Sleep for success The impact the amount of sleep on working students

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SLEEP FOR SUCCESS

Mariana Neuparth Sottomayor Bismark do Agro

The impact of the amount of sleep on working students

Dissertation presented as a partial requirement for obtaining

the Master’s Degree in Statistics and Information

Management, with specialization in Information Analysis and

Management

BOOK SPINE

Sleep for success

The impact of the amount of sleep on working students Mariana Neuparth Sottomayor Bismark do Agro

MEGI

2021

MGI

i

ii NOVA Information Management School

Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa

SLEEP FOR SUCCESS

THE IMPACT OF THE AMOUNT OF SLEEP ON WORKING STUDENTS

by

Mariana Neuparth Sottomayor Bismark do Agro

Dissertation presented as a partial requirement for obtaining the Master’s Degree in Statistics and Information Management, with specialization in Information Analysis and Management

Advisor: Ana Cristina Costa

November 2022

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ABSTRACT

University years are typically a time of exponential intellectual development. Even though sleep is essential for learning, it is typically overlooked. Young adults who work and study simultaneously are more likely to experience sleep deprivation and irregular sleep patterns.

Consolidating a professional, educational and social life is already a challenge. Sleep health affects productivity and quality of life. The purpose of this research was to investigate the impact of sleep on the academic and professional performance of a set of working students from NOVA IMS. A self-report questionnaire was developed with previously established measures. Conclusions were drawn from descriptive analysis and statistical testing. The students’ quality of sleep was moderate, with a tendency to good sleep health, despite being accompanied by poor psychological well-being. There was no statistical support for a difference in sleep patterns between men and women. Findings showed there was no meaningful connection between sleep and academic or professional success. However, there was evidence that success was influenced by factors associated with sleep. Tiredness affected cognitive skills, and results also showed that learning and concentration were impaired as a consequence of poor sleep. Additionally, procrastination decreased work productivity.

The reported hardest aspects of being a working student were managing everything and coping with stress. This study raises awareness on the importance of sleep health on learning and productivity, and provides insights that may be useful to the practice of psychology.

KEYWORDS

Academic performance; Professional success; Sleep; Well-being; Working students

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INDEX

1 Introduction... 1

1.1 Study Objectives ... 2

1.2 Structure ... 3

2 Literature Review ... 4

2.1 Sleep and academic performance ... 6

2.2 Sleep and professional success of working students ... 7

3 Methodology ... 9

3.1 Population and Samples ... 9

3.2 Focus Group ... 10

3.3 Questionnaire ... 11

3.4 Statistical Analysis ... 14

4 Results and Discussion ... 16

4.1 Characterization of the sample ... 16

4.2 Sleep Health... 17

4.3 Mental Health ... 20

4.4 Cognitive Abilities ... 22

4.5 Procrastination ... 23

4.6 Covid pandemic ... 24

4.7 Educational performance and work environment ... 24

4.8 Correlation analysis... 26

4.9 Age group effect on Rate of working while studying ... 28

4.10 Professional success effect on Rate of working while studying ... 28

4.11 Procrastination effect on Professional success ... 29

4.12 Sleep health effect on Procrastination ... 30

4.13 Sleep quality effect on Emotions ... 31

4.14 Sleep health and performance ... 32

4.14.1 Academic performance effect on Sleep health ... 32

4.14.2 Professional success effect on Sleep health ... 32

4.14.3 Rate working while studying effect on Sleep health ... 33

5 Conclusion ... 34

5.1 Recommendations for healthy sleep ... 35

5.2 Improvements for future research directions... 35

References ... 36

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Appendix A. Questionnaire ... 41

Appendix B. Ethics Committee of NOVA IMS ... 49

Appendix C. Statistical test results ... 50

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LIST OF FIGURES

Figure 1. Comparison of eight-hour challenge sleepers with non-incentivised students (King et

al., 2019) ... 6

Figure 2. Methodology flowchart ... 9

Figure 3. Frequency by Sleep Health scores ... 17

Figure 4. Frequency of sleep-related habits ... 18

Figure 5. Means of Sleep Health by gender ... 19

Figure 6. Means of Sleep Health by gender, degree and employment group ... 19

Figure 7. Means of Sleep Health by gender and age group ... 20

Figure 8. Frequency of the effects of poor sleep during the previous month ... 21

Figure 9. Frequency of each emotion as effects of a poor night’s sleep ... 22

Figure 10. Frequency of cognitive abilities impaired by tiredness ... 23

Figure 11. Impact on sleep caused by Covid pandemic ... 24

Figure 12. The most difficult aspects of being a working student ... 25

Figure 13. Support given to working students ... 26

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LIST OF TABLES

Table 1. Scale measurements ... 13

Table 2. Demographics of the sample ... 16

Table 3. Descriptive statistics of Sleep Health (scale: 0-10) ... 17

Table 4. Descriptive statistics of Mental Health (scale: 0-12) ... 20

Table 5. Descriptive statistics of Emotions (scale: 0-12) ... 21

Table 6. Descriptive statistics of Cognitive abilities (scale: 0-12) ... 22

Table 7. Descriptive statistics of Procrastination (scale: 7-35) ... 23

Table 8. Descriptive statistics of GPA and Professional success ... 24

Table 9. Descriptive statistics of Rate Working while Studying (scale: 0-10) ... 25

Table 10. Spearman’s correlation coefficients of the major variables. ... 27

Table 11. Shapiro-Wilk test on rate working while studying by age groups ... 28

Table 12. Kruskal-Wallis test on rate working while studying by age groups ... 28

