High Impact Practices Student Showcase Spring 2025

Night Code: Decoding Nighttime Health Through Data

Night Code: Decoding Nighttime Health Through Data

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Course Code

STA

Course Number

4164

Faculty/Instructor

Professor Nathaniel Simone

Faculty/Instructor Email

nathaniel.simone@ucf.edu

Abstract, Summary, or Creative Statement

Sleep plays a crucial role in maintaining overall health, yet many adults struggle to achieve adequate rest due to lifestyle and health factors. In our project, we explore how variables such as stress levels, BMI, physical activity, blood pressure, and occupation impact sleep patterns using the Sleep Health and Lifestyle Dataset from Kaggle. Through statistical analysis, we examine these relationships, identify trends, and address challenges such as collinearity and interaction effects.

Our project reveals that stress levels negatively affect both sleep duration and quality, while physical activity shows a positive correlation with improved sleep outcomes. Additionally, BMI and blood pressure demonstrate moderate associations with sleep, emphasizing the role of cardiovascular health in sleep patterns. We also explore how different occupations contribute to variations in sleep habits. By applying transformations and interaction terms, we refine our model to enhance predictive accuracy.

This research is relevant to young adults as they transition into professional life, offering insights into the long-term impact of lifestyle choices on sleep health. Understanding these factors can help inform personal wellness strategies, workplace policies, and public health initiatives. By highlighting the significance of sleep, we hope to contribute to ongoing discussions on health and well-being, ultimately encouraging individuals to adopt habits that promote restful and restorative sleep.

Keywords

sleep health; lifestyle factors; multiple regression; stress; awareness; statisical analysis

Night Code: Decoding Nighttime Health Through Data


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