It’s not just what you eat but when: The impact of eating a meal during simulated shift work on driving performance

Published online: 13 Oct 2016.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.Charlotte C. Gupta, Jill Dorrian, Crystal L. Grant, Maja Pajcin, Alison M. Coates, David J. Kennaway, Gary A. Wittert, Leonie K. Heilbronn, Chris B. Della Vedova, and Siobhan Bank

How much is left in your "sleep tank"? Proof of concept for a simple model for sleep history feedback

Available online 07 May 2018Technology-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 concept 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.Jillian Dorrian, Steven Hursh, Lauren Waggoner, Crystal Grant, Maja Pajcin, Charlotte Gupta, Alison Coates, David Kennaway, Gary Wittert, Leonie Heilbronn, Chris Della Vedova, Siobhan Bank

Salivary levels of alpha-amylase are associated with neurobehavioral alertness during extended wakefulness, but not simulated night-shift work

Gupta, CC ORCiD: 0000-0003-2436-3327Sleep 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

The impact of meal timing on performance, sleepiness, gastric upset, and hunger during simulated night shift

Published online in J-STAGE July 25, 2017This 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.Crystal Leigh Grant, Jillian Dorrian, Alison Maree Coates, Maja Pajcin, David John Kennaway, Gary Allen Wittert, Leonie Kaye Heilbronn, Chris Della Vedova, Charlotte Cecilia Gupta, Siobhan Bank

Salivary levels of alpha-amylase are associated with neurobehavioral alertness during extended wakefulness, but not simulated night-shift work

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

Synchronized drowsiness monitoring and simulated driving performance data under 50-hr sleep deprivation: A double-blind placebo-controlled caffeine intervention

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

How much is left in your “sleep tank”? Proof of concept for a simple model for sleep history feedback

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 concept 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

Timing of food intake during simulated night shift impacts glucose metabolism: A controlled study

Eating during the night may increase the risk for obesity and type 2 diabetes in shift workers. This study examined the impact of either eating or not eating a meal at night on glucose metabolism. Participants underwent four nights of simulated night work (SW1–4, 16:00–10:00 h, <50 lux) with a daytime sleep opportunity each day (10:00–16:00 h, <3 lux). Healthy males were assigned to an eating at night (NE; n = 4, meals; 07:00, 19:00 and 01:30 h) or not eating at night (NEN; n = 7, meals; 07:00 h, 09:30, 16:10 and 19:00 h) condition. Meal tolerance tests were conducted post breakfast on pre-night shift (PRE), SW4 and following return to day shift (RTDS), and glucose and insulin area under the curve (AUC) were calculated. Mixed-effects ANOVAs were used with fixed effects of condition and day, and their interactions, and a random effect of subject identifier on the intercept. Fasting glucose and insulin were not altered by day or condition. There were significant effects of day and condition × day (both p < 0.001) for glucose AUC, with increased glucose AUC observed solely in the NE condition from PRE to SW4 (p = 0.05) and PRE to RTDS (p < 0.001). There was also a significant effect of day (p = 0.007) but not condition × day (p = 0.825) for insulin AUC, with increased insulin from PRE to RTDS in both eating at night (p = 0.040) and not eating at night (p = 0.006) conditions. Results in this small, healthy sample suggest that not eating at night may limit the metabolic consequences of simulated night work. Further study is needed to explore whether matching food intake to the biological clock could reduce the burden of type 2 diabetes in shift workers. © 2017 Taylor & Francis Group, LLC

Timing of food intake during simulated night shift impacts glucose metabolism: A controlled study

Eating during the night may increase the risk for obesity and type 2 diabetes in shift workers. This study examined the impact of either eating or not eating a meal at night on glucose metabolism. Participants underwent four nights of simulated night work (SW1–4, 16:00–10:00 h, <50 lux) with a daytime sleep opportunity each day (10:00–16:00 h, <3 lux). Healthy males were assigned to an eating at night (NE; n = 4, meals; 07:00, 19:00 and 01:30 h) or not eating at night (NEN; n = 7, meals; 07:00 h, 09:30, 16:10 and 19:00 h) condition. Meal tolerance tests were conducted post breakfast on pre-night shift (PRE), SW4 and following return to day shift (RTDS), and glucose and insulin area under the curve (AUC) were calculated. Mixed-effects ANOVAs were used with fixed effects of condition and day, and their interactions, and a random effect of subject identifier on the intercept. Fasting glucose and insulin were not altered by day or condition. There were significant effects of day and condition × day (both p < 0.001) for glucose AUC, with increased glucose AUC observed solely in the NE condition from PRE to SW4 (p = 0.05) and PRE to RTDS (p < 0.001). There was also a significant effect of day (p = 0.007) but not condition × day (p = 0.825) for insulin AUC, with increased insulin from PRE to RTDS in both eating at night (p = 0.040) and not eating at night (p = 0.006) conditions. Results in this small, healthy sample suggest that not eating at night may limit the metabolic consequences of simulated night work. Further study is needed to explore whether matching food intake to the biological clock could reduce the burden of type 2 diabetes in shift workers. © 2017 Taylor & Francis Group, LLC