Sleep and Organizational Behavior: Implications for Workplace Productivity and Safety
- Department of Psychology, Clemson University, Clemson, SC, United States
The interaction between sleep and work-related behaviors influence many aspects of employee performance, safety, and health as well as organizational-level success. Although it is well established that quantity and quality of sleep can affect different types of task performance and personal health, the interactions between sleep habits and organizational behaviors have received much less attention. It is important to examine how sleep habits and workplace behaviors relate and the role of the underlying circadian rhythm on the potential impact of sleep and sleepiness in the workplace. Developing a deeper understanding of how sleep habits and sleepiness impact workers and the organization can help provide the necessary background for human resource management to develop more progressive support networks for employees that benefit both the worker and the organization. Human resources and employees should emphasize the impact of good sleep and sleep habits on organizational and individual productivity and safety.
Sleep is an influential component of human health and effective daily functioning and yet is often undervalued in many organizations. Sleep impacts many aspects of employee’s work performance including the ability to adequately respond to rapidly changing work demands and stress-inducing environments and interactions. In the short-term and perhaps at the most basic level, sleep makes us less sleepy and more alert. Poor or inadequate sleep also has a negative impact on many longer-term factors relevant to organizational behavior and personal health including self-control and decision making (Hagger, 2014; Pilcher et al., 2015b), subjective effort (Engle-Friedman and Riela, 2004), immunosuppression (Irwin, 2015), and a variety of performance measures (Lim and Dinges, 2010). Furthermore, our endogenous circadian rhythms impact not only our sleep-wake cycle by encouraging us to sleep at night but also affect our daytime alertness and performance. The organization can also have an impact on our endogenous need for sleep and our circadian rhythms. When organizations create an environment that increases work requirements, this creates additional stress beyond simply the work demands for the employee as well as the employee’s family including poor sleep and other social and health concerns (Mariappanadar, 2014). In addition, many organizational demands, such as shiftwork and travel, challenge our circadian rhythms and create a sustained physiological drive for sleep. As such, sleep and circadian rhythms and their relationship to employee productivity, safety, and health are important concerns for human resource management.
Sleep and Circadian Rhythms
Sleep is often described by two main components, sleep quantity and sleep quality. Sleep quantity is the amount of time spent asleep each night while sleep quality reflects features of sleep related to how well the person slept, such as time taken to fall asleep, number of awakenings during sleep, and how well rested the individual feels after waking (Pilcher et al., 1997). Both sleep quantity and sleep quality are important when considering how sleep impacts daily functioning, and can independently vary based on sleep habits and the presence of health issues or clinical sleep disorders. A recent meta-analysis found that sleep quality was better related than sleep quantity with employee perceptions and emotions such as workload, perceived control, and general strain (Litwiller et al., 2017). Decreased sleep quality is also associated with difficulties with social interactions in the workplace including feelings of ostracism (Chen and Li, 2019). In addition, work schedules can require the person to be awake and functioning very early in the morning, often resulting in decreased sleep quantity for the employee. Or persons with sleep disorders, such as sleep apnea, may have a schedule that allows them to sleep for 7–8 h, but their condition causes them to wake up regularly throughout the night, leading to decreased sleep quantity and poor sleep quality (Lopez et al., 2013). It is important to note that both decreased sleep quantity and poor sleep quality will increase sleepiness during work hours and can negatively impact the worker’s performance and health.
The physiological need for sleep resulting in a daily sleep–wake cycle is a part of and interacts with our endogenous circadian rhythm. Circadian rhythms are physiological and behavioral cycles that occur on approximately a 24-h basis. In terms of our sleep–wake cycle, the desire to sleep is generated as a part of the circadian rhythm to sleep at night as well as a physiological need for sleep that generates roughly at the rate of 1 h of sleep for every 2 h awake. As such, each day we gradually accumulate the need to sleep as a result of being awake but are also influenced by the circadian drive for sleep. Because our daily rhythms are synchronized with the environment to promote sleepiness at the appropriate time, the circadian rhythm acts as an internal clock, and is influenced by bright light exposure, eating, and physical activity. When occupational needs require individuals to shift their regular sleep–wake routine, whether due to shift work scheduling or traveling across time zones, the inertia of the endogenous circadian rhythm can promote sleepiness or wakefulness at inappropriate times leading to occupational health and safety risks.
Another challenge to the endogenous circadian rhythm occurs in workers in the circumpolar regions where sun light exposure and temperature change dramatically due to seasonal variation which also impact sleep habits and can negatively affect alertness and performance (Lan et al., 2017; Morris et al., 2017b). For example, seasonal affective disorder can occur in individuals exposed to extreme changes in environmental conditions, such as dramatic changes in sun light exposure (Rosenthal et al., 1984). Persons living in regions with limited sun light exposure in the winter time can experience seasonal affective disorder where periods of depression occur during the winter months but then gradually decrease as the day length increases. In fact, day length is related to use of antidepressants in United Kingdom (Lansdall-Welfare et al., 2019). These work-related challenges to the circadian system are common occurrences in many work settings and require additional attention when considering the impact of sleep on organizational behaviors and occupational health issues.
Assessment of Sleep and Sleepiness
Sleep is both a physiological event and a personal experience. As such, methods for measuring sleep and sleepiness incorporate both objective and subjective measures. Laboratory measures of sleep include physiological methods such as polysomnography, which includes measures such as eye tracking, brain and muscle activity, respiration, oxygen levels, and cardiac activity to precisely monitor wakefulness and sleep. Although these measures are widely used in clinical settings, there are limitations to standard sleep stage scoring (Pilcher and Schulz, 1987; Watanabe and Watanabe, 2004). Physiological measures of sleepiness can include using polysomnography to monitor how long an individual may take to either fall asleep (multiple sleep latency test; Carskadon and Dement, 1982) or remain awake (maintenance of wakefulness test; Mitler et al., 1982) when in a resting position. Either of these measures can provide an objective estimate of sleepiness when the person should be awake. These physiological methods provide sleep scientists and clinicians the ability to track the onset of sleep, monitor sleep stages, assess physiological sleepiness, and diagnose sleep disorders. However, because of the invasiveness and cost of physiological equipment, this technology is seldom applied in an occupational setting to measure sleep or sleepiness. One method that can help address this issue is actigraphy where portable activity monitors are used to provide an estimate of sleep time in the individual. Although actigraphy is not as accurate as polysomnography, it does provide a useful estimate of sleep–wake time that can be compared within individuals across nights.
Many studies, particularly those in the workplace, ask individuals to record characteristics of their nightly sleep in a sleep log. Individuals record relevant information before and after a night of sleep, including when they believe themselves to have fallen asleep, how long they slept, and how often they awoke during the night (Carney et al., 2012). Scales such as the Pittsburgh Sleep Quality Index ask additional questions about sleep medication use and patterns of behavior to quantify sleep habits (Buysse et al., 1989). These types of subjective measures provide information about sleep habits but are dependent upon the individual’s assessment of their own sleep patterns. In recent years, wearable technology has been used to supplement self-report information, thus providing an objective means of monitoring sleep and sleep habits. Wearable technology allows individuals to track their sleep patterns with relatively little effort. These wearable devices range in price and complexity, but even inexpensive step trackers can use motion tracking to estimate time asleep. Although consumer technology seems to overestimate total sleep time and underestimate sleep disruptions (Kolla et al., 2016), they can provide a meaningful measure of sleep habits when comparing across time within an individual.
