Combatting Bedtime Procrastination Through the Understanding of Time Preferences
In today’s 24-hour society, a sufficient amount of sleep is often neglected due to work or entertainment although the consequences can be fatal. This article suggests how we can explain this behaviour through the behavioural concept of time preferences and how bedtime procrastination differs cross-culturally. Further, it suggests how we could combat this habit to improve the health and work quality of individuals.
Keywords: bedtime procrastination, time preferences, health, cross-cultural differences, MINDSPACE
Biological Anthropology: Did our ancestors get enough sleep? (Mackintosh, A.)
PSYCHOLOGICAL & BEHAVIOURAL SCIENCES
The London School of Economics and Political Science, University of London
Volume 1, Issue 2, pp. 99–105
Received: July 29, 2022
Revision recieved: December 9, 2022
Accepted: December 9, 2022
Published: January 20, 2023
Nguyen, J. (2023). Combatting bedtime procrastination through the understanding of time preferences. Cambridge Journal of Human Behaviour, 1(2), 99–105. https://www.cjhumanbehaviour.com/pbs0008
© Jenny Nguyen. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License.
Obtaining a sufficient amount of sleep is vital to maintain one’s physical and mental health to have a good quality of life. It is recommended that adults should sleep seven to nine hours per night (Cirelli et al., 2017). In 2009, Krueger and Friedman found that approximately 50% of all Americans experience sleepiness during the day, between three to seven times a week, with 35.2% of adults in the U.S. reporting to sleep for six or less hours a night, on average. However, in a modern 24-hour society, sleep insufficiency is an increasingly prominent problem. It is globally prevalent across all age groups and is regarded as a public health epidemic which is often ignored and has high economic costs (Chattu et al., 2018). While chronic sleep illnesses and night shifts may be some of the obvious reasons for insufficient sleep, bedtime procrastination has been introduced as a new area contributing to sleep deprivation. Bedtime procrastination is described as voluntarily not going to bed at the intended time, while no other external factors are prohibiting the individual from doing so (Kroese et al., 2014). The people who practise bedtime procrastination the most are individuals who have busy work schedules during the day. They hence perform bedtime procrastination to gain a few hours of entertainment back in the night by scrolling through their phone or by executing other activities. By doing so, they sacrifice their sleep for leisure time while neglecting the possible health consequences that they might face in the future (Suni & Dimitriu, 2022).
This behaviour is captured by the behavioural science concept of time preferences. People have to make intertemporal choices everyday which involve a trade-off between costs and benefits occurring at different times such as financial or health decisions (Frederick et al., 2002). Yet, people do not always make decisions which maximise their utility, and their choices may not be consistent over time (Edwards, 1954). This is due to a present bias: humans have a preference for immediate gratification, so they may discount the future out of lack of self-control and to resolve uncertainty (Chapman & Coups, 1999). By deciding to not go to bed at the intended time, obtaining a few more hours of the day for entertainment, people prefer to satisfy their present selves due to the uncertainty of the next day where their work schedule is likely to give them no free time (Chesson & Viscusi, 2000; Andersen et al. 2008; Coble & Lusk 2010). However, this can lead to severe health consequences in the future.
Sleep deficiency is related to concentration and memory problems (Ram et al., 2009), obesity, hypertension and cardiovascular disease (Buxton & Marcelli, 2010; Sabanayagam & Shankar, 2010). More than this, it can reduce cognitive processes such as copying, organisation, and problem-solving skills. Sleep-deprived people lack the energy to continue an extended chain of logical thought (Dahl, 1999). The internal state of cognitive impairment hence affects their ability to initiate behaviours affiliated with long-term or abstract goals. As a result, this can decrease their motivation to work (ibid.). Hence, sleep insufficiency caused by bedtime procrastination can lead to severe health issues in both employees as well as students. This issue is likely to cause high economic cost as work quality and work motivation decreases.
Therefore, it is important to limit bedtime procrastination in the population to prevent such risks. Thus, this article aims to investigate the link between bedtime procrastination and the notion of time preferences in a cross-cultural manner, as well as introduce interventions to tackle this issue. The article will initially explain how the term “bedtime procrastination” was coined and compare it to the concept of procrastination. It will then look at cross-cultural differences, focusing on leisure-related smartphone use, to determine the degree to which cultures vary in practising bedtime procrastination. It will then go on to explain the concept of time preferences and how it is linked to the behaviour of bedtime procrastination. Further, it will look at the cross-cultural differences of time preferences to ascertain whether it is correlated with the behaviour of bedtime procrastination cross-culturally to adjust interventions accordingly. Finally, this article will introduce interventions to decrease bedtime procrastination and will state the interventions’ limitations and propose how to make the interventions more effective. This will lead the article to conclude how the understanding of time preferences, self-regulation, and cross-cultural differences can help us understand and limit bedtime procrastination.
WHAT IS BEDTIME PROCRASTINATION?
The term bedtime procrastination was first coined by Kroese and colleagues (2014). They investigated current studies which showed that general procrastination can lead to harmful outcomes in health behaviour and introduced bedtime procrastination as a vital factor related to sufficient sleep and thus affecting well-being (ibid.). As mentioned earlier, bedtime procrastination is defined here as the failing to go to bed at the self-appointed time, while no other external circumstances are preventing the person from doing so (ibid.).
General procrastination is defined as the voluntary delay of a planned course of action despite knowing that one will be worse off for the delay and is associated with self-regulatory failure (Steel, 2007). In Kroese and colleagues’ (2014) study, they conducted a survey which investigated the degree of self-regulation of participants that engaged in bedtime procrastination. The results indicate that bedtime procrastination can be considered as a form of procrastination as it is closely associated with negative self-regulation. Further, they found that bedtime procrastination occurs when people have little mental energy or self-control strength, as the decision to go to bed is typically made at the end of the day when self-control is weakest (Baumeister, 2002).
Magalhães and colleagues (2020) found that bedtime procrastination is not just the failure of going to bed at the intended time but also voluntarily delaying sleeping while already in bed by doing other activities. Nowadays, individuals have access to a wide range of entertainment all accessible from their bed largely through smartphones. The use of screen media by youth, throughout the day and in the period before bedtime, is associated with shorter total sleep time and delayed sleep. Access to a screen-media device in the bedroom at night is associated with shorter sleep duration, later bedtime, and poorer sleep quality (Hale et al., 2018). Thus, sleep procrastination consists of two aspects: procrastination behaviours before going to bed and while in bed.
