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High activity of upper airway dilator muscles is thought to be critical in preventing sleep-related upper airway collapse. To date, most of the research regarding upper airway dilator muscles has focused on the genioglossus muscle, which protrudes the tongue and opens the retroglossal airway. However, collapse commonly occurs in the retropalatal region. We, therefore, aimed to examine the motor control of the palatoglossus muscle as well as investigate breathing route-related changes in genioglossus and palatoglossus motor units. Single motor unit recordings of the genioglossus and palatoglossus were made simultaneously in healthy individuals during wakefulness while breathing through the nose with the mouth closed (NMC), nose with mouth open (NMO), or orally (OMO). The palatoglossus was found to have all five motor unit firing patterns that have been observed in other upper airway dilator muscles, but during nasal breathing had a higher proportion of tonically active but inspiratory modulated motor units as compared with the genioglossus (67% vs. 30%). When still breathing nasally but with the mouth open, the units with an expiratory firing pattern in genioglossus, and all firing patterns in palatoglossus, increased their firing rates compared with nasal breathing with the mouth closed [genioglossus (GG): 17.8 ± 4.9 vs. 23.1 ± 4.8 Hz, palatoglossus (PG): 17.0 ± 4.0 vs. 19.3 ± 4.0 Hz]. Finally, oral breathing resulted in dramatic reductions in the number of palatoglossal motor units that were firing (35 units vs. 92 during nasal breathing). Palatoglossal activity may contribute importantly to airway collapsibility and may provide an alternate pathway for preventing sleep-related airway collapse. NEW & NOTEWORTHY: The firing patterns of motor units in the palatoglossus have until now not been investigated, and how they and motor units in the genioglossus change with breathing route alteration was not known. This study has shown that the palatoglossus contains motor units with all the firing patterns observed in the genioglossus but in different proportions. Furthermore, breathing route changes alter units with different firing patterns differentially in the two muscles. [ABSTRACT FROM AUTHOR]

Upper airway muscle activity is reportedly elevated during slow-wave sleep (SWS) when compared with lighter sleep stages. To uncover the possible mechanisms underlying this elevation, we explored the correlation between different indices of central and reflex inspiratory drive, such as the changes in airway pressure and end-expiratory CO2 and the changes in the genioglossus (GG) and tensor palatini (TP) muscle activity accompanying transitions from the lighter N2 to the deeper N3 stage of non-rapid eye movement (NREM) sleep in healthy young adult men. Forty-six GG and 38 TP continuous electromyographic recordings were obtained from 16 men [age: 20 ± 2.5 (SD) yr; body mass index: 22.5 ± 1.8 kg/m2] during 32 transitions from NREM stages N2 to N3. GG but not TP activity increased following transition into N3 sleep, and the increase was positively correlated with more negative airway pressure, increased end-tidal CO2, increased peak inspiratory flow, and increased minute ventilation. None of these correlations was statistically significant for TP. Complementary GG and TP single motor unit analysis revealed a mild recruitment of GG units and derecruitment of TP units during the N2 to N3 transitions. These findings suggest that, in healthy individuals, the increased GG activity during SWS is driven primarily by reflex stimulation of airway mechanoreceptors and central chemoreceptors.NEW & NOTEWORTHY The characteristic increase in the activity of the upper airway dilator muscle genioglossus during slow-wave sleep (SWS) in young healthy individuals was found to be related to increased stimulation of airway mechanoreceptors and central chemoreceptors. No evidence was found for the presence of a central SWS-specific drive stimulating genioglossus activity in young healthy individuals. However, it remains to be determined whether a central drive exists in obstructive sleep apnea patients.

