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Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes
STUDY OBJECTIVES: Conventional metrics of sleep quantity/depth have serious shortcomings. Odds-Ratio-Product (ORP) is a continuous metric of sleep depth ranging from 0 (very deep sleep) to 2.5 (full-wakefulness). We describe an ORP-based approach that provides information on sleep disorders not appa...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9195236/ https://www.ncbi.nlm.nih.gov/pubmed/35272350 http://dx.doi.org/10.1093/sleep/zsac059 |
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author | Younes, Magdy Gerardy, Bethany Pack, Allan I Kuna, Samuel T Castro-Diehl, Cecilia Redline, Susan |
author_facet | Younes, Magdy Gerardy, Bethany Pack, Allan I Kuna, Samuel T Castro-Diehl, Cecilia Redline, Susan |
author_sort | Younes, Magdy |
collection | PubMed |
description | STUDY OBJECTIVES: Conventional metrics of sleep quantity/depth have serious shortcomings. Odds-Ratio-Product (ORP) is a continuous metric of sleep depth ranging from 0 (very deep sleep) to 2.5 (full-wakefulness). We describe an ORP-based approach that provides information on sleep disorders not apparent from traditional metrics. METHODS: We analyzed records from the Sleep-Heart-Health-Study and a study of performance deficit following sleep deprivation. ORP of all 30-second epochs in each PSG and percent of epochs in each decile of ORPs range were calculated. Percentage of epochs in deep sleep (ORP < 0.50) and in full-wakefulness (ORP > 2.25) were each assigned a rank, 1–3, representing first and second digits, respectively, of nine distinct types (“1,1”, “1,2” … ”3,3”). Prevalence of each type in clinical groups and their associations with demographics, sleepiness (Epworth-Sleepiness-Scale, ESS) and quality of life (QOL; Short-Form-Health-Survey-36) were determined. RESULTS: Three types (“1,1”, “1,2”, “1,3”) were prevalent in OSA and were associated with reduced QOL. Two (“1,3” and “2,3”) were prevalent in insomnia with short-sleep-duration (insomnia-SSD), but only “1,3” was associated with poor sleep depth and reduced QOL, suggesting two phenotypes in insomnia-SSD. ESS was high in types “1,1” and “1,2”, and low in “1,3” and “2,3”. Prevalence of some types increased with age while in others it decreased. Other types were either rare (“1,1” and “3,3”) or high (“2,2”) at all ages. CONCLUSIONS: The proposed ORP histogram offers specific and unique information on the underlying neurophysiological characteristics of sleep disorders not captured by routine metrics, with potential of advancing diagnosis and management of these disorders. |
format | Online Article Text |
id | pubmed-9195236 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-91952362022-06-15 Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes Younes, Magdy Gerardy, Bethany Pack, Allan I Kuna, Samuel T Castro-Diehl, Cecilia Redline, Susan Sleep Sleep, Health, and Disease STUDY OBJECTIVES: Conventional metrics of sleep quantity/depth have serious shortcomings. Odds-Ratio-Product (ORP) is a continuous metric of sleep depth ranging from 0 (very deep sleep) to 2.5 (full-wakefulness). We describe an ORP-based approach that provides information on sleep disorders not apparent from traditional metrics. METHODS: We analyzed records from the Sleep-Heart-Health-Study and a study of performance deficit following sleep deprivation. ORP of all 30-second epochs in each PSG and percent of epochs in each decile of ORPs range were calculated. Percentage of epochs in deep sleep (ORP < 0.50) and in full-wakefulness (ORP > 2.25) were each assigned a rank, 1–3, representing first and second digits, respectively, of nine distinct types (“1,1”, “1,2” … ”3,3”). Prevalence of each type in clinical groups and their associations with demographics, sleepiness (Epworth-Sleepiness-Scale, ESS) and quality of life (QOL; Short-Form-Health-Survey-36) were determined. RESULTS: Three types (“1,1”, “1,2”, “1,3”) were prevalent in OSA and were associated with reduced QOL. Two (“1,3” and “2,3”) were prevalent in insomnia with short-sleep-duration (insomnia-SSD), but only “1,3” was associated with poor sleep depth and reduced QOL, suggesting two phenotypes in insomnia-SSD. ESS was high in types “1,1” and “1,2”, and low in “1,3” and “2,3”. Prevalence of some types increased with age while in others it decreased. Other types were either rare (“1,1” and “3,3”) or high (“2,2”) at all ages. CONCLUSIONS: The proposed ORP histogram offers specific and unique information on the underlying neurophysiological characteristics of sleep disorders not captured by routine metrics, with potential of advancing diagnosis and management of these disorders. Oxford University Press 2022-03-10 /pmc/articles/PMC9195236/ /pubmed/35272350 http://dx.doi.org/10.1093/sleep/zsac059 Text en © Sleep Research Society 2022. Published by Oxford University Press on behalf of the Sleep Research Society. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Sleep, Health, and Disease Younes, Magdy Gerardy, Bethany Pack, Allan I Kuna, Samuel T Castro-Diehl, Cecilia Redline, Susan Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes |
title | Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes |
title_full | Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes |
title_fullStr | Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes |
title_full_unstemmed | Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes |
title_short | Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes |
title_sort | sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes |
topic | Sleep, Health, and Disease |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9195236/ https://www.ncbi.nlm.nih.gov/pubmed/35272350 http://dx.doi.org/10.1093/sleep/zsac059 |
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