Table 13. Shapiro-Wilk test on Rate working while studying by Professional success's subgroups ... 29

Table 14. Kruskal-Wallis test on Rate working while studying by Professional success's subgroups ... 29

Table 15. Nemenyi test on Rate working while studying by Professional success's subgroups ... 29

Table 16. Shapiro-Wilk test on Professional success by procrastination ... 30

Table 17. Mann-Whitney test for Professional success by procrastination... 30

Table 18. Shapiro-Wilk test on Procrastination by Sleep quality’s subgroups ... 30

Table 19. ANOVA on Procrastination by Sleep quality's subgroups ... 31

Table 20. Shapiro-Wilk test on Emotions by Sleep quality’s subgroups ... 31

Table 21. ANOVA on Emotions by Sleep quality's subgroups ... 31

Table 22. Shapiro-Wilk test Sleep health by GPA ... 32

Table 23. Kruskal-Wallis test Sleep health by GPA ... 32

Table 24. Shapiro-Wilk test Sleep health by Professional success ... 33

Table 25. Kruskal-Wallis test Sleep health by Professional success ... 33

Table 26. Shapiro-Wilk test Sleep health by Rate working while studying ... 33

Table 27. Mann-Whitney test Sleep health by Rate working while studying ... 33

Table 28. Shapiro-Wilk test on Sleep health by Technology use ... 50

Table 29. Kruskal-Wallis test on Sleep health by Technology use ... 50

Table 30. Mann-Whitney test Sleep health by gender ... 50

Table 31. Mann-Whitney test Sleep health by gender and degree for full time workers ... 50

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Table 32. Mann-Whitney test Sleep health by gender and degree for part time workers ... 51

Table 33. Shapiro-Wilk test Sleep health by Procrastination ... 51

Table 34. Mann-Whitney test Sleep health by Procrastination ... 51

Table 35. Shapiro-Wilk test on Mental health by Sleep quality’s subgroups ... 51

Table 36. ANOVA on Mental health by Sleep quality's subgroups ... 52

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LIST OF ABBREVIATIONS AND ACRONYMS

ANOVA Analysis of Variance;

ESS Epworth Sleepiness Scale;

FOSQ Functional Outcomes of Sleep Questionnaire;

GPA Grade Point Average;

GPS General Procrastination Scale;

MES Horne-Ostberg Morningness Eveningness Scale;

PSQI Pittsburgh Sleep Quality Index;

SATED Satisfaction with sleep, Alertness during waking hours, Timing of sleep, sleep Efficiency, and sleep Duration;

SQS Sleep Quality Scale;

SUDS Subjective Units of Distress Scale.

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1 INTRODUCTION

Sleep has been shown to be a fundamental component of every person’s overall health, influencing quality of life along with well-being (Jensen & Herr, 1993). Therefore, sleep is a precondition of life, necessary for development and growth.

Sleep experts have attempted to define ideal sleep duration, though, the length depends on multiple factors, for instance, health, age, gender, culture, socioeconomic level, and environment (Reis et al., 2018).

A healthy lifestyle is characterised as the mutual habit of sleep quality, physical activity, a healthy diet, moderate alcohol consumption, and non-smoking (Marques et al., 2019). These behaviours associate to well-being of any individual.

The transition to adulthood is a phase of exponential intellectual growth which, normally, corresponds to the university years. During this time, students follow a strict classes schedule, engage in social events, and lead toward professional paths. Hence, quality sleep is essential in this period of life to consolidate information and reenergize individuals mentally and physically.

Nevertheless, sleep is normally disregarded. The practice of frequently obtaining poor sleep quality can result in dangerous complications (Lund et al., 2010). Several young adults suffer from sleep disorders, such as, sleep deprivation and sleep inconsistency, specialty those who work and study simultaneously. The consequences of chronic sleep deprivation can be extremely serious involving an extended risk of health illnesses as hypertension and diabetes.

The head of the Portuguese Sleep Association, Joaquim Moita, feels that there is a lack of statistical information about sleep health at national and European levels. The scant research shows that Portuguese people have poor sleep hygiene (Soares & Gonçalves, 2019).

Upon the closure of this study, it will confer the advancement of knowledge and add value to the scientific community, namely in the field of psychology.

Taking everything into account, the research has the main purpose of creating awareness and establishing evidence that will contribute to the scientific and the business environment. Furthermore, the conclusions are projected to have practical application and a direct influence on the lifestyle of students, such as improvements to maximize learning achievements, meaning an enriched academic atmosphere and individual working students who will be able to achieve a more successful and healthier lifestyle. Lastly but not least, promoting appropriate sleep and sleep habits could have a significant public health impact through innovative perspectives.

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1.1 S

O

The proposed dissertation, titled "Sleep for Success: The impact of the amount of sleep on working students", aims to investigate the influence of sleep on the academic and professional performance of a set of working students from NOVA IMS.

The study aims to address the following research questions:

§ To what extent does the quality of sleep influence the academic and professional success of the sampled working students?

§ What is the perception of the sampled working students on the relationship between sleep and their mental health?

§ To what degree does female students' sleep differ from those of male students?

Specific objectives:

§ Prepare a survey questionnaire based on a literature review;

§ Use a focus group to validate / improve the survey questions;

§ Evaluate if the sleep health of female students is different from male students.

§ Investigate the perception of students regarding the relationship between sleep and their mental health;

§ Investigate the perception of students regarding the relationship between cognitive skills and sleep health;

§ Investigate the perception of students on which are the most difficult aspects of being working students;

§ Assess if sleep health influences procrastination

§ Investigate the perception of students regarding the effect of sleep on their performance.

