. However, there is little research using objective measures to investigate sleep-loss-related changes in facial appearance. We aimed to assess the effects of sleep deprivation on skin colour, eye openness, mouth curvature and periorbital darkness using objective measures, as well as to replicate previous findings for subjective ratings. We also investigated the extent to which these facial features predicted ratings of fatigue by others and could be used to classify the sleep condition of the person. Subjects (n = 181) were randomised to one night of total sleep deprivation or a night of normal sleep (8-9 hr in bed). The following day facial photographs were taken and, in a subset (n = 141), skin colour was measured using spectrophotometry. A separate set of participants (n = 63) later rated the photographs in terms of health, paleness and fatigue. The photographs were also digitally analysed with respect to eye openness, mouth curvature and periorbital darkness. The results showed that neither sleep deprivation nor the subjects' sleepiness was related to differences in any facial variable. Similarly, there was no difference in subjective ratings between the groups. Decreased skin yellowness, less eye openness, downward mouth curvature and periorbital darkness all predicted increased fatigue ratings by others. However, the combination of appearance variables could not be accurately used to classify sleep condition. These findings have implications for both face-to-face and computerised visual assessment of sleep loss and fatigue.

Keywords: experimental psychology; face; health; perception; skin; sleep loss.

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Article Contents

Introduction, statistical analysis, limitations of the study, acknowledgments, disclosure statements, data availability.

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Sleep deprivation and aging are metabolically linked across tissues

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Arjun Sengupta, Jennifer C Tudor, Danielle Cusmano, Joseph A Baur, Ted Abel, Aalim M Weljie, Sleep deprivation and aging are metabolically linked across tissues, Sleep , Volume 46, Issue 11, November 2023, zsad246, https://doi.org/10.1093/sleep/zsad246

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Insufficient sleep is a concerning hallmark of modern society because sleep deprivation (SD) is a risk factor for neurodegenerative and cardiometabolic disorders. SD imparts an aging-like effect on learning and memory, although little is known about possible common molecular underpinnings of SD and aging. Here, we examine this question by profiling metabolic features across different tissues after acute SD in young adult and aged mice.

Young adult and aged mice were subjected to acute SD for 5 hours. Blood plasma, hippocampus, and liver samples were subjected to UPLC-MS/MS-based metabolic profiling.

SD preferentially impacts peripheral plasma and liver profiles (e.g. ketone body metabolism) whereas the hippocampus is more impacted by aging. We further demonstrate that aged animals exhibit SD-like metabolic features at baseline. Hepatic alterations include parallel changes in nicotinamide metabolism between aging and SD in young animals. Overall, metabolism in young adult animals is more impacted by SD, which in turn induces aging-like features. A set of nine metabolites was classified (79% correct) based on age and sleep status across all four groups.

Our metabolic observations demonstrate striking parallels to previous observations in studies of learning and memory and define a molecular metabolic signature of sleep loss and aging.

Learning and memory are impacted similarly by sleep deprivation (SD) and normal aging. However, little is known about the underlying molecular mechanisms driving this effect. Here, we use UPLC-MS/MS-based metabolic phenotyping and demonstrate that acute SD preferentially perturbs metabolism in young mice compared to old. The effect is reminiscent of molecular signatures of typical aging disorders. As such, older animals are predisposed to the effects of SD, most likely due to aging-related physiological changes. Finally, a common set of metabolites, enriched in pathways like nicotinamide metabolism, were found to be similarly impacted by SD in the plasma and liver of young animals in a pattern that resembles normal aging. These results lay the foundation for investigating mechanistic linkages between effects of SD and aging.

Curtailed sleep is a hallmark of modern society. Sleep deprivation (SD) has been linked to clinical phenotypes such as obesity [ 1 ], diabetes [ 2 ], cardiovascular diseases [ 3 ], and cancer [ 4 ]. Sleep loss alters circulatory metabolic phenotypes [ 5 , 6 ], providing mechanistic clues to the connection between sleep disruption and disease. Sleep disruption directly affects neurological parameters, such as learning and memory in animal models [ 7 ], as well as in humans [ 8 ]

Another hallmark of modern society is a steady increase in average age and life expectancy [ 9 ] in general, concurrent with increased incidence of metabolic diseases that are typical of old age, including cardiovascular diseases, stroke, chronic pulmonary diseases, diabetes, cancer, depression, neurodegenerative disorders, and cognitive impairment [ 10 ]. Interestingly, several of these diseases previously exclusively associated with old age are now occurring in relatively younger populations [ 11–13 ], suggestive of emergent environmental factors. Progressive curtailment of sleep is likely a major factor, given that aging and sleep are intricately linked. Sleep architecture is perturbed with age and several age-related complications are thought to be associated with that perturbation [ 14 ]. Changes in sleep architecture with aging observed in humans have been recapitulated in animal models [ 15 ]. Sleep disruption has also been shown to affect learning and memory to a greater degree in young mice compared to old [ 7 ]. Such studies demonstrate that learning and memory change as a function of both sleep loss and aging. In old animals, the effect of aging outweighs the effect of SD while for young animals, SD remains one of the most prominent determinants of altered learning and memory [ 7 ]. Studies measuring global neurobehavioral performance of human participants post-SD also draw remarkable parallels to observations made in animal studies. For example, older individuals consistently showed less pronounced effects on attention measures post SD, estimated using a psychomotor vigilance test, while young participants demonstrated worsened, aged-like responses [ 16 , 17 ]. Furthermore, younger adult participants demonstrated significantly larger drops in verbal encoding and visuospatial displacement components of working memory, similar to that of older individuals [ 18 ]. Therefore, SD imparts aging-like neurobehavioral and memory phenotypes in younger individuals. However, little is known about the effect of SD on young individuals at the metabolic level compared to the aging phenotype. To address this gap, we have performed a comprehensive metabolomics analysis across tissues in young and old mice participating to SD.

A growing body of literature suggests that altered phenotypes after SD are linked to systemic metabolic perturbations [ 5 , 6 , 19 , 20 ]. Gene expression profiling suggests that prolonged SD could trigger a stress response and immune activation [ 21 ]. This is consistent with the observation that SD-induced oxidative stress at both gene expression and metabolic levels [ 5 , 22 , 23 ]. Aging is also associated with increased generation of reactive oxygen species, which is thought to trigger proliferative and survival signals to prevent aging-related damage at the onset of aging processes [ 24 ]. However, increasing reactive oxygen species can ultimately overwhelm reparative abilities and lead to detrimental effects [ 25 ]. Similarly, age-related metabolic changes are well documented [ 26–28 ]. Redox homeostasis, anti-oxidative responses, and energy metabolism were found to be extensively altered in both mice [ 27 ] and humans with aging [ 29 , 30 ].

Based on these observations, we reasoned that acute sleep loss may create a molecular milieu consistent with accelerated aging which may be reflected in metabolism. Based on our previous studies of systemic sleep loss markers in rats and humans [ 5 , 31 ], we further reasoned that these effects would apply to both central nervous system and peripheral metabolism. To test this hypothesis, we profiled the presence of metabolites in the hippocampus, liver, and blood plasma from sleep-deprived mice across two disparate age groups. Our results suggest that metabolic changes after SD are much more prominent in young adult animals than in aged animals. This is consistent with observations made in learning and working memory [ 7 , 18 ] as well as neurobehavioral paradigms, such as vigilant attention [ 16 , 17 ]. At the pathway level, hepatic ketogenesis appears to be most prominently affected, followed by nicotinamide metabolism, histidine metabolism, glycine/serine/threonine metabolism, and the urea cycle. Aged animals appear to be resistant to effects of SD, unlike the younger animals. However, that appears to be because aging mimics some SD-like metabolic effects. We also report altered circulatory levels of specific metabolites as function of SD in young adult animals, many of which are thought to be associated with diseases of aging. Taken together, our results demonstrate that the metabolic environment following sleep loss in young adult animals is shifted toward that of much older mice.

Sleep deprivation

Young adult (2–4 months of age, N = 20 [10/group]) and aged (22–24 months of age, N = 18 [9/group]) male C57BL/6NIA mice were obtained from the National Institute of Aging mouse colony for these experiments. The number of animals were kept consistent with previous SD and aging experiments [ 7 , 32 , 33 ]. Mice were housed individually on a 12-hour light/dark schedule with lights on at 7:00 am. Food and water were available ad libitum. Mice were randomly assigned to the control or experimental (SD) group. Prior to the day of SD and tissue collection, mice were handled daily for at least 5 days using the same interventions used during SD for 1–2 minutes. The mice in the SD group were sleep-deprived for 5 hours (ZT 0—ZT 5) using the gentle handling method [ 32 ]. This technique is comprised of manual cage tapping, cage jostling, and nestlet disturbance. Earlier studies showed that SD-induced memory consolidation as well as epigenetically demanding processes are impaired during the first half of light phase [ 32 , 34 ]. Control mice were left undisturbed in their home cages. Mice were euthanized immediately after the 5-hour period for blood and tissue collection. Blood plasma, hippocampus, and liver tissue samples were stored at −80°C until sample processing. All experiments were conducted according to the National Institutes of Health guidelines for animal care and use and were approved by the University of Pennsylvania Institutional Animal Care and Use Committee.

Sample processing for UPLC-MS/MS

Samples were processed using slight modifications of previously reported methods [ 31 ]. Briefly, 50 μL plasma, ~50 mg liver tissue, and ~30 mg hippocampi were extracted using 2:2:1 methanol/chloroform/water. The tissues were homogenized, centrifuged, and upper polar layer was collected. The samples were dried using vacuum concentration and reconstituted in 1:1 acetonitrile/water to inject into UPLC-MS. For performing NMR spectroscopy using the plasma sample, a fraction of the polar layer was kept separately.

UPLC-MS/MS experiments

UPLC-MS conditions were adopted from previously reported methods [ 35 ]. Five μL of each sample was injected into XBridge BEH amide column (2.5 μm × 100 mm × 2.1 mm) using an Acquity H-class UPLC system (Waters Corporation, Milford, MA, USA) in analytical triplicates. UPLC gradients start with 15% A (A = 95:5 water/acetonitrile, 20 mM ammonium acetate adjusted to pH 9 using ammonium hydroxide) and 85% B (acetonitrile) at 0.15 mL/min. This was ramped up to 70%A for 10 minutes of isocratic hold. The column was washed after each injection using 98%A and re-equilibrated to starting conditions for 5 minutes post-injection. Waters Xevo TQD MS was used for mass spectrometry with polarity switching (positive mode 3kV, negative mode 2kV). Desolvation gas was set at 450°C with 900 L/hr gas flow. Source temperature was set at 150°C. Data were acquired in multiple reaction monitoring modes. The injection order was randomized to remove run order bias and quality control samples were injected before, during (once after every 10 injections) and after the run to account for instrumental drift. Data were processed using Waters TargetLynx software (v 4.1). Details of measured metabolites along with potential overlaps are described in a separate dataset ( https://doi.org/10.5281/zenodo.8164792 ).

Integrated data was imported in R (v3.2) for further processing. For every metabolite feature, a locally weighted scatterplot smoothing (LOESS) function was fitted to the quality control data. Metabolite features that appeared in less than 50% of the quality control samples were dropped along with features with relative standard deviation > 40%. The data was further normalized with the probabilistic quotient normalization technique using multtest package in R.

NMR spectroscopy and quantification of plasma 3-hydroxybutyrate levels

NMR experiments were performed as previously described [ 36 , 37 ]. Briefly, the dried samples were reconstituted with 200 μL of phosphate buffer (pH ~ 7.0) made in 10% D2O (Cambridge Isotope Laboratory, Andover, MA) containing 0.26 mM internal standard (4,4-dimethyl-4-silapentane-1-sulfonic acid/DSS, Cambridge Isotope Laboratory). All spectra were measured in 3 mm NMR tube (Cortecnet Corp, Brooklyn, NY) for further analysis. The spectra were acquired in 700 MHz AVANCE III HD spectrometer (Bruker Biospin, Billerica, MA) fitted with a triple resonance inverse (TXI) 3 mm probe and samplejet for automation. The pulseprogram took the shape of first transient of a 2-dimensional NOESY and generally of the form RD-90-t-90-tm-90-ACQ (ref). Where RD = relaxation delay, t = small time delay between pulses, tm = mixing time, and ACQ = acquisition. Water signal was suppressed using continuous irradiation during RD and tm. The spectra were acquired using 76 K data points and 14 ppm spectral width. Ninety-six scans were performed and 1 second interscan (relaxation) delay and 0.1 s mixing time were allowed. The FIDs were zero-filled to 128 K; 0.1 Hz of linear broadening was applied followed by Fourier transformation. 3-Hydroxybutyrate levels in the spectra were quantified using targeted profiting technique using Chenomx profiler V8.0 (Edmonton, AB, Canada).

Multivariate analysis

Metabolomics data were imported into Simca-P + 14.0 (Umetrics AB, Sweden). The data was first analyzed using principal component analysis followed by orthogonal partial least square discriminant analysis (OPLS-DA). The model was assessed using cross-validation parameter Q 2 Y and CV-ANOVA p.

Univariate analysis

Analytical triplicates were averaged using the median value. Two-factor ANOVA and unpaired t -test were carried out using in-house scripts written in R v4.1.2. All relevant plots were done using ggplot in scripts written in-house. All P -values were FDR corrected using Benjamini–Hochberg correction.

Enrichment analysis and pathway analysis

Enrichment analysis and pathway analysis were performed using Metaboanalyst 4.0 using KEGG database as the background for enrichment analysis and Metaboanalyst’s in-house disease metabolite database for disease enrichment analysis.

Aging and SD both affect the metabolic profiles

We employed UPLC-MS/MS-based metabolomics analysis to assess the effect of SD (5 hours, ZT0–ZT5) on young adult (2–4 months old, N = 10/group) and aged (20–22 months, N = 9/ group) animals. Metabolic profiles from plasma, liver, and hippocampus samples were subjected to biphasic extraction followed by analysis of the polar fraction by UPLC-MS/MS methods described earlier [ 35 , 38 ]. In total 114, 98, and 179 metabolites were identified from hippocampus, plasma, and liver, respectively. Data quality were assessed using principal component analysis (data not shown). Supervised orthogonal partial least square discriminant analysis (OPLS-DA) [ 39 ] was performed to identify class-specific differences for each tissue type. Simply put, such analysis leverages the group information of the sample set (e.g. SD status and age combinations) and extracts the maximum variance defined by the class identity of the samples. The analysis resulted in significant models (Q 2 = 0.74, 0.77, and 0.42 for plasma, liver, and hippocampus, respectively, CV-ANOVA p << 0.0001 for all models) and metabolic profiles of each tissue could be deconvoluted into two latent variables ( Figure 1 ). The first and major latent variables of models generated from each tissue (along the x -axes of Figure 1 ) distinguished the young adult SD animals from the other three classes (young adult control, aged control, and SD). This component explained 9.6%, 6.5%, and 7.6% of total variation in plasma, liver, and hippocampus metabolic profiles, respectively. This observation suggests that young adult animals are more affected by SD compared to aged animals irrespective of tissue type.

Effect of SD and aging on global metabolic profiles of mice. Orthogonal partial least square—discriminant analysis (OPLS-DA) scores plot of different tissue types across control and SD from both age groups. Each point is a biological sample while the associated error bars represent the deviation of analytical triplicates along each axis. The position of each point is determined by the multivariate concentration of the underlying metabolites of each tissue; similar metabolite levels will lead to clustering in the same area. In each tissue type, the first Latent variable ( x -axis) explains the effect of SD, most prominently observed by separation of the young adult SD group (purple). The second Latent variable ( y -axis) demonstrates similarity between aged samples and young adult SD-group. The model quality is evaluated by 1/7th cross-validation and CV-ANOVA [ 40 ].

The second latent variable, on the other hand, demonstrated that young adult SD animals cluster together with old animals irrespective of tissue type. This variation amounted to 5.8%, 4.5%, and 4.8% of total variation in plasma, liver, and hippocampus, respectively. This observation suggests that SD induces some metabolic variation in young adult animals that resemble aging-like behavior.

