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Health & Lifestyle Sleep Study
Education & Learning Analytics
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About
Unlock insights into sleep health and lifestyle factors with this dataset. It provides a synthetic collection of 400 records across 13 distinct variables, designed for exploring correlations between daily habits and sleep patterns. The dataset includes information on individuals' gender, age, occupation, sleep duration, quality of sleep, physical activity levels, stress levels, BMI category, blood pressure, heart rate, and daily steps. A key feature is the inclusion of sleep disorder presence, specifically noting Insomnia or Sleep Apnea, which allows for analysis of these conditions in relation to other health and lifestyle metrics. This dataset serves as a valuable resource for research and educational purposes.
Columns
- Person ID: An identifier assigned to each individual within the dataset.
- Gender: Indicates the sex of the person, either Male or Female.
- Age: The age of the individual in years, ranging from 27 to 59.
- Occupation: The profession or job role of the individual.
- Sleep Duration (hours): The number of hours a person sleeps per day, with values typically between 5.8 and 8.5 hours.
- Quality of Sleep (scale: 1-10): A subjective rating of sleep quality, where 1 indicates very poor and 10 indicates excellent sleep.
- Physical Activity Level (minutes/day): The daily duration, in minutes, that an individual engages in physical activity, typically between 30 and 90 minutes.
- Stress Level (scale: 1-10): A subjective rating of the stress experienced by the person, ranging from 1 (low stress) to 10 (high stress).
- BMI Category: The Body Mass Index classification of the person (e.g., Underweight, Normal, Overweight).
- Blood Pressure (systolic/diastolic): The individual's blood pressure measurement, presented as systolic over diastolic pressure.
- Heart Rate (bpm): The resting heart rate of the person in beats per minute, typically between 65 and 86 bpm.
- Daily Steps: The number of steps an individual takes per day, ranging from 3,000 to 10,000 steps.
- Sleep Disorder: Denotes the presence or absence of a specific sleep disorder. Values include:
- None: The individual does not exhibit any specific sleep disorder.
- Insomnia: Indicates difficulty falling or staying asleep, leading to inadequate or poor-quality sleep.
- Sleep Apnea: Signifies pauses in breathing during sleep, which can disrupt sleep patterns and pose health risks.
Distribution
This dataset is provided as a CSV file, named
Sleep_health_and_lifestyle_dataset.csv, with a file size of 24.14 kB. It contains 400 rows, each representing a unique individual's data, across 13 distinct columns.Usage
This dataset is ideal for a variety of applications, including:
- Conducting research on the relationship between lifestyle factors and sleep quality.
- Analysing patterns and risk factors associated with common sleep disorders like Insomnia and Sleep Apnea.
- Developing predictive models for sleep health outcomes based on daily habits.
- Exploring the impact of physical activity and stress on cardiovascular health metrics.
- Educational demonstrations in data science, public health, and healthcare analytics.
Coverage
As a synthetic dataset created for illustrative purposes, this data does not have real-world geographic or specific time range coverage. However, it reflects a demographic scope including both male (51%) and female (49%) individuals, with ages ranging from 27 to 59 years. Occupations vary, with Nurses and Doctors being the most common. The BMI categories include Normal, Overweight, and other categories, while sleep disorders are categorised as None, Insomnia, or Sleep Apnea.
License
CC0: Public Domain
Who Can Use It
This dataset is suitable for:
- Data Scientists and Analysts: For exploratory data analysis, feature engineering, and model building related to health outcomes.
- Healthcare Researchers: To study sleep health trends, lifestyle impacts on health, and the prevalence of sleep disorders.
- Students: As a practical resource for learning data cleaning, analysis, and visualisation techniques in a health context.
- Public Health Professionals: To gain insights into population health patterns related to sleep and daily habits.
Dataset Name Suggestions
- Sleep Health & Lifestyle Factors
- Human Sleep & Wellness Data
- Daily Habits & Sleep Patterns
- Health & Lifestyle Sleep Study
Attributes
Original Data Source:Health & Lifestyle Sleep Study
QUALITY