In order to reach the dissertation’s purposes, a questionnaire was developed to collect data on the subject. Before being published and distributed, a focus group assessed and validated the survey. The gathered data was, then, analysed through statistical testing aiming to answer the research questions and meet the objectives of the investigation.

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1.2 S

The present research is divided into chapters. Firstly, the literature review presents an overview of all the information on the framework. Starting with the importance of sleep on general health, and the negative effects of poor sleep habits. Successively, this chapter splits into two topics:

sleep and academic performance, and sleep and professional success of working students. In each subsection, it focuses on several previous investigations on sleep and performance with different approaches and techniques. The study's methodology is explained in the following chapter. The researcher describes the data selection, the collection processes and the analysis chosen to achieve the study purposes. The next part discloses the results obtained in the research, along with their analysis, and are then discussed. Lastly, the conclusion addresses a summary of results of the study, as well as the recommendations for future research on the subject.

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2 LITERATURE REVIEW

Buysse (2014) defined sleep health as “a multidimensional pattern of sleep-wake-fulness, adapted to individual, social, and environmental demands, that promotes physical and mental well- being. Good sleep health is characterized by subjective satisfaction, appropriate timing, adequate duration, high efficiency, and sustained alertness during waking hours.”

Sleep deprivation is established as insufficient sleep to maintain adequate daytime attentiveness (Hershner & Chervin, 2014). Studies have shown that sleep deprivation has numerous effects on performance such as a decrease in reaction-time, less alertness, rise in perceptual and cognitive alterations (Krueger, 1989). In academic terms, the privation of sleep results in a lack of concentration and attention during classes. In other words, there is a loss of productivity, working memory, as well as quality of life. When deprived of decent revitalised sleep, the brain cannot function correctly.

The consequences of sleep deprivation can have an enormous public health impact. Several of the most disastrous human and environmental incidents have been associated with sleep deprivation and fatigue to a certain extent, for instance, the gas tragedy at the Union Carbide chemical plant in India; the nuclear reactor meltdowns at Chernobyl and Three Mile Island; and the grounding of the Exxon Valdez oil tanker (NCSDR, 1994; Moss and Sills, 1981; United States Senate Committee on Energy and National Resources, 1986; USNRC, 1987; Dinges et al., 1989). These accidents resulted in vast monetary costs, yet, there were incomparable with the disastrous effects on the environment and the health of residents (Colten & Altevogt, 2006).

University years are a phase of critical progress and development in different areas simultaneously. Unfortunately, this transition is accompanied with insufficient sleep and daytime sleepiness (Lund et al., 2010). This may prejudice educational functioning, temper and attitude regulation, and driving performance (Hershner & Chervin, 2014).

The life of university students is occupied by amusement distractions, which impairs sleep time and quality (Goyer et al., 2021; Wang & Bíró, 2021). Students experience exponential independence with the smallest supervision, resulting in autonomy and responsibility. Most commonly, adolescents and young adults experience irregularity in sleep schedule from day to day, which is designated as sleep inconsistency (social jet lag). In other words, it corresponds to insufficient sleep during the week succeeded by excessive sleep on the weekends. One of the reasons for this phenomenon is rigorous morning agendas besides staying up late (Okano et al., 2019).

Wang and Bíró (2021) verified that sleep quality is significantly dependent on lifestyle dynamics, mental health issues, social habits and physical influences. Regarding physical activity, they concluded that weight has both a positive and negative effect on the quality of sleep.

However, previous studies conclude that the lack of physical activity negatively impacts sleep satisfaction, principally on male young adults (Edwards & Loprinzi, 2017). Investigation has found that sleep duration and quality improves through the increase in exercise.

Similarly, a healthy and varied diet is beneficial for both sleep and learning performance.

Namely, the relationship was study by Pilato and colleagues on university students demonstrating that

5 drinking more water, including more fruit and seafood on the menu, while eating less processed foods, can result in improved memory, better cognitive functioning and overall better academic outcomes (Pilato et al., 2020).

Entertainment activities and stimulant products, alongside with social pressure, are widely accessible and influence mental health and sleep quality. Students consume substances to cope with anxiety and stress to endure hours of study and concentration (Mwape & Mulenga, 2019). Hence, further triggers for poor sleep include the overconsumption of caffeine, energy drinks, alcohol, drugs and sleep stimulants (Navarro-Martínez et al., 2020; M. E. Patrick et al., 2018; Soós et al., 2021).

While power naps and caffeine can temporarily reduce the effects of sleep deprivation, like increasing concentration, neither can replace sleep. Until today, there is nothing that can substitute a full night of sleep (Walker, 2018). The brain’s ability to learn, to keep memories, to be emotional stable, to reason and to make decisions is impair without proper sleep.

Several researchers have concluded that undergraduates suffer from sleep problems within the context of mental disorders (Goyer et al., 2021; Lund et al., 2010). Peer pressure and evaluations are triggers for stress and anxiety. Students’ mental problems may be beyond the measurement stated in the literature since young people do not acknowledge the problem (Ryan et al., 1998). Although, workings students' mental health may be even worse through the additional difficulty of managing work obligations with academic and personal responsibilities. Consequently, poor sleep quality is becoming a significant problem among university students.

Indicators of mental health and well-being, mindfulness, sleep quality, and morningness all exhibited positive relationships, according to a research with a sample of 305 undergraduates (Howell et al., 2008). Participants who report being more conscious also register better sleep. Awareness may explain well-being directly and indirectly through the improvement of sleep. Higher levels of well-being suggest a predilection for the morning circadian rhythm.