Bedtime procrastination cross-culturally
Understanding cross-cultural differences is vital in order to propose effective interventions as people differ in their psychological reactions to challenges. Incorporating how culture shapes people’s minds will also help to tackle limitations of the interventions.
Smartphones have become one of the primary channels for work and information gathering (Kang & Jung, 2014). However, off-time work-related smartphone use is negatively related to employees’ physical health such as sleep quality and quantity (Lanaj et al., 2014; Xie et al., 2018). A cross-cultural study was conducted to investigate whether off-time work-related smartphone use affected bedtime procrastination in public employees, examining cross-cultural differences from 205 employees from China and 210 employees from the United States (Hu et al., 2022). Results show that off-time work related smartphone use positively influenced bedtime procrastination due to self-control depletion which was consistent between the United States and the Chinese sample. However, smartphone use after hours increased the probability of self-control depletion more strongly in the United States. It seems that employees from individualistic cultures have different psychological reactions when it comes to handling work-related tasks after regular working hours than employees from collectivist countries (Hofstede, 1980). Employees in collectivist countries may believe that they are obliged to fulfil their organisation’s interest over prioritising their own interest. Thus, they may be less likely to view using smartphones for work during after working hours as a threat and feel hence less depleted. Due to their commitment to their organisation’s interests, it is easier for them to blur the boundaries of their work and non-work domains (Smith et al., 1996). Individualistic cultures value the individual and autonomy, while collectivist cultures value the interest of the group over the individual. The United States is an individualistic culture: its people prefer to separate their work and their private life (Lu et al., 2006). Therefore, when they receive work tasks after work hours, it is more of a threat to their autonomy and their self-control depletes, resulting in bedtime procrastination behaviour (Smith et al., 1996).
This research has shown that off-time work related smartphone use seems to be of equal significance to people’s decision to practise bedtime procrastination. This is especially the case for employees from individualistic cultures, with the rationale being that if they have to work beyond normal hours, they may perform more bedtime procrastination to make up for the lost leisure time. Hence, it may be important to further consider how individuals manage their workload during the day, so that there is a smaller chance of bedtime procrastination in the evening. Furthermore, the research has shown how one characteristic associated with bedtime procrastination—self-control depletion—varies cross-culturally. This understanding will be important to draw possible inferences when we are going to compare it to cross-cultural differences in time preferences.
UNDERSTANDING BEDTIME PROCRASTINATION THROUGH TIME PREFERENCES
It is important to mention that there has yet to be research conducted that directly links bedtime procrastination to the behavioural science concept of time preferences. Thus, this article will draw on the underlying characteristics of both domains in order to better understand the real-world behaviour at stake.
Time discounting is regarded as a fundamental characteristic of human decision-making (Frederick et al., 2002). According to standard economic theory, people should be time-consistent in their decision making and should perceive time in a linear fashion. Thus, the date they make a decision should not matter and should always maximise utility. This is reflected in constant discounting and time consistency which indicate that a person’s later preference is consistent with an earlier preference (ibid.).
However, evidence shows that human decision making is actually time-inconsistent, captured by hyperbolic discounting. People do not perceive time linearly and decision making has a temporal dimension which is explained by subjective time perception. It is more likely that people make decisions to satisfy their current self over their future self, even if deciding differently would be more beneficial in the future (Barsky et al., 1997; Chapman & Coups, 1999; Chabris et al., 2008; Bradford, 2009; Sutter et al., 2013). Goods and choices have a higher utility the closer they are to us in time rather than the ones further in the future (Thaler, 1981; Kirby & Maraković, 1995; Coller et al., 2011).
However, people’s subjective time preference suggests that, although there is a present bias, the discounting rate for delaying a decision for one month in the future to one year in the future is smaller than suggested by hyperbolic discounting and instead suggests an individual’s own subjective perception of time (Coller et al., 2011). Furthermore, humans have a systematic tendency to underestimate future wants (ibid.). Additionally, uncertainty is an important factor in our time preferences. Future payouts are often associated with uncertainty, so our inter-temporal trade-offs also depend on perceived risk that come with the delay (Chesson & Viscusi, 2000; Andersen et al., 2008; Coble & Lusk 2010). Lastly, time discounting is correlated with different concepts in psychology such as lack of self-control and impulsiveness (Kirby et al., 1999).
After understanding time preferences and human decision making, we can see that the characteristics of time preferences overlap with the characteristics of bedtime procrastination behaviour. First, because people’s decision making has a temporal dimension due to subjective time perception, people who engage in bedtime procrastination may only incorporate the needs and satisfaction of their present selves into their decision making instead of thinking about the future. They may believe that gaining a few more hours of entertainment now instead of going to sleep has greater utility for them in that moment, although they potentially know about the consequences of tiredness and fatigue the next day.
Thus, they are practising the mentality that goods and choices have a higher utility the closer they are to them in time which is portrayed by hyperbolic discounting. This is further supported by the concept of the hot-to-cold empathy gap (Read & van Leeuwen, 1998). A hot-to-cold gap describes the failing of the hot self (current needs) to appreciate the needs of the cold self (future needs). Most people spend little time in a hot state, but when they are, it is difficult to make future-orientated decisions because their focus is directed to the present. Over-buying when one goes grocery shopping while being hungry is another example to comprehend the empathy gap.
Another reason why people procrastinate their bedtime is due to uncertainty in their time preferences. People’s inter-temporal trade-offs depend on the perceived risk that comes with delay (Chesson & Viscusi, 2000; Andersen et al., 2008; Coble & Lusk, 2010). As people have busy working schedules during the day, they may think that their non-work-related activity is at risk and that there is going to be no time allocated where they can relax and do something they enjoy. Out of that uncertainty, they may not want to take the risk of losing the time for relaxation, so they neglect their sleep to compromise.
Lastly, the fact that time discounting is correlated to a lack of self-regulation highlights the fundamental problem behind bedtime procrastination. As studies have shown, people practise bedtime procrastination because they experience self-control depletion (Hu et al., 2022). Especially since self-control is weaker at the end of the day (Baumeister, 2002), people are more likely to give in to their needs in the present than thinking about the higher utility in the future that would come with a sufficient amount of sleep.
