A novel method to quantify breathing effort from respiratory mechanics and esophageal pressure.

Breathing effort is important to quantify to understand mechanisms underlying central and obstructive sleep apnea, respiratory-related arousals, and the timing and effectiveness of invasive or noninvasive mechanically assisted ventilation. Current quantitative methods to evaluate breathing effort rely on inspiratory esophageal or epiglottic pressure swings or changes in diaphragm electromyographic (EMG) activity, where units are problematic to interpret and compare between individuals and to measured ventilation. This paper derives a novel method to quantify breathing effort in units directly comparable with measured ventilation by applying respiratory mechanics first principles to convert continuous transpulmonary pressure measurements into "attempted" airflow expected to have arisen without upper airway obstruction. The method was evaluated using data from 11 subjects undergoing overnight polysomnography, including six patients with obesity with severe obstructive sleep apnea (OSA), including one who also had frequent central events, and five healthy-weight controls. Classic respiratory mechanics showed excellent fits of airflow and volume to transpulmonary pressures during wake periods of stable unobstructed breathing (means ± SD, r2 = 0.94 ± 0.03), with significantly higher respiratory system resistance in patients compared with healthy controls (11.2 ± 3.3 vs. 7.1 ± 1.9 cmH2O·L-1·s, P = 0.032). Subsequent estimates of attempted airflow from transpulmonary pressure changes clearly highlighted periods of acute and prolonged upper airway obstruction, including within the first few breaths following sleep onset in patients with OSA. This novel technique provides unique quantitative insights into the complex and dynamically changing interrelationships between breathing effort and achieved airflow during periods of obstructed breathing in sleep.NEW & NOTEWORTHY Ineffective breathing efforts with snoring and obstructive sleep apnea (OSA) are challenging to quantify. Measurements of esophageal or epiglottic pressure swings and diaphragm electromyography are useful, but units are problematic to interpret and compare between individuals and to measured ventilation. This paper derives a novel method that uses esophageal pressure and respiratory mechanics first principles to quantify breathing effort as "attempted" flow and volume in units directly comparable with measured airflow, volume, and ventilation.

Regular snoring is associated with uncontrolled hypertension.

Snoring may be a risk factor for cardiovascular disease independent of other co-morbidities. However, most prior studies have relied on subjective, self-report, snoring evaluation. This study assessed snoring prevalence objectively over multiple months using in-home monitoring technology, and its association with hypertension prevalence. In this study, 12,287 participants were monitored nightly for approximately six months using under-the-mattress sensor technology to estimate the average percentage of sleep time spent snoring per night and the estimated apnea-hypopnea index (eAHI). Blood pressure cuff measurements from multiple daytime assessments were averaged to define uncontrolled hypertension based on mean systolic blood pressure≥140 mmHg and/or a mean diastolic blood pressure ≥90 mmHg. Associations between snoring and uncontrolled hypertension were examined using logistic regressions controlled for age, body mass index, sex, and eAHI. Participants were middle-aged (mean ± SD; 50 ± 12 y) and most were male (88%). There were 2467 cases (20%) with uncontrolled hypertension. Approximately 29, 14 and 7% of the study population snored for an average of >10, 20, and 30% per night, respectively. A higher proportion of time spent snoring (75th vs. 5th; 12% vs. 0.04%) was associated with a ~1.9-fold increase (OR [95%CI]; 1.87 [1.63, 2.15]) in uncontrolled hypertension independent of sleep apnea. Multi-night objective snoring assessments and repeat daytime blood pressure recordings in a large global consumer sample, indicate that snoring is common and positively associated with hypertension. These findings highlight the potential clinical utility of simple, objective, and noninvasive methods to detect snoring and its potential adverse health consequences.

Are we getting enough sleep? Frequent irregular sleep found in an analysis of over 11 million nights of objective in-home sleep data.

