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This study examined the impact of eating during simulated night shift on performance and subjective complaints. Subjects were randomized to eating at night (n=5; 23.2 ± 5.5 y) or not eating at night (n=5; 26.2 ± 6.4 y). All participants were given one sleep opportunity of 8 h (22:00 h-06:00 h) before transitioning to the night shift protocol. During the four days of simulated night shift participants were awake from 16:00 h-10:00 h with a daytime sleep of 6 h (10:00 h-16:00 h). In the simulated night shift protocol, meals were provided at ≈0700 h, 1900 h and 0130 h (eating at night); or ≈0700 h, 0930 h, 1410 h and 1900 h (not eating at night). Subjects completed sleepiness, hunger and gastric complaint scales, a Digit Symbol Substitution Task and a 10-min Psychomotor Vigilance Task. Increased sleepiness and performance impairment was evident in both conditions at 0400 h (p < 0.05). Performance impairment at 0400 h was exacerbated when eating at night. Not eating at night was associated with elevated hunger and a small but significant elevation in stomach upset across the night (p < 0.026). Eating at night was associated with elevated bloating on night one, which decreased across the protocol. Restricting food intake may limit performance impairments at night. Dietary recommendations to improve night-shift performance must also consider worker comfort. Refereed/Peer-reviewed

Shiftworkers have impaired performance when driving at night and they also alter their eating patterns during nightshifts. However, it is unknown whether driving at night is influenced by the timing of eating. This study aims to explore the effects of timing of eating on simulated driving performance across four simulated nightshifts. Healthy, non-shiftworking males aged 18–35 years (n = 10) were allocated to either an eating at night (n = 5) or no eating at night (n = 5) condition. During the simulated nightshifts at 1730, 2030 and 0300 h, participants performed a 40-min driving simulation, 3-min Psychomotor Vigilance Task (PVT-B), and recorded their ratings of sleepiness on a subjective scale. Participants had a 6-h sleep opportunity during the day (1000–1600 h). Total 24-h food intake was consistent across groups; however, those in the eating at night condition ate a large meal (30% of 24-h intake) during the nightshift at 0130 h. It was found that participants in both conditions experienced increased sleepiness and PVT-B impairments at 0300 h compared to 1730 and 2030 h (p < 0.001). Further, at 0300 h, those in the eating condition displayed a significant decrease in time spent in the safe zone (p < 0.05; percentage of time within 10 km/h of the speed limit and 0.8 m of the centre of the lane) and significant increases in speed variability (p < 0.001), subjective sleepiness (p < 0.01) and number of crashes (p < 0.01) compared to those in the no eating condition. Results suggest that, for optimal performance, shiftworkers should consider restricting food intake during the night. Refereed/Peer-reviewed

Sleep loss is one of the most common causes of accidents and errors in operational environments. Currently, no single method satisfies all of the requisite criteria of an effective system for assessing the risk of injury prior to safety being compromised. Research has concentrated towards the development of a biomarker for individualized assessment of sleepiness-related deficits in neurobehavioral alertness, with salivary alpha-amylase (sAA) recently reported as a potential biomarker during acute total sleep deprivation. The present study extends on previous research by investigating the association between sAA and neurobehavioral alertness during simulated night-shift work, during individuals are required to work at night when biological processes are strongly promoting sleep and sleep during the day when endogenous processes are promoting wakefulness. In a laboratory-controlled environment, 10 healthy non-shift working males aged 24.7 ± 5.3 years (mean ± SD) underwent four consecutive nights of simulated night-shift work. Between 17:30–04:30 h participants provided saliva samples and completed a 3 min psychomotor vigilance test (PVT-B), 40 min simulated driving task, and 3 min digit symbol substitution test (DSST). Higher sAA levels were associated with faster response speed on the PVT-B, reduced lane variability on the simulated driving task, and improved information processing speed on the DSST during the first night-shift. There were no associations between sAA levels and performance outcomes during subsequent night-shifts. Findings indicate that the usability of sAA to assess the risk of neurobehavioral deficits during shift-work operations is limited. However, the robust circadian rhythm exhibited by sAA during the protocol of circadian misalignment suggests that sAA could serve as a potential circadian marker. Refereed/Peer-reviewed