The unreasonable act of delaying or postponing a task or a series of chores is known as procrastination. Li and colleagues’ research (2020) showed that among undergraduate and graduate students, procrastination was related to shorter sleep duration, poorer sleep quality, and greater social jetlag. They concluded that procrastination may not be a common and harmless behaviour, but a risk factor for poor health.

According to the results of a study on the potential links between procrastination and sleep quality, college students who were habitual procrastinators had lower scores on sleep quality indices.

Procrastination also compromised student performance through poor sleep. Higher stress levels may be a factor in understanding this link (Sirois et al., 2015).

Chronotype also influences sleep and normal function, whether one is a morning or evening type is not a personal choice but rather determined by genetics (Walker, 2018). Additionally, evening chronotype is considered a threat influence for sleep routine (Schlarb et al., 2017). A study on sleep and mental health in young adults in university found that college students with shorter sleep length and later chronotype were more sensitive to suffer depression and anxiety (Dickinson et al., 2018). The same research forecasted that lowest chance of depression and anxiety in sleep hours to be, approximately, 8 hours each night.

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2.1 S

Regardless of vast evidence reinforcing the importance of quality sleep, fewer than 10% of undergraduates maintain the optimal duration of 8 hours per night during finals (King et al., 2019).

These authors performed a study with an 8-hour sleep challenge which disclosed that students embrace positive sleep behaviour through extra credit motivation even during the week of final examinations. Study participants with a more homogeneous sleep schedule performed better academically on the final exam than students with a more inconsistent sleep schedule. Among the students who participated in the challenge, 90% improved their sleep length throughout the incentivised eight-hour trial week, measured by a wearable activity tracker. Figure 1 presents the contrast between the eight-hour challenge sleepers and the non-incentivised students.

Figure 1. Comparison of eight-hour challenge sleepers with non-incentivised students (King et al., 2019) Trockel and colleagues concluded that sleep habits were strongly associated with grade point averages of 1st-year college students (Trockel et al., 2000). These authors found that wake-up times explained the largest amount of variability in grade point averages, and that lower average grades were associated with later wake-up times. According to Barone (2017), these associations were also found for students of other university years.

Privation of sleep is caused by many factors. However, the absence of sleep education and misinformation remain blameworthy. For example, the misbelieve that an all-nighter cramming for a test is beneficial for the learning process. On the contrary, for learning achievements, sleep is necessary so that the brain can transform temporary memory into long-term information. Education towards college students is essential for encouraging sleep health and increase quality (Wang & Bíró, 2021).

Nonetheless, a paper on the outcomes of sleep deprivation in university students found that all-nighter substantially affects physical performance, for example time of reaction, however it does not impair cognitive ability in healthy young students (Patrick et al., 2017).

Currently, electronic devices, such as smartwatches and wristbands, are under development and becoming more popular to monitor physiological data. A research on wearable technology

7 established algorithms to estimate sleep quality, sleepiness level, chronotype, and sleep regularity (de Arriba-Pérez et al., 2018). This study was focused on students to support the self-regulated learning and to increase academic performance. In other words, by comprehending sleep measures students can best determine studying practices, learning approaches and behaviour choices. However, the researchers did not explore whether sleep consciousness altered academic performance directly.

Another research using objective measure, wearable activity tracker, to quantify sleep duration and quality in students discovered that better academic performance correlated with greater sleep consistency, sleep quality, and longer time (Okano et al., 2019). The results proved that more extended sleep duration and more satisfactory sleep quality over the entire month before an evaluation were associated with better test performance. However, sleep duration the night before an exam was not found to be related to higher grades. Therefore, sleep habits are a significant part of memory consolidation. The same investigation discovered that female students tended to experience better quality sleep and with more consistency. In contrast, male students required a prolonged and more frequent sleep schedule to obtain adequate sleep.

The conclusions were consistent with a previous study about pharmacist students (Zeek et al., 2015) and a succeeding paper on adolescent girls in mathematics (Lin et al., 2020), higher grades are associated to longer sleep duration during a normal week. Though the first study concluded that sleep length during the night prior an exam is also imperative.

On the other hand, a study on the impact of covid-19 lockdown in Italy showed that female students had a greater aggravation of sleep quality and of insomnia than male students (Marelli et al., 2021). In general, the pandemic had a substantial effect on sleep and well-being. Moreover, one-third of the sample analysed by those authors exhibited depressive or anxious signs, which likewise negatively influence quality of sleep.

2.2 S

In Portugal, the working-student status is established in the Portuguese Labour Code.

Accordingly, a working student stands as a worker who attends any level of education, such as a postgraduate, master, or doctoral course in an academic institution. A professional training course or an occupation program for temporary employment, with a duration equal to or greater than six months, is also considered (as per articles 89º. to 96º. of the Portuguese Labour Code).

In order to access the status, the student must prove to the employer the academic level enrolled by providing a declaration and schedule of classes and evaluations. Organizations follow general regulations for working students, such as reducing working hours to attend lessons and specific rules for vacations and absences.

Regarding the educational establishment, the legislation regulates that working students are not subject to the minimum classes attendance. Furthermore, it foresees a special exam session and guarantees support services after-work hours for these students.

Students that work full time seek to balance work, university and personal responsibilities simultaneously. Efficient management of time, which is a scarce resource, is challenging for young

8 adults. A study on sleep deprivation showed that students believe that sleep can be postponed consequently, replacing sleep with study or work as a future investment (Barone, 2017). The study participants acknowledged sleep loss as ordinary, bearing in mind the unhealthy side effects.