To further tease apart the effects of aging and SD on metabolic profile of specific tissue types, we performed 2-factor analysis of variance (ANOVA). These results revealed a tissue type dependence on the extent of metabolic perturbation. In the plasma, SD exerted more prominent effect with 46 metabolite levels altered (FDR < 0.2) while aging altered 29 metabolites (FDR < 0.2, Supplementary Figure S1 , Table S1 ). Aging altered 29 metabolites (FDR < 0.2) in liver, while SD altered 36 metabolites, ( Supplementary Figure S1 , Table S1 ). In hippocampus, while the overall metabolic impact is less pronounced, aging seemed to exert a more prominent effect with 11 metabolites affected by aging (FDR < 0.2) while only one, lipoamide, was significantly affected by SD ( Supplementary Figure S1 , Table S1 ). Global analysis across all metabolites suggests that it is a specific subset of metabolites that are responsible for the interaction of aging and sleep as opposed to a generic and broad impact. The correlation of explained variances of the metabolites by age and sleep were minimal (Pearson’s r for liver, plasma, and hippocampus are −0.04, 0.008, and −0.06, respectively, Supplementary Figure S2 ). Taken together, multivariate discriminant analysis revealed that SD impacts the metabolic profile of young adult animals such that a fraction of those changes resemble aging-like characteristics. Moreover, two-factor ANOVA results suggest that aging and SD both significantly impact metabolic profiles; however, the relative weights of SD and aging on the metabolic profile are tissue-type dependent.

Prediction of age and sleep status using perturbed metabolites

We hypothesized that a subset of metabolites perturbed by sleep and aging ( Supplementary Table S1 ) could be used to predict the sleep status and age of the animals. To test that, nine plasma metabolites were selected (GDP, GMP, lactate, oxalate, malate, N1-methyl-4-pyridone-3-carboxamide, hexanoylglycine, propionylcarnitine, and thymine) which are most significantly impacted by both aging and sleep in two-factor ANOVA analysis (FDR < 0.1). It should be noted; however, that our method is not suitable for completely resolving lactate and oxalate as these metabolites have similar mass and overlap with each other (please see dataset at https://doi.org/10.5281/zenodo.8164792 ). From the above panel, a multivariate discriminant analysis model was generated using OPLS-DA 7-fold internal cross-validation. The model was significant (Q 2 (cum) = 0.42, CV-ANOVA p < 0.0001) and showed distinct clustering of the four classes ( Figure 2A ). Receiver-operator characteristic curve for multiple groups ( Figure 2B ) showed successful classification as demonstrated by area under the curve (>0.8 for all classes). At the individual class level, more than 70% of samples were correctly predicted for each class ( Table 1 ). This result suggests that levels of specific plasma metabolite can be useful in predicting the sleep status and/or age of animals.

Classification Table of Prediction of the Four Classes (Young Adult Control and SD, Aged Control and SD) Using Nine Significantly Impacted Metabolites (Supplementary Table S1)

.	Control .	SD .
Young	7/10 (70%)	9/10 (90%)
Aged	7/9 (77.78%)	7/9 (77.78%)
Total—30/38 (78.95%)

.	Control .	SD .
Young	7/10 (70%)	9/10 (90%)
Aged	7/9 (77.78%)	7/9 (77.78%)
Total—30/38 (78.95%)

Classification of the animals using metabolites significantly impacted by SD and aging. Nine plasma metabolites significantly impacted by SD and aging were identified by two-factor analysis of variance. These metabolites were used to build multivariate classification modeling using OPLS-DA modeling resulting in distinct clustering of the four classes (young adult control, young adult SD, aged control, and aged SD) (A), The model quality is evaluated by 1/7th cross-validation and CV-ANOVA [ 40 ]. Receiver-operator characteristics curve was generated to estimate the performance of the classification model (B). Area under the curve from the respective classes in the ROC curve shows significant classification success of the model. Table 1 shows the misclassification table of prediction.

Metabolic changes with acute SD across the age groups

SD induced a subset of metabolic changes that were more prominent in young animals and distinct from the effects of aging per se ( Figure 1 , latent variable 1). To identify SD-induced metabolic alterations, groupwise comparisons were conducted across all tissues by t -test. This analysis provided further evidence that young adult animals are more impacted by SD compared to aged animals, particularly in liver, and plasma. Metabolites were assessed using statistical significance (FDR < 0.2) and effect size (Glass’s delta > 1.5) as a measure of substantive significance. Thus, both statistical and substantive significance were given importance. In plasma, 40 metabolites were statistically significantly altered in the young adult animals post SD ( Figure 3A and B , FDR < 0.2) compared to only seven in the aged animals ( Figure 3A , FDR < 0.2). Among the 40 altered metabolites in young animals, 17 were strongly increased (Glass’s delta > 1.5) and four metabolites were strongly decreased (Glass’s delta < −1.5, Figure 3B , Supplementary Table S1 ). Interestingly, three NAD degradation products (2 N-methyl-pyridone carboxamide derivatives and nicotinamide oxide) were decreased (Glass’s delta < −1.5, Figure 3B ). Overall, these results suggest SD substantially impacted the peripheral metabolic homeostasis of young animals while aged animals remained largely unperturbed.

Differential effect of SD on two separate age groups of animals. Student’s t -test(Young adult control vs. young adult SD and aged control vs. aged SD) was performed to identify the significant metabolites in response to SD across both age groups. Glass’s delta was also computed for each metabolite to judge the effect size relative to the respective control groups. The plots (A) show the significant metabolites (labeled in each panel, colored by fold change to respective control groups) from plasma across the two age groups, respectively. Levels of the significantly altering metabolites are also depicted in a heatmap (B). Significant plasma metabolites from the young adult animals only were subjected to disease enrichment analysis using Metaboanalyst 4.0 and the most significant disease enrichment (FDR < 0.2) are shown in C. Significant liver metabolites were used for pathway analysis and the most significantly impacted pathways (FDR < 0.2) are shown in D. Effect size of each metabolite in both young adult and aged animals from plasma (E) and liver (F) were plotted along with their distribution to demonstrate the global effect of SD across the different age groups. M-2-Pyr—N-methyl-2-pyridone-5-carboxamide, M-4-Pyr—N-methyl-4-pyridone-3-carboxamide,

In liver, 23 metabolites were altered in a statistically significant manner (FDR < 0.2, Supplementary Table S1 ) in young adult animals while no significant alterations were observed in the aged animals. Interestingly, we did not observe any statistically significant hippocampal changes at FDR < 0.2, although, lipoamide was found to increase (FDR = 0.27, Supplementary Table S1 ) post SD in young adult animals.

We then used the metabolites altered in plasma of young adult animals to perform a disease enrichment analysis using Metaboanalyst 4.0 [ 41 ]. We observed that SD signatures in young adult plasma mostly overlap with neurological (seizures, schizophrenia) and metabolic disorders of enzymatic origins ( Figure 3C ).

Using the liver metabolites ( Supplementary Figure S3 ) that are significantly perturbed in the young adult animals post-SD, we performed a pathway analysis to understand the most severely impacted pathways ( Table 2 , Figure 3D ). Ketone body metabolism, butanoate metabolism, glycine/serine/threonine metabolism, urea cycle, histidine metabolism, and nicotinamide metabolism were found to be most significantly impacted (FDR < 0.2). Among the pathways, ketone body metabolism was found to be most significantly impacted ( Figure 3D ). 3-hydroxybutyrate, one end product of ketone body metabolism, was highly elevated in the young adult animals post SD (FDR < 0.2, glass’s delta > 10.0, Supplementary Tale S1 ). Our UPLC-MS/MS method was unable to detect any of the ketone bodies in the blood plasma of the animals. Thus, we performed NMR spectroscopy on these samples to assess the level of 3-hydroxybutyrate and glucose. 3-hydroxybutyrate was significantly elevated ( p < 0.05) in the young SD plasma ( Supplementary Figure S4 ). Glucose was not significantly altered, however, it showed an increasing trend in the young adult SD plasma (data not shown). These observations suggest increased ketogenesis in young adult SD animals independently from any fall in glucose.

Hepatic Pathways Similarly Altered by Aging and SD in Young Adult Animals Obtained by Pathway Analysis (Metaboanalyst 4.0) Using Metabolites That are Similarly Altered by Aging and SD

Pathway .	Total .	Expected .	Hits .	Raw p .	FDR .	Impact .
Nicotinate and nicotinamide metabolism	15	0.069721	2	0.00189	0.090533	0.03
Histidine metabolism	16	0.074369	2	0.002156	0.090533	0
Aminoacyl-tRNA biosynthesis	48	0.22311	2	0.018865	0.52822	0
Arginine biosynthesis	14	0.065073	1	0.063409	1	0
Pantothenate and CoA biosynthesis	19	0.088313	1	0.085204	1	0
beta-Alanine metabolism	21	0.09761	1	0.093799	1	0
Alanine, aspartate and glutamate metabolism	28	0.13015	1	0.12334	1	0.22
Glycine, serine, and threonine metabolism	34	0.15803	1	0.148	1	0.07
Glycerophospholipid metabolism	36	0.16733	1	0.15609	1	0.009
Arginine and proline metabolism	38	0.17663	1	0.16411	1	0.08
Fatty acid degradation	39	0.18127	1	0.16809	1	0

Pathway .	Total .	Expected .	Hits .	Raw p .	FDR .	Impact .
Nicotinate and nicotinamide metabolism	15	0.069721	2	0.00189	0.090533	0.03
Histidine metabolism	16	0.074369	2	0.002156	0.090533	0
Aminoacyl-tRNA biosynthesis	48	0.22311	2	0.018865	0.52822	0
Arginine biosynthesis	14	0.065073	1	0.063409	1	0
Pantothenate and CoA biosynthesis	19	0.088313	1	0.085204	1	0
beta-Alanine metabolism	21	0.09761	1	0.093799	1	0
Alanine, aspartate and glutamate metabolism	28	0.13015	1	0.12334	1	0.22
Glycine, serine, and threonine metabolism	34	0.15803	1	0.148	1	0.07
Glycerophospholipid metabolism	36	0.16733	1	0.15609	1	0.009
Arginine and proline metabolism	38	0.17663	1	0.16411	1	0.08
Fatty acid degradation	39	0.18127	1	0.16809	1	0

We also observed signatures of altered hepatic arginine metabolism ( Figure 3D ) in plasma of young adult SD animals in the form of elevated dimethylarginine (FDR < 0.2, Glass’s delta > 2.0, Figure 3B ). Increased plasma dimethylarginine levels, coupled with decreased urea level in the liver of young adult SD animals suggest decreased urea synthesis and concomitant utilization of arginine to synthesize dimethylarginine and its release into the bloodstream.

Aged animals mimic the metabolic phenotypes associated with SD

Young adult animals are affected significantly more by SD compared to aged animals ( Figure 1 ). Similar observations were previously made in the learning/memory paradigm [ 7 ] primarily because of already worsened memory in aged animals. We asked if the reduced perturbation of aged animals by SD was a result of altered baseline of control animals or resistance to the effects of SD. For this purpose, we compared the effect sizes (Glass’s delta) of metabolites that are perturbed in young adult and aged animals after SD from the following comparisons—young adult control versus young adult SD and aged control versus aged SD. We hypothesized that if the aged controls are non-responsive to SD due to altered baseline in general, the effect sizes of aged metabolites after SD would be relatively low. We selected Glass’s delta analysis over other measures of effect sizes because we could define specific control groups for each analysis. We compared the effect sizes using paired t -tests for the metabolites ( Figure 3E and F ), which were found to be significant for plasma ( p < 0.05). The effect size values are distributed mostly around zero in both aged and young adult animals; however, young adult animals showed another prominent peak around two that is absent in the aged animals ( Figure 3E ). In the liver, several metabolites showed high effect sizes in the young adult animals that were absent in the aged animals ( Figure 3F ). In both liver and plasma, we conclude that SD leads to effective change in the metabolite level only in young animals, while the average metabolite levels in aged SD animals remained similar to the respective control group. These observations suggest that aged animals may appear less sensitive to effects of SD in part because they already exhibit similar metabolic changes at baseline, thereby minimizing the apparent effects of SD.

Overall, metabolomics analysis across different tissue types suggests that young adult animals are more impacted compared to old animals post-SD. Altered plasma metabolic profiles of young adult animals are associated with neurological and metabolic disorders, as well as altered hepatic metabolic pathways. On the other hand, aged animals seem to be much less perturbed by SD most likely because of an elevated baseline.

Specific plasma and liver metabolites are significantly impacted by both sleep and aging

We have shown that SD impacts young adult animals more than aged animals from a metabolic perspective, most likely because the aged animals are already experiencing metabolic effects similar to that of SD because of aging process. This observation raises the possibility of a set of metabolites that changes similarly in response to aging and SD in young adult animals. The presence of such a set of metabolites is already evident from the second component of the tissue-specific OPLS-DA modeling ( Figure 1A–C ). We plotted the second component from the OPLS-DA models of each tissue (plasma, liver, and hippocampus, Figure 4A ) to demonstrate that young SD animals move closer to the aged animals. To identify the specific metabolites related to this signature, we analyzed the effect sizes obtained from the sets of young adult control versus young adult SD and young adult control versus aged control across the tissue types. This approach allowed us to investigate the effects of SD and aging on the metabolite level separately relative to the specific controls followed by identification of commonly perturbed metabolites. In plasma, we found eight metabolites (arginine, cystine, serine, kynurenine, methylguanosine, acetylornithine, lactate, and oxalate, Figure 4B , Supplementary Table S1 ) to be altered similarly by aging and SD (absolute Glass’s delta > 1.0 in both analyses), the majority of which were found to be significantly altered in both analyses (FDR < 0.2). In liver, we observed 11 metabolites (cis-5-tetradecenoylcarnitine, palmitoylcarnitine, octadecadienoylcarnitine, nicotinamide mononuecleotide, nicotinamide riboside, aspartate, LPC 18:1, proline, acetylserine, dimethylglycine/2-aminobutyrate, and imidazoleacetic acid, Supplementary Table S1 , Supplementary Figure S5 ). Three of those metabolites, dimethylglycine/2-aminobutyrate, acetylserine, and imidazoleacetic acid were significantly (FDR < 0.2) altered by both aging and SD. We then performed pathway analysis using the hepatic metabolites to identify the metabolic pathways that are commonly altered by both aging and SD. Nicotinamide metabolism was found to be most strongly altered ( Table 2 ). Finally, hippocampus showed tryptophan and kynurenine to be altered similarly by both aging and SD (Glass’s delta > 1, Supplementary Table S1 ), however, none of them achieved statistical significance in either analysis.

Overlapping metabolic profiles of SD and aging. Metabolites altered similarly in response to SD and aging were identified in plasma and liver. Hippocampus did not show any overlapping metabolite. Significantly altered metabolites (FDR < 0.2) by SD in young adult animals (Young adult control vs. young adult SD) and aging (Young adult control vs. aged control) were identified using Student’s t -test. Directionality of the overlapping metabolites were judged using the effect sizes computed by Glass’s delta relative to the young adult control animals in each analysis. Boxplot representation of second latent variable (LV2) of OPLS-DA scores plots from all tissues are shown in (A) Relative levels of the plasma metabolites are depicted in the boxplots (C). Liver metabolites are shown in Supplementary Figure S3 .

Overall, these observations support the notion that a fraction of metabolic variation induced by SD in young adult animals parallels aging-like features. Hepatic nicotinamide metabolism was altered by both aging and SD in young adult animals while several plasma metabolites were altered similarly by both aging and SD.

Young adult animals display aging-like molecular and clinical phenotypes after acute sleep deprivation

Interest in comprehensive metabolic profiling of SD has grown over the past decade [ 5 , 6 , 19 , 20 , 31 ]. Overlap between phenotypes induced by both aging and SD is striking. For example, SD (experimental or behavioral) was shown to decrease attention and memory [ 7 ], increase lipid oxidation [ 42 ], and increase the rate of telomere depletion [ 38 , 43 , 44 ]. Thus, molecular phenotypes at gene expression and metabolic levels are also expected to overlap between SD and aging. Transcriptomic data from medial prefrontal cortex shows some overlap between genes that are differentially expressed in response to SD in young adult animals and aging process [ 45 ]. As far as we are aware, similar data does not yet exist in the metabolic realm. We demonstrate that a fraction of plasma and liver metabolites are affected similarly by both aging and SD in young adult animals, thereby forming a metabolic milieu that suggests SD makes young seem old. Such effect is most prominent in the blood plasma of the animals ( Figure 4 ). In blood plasma, eight such metabolites were similarly impacted by aging and SD ( Figure 4B ). Among these metabolites, the elevated level of kynurenine is striking ( Figure 4B ). Kynurenine is a metabolite of tryptophan that is reported to increase with aging and drives several aging phenotypes such as sarcopenia [ 46 ] and bone loss [ 47 ]. In addition, tryptophan metabolism via the kynurenine pathway is also thought to drive low-grade chronic inflammation that is associated with aging (“inflammaging”) [ 48 ]. Phenotypes such as sarcopenia and elevated inflammation have been associated with chronic SD [ 49 , 50 ] as well. Although we investigated only an acute SD regimen and could not investigate chronic effects like sarcopenia, it is plausible that chronic accumulation of kynurenine due to SD in young adult animals will lead to similar aging phenotypes [ 46 ].

