Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/130192
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Type: Journal article
Title: Characterisation of cyclic alternating pattern during sleep in older men and women using large population studies
Author: Hartmann, S.
Bruni, O.
Ferri, R.
Redline, S.
Baumert, M.
Citation: Sleep, 2020; 43(7):1-9
Publisher: Oxford University Press
Issue Date: 2020
ISSN: 0161-8105
1550-9109
Statement of
Responsibility: 
Simon Hartmann, Oliviero Bruni, Raffaele Ferri, Susan Redline, Mathias Baumert
Abstract: Study Objectives To assess the microstructural architecture of non-rapid eye movement (NREM) sleep known as cyclic alternating pattern (CAP) in relation to the age, gender, self-reported sleep quality, and the degree of sleep disruption in large community-based cohort studies of older people. Methods We applied a high-performance automated CAP detection system to characterize CAP in 2,811 men from the Osteoporotic Fractures in Men Sleep Study (MrOS) and 426 women from the Study of Osteoporotic Fractures (SOF). CAP was assessed with respect to age and gender and correlated to obstructive apnea–hypopnea index, arousal index (AI-NREM), and periodic limb movements in sleep index. Further, we evaluated CAP across levels of self-reported sleep quality measures using analysis of covariance. Results Age was significantly associated with the number of CAP sequences during NREM sleep (MrOS: p = 0.013, SOF = 0.051). CAP correlated significantly with AI-NREM (MrOS: ρ = 0.30, SOF: ρ = 0.29). CAP rate, especially the A2+A3 index, was inversely related to self-reported quality of sleep, independent of age and sleep disturbance measures. Women experienced significantly fewer A1-phases compared to men, in particular, in slow-wave sleep (N3). Conclusions We demonstrate that automated CAP analysis of large-scale databases can lead to new findings on CAP and its subcomponents. We show that sleep disturbance indices are associated with the CAP rate. Further, the CAP rate is significantly linked to subjectively reported sleep quality, independent from traditionally scored markers of sleep fragmentation. Finally, men and women show differences in the microarchitecture of sleep as identified by CAP, despite similar macro-architecture.
Keywords: Cyclic alternating pattern; deep learning; sleep fragmentation; sleep disorder breathing; sleep studies; sleep quality
Rights: © Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society.
DOI: 10.1093/sleep/zsaa016
Grant ID: http://purl.org/au-research/grants/arc/DP0663345
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