In a research that contrasted working and nonworking students, long work hours were reported by participants with low sleep ratings (Chiang et al., 2020). This might indicate that students who work more have poor sleep health. It was also shown that those who slept poorly had lower grades. Although the researchers were unable to conclude whether poor sleep causes lower GPAs or if students with lower academic performances may have poor sleep owing to other factors not addressed, they did conclude that poor sleep is associated with lower grades.

Spending too much time working can have negative implications, such as sleep deprivation and difficulty maintaining academic performance. A study of 82 working students to assess time spent in class and related factors, found that males slept longer hours, reported extreme drowsiness on Saturdays, worked longer hours, and reported alcohol intake, as a result of spending less time in class.

Long work hours (>40 hours per week) and insufficient sleep may have an impact on lifestyles and academic performance (Nagai-Manelli et al., 2012).

Singleton and Wolfston (2009) analysed the effects of alcohol use on academic achievement.

In their study of liberal arts colleges, they interviewed 236 college students and discovered that excessive drinking reduced students' academic performance by reducing the amount of time to sleep and study. The authors discovered a link between alcohol consumption and sleep, alcohol consumption and academic performance, and sleep and academic performance. As expected, they found that students who consume more alcohol had poor sleep patterns, which has a negative impact on academic performance.

Nowadays, technological addiction affects most young people, who endure hours in front of a screen, including computers, cell phones, and tablets. Sleep difficulties and daytime sleepiness is connected to the regular use of technologies before bed. Also, light exposure from electrical devices impacts sleep (Hershner & Chervin, 2014).

Michel Desmurget, a French neuroscientist, discloses that after thousands of years of evolution human beings are now regressing in terms of cognitive and intellectual abilities due to excessive exposure to screens (Desmurget, 2019). Accordingly, today youths are the first children to have lower IQs than their parents. The specialist also explains that recreational screens time (time spent using a device that does not involve activity or education) prejudice sleep, restrict language acquisition, weaken academic performance, impair concentration, extend the risk of obesity, and damage the brain.

Despite numerous studies, there is still a clear gap in the literature regarding quantitative analysis that identifies and measures the relationship between sleep and productivity, particularly associated with academic performance and work success of working students.

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3 METHODOLOGY

The purpose of this dissertation was to examine the connection between sleep and two types of performance in students: academic performance and job performance. This chapter presents the methodology used and the principal stages are depicted on the following figure.

Initially, an introduction of the research approach, and afterward, the sample and the data- gathering techniques are explained in detail. Additionally, the measurement scales are described.

Lastly, to reach and understand the results, there is a thorough summary of the analysis done.

A quantitative study allows the researcher to investigate and test hypotheses by working with numerical data. Data analysis enables one to approach outcomes, study relationships between variables, and compare differences between and among groups (Velec & Huang, 2014).

For this dissertation, an organized literature search was developed. The type of research used was exploratory, since the aim is to explore the central features of an under-investigation problem.

Moreover, secondary data was collected, data that was previously gathered by someone else.

Figure 2. Methodology flowchart

3.1 P

S

The target population was working students from NOVA IMS, either part-time or full-time workers, and either graduate or undergraduate students.

Two independent samples of students were selected as detailed below. The first one aimed to select five students to be included on the focus group. The main objective of this stage was to assess the adequacy and clarity of the online questionnaire to be replied by students included in the second sample.

The sample of students participating in the focus group was selected by purposive sampling.

The purpose sampling technique, also known as judgement sampling, is an approach in which participants are selected deliberately (purposively) to provide essential information. The researcher

Identification

Screening and elegibility

Focus group

Questionnaire

Data analysis

10 chooses the units to be sampled based on features or experiences the participants own and whether they are available to provide the required data (Etikan et al., 2016). This method was chosen since it is a practical, affordable and time-efficient technique (Taherdoost, 2016). However, the sample results do not generalize to the whole population and, for this reason, it was just chosen for the focus group.

Moreover, it lays open to researcher and sampling biases.

The sample of students participating in the online survey was selected by a volunteer sampling design, which uses a convenience sampling scheme. A non-random sampling method known as convenience sampling occurs when the individuals of the target population who fulfil certain practical requirements are included in the sample (Etikan et al., 2016). These requirements can be ease of access, proximity to the research site, availability at a specific time, or the desire to participate.

Convenience samples are frequently referred to as "accidental samples" because components of the sample may be chosen by chance since they are administratively or physically close to the researcher’s location. Compared to other sampling methods, convenience sampling is a simple and affordable choice (Taherdoost, 2016). The researcher may target known people, such as acquaintances or relatives, to include in the sample, which is more straightforward than recruiting strange individuals.

Bias is the main drawback of convenience sampling.

Volunteer sampling design is a method of convenience sampling, where the researcher seeks respondents to participate in the study (Murairwa, 2015). Volunteers who are willing and qualified to give information on the subject in question. This technique gathers accurate and reliable data from a survey. It is also inexpensive. Since its features, the researcher has little control over the sample structure, so significantly vulnerable to bias.

On two separate occasions, on the 22^nd of July and the 20^th of September, the link to the online survey was distributed via a variety of social media platforms, including LinkedIn, Instagram, and WhatsApp. By reaching student groups from two academic years at these two different times, the response rate was to be increased. Additionally, at the beginning of the fall semester, 1606 NOVA IMS students received a direct email to motivate participation, besides the survey was shared on NOVA IMS Moodle. The data collection period lasted for 11 weeks, even though six of those weeks were holidays, and it was unlikely that students would respond. Since the target population is specific to working students from NOVA IMS, a minimum dimension of 50 responses was established for the sample.

