While sleep is one of the most important factors of physical, psychological, and mental health, its contributions are still widely taken for granted. Sleep has the potential of significantly improving or drastically diminishing one’s productivity. As such, it is necessary for sleep to be studied more deeply to understand how it affects employees. This study seeks to bring out the connection between the number of hours worked and the total number of hours slept per night, while putting into consideration variables such as full-time or part-time employment, and gender.
A literature review will be conducted highlighting existing literature in the contributions of sleep to productivity, and the severity of the consequences of failing to get enough sleep. Further, the differences in sleeping habits between men and women will also be highlighted, in addition to how longer working hours can result in fewer hours of sleep and diminish productivity. A 9-part questionnaire will also be distributed among students to select a target group of 30 individuals, 15 of each gender, to provide the primary source from which correlation between the dependent and independent variables will be derived using the correlation coefficient. The filled questionnaires will be compiled and grouped in terms of gender to allow for a pattern to be established either in agreement with or contrary to the proposed hypotheses.
Key words: sleep, productivity, longer working hours, fewer hours of sleep
Studies of the brain have been around for centuries now, but the progress on our knowledge regarding its inner workings advances at a very slow pace. However, one of the main achievements in neurology – the study of the brain – is the determination of the role of sleep in improving mental acuity and alertness in tackling day-to-day tasks. It is unfortunate that despite the importance of sleep in keeping things in our bodies running smoothly, it is one of the most neglected aspects of leading healthy lives. This study intends to establish a connection between the number of hours worked per day and the number of hours slept at night. Shedding light on this area might help the corporate world to put more stake in ensuring employees get enough sleep on a regular basis to improve their productivity.
As such, this study aims to answer the following questions: Is there a connection between the total hours you work and the number of hours you sleep? Do variables such as gender and job hours (part-time or full-time) affect the amount of sleep you get? The working hypotheses upon which this study is based are: Individuals who work less hours get more sleep than those who work more hours. Those of a male gender who work 20 hours or less get more sleep than females who work the same number of hours.
To accurately draw conclusions from the study, it is necessary to first bring out existing literature on each variable. According to the first hypothesis, individuals working less hours sleep longer than those working longer hours. As such, the variables of interest here are job hours (independent variable) and hours of sleep per night (dependent variable). Further, the second hypothesis connects gender (independent variable) with the aforementioned dependent variable.
Variable 1: Job Hours (Independent variable)
During working hours, the mind is active as is required for the achievement of tasks and objectives. However, for one to fall and stay asleep, it is necessary to be in a relaxed environment to allow the mind to ‘cool down’ in a matter of speaking, and eventually drift off. Therefore, working long hours as is the case in full-time employment implies that the mind is active for a longer time, which necessitates a longer time to ‘cool down’ and fall asleep. According to Dahlgren et al. (2006), longer hours also imply that there is not enough time for recuperation and relaxation, which accumulates stress, making it difficult to fall asleep.
Further, Burgard and Ailshire (2009) explore the concept of workplace experiences ‘following employees home’, which can thus affect their quality of sleep. Their article builds on the premise that the workplace exposes workers to stresses that are otherwise not available at home, which in turn causes them poor quality of sleep. Building on this logically, longer exposure to such stresses in the case of full-time employment causes workers to take these stresses home, minimizing time needed to relax, a claim substantiated by Dahlgren’s article.
Therefore, drawing from these two articles, part-time employment gives workers more time to engage in leisurely activities such as going to the gym or taking yoga classes which reframe the mind and prepare it for the following day’s tasks. This is the kind of recuperation that Dahlgren talks about.
Variable 2: Gender
Much like most other biological aspects, sleep is affected by gender differences, beginning from the most basic concept of circadian rhythms – mental, physical and behavioral changes concurrent with daily cycles often in response to light and darkness. As such, men have longer circadian rhythms as compared to women, which is noticed in the fact that women are often more active in the morning, while men are more active at night (Santhi et al., 2016). Further, men spend less time in deep sleep as compared to women, a statistic which varies depending on age. Also, women get up to 11 minutes more sleep than men, but are more likely to experience sleep problems such as insomnia. This might be due to hormonal changes associated with the female reproductive cycle such as menstruation, pregnancy and menopause (Stallings, 2021).
