Tripolar concentric ring electrodes for capturing localised electroencephalography signals during sleep.

By design, tripolar concentric ring electrodes (TCRE) provide more focal brain activity signals than conventional electroencephalography (EEG) electrodes placed further apart. This study compared spectral characteristics and rates of data loss to noisy epochs with TCRE versus conventional EEG signals recorded during sleep. A total of 20 healthy sleepers (12 females; mean [standard deviation] age 27.8 [9.6] years) underwent a 9-h sleep study. Participants were set up for polysomnography recording with TCRE to assess brain activity from 18 sites and conventional electrodes for EEG, eyes, and muscle movement. A fast Fourier transform using multitaper-based estimation was applied in 5-s epochs to scored sleep. Odds ratios with Bonferroni-adjusted 95% confidence intervals were calculated to determine the proportional differences in the number of noisy epochs between electrode types. Relative power was compared in frequency bands throughout sleep. Linear mixed models showed significant main effects of signal type (p < 0.001) and sleep stage (p < 0.001) on relative spectral power in each power band, with lower relative spectral power across all stages in TCRE versus EEG in alpha, beta, sigma, and theta activity, and greater delta power in all stages. Scalp topography plots showed distinct beta activation in the right parietal lobe with TCRE versus EEG. EEG showed higher rates of noisy epochs compared to TCRE (1.3% versus 0.8%, p < 0.001). TCRE signals showed marked differences in brain activity compared to EEG, consistent with more focal measurements and region-specific differences during sleep. TCRE may be useful for evaluating regional differences in brain activity with reduced muscle artefact compared to conventional EEG.

Estimating vigilance from the pre-work shift sleep using an under-mattress sleep sensor.

Predicting vigilance impairment in high-risk shift work occupations is critical to help to reduce workplace errors and accidents. Current methods rely on multi-night, often manually entered, sleep data. This study developed a machine learning model for predicting vigilance errors based on a single prior sleep period, derived from an under-mattress sensor. Twenty-four healthy volunteers (mean [SD] age = 27.6 [9.5] years, 12 male) attended the laboratory on two separate occasions, 1 month apart, to compare wake performance and sleep under two different lighting conditions. Each condition occurred over an 8 day protocol comprising a baseline sleep opportunity from 10 p.m. to 7 a.m., a 27 h wake period, then daytime sleep opportunities from 10 a.m. to 7 p.m. on days 3-7. From 12 a.m. to 8 a.m. on each of days 4-7, participants completed simulated night shifts that included six 10 min psychomotor vigilance task (PVT) trials per shift. Sleep was assessed using an under-mattress sensor. Using extra-trees machine learning models, PVT performance (reaction times <500 ms, reaction, and lapses) during each night shift was predicted based on the preceding daytime sleep. The final extra-trees model demonstrated moderate accuracy for predicting PVT performance, with standard errors (RMSE) of 19.9 ms (reaction time, 359 [41.6]ms), 0.42 reactions/s (reaction speed, 2.5 [0.6] reactions/s), and 7.2 (lapses, 10.5 [12.3]). The model also correctly classified 84% of trials containing ≥5 lapses (Matthews correlation coefficient = 0.59, F1 = 0.83). Model performance is comparable to current fatigue prediction models that rely upon self-report or manually entered data. This efficient approach may help to manage fatigue and safety in non-standard work schedules.

Insomnia and workplace productivity loss among young working adults: a prospective observational study of clinical sleep disorders in a community cohort.

