Metabolic and hormonal effects of 'catch-up' sleep in men with chronic, repetitive, lifestyle-driven sleep restriction.

OBJECTIVE: Acutely 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. DESIGN: 19 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. MEASUREMENTS: Insulin 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-ß and QUICKI were calculated. RESULTS: Insulin sensitivity was higher following three nights of sleep extension compared to sustained sleep restriction. Fasting insulin, c-peptide, HOMA-IR, HOMA-ß, 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. CONCLUSIONS: Three 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.

An automated algorithm to identify and reject artefacts for quantitative EEG analysis during sleep in patients with sleep-disordered breathing.

PURPOSE: Large quantities of neurophysiological electroencephalogram (EEG) data are routinely collected in the sleep laboratory. These are underutilised due to the burden of managing artefact contamination. The aim of this study was to develop a new tool for automated artefact rejection that facilitates subsequent quantitative analysis of sleep EEG data collected during routine overnight polysomnography (PSG) in subjects with and without sleep-disordered breathing (SDB). METHODS: We evaluated the accuracy of an automated algorithm to detect sleep EEG artefacts against artefacts manually scored by three experienced technologists (reference standard) in 40 PSGs. Spectral power was computed using artefact-free EEG data derived from (1) the reference standard, (2) the algorithm and (3) raw EEG without any prior artefact rejection. RESULTS: The algorithm showed a high level of accuracy of 94.3, 94.7 and 95.8% for detecting artefacts during the entire PSG, NREM sleep and REM sleep, respectively. There was good to moderate sensitivity and excellent specificity of the algorithm detection capabilities during sleep. The EEG spectral power for the reference standard and algorithm was significantly lower than that of the raw, unprocessed EEG signal. CONCLUSIONS: These preliminary findings support an automated way to process EEG artefacts during sleep, providing the opportunity to investigate EEG-based markers of neurobehavioural impairment in sleep disorders in future studies.

The effect of continuous positive airway pressure usage on sleepiness in obstructive sleep apnoea: real effects or expectation of benefit?

RATIONALE: Placebo responses are complex psychobiological phenomena and often involve patient expectation of benefit. With continuous positive airway pressure (CPAP) treatment of obstructive sleep apnoea, greater hours of CPAP use are associated with reduced sleepiness. However, these open-label studies have not controlled for patient expectation of benefit derived from their knowledge of hours of device use. OBJECTIVES: To investigate the relative effectiveness of the use of real or placebo CPAP on daytime sleepiness. METHODS: Patient-level meta-analysis combining data on sleepiness measured by the Epworth Sleepiness Scale from three randomised placebo-controlled crossover trials. Mixed model analysis of variance was used to quantify the effects of real versus placebo device treatment, usage, their interaction and regression to the mean. MEASUREMENTS AND MAIN RESULTS: Duration of real and placebo CPAP use was correlated within patients (r=0.53, p<0.001). High use of real CPAP reduced sleepiness more than high use of placebo (difference 3.0 points; 95% CI 1.7 to 4.3, p<0.001) and more than low use of real CPAP (difference 3.3; 95% CI 1.9 to 4.7, p<0.0001). High use of placebo was superior to low use of placebo (difference 1.5; 95% CI 0.1 to 2.8, p=0.03). Twenty-nine per cent of the effect of high usage of CPAP (4.2 points; 95% CI 3.3 to 5.1) was explained by the expectation of benefit effect associated with high use of placebo (1.2 points ; 95% CI 0.2 to 2.3). CONCLUSIONS: A clinically significant proportion of the effectiveness of high CPAP use in reducing sleepiness is probably caused by patient expectation of benefit.

Residual sleep-disordered breathing during autotitrating continuous positive airway pressure therapy.

