This dataset explores the effects of sleep deprivation on cognitive performance and emotional regulation, based on a 2024 study conducted in the Middle East. It includes 60 participants from diverse backgrounds, capturing data on sleep duration, sleep quality, daytime sleepiness, cognitive function (reaction times, memory accuracy), and emotional stability. Additionally, it records demographic factors such as age, gender, BMI, and lifestyle influences like caffeine intake, physical activity levels, and stress levels.

The study was conducted using standardized cognitive performance tests, including the Stroop Task, N-Back Test, and Psychomotor Vigilance Task (PVT), commonly used in neuroscience and psychology research. This dataset is structured to support statistical analysis, machine learning applications, and behavioral research. It provides valuable insights for sleep research, mental health studies, and cognitive performance analysis, particularly in the context of Middle Eastern populations and lifestyle factors in 2024.

General information

The dataset provides resting-state EEG data (eyes open,partially eyes closed) from 71 participants who underwent two experiments involving normal sleep (NS---session1) and sleep deprivation(SD---session2) .The dataset also provides information on participants' sleepiness and mood states. （Please note here Session 1 (NS) and Session 2 (SD) is not the time order, the time order is counterbalanced across participants and is listed in metadata.）

Dataset

Presentation

The data collection was initiated in March 2019 and was terminated in December 2020. The detailed description of the dataset is currently under working by Chuqin Xiang,Xinrui Fan,Duo Bai,Ke Lv and Xu Lei, and will submit to Scientific Data for publication.

EEG acquisition

• EEG system (Brain Products GmbH, Steing- rabenstr, Germany, 61 electrodes)
• Sampling frequency: 500Hz
• Impedances were kept below 5k

Contact

 * If you have any questions or comments, please contact:
 * Xu Lei: xlei@swu.edu.cn   

Article

Xiang, C., Fan, X., Bai, D. et al. A resting-state EEG dataset for sleep deprivation. Sci Data 11, 427 (2024). https://doi.org/10.1038/s41597-024-03268-2

This statistic depicts the percentage of adults in select countries worldwide who experienced select impacts from sleeplessness as of 2018. It was found that 46 percent of respondents stated that they looked tired after sleeping less than 7 to 9 hours.

How Much Sleep Do Americans Really Get?

The Consequences of Sleep Deprivation

By Makeover Monday [source]

About this dataset

Do you find yourself tossing and turning at night, struggling to fall asleep? You're not alone. A recent study found that the average American adult gets just under seven hours of sleep per night.

But how does this compare to other countries? And what factors contribute to our sleeplessness?

This dataset contains data on the average amount of time Americans spend sleeping, broken down by age group, sex, and activity. The data includes both weekdays and weekends, so you can see how our sleep habits change depending on the day of the week.

So take a look and see if you can find any patterns in the data. Why do you think some groups get more (or less) sleep than others? And what can we do to improve our sleep habits as a nation?

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How to use the dataset

This dataset includes data on the average number of hours per day Americans spend sleeping, broken down by age group, sex, and activity. This can be used to understand patterns in sleep habits among different groups of people, as well as how these patterns may change over time.

To use this dataset effectively, it is important to understand the different variables that are included. The 'Age Group' variable indicates the age group that the data applies to, while the 'Sex' variable indicates whether the data is for male or female respondents. The 'Activity' variable indicates what activity was being undertaken when the respondent was asked about their sleep habits (e.g. 'sleeping', 'working', 'watching TV', etc.), while the 'Type of Days' variable indicates whether the data was collected for weekdays, weekends, or holidays.

Finally, the 'Avg hrs per day sleeping' and 'Standard Error' variables give information on the average amount of time spent sleeping per day, along with a measure of how accurate this estimate is

Research Ideas

• To study the effect of sleep deprivation on health
• To understand the role of sleep in regulating mood and behavior
• To examine the relationship between sleep and cognitive function

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Time Americans Spend Sleeping.csv | Column name | Description | |:-----------------------------|:---------------------------------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | Period | The period of the day the data was collected. (String) | | Avg hrs per day sleeping | The average number of hours per day Americans spend sleeping. (Float) | | Standard Error | The standard error for the average number of hours per day Americans spend sleeping. (Float) | | Type of Days | The type of day the data was collected. (String) | | Age Group | The age group of the Americans surveyed. (String) | | Activity | The activity the Americans surveyed were engaged in. (String) | | Sex | The sex of the Americans surveyed. (String) |

Acknowledgements

If you use this dataset in your research, please credit Makeover Monday [source]

In 2022, in the Netherlands, among individuals aged 18 to 24 years, **** percent of men reported being affected by sleep deprivation, whereas **** percent of women reported to experience the same issue. This statistic depicts the percentage of young population affected by sleep deprivation in the Netherlands in 2022, by gender and age

Between 2013 and 2022, the share of adults in the U.S. not getting enough sleep fluctuated between roughly 33 and 37 percent. In 2022, this figure reached 36.8 percent, the highest share in the given period. This statistic displays the share of adults getting insufficient sleep in the U.S. between 2013 and 2022.