OBJECTIVE: To examine associations between three clinically significant sleep disorders (chronic insomnia, obstructive sleep apnoea, restless legs syndrome) and workplace productivity losses among young Australian adults. DESIGN, SETTING: Prospective, observational study; 22-year follow-up of participants in the longitudinal birth cohort Raine Study (Perth, Western Australia). PARTICIPANTS: Currently employed 22-year-old Raine Study participants who underwent in-laboratory sleep disorder screening for moderate to severe obstructive sleep apnoea (apnoea-hypopnea index of more than fifteen events/hour or obstructive sleep apnoea syndrome) and were assessed for insomnia and restless legs syndrome using validated measures. MAIN OUTCOME MEASURES: Total workplace productivity loss over twelve months, assessed with the World Health Organization Health and Work Performance Questionnaire. RESULTS: Of 1235 contactable 22-year-old Raine Study cohort members, 554 people (44.9%; 294 women [53%]) underwent overnight polysomnography, completed the baseline sleep questionnaire, and completed at least three quarterly workplace productivity assessments. One or more clinically significant sleep disorders were identified in 120 participants (21.7%); 90 participants had insomnia (17%), thirty clinically significant obstructive sleep apnoea (5.4%), and two restless legs syndrome (0.4%). Seventeen people (14% of those with sleep disorders) had previously been diagnosed with a sleep disturbance by a health professional, including fourteen with insomnia. Median total workplace productivity loss was greater for participants with sleep disorders (164 hours/year; interquartile range [IQR], 0-411 hours/year) than for those without sleep disorders (30 hours/year; IQR, 0-202 hours/year); total workplace productivity loss was 40% greater for participants with sleep disorders (adjusted incidence rate ratio, 1.40; bias-corrected and accelerated 95% confidence interval, 1.10-1.76). The estimated population total productivity loss (weighted for disorder prevalence) was 28 644 hours per 1000 young workers per year, primarily attributable to insomnia (28 730 hours/1000 workers/year). CONCLUSION: Insomnia is a risk factor for workplace productivity loss in young workers. Tailored interventions are needed to identify and manage sleep disorders, particularly as most of the sleep disorders detected in the Raine Study had not previously been diagnosed.

Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea.

Rationale: Recent studies suggest that obstructive sleep apnea (OSA) severity can vary markedly from night to night, which may have important implications for diagnosis and management. Objectives: This study aimed to assess OSA prevalence from multinight in-home recordings and the impact of night-to-night variability in OSA severity on diagnostic classification in a large, global, nonrandomly selected community sample from a consumer database of people that purchased a novel, validated, under-mattress sleep analyzer. Methods: A total of 67,278 individuals aged between 18 and 90 years underwent in-home nightly monitoring over an average of approximately 170 nights per participant between July 2020 and March 2021. OSA was defined as a nightly mean apnea-hypopnea index (AHI) of more than 15 events/h. Outcomes were multinight global prevalence and likelihood of OSA misclassification from a single night's AHI value. Measurements and Main Results: More than 11.6 million nights of data were collected and analyzed. OSA global prevalence was 22.6% (95% confidence interval, 20.9-24.3%). The likelihood of misdiagnosis in people with OSA based on a single night ranged between approximately 20% and 50%. Misdiagnosis error rates decreased with increased monitoring nights (e.g., 1-night F1-score = 0.77 vs. 0.94 for 14 nights) and remained stable after 14 nights of monitoring. Conclusions: Multinight in-home monitoring using novel, noninvasive under-mattress sensor technology indicates a global prevalence of moderate to severe OSA of approximately 20%, and that approximately 20% of people diagnosed with a single-night study may be misclassified. These findings highlight the need to consider night-to-night variation in OSA diagnosis and management.

Comorbid insomnia and sleep apnoea is associated with all-cause mortality.

