Introduction

Sleeping Statistics: Sleep is crucial for health and consists of multiple stages. Including Non-Rapid Eye Movement (NREM) and Rapid Eye Movement (REM) sleep.

A full sleep cycle lasts about 90 minutes, with adults typically needing 7-9 hours of sleep per night. The body's internal clock, or circadian rhythm, helps regulate sleep-wake patterns influenced by light and darkness.

Sleep hygiene, such as maintaining a regular schedule and creating a quiet, dark environment, is key for restful sleep.

Quality sleep supports cognitive function, mood regulation, and physical health, while chronic poor sleep is linked to various health risks. Factors like stress, diet, and medications can affect sleep quality.

This statistic displays the results of a survey asking individuals about their sleeping habits in Malaysia in 2018. According to data provided by Ipsos, about 47 percent of Malaysian respondents felt they get enough sleep, while around 20 percent did not consider themselves to be sleeping enough.

This report provides data on statewide sudden, unexpected infant deaths (SUID) as well as risk factors and preventive measures.

Introduction

Sleep Statistics: Sleep is a fundamental component of overall health, yet a significant portion of the adult population fails to obtain the recommended amount. Adults are advised to sleep between seven and nine hours per night. However, only 31% manage to achieve this duration for at least five nights each week. In the United States, approximately 35% of adults report sleeping less than seven hours per night.

The consequences of insufficient sleep are profound. Chronic sleep deprivation is linked to an increased risk of cardiovascular diseases, including heart attacks and strokes. It also elevates the likelihood of developing type 2 diabetes, obesity, and mental health disorders such as depression and anxiety. Moreover, sleep deficiency impairs cognitive functions, leading to decreased attention, memory lapses, and poor decision-making.

The economic impact is equally alarming. In the United States alone, insufficient sleep is estimated to cost over USD 411 billion annually due to lost productivity, increased healthcare expenses, and accidents.

Given these statistics, it is imperative to prioritize quality sleep as a cornerstone of health and well-being. Sleep deprivation can lead to both physical and mental health issues, a higher risk of mortality, and an increased likelihood of accidents. Let's delve deeper into sleep statistics in this article.

The Human Sleep Project (HSP) sleep physiology dataset is a growing collection of clinical polysomnography (PSG) recordings. Beginning with PSG recordings from from ~19K patients evaluated at the Massachusetts General Hospital, the HSP will grow over the coming years to include data from >200K patients, as well as people evaluated outside of the clinical setting.

Dataset

This dataset was created by SURYAPRAKASH C T

Released under GNU Free Documentation License 1.3

Contents

In a survey conducted in 2023 among respondents from India, the majority from different generations stated their favorite pillow was the leading factor that helped people sleep better. However, checking the door was closed was the next leading factor for millennials and Gen X. About 32 percent of boomers indicated sleeping alone, and 27 percent of Gen Z stated complete darkness helped them sleep better.

Online Sleep Survey dataThese data were obtained over the course of several years. The primary purpose was to build a database of subjects from which I could recruit for my Sleep and Decision Making research studies. Data included are basic demographics some self report sleep data, a validated short form measure of morningness/eveningness preferences, and screener questions for anxiety and depressive disorder (as well as self-reported sleep disorder).

This statistic depicts the percentage of U.S. adults who said they got a select number of hours of sleep each night as of 2019, by generation. According to the data, 27 percent of Millennials had about 7 hours of sleep each night at that time. Comparatively, 37 percent of the Silent Generation had 7 hours each night.

The data includes polysomnography (PSG) data collected from both children and adults during full night, restricted night and alternate sleep schedules. Data includes standard recording of electroencephalography, electromyography and electrooculography, and in some cases a further combination of electrocardiography and respiratory measures.

About this dataset

Context

The dataset tabulates the Ten Sleep population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Ten Sleep. The dataset can be utilized to understand the population distribution of Ten Sleep by age. For example, using this dataset, we can identify the largest age group in Ten Sleep.

Key observations

The largest age group in Ten Sleep, WY was for the group of age 60 to 64 years years with a population of 31 (14.49%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Ten Sleep, WY was the 25 to 29 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Ten Sleep is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Ten Sleep total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Ten Sleep Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Ten Sleep by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Ten Sleep across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 61.68% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Ten Sleep is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Ten Sleep total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Ten Sleep Population by Race & Ethnicity. You can refer the same here

