24^th international symposium on shiftwork and working time: innovations in research and practice improving shiftworker health & safety

Introduction

In September 2019, a group of 189 shiftwork and working time professionals convened in Coeur d’Alene, Idaho, USA to participate in the 24^th International Symposium on Shiftwork and Working Time (“Shiftwork2019”). The participants represented 19 countries and included 63 early career researchers. The program was developed by an International Scientific Committee comprised of 14 individuals from 11 countries across 6 continents. The 5-day program included three keynote lectures from esteemed researchers, Drs. Anne Helene Garde, Michael Belzer, and John Axelsson, as well as 8 symposia and eight oral sessions for a total of over 70 oral presentations, and over 100 posters. The meeting, which is designed to foster a sense of community and provide ample networking opportunities, included special evening events following the scientific program each day, including a Welcome Reception, an Early Career Researchers Event, and a Trivia Night. Many conference participants also took part in the satellite events held before and after Shiftwork 2019: Fatigue Risk Management Industry Day, hosted by Washington State University, and the Working Hours, Sleep, & Fatigue Forum, hosted by the National Institute for Occupational Safety and Health (NIOSH). Shiftwork2019 celebrated the 50^th anniversary of the International Symposium on Shiftwork & Working Time, which was first held in Oslo, Norway in 1969. It has since been held approximately every 2 years in locations all around the world. A special 50^th anniversary symposium featured Drs. Stephen Popkin, Frida Fischer, Kazutaka Kogi, and Drew Dawson, who discussed the past, present, and future of this meeting and related fields of research.

The meeting brings together a diverse community with a wide array of interests related to shiftwork and working time, including topics such as circadian rhythms; mental health; shiftwork and disease; sleep and sleepiness; shiftwork and safety; measurement and modeling; policy and education; and working time arrangements. As these areas of work are continuing to grow and progress, we look forward to gathering for our next meeting in 2022 in Japan to learn about the latest scientific advances and to reconnect with colleagues. For those who have not had the opportunity to attend this meeting in the past, we invite you to join us! I am confident that you will find the discussions stimulating and the atmosphere particularly welcoming and collegial.

On behalf of the Working Time Society and the organizers of Shiftwork2019, we are grateful to Dr. Mike Smolensky (editor-in-chief) and Chronobiology International for granting this special double issue to include selected proceedings from Shiftwork2019. With a large number of submissions received, the guest editors of this issue (Drs. Erin Flynn–Evans, Ashleigh Filtness, Tomohide Kubo, and myself) are also appreciative of the many reviewers who dedicated time and effort to helping develop this double issue.

Putting together a special issue amidst the global COVID-19 pandemic posed unusual challenges for authors, reviewers, guest editors, editors, and journal and publishing staff. As each of us dealt with illness, working from home or unstable jobs, changes to childcare arrangements, caring for family and friends, and other strains – we are deeply appreciative of the effort that went into putting this double issue together. We are proud to present the final product and hope that it serves to inform readers, pose new research questions, and build the overall body of work comprising the science of shiftwork and working time.

Working time arrangements

During the present COVID-19 pandemic, flexible working time arrangements have gained significant attention. Yet even prior to this, working time arrangement preferences and associations with variables such as health and work–life balance have been a topic of discussion. In this special issue of Chronobiology International, Wöhrmann and colleagues present a study of nearly 8,000 employees in Germany between the ages of 50 and 65 (Wöhrmann et al. 2020). Employees were asked whether they would prefer to retire early, work until regular retirement age, or work beyond regular retirement age. They were also asked about their current weekly working hours and their desired weekly working hours. A desire for decreased working hours was associated with a preference for early retirement and a desire for increased working hours was associated with a preference for later retirement, suggesting that overwork may lead to an early career end.

Similarly, Brauner et al. cautions against overwork for maintaining work–life balance (Brauner et al. 2020). In their analysis of over 8,500 workers in Germany, satisfaction with work–life balance tended to be higher when employees reported working fewer hours than their preferred schedule, and was lower when employees worked longer hours than their preferred schedule. Yet, analyses did not reveal congruence effects that actual and preferred working hours must align in order to optimize work–life balance. Rather, the importance of work–life balance is in employees not working more hours than preferred and having some control over their schedules.

