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Reduced upper airway muscle activity during sleep is a key contributor to obstructive sleep apnea pathogenesis. Hypoglossal nerve stimulation activates upper airway dilator muscles, including the genioglossus, and has the potential to reduce obstructive sleep apnea severity. The objective of this study was to examine the safety, feasibility and efficacy of a novel hypoglossal nerve stimulation system (HGNS; Apnex Medical, St Paul, MN, USA) in treating obstructive sleep apnea at 12 months following implantation. Thirty-one subjects (35% female, age 52.4 ± 9.4 years) with moderate to severe obstructive sleep apnea and unable to tolerate positive airway pressure underwent surgical implantation and activation of the hypoglossal nerve stimulation system in a prospective single-arm interventional trial. Primary outcomes were changes in obstructive sleep apnea severity (apnea-hypopnea index, from in-laboratory polysomnogram) and sleep-related quality of life [Functional Outcomes of Sleep Questionnaire (FOSQ)]. Hypoglossal nerve stimulation was used on 86 ± 16% of nights for 5.4 ± 1.4 h per night. There was a significant improvement (P < 0.001) from baseline to 12 months in apnea-hypopnea index (45.4 ± 17.5 to 25.3 ± 20.6 events h(-1) ) and Functional Outcomes of Sleep Questionnaire score (14.2 ± 2.0 to 17.0 ± 2.4), as well as other polysomnogram and symptom measures. Outcomes were stable compared with 6 months following implantation. Three serious device-related adverse events occurred: an infection requiring device removal; and two stimulation lead cuff dislodgements requiring replacement. There were no significant adverse events with onset later than 6 months following implantation. Hypoglossal nerve stimulation demonstrated favourable safety, feasibility and efficacy.

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., (© 2024 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)

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., (© 2024 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)

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., (© 2022 European Sleep Research Society.)

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., (© 2021 European Sleep Research Society.)

Little is known about the potential impacts of wind turbine noise (WTN) on sleep. Previous research is limited to cross-sectional studies reporting anecdotal impacts on sleep using inconsistent sleep metrics. This meta-analysis sought to comprehensively review studies evaluating the impact of WTN using widely accepted and validated objective and subjective sleep assessments. Search terms included: "wind farm noise", "wind turbine noise", "wind turbine sound", "wind turbine noise exposure" AND "sleep". Only original articles published in English published after the year 2000 and reporting sleep outcomes in the presence of WTN using polysomnography, actigraphy or psychometrically validated sleep questionnaires were included. Uniform outcomes of the retrieved studies were meta-analysed to examine WTN effects on objective and subjective sleep outcomes. Nine studies were eligible for review and five studies were meta-analysed. Meta-analyses (Hedges' g; 95% confidence interval [CI]) revealed no significant differences in objective sleep onset latency (0.03, 95%  CI -0.34 to 0.41), total sleep time (-0.05, 95%  CI -0.77 to 0.67), sleep efficiency (-0.25, 95%  CI -0.71 to 0.22) or wake after sleep onset (1.25, 95%  CI -2.00 to 4.50) in the presence versus absence of WTN (all p > .05). Subjective sleep estimates were not meta-analysed because measurement outcomes were not sufficiently uniform for comparisons between studies. This systematic review and meta-analysis suggests that WTN does not significantly impact key indicators of objective sleep. Cautious interpretation remains warranted given variable measurement methodologies, WTN interventions, limited sample sizes, and cross-sectional study designs, where cause-and-effect relationships are uncertain. Well-controlled experimental studies using ecologically valid WTN, objective and psychometrically validated sleep assessments are needed to provide conclusive evidence regarding WTN impacts on sleep., (© 2020 European Sleep Research Society.)