In general, amino acids and derivatives such as serine, acetylornithine, arginine, and cystine are elevated in response to SD and aging. Serine was shown to be a regulator of sleep in humans [ 51 ]. Several studies in fruit flies have shown that both L- and D-serine have crucial roles in regulation of sleep [ 52 , 53 ]. Serine is also crucial in the regulation of proliferating immune cells and epigenetic regulation in aging [ 54 ]. Overall, individual plasma metabolic changes induced by SD in young adult animals suggest that SD induces a peripheral metabolic environment that resembles aging.

The effect on the plasma metabolome of the young adult animal may arise from changes in hepatic metabolic pathways induced by SD ( Figures 3D and 5 ). In the liver of the young adult animals, ketone body metabolism was found to be highly enriched due to significant elevation of acetoacetate and 3-hydroxybutyrate ( Figures 3 and 5 ), which in turn could be a result of elevated fatty acid oxidation reportedly promoted by SD [ 55 ]. 3-hydroxybutyrate was also elevated in the plasma of the young adult animals ( Supplementary Figure S4 ), most likely as a direct result of hepatic ketogenesis. Short SD has been shown to elevate ketosis in rodents with impacts on brain energy metabolism [ 56 ]. Specifically, brain glutamate release was shown to be impacted by plasma ketone body levelNicotinamide metabolism was also significantly impacted in the young adult liver as evidenced by the downstream degradation products of NAD ( Figure 5 ), also observed in blood plasma. Elevated nicotinamide mononucleotide and nicotinamide riboside are two of the most prominent signatures overlapping between aging and SD ( Supplementary Figure S5 ,). Both metabolites are involved in NAD biosynthesis but may alternatively produced through certain degradation pathways. Peripheral NAD is depleted with normal aging [ 57 ] most likely by elevated degradation to nicotinamide [ 58 , 59 ]. Plasma NAD showed slight, but insignificant, depletion in young SD animals ( Supplementary Table S1 ). Our results suggest that hepatic changes in NAD metabolism following SD parallel aging in leading to degradation of NAD, which might further lead to alterations in mitochondrial function, a hallmark of aging [ 25 ]. Nevertheless, these results suggest an impact of SD on NAD metabolism that warrants further investigation.

Schematic changes in hepatic metabolic pathways in young adult animals in response to SD and corresponding changes in the plasma. Metabolites altered by SD in young adult livers were subjected to pathway enrichment analysis. Significantly altered hepatic metabolic pathways are presented. Significantly altered plasma metabolites associated with these pathways are shown to be a direct result of changes in hepatic metabolism in response to SD. Metabolites in green are decreased in SD while those in red are increased.

The global metabolic changes induced by SD in young adult animals also resemble phenotypes of aging-related disorders ( Figure 3C , Supplementary Table S2 ). Three urea cycle-related enzymatic disorders—argininosuccinic aciduria, ornithine transcarbamylase (OTC) deficiency, and hyperornithinemia—were found to be enriched in the young adult animals post-SD. Although these disorders are primarily recognized as inborn errors of metabolism [ 60 ], the urea cycle is known to be regulated by the sirtuin SIRT5, which directly affects carbamoylphosphate synthase and OTC activity [ 61 ]. SIRT5 activity is, in turn, regulated by availability of NAD [ 62 ]. It is possible that the aging-induced decrease in NAD bioavailability alters the sirtuin function, thereby altering the function of urea cycle.

Several neurological disorders were also found to be enriched in the young adult SD mice ( Figure 3C ) including different seizure disorders. SD is known to induce epileptiform discharges [ 63 ] and it is believed that lack of sleep increases most seizure events [ 64 ]. On the other hand, aging process also increases epileptic/seizure events [ 64 ]. The relationship between aging and seizure events is thought to be mediated by underlying conditions such as cerebrovascular physiology and neurodegeneration, conditions that are also mediated by chronic SD [ 65 ]. Ketogenesis is thought to promote protective effects from seizure and epileptic events [ 66 ]. It is possible that elevated ketosis in young adult SD animals may be a natural response to SD-induced epileptic features in young adult animals. Therefore, SD in young adult animals induces molecular phenotypes that reflect typical clinical phenotypes of aged individuals.

Young adult animals are more impacted by SD than aged animals

Previous studies explored the relationship between SD and phenotypes such as learning/memory and attention [ 67 , 68 ]. Importantly, SD seems to have a differential effect depending on age. Specifically, young adult animals are more impacted by aging compared to older animals [ 7 , 16 , 67 ]. Such observation suggests that there may be a complex interplay of aging and SD phenotypes at the molecular level. Indeed, we observed similar differential effect of SD on the multivariate metabolic profile across plasma, liver, and hippocampal metabolic profiles ( Figure 1 ). Using a two-factor ANOVA approach, we teased apart the relative contribution of aging and SD towards changes in metabolic profiles. Plasma is impacted much more by SD, while hippocampus alteration is largely driven by aging ( Supplementary Figure S1 ). Interestingly, almost no interaction effect was found across tissues (except 2 in liver—acetylserine and 3-hydroxybutyrate) suggesting the metabolic responses are either unique or parallel. This observation further raises the possibility that metabolic response towards SD may be differentially modulated at different ages. Indeed, we observed young adult animals to be more affected by SD compared to old animals across liver and plasma metabolic profiles. As such, more than 33% of the detected plasma metabolites were impacted by SD in young animals relative to ~6% in aged animals. In liver, ~13% of detected metabolites were impacted by SD in young animals relative to none in older animals.

Overall, this data shows that the metabolic profile of young adult animals is more impacted by SD compared to old animals in plasma and liver. How the aged animals manage to escape the effects of SD remains an outstanding question. One possibility is that the aged animals are more resistant to SD compared to young adult animals. However, data from learning/memory paradigm suggests that aged animals do not recover from working memory impairments caused by SD in the long term as baseline performance also worsens [ 7 ]. Based on these results, we propose that aged animals are resistant to the metabolic effects of SD due to a higher metabolic baseline in older mice, which perhaps leaves less room for SD-induced metabolic changes to occur. We confirmed this hypothesis by computing the effect sizes of all the metabolites separately in young adults and aged animals ( Figure 3E and F ). Specifically, young adult plasma and liver showed several metabolites with moderate to high effect sizes, which was absent in aged animals. This observation suggests that aged animals are already in a “metabolically sleep deprived state” resulting in minimized effect of SD. Earlier epidemiological findings show that the sleep architecture of adults changes with age, as total sleep time, sleep efficiency, %REM, and slow wave sleep decreased over time [ 14 ]. Using the same animal model in a previous study, we have also shown that aged mice have decreased wakefulness during the active period and they could not sustain the sleep–wake states [ 15 ], resulting in more fragmented periods of both wakefulness and NREM sleep. One possibility is that the boundary of sleep and wakefulness becomes more diffuse as individuals age. Therefore, the impact of SD is physiologically blunted in the aged animals compared to the young adult animals.

Metabolic signatures to identify sleep status and age

So far, we described two major variations in this study—first, metabolic pathways that are impacted significantly in young adult animals compared to older animals in response to SD. Second, another set of pathways is impacted similarly by aging and SD in young animals. We also identified a third set of nine plasma metabolites (GDP, GMP, propionylcarnitine, N1-methyl-4-pyridone-3-carboxamide, lactate, oxalate, hexanoylglycine, malate, and thymine) that can be used to classify four groups based on age and sleep status ( Figure 2 ). Several of these metabolites were previously reported to be impacted by aging and sleep [ 5 , 6 , 69 , 70 ]. However, a unique finding here is that the plasma level of these metabolites potentially indicates the sleep status and age group together. When coupled with the fact that SD induces aging-like neurobehavioral characteristics in young animals, this observation suggests a set of biomarkers that could potentially be used to quantify the aging effect of SD on young animals.

To the best of our knowledge, this is the first study to address the overlapping effects of sleep and aging on metabolite profile. However, the primary limitation of the study is its exploratory nature. Despite the existence of considerable amount of knowledge on metabolic effects of aging and emerging knowledge of metabolic effects of SD, the cross-sectional space is unexplored. Though this study, like other omics studies, generates several hypotheses regarding the overlapping metabolic effects of sleep and aging without providing much mechanistic insights, there are several exciting potential avenues for future work to be pursued. For example, it is not yet known whether these metabolic changes are temporary or permanent. We suspect the former given that other effects of acute SD can be rescued with a period of recovery sleep [ 7 ]. Our findings also beg the question of what are the effects of chronic sleep restriction and how it differs from acute SD. Secondly, we used only two extreme age groups in the current study. Such a design helps to identify broad metabolic changes in response to SD and aging. However, this design does not address the temporal changes that are normally associated with aging and the effect of SD thereof. Carefully designed longitudinal studies could address the temporal impact of SD on aging. All of these questions are worth pursuing in the future.

In this study, we have demonstrated that the metabolic effects of SD vary across age groups. Specifically, young adult animals are affected more than old animals. Metabolic effects of SD on aged animals are likely obscured by the effects of aging. This observation parallels the fact that memory impairments induced by SD are normalized in aged animals, but not in young adult animals since memory impairment induced by aging outweighs effects of SD. Moreover, several metabolites are similarly impacted by SD in young adult and aged animals leading to a milieu of metabolites that make “young seem old.” Interestingly, metabolites that are recognized to be associated with aging-related disorders were altered in young adult animals after SD. These observations suggest acute SD affects the metabolic physiology of young adult animals to resemble aging effects. Overall, our findings strengthen the hypothesis that SD affects the physiology of organisms to mimic or perhaps even accelerate aging.

TA and JT are supported by R01 AG 062398. TA was supported by the Brush Family Chair in Biology at the University of Pennsylvania and the Roy J. Carver Chair in Neuroscience at the University of Iowa. AMW and AS are supported by 5R21AG052905.

Financial disclosure: TA serves on the Scientific Advisory Board of EmbarkNeuro and is a scientific advisor to Aditum Bio and Radius Health. TA was supported by the Roy J. Carver Chair of Neuroscience and is supported by NIH RO1 AG 062398. JB reports receiving research funding and materials from the NIH, Pfizer, Elysium Health, and Metro International Biotech; and consulting fees from Pfizer, Elysium Health, and Cytokinetics; he holds a patent for using NAD + precursors in liver injury. Nonfinancial disclosure: None.

Lead contact—Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Aalim M Weljie ([email protected]). Materials availability—The study did not generate any new material. Data and code availability—The dataset is available in zenodo (doi: 10.5281/zenodo.7900329 ), the statistical processing R code is available from the corresponding authors ([email protected], [email protected]).

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Sleep deprivation.

Productivity - Effect of sleep deprivation on.

Effects of Sleep Deprivation

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Rob writes about the intersection of sleep and mental health and previously worked at the National Cancer Institute.

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Table of Contents

What Are the Effects of Sleep Deprivation?

How much sleep is enough, symptoms of sleep deprivation, causes of sleep deprivation, treatment for sleep deprivation, preventing sleep issues.

Sleep deprivation happens when a person does not get the sleep they need to sustain their health and well-being. It is common for people to sacrifice sleep for work, school, or fun, but even one night of inadequate sleep can leave people feeling tired, less productive, and more prone to mistakes the next day.

Nearly half of people in the U.S. have trouble sleeping, and around one-third of adults sleep less than seven hours each night. Without enough sleep, the body begins to accumulate sleep debt.

As sleep debt grows over time, it begins to take a toll on mental and physical health . Long-term sleep deprivation can reduce quality of life and may increase the risk of health issues including obesity, diabetes, and cardiovascular disease.

Learn more about the impacts of sleep deprivation, including its causes, and how prioritizing sleep hygiene can help people get the rest they need.

Research has found that sleep deprivation affects systems throughout the body, leading to a wide range of negative effects.

Daytime sleepiness: Not getting enough sleep is a common cause of people feeling tired during the day Trusted Source UpToDate More than 2 million healthcare providers around the world choose UpToDate to help make appropriate care decisions and drive better health outcomes. UpToDate delivers evidence-based clinical decision support that is clear, actionable, and rich with real-world insights. View Source . Daytime sleepiness can leave a person without the energy to do the things they enjoy and cause problems at work, school, and in relationships.
Impaired mental function: One of the most noticeable effects of sleep loss is cognitive impairment. As sleep debt grows Trusted Source Centers for Disease Control and Prevention (CDC) As the nation’s health protection agency, CDC saves lives and protects people from health threats. View Source , a person becomes less alert and may have difficulty multitasking. Reductions in attention make a sleep-deprived person more prone to mistakes, increasing the risk of a workplace or motor vehicle accident.
Mood changes: Sleep loss can lead to mood changes and make a person feel more anxious or depressed. Without enough sleep, people may feel irritable, frustrated, and unmotivated. They may also struggle to deal with change and to regulate their emotions.
Reduced immune function: Sleep is important for maintaining a healthy immune system, so sleep deprivation can weaken immune function. In fact, research suggests that people who are sleep deprived are less responsive to the flu vaccine and are more likely to get infections like the common cold.
Weight gain: Sleep is important for maintaining a healthy weight Trusted Source National Heart, Lung, and Blood Institute (NHLBI) The NHLBI is the nation's leader in the prevention and treatment of heart, lung, blood and sleep disorders. View Source . Not getting enough sleep can affect appetite and metabolism in ways that can lead to weight gain. Insufficient sleep has been associated with an increased risk of obesity.

Sleep deprivation can have a drastic effect on the ability to safely drive a car. Not only does sleep loss reduce a person’s ability to pay attention Trusted Source National Heart, Lung, and Blood Institute (NHLBI) The NHLBI is the nation's leader in the prevention and treatment of heart, lung, blood and sleep disorders. View Source and react quickly , it can also lead to microsleeps, which involve unknowingly falling asleep for a brief moment. Drowsy driving is linked to tens of thousands of injuries Trusted Source National Highway Traffic Safety Administration (NHTSA) Through enforcing vehicle performance standards and partnerships with state and local governments, NHTSA reduces deaths, injuries and economic losses from motor vehicle crashes. View Source and hundreds of deaths in the U.S. each year.

Insufficient sleep increases risks in almost every system of the body.

When sleep loss becomes a regular occurrence, chronic sleep deprivation can lead to changes in the nervous system, contribute to long-term health complications, and exacerbate chronic medical conditions.

Diabetes: A lack of sleep can make it more difficult for the body to process sugar, contributing to glucose intolerance Trusted Source National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) NIDDK research creates knowledge about and treatments for diseases that are among the most chronic, costly, and consequential for patients, their families, and the Nation. View Source and increasing the risk of type 2 diabetes.
Heart disease: During normal sleep, blood pressure drops in ways that are believed to support heart health. Sleep deprivation prevents this drop in blood pressure and triggers inflammation, heightening the risk of cardiovascular diseases , such as heart disease and stroke.
Mental health conditions: Sleep deprivation is closely linked to mental health Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source . Sleep loss may increase the risk of mental health issues, and those issues can make it harder to get enough sleep.

Experts have created guidelines Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source for the amount of sleep needed to maintain optimal mental health, physical health, and emotional well-being. Without this amount of sleep, people begin to accumulate sleep debt and experience the consequences of sleep deprivation.

Age Group	Age Range
Infant	4-12 months	12-16 hours (including naps)
Toddler	1-2 years	11-14 hours (including naps)
Preschool	3-5 years	10-13 hours (including naps)
School-age	6-12 years	9-12 hours
Teen	13-18 years	8-10 hours
Adult	18 years and older	7 hours or more

Sleep needs vary across the lifespan but also from person to person. This means that how much sleep an individual needs depends on more than just their age. For example, some people may be naturally long or naturally short sleepers who require more or less than the recommended number of hours to wake up feeling rested.

