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Sleep, Health, and Lifestyle Dataset of Professionals
Public Health & Epidemiology
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About
Anonymized data collected from professionals across various fields, focusing on sleep habits, health metrics, and lifestyle factors. This dataset enables the examination of relationships between sleep quality, physical activity, and health indicators such as stress levels and BMI, providing a valuable resource for exploring lifestyle health, sleep patterns, and the impact of occupational stress on overall well-being.
Dataset Features:
- Person ID: Unique identifier for each individual in the dataset.
- Gender: Gender of the individual (Male/Female).
- Age: Age of the individual in years.
- Occupation: Job or profession, including roles such as Software Engineer, Doctor, Sales Representative, Teacher, etc.
- Sleep Duration (hours): Average hours of sleep per night.
- Quality of Sleep: Subjective sleep quality rating on a scale from 1 to 10 (higher values indicate better quality).
- Physical Activity Level: Hours of physical activity per week or rated on a subjective scale.
- Stress Level: Reported stress level on a scale from 1 to 10 (higher values indicate more stress).
- BMI Category: BMI classification (Normal, Overweight, Obese).
- Blood Pressure: Systolic and diastolic blood pressure readings (e.g., 120/80 mmHg).
- Heart Rate: Resting heart rate, measured in beats per minute.
- Daily Steps: Average number of daily steps taken by the individual.
- Sleep Disorder: Type of sleep disorder reported, if any (e.g., None, Sleep Apnea, Insomnia).
Usage:
This dataset is valuable for:
- Sleep and lifestyle studies: To examine how sleep quality and duration correlate with health metrics and lifestyle habits.
- Occupational health research: To understand how various professions impact sleep, stress, and physical activity.
- Health and wellness analytics: To support the development of programs aimed at improving sleep quality and stress management among professionals.
Coverage:
The dataset includes anonymized data from professionals in various occupations, encompassing a range of ages, genders, and health metrics.
License:
CC0 (Public Domain)
Who can use it:
This dataset is useful for researchers, healthcare professionals, workplace wellness program designers, and public health analysts focused on sleep, health, and occupational well-being.
How to use it:
- Predictive modeling: Apply machine learning techniques to predict the likelihood of sleep disorders based on lifestyle factors.
- Data exploration: Perform statistical analysis to identify trends between occupation, sleep, and health.
- Correlation analysis: Analyze the impact of physical activity, sleep quality, and stress levels on overall health metrics.
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