Using a wearable EEG device to examine age trends in sleep macro- and micro-architecture across adolescence
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Study objectives
Adolescence is a period of distinct maturational changes in sleep characteristics. Historically, age trends in sleep physiology have been captured using laboratory-based polysomnography (PSG). However, multiple challenges associated with PSG, including logistical issues, budgetary constraints and ecological validity questions, limit large-scale use. The current study aims to address these challenges by using the Dreem3 headband to measure sleep at home and replicate well-established age-related trends in sleep physiology from late childhood through early adulthood.
Methods
100 typically developing youth (9-26 years) wore a sleep electroencephalography (EEG) device (Dreem3) for 3-4 consecutive nights at home. Sleep EEG data were processed using the Luna pipeline. We used linear mixed models to estimate age-related trends across 8 macro-architecture and 15 micro-architecture variables previously found to be associated with age, and explored age relationships in 24 additional macro- and micro-architecture variables.
Results
At-home sleep studies using Dreem3 replicated established age trends in sleep macro- and micro-architecture, including decreases in percent time spent in non-rapid eye movement (NREM) stage 3 (N3%) sleep and decreases in NREM delta power with increasing age. Exploratory analysis revealed age effects in seven other variables, including decreases in integrated slow spindle activity and NREM cycle duration with increasing age.
Conclusion
Sleep EEG wearables may offer an accessible way to characterize sleep physiology development in large cohorts, setting the stage for understanding how deviations from normative age patterns may put young people at risk for adverse outcomes.
Statement of Significance
Adolescence is a dynamic period characterized by changes in sleep physiology and behavior. While polysomnography has long been widely used for capturing age-related trends, it is resource-intensive and laboratory-bound, which limits the ability to track sleep in an accessible, scalable, and ecologically valid manner. Here, we used a sleep EEG headband, the Dreem3, to examine age-related trends in sleep macro- and micro-architecture across late childhood, adolescence, and early adulthood. We assessed sleep features with previously replicated age effects and explored age associations in other macro- and micro-architecture measures. The at-home wearable sleep EEG device replicated many of the age trends seen in traditional polysomnography. Leveraging accessible sleep EEG devices may provide a more scalable and comprehensive understanding of how sleep changes over adolescence.