In a study of over 800 United States Navy sailors, Shattuck and Matsangas compared the quality of life and sleep habits between daytime workers and shift workers (Shattuck and Matsangas 2020). While daytime workers were found to sleep better, be less sleepy, and report greater vigor and reduced fatigue than shift workers, the daytime workers still showed high rates of short sleep duration, split sleep, excessive daytime sleepiness, and poor sleep quality. Both the daytime workers and shift workers showed similar rates of insomnia, excessive daytime sleepiness with comorbid moderate to severe insomnia symptoms, mood disturbances, and sleep-related behaviors. While daytime workers fared somewhat better than shift workers, results from this study revealed significant fatigue-related issues across the entire sample of US Navy sailors, highlighting the need for widespread cultural change and fatigue management.

Research by Sagherian and Rose compared work hours, daytime naps, and cognitive performance in working adults age 65 or older (Sagherian and Rose 2020). They reported significant associations between working ≥40 h/week and decreased cognitive performance, which may suggest that longer work hours create mental fatigue states in older adults.

The relationship between working time dimensions and well-being of 6,500 health care employees in Finland and Germany was investigated by Karhula and colleagues (Karhula et al. 2020). High work tempo, which is related to the pace or intensity of work, was associated with increased work-life conflict, sleep difficulties, and fatigue. Weekend work was also associated with increased work-life conflict, whereas high working time autonomy – how much control the health-care employees felt they had over their shift and break times – was associated with decreased work-life conflict. If health-care employers would find ways to lessen the pressure of work demands, decrease weekend work, and increase work time autonomy – such as through increasing staffing and allowing workers to determine their break times – it may significantly benefit the well-being of the health-care workforce.

Shiftwork, sleep, and health

Several articles within this special issue investigate the relationship between shiftwork and health. First, Middeldorp et al. compared sickness absenteeism, work performance, and healthcare use due to respiratory infections in shift workers and non-shift workers (Middeldorp et al. 2020). In a study of hospital workers over 6 months, the investigators reported no difference between the shift and non-shift workers for sickness absenteeism and work performance due to influenza-like illness/acute respiratory infection symptoms.

In a study of men residing in rural and urban communities in the Amazon region of Brazil, Martins et al. examined sleep and activity, biological rhythms, and metabolic factors (Martins et al. 2020). The town/urban residents had shorter sleep duration and later sleep onset and offset times, as well as significantly higher body weight, fasting glucose, insulin levels, and insulin resistance than the rural residents, who were in the normal range for weight and metabolic indicators. In this sample, urbanization was associated with an increased presence of risk factors for metabolic disorders, which may be due to less demand for physical effort in typical jobs, as well as increased nighttime activities and light exposure in the town/urban-dwelling population. These findings can be used to inform public health initiatives by highlighting the different sleep and health risk factors associated with urbanization.

A study by Silva-Costa et al. compared lifetime night work exposure and the risk of type 2 diabetes in men and women (Silva-Costa et al. 2020). As part of a longitudinal study of adult health in Brazil, the investigators found that ≥10 y lifetime experience of night work was associated with higher incidence of type 2 diabetes than having never been exposed to night-shift work in women, but not in men. The reason for this gender difference is unclear, and is not the only report of significant gender discrepancies in health effects of shift work in this issue.

Tucker and colleagues examined the associations between shift work and health in both female-dominated and male-dominated occupations (Tucker et al. 2020). Shift work that included night work (as compared to daytime work) predicted higher incidence of sick leave in female-dominated occupations, whereas nightwork predicted greater symptoms of mild depression in male-dominated occupations. These associations remained significant after adjustments for personal circumstances and employment conditions, and for psychosocial working conditions (including psychological and emotional job demands, job control, worktime control, social support at work, persecution at work, and threats or violence at work). While the differences reported may still be due to additional unmeasured aspects of the working environment, the results suggest that night work affects different domains (sickness leave versus mild depression) in female- versus male-dominated occupations.

