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Measuring Sleep Health Disparities with Polysomnography: A Systematic Review of Preliminary Findings
Socioeconomic status (SES) has an unrecognized influence on behavioral risk factors as well as public health strategies related to sleep health disparities. In addition to that, objectively measuring SES’ influence on sleep health is challenging. A systematic review of polysomnography (PSG) studies...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8883971/ https://www.ncbi.nlm.nih.gov/pubmed/35225955 http://dx.doi.org/10.3390/clockssleep4010009 |
Sumario: | Socioeconomic status (SES) has an unrecognized influence on behavioral risk factors as well as public health strategies related to sleep health disparities. In addition to that, objectively measuring SES’ influence on sleep health is challenging. A systematic review of polysomnography (PSG) studies investigating the relation between SES and sleep health disparities is worthy of interest and holds potential for future studies and recommendations. A literature search in databases was conducted following Prisma guidelines. Search strategy identified seven studies fitting within the inclusion criteria. They were all cross-sectional studies with only adults. Except for one study conducted in India, all of these studies took place in western countries. Overall emerging trends are: (1) low SES with its indicators (income, education, occupation and employment) are negatively associated with PSG parameters and (2) environmental factors (outside noise, room temperature and health worries); sex/gender and BMI were the main moderators of the relation between socioeconomic indicators and the variation of sleep recording with PSG. Socioeconomic inequalities in sleep health can be measured objectively. It will be worthy to examine the SES of participants and patients before they undergo PSG investigation. PSG studies should always collect socioeconomic data to discover important connections between SES and PSG. It will be interesting to compare PSG data of people from different SES in longitudinal studies and analyze the intensity of variations through time. |
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