We used the standard and validated Pittsburgh Sleep Quality Index (PSQI), which was developed by researchers at the University of Pittsburgh in 1988 AD. The questionnaire included baseline variables like age, sex, academic year, and questions addressing participants’ sleep habits and quality i.e. PSQI. The PSQI assesses the sleep quality during the previous month and contains 19 self-rated questions that yield seven components: subjective sleep quality sleep, latency, sleep duration, sleep efficiency and sleep disturbance, and daytime dysfunction. Each component is to be assigned a scored that ranges from zero to three, yielding a PSQI score in a range that goes from 0 to 21. A total score of 0 to 4 is considered as normal sleep quality; whereas, scores greater than 4 are categorized as poor sleep quality.Data collected from students through the Google forms were extracted to Google sheets, cleaned in Excel, and then imported and analyzed using STATA 15. Simple descriptive analysis was performed to see the response for every PSQI variable. Then calculation performed following PSQI form administration instructions.

Reaction times in a sleep deprivation study

Description

The average reaction time per day (in milliseconds) for subjects in a sleep deprivation study.

Days 0-1 were adaptation and training (T1/T2), day 2 was baseline (B); sleep deprivation started after day 2.

Format

A data frame with 180 observations on the following 3 variables.

Reaction Average reaction time (ms)

Days Number of days of sleep deprivation

Subject Subject number on which the observation was made.

Details

These data are from the study described in Belenky et al. (2003), for the most sleep-deprived group (3 hours time-in-bed) and for the first 10 days of the study, up to the recovery period. The original study analyzed speed (1/(reaction time)) and treated day as a categorical rather than a continuous predictor.

References

Gregory Belenky, Nancy J. Wesensten, David R. Thorne, Maria L. Thomas, Helen C. Sing, Daniel P. Redmond, Michael B. Russo and Thomas J. Balkin (2003) Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: a sleep dose-response study. Journal of Sleep Research 12, 1–12.

In a survey conducted in New Zealand in 2022, over ** percent of respondents reported not getting enough sleep due to having too much to think about. Over ** percent of respondents reported being anxious or stressed as a reason for not getting enough sleep, while a similar share said they go to bed too late.

The data set is an output of the Track and Know project to be shared with the scientific community. The data set contains the output of a monitoring app recorded during a series of journeys organised in two sub sets. The first subset of data was recorded from journeys made on different days around a circular route by a single driver. On different iterations of the journey the driver determined to drive either; as carefully as possible, normally or poorly. The intention of the poor driving was to imitate sleepy driving with harsh breaking, cornering and acceleration and deliberate lane drifting. The data set is designed to allow the development of algorithms to detect different driving behaviours The second data set was generated by 3 volunteers who were engaged in shift work. The journeys consist of trips to work and home at different times of day and other journeys not related to work. The intention of the data set is to allow comparisons to be made between journeys undertaken by the drivers when sleep replete and sleep deprived (after working a night shift). The data are enriched with weather information pertaining to the date, time and location of each journey.

Adequate sleep is of paramount importance for relieving stress and restoring mental vigor. However, the adverse physiological and pathological responses resulting from sleep insufficiency or sleep deprivation (SD) are becoming increasingly prevalent. Currently, the impact of sleep deficiency on gut microbiota and microbiota-associated human diseases, especially cardiac diseases, remains controversial. Here, we employed the following methods: constructed an experimental sleep-deprivation model in mice; conducted 16S rRNA sequencing to investigate the changes in gut microbiota; through fecal microbiota transplantation (FMT) experiments, transplanted fecal microbiota from sleep-deprived mice to other mice; established an environment with a 30% oxygen concentration to explore the therapeutic effects of oxygen therapy on gut microbiota-associated cardiac fibrosis and dysfunction; and utilized transcriptome data to study the underlying mechanisms of oxygen therapy. The results revealed that: sleep-deprived mice exhibited weakness, depression-like behaviors, and dysfunction in multiple organs. Pathogenic cardiac hypertrophy and fibrosis occurred in sleep-deprived mice, accompanied by poor ejection fraction and fractional shortening. 16S rRNA sequencing indicated that sleep deprivation induced pathogenic effects on gut microbiota, and similar phenomena were also observed in mice that received fecal microbiota from sleep-deprived mice in the FMT experiments. The environment with a 30% oxygen concentration effectively alleviated the pathological impacts on cardiac function. Transcriptome data showed that oxygen therapy targeted several hypoxia-dependent pathways and inhibited the production of cardiac collagen. In conclusion, these results demonstrate the significance of sufficient sleep for gut microbiota and may represent a potential therapeutic strategy, where the oxygen environment exerts a protective effect on insomniacs through gut microbiota.