Night work and nurses

Building on the topic of the effects of night work, four papers within this special issue focused specifically on night work in nurses. Using data from nearly 2,000 nursing staff (both registered nurses and unregistered nursing assistants) across 32 general medical and surgical wards in a large acute hospital in England, Dall’Ora et al. analyzed the association between night shift work and sickness absence over 3 years across more than 600,000 shifts (Dall’Ora et al. 2020). Nursing staff working predominantly night shifts (>75% of their shifts within the past 7 days) were more likely to experience sickness absence compared to nursing staff working only days. Further, sub-group analysis showed an association between a high proportion of night shift work and long-term sickness (7 or more consecutive days), but not short-term sickness. While night shift work is essential to providing around-the-clock medical care, the high rates of long-term sickness absence may reflect chronic ill health and warrants further research into the types of sickness experienced and potential scheduling alternatives – such as shortening night shift durations to allow more time for recovery between shifts or dividing the night shifts between more staff members to have less reliance on steady night shift work.

One possible way to improve health in night working nurses is with on-shift napping. Rotenberg et al. investigated blood pressure measurements and the occurrence of on-shift napping in night working nursing professionals (assistants/technicians and registered nurses; Rotenberg et al. 2020). Within a sample of 449 fixed night shift nursing professionals, 42% reported on-shift napping. Among both the nappers and non-nappers, those who worked most frequent night work (≥5 nights/fortnight) had elevated systolic blood pressure compared to their counterparts working fewer night shifts. However, there were higher diastolic blood pressures and a higher prevalence of self-reported hypertension exclusively among non-nappers who worked >4 night shifts per fortnight, suggesting that on-shift napping may be a viable strategy for improving health in night shift workers (Rangan et al. 2020).

In addition to night shifts, many medical professionals work extended shifts in order to meet the demands for patient care. Because of the fatigue associated with extended work hours, there is a concern for job performance and patient safety. Westley et al. investigated the impact of nurse’s weekly work hours and medication administration near-miss error alerts (Westley et al. 2020). With over 9 million medication administrations within a two-year period, the sample of more than 5,000 nurses triggered 420,706 near-miss alerts (such as off-schedule administration, different dose, inactive order scanned, etc.). Within the sample, more than 40% of nurses worked extended hours (60 hours or more within 7 days). Working extended hours was associated with a 1.3% increase of triggering a near-miss error alert than working less than 60 hours – representing an additional 165 medication error alerts per day. Work hours are not only important for the health and well-being of the nursing professionals, there is also a direct connection to patient safety. As such, hospitals must carefully consider their staffing demands and the frequency and distribution of night and/or extended shifts.

Aside from modifications to shift schedules, improving diets may be a way to improve long-term health in shift workers, including night working nurses. In a pilot test of an electronic food diary, Bigand and colleagues found that registered nurses working night shifts consumed significantly more calories from sugar compared to national recommendations both when on-duty and off-duty (Bigand et al. 2020). Because night work is associated with an increase for diet-related chronic illness (Hales et al. 2017; Laermans and Depoortere 2016), tracking diet and nutrient intake in nurses and other shift-working professionals may serve to identify where nutritional intake deviates from recommendations, and the success of any programs to improve nutritional balance in an effort to protect against metabolic illness.

Night work and meals

In further research related to night work and meals, Jensen and colleagues studied post-prandial blood glucose levels in women working night or day shifts in the health-care sector (Jensen et al. 2020a). They found that post-meal blood glucose levels rose faster and remained elevated for longer following nighttime meals compared to daytime meals. However, there was no difference in total glucose exposure across time following high sugar meals compared to low sugar meals during night shifts. The difference in blood glucose response to meals between day and night shifts may have causal implications for the elevated risk of developing type 2 diabetes associated with shiftwork (Gan et al. 2015; Knutsson and Kempe 2014).