BACKGROUND: Increased mortality has been reported in people with insomnia and in those with obstructive sleep apnoea (OSA). However, these conditions commonly co-occur and the combined effect of comorbid insomnia and sleep apnoea (COMISA) on mortality risk is unknown. This study used Sleep Heart Health Study (SHHS) data to assess associations between COMISA and all-cause mortality risk. METHODS: Insomnia was defined as difficulties falling asleep, maintaining sleep and/or early morning awakenings from sleep ≥16 times per month, and daytime impairments. OSA was defined as an apnoea-hypopnoea index ≥15 events·h-1. COMISA was defined if both conditions were present. Multivariable adjusted Cox proportional hazards models were used to determine the association between COMISA and all-cause mortality (n=1210) over 15 years of follow-up. RESULTS: 5236 participants were included. 2708 (52%) did not have insomnia/OSA (reference group), 170 (3%) had insomnia-alone, 2221 (42%) had OSA-alone and 137 (3%) had COMISA. COMISA participants had a higher prevalence of hypertension (OR 2.00, 95% CI 1.39-2.90) and cardiovascular disease (CVD) (OR 1.70, 95% CI 1.11-2.61) compared with the reference group. Insomnia-alone and OSA-alone were associated with higher risk of hypertension but not CVD compared with the reference group. Compared with the reference group, COMISA was associated with a 47% (hazard ratio 1.47, 95% CI 1.06-2.07) increased risk of mortality. The association between COMISA and mortality was consistent across multiple definitions of OSA and insomnia. CONCLUSIONS: COMISA was associated with higher rates of hypertension and CVD at baseline, and an increased risk of all-cause mortality compared with no insomnia/OSA.

The association of co-morbid insomnia and sleep apnea with prevalent cardiovascular disease and incident cardiovascular events.

Insomnia and obstructive sleep apnea commonly co-occur (co-morbid insomnia and sleep apnea), and their co-occurrence has been associated with worse cardiometabolic and mental health. However, it remains unknown if people with co-morbid insomnia and sleep apnea are at a heightened risk of incident cardiovascular events. This study used longitudinal data from the Sleep Heart Health Study (N = 5803) to investigate potential associations between co-morbid insomnia and sleep apnea and cardiovascular disease prevalence at baseline and cardiovascular event incidence over ~11 years follow-up. Insomnia was defined as self-reported difficulties initiating and/or maintaining sleep AND daytime impairment. Obstructive sleep apnea was defined as an apnea-hypopnea index ≥ 15 events per hr sleep. Co-morbid insomnia and sleep apnea was defined if both conditions were present. Data from 4160 participants were used for this analysis. The prevalence of no insomnia/obstructive sleep apnea, insomnia only, obstructive sleep apnea only and co-morbid insomnia and sleep apnea was 53.2%, 3.1%, 39.9% and 1.9%, respectively. Co-morbid insomnia and sleep apnea was associated with a 75% (odd ratios [95% confidence interval]; 1.75 [1.14, 2.67]) increase in likelihood of having cardiovascular disease at baseline after adjusting for pre-specified confounders. In the unadjusted model, co-morbid insomnia and sleep apnea was associated with a twofold increase (hazard ratio, 95% confidence interval: 2.00 [1.33, 2.99]) in risk of cardiovascular event incidence. However, after adjusting for pre-specified covariates, co-morbid insomnia and sleep apnea was not significantly associated with incident cardiovascular events (hazard ratio 1.38 [0.92, 2.07]). Comparable findings were obtained when an alternative definition of insomnia (difficulties initiating and/or maintaining sleep without daytime impairment) was used.

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.

Annoyance due to amplitude modulated low-frequency wind farm noise: A laboratory study.

This study tested for differences in perceived annoyance and loudness between road traffic noise (RTN) and wind farm noise (WFN) with amplitude modulation (AM) and tonality. Twenty-two participants, who were primarily university students with no previous exposure to WFN and aged between 19 and 29 (mean, 22 years old; standard deviation, 2) years old with normal hearing, underwent a laboratory-based listening test. Each participant rated perceived annoyance and loudness of WFN and RTN samples played at sound pressure levels (SPLs) ranging from 33 to 48 dBA. Probability modeling revealed that participants were the largest source of variability in ratings of perceived annoyance and loudness while noise type and SPL were relatively minor sources. Overall, no differences were found between WFN and RTN perceived annoyance or loudness ratings. On the other hand, no substantial differences in annoyance were found between low-frequency tonal AM and mid-to-high-frequency AM or "swish" WFN.