ABSTRACT Objective: To analyze the contribution of subjective sleep need for daytime sleepiness in adolescents, and to compare questions about sleep, age and body mass index between adolescents who considered to sleep enough and those who reported the need for more sleep. Methods: This is a descriptive, epidemiological and cross-sectional study. Data collection was performed in August 2016, with 773 adolescents aged 14-19 years old, from Paranaguá, Paraná, Southern Brazil. The analysis included the following variables: time in bed, half-sleep phase, sleep need, social jetlag, daytime sleepiness, body mass index and physical activity. Results: The prevalence of adolescents with subjective need for sleep was 73.0%, with an average need of 1.7 extra hours of sleep. These adolescents woke up earlier (p

The Stockholm SleepyBrain Project

Background and Aim ##

Sleepiness is a brain state with pervasive effects on cognitive and affective functioning. However, little is known about the functional mechanisms and correlates of sleepiness in the awake brain. This project aimed to investigate overall effects of sleepiness on brain function with particular regard to emotional processing.

Method and Design ##

We investigated the effects of sleep deprivation using a randomized cross-over design. Resting state functional connectivity was investigated using functional magnetic resonance imaging (fMRI). Emotional contagion was studied using concurrent fMRI and electromyography (EMG) of facial muscles in response to emotional expressions and empathy for pain was investigated using pictures of others receiving pain stimuli. To study emotional reappraisal, participants were instructed to actively up-regulate or down-regulate their emotional responses to picture stimuli. The participants were characterized using several rating scales, biometric information, and blood sampling.

Specific Notes

participants.tsv File

Subject ID list and subject-level variables. Please refer to the participants.json for guidance on how to interpret specific columns in the participants.tsv file.

BIDS dataset

Data were converted from DICOM source files using dcm2niix. The parameters were further extracted from the DICOM files using pydicom and converted to .json format. SeriesDates were anonymized and shifted to pre-1900's years and a subject-based offset added to the month/year that preserves time difference between initial and follow-up visit.

T1- and T2-anatomical scans (anat/*_T{1,2}w.nii.gz) were defaced using the pydeface.py software: https://github.com/poldracklab/pydeface (c1ceeb2)

derivatives Folder

This folder contains the processed output from the MRIQC protocol. MRIQC is an automated processing pipeline designed to compute many image quality metrics for T1 weighted anatomical and T2* weighted functional scans. For more information please see:

https://github.com/poldracklab/mriqc (a5f68f5)

Additional derivatives include:

• Plots of the fMRI event logs
• thumbnail mosaics of the high-resolution T1w and T2w scans used to confirm defacing process.

sourcedata Folder

This folder contains the as-provided source files used to create the BIDS dataset files. The only changes made to these source files were to remove any information that could potentially be used to identify the study participants. Specifically:

• EyeTrackingLogFiles: Files renamed, "TimeValues" and "TimeStamp" entries changed to "REMOVED" within each file.
• PresentationLogFiles: Files renamed, scrubbed of Dates, Subject IDs. These files were used to create the sub-9XXX_ses-{1,2}_task-
• PulseGatingFiles: Files renamed to remove original IDs.
• WorkingMemoryTestResults: Files renamed to remove original IDs., subject IDs altered to 9XXX series randomized IDs. Dates removed. Times-of-day left intact.

Other data that could not be included in raw form due to its binary nature:

• Physiological recordings (EMG): Converted from raw Acknowledge format to compressed .tsv files using the "convert_physio_files.py" script located in the code/ directory within the dataset. The output data are located within the dataset as *_physio.tsv.gz and *_physio.json pairs.

Diffusion Imaging - use these data with caution

Diffusion imaging from the following subjects should be used with caution due to suspicious bval/bvecs tables extracted from the source DICOM files:

sub-9019 sub-9070 sub-9057 sub-9091 sub-9090 sub-9013 sub-9044
sub-9050 sub-9067 sub-9035 sub-9035 sub-9073 sub-9083 sub-9037
sub-9007 sub-9053 sub-9066 sub-9012 sub-9082 sub-9077 sub-9076
sub-9099 sub-9001

Raw Polysomnography Data

Raw polysomnography data is available upon request. Please contact Gustav Nilsonne at gustav.nilsonne@ki.se to request this data.

