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.

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.

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.

Eye Gaze in Autism Spectrum Disorder: A Review of Neural Evidence for the Eye Avoidance Hypothesis.

Reduced eye contact early in life may play a role in the developmental pathways that culminate in a diagnosis of autism spectrum disorder. However, there are contradictory theories regarding the neural mechanisms involved. According to the amygdala theory of autism, reduced eye contact results from a hypoactive amygdala that fails to flag eyes as salient. However, the eye avoidance hypothesis proposes the opposite-that amygdala hyperactivity causes eye avoidance. This review evaluated studies that measured the relationship between eye gaze and activity in the 'social brain' when viewing facial stimuli. Of the reviewed studies, eight of eleven supported the eye avoidance hypothesis. These results suggest eye avoidance may be used to reduce amygdala-related hyperarousal among people on the autism spectrum.

Consecutive Nights of Moderate Sleep Loss Does Not Affect Mood in Healthy Young Males.

Sleep loss causes mood disturbance in non-clinical populations under severe conditions, i.e., two days/nights of sleep deprivation or a week of sleep restriction with 4-5 h in bed each night. However, the effects of more-common types of sleep loss on mood disturbance are not yet known. Therefore, the aim of this study was to examine mood disturbance in healthy adults over a week with nightly time in bed controlled at 5, 6, 7, 8 or 9 h. Participants (n = 115) spent nine nights in the laboratory and were given either 5, 6, 7, 8 or 9 h in bed over seven consecutive nights. Mood was assessed daily using the Profile of Mood States (POMS-2). Mixed-linear effects models examined the effect of time in bed on total mood disturbance and subscales of anger-hostility, confusion-bewilderment, depression-dejection, fatigue-inertia, tension-anxiety, vigour-activity and friendliness. There was no effect of time in bed on total mood disturbance (F(4, 110.42) = 1.31, p = 0.271) or any of the subscales except fatigue-inertia. Fatigue-inertia was higher in the 5 h compared with the 9 h time in bed condition (p = 0.012, d = 0.75). Consecutive nights of moderate sleep loss (i.e., 5-7 h) does not affect mood but does increase fatigue in healthy males.