Traffic noise-induced changes in wake-propensity measured with the Odds-Ratio Product (ORP)

New insights and potential clinical implications of the odds ratio product

The odds ratio product (ORP) is a continuous metric of sleep depth that ranges from 0 (very deep sleep) to 2. 5 (full wakefulness). Its advantage over the conventional method recommended by AASM is that it discloses different levels of stage wake (sleep propensity) and different sleep depths within the same sleep stage. As such, it can be used to identify differences in sleep depth between subjects, and in the same subjects under different circumstances, when differences are not discernible by conventional staging. It also identifies different sleep depths within stage rapid-eye-movement sleep, with possible implications to disorders during this stage. Epoch-by-epoch ORP can be displayed graphically across the night or as average values in conventional sleep stages. In addition, ORP can be reported as % of recording time in specific ORP ranges (e.g., deciles of the total ORP range) where it produces distinct distribution patterns (ORP-architecture) that have been associated with different clinical disorders and outcomes. These patterns offer unique research opportunities to identify different mechanisms and potential therapy for various sleep complaints and disorders. In this review I will discuss how ORP is measured, its validation, differences from delta power, and the various phenotypes, and their postulated mechanisms, identified by ORP architecture and the opportunities for research to advance management of sleep-disordered breathing, insomnia and idiopathic hypersomnia.

Contribution of obstructive sleep apnea to disrupted sleep in a large clinical cohort of patients with suspected obstructive sleep apnea

STUDY OBJECTIVES

The response of sleep depth to CPAP in patients with OSA is unpredictable. The odds-ratio-product (ORP) is a continuous index of sleep depth and wake propensity that distinguishes different sleep depths within sleep stages, and different levels of vigilance during stage wake. When expressed as fractions of time spent in different ORP deciles, nine distinctive patterns are found. Only three of these are associated with OSA. We sought to determine whether sleep depth improves on CPAP exclusively in patients with these three ORP patterns.

METHODS

ORP was measured during the diagnostic and therapeutic components of 576 split-night polysomnographic (PSG) studies. ORP architecture in the diagnostic section was classified into one of the nine possible ORP patterns and the changes in sleep architecture were determined on CPAP for each of these patterns. ORP architecture was similarly determined in the first half of 760 full-night diagnostic PSG studies and the changes in the second half were measured to control for differences in sleep architecture between the early and late portions of sleep time in the absence of CPAP.

RESULTS

Frequency of the three ORP patterns increased progressively with the apnea-hypopnea index. Sleep depth improved significantly on CPAP only in the three ORP patterns associated with OSA. Changes in CPAP in the other six patterns, or in full diagnostic PSG studies, were insignificant or paradoxical.

CONCLUSIONS

ORP architecture types can identify patients in whom OSA adversely affects sleep and whose sleep is expected to improve on CPAP therapy.

Automated identification and assessment of environmental noise sources

Noise pollution is one of the major health risks in urban life. The approach to measurement and identification of noise sources needs to be improved and enhanced to reduce high costs. Long measurement times and the need for expensive equipment and trained personnel must be automated. Simplifying the identification of main noise sources and excluding residual and background noise allows more effective measures. By spatially filtering the acoustic scene and combining unsupervised learning with psychoacoustic features, this paper presents a prototype system capable of automated calculation of the contribution of individual noise sources to the total noise level. Pilot measurements were performed at three different locations in the city of Ljubljana, Slovenia. Equivalent sound pressure levels obtained with the device were compared to the results obtained by manually marking individual parts of each of the three measurements. The proposed approach correctly identified the main noise sources in the vicinity of the measurement points.

An experimental investigation on the impact of wind turbine noise on polysomnography-measured and sleep diary-determined sleep outcomes

Study Objectives

Carefully controlled studies of wind turbine noise (WTN) and sleep are lacking, despite anecdotal complaints from some residents in wind farm areas and known detrimental effects of other noises on sleep. This laboratory-based study investigated the impact of overnight WTN exposure on objective and self-reported sleep outcomes.

Methods

Sixty-eight participants (38 females) aged (mean ± SD) 49.2 ± 19.5 were recruited from four groups; N = 14, living <10 km from a wind farm and reporting WTN related sleep disruption; N = 18, living <10 km from a wind farm and reporting no WTN sleep disruption; N = 18, reporting road traffic noise-related sleep disruption; and N = 18 control participants living in a quiet rural area. All participants underwent in-laboratory polysomnography during four full-night noise exposure conditions in random order: a quiet control night (19 dB(A) background laboratory noise), continuous WTN (25 dB(A)) throughout the night; WTN (25 dB(A)) only during periods of established sleep; and WTN (25 dB(A)) only during periods of wake or light N1 sleep. Group, noise condition, and interaction effects on measures of sleep quantity and quality were examined via linear mixed model analyses.

Results

There were no significant noise condition or group-by-noise condition interaction effects on polysomnographic or sleep diary determined sleep outcomes (all ps > .05).