This study was approved by the institutional review board (IRB) of King Fahad Military Medical Complex – Dhahran, Saudi Arabia (IRB number AFHER-IRB-2020-014). The participants’ consents were obtained by electronic consent methods. This survey-based cross-sectional study utilized a previously validated questionnaire called the “OSAKA questionnaire.” The OSAKA questionnaire is composed of 18 items that are used to assess one’s knowledge and 5 items to assess one’s attitudes concerning dealing with OSA. An online questionnaire was sent between July and August 2020 via email and WhatsApp instant messaging to the Saudi board’s ORL trainee residents, including those in their second year of residency (R2) to their final year of residency (R5) (n = 185). First- year residency trainees were not included, as they were rotating in preparatory rotations and had no previous exposure to ORL clinical training. The survey was conducted anonymously. Along with the OSAKA questionnaire, sociodemographic data and data about previous exposure to sleep practices were also collected. The sociodemographic data included age, gender, level of training, years of ORL practice including residency, year of graduation from medical college, and residency program region (by province). Data reflecting previous exposure to sleep practices were also collected, such as the frequency of exposure to diagnosed OSA patients, patients susceptible to OSA, polysomnography data, obese patients, and surgery directed to treat OSA. We also collected data on trainees’ self-reported awareness and previous consideration of other sleep disorders during their clinical training (such as inadequate sleep hygiene, insomnia, narcolepsy, and periodic limb movement disorder) as well as awareness of other related disorders (such as overlap syndrome and obesity hypoventilation syndrome). We considered those who answered that they were “aware of other sleep disorders” and who gave examples of other sleep disorders as the aware group. The knowledge section of the OSAKA questionnaire was assessed by calculating the total true responses for the knowledge section. The total knowledge score was calculated out of 18. The attitude section of the OSAKA questionnaire consisted of five questions (two for importance and three for confidence). It was calculated out of 5 for each question (the responses “extremely important” and “strongly agree” were scored as scores of 5). The total attitude score was calculated using a maximum score of 25, reflecting the sum score of the five attitude elements. For the confidence questions, we considered those who answered “agree” and “strongly agree” as the confident group in identifying OSA-susceptible patients and managing OSA patients. Exposure to each of the aforementioned clinical exposure items was divided and classified as either no exposure or any number of exposure incidences.

QUT Research Data Respository Dataset and Resources

ISRUC-Sleep is a polysomnographic (PSG) dataset. The data were obtained from human adults, including healthy subjects, and subjects with sleep disorders under the effect of sleep medication. The dataset, which is structured to support different research objectives, comprises three groups of data: (a) data concerning 100 subjects, with one recording session per subject, (b) data gathered from 8 subjects; two recording sessions were performed per subject, which are useful for studies involving changes in the PSG signals over time, (c) data collected from one recording session related to 10 healthy subjects, which are useful for studies involving comparison of healthy subjects with the patients suffering from sleep disorders.

Previous research has shown that sleep deprivation, low quality sleep or inconvenient sleeping times are associated with lower quality of life. However, research of the longitudinal effects of sleep on quality of life is scarce. Hence, we know very little about the long-term effect of changes in sleep duration, sleep quality and the time when individuals sleep on quality of life. Using longitudinal data from three waves of the Czech Household Panel Study (2018–2020) containing responses from up to 4,523 respondents in up to 2,155 households, the study examines the effect of changes in sleep duration, sleep quality and social jetlag on satisfaction with life, happiness, work stress, subjective health and wellbeing. Although sleep duration and timing are important, panel analyses reveal that sleep quality is the strongest predictor of all sleep variables in explaining both within-person and between-person differences in quality of life indicators.

This statistic depicts the percentage of U.S. adults who said they got a select number of hours of sleep each night as of 2019, by gender. According to the data, 28 percent of females had about 7 hours of sleep each night at that time.

Sleep is an intrinsic part of human life, and recent advancements in wearable technology and machine learning have promised continuous and non-invasive methods of tracking sleep health and patterns, providing an important facet to a more holistic understanding of well-being. However, it is still challenging to achieve consistent and reliable real-time estimates of sleep stages using only smartwatches. This is especially true for individuals with irregular sleep patterns or sleep disorders. A major contributing factor is the distinct lack of publicly accessible, large-scale datasets that allow researchers and engineers to validate their wearable sleep staging algorithms against a population with diverse sleep patterns. Here, we present DREAMT, Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology, a new dataset collected from 100 participants, which includes high-resolution signals from a smartwatch, expert sleep technician-annotated sleep stage labels, and clinical metadata related to sleep health and disorders.

People living with dementia experience higher levels of sleep dysfunction compared to healthy older people. Poor sleep is common in Alzheimer’s disease (AD) and dementia with Lewy Bodies (DLB); two common causes of neurodegenerative dementia comprising of approximately 70% of diagnoses. Sleep dysfunction in dementia has been attributed as a significant contributing factor to early admittance into care. (Sloan, 2015, Saheed, 2017, Figuerio et al, 2015, Forbes et al, 2014). Sleep is important for quality of life, health and well-being and when the sleep of both the person with dementia and their caregiver is affected, supporting individuals to live independently at home becomes more challenging. A significant contributing factor to a move out of the home prematurely into institutional care is sleep dysfunction in the person with dementia, resulting in caregiver exhaustion and burnout. Given the complexity of sleep problems, there is a need for tools which can evaluate poor sleep in populations living with dementia.

The Nurolight study sought to explore the impact of poor sleep on people living with dementia and their care partners.

Using the Pittsburgh Sleep Quality Index; a tool designed to evaluate sleep disturbances in populations. It comprises Comprising of 19 self-reported items belonging to one of seven subcategories: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. An additional section of 5 questions relates to partner/roommate reporting and are not scored.

The Nurolight assessed 11 participants (M=6, F=5). The component scores are summed to produce a global score (range 0 to 21). Higher scores indicate poor sleep quality, with a score greater than 5 suggesting significant sleep difficulties. Findings from this study indicate that 81% participants were considered to have significant sleep difficulties.

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