Abstract
References
- Shonia, M. I., Megawat, N. Y., Gunardi, G., & Khasanah, A. (2024). Cluster analysis of sleep health and lifestyle data using Ward algorithm and Euclidean distance. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 30, 107-113.
Abstract
The intention of this study is to identify and assess groups based on their sleep quality and duration, physical activity levels, and stress levels. Next, we will investigate the relationship between sleep habits and stress levels. There were 374 respondents, with a total of 13 variables. The researchers utilized Ward's algorithm to identify groups and Euclidean distance to compare them. This study technique employs statistical computer tools, specifically R. This study's processes begin with data processing, which is followed by data standardization and clustering. There are four categories, namely (1) a group with an average sleep duration of 6 hours and a sleep quality scale worth 6 out of 10, but conducting physical activity less than 30 minutes per day, the stress level is high. (2) in a group with an average sleep duration of 6 hours and a sleep quality scale worth 6 out of 10, but doing physical activity for two hours each day, the stress level is very high. (3) in the group with an average sleep duration of 7 hours, a sleep quality scale of 8 out of 10, and 65 minutes of physical activity each day, the stress level is medium, (4) the group with an average sleep duration of 8 hours and a sleep quality rating of 9 out of 10 maintains a low stress level despite one hour of physical exercise. A dendrogram plot is used in data visualization to show how closely connected the data sets are. This study suggests that a person's sleep habits and daily physical activity have a major impact on their stress level, providing readers and the community with knowledge into how to improve overall health.
References
- Shonia, M. I., Megawat, N. Y., Gunardi, G., & Khasanah, A. (2024). Cluster analysis of sleep health and lifestyle data using Ward algorithm and Euclidean distance. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 30, 107-113.
Details
| Primary Language | English |
|---|---|
| Subjects | Statistics (Other) |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | December 2, 2024 |
| Publication Date | October 30, 2024 |
| Submission Date | February 27, 2024 |
| Acceptance Date | July 3, 2024 |
| Published in Issue | Year 2024Volume: 30 |
