Sleep analysis with smart watch and statistical data mining

Sleep is a primary biological imperative, not only maintaining human life but also guaranteeing the essential balance of physical and psychological health. Adequate and high-quality sleep plays a crucial role in restoring physiological functions, enhancing cognitive performance, and maintaining emotional stability. However, in modern society, sleep quality has been significantly compromised due to factors such as fast-paced lifestyles, increased stress levels, digital dependency, and irregular working hours. These disruptions not only affect individual health but also decrease productivity, concentration, and overall quality of life at the societal level. Therefore, scientific investigation of sleep patterns and the development of data-driven approaches to improve sleep quality have become a major field of interest across multiple disciplines including health sciences, data analytics, and artificial intelligence. With the advancement of wearable technologies, it has become possible to monitor and collect physiological data continuously in daily life. Devices such as smartwatches and fitness trackers provide objective, real-time data on heart rate, sleep duration, movement, and other biological indicators. Among these devices, Fitbit smartwatches are widely used due to their ability to record heart rate variability and sleep phases with a relatively high level of accuracy. In this thesis study, heart rate data collected from Fitbit devices during sleep were analyzed using data mining techniques. The primary aim of the research was to examine sleep quality, duration, and variability through quantitative analysis and to identify significant factors affecting sleep behavior. The entirety of the data analysis and statistical modeling was executed in a Jupyter Notebook utilizing the Python programming language. Various statistical and data mining methods were applied to interpret the collected data effectively. The study initially focused on a three-month winter period, during which daily sleep and heart rate data were gathered and analyzed separately for each month. The analysis included descriptive statistics, data visualizations, and pattern recognition techniques to evaluate parameters such as total time spent in bed, actual sleep duration, and day-to-day variations in sleep efficiency. In the second stage, inferential statistical analyses were carried out to test specific hypotheses regarding sleep behavior. The applied methods included a T-test, three different regression analyses, and two ANOVA (Analysis of Variance) tests. These techniques were employed to investigate several key questions: how sleep habits change throughout the autumn season, how sleep duration varies depending on the day of the week, how sleep deprivation (or sleep debt) affects subsequent days, and how daytime naps influence nighttime sleep quality. Each analysis was structured to provide insight into the behavioral and physiological dimensions of sleep. The findings revealed several noteworthy results. Contrary to some findings reported in previous literature, the ANOVA results demonstrated that sleep deprivation significantly affects physiological performance and subjective well-being in the days following inadequate sleep. The data indicated a cumulative effect of sleep debt, suggesting that recovery requires more than a single night of adequate rest. Regression analyses further revealed that under certain conditions, short daytime naps may have a compensatory and balancing effect on total sleep quality. However, excessive or irregular napping was observed to have a potential negative impact on subsequent nighttime sleep duration and efficiency. In addition to the statistical analyses, data visualizations such as time-series plots, histograms, and regression curves were generated to enhance the interpretability of the results. These visual representations provided a clearer understanding of the relationships between sleep duration, bedtime consistency, heart rate variability, and overall sleep quality. The combined use of exploratory data analysis and inferential statistics allowed for a comprehensive evaluation of both the quantitative and behavioral aspects of sleep. The results underscore the transformative capabilities of utilizing data mining as a core analytical methodology for biological and behavioral research. Through the integration of statistical modeling, data visualization, and computational analysis, it was possible to uncover meaningful patterns that contribute to understanding individual sleep dynamics. Furthermore, the use of open-source tools such as Python and Jupyter Notebook highlights the accessibility and reproducibility of this approach for future researchers. Beyond its immediate findings, this research also carries implications for the future of personalized health technologies. As artificial intelligence (AI) and machine learning methods continue to evolve, the integration of these techniques into wearable devices could enable the development of adaptive systems that monitor sleep in real time and provide personalized recommendations to optimize rest. For instance, algorithms could analyze a user's heart rate variability, daily activity levels, and sleep duration to suggest optimal bedtime schedules or identify early signs of sleep disorders. Such AI-driven feedback systems have the potential to promote healthier sleep habits and, by extension, improve overall well-being. This thesis therefore contributes both methodologically and practically to the growing field of sleep analytics. On a methodological level, it demonstrates how raw physiological data collected from consumer-grade wearable devices can be effectively processed and interpreted using established statistical and data mining methods. On a practical level, it shows how these insights can inform personalized interventions to enhance sleep quality. The study's approach, which combines descriptive, inferential, and predictive analytics, provides a structured framework that can be extended to larger datasets and more diverse populations in future research. In conclusion, the present research illustrates that integrating wearable technology data with data mining and statistical analysis methods can provide valuable insights into human sleep behavior and its determinants. The findings support the view that sleep quality is not only influenced by the amount of time spent asleep but also by behavioral consistency, physiological responses, and external lifestyle factors. As technological capabilities continue their rapid expansion, it is anticipated that future studies will utilize even more sophisticated AI-based models to predict sleep disturbances, recommend individualized interventions, and ultimately improve public health outcomes. This thesis thus represents an important step toward that direction, providing a solid foundation for future interdisciplinary studies combining data science, health informatics, and behavioral research.