Another metric of interest to organizations is the measure of daytime sleepiness. For many researchers and clinicians much of the purpose of tracking nighttime sleep is to better predict daytime fatigue and sleepiness in terms of performance and well-being as well as to estimate the likelihood of falling asleep during the work day. Excessive daytime sleepiness is directly related to risk of at-work injuries, task mistakes, and long-term negative health outcomes (Pagel, 2009). Sleepiness surveys such as the Stanford Sleepiness Scale, that asks individuals to describe their current feeling of sleepiness (Hoddes et al., 1973), and the Epworth Sleepiness Scale, that asks individuals their likelihood of falling asleep given certain scenarios (Johns, 1991) are used regularly in research and clinical settings. Although these sleepiness measures have some limitations (Shahid et al., 2010; Pilcher et al., 2018), both scales can be administered in less than 2 min, making them practical for a workplace setting.
Sleep-related fatigue can also be quantified using performance metrics and non-invasive physiological indices. For example, both simple reaction time on a psychomotor vigilance task and eye movement speed slow proportionately with the severity of sleep deprivation, while instances of attention lapses increase proportionately (Morris et al., 2015). If performance is already being tracked as part of normal job function or can be added as an additional simple task, using a performance metric offers an opportunity to assess the potential detrimental effects of sleepiness with little interruption to the workplace. In addition, less-invasive physiological indices of the circadian rhythm, such as body temperature, show promise in predicting workplace error due to sleepiness (Morris et al., 2017a). When considering potential concerns in occupational health and safety, work environments that regularly challenge the circadian system through shift work, regular travel across time zones, or work in diverse global regions could be designed to assess performance or physiological indices to monitor potential safety and health issues due to sleep disturbances and sleepiness.
Sleep and the Workplace
Human resource management includes supervision of risk factors that impact employee health and well-being as well as productivity in the workplace (Becker and Smidt, 2016). The negative impact of sleep deprivation is one area that is often undervalued by workers and by human resource management. Sleep deprivation resulting either from poor choices related to sleep habits or to occupational requirements such as shift work is a common cause of sleep- and sleepiness-related detriments in the workplace. Sleep deprivation negatively impacts a wide range of employee performance, health, and well-being issues including immune defense reaction (Majde and Krueger, 2005), cardiovascular functioning (Walker et al., 2009), metabolic disorders (Kecklund and Axelsson, 2016), mood disorders (Touitou et al., 2017); affective reactivity (Pilcher et al., 2015a), motivation (Odle-Dusseau et al., 2010), subjective effort (Engle-Friedman and Riela, 2004), accidents in the workplace (Uehli et al., 2014), and performance on many types of vigilance and more complex cognitive tasks (Pilcher et al., 2007; Pilcher et al., 2016). In addition, daytime sleepiness is related to higher mortality rates (Empana et al., 2009), cardiovascular disease and diabetes (Chasens et al., 2009), and fatigue-related accidents (Melamed and Oksenberg, 2002). This wide range of effects related to poor and inadequate sleep has led some to declare a sleep crisis as a public health issue (Barnes and Drake, 2015).
Because of the prevalence of sleep deprivation and daytime sleepiness, health agencies in many countries, such as the US Centers for Disease Control and Prevention, are increasingly monitoring population-based sleep habits (Gelaye et al., 2014). Irregular work hours, such as those seen in shift work schedules, have a negative impact on work performance and can continue over days off (Åkerstedt, 2003). Data collected by the National Center for Health Statistics indicate that 29.9% of working adults average less than 6 h of sleep a night (Luckhaupt et al., 2010). Among specific industries, the highest percentage of individuals experiencing sleep loss were those involved in the management of companies and enterprises (40.5%). Manufacturing and transportation follow closely as the second and third highest industries for sleep loss respectively, largely due to the prevalence of shift work scheduling and work demands across the 24-h day. These patterns of sleep disturbances occur in multiple societies (Åkerstedt, 1998; Dregan and Armstrong, 2011). Furthermore, between 15 and 30 percent of the working population in developed countries are on shift work schedules, often working against the body’s endogenous circadian rhythm and, as such, promoting sleep disturbances and sleep disorders (Boivin and Boudreau, 2014).
Shift work-related sleep disturbances are more common when occupations require workers to sleep during the day, a common result of working at night (Drake et al., 2004). Daytime sleep is challenging for most workers due to a variety of issues such as family demands, environmental light and noise, and the natural circadian pressure to be awake during the day. Some studies suggest that sleep quantity and sleep efficiency are lower in night workers who must sleep during the day compared to rotating shift workers who sleep at different times across the 24-h day depending on their shift work schedule (Drake et al., 2004). However, this may depend on the pattern of night shifts a worker experiences or even individual differences. There is some evidence that permanent night shifts can result in a more stable sleep pattern for many workers than rotating shifts (Pilcher et al., 2000). It is important to note; however, that night shift workers will always have to combat the natural circadian rhythm to be asleep at night and awake during the day. Perhaps the best way for night shift workers to adapt to working at night is to maintain the pattern of being awake at night and sleeping during the day on their days off. Even then, the presence of sunlight during the day will bolster the circadian pressure to be awake during the day and to sleep at night. Although there is some debate among sleep scientists about the relative merits of different shift work schedules, it is clear that night shift work will result in sleep difficulties and, almost always, in sleep loss. The answer is to not require persons to be alert and work at night when their circadian rhythm is encouraging sleepiness and sleep. However, since 24-h-a-day operations are necessary in most societies, the best option is to develop health promotion paradigms and countermeasures to decrease the negative side effects of night shift work.
The need for 24-h-a-day operations in developed countries has increased the likelihood that workers will experience fatigue, sleepiness, and decreased performance skills as part of their daily lives (Åkerstedt, 2007; Arendt, 2010). Evidence also suggests that the more one works, the less time the person sleeps, even on days off (Basner et al., 2007; Krueger and Friedman, 2009). It is important to note; however, that the short-term and long-term impact of sleep deprivation and changes in sleep patterns due to occupational demands vary according to specific characteristics of the work requirements as well as the worker.
Although most organizational research assumes a degree of homogeneity to the working population, individual differences can affect how the working environment impacts the employee. Factors such as gender and age contribute to differences in sleep quantity and quality in addition to general well-being in workers. Studies of employee health and well-being have shown that women shift workers have an increased risk of poor sleep quantity and quality compared to their male counterparts (Chung et al., 2009). This includes difficulty falling asleep, staying asleep, and a higher likelihood to use sleep medications to compensate for poor sleep (Marquie and Foret, 1999). Women also suffer more from work-family imbalance issues than men. Social and family obligations may partially account for the observed gender difference in that women often are responsible for more of the family and home duties than males; however, gender differences in the circadian rhythm may also contribute to the difference seen between females and males (Paschos and FitzGerald, 2017). For example, even in identical environments, the female circadian clock tends to be set earlier and slightly shorter on average than the male circadian clock (Duffy et al., 2011). In addition, growing evidence suggests that many of the differences in sleep between females and males could be related to the lower socioeconomic status of females in comparison to males (Arber et al., 2009). These differences in sleep patterns between females and males can contribute to different responses from females when meeting work demands that challenge the circadian rhythm.