3.2 F

G

A focus group gathered with five working students answered a questionnaire on their perceptions regarding the influence of sleep on their academic and professional performance. This preliminary survey was used to verify whether the questions were clear and well-defined, as well as to assure the questionnaire is adequate to the research objectives.

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3.3 Q

In accordance with the study's purposes, a questionnaire was developed on Qualtrics. The focus group pre-tested it. Subsequently, questions suffered necessary changes and adjustments. The questionnaire was held online to ensure efficiency in obtaining the maximum responders in a brief period (Evans & Mathur, 2005).

The survey enquired young adults who are working students, either part-time or full-time workers. The sample for this study consists of university students from NOVA IMS. Certain questions concern schedules and habits towards understanding their lifestyle. Additionally, questions focus on how the university and the employer support the student, among other questions addressing the specific objectives of the study.

The online questionnaire is based on previously established measures to evaluate sleep, mood, stress and depression. Only the questions of each scale considered relevant to the specific objectives of this study were included. By doing so, participant duration was relieved to promote an increased reply rate.

The Pittsburgh Sleep Quality Index (PSQI) is a self-report survey that evaluates sleep quality during a one-month period (Buysse et al., 1989). The measure consists of 19 individual items, providing a global score based on seven segments: subjective sleep quality, sleep latency (how long to fall asleep), sleep duration, sleep disturbances, habitual sleep efficiency (the % of time in bed that is asleep), use of sleeping medication, and daytime dysfunction. Each item is weighted on a four-point Likert scale (0 – 3) to an overall score ranging from 0 to 21, where lower scores denote a better sleep.

Sleep health was assessed with a metric defined by Buysse (2014), that focuses on five dimensions of sleep: sleep Satisfaction, Alertness during waking hours, Timing of sleep, sleep Efficient and sleep Duration (SATED). These five essential parameters of sleep have been frequently related to health outcomes, hence Buysse considered them the most relevant and appropriate to measure sleep health. The PSQI was previously regarded to assess sleep quality, however, SATED is considered a more efficient metric. The PSQI has more items and it is more complex to score. Moreover, it concentrates on sleep disorders over quality of sleep (Buysse, 2014). SATED is a succinct self-report scale in which responders choose the frequency (rarely/never, sometimes, or usually/always) for each statement.

The total score ranges between 0, indicating poor sleep health and 10, representing good sleep health.

In order to determine excessive daytime sleepiness, the Epworth Sleepiness Scale (ESS) assesses how likely a person would fall asleep while doing ordinary activities (Johns, 1991). Each question rates from 0 (would never doze) to 3 (high chance of dozing). The ESS score sums up to a spectrum between 0 and 24, where higher outcomes represent considerable levels of daytime sleepiness. However, its evaluation of only one specific aspect, the tendency to fall asleep during the day in particular circumstances, is insufficient for assessing the wide concept of sleep health (Buysse, 2014).

The Sleep Quality Scale (SQS) was implemented to investigate the connection between sleep and mental health. The 28-item SQS measures the quality of one's sleep during the previous month (Yi et al., 2006). Six dimensions of sleep quality are considered by the scale: daytime symptoms, sleep restoration, issues starting and sustaining sleep, trouble awakening, and sleep satisfaction. Participants

12 indicate how frequently they display specific sleep habits, from “rarely or never” to “usually or always”.

With a total score that can vary from 0 to 84, higher scores indicate more severe sleep issues. In the current study, 6 statements from the original scale were used.

With the purpose of evaluating individuals’ emotions regarding sleep health, responders were questioned how often a restless night affects the way they feel anxious, depressed, irritable and tired, during the following day.

The Subjective Units of Distress Scale (SUDS) is a self-report measure (Benjamin et al., 2010).

Participants indicate the intensity of their feelings on an ordinary day using a measure of 1 to 10. The scale goes from “no distress; totally relaxed” to “highest anxiety/distress ever felt”. The SUDS was initially considered in the current research, however, the scale is arbitrary and only assesses the specific moment answered, thus it was not considered.

Regarding insomnia, the functional outcomes of sleep questionnaire (FOSQ) evaluate how disorders of excessive sleepiness affects quality of life (Weaver et al., 1997). The scale ranges between 0 and 28, and higher results designate severe clinical insomnia. The FOSQ was used to evaluate the perception of students about the association among cognitive skills and sleep health with questions such as “Do you generally have difficulty concentrating on the things you do because you are sleepy or tired?”.

Lay's General Procrastination Scale (GPS), a 20-item evaluation of trait procrastination, measures a person's propensity to put off starting various tasks and daily life activities (Lay, 1986). For each statement, responders must decide whether the sentence describes them or not by means of a 5-point scale. For example, “I often find myself performing tasks that I had intended to do days before”.

Scores on items 3 and 6 were reversed before being tallied. Higher total scores indicate higher tendency to procrastinate.

Horne and Östberg developed a self-administered questionnaire, the Morningness- Eveningness Scale (MES), to distinguish early birds and night owls (Horne & Östberg, 1976). The MES estimates at which time of day the peak of alertness occurs based on the circadian rhythm (biological clock). The scale results in a value within 16 and 86, where evening types correspond to scores lower than 42, morning types equal to scores higher than 58, and numbers in between indicate intermediate types. The MES was not taken into account, since its assessment does not align with the goals of the current research.

