Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program.
Neil S ZhengJeffrey AnnisHiral MasterLide HanKarla GleichaufJack H ChingMelody NasserPeyton ColemanStacy DesineDouglas M RuderferJohn HernandezLogan Douglas SchneiderFrank E HarrellPublished in: Nature medicine (2024)
Poor sleep health is associated with increased all-cause mortality and incidence of many chronic conditions. Previous studies have relied on cross-sectional and self-reported survey data or polysomnograms, which have limitations with respect to data granularity, sample size and longitudinal information. Here, using objectively measured, longitudinal sleep data from commercial wearable devices linked to electronic health record data from the All of Us Research Program, we show that sleep patterns, including sleep stages, duration and regularity, are associated with chronic disease incidence. Of the 6,785 participants included in this study, 71% were female, 84% self-identified as white and 71% had a college degree; the median age was 50.2 years (interquartile range = 35.7, 61.5) and the median sleep monitoring period was 4.5 years (2.5, 6.5). We found that rapid eye movement sleep and deep sleep were inversely associated with the odds of incident atrial fibrillation and that increased sleep irregularity was associated with increased odds of incident obesity, hyperlipidemia, hypertension, major depressive disorder and generalized anxiety disorder. Moreover, J-shaped associations were observed between average daily sleep duration and hypertension, major depressive disorder and generalized anxiety disorder. These findings show that sleep stages, duration and regularity are all important factors associated with chronic disease development and may inform evidence-based recommendations on healthy sleeping habits.
Keyphrases
- major depressive disorder
- physical activity
- electronic health record
- sleep quality
- cross sectional
- atrial fibrillation
- blood pressure
- cardiovascular disease
- heart failure
- big data
- risk factors
- metabolic syndrome
- adipose tissue
- coronary artery disease
- insulin resistance
- skeletal muscle
- machine learning
- body mass index
- mitral valve
- deep learning
- clinical practice
- left atrial