This paper presents the 60-s time-resolution segment from our 50-h total sleep deprivation (TSD) dataset (Aidman et al., 2018) [1] that captures minute-by-minute dynamics of driving performance (lane keeping and speed variability) along with objective, oculography-derived drowsiness estimates synchronised to the same 1-min driving epochs. Eleven participants (5 females, aged 18–28) were randomised into caffeine (administered in four 200 mg doses via chewing gum in the early morning hours) or placebo groups. Every three hours they performed a 40 min simulated drive in a medium fidelity driving simulator, while their drowsiness was continuously measured with a spectacle frame-mounted infra-red alertness monitoring system. The dataset covers 15 driving periods of 40 min each, and thus contains over 600 data points of paired data per participant. The 1-min time resolution enables detailed time-series analyses of both time-since-wake and time-on-task performance dynamics and associated drowsiness levels. It also enables direct examination of the relationships between drowsiness and task performance measures. The question of how these relationships might change under various intervention conditions (caffeine in our case) seems worth further investigation Refereed/Peer-reviewed

Aim: The study examined the moderating effect of repeat-dose, chewing gum-administered caffeine on the well-established relationship between drowsiness and driving performance, under the conditions of accumulating sleep loss. Method: 50-h sleep deprivation protocol with a double-blind, placebo-controlled design. Eleven volunteers (6 male), aged 18-28 years were screened for pre-existing medical conditions (including sleep disturbances), tobacco and recreational drug use, recent time-zone travel and shift-work. They were randomly allocated to placebo or caffeine group and administered 4 oral doses of either caffeinated gum pellets (200 mg/dose) or non-caffeinated placebo gum every two hours (01:00, 03:00, 05:00, 07:00) on the first and second nights of the protocol. Participants were constantly monitored and remained awake for 50 h, while performing 15 identical, evenly-spaced 40-min monotonous driving tasks in a medium-fidelity moving-base driving simulator. Their drowsiness was monitored with a spectacle frame-mounted infra-red sensor registering ocular parameters and converting them into a Johns Drowsiness Scale (JDS) score every 60 s. Lane keeping and speed variability measures were used to assess driving performance. Results: Driving performance declined and drowsiness increased from the first simulated drive to the last. When driving performance was examined in one-minute epochs synchronised with JDS scores, both lateral lane positioning and speed variability were found to be associated with drowsiness. The strength of this association was significantly weaker in the caffeine group, compared to placebo. Placebo group replicated the linear relationship between drowsiness and driving errors across the full range of JDS scores. This pattern was significantly weaker under the caffeine condition, and was even reversed at the upper range of JDS, with higher JDS scores not resulting in further degradation of driving performance. This dissociation between drowsiness and driving errors persisted across the 24-h cycle under the caffeine condition, despite caffeine being administered only during early morning hours. Conclusion: Strategically timed, repeat 200 mg doses of caffeine administered via chewing gum can mitigate fatigue-induced impairments in driving performance by not only reducing drowsiness but also by significantly weakening its impact on driving errors. This dual effect of sustained drowsiness reduction and the dissociation between drowsiness levels and driving errors seems worth further investigation as it might offer an effective emergency countermeasure against driver drowsiness and its subsequent conversion into potentially fatal driving errors. Refereed/Peer-reviewed