How much sleep a person needs also depends on their health and typical daily activities. In the short-term, the need for sleep is temporarily increased after demanding activities, when a person is sick, or when recovering from a period of sleep deprivation.

Additionally, avoiding sleep deprivation is about more than just spending enough hours in bed. Healthy and restorative rest also depends on the quality of sleep. So even if a person gets the right amount of hours of sleep, they may still be sleep deprived if their sleep quality is reduced from waking up too often at night.

The symptoms of sleep deprivation may be obvious or subtle depending on how much sleep is missed and how accustomed a person is to sleep deprivation. Signs to watch out for include:

Waking up feeling unrefreshed
Daytime sleepiness
Falling asleep unexpectedly during the day
Difficulty functioning at home, work, or school
Trouble concentrating and slow reaction times
Mood changes and problems controlling emotions
Spending more than 30 minutes trying to fall asleep
Feeling tiredness in the morning despite a full night of sleep
Waking up frequently during the night
Snoring loudly or gasping for air while sleeping

Some symptoms of sleep deprivation may look different in children than in adults. In addition to dozing off during the day, children with sleep deprivation may exhibit an increase in energy or hyperactivity. Children may also have frequent changes in mood, difficulty controlling their behavior, or poor academic achievement.

There are many potential causes of sleep deprivation, ranging from natural changes in the body as people age to an undiagnosed medical condition or sleep disorder.

In teens, sleep deprivation can develop because of changes during puberty that lead adolescents to prefer later bedtimes. This natural preference for late nights often conflicts with early morning school schedules, making it difficult for teens to get the sleep they need.

In women and people assigned female at birth, sleep loss can occur at certain times during their menstrual cycle. People commonly have fragmented sleep in the week before their period begins Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source . Sleep loss is also common during and after pregnancy and during menopause.

Other causes of sleep deprivation include poor sleep habits, busy schedules, and health issues that interfere with getting enough quality rest.

Poor sleep habits: Daytime habits can either help or hinder nighttime sleep. Sleep deprivation can be caused by poor sleep habits, such as using a cell phone, TV, or other electronics in bed, drinking caffeine too close to bedtime, or having an inconsistent sleep schedule.
Full schedules: A common reason for losing sleep is a busy schedule that involves activities in the late evening. People who work late or overbook themselves in the evening may sacrifice sleep in hopes of sleeping in on the weekend. Unfortunately, extra sleep on the weekend is not able to fully compensate for lost sleep during the week.
Stress: Stress is a natural reaction to challenging situations, but if left unchecked it can make it more difficult to fall asleep Trusted Source Medline Plus MedlinePlus is an online health information resource for patients and their families and friends. View Source at night. Excess stress causes the body to release hormones that trigger alertness, which can interfere with normal sleep.
Issues in the sleep environment: A person’s sleep environment can have a significant impact on their sleep. People who live in noisy areas may find it difficult to get quality sleep. Sleep can also be interrupted by too much ambient light and temperatures that are too hot or too cold.
Medical conditions: Many medical problems can interfere with sleep, including pain, heart failure, and asthma. Some medical conditions may flare up Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source or get worse at night, like acid reflux or chronic obstructive pulmonary disease (COPD).
Medications and substances: A wide variety of medications can interrupt sleep or make it more challenging to doze off. These include certain steroids, decongestants, pain medications, and drugs used to treat anxiety and depression.
Mental health conditions: Several mental health conditions are linked to sleep challenges, including depression, anxiety, and bipolar disorder, as well as attention deficit hyperactivity disorder (ADHD) Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source , and autism spectrum disorder.

Another potential cause of sleep deprivation is an undiagnosed or untreated sleep disorder . In fact, as many as 70 million people in the U.S. live with a chronic sleep disorder. Sleep disorders that can make it difficult to get enough sleep include insomnia, obstructive sleep apnea, and restless legs syndrome.

Treatment for sleep deprivation involves finding ways to get more hours of high-quality sleep. The best approach to achieving this depends on the cause of an individual’s sleep problems. In people with persistent sleep deprivation, it may take several weeks or longer Trusted Source Merck Manual|MSD Manuals View Source to resolve the symptoms of sleep loss.

When symptoms of sleep loss continue despite an improvement in sleep habits, working with a doctor is an important step in addressing the causes of sleep problems. To help determine the cause of sleep deprivation, a doctor may ask questions related to routines and sleep habits, such as:

What time do you go to bed and wake up each day, including on the weekends?
What is your work schedule?
Is sleep refreshing or is it difficult to get up in the morning?
Do you wake up often during the night?
How often do you take daytime naps?
Does daytime sleepiness ever interfere with your life?

Using this information, a doctor may recommend additional tests to find the source of sleep issues. A doctor may suggest starting a sleep diary to keep track of symptoms and habits that may be causing sleep deprivation.

To reduce the risk of sleep deprivation, it is important to take steps to improve sleep hygiene .

Make sleep a priority: Prioritize sleep health by creating a comfortable sleep environment and keeping a consistent sleep schedule. This means going to sleep and waking up at around the same time each day and avoiding the temptation to stay up later or sleep in on the weekends.
Combat stress: To combat bedtime stress, give yourself plenty of time to wind down from the day. Use this time to listen to calming music, stretch, or write in a journal. Boost your ability to relieve stress by trying out new relaxation techniques and seeing what helps the most.
Time your light exposure: Ambient light can signal to the body whether it is time to be awake or prepare for sleep, so be intentional about light exposure. Try to get at least 30 minutes of sunlight exposure during the day and then dim or turn off lights in the evening. Shut off phones, TVs, and computers at least an hour before bed.
Watch your caffeine intake: Caffeine can linger in the body for eight or more hours, so consuming caffeine in the afternoon may affect how long it takes to doze off at bedtime.
Nap wisely: Although they cannot replace quality nighttime rest, naps can be a helpful tool to improve daytime alertness. If naps are too long or poorly timed, though, they can make it more difficult to fall asleep in the evening. Adults should aim for naps that are no longer than 20 minutes and should avoid napping in the late afternoon.
Stay active: Regular exercise can make it easier to get to sleep at bedtime. Try to get at least 30 minutes of physical activity every day, but it is best to avoid highly strenuous exercise too close to bedtime.

Medical Disclaimer: The content on this page should not be taken as medical advice or used as a recommendation for any specific treatment or medication. Always consult your doctor before taking a new medication or changing your current treatment.

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The impact of dietary habits on sleep deprivation and glucose control in school-aged children with type 1 diabetes: a cross-sectional study.

1. Introduction

2. materials and methods, 2.1. design and setting, 2.2. variables and measurements, 2.2.1. sleep deprivation scale for children and adolescents, 2.2.2. dietary habits index, 2.3. data analysis, 2.4. ethical considerations, 4. discussion, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Characteristics	n (%)/Mean ± SD	Min–Max

Female	47 (47)
Male	53 (53)
Age (year)	10.14 ± 1.79	7.00–13.00
Height (cm)	140.79 ± 14.27	105–170
BMI (kg/m )	19.02 ± 3.96	12.98–44.62
Education level
No education	1 (1)
Primary school	34 (34)
Secondary school	65 (65)
Physical activity level
None	23 (23)
Lower than 30 min	32 (32)
More than 30 min	45 (45)
HbA (%)	8.28 ± 1.82	5.90–13.00
Duration of Diabetes (year)	2.81 ± 2.01	0–10
Age at diabetes diagnosis	7.05 ± 2.21	2–11
Presence of diabetes in the family
Yes	60 (60)
No	40 (40)
The degree of proximity
First degree	28 (28)
Second degree	30 (30)
Third degree	1 (1)
Fourth degree	1 (1)
Presence of other diseases
Yes	26 (26)
No	74 (74)
Names of other diseases
Hypertension	1 (3.8)
Celiac disease	18 (69.2)
Other	7 (27)
Frequency of blood glucose monitoring
Once a day	6 (6)
Twice a day	61 (61)
Three times a day	7 (7)
Every hour	3 (3)
Every 30 min	23 (23)
Meal time a day
Total number of meals	5	3–6
Number of main meals	3	2–4
Number of snacks	2	0–4

Scales	M ± SD	Min–Max
Dietary Habits Index Score	11.59 ± 3.36 *	3–18
Sleep Deprivation Scale for Children and Adolescents Score	32.14 ± 11.46	15–60

Variables		Dietary Habits Index Score	Sleep Deprivation Scale for Children and Adolescents Score	HbA
Dietary Habits Index Score	r	1
Dietary Habits Index Score	p
Sleep Deprivation Scale for Children and Adolescents Score	r	0.307	1
Sleep Deprivation Scale for Children and Adolescents Score	p	* 0.002
HbA	r	0.528	0.181	1
HbA	p	** <0.001	0.075

Variables	Beta	Standard Error	β *	t **	p	95% CI

Constant	4.876	0.585		8.340	0.000	3.715	6.036
Dietary Habits	0.289	0.048	0.528	5.996	0.000	0.194	0.385
R ***	0.528
R ****	0.279
F *****	35.952
p	<0.001
DW	1.776

Variables	Beta	Standard Error	β *	t **	p	95% CI

Constant	19.907	4.052		4.913	0.000	11.863	27.952
Dietary Habits	1.057	0.336	0.307	3.146	0.002	0.390	1.724
R ***	0.307
R ****	0.094
F *****	9.895
p	>0.001
DW	2.270

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Share and Cite

Askin Ceran, M.; Keser, M.G.; Bektas, M.; Unusan, N.; Selver Eklioglu, B. The Impact of Dietary Habits on Sleep Deprivation and Glucose Control in School-Aged Children with Type 1 Diabetes: A Cross-Sectional Study. Children 2024 , 11 , 779. https://doi.org/10.3390/children11070779

Askin Ceran M, Keser MG, Bektas M, Unusan N, Selver Eklioglu B. The Impact of Dietary Habits on Sleep Deprivation and Glucose Control in School-Aged Children with Type 1 Diabetes: A Cross-Sectional Study. Children . 2024; 11(7):779. https://doi.org/10.3390/children11070779

Askin Ceran, Merve, Muteber Gizem Keser, Murat Bektas, Nurhan Unusan, and Beray Selver Eklioglu. 2024. "The Impact of Dietary Habits on Sleep Deprivation and Glucose Control in School-Aged Children with Type 1 Diabetes: A Cross-Sectional Study" Children 11, no. 7: 779. https://doi.org/10.3390/children11070779

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DOI: 10.1016/j.ynstr.2024.100655
Corpus ID: 270491838

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Open access
Published: 28 June 2024

Adolescents’ short-form video addiction and sleep quality: the mediating role of social anxiety

Li Jiang 1 &
Yizoon Yoo 2

BMC Psychology volume 12 , Article number: 369 ( 2024 ) Cite this article

19 Accesses

Metrics details

Adolescence is a critical period for individual growth and development. Insufficient sleep adversely affects adolescents’ physical development, blood pressure, vision, and cognitive function. This study examined the effect of short-form video addiction on adolescents’ sleep quality, as well as the mediating role of social anxiety, to identify methods for improving adolescents’ sleep quality in the Internet era.

A questionnaire survey was conducted in this cross-sectional study on 1629 adolescents recruited from three high schools. Their short-form video addiction, social anxiety, and sleep quality were evaluated using corresponding scales. Pearson correlation analysis was carried out to analyze the relationships among short‐form video addiction, sleep quality, and social anxiety. Mediating effect analysis was constructed using AMOS 20.0 statistical software.

Participants’ sleep quality score is 6.12 ± 3.29 points. The detection rate of sleep quality among them is 31.06%. Short‐form video addiction, sleep quality, and social anxiety are significantly correlated ( r = 0.439, 0.404, 0.457, P < 0.001). The direct effect of short-form video addiction on sleep quality is 0.248, accounting for 62.4% of the total effect. The indirect effect exerted through social anxiety is 0.149, accounting for 37.6%.

Conclusions

Sleep disorders are very common among Chinese adolescents. Short‐form video addiction is positively correlated with adolescents’ sleep quality and social anxiety. Social anxiety partially mediates the relationship between short-form video addiction and sleep quality. The adverse effects of short-form video addiction and social anxiety on the sleep quality of this group must be minimized. Schools are recommended to implement measures to promote sleep quality among adolescents.

Peer Review reports

Introduction

Sleep is one of the most basic physiological requirements for human beings. Adequate sleep not only promotes bodily growth, physical development, and immune function regulation but also contributes significantly to mental health [ 1 ]. Adolescents are in a special period of physical, cognitive, emotional, and interpersonal changes, during which sleep has unique implications for their growth [ 2 ]. Compared with college students and children, adolescents have more prominent sleep problems, with a higher incidence of sleep disorder and a trend of increasing deterioration [ 3 ]. Besides short sleep hours, adolescents have serious problems with sleep quality [ 3 ]. Sleep problems lead to a series of adverse outcomes, including decreased immune function, obesity, traffic accidents, substance abuse, depression, and suicidal tendency [ 1 , 2 , 3 ]. Psychological factors such as resilience, loneliness, depression, and stress can decrease individuals’ sleep quality [ 4 ]. Physiological and behavioral factors such as fatigue and excessive use of the Internet and smartphones can also affect adolescents’ sleep quality [ 5 ].

Short-form video, a new type of Internet-based content dissemination method emerging in recent years, has gradually become an important factor affecting adolescents’ sleep quality. Short-form videos are highly popular because of their rich and diversified content, favorable human–computer interaction mode, easy access, and short viewing time [ 6 ]. The number of short-form video users in China had reached 934 million by December 2021 [ 7 ]. As pointed out in “Blue Book of Teenagers: Annual Report on the Internet Use by Minors in China (2021),” adolescents have become the largest group of short-form video users [ 7 ]. A study on the negative effects of Internet use reveals that an increase in online time can lead to emotional changes, depression, and intense psychological conflicts, negatively affecting daily life. As a new form of Internet addiction, short-form video addiction refers to a chronic or periodic obsession state featured by the repeated use of short-form video apps (such as Tiktok, Kwai, etc.), resulting in strong and continuous craving and addiction [ 8 ]. Among the youth population in China, 49.3% of underage netizens watch short videos online. In addition, 60.4% of adolescent short video users reported watching short videos on Tiktok, and 59.3% of them reported watching them on Kwai [ 9 ]. Ye et al. [ 10 ] found in their research on college students that short-form video addiction exacerbates the negative effects on students’ academic and physical and mental health, for example, reducing learning motivation and increasing depression incidence. In short, this addictive behavior causes many negative effects, including psychological problems [ 10 , 11 ]. Zhang et al. [ 12 ] conducted a cross-sectional survey on Internet addiction and sleep quality in Vietnamese adolescents and found a significant positive correlation between Internet addiction and sleep quality. Internet addiction is a high-risk factor for poor sleep quality [ 12 ]. Short-form video addiction, as a new form of Internet addiction, exhibits the general characteristics of addictive behavior, such as strong and sustained cravings and psychological and behavioral addiction. Therefore, short-form video addiction may affect adolescents’ sleep quality, and the mechanism through which short-form video addiction affects sleep quality must be investigated.

Social anxiety is a phenomenon individuals experience in multiple social situations caused by fear that their words and actions may be negatively evaluated by others. Severe social anxiety can also cause social anxiety disorders in individuals [ 13 , 14 ]. A study using the cross-lagged model to examine the relationship between sleep and psychological symptoms finds that high levels of anxiety predict a sustained decrease in sleep time and quality [ 15 ]. Another study on young people shows that low levels of positive emotions and high levels of negative emotions are closely related to poor sleep quality [ 12 ]. Liu et al. conducted an online questionnaire survey on 1402 middle school students in China and found that social anxiety significantly predicts poor sleep quality [ 16 ]. People without sufficient sleep experience a decrease in their perception of happy emotions [ 16 ]. This biased emotional perception is associated with social dysfunction and psychological problems, and social anxiety is a common psychological problem among adolescents. Therefore, social anxiety, as a type of anxiety [ 17 ], negatively affects adolescents’ sleep quality.