Cross-cultural differences in the delay of gratification
The research of Lamm and colleagues (2017) show that the differences between culture-specific modes of self-regulation also overlap with the study of bedtime procrastination cross-culturally conducted by Hu and colleagues (2022). Lamm and colleagues compared the self-regulatory abilities of German middle-class and rural Cameroonian Nso preschoolers using the Marshmallow test (see Mischel et al., 1972), where they instruct children to not eat the marshmallow in front of them right away but to wait to receive a bigger treat.
Results show that Nso preschoolers showed greater self-regulation abilities than German preschoolers. This is said to be due to the Nso being from a culture that nurtures interdependent selves. The culture is characterised by social hierarchy, where the socialisation goal is to develop social responsibility, including respect for elders and solidarity among peers (Nsamenang & Lamb, 1992). Whereas, German culture favours independent selves. They promote ideals like individualism, the concept of equality, and being free from constraints (Markus & Conner, 2013).
Thus, according to the cultural model of psychological autonomy, which is pursued by Germans, self-regulation is a voluntary achievement that stems from the child’s intrinsic motivation. Therefore, waiting for gratification results in an inconsistency within self-perception of a free and self-determined individual (Lamm et al., 2017). The development of self-regulation in Nso children constitutes an unconditional achievement that stems from children’s sense of belongingness and responsibility to the group (Jensen & Keller, 2015).
These findings show that self-regulation is based on different underlying processes which are affected by the cultural environment. Drawing back from the research about cross-cultural differences in bedtime procrastination, one can see that the underlying characteristics of delayed gratification are similar to the characteristics of bedtime procrastination. The relationship between the two behaviours emerge when looking at the results of the study. The Nso children come from a collectivist culture and the German children are from an individualistic culture. Thus, the differences in the ability to delay gratification in general contexts and the depletion of self-control when practising bedtime procrastination in Western and non-Western cultures seem to coincide with their respective cultures. The comparison of the cross-cultural differences in both domains will be of great benefit to further respond to limitations of interventions which try to combat procrastination.
INTERVENTIONS TO LIMIT BEDTIME PROCRASTINATION
To limit bedtime procrastination, we can use points from the MINDSPACE checklist which lists nine of the most robust ways to influence one’s behaviour consisting of Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitments, and Ego (Institute for Government, 2010).
First, as smartphone use is highly correlated with increased bedtime procrastination (Suh et al., 2021), it is reasonable to try limiting the usage of media devices before bedtime. One way to do this is using defaults which are pre-set options. Individuals can regulate the amount of time they spend on certain apps. This could be done through a regulating function that could prevent access to entertainment apps during the intended sleeping period from, for example, 11pm to 7am. This will help to decrease bedtime procrastination and promote overall productivity during the day.
Second, commitment, or adhering to a behaviour before enacting it, can be effective in changing bedtime procrastination behaviour. Individuals have to make a promise to themselves that they are going to pursue the goal in the long-term and embody that mentality. As low self-regulation is the main factor for bedtime procrastination and failing to delay gratification, integrating habit formulations such as intention implementation can help an individual regulate their self-control ability (Loft & Cameron, 2013). Implementation intentions are schedules that link key situational cues to the intended actions. For instance, practising sleep hygiene behaviours such as brushing one’s teeth or reading a book prior to bed directly encourages relaxing behaviours and timely sleep preparation that can prevent bedtime procrastination. This intervention will also prevent situations like experiencing hot-to-cold empathy gaps. Routines can limit the feeling of uncertainty as one will be aware of what their routine is the next day. Thus, they can better adjust their time preferences and time management.
Third, using priming to influence one’s action through subconscious cues can be beneficial to combat bedtime procrastination. Dissociating the bedroom from anything other than sleep and rest will subconsciously prime the mind to not engage in other activities when it is time to go to bed. Further, this can be enhanced by placing a designated scent into the bedroom. This can rewire one’s mindset about the bedroom since combining multiple senses can reinforce desired perceptions and behaviours (Sperdin, 2010).
Finally, through affect, we can powerfully shape our actions through emotional associations. Thus, mental imagery techniques can also be incorporated to encourage desired behaviours. Imagery tasks describe how one visualises the behavioural steps leading towards a desired goal which can increase behavioural adherence and goal attainment (Chan & Cameron, 2012; Hagger et al., 2012; Taylor et al., 1998). Evidence suggests that when emotional associations and vivid perceptual experiences are combined, it can enhance motivation, salience of goals, and specify aspects of the behavioural process (Chan, 2007).
Bearing this in mind, the interventions are in no way mutually exclusive, and it may bring bigger benefits if more than one intervention is implemented at the same time. The problem with following only one intervention, for instance limiting smartphone use, is that it may not solve the root of the problem, since individuals may enjoy some important social time or entertainment on their smartphones which, if cut, could be detrimental to mental health, as seen in adolescents (Orben et al., 2020).
Regulating the use of smartphones can be difficult and is bound to the individual’s willingness to change their habits. However, it would be effective to suggest that changes should be undergone in small steps. Limiting the time of using certain apps can be done gradually; for instance, by using social media one hour less than usual in a day. Afterwards, usage can be further gradually decreased. Through incorporating structural changes into defaults, levels of reactance could be lessened.
Also, adapting routines and sticking to them can be challenging. Thus, individuals should tell a friend or a family member about their goal, so they will feel more commitment to adhere to the routine. The more individuals voice their goals to others, the more likely they are going to abide by it (Ellingsen & Johannesson, 2004; Ismayilov & Potters, 2017; Vanberg, 2008).
As we have seen, there are cross-cultural differences in people’s time preferences. Thus, when proposing interventions, cultural structures and traits need to be considered. As individualist cultures are more negatively affected by bedtime procrastination and have difficulty to delay their gratification, interventions that promote autonomy and intrinsic motivation can be more effective to increase self-regulation tendencies. Collectivist cultures favour the interest of the group; thus, using interventions that encourage norm behaviour will decrease bedtime procrastination as they are more likely to be influenced by what others do.
Interventions often face scaling problems, where they are most likely only effective on a micro-level and less effective on a macro-level. As the research on bedtime procrastination is limited, to make bedtime procrastination interventions effective on the wider population, further research must be conducted looking into more demographic variables for the interventions to be successful in different perspectives.