Therefore, by most objective merits, women sleep longer and better than men, assuming sleeping problems and disturbances are at a minimum or entirely absent. However, this goes against the second hypothesis which states that men working 20 hours or less per week get more sleep than women working the same hours, which implies that the aspect of daily activities has a stronger correlation than that of gender in terms of hours slept every night. By analyzing these three variables, a reason for this discordance can be derived and understood.
Variable 3: Hours Slept
As the dependent variable, the concept of hours slept is affected both by gender and by hours worked per day. This aspect is heavily researched on, with articles such as one by Philibert (2005) which explores the relationship between sleep loss and the performance of non-physicians and residents in a hospital. Philibert expresses the importance of cognitive skills in ensuring high quality patient care, bringing out how sleep loss and sleep deprivation can cause life-threatening errors not only to patients but also to fellow workers. According to WELCOA (2018), failure to get adequate sleep affects one’s mood, which in turn impairs concentration and focus, thus drastically reducing productivity. Further, failure to get enough sleep resulted in more than 40% of adults falling asleep during work or even while driving.
The effects of reduced productivity are not only felt in the workplace, but also in the economy. According to the RAND Corporation, more than $400 billion is lost annually in the US economy due to poor sleep. Additionally, poor sleep causes a vicious cycle in which an employee fails to get enough sleep, gets overwhelmed by work the next day, which increases their anxiety and stress, thus affecting their ability to fall asleep at night. Therefore, according to existing literature, it is necessary to adopt positive sleeping practices and behaviors not only for employees’ benefits, but also for the good of the economy at large.
How this Study Fits into Existing Literature
This study seeks to establish a connection between and among the three aforementioned variables. Previous study only correlate two of these variables. For example, the article by Dahlgren showcases the relationship between working long hours and sleep and stress levels, which are two of the desired variables for this study. Moreover, Burgard and Ailshire express how employees facing stresses at work end up carrying them home, which implies that such stresses are always with them even when they are supposed to be unwinding and relaxing. Due to this, they end up sleeping poorly. This article also highlights similar variables to those by Dahlgren.
Nevertheless, the differences in sleep in men and women have been discussed by Santhi et al. and Stallings, which provide a baseline regarding how gender defines sleeping habits and patterns. The effect of poor sleep on productivity in the workplace has also been explored separately. As such, each of the sections above in this literature review only describe how the variables are related with each other, but not how the independent variables work in tandem to determine the nature of the dependent variable. This study thus fits into existing research by filling this gap, and by adding to existing knowledge regarding sleep and relaxation.
My participants will include 30+ (15 male and 15 female selected at random) students who are currently enrolled at Louisiana State University Alexandria and at the local college in my area Georgia State University ages 18 and up. The equal number of male and female participants is intended for highlighting the correlation between sleep, hours worked, and gender. I intend on recruiting them using both of the campuses Facebook group pages and some student email listings. Partakers will have the option to freely take part in a survey that will assist with a psychological research study.
There will be no age maximum on this study, nor will race and/or gender be a deciding factor in picking the participants although they will be asked about the latter at the end of the survey process for the results. While I assume that individuals who work full or part-time hours may be more beneficial to this study, anyone one who is employed and a college student over the age of 18 are encouraged to participate. However, the inclusion of individuals from different ages might cause problems correlating the data because each variable is affected differently according to age. Therefore, further research is required to accurately examine the variables across different age groups for better results.
The survey consists of 9 multiple-choice questions that should take no more than 3 minutes to complete. The survey was created by Fernandez (2021) and utilizes questions relating to demographics and sleep habits. Also available upon start of the survey, questions 1-3 will be the Informed consent, that contains the researcher’s contact information, guarantee that there are nor will be any penalties for not completing and/or participating in the survey, and ensure all information retained from survey will stay anonymous and will not be used for any other purposes outside of this study. Following consent, the second portion of questions (3-6) refer to participant’s employment status, job classification (part-time or full-time) and average hourly amount of sleep on a daily basis. The remaining questions focus on race, age, and gender. The assessment has a significant level of legitimacy due to the questions aiming to find the connection between work and sleep. Limitations that could affect the reliability and validity of the study would be if participants choose to answer questions with no honest and/or honorable intention.