Technology-supported methods for sleep recording are becoming increasingly affordable. Sleep history feedback may help with fatigue-related decision making - Should I drive? Am I fit for work? This study examines a "sleep tank" model (SleepTank ™ ), which is analogous to the fuel tank in a car, refilled by sleep, and depleted during wake. Required inputs are sleep period time and sleep efficiency (provided by many consumer-grade actigraphs). Outputs include suggested hours remaining to "get sleep" and percentage remaining in tank (Tank%). Initial proof of co ncept analyses were conducted using data from a laboratory-based simulated nightshift study. Ten, healthy males (18-35y) undertook an 8h baseline sleep opportunity and daytime performance testing (BL), followed by four simulated nightshifts (2000 h-0600 h), with daytime sleep opportunities (1000 h-1600 h), then an 8 h night-time sleep opportunity to return to daytime schedule (RTDS), followed by daytime performance testing. Psychomotor Vigilance Task (PVT) and Karolinska Sleepiness Scale were performed at 1200 h on BL and RTDS, and at 1830 h, 2130 h 0000 h and 0400 h each nightshift. A 40-minute York Driving Simulation was performed at 1730 h, 2030 h and 0300 h on each nightshift. Model outputs were calculated using sleep period timing and sleep efficiency (from polysomnography) for each participant. Tank% was a significant predictor of PVT lapses (p < 0.001), and KSS (p < 0.001), such that every 5% reduction resulted in an increase of two lapses, or one point on the KSS. Tank% was also a significant predictor of %time in the Safe Zone from the driving simulator (p = 0.001), such that every 1% increase in the tank resulted in a 0.75% increase in time spent in the Safe Zone. Initial examination of the correspondence between model predictions and performance and sleepiness measures indicated relatively good predictive value. Results provide tentative evidence that this "sleep tank" model may be an informative tool to aid in individual decision-making based on sleep history. Refereed/Peer-reviewed

Shift work is a risk factor for chronic diseases such as Type 2 diabetes. Food choice may play a role, however simply eating at night when the body is primed for sleep may have implications for health. This study examined the impact of consuming a big versus small snack at night on glucose metabolism. N = 31 healthy subjects (21–35 y; 18 F) participated in a simulated nightshift laboratory study that included one baseline night of sleep (22:00 h-07:00 h) and one night awake with allocation to either a big snack (2100 kJ) or small snack (840 kJ) group. The snack was consumed between 00:00–00:30 h and consisted of low fat milk, a sandwich, chips and fruit (big snack) or half sandwich and fruit (small snack). Subjects ate an identical mixed meal breakfast (2100 kJ) at 08:30 h after one full night of sleep and a simulated nightshift. Interstitial glucose was measured continuously during the entire study using Medtronic Continual Glucose Monitors. Only subjects with identical breakfast consumption and complete datasets were analysed (N = 20). Glucose data were averaged into 5-minute bins and area under the curve (AUC) was calculated for 90 min post-breakfast. Pre-breakfast, glucose levels were not significantly different between Day1 and Day2, nor were they different between snack groups (p > 0.05). A snack group by day interaction effect was found (F1,16 = 5.36, p = 0.034) and post-hocs revealed that in the big snack group, AUC response to breakfast was significantly higher following nightshift (Day2) compared to Day1 (p = 0.001). This translated to a 20.8% (SEM 5.6) increase. AUC was not significantly different between days in the small snack group. Consuming a big snack at 00:00 h impaired the glucose response to breakfast at 08:30 h, compared to a smaller snack. Further research in this area will inform dietary advice for shift workers, which could include recommendations on how much to eat as well as content., Highlights • Eating at night when the body is primed for sleep may have implications for health of shift workers. • This study examined the impact of consuming a big versus small snack at night on glucose metabolism. • Consuming a big snack at midnight impaired the glucose response to breakfast at 08:30 h, compared to consuming a smaller snack at midnight.

During sleep deprivation, neurobehavioral functions requiring sustained levels of attention and alertness are significantly impaired. Discrepancies between subjective measures of sleepiness and objective performance during sustained operations have led to interest in physiological monitoring of operator performance. Alertness, vigilance, and arousal are modulated by the wake-promoting actions of the central noradrenergic system. Salivary alpha-amylase (sAA) has been proposed as a sensitive peripheral measure of noradrenergic activity, but limited research has investigated the relationship between sAA and performance. In a laboratory-controlled environment, we investigated the relationship between sAA levels, subjective sleepiness, and performance during two days (50 h) of total sleep deprivation. Beginning at 09:00, twelve healthy participants (5 females) aged 22.5 ± 2.5 years (mean ± SD) provided saliva samples, recorded ratings of subjective sleepiness, completed a brief 3-min psychomotor vigilance task (PVT-B) and performed a 40-min simulated driving task, at regular 3 h intervals during wakefulness. Ratings of subjective sleepiness exhibited a constant linear increase (p