Though MINDSPACE is shown to be a robust way to change behaviour, it fails to take the role of social networks into consideration as it primarily focuses on the behaviour change in individuals (Robinson, 2011). Hence, the concept of MINDSPACE must be further developed to be effective on larger scales.
Ultimately, understanding the notion of time preferences helps explain the underlying behaviour of bedtime procrastination and provides a foundation to introduce interventions to limit it. Insufficient sleep presents a significant risk to health and work productivity and is caused partly through different lifestyles and sleep behaviour choices that undermine sleep quantity and quality. As human decision making is inconsistent and people have the tendency to value their present selves over their future selves, people often make decisions that do not always maximise utility as they tend to seek immediate gratification (Barsky et al., 1997; Chapman & Coups, 1999; Chabris et al., 2008; Bradford, 2009; Sutter et al., 2013). This behaviour can be observed in people who practise bedtime procrastination, as they allow their present selves to obtain more free time in the night at the cost of experiencing the negative effects of insufficient sleep in the long-term.
Cross-cultural studies have shown that the differences in the ability to delay gratification and the lack of self-control when practising bedtime procrastination in Western and non-Western cultures seem to coincide with their respective culture type. People from individualistic cultures show greater self-depletion than individuals from collectivist cultures (Hu et al., 2022). These results are also reflected in the degree to which people from different cultures are more likely to engage in bedtime procrastination. Thus, context matters and incorporating factors such as cross-cultural differences when designing interventions is crucial to make them applicable at a wider scope and more effective.
As a result of people failing to produce high quality work due to sleep insufficiency, intervention using four concepts of MINDSPACE were proposed (Institute of Government, 2010). These would limit bedtime procrastination and prevent high economic costs in the long run. Evidence shows that the methods of defaults, commitment, affect, and priming are most effective in combatting bedtime procrastination behaviour as they can change behaviour to improve self-regulation (Loft & Cameron, 2013). However, there are limitations to such methods, to which alternative approaches offer opportunities to limit such potential shortfalls and increase the success rates of intervention.
However, in order to successfully limit bedtime procrastination, further research in this domain needs to be conducted to investigate and provide solid evidence that bedtime procrastination is directly associated with the concept of time discounting. Also, more research on bedtime procrastination, looking at wider demographic variables for example, is essential to provide more data from different perspectives. This will enhance our understanding of real-world behaviour regarding bedtime procrastination and will enable policymakers to introduce better and more precise interventions, ensuring high effectiveness for combatting bedtime procrastination.
Suggestions for future research
As the main objective of this article was to draw inferences between bedtime procrastination and the notion of time preferences in a cross-cultural manner, further research needs to be conducted, as the current literature on this topic is scarce. Future research could be conducted by directly testing the link between bedtime procrastination and time preferences, for example. Moreover, the effectiveness of the suggested interventions in this article need to be empirically tested to make possible improvements. Lastly, further research on MINDSPACE interventions need to be conducted to ensure that they are effective on a larger scale.
Did our ancestors get enough sleep?
Jesus College, University of Cambridge
Sleep deficiency is a public health epidemic that affects vast numbers of people, with serious consequences to health and productivity. Whilst often proposed to be an issue of obtaining insufficient sleep durations, people from non-industrialised societies have been found to obtain similar amounts of sleep compared to those from industrialised societies without displaying the same signs of sleep deficiency. This commentary draws on ethnographic research to argue that sleep deficiency in industrialised populations actually stems from poor sleep quality, then examines potential causes of poor sleep quality, and evaluates whether people from across both industrialised and non-industrialised societies can benefit from longer sleep durations.
As highlighted by Nguyen in this article, sleep deficiency is now regarded as a public health epidemic, affecting all age groups across the world. Obtaining insufficient sleep can have serious health consequences, in the U.S being linked to 7 out of the 15 leading causes of death (Kochanek et al., 2014), and can harm cognitive performance in concentration, memory, and organisation (Ram et al., 2009). Such cognitive impairment negatively impacts the ability of individuals to pursue long-term goals and reduces their motivation to work (Dahl, 1999 as cited in Nguyen’s article). Therefore, addressing this epidemic is not only a public health matter, but also an economic concern due to the resulting decrease in the productivity of workers.
The study of bedtime procrastination (the failure of an individual to go to sleep at an intended time, despite no external factors preventing them from doing so) has been introduced in the last decade as an important factor for understanding and explaining the epidemic in sleep deficiency (Kroese et al., 2014 as cited in Nguyen’s article). As Nguyen points out, this issue is thought to be exacerbated by modern technology, whereby individuals choose to sacrifice their sleep in order to gain a few extra hours of entertainment from their devices. The author describes this phenomenon as a perfect example of future discounting, whereby an individual tends to value actions which yield short-term rewards more highly than those with delayed returns. Despite this, ethnographic evidence suggests that our ancestors did not sleep any more than us. This essay will explore whether or not the Homo sapiens ancestors of all contemporary humans really got better sleep than people living in industrialised societies, by drawing on ethnographic evidence, evolutionary theory, and modern sleep data.
Evidence from non-industrial societies
We cannot measure sleep by looking at the fossil record. However, the sleeping patterns of people from non-industrialised societies prove to be a valuable resource for understanding the sleep patterns of the ancestors of all contemporary humans, since both groups share similar lifestyles and environments. A study which measured the sleep patterns of San, Hadza, and Tsimane individuals found that people from these hunter-gatherer societies appear to not sleep any more than people from industrialised populations—averaging 5.7–7.1 hours each night and falling asleep several hours after sunset (Yetish et al., 2015). Additionally, whilst wake-up times were found to be very consistent in these groups, usually occurring before sunrise, sleep onset times were irregular from day-to-day. This irregularity must have been mostly due to variance in individual choice, since the environmental factors mediating sleep remained constant, and only 5% of participants reported trouble falling asleep; therefore, it is likely that bedtime procrastination was still a common phenomenon before the development of the modern technology thought to delay sleep (ibid.).
As concluded by Yetish and colleagues, the similarity of these sleeping habits across all three societies, despite their ancient geographical separation, suggests that they represent core human sleep patterns likely characteristic of what Homo sapiens would have displayed in their environment of evolutionary adaptedness (EEA). However, despite similar sleep durations and tendencies to procrastinate at bedtime, these societies do not display the same symptoms of sleep insufficiency that industrialised populations do.
