Sleep duration and chronic fatigue are differently associated with the dietary profile of shift workers

Shift work has been associated with dietary changes. This study examined factors associated with the dietary profiles of shift workers from several industries (n = 118, 57 male; age = 43.4 ± 9.9 years) employed on permanent mornings, nights, or rotating 8-h or 12-h shifts. The dietary profile was assessed using a Food Frequency Questionnaire. Shift-related (e.g., sleep duration and fatigue), work-related (e.g., industry), and demographic factors (e.g., BMI) were measured using a modified version of the Standard Shift work Index. Mean daily energy intake was 8628 ± 3161 kJ. As a percentage of daily energy intake, all workers reported lower than recommended levels of carbohydrate (CHO, 45%-65%). Protein was within recommended levels (15%-25%). Permanent night workers were the only group to report higher than recommended fat intake (20%-35%). However, all workers reported higher than recommended levels of saturated fat (>10%) with those on permanent nights reporting significantly higher levels than other groups (Mean = 15.5% ± 3.1%, p < 0.05). Shorter sleep durations and decreased fatigue were associated with higher CHO intake (p ≤ 0.05) whereas increased fatigue and longer sleep durations were associated with higher intake of fat (p ≤ 0.05). Findings demonstrate sleep duration, fatigue, and shift schedule are associated with the dietary profile of shift workers

Timing of Australian flight attendant food and beverage while crewing: A preliminary investigation

Flight attendants experience circadian misalignment and disrupted sleep and eating patterns. This survey study examined working time, sleep, and eating frequency in a sample (n=21, 4 males, 17 females) of Australian flight attendants (mean age=41.8 yr, SD=12.0 yr, mean BMI=23.8 kg/m2, SD=4.1 kg/m2). Respondents indicated frequencies of snack, meal, and caffeine consumption during their last shift. Reported sleep duration on workdays (mean=4.6 h, SD=1.9 h) was significantly lower than on days off (M=7.2 h, SD=1.2 h, p<0.001), and significantly lower than perceived sleep need (M=8.1 h, SD=0.8 h, p<0.001). Food intake was distributed throughout shifts and across the 24 h period, with eating patterns incongruent with biological eating periods. Time available, food available, and work breaks were the most endorsed reasons for food consumption. Caffeine use and reports of gastrointestinal disturbance were common. Working time disrupts sleep and temporal eating patterns in flight attendants and further research into nutritional and dietary-related countermeasures may be beneficial to improving worker health and reducing circadian disruption

Altering meal timing to improve cognitive performance during simulated nightshifts

Altering meal timing could improve cognition, alertness, and thus safety during the nightshift. This study investigated the differential impact of consuming a meal, snack, or not eating during the nightshift on cognitive performance (ANZCTR12615001107516). 39 healthy participants (59% male, age mean±SD: 24.5 ± 5.0y) completed a 7-day laboratory study and underwent four simulated nightshifts. Participants were randomly allocated to: Meal at Night (MN; n= 12), Snack at Night (SN; n = 13) or No Eating at Night (NE; n = 14). At 00:30 h, MN consumed a meal and SN consumed a snack (30% and 10% of 24 h energy intake respectively). NE did not eat during the nightshift. Macronutrient intake was constant across conditions. At 20:00 h, 22:30 h, 01:30 h, and 04:00 h, participants completed the 3-min Psychomotor Vigilance Task (PVT-B), 40-min driving simulator, post-drive PVT-B, subjective sleepiness scale, 2-choice Reaction Time task, and Running Memory task. Objective sleep was recorded for each of the day sleeps using Actigraphy and for the third day sleep, Polysomnography was used. Performance was compared between conditions using mixed model analyses. Significant two-way interactions were found. At 04:00 h, SN displayed increased time spent in the safe zone (p  355 ms; p < .001), and reaction time on the 2-choice reaction time task (p < .001) and running memory task (p < .001) compared to MN and NE. MN reported greater subjective sleepiness at 04:00 h (p < .001) compared to SN and NE. There was no difference in objective sleep between eating conditions. Eating a large meal during the nightshift impairs cognitive performance and sleepiness above the effects of time of night alone. For improved performance, shiftworkers should opt for a snack at night

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