Another study of meals in night shift workers compared the effects of meal composition on food perceptions later during the day (Silva et al. 2020). In a randomized crossover study, 14 male night shift workers underwent two isocaloric dietary events at 01:00 h during their night shift, with a 6-day washout period between them. One meal was high-protein/moderate-carbohydrate (HP/MCHO) and the other was low-protein/high-carbohydrate (LP/HCHO). The workers documented foods they consumed before and after the meal, as well as their hunger, enjoyment of the meals, and satiety. The meal composition impacted later appetite, with greater appetite for salty food snacks after the HP/MCHO meal than after the LP/HCHO meal. During the day following the HP/MCHO meal, there was a better distribution of nutrients, with a higher percentage of carbohydrate consumption during lunch and lower percentage of fat consumption during dinner, compared to the day following the LP/HCHO meal. While there was no immediate difference in perceptions of hunger before, enjoyment of the meal, and satiety after the meal between the HP/MCHO and LP/HCHO meals, the results suggest that meal composition during night shifts may impact hunger and food preferences on the following day. This finding can serve to inform future research aimed at preventing the nutritional-associated problems common in shiftworkers, such as obesity and metabolic disorders (Balieiro et al. 2014; Liu et al. 2018).

Melatonin and circadian misalignment

Melatonin is a hormone released by the pineal gland with a diurnal pattern of expression regulated by the body’s master circadian clock in the suprachiasmatic nucleus of the hypothalamus (Emens and Burgess 2015). The expression and timing of endogenous melatonin can be used as an indicator of circadian timing. Melatonin can be assessed in several ways, as illustrated by the following three papers.

In a study with 73 Danish police officers, Jensen et al. compared levels of 6-sulfatoxymelatonin, the major metabolite of melatonin, which approximately reflects the blood concentration of melatonin (Arendt et al. 1985), in morning urine samples on the last recovery day after three different night schedules: two, four, and seven consecutive night shifts followed by a corresponding number of days for recovery (Jensen et al. 2020b). Previously, these authors have demonstrated that salivary melatonin is no different between the three recovery timepoints (Jensen et al. 2016). In the present report, the authors similarly found no difference in urinary 6-sulfatoxymelatonin concentration between the three recovery timepoints. Together, these results suggest that any shifting or dampening of melatonin rhythms that may have occurred during the two, four, and seven night shift cycles appears to have returned to normal in both the salivary melatonin and blood concentration of melatonin following the two, four, and seven recovery days, respectively.

St. Hilaire et al. used mathematical modeling of sleep/wake and light exposure data from two simulation studies to predict circadian phase shifts, identified with the time of dim light melatonin onset (DLMO) as measured by salivary melatonin samples (St. Hilaire et al. 2020). Healthy, non-shift working adults (age 50–66) completed a 10 d laboratory-field study, which involved four consecutive days with simulated day shifts (07:00–15:00 h), an off day, and four simulated night shifts (23:00–07:00 h). Participants were randomized to one of four interventions during the simulated night shifts and had salivary melatonin sampled during the final day. The model used sleep-wake and light-dark pattern data from a wrist monitor to predict circadian phase in simulation 1, as well as using known light levels measured at the level of the eye during simulated night shifts in simulation 2. The model predicted the estimated phase shift within 2 hours of the observed phase shift in approximately 80% of individuals for both simulations, and no individual had an actual phase shift greater than 3 hours from the model-predicted phase shift. Simulation 2, which incorporated known light levels during the simulated night shifts, performed better than simulation 1 (using sleep-wake and light-dark data from the wrist monitor alone), specifically in the subgroup of participants who had bright light exposure as part of their intervention during the simulated night shifts. In shiftwork research, sleep-wake and light-dark rhythm activity can be measured with a wrist activity monitor to estimate the phase shift in individuals.