A disparity in sleep quality
The most likely explanation for the disparity in the prevalence of symptoms associated with sleep deficiency between hunter-gatherer societies and industrialised populations is a difference in sleep quality. Although people from non-industrialised societies do not appear to get any more sleep than those from industrialised societies, the lower prevalence of symptoms related to sleep deficiency suggests the sleep they do get is more effective at fulfilling its functions. Whilst many factors are known to influence sleep quality, the aforementioned study (Yetish et al., 2015) highlights two key differences between the lifestyles of industrialised versus non-industrialised societies which are likely to influence this disparity: light exposure and temperature exposure.
Light plays a major role in controlling our sleep and circadian rhythms. Adequate exposure to bright, low-angle sunlight each morning is an important trigger for setting our circadian clock (Blume et al., 2019), and is shown to be a significant predictor of sleep quality (Figuerio et al., 2017). People in industrialised societies will likely spend most of their mornings indoors, missing out on this crucial window for light exposure, whereas the people studied in Yetish and colleagues’ (2015) study receive their highest amounts of sunlight during the morning. Additionally, exposure to bright artificial light late in the evening, a phenomena common in industrialised societies but largely absent amongst hunter-gatherer societies, has been shown to be a significant predictor of poor sleep quality (Alshobaili & AlYousefi, 2019; Cho et al., 2015). All of this evidence suggests that the light exposure patterns of people in industrial societies negatively influence sleep quality.
Yetish and colleagues (2015) also found that the daily cycle of atmospheric temperature change is likely to be a potent natural regulator of sleep. In all three societies, during both winter and summer, sleep onset reliably occurred during the evening period of decreasing ambient temperature and wake onset occurred near the lowest point of the daily temperature rhythm. Surprisingly, this effect was not altered by light levels. This is highlighted by the fact that during the summer, the San society actually awoke on average one hour after sunrise, which correlated with the shifting of the temperature minimum into the light period in this region. On top of this, all societies slept roughly one hour longer in winter. People of industrial societies, living in insulated buildings, will not experience such changes in atmospheric temperature while sleeping. Therefore, although more research is required to directly address this, the absence of these natural ambient temperature changes could also contribute to poorer sleep quality. Since this study, the timing and consistency of sleep has been shown to influence sleep quality (Moderie et al., 2020; Chaput et al., 2020), as has thermal environment (Okamoto-Mizuno & Mizuno, 2012).
How much sleep do humans really need?
Whilst superior sleep quality may explain the lack of sleep insufficiency symptoms in these societies, it still does not explain why humans sleep so little compared to our primate relatives. In fact, a predictive model of primate sleep, based on factors such as body mass, brain size, and diet, concluded that humans should be sleeping about 9.5 hours a night (Nunn & Samson, 2018), not the 5.7–7.1 hours observed in this study (Yetish et al., 2015). However, this model is limited by the fact that it only uses sleep data from captive animals.
Evolutionary explanations for this tend to centre around the idea that, as our ancestors moved from trees to the ground, and hence started to sleep there, they became more vulnerable to predation (Nunn & Samson, 2018). The new selective pressures brought about by sleeping on the ground may have selected for periods of shorter, more REM-dense sleep (Samson, 2021). This theory is lent some credence by the finding that mammals at greater risk of predation tend to sleep less (Capellini et al., 2008). Various hunter-gatherer societies have been observed to typically cook meals in the evening and stay up by the fire well past sunset, telling stories and exchanging information (Wiessner, 2014). No other primates can produce light after dark (Parker et al., 2016), so, perhaps the fact that our ancestors managed to make these dark hours productive is another reason it became adaptive for them to condense their sleep into shorter periods (Preston, 2022).
However, if we evolved to sleep less as a trade-off to reduce predation and/or promote socialising, can humans today benefit from achieving more sleep than our ancestors would have gotten?
A meta-analysis of 16 studies, looking at the relationship between sleep duration and all-cause mortality across all ages, found that both short (< 6 hours) and long ( > 8 hours) sleep durations are associated with increased risk of death (Cappuccio et al., 2010). Similarly, a study using UK BioBank data from nearly 500,000 middle-aged participants concluded that 7 hours of sleep is the optimum daily amount for cognition and mental health (Li et al., 2022), whilst another study, using a global sample of over 10,000 people, showed that 7.4 hours is the optimum for high-level cognitive abilities (Wild et al., 2018). Despite this, researchers at Fitbit found that people who slept an average of only 5–6.5 hours a night performed better on a cognitive test than those who slept more or less (Redford, 2018).
Whilst this result from Fitbit may seem confusing at first, it can be explained. The first three studies all measured sleep duration as the time between sleep onset and offset, and gathered this data through surveys. This approach is flawed, however, because most people will periodically wake-up throughout the night without realising it (Patel et al., 2022), resulting in less sleep achieved than expected. The Fitbit study, as well as the study by Yetish and colleagues (2015), use wearable devices to accurately track the total time spent asleep, and find significantly lower sleep duration values than those estimated using the previous method. These differences in the way studies measure sleep duration make it difficult to reliably compare results; however, it does not seem to be the case that obtaining any more sleep than our hunter-gatherer counterparts will bring us any significant benefits (given that the sleep we do obtain is of good quality).
It seems clear that the sleep insufficiency epidemic is much more complicated than a simple case of individuals not sleeping enough. Despite their average sleep durations being on the low-end of those observed in industrialised populations, the hunter-gatherer populations from southern Africa and Bolivia discussed in this commentary do not frequently display symptoms of sleep deficiency, and rarely report trouble falling asleep. This could be explained by an evolutionary mismatch between how our ancestors lived and how people of industrialised societies live today, whereby the lifestyles of the latter group are negatively affecting the quality of their sleep. Despite this, it is difficult to conclude how much sleep is optimum for humans. Whilst comparative primatology predicts that we require significantly more sleep than we obtain, such models are limited by the fact that almost all primate sleep data comes from captive animals, and unique aspects of our species predict that we evolved to sleep in shorter periods. Most contemporary research seems to agree on approximately 7 hours as the optimum amount of sleep to obtain; however, this value is inflated by researchers failing to account for the difference between sleep period (time between onset and offset) and sleep duration (time actually spent in a sleep state). Though not yet quantified, when accounting for this difference, I argue the optimum may well fall closer to 6 hours per night (given that the sleep obtained is of good quality). It is important to stress that these values are averages, based on research skewed towards middle-aged adults. Hence, when making decisions about their own sleep, people should account for individual variance in sleep requirements, which can be affected by a multitude of factors (Chaput et al., 2018).