Obstructive sleep apnoea (OSA) is often treated with autotitrating continuous positive airway pressure (autoCPAP) devices. Clinical and bench tests of these devices have suggested performance limitations. These studies do not indicate whether this is a failure to detect or a failure to respond to airway obstruction. In this randomised, crossover trial, 34 patients with moderate-to-severe OSA underwent polysomnography on two laboratory visits. The autoCPAP device was randomly set to a fixed subtherapeutic pressure (detection assessment) or autotitrating mode (response assessment). Airflow was measured both from the autoCPAP (autoCPAP flow) and directly from the nasal mask, and recorded on polysomnography. Apnoea/hypopnoea indices (AHIs) measured at the two sites and from the autoCPAP download report were compared. Regarding detection, the AHI measured from the nasal mask showed good agreement with the autoCPAP flow AHI, but agreement was lower with the autoCPAP report AHI. In autotitrating mode, there was significant misclassification of those with and without OSA (AHI ≥ 10 events · h(-1)) on the autoCPAP report. Regarding response, residual OSA (AHI ≥ 10 events · h(-1)) was still evident in 24% of patients during autotitration. In some patients, autoCPAP fails to detect and/or respond to sleep apnoea. Clinicians should consider limitations of each device and use caution when using autoCPAP report statistics to verify effective treatment.

Reducing aerosol dispersion by high flow therapy in COVID-19: High resolution computational fluid dynamics simulations of particle behavior during high velocity nasal insufflation with a simple surgical mask.

Objective: All respiratory care represents some risk of becoming an aerosol-generating procedure (AGP) during COVID-19 patient management. Personal protective equipment (PPE) and environmental control/engineering is advised. High velocity nasal insufflation (HVNI) and high flow nasal cannula (HFNC) deliver high flow oxygen (HFO) therapy, established as a competent means of supporting oxygenation for acute respiratory distress patients, including that precipitated by COVID-19. Although unlikely to present a disproportionate particle dispersal risk, AGP from HFO continues to be a concern. Previously, we published a preliminary model. Here, we present a subsequent highresolution simulation (higher complexity/reliability) to provide a more accurate and precise particle characterization on the effect of surgical masks on patients during HVNI, low-flow oxygen therapy (LFO2), and tidal breathing. Methods: This in silico modeling study of HVNI, LFO2, and tidal breathing presents ANSYS fluent computational fluid dynamics simulations that evaluate the effect of Type I surgical mask use over patient face on particle/droplet behavior. Results: This in silico modeling simulation study of HVNI (40 L min-1) with a simulated surgical mask suggests 88.8% capture of exhaled particulate mass in the mask, compared to 77.4% in LFO2 (6 L min-1) capture, with particle distribution escaping to the room (> 1 m from face) lower for HVNI+Mask versus LFO2+Mask (8.23% vs 17.2%). The overwhelming proportion of particulate escape was associated with mask-fit designed model gaps. Particle dispersion was associated with lower velocity. Conclusions: These simulations suggest employing a surgical mask over the HVNI interface may be useful in reduction of particulate mass distribution associated with AGPs.

Randomised cross-over study of automated oxygen control for preterm infants receiving nasal high flow.

OBJECTIVE: To evaluate a prototype automated controller (IntellO2) of the inspired fraction of oxygen (FiO2) in maintaining a target range of oxygen saturation (SpO2) in preterm babies receiving nasal high flow (HF) via the Vapotherm Precision Flow. DESIGN: Prospective two-centre order-randomised cross-over study. SETTING: Neonatal intensive care units. PATIENTS: Preterm infants receiving HF with FiO2 ≥25%. INTERVENTION: Automated versus manual control of FiO2 to maintain a target SpO2 range of 90%-95% (or 90%-100% if FiO2=21%). MAIN OUTCOME MEASURES: The primary outcome measure was per cent of time spent within target SpO2 range. Secondary outcomes included the overall proportion and durations of SpO2 within specified hyperoxic and hypoxic ranges and the number of in-range episodes per hour. RESULTS: Data were analysed from 30 preterm infants with median (IQR) gestation at birth of 26 (24-27) weeks, study age of 29 (18-53) days and study weight 1080 (959-1443) g. The target SpO2 range was achieved 80% of the time on automated (IntellO2) control (IQR 70%-87%) compared with 49% under manual control (IQR 40%-57%; p<0.0001). There were fewer episodes of SpO2 below 80% lasting at least 60 s under automated control (0 (IQR 0-1.25)) compared with manual control (5 (IQR 2.75-14)). There were no differences in the number of episodes per hour of SpO2 above 98% (4.5 (IQR 1.8-8.5) vs 5.5 (IQR 1.9-14); p=0.572) between the study arms. CONCLUSIONS: The IntellO2 automated oxygen controller maintained patients in the target SpO2 range significantly better than manual adjustments in preterm babies receiving HF. TRIAL REGISTRATION NUMBER: NCT02074774.

