Normal Sleep Architecture

Deatherage, Joseph R.; Roden, R. David; Zouhary, Kenneth

doi:10.1053/j.sodo.2009.01.002

Cited by 10 publications

(8 citation statements)

References 1 publication

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“…Older age is related to changes in sleep architecture and patterns, such as reduced electroencephalogram slow-wave activity and a reduced slow-wave response to sleep deprivation. [4] However, younger workers have been found to be at greater risk for poor sleep quality because of their high work stress, yet low ability to cope with the stress. [5] Those who are female,[1, 2] not married,[6] and less educated[6] have been shown to be at greater risk of poor sleep quality[1, 2, 6].…”

Section: Introductionmentioning

confidence: 99%

“…[7] In addition, bidirectional inverse relationships between sleep quality and pain,[8] medical comorbidities,[9] and depression[2, 5] have been studied. Among health behaviors, smoking,[4, 9] problem drinking,[4, 9] physical inactivity,[9] and obesity[9–11] have been associated with poor sleep quality. Moreover, blue collar workers generally report high job stress (partially due to lack of control over their tasks or schedule, and repeated and monotonous work)[12] and particularly Operating Engineers (heavy equipment operators) has been shown to be at greater risk of being exposed to loud noises at work[13] and engage in many of the health behavioral factors,[14] all of which have been associated with poor sleep quality.…”

Section: Introductionmentioning

confidence: 99%

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Factors Associated With Sleep Quality Among Operating Engineers

et al. 2013

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Blue collar workers generally report high job stress and are exposed to loud noises at work and engage in many of the health behavioral factors, all of which have been associated with poor sleep quality. However, sleep quality of blue collar workers has not been studied extensively, and no studies have focused Operating Engineers (heavy equipment operators) among whom daytime fatigue would place them at high risk for accidents. Therefore, the purpose of this study was to determine variables associated with sleep quality among Operating Engineers. This was a cross-sectional survey design with a dependent variable of sleep quality and independent variables of personal and related health behavioral factors. A convenience sample of 498 Operating Engineers was recruited from approximately 16,000 Operating Engineers from entire State of Michigan in 2008. Linear regression was used to determine personal and related health behavior factors associated with sleep quality. Multivariate analyses showed that personal factors related to poor sleep quality were younger age, female sex, higher pain, more medical comorbidities and depressive symptoms and behavioral factors related to poor sleep quality were nicotine dependence. While sleep scores were similar to population norms, approximately 34% (n=143) showed interest in health services for sleep problems. While many personal factors are not changeable, interventions to improve sleep hygiene as well as interventions to treat pain, depression and smoking may improve sleep quality resulting in less absenteeism, fatal work accidents, use of sick leave, work disability, medical comorbidities, as well as subsequent mortality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Factors Associated With Sleep Quality Among Operating Engineers

et al. 2013

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“…In a healthy person, sleep progresses in a cyclic alternating pattern between Stages N1, N2, and N3, followed by REM sleep. Interruption or truncation sleep due to insufficient sleep duration, environmental disturbances from noise and light, consumption of alcohol or medication, or sleep disorders resulting from circadian misalignment, can negatively affect SQ [44], [45].…”

Section: ) Contextual Information Of Shift-worker Sleepiness A: Total Sleep Drivementioning

confidence: 99%

Maritime Shift Workers Sleepiness Detection System With Multi-Modality Cues

et al. 2019

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Sleepiness has been recognized as a causal factor in many round-the-clock industries. While individuals can subjectively express their momentary sleepiness level, sleepiness-related contextual factors (CF) can influence their perception of sleepiness and cognitive performance. In this paper, the selfreported sleepiness value (vSRS) was improved by transforming it into a kernel density estimate and the assignment of the class's score is done using a likelihood ratio test (IvSRS). We integrated multiple CF and IvSRS to model sleepiness using a Bayesian network (BN). The BN produced a single probability estimate calculated based on the prior and posterior probability of the CF and IvSRS. The results showed IvSRS performed better (p < 0.05) in classifying sleepiness to three states, compared to non-modified vSRS.Considering each CF and IvSRS as stand alone indicators, integrating all these information under a BN significantly improved the systems performance (p ≤ 0.05). In addition to being able to function well in the event of missing vSRS, the proposed system has a prediction horizon of 12 h, with F 1 -measure > 78%.

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“…This refers to the different phases of sleep an individual goes through. The relative proportions of sleep phases within the sleep architecture determine the sleep quality an individual is getting [4].…”

Section: Introductionmentioning

confidence: 99%

Computational Analysis of the Adaptive Causal Relationships Between Cannabis, Anxiety and Sleep

Leeuwen

Wolthuis

Treur

2020

Lecture Notes in Computer Science

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In this paper an adaptive computational temporal-causal network model is presented to analyse the dynamic and adaptive relationships between cannabis usage, anxiety and sleep. The model has been used to simulate different well-known scenarios varying from intermittent usage to longer periods of usage interrupted by attempts to quit and to constant usage based on full addiction. It is described how the model has been verified and validated by empirical information from the literature.

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Normal Sleep Architecture

Cited by 10 publications

References 1 publication

Factors Associated With Sleep Quality Among Operating Engineers

Factors Associated With Sleep Quality Among Operating Engineers

Maritime Shift Workers Sleepiness Detection System With Multi-Modality Cues

Computational Analysis of the Adaptive Causal Relationships Between Cannabis, Anxiety and Sleep

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