OBJECTIVES: Evidence-based guidelines recommend that adults should sleep 7-9 h/night for optimal health and function. This study used noninvasive, multinight, objective sleep monitoring to determine average sleep duration and sleep duration variability in a large global community sample, and how often participants met the recommended sleep duration range. METHODS: Data were analyzed from registered users of the Withings under-mattress Sleep Analyzer (predominantly located in Europe and North America) who had ≥28 nights of sleep recordings, averaging ≥4 per week. Sleep durations (the average and standard deviation) were assessed across a ∼9-month period. Associations between age groups, sex, and sleep duration were assessed using linear and logistic regressions, and proportions of participants within (7-9 hours) or outside (<7 hours or >9 hours) the recommended sleep duration range were calculated. RESULTS: The sample consisted of 67,254 adults (52,523 males, 14,731 females; aged mean ± SD 50 ± 12 years). About 30% of adults demonstrated an average sleep duration outside the recommended 7-9 h/night. Even in participants with an average sleep duration within 7-9 hours, about 40% of nights were outside this range. Only 15% of participants slept between 7 and 9 hours for at least 5 nights per week. Female participants had significantly longer sleep durations than male participants, and middle-aged participants had shorter sleep durations than younger or older participants. CONCLUSIONS: These findings indicate that a considerable proportion of adults are not regularly sleeping the recommended 7-9 h/night. Even among those who do, irregular sleep is prevalent. These novel data raise several important questions regarding sleep requirements and the need for improved sleep health policy and advocacy.

Establishing the acute physiological and sleep disruption characteristics of wind farm versus road traffic noise disturbances in sleep: a randomized controlled trial protocol.

Study Objectives: Despite the global expansion of wind farms, effects of wind farm noise (WFN) on sleep remain poorly understood. This protocol details a randomized controlled trial designed to compare the sleep disruption characteristics of WFN versus road traffic noise (RTN). Methods: This study was a prospective, seven night within-subjects randomized controlled in-laboratory polysomnography-based trial. Four groups of adults were recruited from; <10 km away from a wind farm, including those with, and another group without, noise-related complaints; an urban RTN exposed group; and a group from a quiet rural area. Following an acclimation night, participants were exposed, in random order, to two separate nights with 20-s or 3-min duration WFN and RTN noise samples reproduced at multiple sound pressure levels during established sleep. Four other nights tested for continuous WFN exposure during wake and/or sleep on sleep outcomes. Results: The primary analyses will assess changes in electroencephalography (EEG) assessed as micro-arousals (EEG shifts to faster frequencies lasting 3-15 s) and awakenings (>15 s events) from sleep by each noise type with acute (20-s) and more sustained (3-min) noise exposures. Secondary analyses will compare dose-response effects of sound pressure level and noise type on EEG K-complex probabilities and quantitative EEG measures, and cardiovascular activation responses. Group effects, self-reported noise sensitivity, and wake versus sleep noise exposure effects will also be examined. Conclusions: This study will help to clarify if wind farm noise has different sleep disruption characteristics compared to road traffic noise.

Single-Night Diagnosis of Sleep Apnea Contributes to Inconsistent Cardiovascular Outcome Findings.

BACKGROUND: Single-night disease misclassification of OSA due to night-to-night variability may contribute to inconsistent findings in OSA trials. RESEARCH QUESTION: Does multinight quantification of OSA severity provide more precise estimates of associations with incident hypertension? STUDY DESIGN AND METHODS: A total of 3,831 participants without hypertension at baseline were included in simulation analyses. Included participants had ≥ 28 days of nightly apnea-hypopnea index (AHI) recordings via an under-mattress sensor and ≥ three separate BP measurements over a 3-month baseline period followed by ≥ three separate BP measurements 6 to 9 months postbaseline. Incident hypertension was defined as a mean systolic BP ≥ 140 mm Hg or a mean diastolic BP ≥ 90 mm Hg. Simulated trials (1,000) were performed, using bootstrap methods to investigate the effect of variable numbers of nights (x = 1-56 per participant) to quantify AHI and the ability to detect associations between OSA and incident hypertension via logistic regression adjusted for age, sex, and BMI. RESULTS: Participants were middle-aged (mean ± SD, 52 ± 12 y), mostly male (91%), and overweight (BMI, 28 ± 5 kg/m2). Single-night quantification of OSA failed to detect an association with hypertension risk in 42% of simulated trials (α = .05). Conversely, 100% of trials detected an association when AHI was quantified over ≥ 28 nights. Point estimates of hypertension risk were also 50% higher and uncertainty was five times lower during multinight vs single-night simulation trials. INTERPRETATION: Multinight monitoring of OSA allows for better estimates of hypertension risk and potentially other adverse health outcomes associated with OSA. These findings have important implications for clinical care and OSA trial design.