The purpose of this study will be discovered using a non-experimental design and Factorial ANOVA. This study consists of 2 independent variables (gender and hours worked) and 1 dependent variable (sleep). The survey, which will be posted on Google Forms, will be distributed via a post on Facebook with the link attached and can be accessed from their personal Facebook app. First, participants who attend LSUA will receive an email Dr. Elder in the psychology department that invites them to take part in various surveys from students who are currently enrolled in PSYC 4017 to complete their research. If the student wishes to participate, a list of survey links will be populated on the screen and students will have the option to select from the selection of surveys offers. At the conclusion of the survey, there will be no additional requirements from participants.
The filled forms will then be examined and analyzed for any similarities and correlations important to the establishment of a pattern. Selection of filled forms will be done by collecting the first 15 forms filled by male students, and the first 15 forms filled by female students, while checking for their validity. It might be impossible to divide them according to age since the targeted demographic is that of college students who are between ages 22 and 34. Further, the small size of the group might make it impossible to divide them according to age groups, which can also be attributed to the fact that doing so would fall outside the scope of objectives that this project intends to achieve.
In this study, the first research question is whether or not the number of hours you sleep is affected by the number of hours you work. It is to be expected that there will be some considerable correlations between the two variables, which also shows a relationship with the hypothesis for this study that states that employees who work less hours get more sleep than those employees who work more hours. The second hypothesis states that female employees will receive less sleep per night than male employees. If there is a substantial correlation between the two, the Pearson Correlation should be applicable since I would like to find the correlation between the number of hours working and the hours of sleep. I do not feel that I can make use of Factorial ANOVA since we will only be comparing the sleep hours between male employees and female employees, which can be analyzed by an independent-samples t-test. However, if necessary, using Factorial ANOVA, I would change the second independent variable to the current position of the employee, whether or not the employee is freelance, regular, supervisor, and manager, so it would truly be a factorial design on its own. Therefore, if the correlation is indeed proved to be positive, it would lead to the accepting the null hypothesis as the experimental hypothesis.
Based on data collected from the literature review, it is possible to draw the conclusion that individuals that work less hours such as part-time employee sleep more than those working full-time jobs. Therefore, this study accepts the first the hypothesis which connects these two variables, describing that long working hours imply less hours of sleep which increase stress carried forward to the next day and affect sleep even further. Therefore, companies are encouraged to implement measures such as maximum working hours to prevent employees from overworking, which might cause more harm than good.
Further, the articles discussed above also lead to the conclusion that gender affects the number of hours slept per night. Without considering the aspect of working, women sleep longer and better than men. However, the second hypothesis stipulates that males working less than 20 hours a day sleep better than females working the same hours. The discord between existing literature and the findings of this study indicates the presence of another variable which is unaccounted for. Future studies should focus on the amount of sleep received by men and women in working conditions.
Burgard, S. A., & Ailshire, J. A. (2009). Putting work to bed: stressful experiences on the job and sleep quality. Journal of health and social behavior, 50(4), 476-492.
Dahlgren, A., Kecklund, G., & Åkerstedt, T. (2006). Overtime work and its effects on sleep, sleepiness, cortisol and blood pressure in an experimental field study. Scandinavian journal of work, environment & health, 318-327.
Fernandez, E. How Much Sleep Are You Getting? (2021). Retrieved May 22, 2021, from https://docs.google.com/forms/d/1C7gICLWXH9WMYOxzU4rAkGgzMSnKKvi6-dkEshEYKVc
Philibert, I. (2005). Sleep loss and performance in residents and non-physicians: a meta-analytic examination. Sleep, 28(11), 1392-1402.
Santhi, N., Lazar, A. S., McCabe, P. J., Lo, J. C., Groeger, J. A., & Dijk, D. J. (2016). Sex differences in the circadian regulation of sleep and waking cognition in humans. Proceedings of the National Academy of Sciences, 113(19), E2730-E2739.
Stallings, M. (2021, March 30). Men’s and Women’s Sleep Habits. Sleep.org. https://www.sleep.org/sleep-for-men-and-women/
WELCOA. (2018, December 14). The Effects of Poor Sleep in the Workplace. WELCOA. https://www.welcoa.org/blog/effects-poor-sleep-workplace/