Andersen, S., Harrison, G. W., Lau, M. I., & Rutström, E. E. (2008). Eliciting risk and time preferences. Econometrica, 76(3), 583–618. https://doi.org/10.1111/j.1468-0262.2008.00848.x
Barsky, R. B., Juster, F. T., Kimball, M. S., & Shapiro, M. D. (1997). Preference parameters and behavioral heterogeneity: An experimental approach in the Health and Retirement Study. The Quarterly Journal of Economics, 112(2), 537–579. https://doi.org/10.1162/003355397555280
Baumeister, R. F. (2002). Yielding to temptation: Self‐control failure, impulsive purchasing, and consumer behavior. Journal of Consumer Research, 28(4), 670–676. https://doi.org/10.1086/338209
Bradford, W. D. (2009). The association between individual time preferences and Health Maintenance Habits. Medical Decision Making, 30(1), 99–112. https://doi.org/10.1177/0272989×09342276
Buxton, O. M., & Marcelli, E. (2010). Short and long sleep are positively associated with obesity, diabetes, hypertension, and cardiovascular disease among adults in the United States. Social Science & Medicine, 71(5), 1027–1036. https://doi.org/10.1016/j.socscimed.2010.05.041
Chabris, C. F., Laibson, D., Morris, C. L., Schuldt, J. P., & Taubinsky, D. (2008). Individual laboratory-measured discount rates predict field behavior. Journal of Risk and Uncertainty, 37(2–3), 237–269. https://doi.org/10.1007/s11166-008-9053-x
Chan, C. K. Y. (2007). Approach and avoidance motivations: Development of a new assessment tool and an intervention using a self-regulatory perspective [Doctoral Thesis, University of Auckland]. http://hdl.handle.net/2292/51032
Chan, C. K. Y., & Cameron, L. D. (2012). Promoting physical activity with goal-oriented mental imagery: A randomized controlled trial. Journal of Behavioral Medicine, 35(3), 347–363. https://doi.org/10.1007/s10865-011-9360-6
Chapman, G. B., & Coups, E. J. (1999). Time preferences and preventive health behavior. Medical Decision Making, 19(3), 307–314. https://doi.org/10.1177/0272989×9901900309
Chattu, V., Manzar, M., Kumary, S., Burman, D., Spence, D., & Pandi-Perumal, S. (2018). The global problem of insufficient sleep and its serious public health implications. Healthcare, 7(1), 1. https://doi.org/10.3390/healthcare7010001
Chesson, H., & Viscusi, W. K. (2000). The heterogeneity of time-risk tradeoffs. Journal of Behavioral Decision Making, 13(2), 251–258. https://doi.org/10.1002/(SICI)1099-0771(200004/06)13:2%3C251::AID-BDM328%3E3.0.CO;2-P
Cirelli, C., Benca, R., & Eichler, A. F. (2017). Insufficient sleep: Definition, epidemiology, and adverse outcomes. UpToDate. Retrieved May 2, 2022, from https://www.uptodate.com/contents/insufficient-sleep-definition-epidemiology-and-adverse-outcomes#topicContent
Coble, K. H., & Lusk, J. L. (2010). At the nexus of risk and time preferences: An experimental investigation. Journal of Risk and Uncertainty, 41(1), 67–79. https://doi.org/10.1007/s11166-010-9096-7
Coller, M., Harrison, G. W., & Rutstrom, E. E. (2011). Latent process heterogeneity in discounting behavior. Oxford Economic Papers, 64(2), 375–391. https://doi.org/10.1093/oep/gpr019
Dahl, R. R. (1999). The consequences of insufficient sleep for adolescents: Links between sleep and emotional regulation. Phi Delta Kappan, 80(5), 354–359. https://www.spps.org/cms/lib010/MN01910242/Centricity/Domain/7352/conseqsleepdep-dahl.pdf
Edwards, W. (1954). The theory of decision making. Psychological Bulletin, 51(4), 380–417. https://doi.org/10.1037/h0053870
Ellingsen, T., & Johannesson, M. (2004). Promises, threats and fairness. The Economic Journal, 114(495), 397–420. https://doi.org/10.1111/j.1468-0297.2004.00214.x
Frederick, S., Loewenstein, G., & O’donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature, 40(2), 351–401. https://doi.org/10.1257/jel.40.2.351
Hagger, M. S., Lonsdale, A., & Chatzisarantis, N. L. (2012). A theory-based intervention to reduce alcohol drinking in excess of guideline limits among undergraduate students. British Journal of Health Psychology, 17(1), 18–43. https://doi.org/10.1111/j.2044-8287.2010.02011.x
Hale, L., Kirschen, G. W., LeBourgeois, M. K., Gradisar, M., Garrison, M. M., Montgomery-Downs, H., Kirschen, H., McHale, S. M., Chang, A.-M., & Buxton, O. M. (2018). Youth screen media habits and sleep. Child and Adolescent Psychiatric Clinics of North America, 27(2), 229–245. https://doi.org/10.1016/j.chc.2017.11.014
Hofstede, G. (1980). Culture and organizations. International Studies of Management & Organization, 10(4), 15–41. https://doi.org/10.1080/00208825.1980.11656300
Hu, W., Ye, Z., & Zhang, Z. (2022). Off-time work-related smartphone use and bedtime procrastination of public employees: A cross-cultural study. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.850802
Institute for Government. (2010). MINDSPACE: Influence behaviour through public policy. https://www.instituteforgovernment.org.uk/publications/mindspace
Ismayilov, H., & Potters, J. (2017). Elicited vs. voluntary promises. Journal of Economic Psychology, 62, 295–312. https://doi.org/10.1016/j.joep.2017.07.005
Jensen, L. A., & Keller, H. (2015). Dual and communal parenting. The Oxford Handbook of Human Development and Culture. https://doi.org/10.1093/oxfordhb/9780199948550.013.36
Kang, S., & Jung, J. (2014). Mobile communication for human needs: A comparison of smartphone use between the US and Korea. Computers in Human Behavior, 35, 376–387. https://doi.org/10.1016/j.chb.2014.03.024