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.

The effect of wind turbine noise on polysomnographically measured and self-reported sleep latency in wind turbine noise naïve participants.

STUDY OBJECTIVES: Wind turbine noise (WTN) exposure could potentially interfere with the initiation of sleep. However, effects on objectively assessed sleep latency are largely unknown. This study sought to assess the impact of WTN on polysomnographically measured and sleep diary-determined sleep latency compared to control background noise alone in healthy good sleepers without habitual prior WTN exposure. METHODS: Twenty-three WTN naïve urban residents (mean ± SD age: 21.7 ± 2.1 years, range 18-29, 13 females) attended the sleep laboratory for two polysomnography studies, one week apart. Participants were blind to noise conditions and only informed that they may or may not hear noise during each night. During the sleep onset period, participants were exposed to counterbalanced nights of WTN at 33 dB(A), the upper end of expected indoor values; or background noise alone as the control condition (23 dB(A)). RESULTS: Linear mixed model analysis revealed no differences in log10 normalized objective or subjective sleep latency between the WTN versus control nights (median [interquartile range] objective 16.5 [11.0 to 18.5] vs. 16.5 [10.5 to 29.0] min, p = .401; subjective 20.0 [15.0 to 25.0] vs. 15.0 [10.0 to 30.0] min, p = .907). CONCLUSIONS: Although undetected small effects cannot be ruled out, these results do not support that WTN extends sleep latency in young urban-dwelling individuals without prior WTN exposure.

EEG power spectral responses to wind farm compared with road traffic noise during sleep: A laboratory study.

Wind turbine noise is dominated by low frequencies for which effects on sleep relative to more common environmental noise sources such as road traffic noise remain unknown. This study examined the effect of wind turbine noise compared with road traffic noise on sleep using quantitative electroencephalogram power spectral analysis. Twenty-three participants were exposed to 3-min samples of wind turbine noise and road traffic noise at three sound pressure levels (33, 38 and 43 dBA) in randomised order during established sleep. Acute (0-30 s) and more sustained (30-180 s) effects of noise presentations during N2 and N3 sleep were examined using spectral analysis of changes in electroencephalogram power frequency ranges across time in 5-s intervals. Both noise types produced time- and sound pressure level-dependent increases in electroencephalogram power, but with significant noise type by sound pressure level interactions in beta, alpha, theta and delta frequency bands (all p < 0.05). Wind turbine noise showed significantly lower delta, theta and beta activity immediately following noise onset compared with road traffic noise (all p < 0.05). However, alpha activity was higher for wind turbine noise played at lower sound pressure levels (33 dBA [p = 0.001] and 38 dBA [p = 0.003]) compared with traffic noise during N2 sleep. These findings support that spectral analyses show subtle effects of noise on sleep and that electroencephalogram changes following wind turbine noise and road traffic noise onset differ depending on sound pressure levels; however, these effects were mostly transient and had little impact on conventionally scored sleep. Further studies are needed to establish if electroencephalogram changes associated with modest environmental noise exposures have significant impacts on sleep quality and next-day functioning.

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences.

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for "non-circadian rhythm" sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The "endpoint" of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.