A significant positive correlation is also observed between Internet addiction and social anxiety. Li et al. [ 18 ] suggested that as Internet addiction increases, adolescents spend more time and energy on the Internet and less on activities such as interpersonal communication. This imbalance results in decreased self-efficacy and increased loneliness, ultimately leading to social anxiety. According to the “uses and gratifications approach,” short-form video and other Internet social media provide adolescents who are unable to meet their belonging needs or maintain social relationships in real life with more opportunities to interact with others [ 18 ]. However, long-term addiction to virtual social media greatly increases the risk of individuals relying on short-form video. Adolescence is a critical period for the development of social anxiety [ 19 ]. Currently, studies on the relationship between short-form video addiction and social anxiety in this group are few. Hence, examining the internal relationship between short-form video addiction and social anxiety is necessary.

In summary, short-form video addiction may affect adolescents’ sleep quality, social anxiety may also affect their sleep quality, and short-form video addiction may significantly predict social anxiety. Focusing on adolescents, this study explores the mechanism through which short-form video addiction affect their sleep quality. The goal is to provide valuable reference for preventing short-form video addiction and improving sleep quality among adolescents.

Research methods

Research participants.

With reference to previous experience, large-scale sample surveys aim to make the selected samples representative. According to Kline [ 20 ], the sample size must be more than 20 times the number of items. Therefore, the sample size should be at least 860, considering the 43 items.

Using the stratified cluster sampling method, a questionnaire survey was conducted between June and July 2023 on first-year and second-year high school adolescents in Shandong Province, China. Third-year high school students were excluded in the scope of this survey because of their need to spend more time preparing for the upcoming college entrance examination in China. After obtaining informed consent from each participant, copies of the questionnaire were distributed to each class. The survey was conducted in the form of collectively filling out the printed questionnaire. Members of the research team explained the filling requirement before they asked participants to fill out the questionnaire. They were required to complete the questionnaire within the specified time. Those who met the following criteria were included: those with Chinese nationality; those proficient in Chinese with basic listening, speaking, reading, and writing skills; first- and second-year high school students; those without other confirmed mental illness; and those who have not participated in similar research before. Exclusion criteria were those who did not meet the inclusion criteria and those with reading disorders.

A total of 1655 copies of the questionnaire were distributed, and 1650 copies were collected. After checking the copies of the questionnaire collected, 21 copies were found to be filled out regularly, with all items unanswered or with some items unanswered. These copies were considered invalid. After 21 copies with regular or missing filling were excluded, 1629 valid copies remained, with an effective recovery rate of 98.4%. Among them, 832 and 797 copies were collected from first-year and second-year high school students, respectively, with an average age of 16.54 + 0.98 years old. The questionnaires were filled out by 831 male students (51.0%) and 798 female students (49.0%). Table 1 provides additional demographic information. Before conducting the survey, members of the research team explained the significance, methods, and precautions of the survey to all the participants, guardians, and school administrators. They emphasizes that the survey was conducted anonymously and that all data are for scientific research purposes only. The personal information of the participants would be strictly protected. Informed consent to participate was obtained from all of the participants in the study. This survey meets the requirements of the Ethics Committee.

Measurement instruments

Short-form video addiction scale [ 21 ].

This study referred to the Short Video-dominated Social Media Dependence Scale adapted by Hu et al. [ 21 ]. The scale was revised from the Social Network Dependence Scale prepared by Milošević-Đorđević [ 22 ]. “Social networking sites” in each item was changed to “short video social networking sites”. The scale consists of 6 items, scored from 1 indicating “highly disagree” to 5 indicating “highly agree” on a 5-point scale. A higher score means a higher degree of short-form video addiction. The internal consistency of this scale for measuring short-form video addiction among Chinese adolescents is 0.732 [ 21 ].

Social interaction anxiety scale (SIAS) [ 23 ]

SIAS was prepared by Mattick and Clarke [ 24 ] and revised by Chinese scholars Ye et al. [ 23 ]. SIAS measures anxiety and fear of expressing and being observed in social situations based on the description of social phobia in DSM-III-R. It contains 19 items, with the items 8 and 10 being reverse-scored. Items are scored from 1 (completely disagree) to 5 (completely agree) on a 5-point scale. A higher score indicates a higher level of social anxiety. When SIAS is used with Chinese adolescents, the Cronbach’s α coefficient is 0.874 [ 23 ].

Pittsburgh sleep quality index (PSQI) [ 25 ]

PSQI is a self-rating sleep quality scale compiled by Buysse et al. [ 26 ] and then translated into Chinese by Liu et al. [ 25 ] for application in China. PSQI consists of 18 items, including 3 fill-in-the-blank items, 5 multiple-choice items, and 10 self-rating items. The total score on this scale is the sum of scores in seven factors, namely, sleep quality, sleep latency, sleep hours, sleep efficiency, sleep disorder, hypnotics, and daytime dysfunction. The total score on this scale ranges from 0 to 21 points. A higher score means poorer sleep quality. A total score greater than 7 on this scale is considered indicative of a sleep disorder. The Cronbach’s α coefficient of PSQI is 0.77 when applied to the Chinese population.

Statistical analysis

The data were analyzed using SPSS 20.0 statistical software, and the quantitative data that followed normal distribution were expressed as mean ± standard deviation. Independent sample t-test was used for comparison between two groups; one-way ANOVA was used for comparison among multiple groups. Student–Newman–Keuls method was used for pairwise comparison; Pearson correlation analysis was used for analyzing the correlation between two variables. AMOS20.0 statistical software was used for analysis of mediating effect, and the maximum likelihood method was used for parameter estimation. A difference was considered statistically significant if P < 0.05.

Quality control

The following measures were taken to reduce response bias or social desirability effects in the self-report measures: (1) Prior to data collection, all members of the research team participated in collective training. The training included selecting participants strictly according to the inclusion criteria; specifying the wordings used for offering questionnaire filling guidance and explanation; discussing the problems and solutions possibly encountered during questionnaire collection; and standardizing the steps of questionnaire distribution, filling out, and collection. (2) During data collection, participants were informed that this study would be conducted anonymously without privacy disclosure to obtain their cooperation. After questionnaire completion, the researchers checked all items individually to ensure data integrity. (3) After summarizing the collected data, two members verified and input them. (4) While the participants were filling out the questionnaires, the researchers did not provide any suggestions or hints related to the study results.

Analysis of status and differences in short-form video addiction, social anxiety, and sleep quality

Adolescents have a short-form video addiction score of 13.79 ± 4.36, social anxiety score of 42.76 ± 14.23, and sleep quality score of 6.12 ± 3.29 (detection rate = 31.06%). No statistically significant differences are found in the short‐form video addiction score based on grade, only child status, gender, and family residence ( P > 0.05). However, statistically significant differences are observed based on academic performance, family economic condition, and exercise frequency ( P < 0.05). No statistically significant differences are found in the social anxiety score based on grade, only child status, gender, family residence, and exercise frequency ( P > 0.05). Statistically significant differences are observed though based on academic performance and family economic condition ( P < 0.05). No statistically significant differences are found ( P > 0.05) in the sleep quality score based on grade, only child status, and family residence. Yet, statistically significant differences are noted ( P < 0.05) based on gender, academic performance, family economic condition, and exercise frequency. The results are shown in Table 1 .

The Pearson correlation analysis shows pairwise positive correlations among short-form video addiction, social anxiety, and sleep quality. As shown in Table 2 , short‐form video addiction is significantly positively correlated with sleep quality ( r = 0.404, P < 0.05), short‐form video addiction is significantly positively correlated with social anxiety ( r = 0.439, P < 0.05), and sleep quality is significantly positively correlated with social anxiety ( r = 0.457, P < 0.05).

Analysis of mediating effect

To further explore the relationships among variables and test whether social anxiety mediates the relationship between short-form video addiction and sleep quality, this study established a structural equation model using Amos 21.0. The model had short‐form video addiction as the independent variable, social anxiety as the mediating variable, and sleep quality as the dependent variable. The specific path is shown in Fig. 1 . The results of parameter test show that the path coefficients of the three paths are statistically significant ( P < 0.05), as shown in Table 3 .

Mediating effect model (standard regression coefficient)

To further examine the direct and indirect effects of short-form video addiction on sleep quality, this study used the bootstrap self-sampling method to calculate the effect values and 95% confidence intervals. The results are shown in Table 4 . The direct effect of short‐form video addiction on sleep quality is 0.248 (95% CI: 0.199–0.296), accounting for 62.4% of the total effect value (0.397). The indirect effect exerted through social anxiety is 0.149 (95% CI: 0.120–0.183), accounting for 37.6%. These results suggest that social anxiety partially mediates the effect of short‐form video addiction on sleep quality.

Discussions

Status and characteristics of adolescents’ sleep quality.

In this study, the total sleep quality score is 6.12 ± 3.29, and the detection rate of sleep disorder is 31.06%. These results are similar to the finding from other studies [ 27 , 28 ]. Adolescents’ sleep problems still require attention from the education department. Further analysis reveals no significant differences in sleep quality among adolescents based on grade, only child status, and family residence ( P > 0.05). The similarity in the learning content and environment for first-year and second-year high school students likely explains the lack of significant difference in sleep quality by grade. No significant differences are found in the sleep quality between adolescents who are the only child and who are not and between those who live in rural areas and those who live in urban areas. However, some studies report that only children experience higher levels of emotional warmth and understanding from their parents than non-only children. Their families generally adopt a warm and sympathetic parenting style to establish an affectionate and trusting atmosphere, so only children have higher sleep quality than non-only children [ 29 ]. This finding may be related to the emphasis recently placed by school administrators and the educational community on strengthening mental health education among high school students. For example, family-school cooperative management programs such as “Family–School Alliance” help cultivate a sense of security and trust among adolescents, stabilize their emotions, and ultimately improve their sleep quality [ 30 ]. This reason also account for the significant difference in sleep quality among adolescents with different family residences.

By contrast, significant differences are found in sleep quality among adolescents based on gender, academic performance, family economic condition, and exercise frequency ( P < 0.05). Further analysis indicates that girls have poorer sleep quality than boys. This result is probably due to physiological and personality factors that make girls more prone to tension and anxiety. In higher grades, academic performance significantly affects adolescents’ development of mental health [ 28 , 31 ]. Poor academic performance and greater learning difficulty lead to increased learning pressure, so high-school students experiencing both are prone to negative emotions such as learning anxiety. In addition, the subjective and objective factors such as self-isolation lead to poorer academic performance and sleep quality among adolescents. In addition, this study observes certain relationships between family economic condition and exercise frequency and adolescents’ sleep quality, which is consistent with previous research results [ 3 , 32 ]. Poorer family economic conditions worsen sleep quality. The reason is the a significant gap in living and educational conditions between those with good family economic conditions and those with poor economic conditions. The latter tends to have increased learning time, perceive pressure and mental tension, and suffer from poorer sleep quality [ 3 ]. In this study, 115 high school students have no exercise within a week, and 938 exercise one to three days per week, which to some extent indicates that their physical exercise is worrying. This frequency may be related to high school students having more learning tasks, less free leisure time, and insufficient self-awareness. Strengthening physical exercise is beneficial for reducing the occurrence of anxiety symptoms, and adolescents who exercise more frequently are more likely to have longer sleep hours compared with their peers who exercise less frequently [ 3 , 32 ]. In the past decade, short-term intervention studies find a significant correlation between objectively measured sleep hours and physical exercise [ 3 , 33 , 34 ].

Correlation analysis of short-form video addiction, social anxiety, and sleep quality

This study finds that short-form video addiction significantly predicts sleep quality. A higher degree of short‐form video addiction among adolescents results in poorer sleep quality. This result is consistent with the findings from previous studies on Internet addiction among adolescents [ 4 , 5 , 31 ]. During the daytime, high school students may be prohibited by teachers or school administrators from using mobile phones in the classroom because of overloaded academic tasks [ 35 ]. Many adolescents may excessively watch short-form video online before going to bed at night, thus decreasing their sleep hours. Besides, the blue light emitted by electronic mobile devices at night can interfere with the secretion of melatonin, a hormone that regulates sleep [ 36 , 37 ]. The radio frequency electromagnetic fields generated by electronic mobile devices can also disrupt normal blood flow and metabolic functions in the brain, thereby negatively affecting adolescents’ sleep quality [ 38 ]. Therefore, this study provides new ideas and references for improving the sleep quality of adolescents.

This study reveals a significant positive correlation between short-form video addiction and social anxiety among adolescents. This outcome is consistent with the research finding that Internet addiction leads to an increase in social anxiety among adolescents [ 14 , 39 , 40 ]. On the one hand, adolescents who rely on short-form video consume plenty of time to watch short-form video. The resulting difficulty in focusing their mind on learning or daily interpersonal communication likely leads to setbacks such as decreased academic performance and hindered interpersonal communication, thus giving rise to social anxiety among adolescents [ 39 ]. On the other hand, short-form video platforms feature both high-quality and poor-quality videos, with many videos exhibiting a materialistic bias. Negative content can trigger upward social comparison among adolescents. Long-term upward social comparison on social networking sites likely generates negative emotions, such as depression and jealousy, among adolescents, which are positive correlated with social anxiety [ 41 ]. Therefore, negative content further increases the level of social anxiety level coming from short-form video addiction [ 41 ]. Research indicates that short‐form video addiction indirectly reduces real-life interactions and contributes to interpersonal relationship barriers [ 41 ].

Moreover, social anxiety can increase the use of short videos among adolescents. At present, social anxiety is considered one of the predominant risk factors causing mobile phone addiction [ 42 , 43 ], and the findings of these studies to some extent support the results of the current study. According to the “uses and gratifications approach” [ 18 ], adolescents who struggle to meet their need for belonging or maintain social relationships in real life may turn to short videos for opportunities to interact with others, efficiently satisfying their need for interpersonal communication [ 18 ]. Doing so reduces the risk of poor interpersonal relationships and alleviates the pressure arising from unfavorable social environments. As suggested by social cognitive theory, individuals with social anxiety are prone to negative evaluations of their environment and other people. Those with poorer social support systems tend to have more severe social avoidance tendencies, are less able to integrate into the group, or are more likely to be excluded by the group [ 43 , 44 ]. Thus, they may seek social connections and a sense of belonging by watching short videos, ultimately leading to addiction.

Mediating role

The mediating effect analysis shows that short-form video addiction has a positive predictive effect on sleep quality (β = 0.248, P < 0.05) and that social anxiety partially mediates the relationship between short‐form video addiction and sleep quality among adolescents (β = 0.149, P < 0.05). In other words, short‐form video addiction not only directly predicts sleep quality but also indirectly affects sleep quality through social anxiety. The reasons are as follows: (1) Adolescents addicted to short-form videos tend to watch them without supervision from teachers or parents on their mobile phones during night breaks. The electromagnetic radiation generated by mobile phones affects the nervous system, disrupts brain function and metabolism, delays latency, cause dizziness and headaches, and decreases sleep quality [ 35 , 36 , 37 ]. (2) Loneliness comes from individuals’ failure to reach their ideal interpersonal communication level. Short‐form video addiction has become an important external factor contributing to loneliness [ 39 ]. Excessive Internet use can cause poor adaptation, leading individuals to immerse themselves in the virtual world, which reduces social interactions and creates interpersonal relationship barriers in real life [ 39 , 45 ]. Ultimately, excessive Internet use exacerbates social anxiety. The rich and diverse content of short-form videos and highly perceivable enjoyment they provide cause adolescents to overuse these videos, resulting in their reduced interpersonal communication and social skills in real life. When facing the real world again, these adolescents feel more detached from society and have a higher level of social anxiety. People with anxiety are likely to have poorer sleep quality and more prominent problems during daytime such as drowsiness, fatigue, and lack of concentration [ 46 ]. Individuals with high anxiety also harbor negative attitudes and emotions, exhibiting hostility toward external stimuli. Hostility is significantly correlated with poor sleep quality and ultimately decreases overall sleep quality [ 33 , 46 ]. Therefore, alleviating adolescents’ social anxiety can help alleviate their short‐form video addiction and improve their sleep quality.

In summary, adolescents’ short-form video addiction has become a social problem that negatively affects their physical and mental health. This study explores the effect of short-form video addiction on the sleep quality of adolescents, and the results indicate the need to actively address sleep quality issues and short‐form video addiction. In addition, alleviating social anxiety can alleviate the negative impact of short‐form video addiction on sleep quality, thereby improving the sleep quality of adolescents and supporting their healthy development.