Age is also a primary predictor of sleep and behavior, an issue that has become increasingly important as the average workforce age increases (Ng and Feldman, 2010). Although the time spent in bed remains relatively stable throughout the working adult years, there are several factors that contribute to poorer sleep in later adulthood. As early as the middle twenties, sleep patterns begin to shift and continue to change throughout adulthood and into retirement age (Ohayon et al., 2004). With age comes a decrease in the amount of deep sleep, generally associated with poorer cognitive performance, and an increase in the number of awakenings during the night, which is linked to less sleep quantity and poorer sleep quality (Crowley, 2011). These changes in sleep in adulthood can have a negative effect on individual health, decreased cognitive and physical ability during the work day, and a higher risk of injury. Although we do not yet fully understand these age-related changes in sleep, one contributing factor is an age-related decline in the endogenous circadian rhythm output which may help destabilize the sleep-wake cycle (Nakamura et al., 2011). However, it is important to note that lifestyle habits can also play a role in how individuals adapt to the potential detrimental effects of working conditions that challenge the sleep-wake cycle. For example, one study in nurses found the highest prevalence of excessive daytime sleepiness (29.3%) in those 20–29 years of age, compared to those in their 30 s (24.7%), 40 s (15.5%), or 50 s (12.3%), largely due to social choices such as maintaining or not maintaining regular sleep schedules (Suzuki et al., 2005).
Performance in the workplace can also be moderated by the endogenous circadian rhythm. The endogenous circadian rhythm in a few individuals can be slightly shorter than 24 h but averages 24.2 h in length (Czeisler et al., 1999). There is evidence that this variation in the circadian rhythm can be used to define individuals as morning types who have shorter circadian periods or as evening types who have longer circadian periods, with most individuals somewhere between the two extremes (Duffy et al., 2001). A convenient method to determine a person’s chronotype is through self-report questionnaires (Horne and Östberg, 1976) which can be easily administered in a variety of settings. For example, multi-country data gathered through a self-report survey suggest that chronotype affects sleep duration and itself is dependent on age and sex (Roenneberg et al., 2007).
Individuals who do fall in the extremes of morningness and eveningness can experience difficulties with on-the-job performance and functioning. Those with a disposition toward morningness are found to be more productive during the earlier hours of the day, while those who identify as evening types tend to be more productive during the later portion of the day (Preckel et al., 2011). In addition, the chronotypes follow age-related patterns with early career individuals in their 20 s tending toward eveningness and later career individuals tending toward morningness (Monk and Kupfer, 2007; Preckel et al., 2011). The morningness versus eveningness of an individual also impacts sleep and social activities. Circadian rhythms contribute to less sleep quantity and poorer sleep quality in persons with eveningness which will in turn result in performance degradation in the workplace as discussed previously. In a social context, eveningness can negatively impact social interactions and contribute to feelings of stress in the earlier part of the work day (Mecacci and Rocchetti, 1998; Cofer et al., 1999). Furthermore, changes in exposure to sunlight due to where a person lives within a given time zone can affect the circadian clock. It has been shown that persons living on the eastern edge of a time zone tend to be more likely to be morning people while persons in subtropical regions tend toward eveningness compared to those who live in more northern regions (Randler, 2008). It is also interesting to note that chronotypes are influenced by how the pressure or need to sleep is dissipated by sleeping (Mongrain et al., 2006), thus adding to the complexity of workers better managing their sleep to improve their work-related performance.
Race and cross-cultural differences have also emerged as relevant factors when considering employee sleep and health (Grandner et al., 2016). A meta-analysis concluded that African Americans slept less than white Americans each night and had less deep sleep overall (Ruiter et al., 2011). Moreover, minority groups, in general, sleep less than non-minorities (Jean-Louis et al., 2000), while African/Caribbean immigrants sleep less than white Americans even after controlling for education and occupation (Ertel et al., 2011). Sleep quantity; however, is not the only potential concern when examining the effects of sleep on organizational behaviors and occupational health issues. For example, sleep quality is better related to health and well-being than sleep quantity in persons sleeping about 7 h a night (Pilcher et al., 1997). Sleep quality also seems to be a mitigating factor when examining the effects of socioeconomic status, race, and sleep on health (Moore et al., 2002) suggesting that both lower socioeconomic status and poor sleep negatively affect health in minority groups. Measures related to sleep also vary across different countries and cultures. Germans, for example, differ in their sleep-wake patterns and chronotypes from Indians and Slovakians (Randler et al., 2015) while sleep problems related to aging relates to increased health and well-being issues in multiple countries across Africa and Asia (Stranges et al., 2012).
A variety of other factors impact the ability of individuals to maintain a healthy sleep–wake cycle. Persons suffering from clinical sleep disorders, such as insomnia and sleep apnea, have more work impairments (Swanson et al., 2011) and are more likely to have substance use disorders than normal sleepers (Fortuna et al., 2018). The presence of sleep disorders has also been used to predict risk of substance use in the future (Wong et al., 2004). Research suggests there is a bidirectional relationship between sleep and substance use that can decrease occupational productivity (Mullins et al., 2014). In addition, cultural standards influence sleep. Social demands such as school or work start times alter the sleep–wake cycle (Jenni and O’Connor, 2005). Personal decisions for sleep time within a family such as earlier or later bedtimes for children can change the sleep habits of the parents and negatively affect their work performance the next day (Giannotti and Cortesi, 2009). Finally, cultural expectations for sleep habits can impact sleep and work performance and can range widely across cultures both for sleeping at night and daytime napping which can impact on-the-job performance. As we have seen, decreased sleeping at night negatively impacts work performance. In contrast, daytime napping can help mitigate this effect and potentially improve performance after the nap; however, this effect varies across individuals.
Poor and inadequate sleep results in a variety of cognitive deficits, including an inability to maintain attention, decreased alertness, delayed reaction time, dulled auditory and visual perception, altered emotional processing, and a general inability to think clearly (Lim and Dinges, 2010). When considering sleep-deprived workers, this results in a decrease in job-related performance and a propensity for errors. For example, many studies have found that sleep deprivation and sleepiness result in an increased likelihood of medical-related errors. Excessive daytime sleepiness has been found to predict drug administration errors and incorrect operation of medical equipment (Suzuki et al., 2005). In addition, nurses alter their medical-related decision-making across a 12-h shift (McClelland et al., 2013) and on-call scheduling results in performance decrements in physicians (Pitkanen et al., 2008).