Because collections of saliva, blood, or urine can be costly, invasive, or burdensome for shift workers, another study by Reiter et al. estimated DLMO using sleep markers derived from questionnaires, diaries, and wrist activity monitors (Reiter et al. 2020). A sample of 72 healthy adults completed two sleep questionnaires (the Pittsburgh Sleep Quality Index (PSQI) and the Munich Chronotype Questionnaire (MCTQ)). They also completed daily sleep diaries and wore a wrist activity monitor (actigraph) for one week prior to entering the sleep laboratory. During the second evening in the laboratory, saliva samples were collected hourly in dim light to determine DLMO. The mean time of DLMO was 21:37 h, with a range from 19:12 to 00:47 h. The investigators used regression and Bland–Altman techniques to assess the potential of sleep markers from the questionnaires, sleep diary, and actigraphy to estimate circadian phase. Sleep-wake data collected with diary and actigraphy provided better estimates of DLMO than the questionnaire markers, with sleep midpoint time as typically the strongest predictor of DLMO, although none of the methods tested offered a precise prediction of DLMO. Yet, depending on the constraints in biological sampling to determine circadian phase in field research settings, midpoints from diary or actigraphy may provide suitable estimates of DLMO.

Sleep loss, safety, and performance

Because of the timing and duration of shiftwork schedules (e.g., night work, extended shifts), shiftwork has been found to result in shortened and disrupted sleep, as well as increased daytime sleepiness (Åkerstedt 2003; Van Dongen and Dinges 2005). This can produce an increase in performance and safety deficits (Folkard et al. 2005; Van Dongen et al. 2016). In order to protect both performance and safety in shiftworkers, it is essential to understand what types of deficits individuals experience during sleep loss, as well as when they are most vulnerable.

In a fatigue- and driving safety-related study, Roach et al. evaluated the impact of working multiple, consecutive night shifts on driving crash risk during a simulated 20-min morning commute (Roach et al. 2020). During a laboratory study, 72 healthy adults (ages 18–35 y) experience 7 consecutive night shifts (23:00–07:00 h), that each started and ended with a 20-min simulated drive, representing the commute to and from work. During the simulated morning commute (after each night shift), accident risk was highest at the start of the week, and gradually lessened across the middle and end of the simulated week of night shift work. This suggests that the largest effect of night work on driving safety occurs at the start of the work week, when individuals may experience an abrupt misalignment of their internal circadian clock and their sleep and work times, with some adaptation during the week. Importantly, in this laboratory study, participants had a 9 h sleep opportunity prior to the first night shift and 7 h sleep opportunities during each of the following days; in many real-world settings, night shift workers may have shorter sleep opportunities between shifts, indicating that the real-world accident risk following night shifts may be even greater.

Sleepiness is a risk factor for driving accidents, although not all such accidents are limited to falling asleep at the wheel (Åkerstedt 2000; Beanland et al. 2013; Tefft 2010). During a simulated driving task, Filtness et al. investigated whether change blindness may contribute to fatigue-related driving accidents (Filtness et al. 2020). A change detection task was administered during a 45-min simulated drive following one normal night of sleep (7–8 h) and following one night of sleep restriction (5 h) in a counterbalanced order. During each simulated drive, participants were cued 20 times by the visual screen turning blank for 500 ms. After the screen reappeared, participants were asked whether there were any changes (with 12 instances having a change and 8 instances having no change). There were four additional unexpected changes, including the language of the road signs changing from English to German. Interestingly, sleep loss did not impact accuracy for the cued or unexpected changes, suggesting that change blindness is likely not a significant contributor to sleep-related driving accidents.

With a similar goal of uncovering what functions are more or less vulnerable to the effects of sleep loss – and thus contribute to fatigue-related errors and accidents – Hudson et al. used a variation on the psychomotor vigilance test (PVT) to study the effects of sleep deprivation on inhibitory control (Hudson et al. 2020). A “stop-signal” PVT was administered twice in a laboratory study: once at a well-rested baseline and once after 34.5 hours of continuous wakefulness. On 75% of trials, the stimulus was a green bullseye, and participants were instructed to respond as quickly as possible with a keypress. On the remaining 25% of trials, the stimulus was a red bullseye, and participants were instructed to not press the response key. While accuracy (responding versus not responding to the green or red stimuli, respectively) was not reduced during sleep deprivation, response times to the green bullseyes were significantly slower. Although inhibitory control remained intact, this reflects a speed/accuracy trade-off, whereby participants slowed their responding in order to preserve accuracy. Under conditions of sleep loss with low time pressure, this may be an effective way to preserve accuracy, but with relatively high time pressure for making decisions, accuracy may become impaired.