Limitations and future direction

This study has some limitations. First, all the data were collected from subjective reports by the participants, which may probably lead to errors, such as memory bias and social desirability bias). Second, the sample was limited to students from three high schools in Shandong Province, China, which may introduce selection bias. Besides, this cross-sectional study lacked follow-up investigation. Future research should collect data from multiple sources (such as individuals, peers, parents, teachers, etc.) to measure relevant variables more objectively. Longitudinal tracking can be conducted to expand the survey scope and verify the internal connections among loneliness, short-form video addiction, and sleep quality as well as the corresponding mechanisms of action.

The results of this study can provide empirical support and beneficial insights for improving adolescents’ sleep quality, weakening the impact of short-form video addiction in the era of mobile Internet, and maintaining adolescents’ physical and mental health. Sleep quality remains a prominent problem among adolescents that require attention from the education department. Short‐form video addiction has a significant direct effect on adolescents’ sleep quality. Adolescents should learn to consciously suppress their desire to watch short-form video, reduce the frequency of their Internet use, and engage in self-control before falling asleep to ensure sufficient sleep hours. In addition, short‐form video addiction increases social anxiety among adolescents and leads to more negative emotions and less positive emotions, thereby undermining their sleep quality. In response, parents and teachers should consciously guide adolescents to master socializing strategies and improve their interpersonal skills to weaken the mediating effect of negative emotions such as anxiety on the relationship between short‐form video addiction and sleep quality.

Data availability

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

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Research explores reversing memory loss with existing drugs

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The loss of social memories caused by sleep deprivation could potentially be reversed using currently available drugs, according to a study in mice presented today (Friday) at the Federation of European Neuroscience Societies (FENS) Forum 2024. Lack of sleep is known to affect the brain, including memory, in mice and in humans, but research is beginning to show that these memories are not lost, they are just 'hidden' in the brain and difficult to retrieve. The new research shows that access to these otherwise hidden social memories can be restored in mice with a drug currently used to treat asthma and chronic obstructive pulmonary disease. The team of researchers have also shown that another drug currently used to treat erectile dysfunction can restore access to spatial memories. Researchers say these spatial memories in mice are akin to humans remembering where they put their keys the night before, whereas the social memories could be compared with remembering a new person you met. The research was presented by Dr Robbert Havekes from the University of Groningen in the Netherlands. He said: "Ever since starting as a PhD student, many years ago, I have been intrigued by the observation that even a single period of sleep deprivation can have a major impact on memory processes and the brain as a whole. The early work published years ago helped us identify some of the molecular mechanisms that mediate amnesia.

By manipulating these pathways specifically in the hippocampus, we have been able to make memory processes resilient to the negative impact of sleep deprivation. In our new studies, we have examined whether we could reverse amnesia even days after the initial learning event and period of sleep deprivation." Dr Robbert Havekes, University of Groningen

The new studies, presented at the FENS Forum and funded by the Air Force Office of Scientific Research (AFOSR), were conducted by Dr Havekes' PhD students Adithya Sarma and Camilla Paraciani, who will also be presenting their work as poster presentations. To study social memories in the lab, the researchers gave mice the opportunity to choose between interacting with a mouse they have never encountered before or a sibling from their own cage. Under normal circumstances, the mice prefer interacting with the new mouse over their litter-mate that they already know. Given the same choice the next day, mice will interact to a similar extent with both their litter-mate and the mouse they met the day before as both mice are now considered familiar. However, if the mice are sleep-deprived after their first encounter then the next day they still prefer to interact with the new mouse as if they never met it before. These findings suggest that they simply cannot recall their previous encounter. The team found they were able to permanently restore these hidden social memories, first using a technique called optogenetic engram technology. This technique allows them to identify neurons in the brain that together form a memory (known as a memory engram) for a specific experience and alter those neurons so they can be reactivated by light. Researchers can then use light to reactivate this specific group of neurons resulting in the recall of the specific experience (in this case a social memory). They were also able to restore the mice's social memories by treating them with roflumilast, a type of anti-inflammatory drug, approved by the US Food and Drug Administration, that is used to treat chronic obstructive pulmonary disease. Dr Havekes says this finding is particularly interesting as it provides a stepping stone towards studies of sleep deprivation and memory in humans, and he is now collaborating with another research group that is embarking on human studies. In parallel, the same researchers have investigated the loss of spatial memory caused by sleep deprivation by studying mice's abilities to learn and remember the location of individual objects. A brief period of sleep deprivation following training meant the mice could not recall the original locations of the object and so they did not notice when an object was moved to a new location during a test. As with the social memories, access to these spatial memories could be restored by treating the mice with another drug, vardenafil, that is currently used to treat erectile dysfunction. This is a second drug that is approved by the US Food and Drug Administration that the researchers have successfully used to reverse amnesia in mice. Dr Havekes said: "We have been able to show that sleep deprivation leads to amnesia in the case of specific spatial and social recognition memories. This amnesia can be reversed days later after the initial learning experience and sleep deprivation episode using drugs already approved for human consumption. We now want to focus on understanding what processes are at the core of these accessible and inaccessible memories. In the long term, we hope that these fundamental studies will help pave the way for studies in humans aimed at reversing forgetfulness by restoring access to otherwise inaccessible information in the brain." Professor Richard Roche is chair of the FENS Forum communication committee and Deputy Head of the Department of Psychology at Maynooth University, Maynooth, County Kildare, Ireland, and was not involved in the research. He said: "This research shows that social and spatial memories seemingly lost through sleep-deprivation can be recovered. Although these studies were carried out in mice, they suggest that it may be possible to recover people's lost social and spatial memories using certain drug treatments that are already approved for human use. There are many situations where people cannot get the amount of sleep they need, so this area of research has obvious potential. However, it will take time and a lot more work to move this research from mice into humans."

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Tags: Amnesia , Anti-Inflammatory , Asthma , Brain , Chronic , Chronic Obstructive Pulmonary Disease , Drugs , Erectile Dysfunction , Food , Hippocampus , Neurons , Neuroscience , Psychology , Research , Sleep , students , Technology

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Memories lost due to sleep deprivation restored by existing drugs

Memories seemingly lost as a result of sleep deprivation were restored using existing drugs used to treat asthma and erectile dysfunction, according to new research. The research suggests these memories are merely hidden, not lost, and offers a way to retrieve them.

We’ve known for a while how a lack of sleep can negatively affect the brain. Studies have found that just one night of disturbed sleep increases Alzheimer’s disease-related amyloid-beta peptides. In addition to making someone unfocused, a lack of sleep can negatively affect the brain’s hippocampus, which is key to making memories .

In new research presented at this month’s Federation of European Neuroscience Societies (FENS) Forum in Vienna, researchers identified existing drugs that can restore memories seemingly lost because of sleep deprivation.

“By manipulating these pathways specifically in the hippocampus, we have been able to make memory processes resilient to the negative impact of sleep deprivation,” said Dr Robbert Havekes, association professor in neuroscience at the University of Groningen in the Netherlands. “In our new studies, we have examined whether we could reverse amnesia even days after the initial learning event and period of sleep deprivation.”

The hippocampus works like the brain’s librarian, forming short-term memories that are consolidated into long-term ones, labeled and stored for later retrieval. Its role extends to both spatial and social memory. Spatial memory is our short- and long-term memory of places, events, and things in the world. It’s how we remember the route to the grocery store and find things after we’ve put them down. Social memory enables us to distinguish a familiar face from one we don’t know, so we can not only engage in meaningful relationships but express appropriate behavioral responses based on previous encounters.

Focusing on social memory, the researchers gave mice a choice of interacting with siblings or a mouse they’d never met before. Normally, mice prefer to interact with the unknown mouse over their siblings, and then, the next day, they will spend a similar amount of time with their siblings and the newly met mouse, who’s now considered familiar. However, the researchers found that mice who were sleep-deprived after first meeting the unknown mouse would interact with it the following day as if it was the first time, suggesting that that first encounter had been forgotten.

It's proposed that an experience activates a population of neurons that undergo permanent chemical and/or physical changes to become an ‘engram’ or memory trace and that an engram’s reactivation leads to memory retrieval. Here, the researchers used optogenetics, a technique that uses light to control the activity of specific neurons, to identify the neurons that formed the specific mouse-meeting memory and reactivated their engram, restoring the mice’s hidden social memory of that first meeting. They found that roflumilast (sold as Daxas, Daliresp and others), used to reduce airway irritation and swelling in severe asthma and chronic obstructive pulmonary disease (COPD), also produced the same social-memory-restoring effect.

The researchers conducted parallel experiments on spatial memory. Mice who were sleep-deprived couldn’t recall the locations of individual objects and so didn’t notice when an object was moved to a new location during testing. As with social memories, hidden spatial memories were restored by the drug vardenafil (Levitra and others), used to treat erectile dysfunction or impotence.

“We have been able to show that sleep deprivation leads to amnesia in the case of specific spatial and social recognition memories,” Havekes said. “This amnesia can be reversed days later after the initial learning experience and sleep deprivation episode using drugs already approved for human consumption. We now want to focus on understanding what processes are at the core of these accessible and inaccessible memories. In the long term, we hope that these fundamental studies will help pave the way for studies aimed at reversing forgetfulness by restoring access to otherwise inaccessible information in the brain.”

The research has only been presented at the FENS Forum 2024 and hasn’t been published or peer-reviewed yet.

Source: FENS via EurekAlert!

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Sleepiness, sleep deprivation, quality of life, mental symptoms and perception of academic environment in medical students

Bruno perotta.

1 Mackenzie Evangelical School of Medicine – Parana, Curitiba, Brazil

2 Department of Medicine, School of Medicine of the University of Sao Paulo, Sao Paulo, Brazil

Fernanda M. Arantes-Costa

3 Center for Development of Medical Education, School of Medicine of University of Sao Paulo, Sao Paulo, Brazil

Sylvia C. Enns

Ernesto a. figueiro-filho.

4 Department of Gynecology & Obstetrics, University of Toronto, Toronto, Canada

Helena Paro

5 Department of Gynecology & Obstetrics, Federal University of Uberlandia, Uberlandia, Brazil

Itamar S. Santos

Geraldo lorenzi-filho.

6 Department of Cardio-Pneumology, School of Medicine of the University of Sao Paulo, Sao Paulo, Brazil

Milton A. Martins

Patricia z. tempski, associated data.

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

It has been previously shown that a high percentage of medical students have sleep problems that interfere with academic performance and mental health.

To study the impact of sleep quality, daytime somnolence, and sleep deprivation on medical students, we analyzed data from a multicenter study with medical students in Brazil (22 medical schools, 1350 randomized medical students). We applied questionnaires of daytime sleepiness, quality of sleep, quality of life, anxiety and depression symptoms and perception of educational environment.

37.8% of medical students presented mild values of daytime sleepiness (Epworth Sleepiness Scale - ESS) and 8.7% presented moderate/severe values. The percentage of female medical students that presented ESS values high or very high was significantly greater than male medical students ( p < 0.05). Students with lower ESS scores presented significantly greater scores of quality of life and perception of educational environment and lower scores of depression and anxiety symptoms, and these relationships showed a dose-effect pattern. Medical students reporting more sleep deprivation showed significantly greater odds ratios of presenting anxiety and depression symptoms and lower odds of good quality of life or perception of educational environment.

Conclusions

There is a significant association between sleep deprivation and daytime sleepiness with the perception of quality of life and educational environment in medical students.

Sleep problems are very frequent in the general population and medical students are one group that is vulnerable to poor sleep [ 1 , 2 ]. The prevalence of sleep disturbances in medical students is higher than in non-medical students [ 1 , 3 ]. There are many reasons to the high prevalence of sleep problems in medical students, including many hours of classes and study, clinical clerkships that include overnight work, emotional stress, choices concerning lifestyle and many hours using virtual social media [ 4 , 5 ].

There is evidence that enough good quality sleep is important for long term learning, for neurocognitive and psychomotor performance and for physical and mental health [ 6 ]. In addition, sleep deprivation in medical students can make them more vulnerable to depressive and anxiety disorders [ 1 ]. Moreover, there are concerns related to patient safety when health professionals are sleep deprived. A review by Curcio et al. suggested that student learning and academic performance are closely related to sleep quantity and quality [ 7 ].

To our knowledge there was no previous work that evaluated the relationship between sleep quality and sleep deprivation with quality of life and perception of academic environment in medical students. To better understand the impact of sleep quality and quantity on medical students we analyzed data from a multicenter study with medical students in Brazil [ 8 – 11 ]. The purpose of this study was to evaluate the relationship between sleep deprivation, sleep quality and daytime sleepiness, and quality of life, perception of academic environment and symptoms of depression and anxiety.

Study design and sample

We performed this protocol as part of a multicentric study with 22 Brazilian medical schools (VERAS study, translated to English as “Students’ and Residents’ life in health professions”). Detailed description of this study was previously published [ 8 – 11 ]. Schools participating in the study were from all regions of Brazil, and with a diverse legal status and location (13 public and 9 private, 13 in state capital cities and 9 in other cities). The research protocol was approved by the Ethics Committee of the School of Medicine of the University of Sao Paulo. All medical schools included approved the study.

When our study was performed, Brazil had 153 medical schools with at least one graduating class, with approximately 86,000 medical students. The sample size of the study was defined to enable an effect size of 0.165, with 80% power at a 0.05 significance level, when comparing two samples of equal size. We then increased the sample to 1650 students to account for 30% loss of participants [ 8 – 11 ].

Sixty students were randomly selected from each of 22 medical schools. Five male and five female medical students were selected from each year of the undergraduate program. The selection was performed using a computer-generated list of random numbers [ 10 ]. Students were invited to participate by e-mail and social media. Participation was voluntary, without any compensation or incentive. We guaranteed both confidentiality and anonymity, and participating students completed an informed consent form [ 8 – 11 ].

Data collection

Students accessed an electronic survey platform, that was designed specifically for the study and had 10 days to complete the survey (thirteen questionnaires). After finishing the survey, voluntary received feedback on their scores. Medical students received their score for each domain of each questionnaire and information about the meaning of each result. We offered to the students the opportunity to contact the research group for guidance and/or emotional support. Confidentiality and anonymity were guaranteed in the consent form [ 8 – 11 ].

Instruments

To assess daytime sleepiness, we used the Epworth Sleepiness Scale (ESS) [ 12 ]. This questionnaire consists of 8 self-rated items, each scored from 0 to 3, that measure a subject’s habitual “likelihood of dozing or falling asleep” in common situations of daily living. The final score is the sum of individual items (scores 0–24). Values > 10 are considered excessive daytime sleepiness and values > 15 are considered severe sleepiness. ESS was translated and validated to Brazilian Portuguese [ 13 ].

To assess sleep quality, we used Pittsburgh Sleep Quality Index (PSQI) [ 14 ]. This questionnaire has 19 items to evaluate subjective sleep quality. We used only the global score of PSQI (range 0 to 21). Higher scores indicate worse sleep quality. Values > 5 are considered poor quality of sleep [ 14 ]. PSQI had been previously translated and validated to Brazilian Portuguese [ 13 ].

To assess sleep deprivation, we calculated the difference between mean hours of sleep during weekends and mean hours of sleep during weekdays, that was called Sleep Deprivation Index (SDI). SDI was derived from the questions: a) How many hours, on average, did you sleep on weekdays during the last 2 weeks? b) On weekends, if nobody wakes you up, how many hours, on average, do you sleep?

To assess quality of life (QoL) we used three questionnaires:

WHOQOL-BREF, that has 26 items with four domains: environment, psychological, social relationships, and physical health [ 15 ]. Answers are given on a 5-point Likert scale and points within each domain are transformed to a score from 0 to 100. Higher scores represent better QoL (WHOQOL GROUP 1995). This questionnaire was translated and validated to Brazilian Portuguese [ 16 ].
QoL self-assessment, that consisted of two questions to evaluate students’ perception regarding their overall QoL and QoL related to medical school (MSQoL) on a scale from 0 to 10. The items were [ 1 ] rate your overall quality of life [ 2 ]; rate your quality of life in medical school [ 8 , 10 ].
VERAS-Q that is a questionnaire created to evaluate quality of life from students in the health professions. This questionnaire has 45 statements on a 5-points Likert scale divided in four domains (time management, psychological, physical health and learning environment) and a global score [ 17 , 18 ].

