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Lifestyle Factors and Sleep Quality Analysis
Data Science and Analytics
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About
Explores the effects of sleep deprivation on cognitive performance and emotional regulation. This information is based on a 2024 study conducted in the Middle East with 60 participants from diverse backgrounds. The data captures details on sleep duration, quality, cognitive function metrics like reaction times, and emotional stability. It also includes demographic and lifestyle factors such as age, gender, caffeine intake, and stress levels. The study utilised standardised cognitive tests, including the Stroop Task, N-Back Test, and Psychomotor Vigilance Task (PVT).
Columns
- Participant_ID: A unique identifier for each participant in the study.
- Sleep_Hours: The number of hours a participant slept.
- Sleep_Quality_Score: A score representing the quality of a participant's sleep.
- Daytime_Sleepiness: A score indicating the level of sleepiness experienced by the participant during the day.
- Stroop_Task_Reaction_Time: The reaction time recorded during the Stroop Task, a test of cognitive function.
- N_Back_Accuracy: The accuracy score from the N-Back Test, which measures working memory.
- Emotion_Regulation_Score: A score indicating a participant's ability to regulate their emotions.
- PVT_Reaction_Time: The reaction time measured by the Psychomotor Vigilance Task (PVT).
- Age: The age of the participant.
- Gender: The gender of the participant (Male or Female).
- BMI: The Body Mass Index of the participant.
- Caffeine_Intake: The amount of caffeine consumed by the participant.
- Physical_Activity_Level: A score representing the participant's level of physical activity.
- Stress_Level: A score indicating the participant's level of stress.
Distribution
The data is available in a tabular format as a single CSV file named
sleep_deprivation_dataset_detailed.csv. It has a size of 3.58 kB and contains data for 60 participants across 14 columns.Usage
This data is ideal for statistical analysis, machine learning applications, and behavioural research. Specific use cases include analysing the impact of sleep on cognitive performance, building predictive models for mental health indicators, and studying lifestyle factors affecting sleep in Middle Eastern populations.
Coverage
The data covers a geographic scope of the Middle East and was collected during the year 2024. It includes 60 participants from diverse backgrounds, with demographic data covering age and gender (62% Male, 38% Female).
License
CC0: Public Domain
Who Can Use It
- Sleep Researchers: Can analyse the relationships between sleep patterns, cognitive tasks, and emotional states.
- Data Scientists and Machine Learning Engineers: Can use the data to build models predicting cognitive performance or stress levels based on sleep and lifestyle factors.
- Mental Health Professionals: Can gain insights into how sleep deprivation correlates with emotional regulation and stress.
- Students and Academics: Can use this beginner-friendly, tabular dataset for projects in psychology, neuroscience, or data analysis.
Dataset Name Suggestions
- Sleep Deprivation & Cognitive Performance (Middle East 2024)
- Cognitive and Emotional Impact of Sleep Deprivation
- Middle East Sleep Study 2024
- Lifestyle Factors and Sleep Quality Analysis
Attributes
Original Data Source: Lifestyle Factors and Sleep Quality Analysis
QUALITY