Visualizing Sleep Efficiency Patterns: Data to Insights

This study aims to investigate the impact of various factors, including age, gender, and lifestyle behaviors such as smoking, alcohol consumption, and exercise, on sleep efficiency and patterns using a dataset collected from a study conducted in Morocco. The dataset comprises 386 participants, from 4 different age groups consisting of 2.9% adolescents, 22.9 % Young adults, 42.3% middle Adults, and 31.9% Late adults. Various statistical visualization tools of R programming language, like boxplots, scatterplots, grouping, correlation plots, pie charts, and mosaic plots, were used to analyze important patterns between these observed metrics. It was observed that sleep efficiency varies tremendously between age groups, ranging from 0.6 to 0.95, while sleep duration is constant throughout, ranging from 7 to 9 hours. Furthermore, on average, women struggle more with sleeping due to inconsistent sleep patterns shown through larger variations and outliers. Additionally, sleep efficiency had a significant relationship with age, Rapid Eye Movement (REM) sleep percentage, deep sleep percentage, alcohol consumption, smoking, and regular exercise. A very strong positive correlation was seen between sleep efficiency and deep sleep percentage (0.79) and a negative correlation between sleep efficiency and light sleep percentage (-0.82), awakenings (-0.57), and alcohol consumption (-0.45), respectively. Inactive lifestyles significantly impact sleep efficiency as indicated by greater Pearson residuals in the inactive groups, especially among females with low activity levels. These findings emphasize the significance of lifestyle factors on sleep quality and offer insights for enhancing sleep efficiency in various demographic groups.