Known Issues

-sub-9066/ses-1/func/sub-9066_ses-1_task-hands_events.tsv does not have all of the columns present in the other events files. It only has 'onset', 'duration' and 'condition'.

According to a survey from 2024, around eight percent of college students in the United States had extremely difficulty falling asleep for seven of the last seven days. This statistic shows the percentage of college students in the U.S. who had an extremely hard time falling asleep within the past seven days as of fall 2024.

The data here include one large, multi-paradigm study on the impact of sleep deprivation on mentalizing and cognition and a series of studies on the impact of anxiety on belief and desire reasoning. The rationale for the broader project was to consider the hypothesis that anxiety and sleep deprivation impact mentalizing in distinct ways for distinct reasons. Mentalizing (also known as theory of mind) is our ability to understand other people’s mental states. There are good reasons to expect that sleep deprivation might impact mentalizing. Sleep deprivation impacts processes associated with mentalizing, including executive functioning, and other social processes, such as emotion recognition. We wanted to provide the first detailed consideration of whether sleep deprivation impacted mentalizing itself.

Participants took part in paradigms when rested and when sleep deprived. These included three mentalizing tasks: the reading the mind in the eyes task, the belief-desire reasoning task and the emotional egocentricity touch paradigm. These allowed us to test the impact of sleep deprivation on mentalizing ability, egocentrism and self-other distinction. Sleep deprivation only negatively impacted overall performance. We concluded that sleep deprivation may impact overall processing performance at mentalizing, rather than specific processes, such as the ability to inhibit egocentrism or distinguish between self and other perspectives.

It has been proposed that anxiety makes us more egocentric, to overcome uncertainty. We wished to examine this in belief reasoning. In study one, participants completed a belief-desire reasoning task when anxious (manipulated with an autobiographical writing task) and when relaxed. There was no impact of anxiety on performance. To understand this, we conducted two follow-up studies. The first examined the impact of the independent variable, by using a threat-of-shock paradigm. The second, the impact of the dependent variable, through requiring participants to infer the belief of the character. Both replicated the original findings.

Poor sleep and anxiety pose significant economic and social challenges. Inadequate sleep costs the UK economy 1.3-1.9% of Gross Domestic Product each year and has significant health and social consequences. Common mental health difficulties, such as anxiety, account for 17.6 million sick days yearly and reduce GDP by around 1.3%. Additional to this are the indirect costs of poor sleep and anxiety - the costs to our social understanding, social interaction and social relationships. Understanding other people's perspectives, beliefs, desires and feelings is important. The sophistication with which humans do this is one of the things that has led to our success as a species. We live in large communities and cooperate to meet shared goals. We learn from others, teach our children and care for those more vulnerable than ourselves. Each of these activities relies on our understanding of what other people see, believe, want or feel. Sometimes others tell us this information, but sometimes we have to infer it from the way they act.

Everyday experience suggests poor sleep and anxiety affect our social understanding. Who has not noticed anxious students nervously ignoring each other as they wait for an exam? How many of us have spent time apologizing to our mothers when we forgot to send them a birthday card because we were exhausted after over-working towards a deadline? Emerging experimental evidence supports the suggestion that sleep deprivation and anxiety can change our social understanding and that those with long-term experiences of sleep problems or anxiety experience social difficulties. What is less clear is exactly how sleep and anxiety impact on our social understanding and how variability in these experiences impacts our longer-term social functioning. One hypothesis that is consistent with the literature is that anxiety makes us more selfish, encouraging us to focus on our own point of view at the expense of others. Another is that sleep deprivation makes us more muddled, making it harder to distinguish between our own point of view and other people's. A third is that those with better sleep and lower levels of anxiety will function better in the longer term because they understand people better.

To look at the impact of sleep deprivation, we will test people on three tasks, after having them remain awake for a whole night. The tasks will investigate their ability to think about other people's thoughts and feelings. Their responses will be informative as to whether sleep deprivation has caused them to have difficulties in distinguishing between their own thoughts and feelings, and other people's. To look at the impact of anxiety, we will induce anxiety in people and then have them do the very same tasks. People will be made anxious by recalling and writing about a time they felt anxious. Here, we predict people's responses will show evidence of increased selfishness in their judgments. To look at the relationship between long-term, everyday tendencies towards anxiety and poor sleep, and social difficulties, participants will complete a number of questionnaires and short experimental tasks. We will use sophisticated statistical modelling to test the relationship between people's traits in these areas.