To assess the perception of the educational environment in medical school we used DREEM (Dundee Ready Education Environment Measure), a 50-item questionnaire which evaluates educational environment perceptions. This questionnaire has 5 domains: perceptions of learning, perceptions of teachers, academic self-perceptions, perceptions of atmosphere, and social self-perceptions [ 19 , 20 ]. Answers are given on a 5-point Likert scale. This questionnaire was translated and validated to Brazilian Portuguese [ 21 ].

To assess emotional symptoms, we used Beck Depression Inventory (BDI) and State Trait Anxiety Inventory (STAI). BDI is a 21-item questionnaire to measure depression symptoms [ 22 ]. Scores of each item vary from 0 to 3 according to increasing symptom intensity. This questionnaire was translated and validated to Brazilian Portuguese [ 23 ]. STAI has a scale with 20 items each evaluating the intensity of state-anxiety and of trait-anxiety symptoms [ 24 ]. This questionnaire was also previously translated and validated to Brazilian Portuguese [ 23 ].

The results of the reliability analyses performed using the Cronbach’s α coefficient demonstrated that the data had and α value between 0.65 and 0.94 for all domains of the questionnaires (data not shown).

Statistical analysis

Students were divided according the results of ESS in three groups, respectively ESS ≤ 10, 10 < ESS < 16 and ESS > 16. Comparisons among these three groups were performed using one-way ANOVA followed by Dunn test.

We divided medical students in three groups according to quartiles of sleep deprivation. We present categorical variables as counts and proportions and their distributions across sleep deprivation groups are analyzed using chi-squared trend tests for proportions. Quality of life (Overall, medical school-related, WHOQOL and VERAS-Q), mental symptoms (BDI, STAI-state and STAI-trait), and DREEM scores are presented as medians and interquartile ranges and their distributions across sleep deprivation groups are analyzed using the Jonckheere-Terpstra trend test. We built binary logistic regression models to study the association between sleep deprivation and daytime sleepiness, and the association between sleep deprivation and high scores in each of these scales. High scores were defined as a score equal or above the median for the whole sample. Binary logistic models are presented adjusted for age, sex, and year of medical school. Significance level was set at 0.05. Analyses were performed using R software, version 3.2.0.

As previously shown, of 1650 randomly selected students, 1350 (81.8%) accepted to participate and completed the study [ 8 – 11 ]. The main reason to refuse to participate in the study (16.6%) was lack of time. Their ages ranged between 17 and 40 (22.8 ± 1.3) years old.

From the 1350 participants, 714 (52.9%) were women, 459 (34.0%) were in the 1st or 2nd year of medical school (basic sciences), 491 (36.4%) were in the 3rd or 4th year of medical school (clinical sciences) and 400 (29.6%) in the last 2 years of medical course (clerkships).

Table 1 shows the results of Epworth Daytime Sleepiness Scale (ESS): 37;8% medical students presented high values of ESS and 8.7% presented very high values. The percentages of female medical students that presented ESS values high or very high were significantly greater than male medical students.

Results of the Epworth Daytime Sleepiness Scale (ESS) in all medical students evaluated

ESS results	Males	Females	Total
0–10	388 (61.0%)	334 (46.8%)*	722 (53.5%)
11–15	211 (33.2%)	299 (41.9%)*	510 (37.8%)
16–24	37 (5.8%)	81 (11.3%)*	118 (8.7%)

* P < 0.05 compared to males

Figure 1 shows the distribution of self-related sleep hours during weekdays (A), weekends (B), difference between mean weekend and weekday sleep hours (C) and ESS scores of medical students (D).

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Sleep pattern and daily sleepiness among medical students. Histograms represent the distribution of self-related sleep hours during weekdays ( a ), weekends ( b ), difference between mean weekend and weekday sleep hours ( c ) and Epworth Sleepiness Scale (ESS) scores of medical students ( d ). Gray bars represent normal values of ESS and black bars represent increased daytime somnolence

We evaluated the differences in the results of questionnaires of quality of life, education environment, and depression and anxiety symptoms among medical students with normal values of ESS (< 10), students with values between 11 and 15 and students with values > 15.

The association between excessive daytime sleepiness and quality of life is shown in Fig. 2 . We observed a dose-effect pattern, with lower values of ESS corresponding to higher values of quality of life scores. We observed statistically significant differences among the three groups in all domains of WHOQOL-BREF and VERAS-Q questionnaires and in the scores of quality of life in general and medical school-related quality of life.

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Quality of life of medical students decreases with higher daily sleepiness scores. Mean and standard error values of VERAS-Q ( a ), self-evaluation of QoL ( b ) and WHOQOL-BREF questionnaires ( c ) in the three groups of medical students based on ESS scores. * p < 0.05 compared to ESS 0–10; § p < 0.05 compared to ESS 16–24; # p < 0.05 compared to ESS 11–15

We also observed that students with higher values of ESS presented a worse perception of education environment. Both in global DREEM score and in the five domains of DREEM there were statistically significant differences among the three groups of medical students concerning the results of ESS (Fig. 3 ).

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Medical students that presents higher daily sleepiness scores (ESS) showed lower perception of educational environment. Bars indicate mean (and standard error) values of DREEM global ( a ) and domain scores ( b ) * p < 0.05 compared to ESS 0–10; § p < 0.05 compared to ESS 16–24; # p < 0.05 compared to ESS 11–15

Higher scores of daytime sleepiness were also associated with higher scores of depression symptoms and with state and trait anxiety scores. We also observed a dose-response relationship and the differences were statistically significant among the three groups of ESS values (Fig. 4 ).

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Higher scores of sleepiness are associated with higher scores of depression and anxiety. Beck Depression Inventory (BDI ( a ) and State and Trait Anxiety scores ( b ) (Means and standard errors). BDI scores range from 0 to 21 and STAI scores range from 20 to 80. * p < 0.05 compared to ESS 0–10; § p < 0.05 compared to ESS 16–24; # p < 0.05 compared to ESS 11–15

Medical students that presented higher ESS scores showed lower quality of sleep measured by PSQI. PSQI global score range from 0 to 21, lower scores represent better quality of sleep (Fig. 5 a). Figure 5 b shows the distribution of PSQI scores in all medical students.

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Medical students that presents higher daily sleepiness score showed lower quality of sleep measured by PSQI-Br. PSQI-Br global score ranges from 0 to 21, and lower scores represent better quality of sleep. a Mean (and standard error) values of PSQI-Br global scores. b Distribution of number of medical students with each value of PSQI-Br. Values higher than 5 indicate poor quality of sleep (gray bars). * p < 0.05 compared to ESS 0–10; § p < 0.05 compared to ESS 16–24; # p < 0.05 compared to ESS 11–15

We divided medical students in four quartiles concerning the values of this sleep deprivation index (SDI), with SDI respectively ≤2 (Q1), =3 (Q2), =4 (Q3) and > 4 (Q4) hours. Table 2 shows the distribution of medical students and values of the studied questionnaires according to deprivation groups (Q1, Q2 + Q3 and Q4).

Description of the study sample, according to sleep deprivation groups, from Sleep Deprivation Index

	Q1 ( = 536) ≤ 2 h	Q2-Q3 ( = 564) 3 and 4 h	Q4 ( = 238) > 4 h	Total ( = 1338)
Age (mean ± SD)	23.0 ± 3.0	22.5 ± 2.9	22.6 ± 3.0	22.7 ± 3.0
Year of medical school (N (%))
1st/2nd (Basic)	175 (32.6%)	196 (34.8%)	83 (34.9%)	454 (33.9%)
3rd/4th (Clinical)	195 (36.4%)	209 (37.1%)	81 (34.0%)	485 (36.2%)
5th/6th (Clerkship)	166 (31.0%)	159 (28.2%)	74 (31.1%)	399 (29.8%)
Female sex - N (%)	276 (51.5%)	304 (53.9%)	131 (55.0%)	711 (53.1%)
WHOQOL (median [P25 - P75])
Physical				67.9 [53.6–75.0]
Psychological				62.5 [54.2–75.0]
Social Relationships				66.7 [50.0–75.0]
Environment				65.6 [53.1–75.0]
VERAS-Q (median [P25 - P75])
Time use				36.4 [25.0–47.7]
Psychological				50.0 [39.6–62.5]
Physical Environment				53.1 [40.6–68.8] 57.1 [48.2–66.1]
Quality of life (median [P25 - P75])
Overall	8.0 [7.0–9.0]	8.0 [7.0–9.0]	8.0 [7.0–9.0]	8.0 [7.0–9.0]
Medical school-related				7.0 [6.0–8.0]
Mental symptoms (median [P25 - P75])
Depression (BDI)				8.0 [4.0–13.0]
Anxiety-state				43.0 [35.0–52.0]
Anxiety-trait				45.0 [37.0–53.0]
DREEM (median [P25 - P75])
Perceptions of learning				28.0 [23.0–33.0]
Perception of teachers	28.0 [23.0–32.0]	28.0 [24.0–33.0]	27.0 [23.0–31.0]	28.0 [23.0–32.0]
Perceptions of the atmosphere				19.0 [15.2–22.0]
Academic self-perceptions				30.0 [25.0–35.0]
Social self-perceptions				16.0 [13.0–19.0]
Global				120.0 [101.0–139.0]

Groups that showed statistically significant differences ( P < 0.05) are in bold

Table 3 shows the results of binary logistic regression models. We show the odds ratios (and 95% confidence intervals) for the association between sleep deprivation groups and high quality of life, depression and anxiety symptoms and perception of academic environment. The results are presented crude and adjusted for age, sex and year of medical school. Group Q1 was used as reference and the odds ratio that were statistically significant are presented in bold.

Odds ratios (and 95% confidence intervals) for the association between sleep deprivation groups and high quality of life, mental symptoms, and DREEM scores

	Crude			Adjusted
	Q1 ≤ 2 h	Q2-Q3 3 and 4 h	Q4 > 4 h	Q1 ≤ 2 h	Q2-Q3 3 and 4 h	Q4 > 4 h
WHOQOL
Physical	Ref (1.0)			Ref (1.0)
Psychological	Ref (1.0)			Ref (1.0)
Social Relationships	Ref (1.0)			Ref (1.0)
Environment	Ref (1.0)	0.88 (0.69–1.11)		Ref (1.0)	0.84 (0.66–1.07)
VERAS-Q
Time use	Ref (1.0)			Ref (1.0)
Psychological	Ref (1.0)			Ref (1.0)
Physical	Ref (1.0)			Ref (1.0)
Environment	Ref (1.0)	0.85 (0.67–1.07)		Ref (1.0)	0.81 (0.63–1.03)
Quality of life
Overall	Ref (1.0)	0.97 (0.75–1.25)	0.76 (0.55–1.04)	Ref (1.0)	0.94 (0.73–1.21)	0.74 (0.54–1.01)
Medical school-related	Ref (1.0)			Ref (1.0)
Mental symptoms
BDI	Ref (1.0)			Ref (1.0)
Anxiety-state	Ref (1.0)	1.07 (0.85–1.36)		Ref (1.0)	1.07 (0.84–1.36)
Anxiety-trait	Ref (1.0)			Ref (1.0)
DREEM
Perceptions of learning	Ref (1.0)	0.81 (0.64–1.02)		Ref (1.0)
Perception of teachers	Ref (1.0)	1.07 (0.84–1.36)		Ref (1.0)	1.04 (0.81–1.32)
Perceptions of atmosphere	Ref (1.0)	0.80 (0.63–1.02)		Ref (1.0)	0.80 (0.63–1.02)
Academic self-perceptions	Ref (1.0)	0.92 (0.73–1.17)		Ref (1.0)	0.91 (0.72–1.16)
Social self-perceptions	Ref (1.0)			Ref (1.0)
Global	Ref (1.0)	0.90 (0.71–1.14)		Ref (1.0)	0.89 (0.70–1.13)

High scores are defined as those equal of above the median for the whole sample. P -values below 0.05 are in bold. Adjusted models are adjusted for age, sex and year of medical school

Groups with higher sleep deprivation (Q2 + Q3 and Q4) had lower odds for higher scores of quality of life in all domains of VERAS-Q and WHOQOL-BREF questionnaires with the exception of environment domains of group Q2 + Q3. Interestingly, lower odds for quality of life were observed in the groups with sleep deprivation only for medical school-related quality of live but not for overall QoL.

We observed higher odds for depression symptoms in medical students with higher differences between weekends and weekdays sleep hours (SDI). Medical student that reported more than 4 h of SDI had an odds ratio of 3.01 (2.16 to 4.19) of higher depression symptoms compared to students with a SDI less than 3. We also observed higher odds rations of higher anxiety symptoms for state anxiety in Group Q4 and for trait anxiety in groups Q2 + Q3 and Q4.

When we studied the odds ratios of higher DREEM scores, we observed statistically significant lower odds ratios in Group Q4 compared to Q1 in global DREEM scores and in all DREEM domains (learning, teachers, educational atmosphere, academic and social self-perception). Group Q2 + Q3 presented lower odds only in two domains (perception of learning and social self-perception).

Table 4 shows the results of a binary logistic regression model for the association between sleep deprivation index (SDI) and daytime sleepiness (ESS). We show the odds ratios (and 95% confidence intervals) for the association between sleep deprivation groups and daytime sleepiness. The results were adjusted for age, sex and year of medical school. Group Q1 was used as reference. Students in the quartile 2 and quartile 3 of the SDI had an increase of 59,9% the odds of having pathologic values of daytime sleepiness, in comparison with Q1. In addition, students in the quartile 4 of SDI had an increase of 122,8% in the odds of having pathologic values of daytime sleepiness, in comparison with Q1 group.

Results of binary logistic regression models for the association between sleep deprivation index (SDI) and Epworth scale (daytime sleepiness)

	SDI	Adjusted data OR (95% CI)	P
Epworth > 10	Q1 (≤ 2 h)	Ref (1.0)
	Q2 + Q3 (3 and 4 h)		< 0.001
	Q4 (≥ 4 h)		< 0.001

Our data reveal consistent associations between daytime sleepiness and sleep deprivation and worse perception of quality of life and academic environment, and anxiety and depression symptoms in medical students. A dose-response relationship was observed for these associations.

In our study, there was a high frequency of students who had high scores on the Epworth scale (46.5%). This number, if compared with most studies involving medical students, was impressive. A study from Malaysia showed a percentage of 35.0% [ 25 ] of high scores on the Epworth scale. In India, this value was 30.6% [ 26 ]. Our data also showed that females had greater daytime sleepiness in relation to the males.

Our results showed that there was also a high percentage of students who had poor sleep quality by PSQI (62.2%). This number was higher than other studies in medical students, with scores ranging from 19.0% in China [ 27 ], 38.9% in Brazil [ 28 ] and 40.0% in Lithuania [ 29 ]. A national study, which evaluated the general adult population, showed a mean of 4.9 of the overall PSQI score and worse scores in females [ 30 ]. Our data did not show differences between males and females, and we observed a worse mean of the overall PSQI score.

Some studies have evaluated sleep in healthy young general population, identifying habitual sleep ranges from 7.0 to 8.5 h, and their determinants are social factors and lifestyle [ 31 – 34 ]. However, when offered the opportunity of extended sleep time in experiments with protected hours, the amount of nocturnal sleep can increase more than 1 h, ranging from 8.4 to 8.9 h [ 31 – 36 ]. The recommendation of the National Sleep Foundation is that individuals from 18 to 25 years of age sleep between 7 and 9 h [ 37 ]. The extended period of sleep brings potential benefits to the individual because this implies that all phases of sleep are respected, allowing physical and mental restoration [ 32 ]. One practical way in which people compensate for the lack of sleep that may incorporate into their routine is a short nap throughout the day.

The difference between the hours of sleep in the week and at the weekend associated with not meeting the actual need for sleep suggests that many students in our study had chronic sleep deprivation. The smaller mean hours of sleep during the week in the group with worse daytime sleepiness scores (Epworth> 10) also reinforce this data. Other studies have shown that young adults have sleep deprivation from one to three hours at night during the week, with a much longer sleep duration and wake-up time later at weekends [ 38 ]. Coupled with this behavior, many medical students view sleep deprivation as a symbol of dedication to the profession [ 39 ]. This aspect has a strong influence of the hidden curriculum, which concerns the student’s socialization in the process of becoming a doctor, or the construction of their professional identity, acquiring habits and behaviors patterns of their peers and models [ 40 ]. The common sense is that the successful doctor is the one who is too busy to abstain from hours of leisure, socializing and self-care, in favor of the health care of others [ 41 ]. This model that underestimates self-care can be assimilated and reproduced by students, sacrificing their hours of sleep for other interests.