Kirby, K. N., & Maraković, N. N. (1995). Modeling myopic decisions: Evidence for hyperbolic delay-discounting within subjects and amounts. Organizational Behavior and Human Decision Processes, 64(1), 22–30. https://doi.org/10.1006/obhd.1995.1086
Kirby, K. N., Petry, N. M., & Bickel, W. K. (1999). Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls. Journal of Experimental Psychology: General, 128(1), 78–87. https://doi.org/10.1037/0096-3422.214.171.124
Kroese, F. M., De Ridder, D. T., Evers, C., & Adriaanse, M. A. (2014). Bedtime procrastination: Introducing a new area of procrastination. Frontiers in Psychology, 5, 611. https://doi.org/10.3389/fpsyg.2014.00611
Krueger, P. M., & Friedman, E. M. (2009). Sleep duration in the United States: A cross-sectional population-based study. American Journal of Epidemiology, 169(9), 1052–1063. https://doi.org/10.1093/aje/kwp023
Lamm, B., Keller, H., Teiser, J., Gudi, H., Yovsi, R. D., Freitag, C., Poloczek, S., Fassbender, I., Suhrke, J., Teubert, M., Vöhringer, I., Knopf, M., Schwarzer, G., & Lohaus, A. (2017). Waiting for the second treat: Developing culture-specific modes of self-regulation. Child Development, 89(3). https://doi.org/10.1111/cdev.12847
Lanaj, K., Johnson, R. E., & Barnes, C. M. (2014). Beginning the workday yet already depleted? Consequences of late-night smartphone use and sleep. Organizational Behavior and Human Decision Processes, 124(1), 11–23. https://doi.org/10.1016/j.obhdp.2014.01.001
Loft, M. H., & Cameron, L. D. (2013). Using mental imagery to deliver self-regulation techniques to improve sleep behaviors. Annals of Behavioral Medicine, 46(3), 260–272. https://doi.org/10.1007/s12160-013-9503-9
Lu, L., Gilmour, R., Kao, S. F., & Huang, M. T. (2006). A cross‐cultural study of work/family demands, work/family conflict and wellbeing: The Taiwanese vs British. Career Development International, 11(1), 9–27. https://doi.org/10.1108/13620430610642354
Magalhães, P., Cruz, V., Teixeira, S., Fuentes, S., & Rosário, P. (2020). An exploratory study on sleep procrastination: Bedtime vs. while-in-bed procrastination. International Journal of Environmental Research and Public Health, 17(16), 5892. https://doi.org/10.3390/ijerph17165892
Markus, H. R., & Conner, A. (2013). Clash! 8 cultural conflicts that make us who we are. Hudson Street Press. ISBN: 9781594630989
Mischel, W., Ebbesen, E. B., & Raskoff Zeiss, A. (1972). Cognitive and attentional mechanisms in delay of gratification. Journal of Personality and Social Psychology, 21(2), 204–218. https://doi.org/10.1037/h0032198
Nsamenang, A. B., & Lamb, M. E. (1992). The acquisition of socio-cognitive competence by Nso children in the Bamenda grassfields of Northwest Cameroon. International Journal of Behavioral Development, 16(3), 429–441. https://doi.org/10.1177/016502549301600304
Orben, A., Tomova, L., & Blakemore, S. J. (2020). The effects of social deprivation on adolescent development and mental health. The Lancet Child & Adolescent Health, 4(8), 634–640. https://doi.org/10.1016/S2352-4642(20)30186-3
Ram, S., Seirawan, H., Kumar, S. K., & Clark, G. T. (2009). Prevalence and impact of sleep disorders and sleep habits in the United States. Sleep and Breathing, 14(1), 63–70. https://doi.org/10.1007/s11325-009-0281-3
Read, D., & van Leeuwen, B. (1998). Predicting hunger: The effects of appetite and delay on choice. Organizational Behavior and Human Decision Processes, 76(2), 189–205. https://doi.org/10.1006/obhd.1998.2803
Robinson, L. (2011, February 2). Changing behaviour without changing minds – the mindspace guide. Changeology. Retrieved May 2, 2022, from https://changeologyblog.wordpress.com/2011/02/02/changing-behaviour-without-changing-minds-the/
Sabanayagam, C., & Shankar, A. (2010). Sleep duration and cardiovascular disease: results from the National Health Interview Survey. Sleep, 33(8), 1037–1042. https://doi.org/10.1093/sleep/33.8.1037
Smith, P. B., Dugan, S., & Trompenaars, F. (1996). National culture and the values of organizational employees. Journal of Cross-Cultural Psychology, 27(2), 231–264. https://doi.org/10.1177/0022022196272006
Sperdin, H. F. (2010). The behavioral relevance of multisensory neural response interactions. Frontiers in Neuroscience. https://doi.org/10.3389/neuro.01.009.2010
Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133(1), 65–94. https://doi.org/10.1037/0033-2909.133.1.65
Suh, S., Cho, N., Jeoung, S., & An, H. (2021). Developing a psychological intervention for decreasing bedtime procrastination: The Bed-Pro Study. Behavioral Sleep Medicine, 1–15. https://doi.org/10.1080/15402002.2021.1979004
Suni, E., & Dimitriu, A. (2022). Mental Health and Sleep. Sleep Foundation. https://www.sleepfoundation.org/mental-health
Sutter, M., Kocher, M. G., Glätzle-Rützler, D., & Trautmann, S. T. (2013). Impatience and uncertainty: Experimental decisions predict adolescents’ field behavior. American Economic Review, 103(1), 510–531. https://doi.org/10.1257/aer.103.1.510
Taylor, S. E., Pham, L. B., Rivkin, I. D., & Armor, D. A. (1998). Harnessing the imagination: Mental simulation, self-regulation, and coping. American Psychologist, 53(4), 429–439. https://doi.org/10.1037/0003-066x.53.4.429
Thaler, R. (1981). Some empirical evidence on dynamic inconsistency. Economics Letters, 8(3), 201–207. https://doi.org/10.1016/0165-1765(81)90067-7