Each of these experiments will provide exciting new evidence for how everyday experiences impact on our social understanding. They will also open up new avenues for investigation, looking at the impact of these findings. Clearer evidence for the way in which poor sleep and anxiety impact social understanding is highly relevant to clinical populations, such as those with insomnia, generalized anxiety disorder and autism. Further, specialist populations, such as new parents, junior doctors or soldiers in combat have to experience poor sleep and anxiety at the same time as making crucial social judgments. The novel insights offered by our project will provide a model for understanding how their abilities are affected.

RNA-seq data and DNA methylation array data from 15 study participants. Biopsies were takes from adipose tissue and muscle, on two occasions: after a night of normal sleep and after a night of total sleep deprivation.

Sleep deprivation (SD) adversely affects brain function and is accompanied by frequency dependent changes in EEG. Recent studies have suggested that BOLD fluctuations pertain to a spatiotemporal organization with different frequencies. The present study aimed to investigate the frequency-dependent SD-related brain oscillatory activity by using the amplitude of low-frequency fluctuation (ALFF) analysis. The ALFF changes were measured across different frequencies (Slow-4: 0.027–0.073 Hz; Slow-5: 0.01–0.027 Hz; and Typical band: 0.01–0.08 Hz) in 24 h SD as compared to rested wakeful during resting-state fMRI. Sixteen volunteers underwent two fMRI sessions, once during rested wakefulness and once after 24 h of SD. SD showed prominently decreased ALFF in the right inferior parietal lobule (IPL), bilateral orbitofrontal cortex (OFC) and dorsolateral prefrontal cortex (DLPFC), while increased ALFF in the visual cortex, left sensorimotor cortex and fusiform gyrus. Across the Slow-4 and Slow-5, results differed significantly in the OFC, DLPFC, thalamus and caudate in comparison to typical frequency band; and Slow-4 showed greater differences. In addition, negative correlations of behavior performance and ALFF patterns were found mainly in the right IPL across the typical frequency band. These observations provided novel insights about the physiological responses of SD, identified how it disturbs the brain rhythms, and linked SD with frequency-dependent alterations in amplitude patterns.

This dataset was used to generate the figures in the Manuscript: "Sleep Deprivation Improves Behavioral Performance in Zebrafish Larvae".

Impaired sleep is a common aspect of aging and often precedes the onset of Alzheimer's disease. Here, we compare the effects of sleep deprivation in young wild-type mice and their APP/PS1 littermates, a murine model of Alzheimer's disease. After 7 h of sleep deprivation, both genotypes exhibit an increase in EEG slow-wave activity. However, only the wild-type mice demonstrate an increase in the power of infraslow norepinephrine oscillations, which are characteristic of healthy non-rapid eye movement sleep. Notably, the APP/PS1 mice fail to enhance norepinephrine oscillations 24 h after sleep deprivation, coinciding with an accumulation of cerebral amyloid-β protein. Proteome analysis of cerebrospinal fluid and extracellular fluid further supports these findings by showing altered protein clearance in APP/PS1 mice. We propose that the suppression of infraslow norepinephrine oscillations following sleep deprivation contributes to increased vulnerability to sleep loss and heightens the risk of developing amyloid pathology in early stages of Alzheimer's disease.

Abstract: Introduction: Sleep is a physiological state that occurs cyclically in many living beings of the animal kingdom, and the behaviours of rest and activity, that form the sleep-wake cycle, have been the subject of studies. Undergraduate medical students are faced with a lack of time and exhaustion, since the course entails full time study, which fact means that the students put off basic activities more and more until the end of the day, and thus develop sleeping disorders. Objective: To analyze the quality of sleep and the incidence of sleeping disorders among medical students. Method: The research was conducted individually with medical students from a college in the south of Minas Gerais, via the Google Forms platform, whereby the student answered two self-administered questionnaires. The first encompassed questions about gender and year of undergraduate study and the second assessed quality of sleep, referring to the Pittsburgh Sleep Quality Index. Bioestat 5 and Excel 365 programs were used for statistical analysis. Result: Analysis of the study found sleep disturbance among 20.5% of the students and poor or very poor quality of sleep among 40.2% of the students. Conclusion: The research showed that the quality of sleep among medical students is lower than that of the general population, and is directly related to the progression of the course. It was concluded that the students in this study have, on average, fewer hours of sleep than the rest of Brazilians.