Previous research has found a substantial impairment during conditions of sleep deprivation on tasks requiring cognitive flexibility, that is, when circumstances (such as task demands) unexpectedly change and individuals must recognize and adapt their behavior (Honn et al. 2019; Whitney et al. 2015). Building on this work, Lawrence-Sidebottom et al. created a modified version of the previously used go/no-go reversal-learning task (GNGr) with simulated attentional lapsing to investigate the role of information acquisition failures in cognitive flexibility impairment (Lawrence-Sidebottom et al. 2020). This task, which was administered to well-rested adults, requires learning stimulus response contingencies through feedback, and altering response patterns to those stimuli following an unannounced contingency reversal. Attentional lapsing was simulated by masking either a portion of the stimuli, a portion of the feedback, or alternating between masking stimuli and feedback, as well as a no masking control condition. The rate of masking gradually increased across the task duration to mimic the time-on-task effect on the PVT during sleep deprivation. None of the masking conditions produced results similar to the original data, which was collected during sleep deprivation. This indicates that the cognitive flexibility deficits in the GNGr during sleep loss are independent from the attentional lapses typically observed in tasks such as the PVT.

Genetic contribution

Individuals vary in their adaptation to night shift work as well as their performance during sleep deprivation, which may have a genetic basis (Crowley et al. 2004; Van Dongen et al. 2004). Satterfield et al. investigated the possible association of a Val66Met single nucleotide polymorphism of brain-derived neurotrophic factor (BDNF) with sleep, performance, and circulating levels of interleukin-6 (IL-6) during a simulated night shift study. Participants completed two 5-day weeks of simulated night shift schedules, separated by a two-night simulated weekend, when sleep times reverted back to nighttime hours. While individuals who were homozygous for the Val allele showed reduced total sleep time, sleep latency, rapid eye movement (REM) sleep and increased wake after sleep onset during both weeks of simulated night shift work, the heterozygotes mimicked these patterns during the first week but not in the second week, suggesting a possible degree of circadian adaptation to the night shift schedule. Further, the heterozygous individuals had less nighttime performance impairment after the first week of simulated night shift work and a blunted IL-6 temporal pattern, indicating that BDNF genotype for this particular gene may impact circadian adaptation.

In relation to performance during sleep deprivation, another genetic polymorphism (dopamine receptor D₂ C957T) was investigated by Muck and colleagues (Muck et al. 2020). A 10-min PVT was administered 15 times during a 38-h total sleep deprivation laboratory study. The severity of the time-on-task effect was associated with the dopamine receptor D₂ polymorphism, such that individuals homozygous for the T allele experienced the greatest time-on-task impairments during sleep deprivation. This evidence suggests that the genetic determinants of dopamine availability play a role in resilience or vulnerability to sleep deprivation.

Along with the BDNF and dopamine receptor D₂ polymorphisms, a tumor necrosis factor alpha (TNFα) genotype is also associated with resilience to the effects of sleep deprivation on PVT performance (Skeiky et al. 2020). In an 18-day laboratory study with a randomized, double-blind, placebo-controlled crossover study, individuals underwent three periods of 48 h of total sleep deprivation, each preceded by three nights with 10-h sleep opportunities. During each of the three sleep deprivation periods, participants received either 200 mg caffeine, 300 mg caffeine, or 0 mg caffeine (placebo) in gum every 12 h at 13:00 h and 01:00 h, with the same dose administered at four time points per sleep deprivation period (after 6, 18, 30, and 42 h awake) and the order of doses randomized between the three sleep deprivation periods, and a 10-min PVT administered every 2 h of wakefulness. Individuals with the A allele (either homozygous or heterozygous) for TNFα G308A performed significantly better on the PVT during sleep deprivation than individuals homozygous for the G allele. However, neither allele was associated with increased benefit from caffeine, suggesting a distinct underlying mechanism for the impact of the TNFα G308A polymorphism and caffeine on performance during sleep deprivation.

