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Five million nights: temporal dynamics in human sleep phenotypes.
Varun K Viswanath1, Wendy Hartogenesis2, Stephan Dilchert3
1Department of Electrical and Computer Engineering, Jacobs School of Engineering, University of California, San Diego, CA, USA. vkviswan@ucsd.edu.
NPJ Digital Medicine
|June 20, 2024
Summary
Individuals shift between 13 sleep phenotypes, offering new insights into health. Tracking these sleep pattern changes reveals more about chronic and acute illnesses than static sleep types alone.
Area of Science:
- Wearable technology and sleep science.
- Longitudinal health data analysis.
- Personalized sleep medicine.
Background:
- Affordable wearable devices enable widespread sleep monitoring.
- Converting sleep data into actionable health insights is challenging due to individual variability.
- Existing quantitative sleep phenotype models often rely on cross-sectional data, limiting longitudinal analysis.
Purpose of the Study:
- To determine if individual sleep phenotypes change over time.
- To investigate if these temporal changes in sleep phenotypes provide insights into acute illnesses like flu or COVID-19.
- To compare the information gained from longitudinal sleep dynamics versus static phenotype membership for health condition elucidation.
Main Methods:
- Analysis of five million nights of wearable sleep data.
- Identification and classification of sleep phenotypes based on parameter combinations.
- Statistical comparison of sleep phenotype transition patterns between different health groups and over time.
Main Results:
- Identification of 13 distinct sleep phenotypes related to sleep quality.
- Evidence of significant transitions between sleep phenotypes over time, both between individuals and within individuals during acute illness.
- Longitudinal transition patterns provided 2-10 times more information on chronic and acute health conditions compared to cross-sectional phenotype membership.
Conclusions:
- Sleep phenotypes are dynamic and change over time within individuals.
- Analyzing the temporal dynamics of sleep phenotype transitions offers significant potential for understanding and monitoring chronic and acute health conditions.
- Future development of longitudinal sleep analyses should leverage these temporal dynamics for improved health insights.
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