Introduction: Driver drowsiness detection technology that assesses eye blinks is increasingly being used as a safety intervention in the transport industry. It is unclear how alcohol consumption to common legal driving limits impacts upon this technology. The aim of the study was to assess the impact of a blood alcohol content (BAC) of 0.05% and of 0.08% on drowsiness detection technology during simulated driving. Methods: Participants completed a 60‐min driving simulation and sleepiness questionnaire under three conditions: 1–0.00% BAC, 2–0.05% BAC and 3–0.08% BAC. During the driving simulation task participants wore a commercial eye blink drowsiness detection technology (Optalert) with the drowsiness alarms silenced. Results: Twelve participants (3 female) completed all alcohol conditions. Relative to baseline, all eye blink parameters were affected at 0.08% BAC (all p < 0.05), whereas 0.05% BAC only affected the composite eye blink drowsiness measure (the Johns Drowsiness Scale). Conclusions: Alcohol consumption to 0.08% BAC impaired eye blink measures to a level that would be considered a moderate drowsiness risk. Therefore, employers should be aware that drowsiness alerts from these technologies may increase after alcohol consumption. [ABSTRACT FROM AUTHOR]

Summary: Drowsy driving is a major cause of fatal and serious injury motor vehicle accidents. The inability objectively to assess drowsiness has hindered the assessment of fitness to drive and the development of drowsy driving regulations. This study evaluated whether spontaneous eye blink parameters measured briefly pre‐ and post‐drive could be used to detect drowsy driving impairment. Twelve healthy participants (6 female) drove an instrumented vehicle for 2 h on a closed‐loop track during a rested (8–10 h awake) and an extended wake condition (32–34 h awake). Pre‐ and post‐drive, the participants completed a 5 min eye blink task, a psychomotor vigilance task (PVT), and the Karolinska sleepiness scale (KSS). Whole drive impairment was defined as >3.5 lane departures per hour. Severe end of drive impairment was defined as ≥2 lane departures in the last 15 min. The pre‐drive % of time with eyes closed best predicted the whole drive impairment (area under the curve [AUC] 0.87). KSS had similar prediction ability (AUC 0.85), while PVT reaction time (AUC 0.72) was less accurate. The composite eye blink parameter, the Johns drowsiness scale was the best retrospective detector of severe end of drive impairment (AUC 0.99). The PVT reaction time (AUC 0.92) and the KSS (AUC 0.93) were less accurate. Eye blink parameters detected drowsy driving impairment with an accuracy that was similar to, or marginally better than, PVT and KSS. As eye blink measures are simple to measure, are objective and have high accuracy, they present an ideal option for the assessment of fitness for duty and roadside drowsiness. [ABSTRACT FROM AUTHOR]

Summary: This study aimed to examine the impact of break duration between consecutive shifts, time of break onset, and prior shift duration on total sleep time (TST) between shifts in heavy vehicle drivers (HVDs), and to assess the interaction between break duration and time of break onset. The sleep (actigraphy and sleep diaries) and work shifts (work diaries) of 27 HVDs were monitored during their usual work schedule for up to 9 weeks. Differences in TST between consecutive shifts and days off were assessed. Linear mixed models (followed by pairwise comparisons) assessed whether break duration, prior shift duration, time of break onset, and the interaction between break duration and break onset were related to TST between shifts. Investigators found TST between consecutive shifts (mean [SD] 6.38 [1.38] h) was significantly less than on days off (mean [SD] 7.63 [1.93] h; p < 0.001). Breaks starting between 12:01 and 8:00 a.m. led to shorter sleep (p < 0.05) compared to breaks starting between 4:01 and 8:00 p.m. Break durations up to 7, 9, and 11 h (Australian and European minimum break durations) resulted in a mean (SD) of 4.76 (1.06), 5.66 (0.77), and 6.41 (1.06) h of sleep, respectively. The impact of shift duration prior to the break and the interaction between break duration and time of break were not significant. HVDs' sleep between workdays is influenced independently by break duration and time of break onset. This naturalistic study provides evidence that current break regulations prevent sufficient sleep duration in this industry. Work regulations should evaluate appropriate break durations and break onset times to allow longer sleep opportunities for HVDs. [ABSTRACT FROM AUTHOR]