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Background: Large electricity-generating wind turbines emit both audible sound and inaudible infrasound at very low frequencies that are outside of the normal human range of hearing. Sufferers of wind turbine syndrome (WTS) have attributed their ill-health and particularly their sleep disturbance to the signature pattern of infrasound. Critics have argued that these symptoms are psychological in origin and are attributable to nocebo effects. Objectives: We aimed to test the effects of 72 h of infrasound (1.6-20 Hz at a sound level of ~ 90 dB pk re 20 pPa, simulating a wind turbine infrasound signature) exposure on human physiology, particularly sleep. Methods: We conducted a randomized double-blind triple-arm crossover laboratory-based study of 72 h exposure with a >10-d washout conducted in a noise-insulated sleep laboratory in the style of a studio apartment. The exposures were infrasound (~90 dB pk), sham infrasound (same speakers not generating infrasound), and traffic noise exposure [active control; at a sound pressure level of 40-50 dB L[A.sub.eq,night] and 70 dB LA[F.sub.max] transient maxima, night (2200 to 0700 hours)]. The following physiological and psychological measures and systems were tested for their sensitivity to infrasound: wake after sleep onset (WASO; primary outcome) and other measures of sleep physiology, wake electroencephalography, WTS symptoms, cardiovascular physiology, and neurobehavioral performance. Results: We randomized 37 noise-sensitive but otherwise healthy adults (18-72 years of age; 51% female) into the study before a COVID19-related public health order forced the study to close. WASO was not affected by infrasound compared with sham infrasound (-1.36 min; 95% CI: -6.60, 3.88, p = 0.60) but was worsened by the active control traffic exposure compared with sham by 6.07 min (95% CI: 0.75, 11.39, p = 0.02). Infrasound did not worsen any subjective or objective measures used. Discussion: Our findings did not support the idea that infrasound causes WTS. High level, but inaudible, infrasound did not appear to perturb any physiological or psychological measure tested in these study participants. https://doi.org/10.1289/EHP10757, Introduction Large electricity-generating wind turbines emit both audible sound and inaudible infrasound at very low frequencies that are outside of the normal human range of hearing. There have been several [...]

ObjectiveAcutely restricting sleep worsens insulin sensitivity in healthy individuals whose usual sleep is normal in duration and pattern. The effect of recovery or weekend 'catch-up' sleep on insulin sensitivity and metabolically active hormones in individuals with chronic sleep restriction who regularly 'catch-up' on sleep at weekends is as yet unstudied.Design19 men (mean ± SEM age 28·6 ± 2·0 years, BMI 26·0 ± 0·8 kg/m(2) ) with at least 6 months' history (5·1 ± 0·9 years) of lifestyle-driven, restricted sleep during the working week (373 ± 6·6 min/night) with regular weekend 'catch-up' sleep (weekend sleep extension 37·4 ± 2·3%) completed an in-laboratory, randomized, crossover study comprising two of three conditions, stratified by age. Conditions were 3 weekend nights of 10 hours, 6 hours or 10 hours time-in-bed with slow wave sleep (SWS) suppression using targeted acoustic stimuli.MeasurementsInsulin sensitivity was measured in the morning following the 3rd intervention night by minimal modelling of 19 samples collected during a 2-h oral glucose tolerance test. Glucose, insulin, c-peptide, leptin, peptide YY (PYY), ghrelin, cortisol, testosterone and luteinizing hormone (LH) were measured from daily fasting blood samples; HOMA-IR, HOMA-β and QUICKI were calculated.ResultsInsulin sensitivity was higher following three nights of sleep extension compared to sustained sleep restriction. Fasting insulin, c-peptide, HOMA-IR, HOMA-β, leptin and PYY decreased with 'catch-up' sleep, QUICKI and testosterone increased, while morning cortisol and LH did not change. Targeted acoustic stimuli reduced SWS by 23%, but did not alter insulin sensitivity.ConclusionsThree nights of 'catch-up' sleep improved insulin sensitivity in men with chronic, repetitive sleep restriction. Methods to improve metabolic health by optimizing sleep are plausible.

Background and objective: Use of in-laboratory polysomnography (PSG) to diagnose obstructive sleep apnoea (OSA) is cost and resource intensive. Questionnaires, physical measurements and home monitors have been studied as potential simpler alternatives. This study aimed to develop a diagnostic model for OSA for use in primary care. Methods: Primary care practitioners were trained to recognize symptoms of sleep apnoea and recruited patients based on the clinical need to investigate OSA. Assessment was by symptom questionnaires, anthropomorphic measurements, digital facial photography, and a single-channel nasal flow monitor (Flow Wizard ©, DiagnoseIT, Sydney, Australia) worn at home for 3 nights. The in-laboratory PSG was the reference test, with OSA defined as apnoea-hypopnoea index (AHI) =10 events/h. Results: In the model development phase, 25 primary care practitioners studied 315 patients in whom they suspected OSA, of which 57% had AHI=10 and 22% had AHI=30. Published OSA questionnaires provided low to moderate prediction of OSA (area under the curve [AUC] 0.53-0.73). The nasal flow monitor alone yielded high accuracy for predicting OSA with AUC of 0.87. Sensitivity was 0.87 and specificity 0.77 at a threshold respiratory event index (REI) of 18 events/h. A model adding age, gender, symptoms and BMI to the nasal flow monitor REI only modestly improved OSA prediction (AUC 0.89), with similar AUC (0.88) confirmed in the validation population of 114 patients. Conclusion: Sleep apnoea can be diagnosed in the primary care setting with a combination of clinical judgement and portable monitor test outcomes. [ABSTRACT FROM AUTHOR]

Summary: Therapeutic‐continuous positive airway pressure seems to increase weight compared with placebo‐continuous positive airway pressure. It is not known whether weight gain with therapeutic‐continuous positive airway pressure dose is dependent or whether it causes metabolic dysfunction. Data synthesis of three randomised placebo‐continuous positive airway pressure‐controlled trials (2–3 months) was performed to test whether there is a dose‐dependent effect of continuous positive airway pressure on weight. Fasting glucose, insulin, insulin resistance (homeostatic model assessment), lipids and visceral abdominal fat were also tested to determine any effect on metabolic function. Mixed‐model analysis of variance was used to quantify these effects. One‐hundred and twenty‐eight patients were analysed. Overall there was a small increase in weight with therapeutic‐continuous positive airway pressure use compared with placebo‐continuous positive airway pressure (difference: 1.17 kg; 0.37–1.97, p = 0.005), which was greater with high‐use therapeutic‐continuous positive airway pressure compared with high‐use placebo‐continuous positive airway pressure (1.45 kg; 0.10–2.80, p = 0.04). Continuous positive airway pressure use as a continuous variable was also significantly associated with weight change in continuous positive airway pressure users (0.30 kg hr−1 night−1; 0.04–0.56, p = 0.001), but not in placebo users (0.04 kg hr−1 night−1; −0.22 to 0.26, p = 0.76). Neither therapeutic‐continuous positive airway pressure nor the dose of therapeutic‐continuous positive airway pressure caused any changes to metabolic outcomes. The weight gain effects of medium‐term therapeutic‐continuous positive airway pressure appear modest and are not accompanied by any adverse metabolic effects. [ABSTRACT FROM AUTHOR]