In addition to questions related to the previous scales, questions associated with educational performance, demographic information, and work environment, were also included. To assess success, both professional and academic, students were asked to evaluate their perceptions and report their average grades. Participants were also questioned about their habits and perspectives on how technology, caffeinated beverages, sleep aids, and alcohol affects their sleep.

Between July 12^th and July 20^th, a pre-test was conducted, the focus group validated the proposed questionnaire (Appendix A). Hence, it was used on the online survey. Afterwards, the Ethics Committee of NOVA IMS approved the questionnaire on July 22^nd (Appendix B).

13 As mentioned, the measurement items assessed were adjusted from prior research in the literature to ensure the accuracy of the data gathered. Table 1 lists all constructs, scales, indicators, and correspondent authors.

Construct Scale Indicators Adapted from

Sleep Health

(rarely/never, sometimes, or usually/always)

Are you satisfied with your sleep?

Do you stay awake all day without dozing?

Are you asleep (or trying to sleep) between 2 am and 4 am?

Do you spend less than 30 minutes awake at night?

Do you sleep between 6 and 8 hours per day?

SATED (Buysse, 2014)

Mental Health

(rarely/never, sometimes, usually/always)

Did poor sleep give you headaches?

Did poor sleep make hard for you to think?

Did poor sleep make you lose interest in work or study?

Did poor sleep cause you to make mistakes at work/school?

Did sleepiness interfere with your daily life?

Did poor sleep make your life painful?

SQS

(Yi et al., 2006)

Emotions

(yes extreme, yes moderate, yes a little, no)

Does a poor night’s sleep make you...

Depressed?

Anxious?

Irritable?

Tired?

-

Cognitive Abilities

(yes extreme, yes moderate, yes a little, no)

Do you generally have difficulty ... because you are sleepy or tired?

concentrating on the things you do remembering things

performing employed work learning/studying

FOSQ (Weaver et al., 1997)

Procrastination

5-point Likert scale (strongly disagree to strongly agree)

I often find myself performing tasks that I had intended to do days before.

I do not do assignments until just before they are to be handed in.

When it is time to get up in the morning, I often get right out of bed.

I generally delay before starting on work I have to do.

In preparing for some deadline, I often waste time by doing other things.

I often have a task finished sooner than necessary.

I am continually saying "I'll do it tomorrow".

GPS (Lay, 1986)

Table 1. Scale measurements

14

3.4 S

A

The gathered data was statistically analysed. Initially, an exploratory and descriptive analysis were done to provide an overview of the dataset, presenting the insights with tables and charts. The research design implements statistical tests and analytical methods. Additionally, in order to assess the internal consistency of the instrument used, the Cronbach's Alpha Coefficient was calculated.

Concerning inferential statistics, a set of parametric and nonparametric tests were applied to evaluate and comprehend how the variables were differentiated and associated, in accordance with the objectives and hypotheses in view. Otherwise stated, conclusions were drawn at the 5%

significance level.

Variables in a metric scale (Sleep health, Mental health, Cognitive abilities, Procrastination, GPA) or a continuous scale (Professional success and Rate of working while studying) were subjected to the Shapiro-Wilk distribution fitting test to investigate the normality assumption of the one-way ANOVA. The F-test of ANOVA was applied if the null hypothesis of the Shapiro-Wilk test was not rejected for all samples of the k groups defined by the factor (age group, or Professional success subgroups, Sleep health subgroups), and if the samples verified the homoscedasticity condition using the Levene’s test. The statistical hypotheses of the Shapiro-Wilk test are:

!0 ∶ $ℎ& ()*+,& -.*&( /0.* ) 1.0*), +.+2,)$3.4, 63$ℎ 2474.64 +)0)*&$&0(

!1 ∶ $ℎ& ()*+,& 9.&( 4.$ -.*& /0.* ) 1.0*), +.+2,)$3.4

The Levene’s test was performed with the aim of checking the equality of variances of the populations. The test statistic with the mean as the centre of each group was used in case the populations followed a Normal distribution. In case the Shapiro-Wilk test concluded that the populations did not follow a Normal distribution, then the test statistic considered the median as the centre of each group. This latter test statistic was also applied to variables in an ordinal scale. The hypotheses of the Levene’s test are:

!0 ∶ :ℎ& 7 +.+2,)$3.4( ℎ);& $ℎ& ()*& (-),& +)0)*&$&0

!1 ∶ <$ ,&)($ .4& ./ $ℎ& +.+2,)$3.4( ℎ)( ) 93//&0&4$ (-),& +)0)*&$&0

Since each observation is related to a different student, the observations are independent of each other. In case there was evidence of normality and homoscedasticity, one-way ANOVA was performed to test if the means are identical in all levels of the factor. To illustrate, the hypotheses are:

!0 ∶ =_>= =_@ = ⋯ = =_B

!1 ∶ ∃_D,E(DGE) =_D ≠ =_E

(At least one pair of means is different.)

In case the normality assumption was not met, the nonparametric Kruskal-Wallis test was implemented instead of the one-way ANOVA:

!0 ∶ :ℎ& 7 ()*+,&( -.*& /0.* 39&4$3-), +.+2,)$3.4(

15

!1 ∶ )$ ,&)($ .4& ./ $ℎ& 7 ()*+,&( 9.&( 4.$ -.*& /0.* 39&4$3-), +.+2,)$3.4(

The ANOVA and its nonparametric counterpart, the Kruskal-Wallis test, do not allow determining which groups are significantly different. As a matter of fact, to reach that conclusion, post hoc tests, also known as multiple comparisons tests, must be implemented. Succeeding the ANOVA, the well-known Tukey-Kramer test was applied.