Vanberg, C. (2008). Why do people keep their promises? An experimental test of two explanations. Econometrica, 76(6), 1467–1480. https://doi.org/10.3982/ECTA7673
Xie, X., Dong, Y., & Wang, J. (2018). Sleep quality as a mediator of problematic smartphone use and clinical health symptoms. Journal of Behavioral Addictions, 7(2), 466–472. https://doi.org/10.1556/2006.7.2018.40
Alshobaili, F. A., & AlYousefi, N. A. (2019). The effect of smartphone usage at bedtime on sleep quality among Saudi non-medical staff at King Saud University Medical City. Journal of Family Medicine and Primary Care, 8(6), 1953–1957. https://doi.org/10.4103/jfmpc.jfmpc_269_19
Blume, C., Garbazza, C., & Spitschan, M. (2019). Effects of light on human circadian rhythms, sleep and mood. Somnologie, 23(3), 147–156. https://doi.org/10.1007/s11818-019-00215-x
Capellini, I., Barton, R.A., McNamara, P., Preston, B.T., & Nunn, C.L. (2008). Phylogenetic analysis of the ecology and evolution of mammalian sleep. Evolution, 62, 1764–1776. https://doi.org/10.1111/j.1558-5646.2008.00392.x
Cappuccio, F. P., D’Elia, L., Strazzullo, P., & Miller, M. A. (2010). Sleep duration and all-cause mortality: A systematic review and meta-analysis of prospective studies. Sleep, 33(5), 585–592. https://doi.org/10.1093/sleep/33.5.585
Chaput, J. P., Dutil, C., & Sampasa-Kanyinga, H. (2018). Sleeping hours: What is the ideal number and how does age impact this? Nature and Science of Sleep, 10, 421–430. https://doi.org/10.2147/NSS.S163071
Chaput, J. P., Dutil, C., Featherstone, R., Ross, R., Giangregorio, L., Saunders, T. J., Janssen, I., Poitras, V. J., Kho, M. E., Ross-White, A., Zankar, S., & Carrier, J. (2020). Sleep timing, sleep consistency, and health in adults: A systematic review. Applied Physiology, Nutrition, and Metabolism, 45(10, Suppl. 2), S232–S247. https://doi.org/10.1139/apnm-2020-0032
Cho, Y., Ryu, S. H., Lee, B. R., Kim, K. H., Lee, E., & Choi, J. (2015). Effects of artificial light at night on human health: A literature review of observational and experimental studies applied to exposure assessment. Chronobiology International, 32(9), 1294–1310. https://doi.org/10.3109/07420528.2015.1073158
Figueiro, M. G., Steverson, B., Heerwagen, J., Kampschroer, K., Hunter, C. M., Gonzales, K., Plitnick, B., & Rea, M. S. (2017). The impact of daytime light exposures on sleep and mood in office workers. Sleep Health, 3(3), 204–215. https://doi.org/10.1016/j.sleh.2017.03.005
Kochanek, K. D., Murphy, S. L., Xu, J., & Arias, E. (2014). Mortality in the United States, 2013. NCHS Data Brief, (178), 1–8. PMID: 25549183
Li, Y., Sahakian, B.J., Kang, J., Langley, C., Zhang W., Xie, C., Xiang, S., Yu, J., Cheng, W., & Feng, J. (2022). The brain structure and genetic mechanisms underlying the nonlinear association between sleep duration, cognition and mental health. Nature Aging, 2, 425–437. https://doi.org/10.1038/s43587-022-00210-2
Moderie, C., Van der Maren, S., Paquet, J., & Dumont, M. (2020). Impact of mandatory wake time on sleep timing, sleep quality and rest-activity cycle in college and university students complaining of a delayed sleep schedule: An actigraphy study. Nature and Science of Sleep, 12, 365–375. https://doi.org/10.2147/NSS.S251743
Nunn, C. L., & Samson, D. R. (2018). Sleep in a comparative context: Investigating how human sleep differs from sleep in other primates. American Journal of Physical Anthropology, 166(3), 601–612. https://doi.org/10.1002/ajpa.23427
Okamoto-Mizuno, K., & Mizuno, K. (2012). Effects of thermal environment on sleep and circadian rhythm. Journal of Physiological Anthropology, 31(1), 14. https://doi.org/10.1186/1880-6805-31-14
Parker, C. H., Keefe, E. R., Herzog, N. M., O’connell, J. F., & Hawkes, K. (2016). The pyrophilic primate hypothesis. Evolutionary Anthropology, 25(2), 54–63. https://doi.org/10.1002/evan.21475
Patel, A. K., Reddy, V., Shumway, K. R., & Araujo, J. F. (2022). Physiology, sleep stages. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK526132/
Preston, E. (2022). The awake ape: Why people sleep less than our primate relatives. Knowable Magazine. https://doi.org/10.1146/knowable-042822-1
Ram, S., Seirawan, H., Kumar, S. K., & Clark, G. T. (2009). Prevalence and impact of sleep disorders and sleep habits in the United States. Sleep and Breathing, 14(1), 63–70. https://doi.org/10.1007/s11325-009-0281-3
Redford, G. (2018, October 19). What playing this game can tell you about your brain power. Fitbit News. https://blog.fitbit.com/brain-power/
Samson, D. (2021). The human sleep paradox: The unexpected sleeping habits of Homo sapiens. Annual Review of Anthropology, 50(1), 259–274. https://doi.org/10.1146/annurev-anthro-010220-075523
Wiessner P. W. (2014). Embers of society: Firelight talk among the Ju/’hoansi Bushmen. Proceedings of the National Academy of Sciences of the United States of America, 111(39), 14027–14035. https://doi.org/10.1073/pnas.1404212111
Wild, C. J., Nichols, E. S., Battista, M. E., Stojanoski, B., & Owen, A. M. (2018). Dissociable effects of self-reported daily sleep duration on high-level cognitive abilities. Sleep, 41(12), Article zsy182. https://doi.org/10.1093/sleep/zsy182
Yetish, G., Kaplan, H., Gurven, M., Wood, B., Pontzer, H., Manger, P. R., Wilson, C., McGregor, R., & Siegel, J. M. (2015). Natural sleep and its seasonal variations in three pre-industrial societies. Current Biology, 25(21), 2862–2868. https://doi.org/10.1016/j.cub.2015.09.046