Correspondence between physiological and behavioural responses to vibratory stimuli during the sleep onset period: A quantitative electroencephalography analysis.

Behavioural responses to auditory stimuli cease in late N1 or early N2 sleep. Yet, responsiveness to minimal intensity tactile stimuli and the correspondence with sleep microstructure during the sleep onset period is unknown. The aim of the present study was to investigate sleep microstructure using quantitative electroencephalography analysis when participants behaviourally responded to minimal intensity vibratory stimuli compared to when participants did not respond to stimuli during the sleep onset period. Eighteen participants wore a device that emitted vibratory stimuli to which individuals responded by tapping their index finger. A fast Fourier transform using multitaper-based estimation was applied to electroencephalography signals in 5-s epochs. Participants exhibited increases in higher frequencies 5 s before and immediately after the stimulus presentation when they responded to the stimulus compared to when they did not respond during all sleep stages. They also had greater delta power after stimulus onset when they did not respond to stimuli presented in N1 and N2 sleep compared to when they did respond. Participants responded to a significantly greater proportion of stimuli in wake than in N1 sleep (p < .001, d = 2.38), which was also significantly greater than the proportion of responses in N2 sleep (p < .001, d = 1.12). Participants showed wake-like sleep microstructure when they responded to vibratory stimuli and sleep-like microstructure when they did not respond during all sleep stages. The present study adds to the body of evidence characterising N1 sleep as a transitional period between sleep and wake containing rapid fluctuations between these two states.

Amplitude modulated wind farm noise relationship with annoyance: A year-long field study.

This paper presents results from a one-year study of indoor annoyance and self-reported sleep times for two participants located near different wind farms. Continuous measurements of outdoor and indoor noise and meteorological conditions were taken at each location for the duration of the study. In at least 50% of the annoyance recordings, participants described noise as "swish" or "swoosh." Furthermore, the majority of the annoyance recordings occurred at nighttime and in the early morning. The third quartile of A-weighted indoor sound pressure level [SPL(A)], between 27 and 31 dBA, was associated with an 88% increased probability of annoyance compared to the lowest reference quartile, which was between 12 and 22 dBA [odds ratio and 95% confidence intervals, 7.72 (2.61,22.8), p < 0.001]. The outdoor SPL(A) was also predictive of annoyance but only between 40 and 45 dBA. The outdoor prevalence of amplitude modulation (AM), defined as the percentage of time that AM was detectable by an algorithm for each annoyance period, was also associated with annoyance. Self-reported sleep efficiency (time spent asleep relative to time in bed available for sleep) was significantly associated with nighttime annoyance (ß = -0.66, p = 0.02) but only explained a small fraction of the variance (R2 = 5%).

Reconsidering screening thresholds in health assessments for obstructive sleep apnea using operational and safety incident data.