Impaired Neurobehavioural Performance in Untreated Obstructive Sleep Apnea Patients Using a Novel Standardised Test Battery.

OBJECTIVE/BACKGROUND: Although polysomnography (PSG) is the gold-standard measure for assessing disease severity in obstructive sleep apnea (OSA), it has limited value in identifying individuals experiencing significant neurobehavioural dysfunction. This study used a brief and novel computerised test battery to examine neurobehavioural function in adults with and without OSA. PATIENTS/METHODS: 204 patients with untreated OSA [age 49.3 (12.5) years; body mass index, [BMI] 33.6 (8.0) kg/m2; Epworth sleepiness scale 12 (4.9)/24; apnea hypopnea index 33.6 (25.8)/h] and 50 non-OSA participants [age 39.2 (14.0) years; BMI 25.8 (4.2) kg/m2, ESS 3.6 (2.3)/24]. All participants completed a computerised neurobehavioural battery during the daytime in the sleep clinic. The OSA group subsequently underwent an overnight PSG. The 30 min test battery assessed cognitive domains of visual spatial scanning and selective attention (Letter Cancellation Test), executive function (Stroop task) and working memory (2- and 3-Back tasks), and a validated sustained attention task (psychomotor vigilance task, PVT). Group differences in performance were compared. Associations between disease severity and performance were examined in the OSA group. RESULTS: After controlling for age, gender and education, OSA patients demonstrated impaired performance on the Stroop-Text, 2 and 3-Back tasks, and the PVT compared with the non-OSA group. OSA patients had worse performance on the LCT with fewer average hits albeit with better accuracy. Some OSA polysomnographic disease severity measures were weakly correlated with performance. CONCLUSIONS: This brief test battery may provide a sensitive, standardised method of assessing daytime dysfunction in OSA.

Assessing sleep disturbance in low back pain: the validity of portable instruments.

Although portable instruments have been used in the assessment of sleep disturbance for patients with low back pain (LBP), the accuracy of the instruments in detecting sleep/wake episodes for this population is unknown. This study investigated the criterion validity of two portable instruments (Armband and Actiwatch) for assessing sleep disturbance in patients with LBP. 50 patients with LBP performed simultaneous overnight sleep recordings in a university sleep laboratory. All 50 participants were assessed by Polysomnography (PSG) and the Armband and a subgroup of 33 participants wore an Actiwatch. Criterion validity was determined by calculating epoch-by-epoch agreement, sensitivity, specificity and prevalence and bias- adjusted kappa (PABAK) for sleep versus wake between each instrument and PSG. The relationship between PSG and the two instruments was assessed using intraclass correlation coefficients (ICC 2, 1). The study participants showed symptoms of sub-threshold insomnia (mean ISI = 13.2, 95% CI = 6.36) and poor sleep quality (mean PSQI = 9.20, 95% CI = 4.27). Observed agreement with PSG was 85% and 88% for the Armband and Actiwatch. Sensitivity was 0.90 for both instruments and specificity was 0.54 and 0.67 and PABAK of 0.69 and 0.77 for the Armband and Actiwatch respectively. The ICC (95%CI) was 0.76 (0.61 to 0.86) and 0.80 (0.46 to 0.92) for total sleep time, 0.52 (0.29 to 0.70) and 0.55 (0.14 to 0.77) for sleep efficiency, 0.64 (0.45 to 0.78) and 0.52 (0.23 to 0.73) for wake after sleep onset and 0.13 (-0.15 to 0.39) and 0.33 (-0.05 to 0.63) for sleep onset latency, for the Armband and Actiwatch, respectively. The findings showed that both instruments have varied criterion validity across the sleep parameters from excellent validity for measures of total sleep time, good validity for measures of sleep efficiency and wake after onset to poor validity for sleep onset latency.