Similarly, the duration of slow wave sleep (i.e., stage N3 sleep or “deep” sleep) has been found to be trait-like, with profound inter-individual differences that are stable and vary systematically across multiple nights of sleep (Gander et al. 2010; Tucker et al. 2007). Erwin et al. expanded on this work to determine the potential impact on this trait of the sleep periods occurring during the daytime following sleep deprivation, as well as prior caffeine consumption (Erwin et al. 2020). Robust inter-individual differences in slow wave sleep duration were maintained during daytime sleep after total sleep deprivation and were not impacted by prior consumption of caffeine.

Sleep inertia countermeasures

In many shiftwork industries, a prominent concern regarding on-shift napping is sleep inertia, the temporary period of grogginess after awakening (Edwards et al. 2013). Depending on how quickly an individual needs to begin work after awakening, there is a threat that sleep inertia will increase likelihood of making a critical mistake. In order to experience the alerting benefits of a nap with reduced risk of sleep inertia, a “caffeine-nap” has been proposed – which is consuming caffeine prior to a brief (e.g., 15–30 min) nap so that the alerting effects of the caffeine, which can take 20–30 min to reach its peak effect, will counteract the sleep inertia. Previous research has shown that an afternoon caffeine-nap with 150 mg of caffeine followed by a 15 min nap improved performance and subjective sleepiness more than either 200 mg of caffeine or a 15 min nap alone (Reyner and Horne 1997). Centofanti et al. conducted a pilot study of a caffeine-nap protocol (200 mg caffeine followed by a 30 min nap) at 03:30 h to test the efficacy of this combination countermeasure against sleep inertia-related performance deficits (Centofanti et al. 2020). Compared to a placebo followed by 30 min nap, the caffeine-nap protocol resulted in improved vigilant attention and subjective fatigue during the 45 min following the nap, suggesting that a caffeine-nap protocol may be an effective way to improve alertness with less risk of sleep inertia during night shifts.

As shift workers on call have reported an “adrenaline rush” when awakening to a call – which they believe reduced sleep inertia – another potential sleep inertia countermeasure may be brief exercise to increase sympathetic activity (Paterson et al. 2016). In a small pilot study, Kovac et al. tested the effects of a 30 s maximal sprint upon awakening at 02:00 h (Kovac et al. 2020). Compared to when participants awakened and maintained sedentary behavior, the brief exercise produced a temporary increase in sympathetic activity at 5 min post-exercise, measured by plasma noradrenaline levels, and reduced subjective sleepiness. While no cognitive data were collected to measure the performance impact of sleep inertia or benefit of the countermeasure, these findings provide preliminary support for brief exercise as a potential short-term countermeasure for sleep inertia.

Shiftwork in transportation industries

In aviation and other shiftwork industries, multi-faceted fatigue risk management (FRM) approaches can be used to improve safety and fatigue. Rangan et al. provide examples of an FRM system in a commercial cargo aviation operation that includes both predictive and proactive approaches to managing fatigue risk. This system includes elements such as predictive duty scheduling using a biomathematical model of fatigue and proactive sleep opportunity management, such as through providing quiet, comfortable, secure sleep rooms for pilots to use before or after flights, as well as a wake-up call program that involves delaying the wake-up call if the flight should be delayed in an effort to maximize the available sleep opportunity for the pilot. Implementation of such tools may have widespread benefits within other transportation and shift work operations.

In some global regions, one fatigue countermeasure in commercial aviation is controlled rest, which is a short, unscheduled nap taken by pilots while on the flightdeck to counteract fatigue ([ICAO] International Civil Aviation Organisation 2015). Under the ICAO guidance, this allows for rest during non-critical phases of flight (e.g., cruise phase) to improve alertness for the remainder of the flight. The implementation of controlled rest in naturalistic settings has not been widely studied; as such, Hilditch et al. collected data from over 40 pilots from a long-haul operation over approximately a 2-week period (Hilditch et al. 2020). Of the 239 flights analyzed, controlled rest was attempted on 46% of flights, with 10% of flights including two periods of controlled rest. Sleep was obtained in 80% of the attempted controlled rest periods (i.e., when pilots noted that they were attempting a nap with controlled rest), with the average sleep duration of 43 min. This report provides evidence of the frequent use of controlled rest for managing fatigue in a long-haul aviation operation.