Purpose: Residential support workers (RSW) engage in overnight "sleepover" shifts as well as more traditional "standup" night shifts. While research has investigated the consequences of night and on-call work for sleep in other industries, the sleep of RSW has not been evaluated. Method: In a single-provider case study, six employees completed the Pittsburgh Sleep Quality Index (PSQI), the Depression Anxiety Stress Scale (DASS), a 2-week sleep diary, and a 30 min interview, and four also completed the Shirom-Melamed Burnout Measure (SMBM). Results: Participants reported sleep of poor quality, low-mild DASS scores, and evidence of SMBM scores that were elevated relative to norms. Sleep was significantly lower (p < 0.01) following "standup" shifts (mean = 4.1, SD = 1.8 h) and during "sleepover" shifts (mean = 5.6, SD = 2.0 h) compared to non-shift nights (mean = 7.3, SD = 2.3 h). Interviews suggested that sleep fluctuates with level of patient care, colleague assistance, stress, and the quality of the sleeping environment (including bed comfort, light, noise and perceived safety). Conclusions: Findings suggest that this group have sleep that is insufficient and of poor quality and that they may be at risk of burnout. Consideration of ways to optimise sleeping conditions at work (e.g. through noise or stress reduction) would be beneficial. Research in this area has the potential to facilitate improvements in health and safety in this growing industry.

Aims/Hypothesis: The Andres clamp technique, which requires accurate and timely determination of glucose, utilizes the Beckman or Yellow Springs Instruments (YSI) glucose analyzers. Both instruments require maintenance, a dedicated operator, preparation of a plasma sample, and a duplicate measurement that takes ≥2 minutes. The Nova StatStrip glucose meter was evaluated for accuracy, reliability, and near-real-time availability of glucose. Methods: Blood samples from 24 patients who underwent 6-hour clamp studies and 12 patients who had a standardized meal tolerance test (SMT) were measured. Specimens were analyzed simultaneously and immediately upon collection by Beckman, YSI, and Nova. Results: Of 1004 data pairs for the Nova device versus Beckman, the Nova data points ranged from 32 to 444, while Beckman ranged from 42 to 412. The coefficient for the slope of Beckman versus Nova was 1.009 ( r = 0.978). Using error grid analysis, the number and percentage of values for Nova were 976 (97.2%) in the A zone and 28 (2.8%) in the B zone. Of 399 data pairs for the Nova device versus YSI, the Nova data points ranged from 46 to 255, whereas YSI ranged from 47 to 231. The coefficient for the slope of YSI versus Nova was 1.023 ( r = 0.989). All Nova readings fell in the A zone. Time required for final reading, in duplicate, was 15 seconds for Nova and 120–180 seconds for Beckman and YSI. Conclusions: The simplicity of Nova and its reliability, accuracy, and speed make it an acceptable replacement device for Beckman and YSI in the conduct of clamps, especially when perturbations require rapid glucose determination.

The current study investigated the effects of repeated caffeine administration on performance and subjective reports of sleepiness and fatigue during 50 h extended wakefulness. Twenty-four, non-smokers aged 22.5 ± 2.9 y (mean ± SD) remained awake for two nights (50 h) in a controlled laboratory environment. During this period, 200 mg of caffeine or placebo gum was administered at 01:00, 03:00, 05:00 and 07:00 on both nights (total of 800 mg/night). Neurobehavioral performance and subjective reports were assessed throughout the wake period. Caffeine improved performance compared to placebo, but did not affect overall ratings of subjective sleepiness and fatigue. Performance and sleepiness worsened with increasing time awake for both conditions. However, caffeine slowed performance impairments such that after 50 h of wakefulness performance was better following caffeine administration compared to placebo. Caffeine also slowed the increase in subjective sleepiness and performance ratings, but only during the first night of wakefulness. After two nights of sleep deprivation, there was no difference in sleepiness ratings between the two conditions. These results demonstrate that strategic administration of caffeine effectively mitigates performance impairments associated with 50 h wakefulness but does not improve overall subjective assessments of sleepiness, fatigue and performance. Results indicate that while performance impairment is alleviated, individuals may continue to report feelings of sleepiness. Individuals who use caffeine as a countermeasure in sustained operations may feel as though caffeine is not effective despite impairments in objective performance being largely mitigated.