Specialists in time management suggest that the agenda begins by delimiting the necessary hours of sleep and from there the other daily tasks are distributed. The question that arises is that there is a desire among the students to include all complementary training opportunities to the formal curriculum, often causing harm to their health. This overload can be motivated both by the competitiveness among the students and by the generational multitasking characteristic [ 42 ].

Few data exist on the medical student’s routine in the past. A 1968 study in England found that on average the medical student slept eight hours a day and that the amount of sleep did not change between the week and the weekend [ 43 ]. An Australian study reported the worst academic performance when waking later in the morning, especially at weekends [ 44 ]. The same author, years later, after developing the Epworth scale, found an average of this score of 7.6 [ 45 ], whereas in our data the average daytime sleepiness score was 10.3. The analysis of these studies shows that in addition to the cultural differences, it is necessary to highlight the historicity of the samples.

Some authors compared the sleep of medical students with that of other courses. There is a large percentage of college students in general who sleep less than 7 h per night, ranging from 24 to 49% [ 46 ]. Medical students had worse PSQI scores in relation to Law and Economics courses in Lithuania [ 29 ].

Several studies have reported the relationship between daytime sleepiness and academic performance. There were better performances in students who slept earlier and who had greater hours of sleep during the week. Sleep deprivation has negative effects on emotional intelligence, including the ability to demonstrate empathy [ 47 – 49 ]. Of course, these studies report only associations, and cause-effect of sleepiness versus academic performance or emotional abilities cannot be precisely established.

In the same context, it is unclear whether sleepiness leads to deterioration of the student’s mental health, or whether drowsiness can be one of the consequences of anxiety or depression. A national study revealed an increased risk of minor psychiatric disorders among students with sleepiness, sleep interruption, insomnia, and sleep hours of less than 7 h [ 50 ]. Loayza et al. [ 50 ] suggest that the evaluation of sleepiness in medical students can be a good tool for psychiatric screening and preventive measures.

The overall PSQI scores were related to the range of ESS scores, that is, there was a positive association of the instruments, indicating that the higher the PSQI Global score (meaning poorer sleep quality), the greater the tendency of the individual have an ESS altered score (indicating greater daytime sleepiness).

Few studies compared WHOQOL-BREF with Epworth scale, and these studies were from specific populations, such as elderly patients with chronic pain or sleep apnea [ 51 – 53 ]. All studies revealed a relationship between sleepiness and decreased the quality of life.

Our DREEM results show that students had a more positive than negative perception of educational environment (total score between 101 and 150), according to the syntax of DREEM [ 19 ]. The mean of the global score was similar to the results of other studies conducted in developing countries such as Iran, India, Kuwait and Sri Lanka [ 54 – 57 ].

Odds Ratio (OR) values were significant for most associations between sleep and quality of life and educational environment. These logistic regression results are robust because they carefully exclude confounding factors such as age, sex, and course year. With this analysis, the impact of sleep deprivation on the medical student’s quality of life confirms the practical relevance of this issue. However, data on quality of life are multifactorial and sleepiness is not an isolated factor in the worsening of the quality of life and in the perception of the educational environment. It is worth mentioning that only the group with the highest drowsiness (Epworth quartile 4) presented a significant association of ORs for the domains of DREEM and Global score, except for perception of learning and social relation’s domains, which also showed significance in the intermediate drowsiness group (quartiles 2 + 3).

The present study has some strengths: the original format, the national multicenter design, with an expressive number of randomized respondents, a low number of losses, a high response rate and a variety of instruments that analyze the quality of life, sleep, emotional symptoms, and medical student educational environment. Another positive aspect of the study was the possibility for respondents to receive feedback on their results and the opportunity for support and guidance.

Our study has as limitations the transversal design that does not allow us to analyze causality and the fact that the results are generalizable only to the universe of Brazilian students, although we can infer that they are similar to those found in other cultures. There are some limitations of studies that use self-reports. Specifically, in relation to studies of sleep, the results can be compared with more objective measures, such as polysomnography or actigraphy. More stressed individuals tend to report more sleepiness and fatigue in relation to people who are less stressed [ 58 ]. Concerning quality of life, individuals with more critical views may negatively direct their responses to some items.

Sleep deprivation and daytime sleepiness are associated to a worse the perception of quality of life and educational environment and depression and anxiety symptoms in medical students.

Curricular changes that include redistribution of academic activities, individual orientation for mentoring activity, health promotion programs and protected hours for study and leisure are valid strategies to assist the student in the management of his/her time, which indirectly can improve his / her learning, sleep and decrease their daytime sleepiness, ultimately improving the medical student’s quality of life.

Acknowledgements

The authors acknowledge the students and the medical schools that participated in the study: Universidade Federal do Rio de Janeiro, Universidade Federal de Ciências da Saúde de Porto Alegre, Universidade Estadual do Piauí, Faculdade de Medicina de Petrópolis, Faculdade de Ciências Médicas da Paraíba, Pontifícia Universidade Católica de São Paulo, Universidade Federal do Ceará, Universidade Federal de Goias, Universidade Federal de Mato Grosso do Sul, Escola Baiana de Medicina e Saúde Pública, Faculdade de Medicina de Marília, Faculdade de Medicina de São José do Rio Preto, Faculdade de Ciências Médicas da Paraíba, Faculdade Evangélica do Paraná, Faculdade de Medicina do ABC, Fundação Universidade Federal de Rondônia, Pontifícia Universidade Católica do Rio Grande do Sul, Universidade Federal do Tocantins, Universidade Federal de Uberlândia, Universidade Estadual Paulista Júlio de Mesquita Filho, Centro Universitário Serra dos Orgaos, Universidade de Fortaleza and Universidade de Passo Fundo.

Abbreviations

ESS	Epworth Sleepiness Scale
PSQI	Pittsburgh Sleep Quality Index
SDI	Sleep Deprivation Index
QoL	Overall quality of life
MSQoL	Medical School related quality of life
BDI	Beck Depression Inventory
STAI	State Trait Anxiety Inventory
DREEM	Dundee Ready Education Environment Measure
VERAS-Q	Questionnaire to evaluate quality of life in students of health professions

Authors’ contributions

Study design: BP, HP, GL, MAM, PZT. Data collection: BP, EAF, HP, MAM, PZT. Data analysis: BP, FMA, SCE, ISS, GL. Writing of manuscript: BP, FMA, SCE, ISS, MAM, PZT. Review and approval of manuscript: BP, FMA, SCE, EAF, HP, ISS, GL, MAM, PZT.

This study was supported by the following Brazilian Scientific Agencies: FAPESP (Sao Paulo), CNPq (Brazil) and CAPES (Brazil).

Availability of data and materials

Ethics approval and consent to participate.

The research protocol was approved by the Ethics Committee of the School of Medicine of the University of Sao Paulo (protocol number 181/11). All medical schools included approved the study.

Our study was performed according to STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting observational studies.

Participation was voluntary, and we did not offer any compensation or incentive. We guaranteed both confidentiality and anonymity, and participating students completed an informed consent form.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests concerning this manuscript.

Publisher’s Note

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

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Effects of Sleep Deprivation on Performance: A Meta-Analysis
To quantitatively describe the effects of sleep loss, we used meta-analysis, a technique relatively new to the sleep research field, to mathematically summarize data from 19 original research studies. Results of our analysis of 143 study coefficients and a total sample size of 1,932 suggest that overall sleep deprivation strongly impairs human ...
Addressing the Sleep Deprivation Epidemic in Adolescents: Findings from
Healthy People 2030 has prioritized sleep and identified an objective to increase the proportion of students in grades 9 through 12 who get sufficient sleep. ... conducted by the Centers for Disease Control (CDC). Severe sleep deprivation was defined as 6 hours or less sleep per night on school nights. Binary logistic regressions were used to ...
The effects of insufficient sleep and adequate sleep on cognitive
Study objectives. Although sleep affects a range of waking behaviors, the majority of studies have focused on sleep loss with relatively little attention on sustained periods of adequate sleep. ... A meta-analysis of the impact of short-term sleep deprivation on cognitive variables. Psychol Bull. 2010; 136: 375-389. ... Working memory is a ...
(PDF) The Effects Of Sleep Deprivation Towards The ...
The objective of this study is to evaluate the relationship between sleep deprivation and study habits with academic performance among students during ODL. A cross-sectional survey is used through ...
The effect of sleep deprivation on objective and subjective ...
However, there is little research using objective measures to investigate sleep-loss-related changes in facial appearance. We aimed to assess the effects of sleep deprivation on skin colour, eye openness, mouth curvature and periorbital darkness using objective measures, as well as to replicate previous findings for subjective ratings.
Sleep deprivation and aging are metabolically linked across tissues
Study objectives. Insufficient sleep is a concerning hallmark of modern society because sleep deprivation (SD) is a risk factor for neurodegenerative and cardiometabolic disorders. SD imparts an aging-like effect on learning and memory, although little is known about possible common molecular underpinnings of SD and aging.
The Effects of Sleep Deprivation on College Students
Lack of sleep resulted in inadequate psychomotor performance and poor brain function. In order to combat the effects of sleep deprivation on one's physiological health, napping for a. short period of time and early in the day can be used to maintain optimal brain function. In.
A systematic review of sleep deprivation and ...
Sleep deprivation affects regions of the prefrontal cortex ... 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. ... Journal of Sleep Research ...
Assessment of effects of total sleep deprivation and subsequent
Background Sleep is critical for maintaining homeostasis in bodily and neurobehavioral functions. This homeostasis can be disturbed by sleep interruption and restored to normal by subsequent recovery sleep. Most research regarding recovery sleep (RS) effects has been conducted in specialized sleep laboratories, whereas small, less-well equipped research units may lack the possibilities to run ...
The Extraordinary Importance of Sleep
New sleep deprivation studies confirm the relationship between inadequate sleep and a wide range of disorders, such as hypertension, obesity and type-2 diabetes, cardiovascular disease, impaired immune functioning, and more. ... Achieving the right balance of subjective and objective measures of sleep is an important goal in both research and ...
(PDF) The effect of sleep deprivation on objective and subjective
The faces of people who are sleep deprived are perceived by others as looking paler, less healthy and less attractive compared to when well rested. However, there is little research using ...
The Effects of Sleep Deprivation on Individual Productivity
Long-term total sleep deprivation: no sleep for more than 48 hours Nocturnal awakenings: waking up during a period of sleep Limitations As with all correlational studies, the findings from the current study cannot reveal causal relationships between sleep deprivation and personal productivity. Another key limitation has been identified.
Sleeping Habits and Sleep Deprivation Among College Students by Jane
The following steps will be taken to gain knowledge about sleep deprivation among college students: 1. Assess contributing factors of sleep deprivation by conducting a survey with college students. 2. Evaluate results of sleep deprivation surveys and formulate recommendations for future research and interventions.
Effects of Sleep Deprivation
Research has found that sleep deprivation affects systems throughout the body, leading to a wide range of negative effects. Daytime sleepiness: Not getting enough sleep is a common cause of people feeling tired during the day Trusted Source UpToDate More than 2 million healthcare providers around the world choose UpToDate to help make ...
Chapter 4. Methodology for the Assessment of Sleep
following techniques will be covered in this chapter including: polysomnography, actigraphy, brain imaging, hormone assessment, and subjective measures including self-report. questionnaires and ...
The Impact of Dietary Habits on Sleep Deprivation and Glucose Control
Diet plays a crucial role in managing type 1 diabetes (T1DM). Background/Objectives:This study aimed to determine the impact of nutritional habits on sleep deprivation and glucose control in school-aged children with T1DM. Methods: In this cross-sectional study, nutritional habits and sleep deprivation were assessed in 100 school-aged children with T1DM, aged 7-13 years. The Dietary Habits ...
The role of objective sleep in implicit and explicit affect regulation
DOI: 10.1016/j.ynstr.2024.100655 Corpus ID: 270491838; The role of objective sleep in implicit and explicit affect regulation: A comprehensive review @article{Straus2024TheRO, title={The role of objective sleep in implicit and explicit affect regulation: A comprehensive review}, author={Laura D. Straus and Maia ten Brink and Pilleriin Sikka and Radhika Srivastava and James J. Gross and Peter J ...
How Sleep Deprivation Affects Your Memory: New Brain Waves Discovered
Even after the sleep-deprived mice were allowed to sleep again, their SWRs never quite reached the strength and consistency found in mice that had normal sleep. The results in this study further demonstrate how critical sleep is for memory and suggest that long term sleep deprivation could have a lasting effect on memory.
Sleep deprivation: Impact on cognitive performance
People who are exposed to sleep loss usually experience a decline in cognitive performance and changes in mood (for meta-analyses, see Pilcher and Huffcutt 1996; Philibert 2005 ). Sleep deprivation is a study design to assess the effects of sleep loss. In acute total SD protocols, the subjects are kept awake continuously, generally for 24-72 ...
Adolescents' short-form video addiction and sleep quality: the
In addition, the subjective and objective factors such as self-isolation lead to poorer academic performance and sleep quality among adolescents. In addition, this study observes certain relationships between family economic condition and exercise frequency and adolescents' sleep quality, which is consistent with previous research results [ 3 ...
Research explores reversing memory loss with existing drugs
The loss of social memories caused by sleep deprivation could potentially be reversed using currently available drugs, according to a study in mice presented today (Friday) at the Federation of ...
Sleep Deprivation
Sleep loss from various causes is prevalent and often missed by physicians as a treatable health problem. There are strong associations with serious medical conditions such as diabetes mellitus & insulin resistance, hypertension, obesity, obstructive sleep apnea, depression, and anxiety. These medical and psychiatric comorbidities put a person at risk for heart attack and stroke. Other adverse ...
Memories lost due to sleep deprivation restored by existing drugs
Memories seemingly lost as a result of sleep deprivation were restored using existing drugs used to treat asthma and erectile dysfunction, according to new research. The research suggests these ...
Sleepiness, sleep deprivation, quality of life, mental symptoms and
Background. Sleep problems are very frequent in the general population and medical students are one group that is vulnerable to poor sleep [1, 2].The prevalence of sleep disturbances in medical students is higher than in non-medical students [1, 3].There are many reasons to the high prevalence of sleep problems in medical students, including many hours of classes and study, clinical clerkships ...

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The effects of insufficient sleep and adequate sleep on cognitive function in healthy adults

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The effect of sleep deprivation on objective and subjective measures of facial appearance

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Sleep deprivation and aging are metabolically linked across tissues

Sleep deprivation

Sample processing for UPLC-MS/MS

UPLC-MS/MS experiments

NMR spectroscopy and quantification of plasma 3-hydroxybutyrate levels

Multivariate analysis

Univariate analysis

Enrichment analysis and pathway analysis

Aging and SD both affect the metabolic profiles

Prediction of age and sleep status using perturbed metabolites

Metabolic changes with acute SD across the age groups

Aged animals mimic the metabolic phenotypes associated with SD

Specific plasma and liver metabolites are significantly impacted by both sleep and aging

Young adult animals display aging-like molecular and clinical phenotypes after acute sleep deprivation

Young adult animals are more impacted by SD than aged animals

Metabolic signatures to identify sleep status and age

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Effects of Sleep Deprivation

What Are the Effects of Sleep Deprivation?

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Sleep Deprivation and Reaction Time

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The role of objective sleep in implicit and explicit affect regulation: A comprehensive review

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Expressive suppression and cognitive reappraisal in veterans with PTSD: Results from the mind your heart study.

Combined cognitive-behavioral therapy for fibromyalgia: Effects on polysomnographic parameters and perceived sleep quality

Stress, sleep, and the selective consolidation of emotional memories

Adolescents’ short-form video addiction and sleep quality: the mediating role of social anxiety

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Social interaction anxiety scale (SIAS) [ 23 ]

Pittsburgh sleep quality index (PSQI) [ 25 ]

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Analysis of status and differences in short-form video addiction, social anxiety, and sleep quality

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Correlation analysis of short-form video addiction, social anxiety, and sleep quality

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