The rail industry in Australia screens workers for probable obstructive sleep apnea (OSA) due to known safety risks. However, existing criteria to trigger screening only identify a small proportion of workers with OSA. The current study sought to examine the relationship between OSA risk and rail incidents in real-world data from Australian train drivers, and conducted a proof of concept analysis to determine whether more conservative screening criteria are justified. Health assessment (2016-2018) and subsequent rail incident data (2016-2020) were collected from two passenger rail service providers. Predictors included OSA status (confirmed no OSA with a sleep study, controlled OSA, unknown OSA [no recorded sleep assessment data] and confirmed OSA with no indication of treatment); OSA risk according to the current Standard, and OSA risk according to more conservative clinical markers (BMI threshold and cardiometabolic burden). Coded rail safety incidents involving the train driver were included. Data were analysed using zero-inflated negative binomial models to account for over-dispersion with high 0 counts, and rail safety incidents are reported using Incidence Risk Ratios (IRRs). A total of 751 train drivers, typically middle-aged, overweight to obese and mostly men, were included in analyses. There were 43 (5.7%) drivers with confirmed OSA, 62 (8.2%) with controlled OSA, 13 (1.7%) with confirmed no OSA and 633 (84.4%) drivers with unknown OSA. Of the 633 train drivers with unknown OSA status, 21 (3.3%) met 'at risk' criteria for OSA according to the Standard, and incidents were 61% greater (IRR: 1.61, 95% Confidence Interval (CI) 1.02-2.56) in the years following their health assessment compared to drivers who did not meet 'at risk' criteria. A more conservative OSA risk status using lower BMI threshold and cardiometabolic burden identified an additional 30 'at risk' train drivers who had 46% greater incidents compared to drivers who did not meet risk criteria (IRR (95% CI) 1.46 (1.00-2.13)). Our more conservative OSA risk criteria identified more workers, with greater prospective incidents. These findings suggest that existing validated tools could be considered in future iterations of the Standard in order to more sensitively screen for OSA.

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.

Power-law properties of nocturnal arrhythmia avalanches: a novel marker for incident cardiovascular events.

BACKGROUND: Bursting non-sustained cardiac arrhythmia events, are a common observation during sleep. OBJECTIVES: We hypothesized nocturnal arrhythmia episode durations could follow a power-law, whose exponent could predict long-term clinical outcomes. METHODS: We defined 'nocturnal arrhythmia avalanche' (NAA) as any instance of a drop in electrocardiogram (ECG) template-matched R-R intervals ≥30% of R-R baseline, followed by a return to 90% of the baseline. We studied NAA in ECG recordings obtained from the Sleep Heart Health Study (SHHS), the Osteoporotic Fractures in Men Study (MrOS) Sleep and Multi-Ethnic Study of Atherosclerosis (MESA) studies. The association of the nocturnal arrhythmia durations with a power-law distribution was evaluated, and the association of derived power-law exponents (α) with major adverse cardiovascular events and mortality assessed with multivariable Cox regression. RESULTS: n=9176 participants were studied. NAA episodes distribution was with a consistent power-law versus comparator distributions in all datasets studied (Positive log likelihood ratio of power-law vs. exponential in MESA: 83%; SHHS: 69%; MrOS: 81%; power-law vs. log-normal in MESA: 95%; SHHS: 35% and MrOS: 64%). The NAA power law exponent (α) showed a significant association of with adverse CV outcomes (Association with CV mortality: SHHS (HR = 1.39[1.07-1.79], p=0.012); MrOS (HR = 1.42[1.02-1.94], p=0.039; Association with CV events: MESA (HR = 3.46[1.46-8.21], p=0.005)) in multivariable Cox regression, after adjusting for conventional CV risk factors and nocturnal ectopic rate. CONCLUSION: The NAA power-law exponent is a reproducible, predictive marker for incident cardiovascular events and mortality.

A circadian-informed lighting intervention accelerates circadian adjustment to a night work schedule in a submarine lighting environment.