A randomized crossover trial of the effect of a novel method of pressure control (SensAwake) in automatic continuous positive airway pressure therapy to treat sleep disordered breathing.

OBJECTIVES: To study the acute effect of the new SensAwake CPAP modality (reducing pressure on awakenings) on wake after sleep onset (WASO) and other polysomnographic measures in patients with obstructive sleep apnea (OSA). STUDY DESIGN: Randomized crossover trial comparing an automatic continuous positive airway pressure device (AutoCPAP) with and without SensAwake on sleep architecture. CPAP naive patients received each therapy for a single night in the laboratory with at least 1-week washout. Both patients' and technicians' subjective satisfaction was assessed. Pressure data measured and stored by the AutoCPAP device were also analyzed. RESULTS: OSA was controlled adequately by both modes (SensAwake ON apnea hypopnea index ± SD, AHI = 5.3 ± 5.6/h vs. SensAwake OFF = 5.4 ± 5.8, p = 0.9) in the 42 patients who completed the protocol. Mean and 90% pressures were significantly lower with SensAwake (mean ON = 6.9 ± 1.9 vs. OFF = 7.7 ± 2.5 cm H(2)O, p < 0.05; 90% pressure ON = 9.6 ± 2.7 vs. OFF = 10.6 ± 2.7 cm H(2)O, p < 0.02). SensAwake did not improve WASO (ON = 74 ± 54 min vs. OFF = 78 ± 51 min, p = 0.6). There were no differences in other sleep architecture measures or patient satisfaction between the 2 modalities. AutoCPAP-measured AHI closely approximated PSG-derived (ROC AUC = 0.81 [95% CI 0.71-0.92], p = 0.0001). CONCLUSIONS: SensAwake provides similar control of the AHI to the standard AutoCPAP mode but does so at lower mean and 90% pressures. However, no measure of sleep architecture was significantly improved by the SensAwake mode during this initial acute exposure. The internal AutoCPAP AHI detection and calculation was similar to PSG-derived AHI measures. Longer term studies are needed to evaluate any long-term influence of SensAwake on WASO.

The validity of wrist actimetry assessment of sleep with and without sleep apnea.

INTRODUCTION: It is unclear whether actimetry can be reliably used to measure sleep in severe obstructive sleep apnea (OSA) patients. We compared polysomnography (PSG) with actimetric assessment of sleep on an epoch-by-epoch basis in subjects with and without OSA. METHODS: 21 subjects were recorded with simultaneous overnight standard PSG and actimetry. RESULTS: 10 subjects with apnea-hypopnea index (AHI) <10 (6.5 +/- 2.8/h) were classified as non-OSA subjects and 11 subjects with AHI >10 (42.0 +/- 27.3/h) were classified as OSA subjects. Overall sensitivity and specificity for actimetry to identify sleep was 94.6% and 40.6%, respectively, with an overall mean sleep/wake simple agreement of 84.6% and kappa of 0.38. There was no difference in agreement between non-OSA and OSA subjects (simple agreement: 83% vs. 86%, p = 0.73; kappa: 0.35 vs. 0.40, p = 0.73). The kappa agreement did not correlate with PSG arousal index (r = -0.21, p = 0.36) but declined with reduced sleep efficiency (r = 0.66, p = 0.001). There was no systematic difference (all p > 0.40) between actimetry and PSG in sleep latency, total sleep time and sleep efficiency, although correlations between the measurements using the two techniques were generally poor. However, while actimetry systematically underestimated wake after sleep onset (WASO) (35.5 +/- 18.8 vs. 59.4 +/- 35.1, p = 0.009), fragmentation index measured by actimetry only underestimated arousal index measured by PSG in OSA patients (23.9 +/- 17.8 vs. 33.1 +/- 18.5, p = 0.04). CONCLUSIONS: Contrary to prior reports, epoch-by-epoch comparison of sleep/wake scoring showed similar fair agreement between actimetry and PSG in subjects with or without OSA. Fragmentation index by actimetry may underestimate arousals caused by respiratory events and offer misleading results in severe OSA patients.