Sleep loss can also promote injury both during work and outside of work hours. Employees at four United States corporations were surveyed about sleep habits and those reporting insufficient sleep were nearly twice as likely to unintentionally sleep during work, fall asleep while driving, and injure themselves at home due to sleepiness compared to good sleepers (Rosekind et al., 2010). Workers suffering from obstructive sleep apnea and the resulting sleep loss and daytime sleepiness are twice as likely to injure themselves while at work than those without sleep apnea (Garbarino et al., 2016; Hirsch et al., 2016). Furthermore, in a study of 160 fatal occupational accidents, sleep difficulties were more predictive of fatal accidents than how physically strenuous the work was, how hectic the work was, the age of the individual, or whether the individual was working overtime (Åkerstedt et al., 2002). More specifically, risk of occupational injury due to sleepiness is particularly high in the transportation industry. In one sample, one-in-four professional drivers were affected by insomnia, which doubled their risk of crashing while driving for work and tripled their risk of having a near-miss accident compared to non-insomniac drivers (Garbarino et al., 2017).
As noted earlier, sleep deprivation negatively impacts motivation to perform well when working. It is important to understand how sustained performance under sleep loss conditions impacts subjective perceptions as those could be the first indicator of a potential negative reaction by the individual (Jones et al., 2006). It is well documented that sleep deprivation negatively impacts mood and increases sleepiness (Pilcher and Huffcutt, 1996; Driesen et al., 2010). The type of task could also affect how the worker reacts when sleep deprived with vigilance tasks being more negatively affected than more complex tasks (Odle-Dusseau et al., 2010). The desynchronization of the sleep-wake cycle as well as the loss of sleep as seen in many occupations can be a direct cause of stress in individuals (Jaffe et al., 1996). Furthermore, Kucharczyk et al. (2012) found that persons suffering from insomnia are more likely to struggle with professional development in general, resulting in a lower likelihood of job promotion, and a higher probability of dismissal. Although less research has focused on the individual’s perceptions when working under sleep-deprived conditions as well as the broader implications of sleepiness in the workplace, the evidence suggests that these could be important issues for human resource management.
Sleep plays a unique role in work behaviors due to the impact of sleep loss and sleepiness on workers’ overall situational and emotional processing. van der Helm et al. (2010) showed that sleep deprivation impairs the ability to judge human facial emotion which could disrupt affective social cues valuable to interpersonal communication. In addition, sleep deprivation negatively impacts the individual’s response to positive stimuli (Pilcher et al., 2015a) which could result in more focused responding to perceived negative events. In an occupational setting, this can result in miscommunication, social misjudgment, and tension between coworkers. Barber and Budnick (2015) found that sleepiness during work was related to aggressive behaviors in the workplace. Tired individuals were more likely to rationalize using aggressive behavior and avoiding rules perceived as unfair (Berry et al., 2010). These types of potential issues are of particular concern in that workers are more likely to fully engage in their workplace when they have emotional and cognitive investment in the system (Voronov and Vince, 2012) and a strong work identity (Knez, 2016), something that sleep loss and sleepiness could negatively impact.
Sleep deprivation is also related to employee absenteeism. Non-shift work employees across a variety of blue-collar and clerical occupations who self-report more daytime sleepiness are significantly more likely to take work absences (Philip et al., 2001) and more likely to arrive late or leave early (Swanson et al., 2011). Although absenteeism could be explained by considering the relationship between sleep and illness, other research supports the possibility of willful absenteeism. Barnes et al. (2011) found that poor sleep quantity and quality could predict unethical behaviors in a work setting. In addition, workers with poorer sleep were more likely to lie about their own performance scores and were rated as showing more unethical behavior on an ethics scale by their supervisors (Akaah, 1992). This included actions such as misusing sick days and company time, claiming credit for someone else’s work, and divulging confidential company information.
A variety of factors in and out of the workplace can contribute to sleep loss and sleepiness, including many psychosocial work factors. Social support impacts the ability to react to challenging conditions and exert the self-control needed to successfully navigate many stress-inducing situations (Pilcher and Bryant, 2016). In a sample of Japanese workers, decreased social support and interpersonal conflicts at work were related to an increase in risk of insomnia (Nakata et al., 2004). Similarly, research shows that social exclusion at work increases risk for sleep disturbances (Pereira et al., 2013). These issues likely stem from increased physiological arousal due to stress and anxiety as well as a negative shift in the victim’s sense of self and feelings of support for work mates. Negative events outside of the workplace similarly contribute to sleep loss, commonly through parasomnias such as nightmares in the case of traumatic events (Mysliwiec et al., 2014). Indeed, feelings of stress from daily life, including psychosocial stress but also everyday challenges, predict day to day sleep quality (Åkerstedt et al., 2012) and thus impact on-the-job behaviors.
Sleep and Health
Many people recognize that maintaining good sleep habits is part of making healthy lifestyle choices; however, it can be surprisingly difficult to maintain good sleep habits which can lead to negative consequences on long-term health. Sleep scientists have found numerous links between sleep and health. Poor sleep over a longer period of time is related to increased risk of cardiovascular disease and diabetes (Chasens et al., 2009) and higher risk of developing dementia and Alzheimer’s disease (Benedict et al., 2015). Studies have also found increased mortality rates associated with either shorter sleep periods (less than 3.5–4.5 h) or longer sleep periods (greater than 8 h) (Kripke et al., 2002) as well as with excessive daytime sleepiness (Empana et al., 2009). Moreover, shift workers have a higher risk factor for developing health issues. One review study concluded that shift workers, particularly night shift workers, experience more severe gastrointestinal, neuro-psychological, and cardiovascular issues than non-shift workers (Costa, 1996). A second review study concluded that shift workers are more likely to suffer from peptic ulcer disease and coronary heart disease (Knutsson, 2003) than non-shift workers. More generally, night shift workers can experience a wide range of long-term issues that can contribute to poor health over time. Nurses working the night shift, for example, have significantly higher incidences of obesity, higher caloric intake and tobacco use, and poorer sleep (Ramin et al., 2015). Finally, shift workers experience a wide range of issues that can negatively affect long-term health including concerns with families and social life, increased fatigue, and increased risk of on-the-job accidents (Harrington, 2001).
There is also increasing evidence that sleep is specifically involved in the functioning of the immune system (Bryant et al., 2004). Research suggests that proper functioning of many components of the immune system such as T-cells and inflammation markers as well as necessary endocrine functioning depend on the timing of sleep and the endogenous circadian rhythm to sleep at night (Lange et al., 2010). More specifically, immunology studies show that sleep loss is associated with increased secretion of proinflammatory cells while curtailing the antiviral immune responses (Irwin, 2015). Other studies also suggest a link between exposure to viruses, poor sleep, and developing an illness. For example, when exposed to the rhinovirus, participants who averaged less than 7 h of sleep were three times more likely to show clinical symptoms of the common cold than those who slept 8 h (Cohen et al., 2009). Cohen and colleagues also found that people with low sleep efficiency were five times more likely to show cold symptoms when exposed to the rhinovirus. Furthermore, just feeling rested after sleeping does not seem to be enough to ensure a properly functioning immune system. Although employees who sleep for fewer hours can report deep sleep and may even feel rested upon awakening, they are still at risk for developing an illness (Vgontzas et al., 2004). Sleep deprivation also appears to attenuate the antibody response to influenza and hepatitis vaccinations (Spiegel et al., 2002). This effect is further compounded with stress, as higher subjective reports of stress with poor sleep further diminishes the humoral immune response to immunization (Miller et al., 2004).