One potential component of fatigue in aviation, particularly in short-haul operations, is workload. In a study of 95 short-haul commercial airline pilots, Arsintescu et al. evaluated the relationship between subjective workload, as measured by the NASA Task Load Index, performance, as measured by a 5-min PVT, and subjective fatigue, as measured by the Samn-Perelli scale at top-of-descent for over 3,000 flights (Arsintescu et al. 2020). Sleep duration was measured with wrist actigraphy through the duration of the study. Subjective workload was found to weakly correlate with PVT lapses, response speed, Samn-Perelli fatigue, number of flight sectors flown, prior sleep duration, and flight duration. These results support the interconnectedness of workload, performance, and fatigue in short-haul aviation.

In another around-the-clock transportation industry, maritime pilots, who guide ships in and out of port, often work on-demand schedules that may result in fatigue. Gregory and colleagues collected scheduling data from 61 maritime pilots over a one-year period (Gregory et al. 2020). Because maritime pilots work on-call as needed, work schedules and demands often fluctuate. On average, the maritime pilots sampled worked approximately 35 h per work week, with each work period averaging less than 8 h. However, there were instances of work weeks exceeding 50 h, frequent night or rotating shifts, and occasional shifts exceeding 12 h. While the majority of work schedules studied were not predicted to be unsafe from a fatigue perspective, fatigue-related risk from the remaining schedules should be evaluated and addressed in order to improve safety in maritime pilot operations.

City bus drivers tend to work long shifts, which may start with a morning commute shift and end with an evening commute shift, and have reported high incidence of fatigue as well as falling asleep at the wheel or having an accident or near miss due to sleepiness (Anund et al. 2016; Deza-Becerra et al. 2017; Vennelle et al. 2010). In a qualitative study, Pilkington-Chaney et al. conducted focus group interviews with 62 London city bus drivers on how they manage sleepiness and fatigue (Pilkington-Cheney et al. 2020). The drivers reported using several effective fatigue management strategies, such as naps and caffeine consumption, as well as several less effective strategies (e.g., opening the window). Importantly, the drivers identified some workplace barriers that limit availability of fatigue management strategies, such as needing to adhere to a strict schedule, not being allowed to drink coffee or other beverages unless the bus is fully parked, and a lack of available facilities for napping, eating, or using the restroom. Several drivers also mentioned a lack of support or discussion related to managing fatigue. This study highlighted the need for education regarding effective fatigue management in drivers, and also identified areas where operators may be able to address workplace constraints to improve fatigue management.

Discussion forum

Last, we close this special issue of Chronobiology International with a discussion forum concerning work–life balance in school teachers. As teaching is a profession that often involves work outside of work hours (e.g., grading tests, planning lessons), work–life balance can become unbalanced as work tasks cut into leisure time. Silva and Fischer interviewed 29 elementary school teachers and describe three typologies that were represented (Silva and Fischer 2020). These typologies were duty-duty, in which there is no perception of leisure or balance with work duties; duty-need, in which work demands may intrude into leisure or personal time, but the individual still feels a sense of accomplishment and that personal and professional needs are being met; and duty-pleasure typology, which was the least common (found in only 4 of the 29 teachers interviewed), including balance of duty and pleasure, and with some job duties bringing a sense of pleasure. These typologies can be helpful in future investigations of work–life balance in teachers, and may also be applied in other domains.

Conclusion

The impacts of shiftwork are varied and far reaching, including effects on work–life balance, sleep, performance, health, safety, and more across a widespread range of domains. Within shiftwork and working time research, there is a wealth of innovations in research and practice improving shiftworker health and safety, as is reflected in this special issue of Chronobiology International with selected proceedings from the 24^th International Symposium on Shiftwork and Working Time.