In Simple Terms
Scientists used to think people were either early risers or night owls. But a new study shows there are actually five different sleep patterns. This discovery helps us understand how our sleep habits affect our health.
Rethinking Sleep Patterns
For a long time, people have been classified into two sleep categories: the “early bird” who wakes with the sunrise, and the “night owl” who stays up late. This simple division was thought to explain differences in health among individuals. However, recent research is challenging this idea, offering new insights into the biological diversity of sleep schedules.
Discovering New Sleep Types
A recent study published in Nature Communications involved researchers from McGill University using machine learning algorithms to analyze brain imaging data and questionnaires from 27,000 UK participants. Instead of finding just two types, they identified five distinct sleep patterns, each with unique biological and behavioral traits.
Among these five patterns, there are three types of night owls and two types of early birds. One type of night owl, called the “high-performing owl,” shows strong cognitive abilities despite a tendency for risk-taking and emotional regulation challenges. Another type, the “low-energy owl,” is linked to lower physical activity, higher smoking rates, and health issues like depression, heart disease, and diabetes.
Health and Biological Dimensions
The third type of night owl is characterized by masculine tendencies, with higher rates of cigarette and alcohol consumption and elevated testosterone levels. This might explain why the night owl pattern is more common among men.
On the other hand, the first type of early bird, known as the “classic bird,” is associated with good health traits such as efficient brain networks, lower smoking and alcohol rates, and emotional stability. The second type, more common among women, shows higher rates of depression symptoms and menstrual issues.
Beyond the Patterns: Possible Explanations
The findings suggest these patterns may result from a complex interplay of genes, hormonal changes, and environmental factors like work schedules or light exposure. However, it remains unclear how each of these factors shapes an individual’s sleep pattern.
Charlene Gamaldo from Johns Hopkins University, who was not involved in the study, emphasizes the importance of big data and machine learning in opening new avenues for understanding sleep patterns. She notes that relying on self-reported data requires further research to determine the causal relationship between these patterns and health outcomes.
Conclusion
This study offers a fresh perspective on the diversity of human sleep patterns, contributing to a deeper understanding of how sleep impacts overall health. While these findings require further investigation to establish causal links, they pave the way for future research into the genetic and environmental factors influencing sleep patterns.