Even relatively short-term sleep loss can have adverse effects on key health indicators. In general, short-term sleep restriction can decrease glucose tolerance, increase blood pressure, increase reactivity of the sympathetic nervous system, decrease leptin levels, and increase immunological markers of inflammation (Alvarez and Ayas, 2004). One night of sleep deprivation results in increased systolic blood pressure in middle-aged adults (Kato et al., 2000) and in elevated diastolic blood pressure in young healthy adults with a family history of hypertension (McCubbin et al., 2010). Partial sleep deprivation also has negative effects on several physiological indicators of health. For example, blood pressure is increased in persons reporting 3.6 h of sleep at night (Tochikubo et al., 1996). Approximately 4 h of sleep at night is also related to impaired glucose reactivity, increased sympathetic response, higher cortisol levels, and reduced leptin levels (Spiegel et al., 1999) as well as increased levels of C-reactive protein, an indicator of inflammation related to heart disease (Meier-Ewert et al., 2004). Both total and partial sleep deprivation have negative effects on many health-related physiological indices suggesting that helping workers prioritize good sleep habits is essential for good health in the workers and long-term organizational success.
Human Resources Impact
When organizations go beyond the goal of financial gain to embrace corporate social responsibility and support the good of society, employees work harder and better contribute to the effectiveness of the organization. Better integrating the concept of corporate social responsibility with well-being of employees could also have positive effects on the organization (Glavas, 2016). Although many human resource models incorporate necessary functions for organizational success such as hiring and staffing practices, benefits and compensation, and training for employees, it is less common for human resource divisions to focus on employee well-being.
Work-related stress and strains are related to the employee’s perceived well-being but can be compensated for through social support (Giorgi et al., 2017). Human resources could be part of that social support system. Organizations can benefit if human resources create more of a partnership with their workers and include the well-being of workers in the different decision processes that typically take place in human resources. For example, human resources could focus on job design and anti-harassment practices that would benefit the workers and increase performance rates (Guest, 2002).
Sleep loss and sleepiness is one well-being issue that directly impacts the functioning of employees on many levels. Although less research has examined the impact of sleep loss and sleepiness at the organizational level, these are important issues for employees, managers, and human resources. For example, research suggests that changes in behavior due to sleep loss could be considered during personnel selection. Person-specific sensitivity to the negative effects of sleep deprivation has been considered as a trait characteristic (Van Dongen, 2006) that could be used when hiring individuals for shift work. Furthermore, employee selection tools on aggressive behaviors could be used as an indicator of employee responses in work environments associated with sleep loss (Barber and Budnick, 2015). The implications of these findings suggest that human resources could mitigate future personnel problems at the level of hiring or when assigning employees to a new work position by screening for individual responsiveness to sleep loss and sleepiness. The potential benefits of organizations taking these steps are more apparent when considering that individuals do not appear to self-select occupations based on their own ability to cope with sleepiness-inducing work environments or schedules (Van Dongen, 2006).
Research suggests that working conditions can negatively impact employee health (Urtasun and Nuñez, 2018). As such, it is important to consider how human resources can promote healthier work environments. One method is by promoting workplace practices that could curtail on-the-job sleepiness. Caruso and Hitchcock (2010) have suggested that traditional tactics for better managing sleepiness related to shift work, such as providing for nap breaks, are promising countermeasures. Human resources could also consider work scheduling practices that provides flexible working hours for employees where employees could choose either earlier or later work hours to better match their chronotype or to better match their family or other nonwork-related demands. Organizations can also provide adaptation time for workers to adapt to new time zones when traveling. In addition, there is growing evidence for the use of light exposure to promote alertness. Light is connected to the circadian rhythm and sleep–wake cycle and may help establish alertness at appropriate times. Research suggests that allowing for regular exposure to natural sunlight during the day can help ward off daytime sleepiness (Caldwell et al., 2009). In the case of night shift work, brief half-hour exposure to bright artificial light during the night has been shown to reduce subjective sleepiness and encourage alertness (Cajochen et al., 2000; Smolders and de Kort, 2014). Although there is much work yet to do at the organizational level, current research suggests several possible workplace interventions that human resource divisions could explore as potential countermeasures to help employees better manage their on-the-job alertness levels.
Conclusion and Implications
In recent years, some organizations have started to emphasize better understanding how sleep habits and sleepiness affect their employees and the workplace. Although research has connected poor sleep to decrements in many major cognitive processes as well as negative health outcomes, the literature addressing occupational interventions is still developing. The current summary relating sleep habits and sleep deprivation with organizational behavior (see Table 1 for a summary of the cited literature in this review), will help provide background for new research.
Shift work scheduling, travel across multiple time zones, and working in the circumpolar regions contribute to on-the-job sleepiness, circadian disruption, and decreased task performance. Best practices for organizational intervention to mitigate these work-related hazards require additional research. At least some of this difficulty comes from the complex interaction between employee individual differences and differences in occupations (Van Dongen, 2006). In addition, the choice by the worker on whether to prioritize sleep has an impact. Many workers are willing to exchange personal sleep time to increase time at work and time with their family (Barnes et al., 2012). Although organizations cannot control each employee’s sleep times, human resource management can investigate more thorough education programs as well as potential actions in the workplace that would help workers maintain better sleep habits and alertness when on-the-job. Personnel scheduling and responsibilities, demographics, and personal traits contribute to sleep habits and the risk of sleepiness in the workplace as well as a decrease in occupational-related performance. However, the degree to which this on-the-job sleepiness limits job performance varies depending on individual tolerances to job demands as well as the job characteristics. As such, implementing best practices and interventions in at-risk occupations is a challenging but necessary process for workers, human resources, and organizations.
Current research supports introducing workplace programs to support healthy sleep habits. Some organizations may assume that employees will make responsible and rational decisions about their sleep health. However, without education, it is difficult for many people to fully appreciate how many aspects of performance and health are negatively impacted by poor sleep habits and sleep loss. Although the effectiveness of workplace health promotion programs depends on the type of intervention and characteristics of the workers (Rongen et al., 2013), it is essential to develop and test health promotion programs that address specific occupational concerns. For example, health promotion programs that utilize wearable technology, such as creating teams to use step counters to track movement, show greater participation and less attrition than with individual participants (Glance et al., 2016). Health promotion programs could provide a range of effort such as, educational materials, classes, and health-promotion events that could help inform employees of the importance of prioritizing sleep as well as indicating the organization’s commitment to helping the employees. The programs could also focus on using current technology to better track and improve sleep habits and alertness when working. Better developing and maintaining health promotion programs such as these could benefit the employees as well as the organization.