Conclusions

These results do not support that WTN at 25 dB(A) impacts sleep outcomes in participants with or without prior WTN exposure or self-reported habitual noise-related sleep disruption. These findings do not rule out effects at higher noise exposure levels or potential effects of WTN on more sensitive markers of sleep disruption.

Clinical Trial Registration

ACTRN12619000501145, UTN U1111-1229-6126. Establishing the physiological and sleep disruption characteristics of noise disturbances in sleep. https://www.anzctr.org.au/. This study was prospectively registered on the Australian and New Zealand Clinical Trial Registry.

Environmental Noise and Effects on Sleep: An Update to the WHO Systematic Review and Meta-Analysis

BACKGROUND

Nighttime noise carries a significant disease burden. The World Health Organization (WHO) recently published guidelines for the regulation of environmental noise based on a review of evidence published up to the year 2015 on the effects of environmental noise on sleep.

OBJECTIVES

This systematic review and meta-analysis will update the WHO evidence review on the effects of environmental noise on sleep disturbance to include more recent studies.

METHODS

Investigations of self-reported sleep among residents exposed to environmental traffic noise at home were identified using Scopus, PubMed, Embase, and PsycINFO. Awakenings, falling asleep, and sleep disturbance were the three outcomes included. Extracted data were used to derive exposure-response relationships for the probability of being highly sleep disturbed by nighttime noise [average outdoor A-weighted noise level (Lnight) 2300-0700 hours] for aircraft, road, and rail traffic noise, individually. The overall quality of evidence was assessed using Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria.

RESULTS

Eleven studies (n=109,070 responses) were included in addition to 25 studies (n=64,090 responses) from the original WHO analysis. When sleep disturbance questions specifically mentioned noise as the source of disturbance, there was moderate quality of evidence for the probability of being highly sleep disturbed per 10-dB increase in Lnight for aircraft [odds ratio (OR)=2.18; 95% confidence interval (CI): 2.01, 2.36], road (OR=2.52; 95% CI: 2.28, 2.79), and railway (OR=2.97; 95% CI: 2.57, 3.43) noise. When noise was not mentioned, there was low to very low quality of evidence for being sleep disturbed per 10-dB increase in Lnight for aircraft (OR=1.52; 95% CI: 1.20, 1.93), road (OR=1.14; 95% CI: 1.08, 1.21), and railway (OR=1.17; 95% CI: 0.91, 1.49) noise. Compared with the original WHO review, the exposure-response relationships closely agreed at low (40 dB Lnight) levels for all traffic types but indicated greater disturbance by aircraft traffic at high noise levels. Sleep disturbance was not significantly different between European and non-European studies.

DISCUSSION

Available evidence suggests that transportation noise is negatively associated with self-reported sleep. Sleep disturbance in this updated meta-analysis was comparable to the original WHO review at low nighttime noise levels. These low levels correspond to the recent WHO noise limit recommendations for nighttime noise, and so these findings do not suggest these WHO recommendations need revisiting. Deviations from the WHO review in this updated analysis suggest that populations exposed to high levels of aircraft noise may be at greater risk of sleep disturbance than determined previously. https://doi.org/10.1289/EHP10197.

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 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.

A novel EEG marker predicts perceived sleepiness and poor sleep quality

STUDY OBJECTIVES

To determine if a novel EEG-derived continuous index of sleep depth/alertness, the odds ratio product (ORP), predicts self-reported daytime sleepiness and poor sleep quality in two large population-based cohorts.

METHODS

ORP values which range from 0 (deep sleep) to 2.5 (fully alert) were calculated in 3s intervals during awake periods (ORPwake) and NREM sleep (ORPNREM) determined from home sleep studies in the HypnoLaus (N = 2162: 1106 females, 1056 males) and men androgen inflammation lifestyle environment and stress (MAILES) cohorts (N = 754 males). Logistic regression was used to examine associations between ORPwake, ORPNREM, and traditional polysomnography measures (as comparators) with excessive sleepiness (Epworth sleepiness scale >10) and poor sleep quality (Pittsburgh sleep quality index >5) and insomnia symptoms.

RESULTS

High ORPwake was associated with a ~30% increase in poor sleep quality in both HypnoLaus (odds ratio, OR, and 95% CI) 1.28 (1.09, 1.51), and MAILES 1.36 (1.10, 1.68). High ORPwake was also associated with a ~28% decrease in excessive daytime sleepiness in the MAILES dataset. ORPNREM was associated with a ~30% increase in poor sleep quality in HypnoLaus but not in MAILES. No consistent associations across cohorts were detected using traditional polysomnography markers.

CONCLUSIONS

ORP, a novel EEG-derived metric, measured during wake periods predicts poor sleep quality in two independent cohorts. Consistent with insomnia symptomatology of poor perceived sleep in the absence of excessive daytime sleepiness, ORPwake may provide valuable objective mechanistic insight into physiological hyperarousal.