STUDY OBJECTIVE: Night work has detrimental impacts on sleep and performance, primarily due to misalignment between sleep-wake schedules and underlying circadian rhythms. This study tested whether circadian-informed lighting accelerated circadian phase delay, and thus adjustment to night work, compared to blue-depleted standard lighting under simulated submariner work conditions. METHODS: Nineteen healthy sleepers (12 males; mean±SD aged 29 ±10 y) participated in two separate 8-day visits approximately one month apart to receive, in random order, circadian-informed lighting (blue-enriched and dim, blue-depleted lighting at specific times) and standard lighting (dim, blue-depleted lighting). After an adaptation night (day 1), salivary dim light melatonin onset (DLMO) assessment was undertaken from 18:00-02:00 on days 2-3. During days 3-7, participants completed simulated night work from 00:00-08:00 and a sleep period from 10:00-19:00. Post-condition DLMO assessment occurred from 21:00-13:00 on days 7-8. Ingestible capsules continuously sampled temperature to estimate daily core body temperature minimum (Tmin) time. Tmin and DLMO circadian delays were compared between conditions using mixed effects models. RESULTS: There were significant condition-by-day interactions in Tmin and DLMO delays (both p<0.001). After four simulated night shifts, circadian-informed lighting produced a mean [95%CI] 4.3 [3.3 to 5.4] h greater delay in Tmin timing and a 4.2 [3 to 5.6] h greater delay in DLMO timing compared to standard lighting. CONCLUSIONS: Circadian-informed lighting accelerates adjustment to shiftwork in a simulated submariner work environment. Circadian lighting interventions warrant consideration in any dimly lit and blue-depleted work environments where circadian adjustment is relevant to help enhance human performance, safety, and health.

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.

Emerging applications of objective sleep assessments towards the improved management of insomnia.

Self-reported sleep difficulties are the primary concern associated with diagnosis and treatment of chronic insomnia. This said, in-home sleep monitoring technology in combination with self-reported sleep outcomes may usefully assist with the management of insomnia. The rapid acceleration in consumer sleep technology capabilities together with their growing use by consumers means that the implementation of clinically useful techniques to more precisely diagnose and better treat insomnia are now possible. This review describes emerging techniques which may facilitate better identification and management of insomnia through objective sleep monitoring. Diagnostic techniques covered include insomnia phenotyping, better detection of comorbid sleep disorders, and identification of patients potentially at greatest risk of adverse outcomes. Treatment techniques reviewed include the administration of therapies (e.g., Intensive Sleep Retraining, digital treatment programs), methods to assess and improve treatment adherence, and sleep feedback to address concerns about sleep and sleep loss. Gaps in sleep device capabilities are also discussed, such as the practical assessment of circadian rhythms. Proof-of-concept studies remain needed to test these sleep monitoring-supported techniques in insomnia patient populations, with the goal to progress towards more precise diagnoses and efficacious treatments for individuals with insomnia.

Sleep Irregularity Is Associated With Hypertension: Findings From Over 2 Million Nights With a Large Global Population Sample.

BACKGROUND: Irregularities in sleep duration and sleep timing have emerged as potential risk factors for hypertension. This study examined associations between irregularity in sleep duration and timing with hypertension in a large, global sample over multiple months. METHODS: Data from 12 287 adults, who used an under-mattress device to monitor sleep duration and timing and also provided blood pressure recordings on ≥5 separate occasions, were analyzed. Sleep duration irregularity was assessed as the SD in total sleep time across the ≈9-month recording period. Sleep timing irregularity was assessed as SDs in sleep onset time, sleep midpoint, and sleep offset time. Logistic regressions were conducted to investigate associations between sleep irregularity and hypertension, defined as median systolic blood pressure ≥140 mm Hg or median diastolic blood pressure ≥90 mm Hg. RESULTS: Participants were middle-aged (mean±SD, 50±12 years), mostly men (88%) and overweight (body mass index, 28±6 kg/m-2). Sleep duration irregularity was consistently associated with an ≈9% to 17% increase in hypertension independently of the total sleep time. A ≈34-minute increase in sleep onset time irregularity was associated with a 32% increase in hypertension (1.32 [1.20-1.45]). A 32-minute increase in sleep midpoint irregularity was associated with an 18% increase in hypertension (1.18 [1.09-1.29]), while a 43-minute increase in sleep offset time irregularity was associated with an 8.9% increase in hypertension (1.09 [1.001-1.18]). CONCLUSIONS: These findings support that sleep irregularity, both in duration and timing, is a risk marker for poor cardiovascular health. Further mechanistic investigations of temporal relationships between day-to-day fluctuations in sleep duration and timing, next-day blood pressure, and other cardiovascular outcomes are warranted.