Both authors contributed in the conceptual development of the manuscript and writing the manuscript.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Åkerstedt, T., Fredlund, P., Gillberg, M., and Jansson, B. (2002). A prospective study of fatal occupational accidents–relationship to sleeping difficulties and occupational factors. J. Sleep Res. 11, 69–71. doi: 10.1046/j.1365-2869.2002.00287.x
Åkerstedt, T., Orsini, N., Petersen, H., Axelsson, J., Lekander, M., and Kecklund, G. (2012). Predicting sleep quality from stress and prior sleep–a study of day-to-day covariation across six weeks. Sleep Med. 13, 674–679. doi: 10.1016/j.sleep.2011.12.013
Barnes, C. M., Wagner, D. T., and Ghumman, S. (2012). Borrowing from sleep to pay work and family: expanding time-based conflict to the broader nonwork domain. Pers. Psychol. 65, 789–819. doi: 10.1111/peps.12002
Basner, M., Fomberstein, K. M., Razavi, F. M., Banks, S., William, J. H., Rosa, R. R., et al. (2007). American time use survey: sleep time and its relationship to waking activities. Sleep 30, 1085–1095. doi: 10.1093/sleep/30.9.1085
Benedict, C., Byberg, L., Cedernaes, J., Hogenkamp, P. S., Giedratis, V., Kilander, L., et al. (2015). Self-reported sleep disturbance is associated with Alzheimer’s disease risk in men. Alzheimers Dement. 11, 1090–1097. doi: 10.1016/j.jalz.2014.08.104
Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., and Kupfer, D. J. (1989). The pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 28, 193–213. doi: 10.1016/0165-1781(89)90047-4
Cajochen, C., Zeitzer, J. M., Czeisler, C. A., and Dijk, D. (2000). Dose-response relationship for light intensity and ocular and electroencephalographic correlates of human alertness. Behav. Brain Res. 115, 75–83. doi: 10.1016/s0166-4328(00)00236-9
Caldwell, J. A., Mallis, M. M., Caldwell, J. L., Paul, M. A., Miller, J. C., and Neri, D. F. (2009). Fatigue countermeasures in aviation. Aviat. Space Environ. Med. 80, 29–59. doi: 10.3357/asem.2435.2009
Carney, C. E., Buysse, D. J., Ancoli-Israel, S., Edinger, J. D., Krystal, A. D., Lichstein, K. L., et al. (2012). The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep 35, 287–302. doi: 10.5665/sleep.1642
Cofer, L. F., Grice, J. W., Sethre-Hofstad, L., Radi, C. J., Zimmermann, L. K., Palmer-Seal, D., et al. (1999). Developmental perspectives on morningness-eveningness and social interactions. Hum. Dev. 42, 169–198. doi: 10.1159/000022623
Cohen, S., Doyle, W. J., Alper, C. M., Janicki-Deverts, D., and Turner, R. B. (2009). Sleep habits and susceptibility to the common cold. Arch. Intern. Med. 169, 62–67. doi: 10.1001/archinternmed.2008.505
Czeisler, C. A., Duffy, J. F., Shanahan, T. L., Brown, E. N., Mitchell, J. F., Rimmer, D. W., et al. (1999). Stability, precision, and near-24-hour period of the human circadian pacemaker. Science 284, 2177–2181. doi: 10.1126/science.284.5423.2177
Drake, C. L., Roehrs, T., Richardson, G., Walsh, J. K., and Roth, T. (2004). Shift work sleep disorder: prevalence and consequences beyond that of symptomatic day workers. Sleep 27, 1453–1462. doi: 10.1093/sleep/27.8.1453
Dregan, A., and Armstrong, D. (2011). Cross-country variation in sleep disturbance among working and older age groups: an analysis based on the European Social Survey. Int. Psychogeriatr. 23, 1413–1420. doi: 10.1017/S1041610211000664
Driesen, K., Jansen, N. W., Kant, I., Mohren, D. C., and van Amelsvoort, L. G. (2010). Depressed mood in the working population: associations with work schedules and working hours. Chronobiol. Int. 27, 1062–1079. doi: 10.3109/07420528.2010.489877
Duffy, J. F., Cain, S. W., Chang, A. M., Phillips, A. J., Munch, M. Y., Gronfier, C., et al. (2011). Sex difference in the near-24-hour intrinsic period of the human circadian timing system. Proc. Natl. Acad. Sci. U.S.A. 108, 15602–15608. doi: 10.1073/pnas.1010666108
Duffy, J. F., Rimmer, D. W., and Czeisler, C. A. (2001). Association of intrinsic circadian period with morningness-eveningness, usual wake time, and circadian phase. Behav. Neurosci. 115, 895–899. doi: 10.1037/0735-7044.115.4.895
Empana, J. P., Dauvilliers, Y., Dartigues, J. F., Ritchie, K., Gariepy, J., Jouven, X. P., et al. (2009). Excessive daytime sleepiness is an independent risk indicator for cardiovascular mortality in community-dwelling elderly: the three city study. Stroke 40, 1219–1224. doi: 10.1161/STROKEAHA.108.530824
Ertel, K. A., Berkman, L. F., and Buxton, O. M. (2011). Socioeconomic status, occupational characteristics, and sleep duration in african/caribbean immigrants and US white health care workers. Sleep 34, 509–518. doi: 10.1093/sleep/34.4.509
Fortuna, L. R., Cook, B., Porche, M. V., Wang, Y., Amaris, A. M., and Alegria, M. (2018). Sleep disturbance as a predictor of time to drug and alcohol use treatment in primary care. Sleep Med. 42, 31–37. doi: 10.1016/j.sleep.2017.12.009
Garbarino, S., Guglielmi, O., Sanna, A., Mancardi, G. L., and Magnavita, N. (2016). Risk of occupational accidents in workers with obstructive sleep apnea: systematic review and meta-analysis. Sleep 39, 1211–1218. doi: 10.5665/sleep.5834
Garbarino, S., Magnavita, N., Guglielmi, O., Maestri, M., Dini, G., Bersi, F. M., et al. (2017). Insomnia is associated with road accidents. Further evidence from a study on truck drivers. PLoS One 12:e0187256. doi: 10.1371/journal.pone.0187256
Gelaye, B., Lohsoonthorn, V., Lertmeharit, S., Pensuksan, W. C., Sanchez, S. E., Lemma, S., et al. (2014). Construct validity and factor structure of the pittsburgh sleep quality index and epworth sleepiness scale in a multi-national study of African, south east Asian and South American college students. PLoS One 9:e116383. doi: 10.1371/journal.pone.0116383
Giorgi, G., Arcangeli, G., Perminiene, M., Lorini, C., Ariza-Montes, A., Fiz-Perez, J., et al. (2017). Work-related stress in the banking sector: a review of incidence, correlated factors, and major consequences. Front. Psychol. 8:2166. doi: 10.3389/fpsyg.2017.02166
Glance, D. G., Ooi, E., Berman, Y., Glance, C. F., and Barrett, H. R. (2016). “Impact of a digital activity tracker-based workplace activity program on health and wellbeing,” in Proceedings of the 6th International Conference on Digital Health Conference, (New York, NY: Association for Computing Machinery), 37–41.
Grandner, M. A., Williams, N. J., Knutson, K. L., Roberts, D., and Jean-Louis, G. (2016). Sleep disparity, race/ethnicity, and socioeconomic position. Sleep Med. 18, 7–18. doi: 10.1016/j.sleep.2015.01.020
Hirsch, A. J., Park, J. E., Daniele, P. R., Fleetham, J., Ryan, C. F., and Ayas, N. T. (2016). Obstructive sleep apnoea and frequency of occupational injury. Thorax 71, 664–666. doi: 10.1136/thoraxjnl-2015-207994
Jaffe, M. P., Smolensky, M. H., and Wun, C. C. (1996). Sleep quality and physical and social well-being in north american petrochemical shift workers. South. Med. J. 89, 305–312. doi: 10.1097/00007611-199603000-00009
Jean-Louis, G., Kripke, D. F., Ancoli-Israel, S., Klauber, M. R., and Sepulveda, R. S. (2000). Sleep duration, illumination, and activity patterns in a population sample: effects of gender and ethnicity. Biol. Psychiatry 47, 921–927. doi: 10.1016/s0006-3223(99)00169-9
Jones, C. B., Dorrian, J., Jay, S. M., Lamond, N., Ferguson, S., and Dawson, D. (2006). Self-awareness of impairment and the decision to drive after an extended period of wakefulness. Chronobiol. Int. 23, 1253–1263. doi: 10.1080/07420520601083391
Kato, M., Phillips, B. G., Sigurdsson, G., Narkiewicz, K., Pesek, C. A., and Somers, V. K. (2000). Effects of sleep deprivation on neural circulatory control. Hypertension 35, 1173–1175. doi: 10.1161/01.hyp.35.5.1173
Kolla, B. P., Mansukhani, S., and Mansukhani, M. P. (2016). Consumer sleep tracking devices: a review of mechanisms, validity and utility. Expert Rev. Med. Devices 13, 497–506. doi: 10.1586/17434440.2016.1171708
Lansdall-Welfare, T., Lightman, S., and Cristianini, N. (2019). Seasonal variation in antidepressant prescriptions, environmental light and web queries for seasonal affective disorder. Br. J. Psychiatry 215, 481–484. doi: 10.1192/bjp.2019.40
Lopez, R., Jaussent, I., Scholz, S., Bayard, S., Montplaisir, J., and Dauvilliers, Y. (2013). Functional impairment in adult sleepwalkers: a case-control study. Sleep 36, 345–351. doi: 10.5665/sleep.2446
Luckhaupt, S. E., Tak, S., and Calvert, G. M. (2010). The prevalence of short sleep duration by industry and occupation in the national health interview survey. Sleep 33, 149–159. doi: 10.1093/sleep/33.2.149
McCubbin, J. A., Pilcher, J. J., and Moore, D. D. (2010). Blood pressure increases during a simulated night shift in persons at risk for hypertension. Int. J. Behav. Med. 17, 314–320. doi: 10.1007/s12529-010-9117-6
Meier-Ewert, H. K., Ridker, P. M., Rifai, N., Regan, M. M., Price, N. J., Dinges, D. F., et al. (2004). Effect of sleep loss on C-reactive protein, an inflammatory marker of cardiovascular risk. J. Am. Coll. Cardiol. 43, 678–683. doi: 10.1016/j.jacc.2003.07.050
Miller, G. E., Cohen, S., Pressman, S., Barkin, A., Rabin, B. S., and Treanor, J. J. (2004). Psychological stress and antibody response to influenza vaccination: when is the critical period for stress, and how does it get inside the body? Psychosom,. Medicine 66, 215–223. doi: 10.1097/01.psy.0000116718.54414.9e
Mitler, M. M., Gujavarty, K. S., and Browman, C. P. (1982). Maintenance of wakefulness test: a polysomnographic technique for evaluating treatment efficacy in patients with excessive somnolence. Electroencephalogr. Clin. Neurophysiol. 53, 658–661. doi: 10.1016/0013-4694(82)90142-0
Morris, D. M., Pilcher, J. J., Mulvihill, J. B., and Vander Wood, M. A. (2017a). Performance awareness: predicting cognitive performance during simulated shiftwork using chronobiological measures. Appl. Ergon. 63, 9–16. doi: 10.1016/j.apergo.2017.03.009
Morris, D. M., Pilcher, J. J., and Powell, R. B. (2017b). Task-dependent cold stress during expeditions in antarctic environments. Int. J. Circumpolar Health 76, 1–7. doi: 10.1080/22423982.2017.1379306
Morris, D. M., Pilcher, J. J., and Switzer, F. S. I. I. I. (2015). Lane heading difference: an innovative model for drowsy driving detection using retrospective analysis around curves. Accid. Anal. Prevent. 80, 117–124. doi: 10.1016/j.aap.2015.04.007
Mullins, H. M., Cortina, J. M., Drake, C. L., and Dalal, R. S. (2014). Sleepiness at work: a review and framework of how the physiology of sleepiness impacts the workplace. J. Appl. Psychol. 99, 1096–1112. doi: 10.1037/a0037885
Mysliwiec, V., O’Reilly, B., Polchinski, J., Kwon, H. P., Germain, A., and Roth, B. J. (2014). Trauma associated sleep disorder: a proposed parasomnia encompassing disruptive nocturnal behaviors, nightmares, and REM without atonia in trauma survivors. J. Clin. Sleep Med. 10, 1143–1148. doi: 10.5664/jcsm.4120
Nakamura, T. J., Nakamura, W., Yamazaki, S., Kudo, T., Cutler, T., Colwell, C. S., et al. (2011). Age-related decline in circadian output. J. Neurosci. 31, 10201–10205. doi: 10.1523/JNEUROSCI.0451-11.2011
Nakata, A., Haratani, T., Takahashi, M., Kawakami, N., Heihachiro, A., Kobayashi, F., et al. (2004). Job stress, social support, and prevalence of insomnia in a population of Japanese daytime workers. Soc. Sci. Med. 59, 1719–1730. doi: 10.1016/j.socscimed.2004.02.002
Odle-Dusseau, H. N., Bradley, J. L., and Pilcher, J. J. (2010). Subjective perceptions of the effects of sustained performance under sleep-deprivation conditions. Chronobiol. Int. 27, 318–333. doi: 10.3109/07420520903502226
Ohayon, M. M., Carskadon, M. A., Guilleminault, C., and Vitiello, M. V. (2004). Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep 27, 1255–1273. doi: 10.1093/sleep/27.7.1255
Philip, P., Taillard, J., Niedhammer, I., Guilleminault, C., and Bioulac, B. (2001). Is there a link between subjective daytime somnolence and sickness absenteeism? A study in a working population. J. Sleep Res. 10, 111–115. doi: 10.1046/j.1365-2869.2001.00253.x
Pilcher, J. J., Band, D., Odle-Dusseau, H. N., and Muth, E. R. (2007). Human performance under sustained operations and acute sleep deprivation conditions: toward a model of controlled attention. Aviat. Space Environ. Med. 78, B15–B24.
Pilcher, J. J., Ginter, D. R., and Sadowsky, B. (1997). Sleep quality versus sleep quantity: relationships between sleep and measures of health, well-being and sleepiness in college students. J. Psychosom. Res. 42, 583–596. doi: 10.1016/s0022-3999(97)00004-4
Pilcher, J. J., Jennings, K. S., Phillips, G. E., and McCubbin, J. A. (2016). Auditory attention and comprehension during a simulated night shift: effects of task characteristics. Hum. Factors 58, 1031–1043. doi: 10.1177/0018720816654377
Pilcher, J. J., Switzer, F. S. I. I. I., Munc, A., Donnelly, J., Jellen, J. C., and Lamm, C. (2018). Psychometric properties of the Epworth Sleepiness Scale: a factor analysis and item-response theory approach. Chronobiol. Int. 35, 533–545. doi: 10.1080/07420528.2017.1420075
Pitkanen, M., Hurn, J., and Kopelman, M. D. (2008). Doctors’ health and fitness to practise: performance problems in doctors and cognitive impairments. Occup. Med. 58, 328–333. doi: 10.1093/occmed/kqn080
Preckel, F., Lipnevich, A. A., Schneider, S., and Roberts, R. D. (2011). Chronotype, cognitive abilities, and academic achievement: a meta-analytic investigation. Learn. Individ. Diff. 21, 483–492. doi: 10.1016/j.lindif.2011.07.003
Ramin, C., Devore, E. E., Wang, W., Pierre-Paul, J., Wegrzyn, L. R., and Schernhammer, E. S. (2015). Night shift work at specific age ranges and chronic disease risk factors. Occup. Environ. Med. 72, 100–107. doi: 10.1136/oemed-2014-102292
Randler, C., Prokop, P., Sahu, S., and Haldar, P. (2015). Cross-cultural comparison of seven morningness and sleep-wake measures from Germany, India and Slovakia. Int. J. Psychol. 50, 279–287. doi: 10.1002/ijop.12098
Rosekind, M. R., Gregory, K. B., Mallis, M. M., Brandt, S. L., Seal, B., and Lerner, D. (2010). The cost of poor sleep: workplace productivity loss and associated costs. J. Occup. Environ. Med. 52, 91–98. doi: 10.1097/JOM.0b013e3181c78c30
Rosenthal, N. E., Sack, D. A., Gillin, J. C., Lewy, A. J., Goodwin, F. K., Davenport, Y., et al. (1984). Seasonal affective disorder: a description of the syndrome and preliminary findings with light therapy. Arch. Gen. Psychiatry 41, 72–80.
Ruiter, M. E., DeCoster, J., Jacobs, L., and Lichstein, K. L. (2011). Normal sleep in african-americans and caucasian-americans: a meta-analysis. Sleep Med. 12, 209–214. doi: 10.1016/j.sleep.2010.12.010
Smolders, K. C., and de Kort, Y. A. (2014). Bright light and mental fatigue: effects on alertness, vitality, performance and physiological arousal. J. Environ. Psychol. 39, 77–91. doi: 10.1016/j.jenvp.2013.12.010
Stranges, S., Tigbe, W., Gómez-Olivé, F. X., Thorogood, M., and Kandala, N. B. (2012). Sleep problems: an emerging global epidemic? Findings from the INDEPTH WHO-SAGE study among more than 40,000 older adults from 8 countries across Africa and Asia. Sleep 35, 1173–1181. doi: 10.5665/sleep.2012
Suzuki, K., Ohida, T., Kaneita, Y., Yokoyama, E., and Uchiyama, M. (2005). Daytime sleepiness, sleep habits and occupational accidents among hospital nurses. J. Adv. Nurs. 52, 445–453. doi: 10.1111/j.1365-2648.2005.03610.x
Swanson, L. M., Arnedt, J., Rosekind, M. R., Belenky, G., Balkin, T. J., and Drake, C. (2011). Sleep disorders and work performance: findings from the 2008 national sleep foundation sleep in america poll. J. Sleep Res. 20, 487–494. doi: 10.1111/j.1365-2869.2010.00890.x
Tochikubo, O., Ikeda, A., Miyajima, E., and Ishii, M. (1996). Effects of insufficient sleep on blood pressure monitored by a new multibiomedical recorder. Hypertension 27, 1318–1324. doi: 10.1161/01.hyp.27.6.1318
Touitou, Y., Reinberg, A., and Touitou, D. (2017). Association between light at night, melatonin secretion, sleep deprivation, and the internal clock: health impacts and mechanisms of circadian disruption. Life Sci. 173, 94–106. doi: 10.1016/j.lfs.2017.02.008
Uehli, K., Mehta, A. J., Miedinger, D., Hug, K., Schindler, C., Holsboer-Trachsler, E., et al. (2014). Sleep problems and work injuries: a systematic review and meta-analysis. Sleep Med. Rev. 18, 61–73. doi: 10.1016/j.smrv.2013.01.004
Urtasun, A., and Nuñez, I. (2018). Healthy working days: the (positive) effect of work effort on occupational health from a human capital approach. Soc. Sci. Med. 202, 79–88. doi: 10.1016/j.socscimed.2018.02.028
Vgontzas, A. N., Zoumakis, E., Bixler, E. O., Lin, H., Follett, H., Kales, A., et al. (2004). Adverse effects of modest sleep restriction on sleepiness, performance, and inflammatory cytokines. J. Clin. Endocrinol. Metab. 89, 2119–2126. doi: 10.1210/jc.2003-031562
Walker, A. D., Muth, E. R., Odle-Dusseau, H. N., Moore, D. W., and Pilcher, J. J. (2009). The effects of 28 hours of sleep deprivation on respiratory sinus arrhythmia during tasks with low and high controlled attention demands. Psychophysiology 46, 217–224. doi: 10.1111/j.1469-8986.2008.00718.x
Wong, M. M., Brower, K. J., Fitzgerald, H. E., and Zucker, R. A. (2004). Sleep problems in early childhood and early onset of alcohol and other drug use in adolescence. Alcohol. Clin. Exp. Res. 28, 578–587. doi: 10.1097/01.alc.0000121651.75952.39
Keywords: sleep, biological rhythm, sleep loss, individual performance, organizational behavior, occupational health
Citation: Pilcher JJ and Morris DM (2020) Sleep and Organizational Behavior: Implications for Workplace Productivity and Safety. Front. Psychol. 11:45. doi: 10.3389/fpsyg.2020.00045
Received: 02 May 2019; Accepted: 08 January 2020;
Published: 31 January 2020.
Edited by:Con Stough, Swinburne University of Technology, Australia
Reviewed by:Greta Mazzetti, University of Bologna, Italy
Malcolm Von Schantz, University of Surrey, United Kingdom
Copyright © 2020 Pilcher and Morris. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: June J. Pilcher, email@example.com
†ORCID: June J. Pilcher, orcid.org/0000-0003-1070-6608
‡Present address: Drew M. Morris, Centre College, Danville, KY, United States