Succeeding the Kruskal-Wallis test, a nonparametric multiple comparison tests was implemented to assess whether two populations have the same median, namely the Nemenyi test:

!0 ∶ ∀_D,E(DGE) =K_D= =K_E

!1 ∶ ∃_D,E(DGE) =K_D ≠ =K_E

(At least one pair of medians is different.)

To compare only two groups, the Wilcoxon-Mann-Whitney¹ test was executed, which is the nonparametric equivalent of the two-sample t-test. The statistical hypotheses can be formulated as follows (Nachar, 2008):

!0 ∶ The two groups come from the same population

!1 ∶ : The first group data distribution differs from the second group data distribution Rejecting !0 means one of the populations tends to have either smaller or larger values than the other, thus the populations are different.

The Pearson’s correlation coefficient measures linear correlation. For this reason, the corresponding parametric test was only applied to the two continuous variables (Professional success and Rate working while studying). In order to understand how the variables relate and to measure their degree of association, the Spearman’s rank correlation test was performed between the main variables.

Throughout this project, computations were done using primarily Microsoft Excel by means of the Real Statistics Resource Pack software (Release 8.4 for Excel 365).

1 This test is also known as the Mann Whitney U test or Wilcoxon rank sum test.

16

4 RESULTS AND DISCUSSION

4.1 C

The answers from 227 students were collected using a convenience sampling scheme as described in the Methodology section. Since 69 students responded to less than 40% of the questionnaire, those responses were not analysed, whereas 39 participants missed only 1 to 10 questions, and so these responses were considered. Therefore, 119 complete answers and 39 incomplete answers were suitable for data analysis, hence, a total of 158. A response rate could not be calculated since the size of the target population is unknown, and it was not possible to account for the number of people who received or engaged with the link to the online survey.

Regardless of the fact that all scales were adapted from previously published research, all constructs underwent a reliability analysis. With a Cronbach's Alpha of 0.74, the internal consistency of the questionnaire is acceptable.

The collected sample is mainly represented by female participants (61%). The majority of students age between 21 to 26 years old (71%). Additionally, 83% of participants are full-time employers and the most common degree pursued is a master's degree (82%). There are no records of non-binary/third gender nor “prefer not to say”, moreover, no one is pursuing a Doctoral degree (PhD).

The demographic information is shown in Table 2.

Characteristics (N=158) n %

Gender Female 96 60.76%

Male 62 39.24%

Age

18 - 27 123 77.85%

28 - 37 25 15.82%

38 - 47 5 3.16%

48 - 57 5 3.16%

Employment Full time - 8 hours 131 82.91%

Part time - 4 hours 27 17.09%

Study program

Bachelor's degree 7 4.43%

Master's degree 130 82.28%

Postgraduate 21 13.29%

Table 2. Demographics of the sample

17

4.2 S

H

The participants' overall sleep health score represented their contentment with sleep, alertness during the day, timing, efficiency, and length of sleep (Buysse, 2014). The range of the scale is between 0 and 10, higher values represent good sleep health. The sample as a whole had an average sleep health score of 6.77 (± 1.6). Table 3 shows the descriptive statistics of this variable.

Sleep Health

n 146

Mean 6.7740

Standard Error 0.1335 Standard Deviation 1.6136 Sample Variance 2.6037

Table 3. Descriptive statistics of Sleep Health (scale: 0-10)

Participants sleep health was moderate, with a tendency toward good sleep health, according to these sleep scores. However, 22% of the sample sleeps poorly, which mean 32 students with a score equal to or lower than 5 (Figure 3).

Figure 3. Frequency by Sleep Health scores

Inadequate sleep may be explained with high rates of consuming alcohol, sleeping pills, caffeine beverages in the afternoon and using technology before bed (Chiang et al., 2020). Figure 4 presents the samples’ sleep-related behaviors.

The working students in this study reported to drink alcohol beverages occasionally (66%).

However, the perception reported was that alcohol does not affect sleep, never 66% and occasionally 27%. Drinking alcohol has been linked to reduced sleep duration and quality (Singleton & Wolfston, 2009) .

18 The participants’ consumption of caffeinated beverages in the afternoon or evening was low, sometimes 39% and never 34%. Moreover, 49% of the working students believe that caffeine never disturbs sleep and 16% that it only disturbs sometimes. One of the causes of sleep debt may be caffeine. Regardless it is used to treat sleep deprivation and fatigue (M. E. Patrick et al., 2018).

Conversely, the use of technology within an hour of going to bed was extremely common among the sample: 46% always use gadgets before sleeping and 36% reported most of the time. The opinions on the implications for sleep were that it affected occasionally (47%), about half of the time (14%) and most of the time (15%). Particularly, smart phone addiction can make it very challenging for users to quit using technology before bed, which can increase bedtime procrastination and result in shorter duration and lower-quality sleep (Zhang & Wu, 2020).

Sleep aids intel pills and relaxing drinks to assist sleeping, the use was very low, never 68% and sometimes 25%.

Figure 4. Frequency of sleep-related habits

Note: Caffeinated beverages during the afternoon or evening and use of technology within an hour of going to bed

The most frequent harmful habit was undoubtedly the usage of technology. The majority of people who have trouble sleeping, whose sleep score was below or equal to 5, typically use electronics before sleeping (81%). Further investigation was conducted. However, statistical testing did not yield enough proof that using technology during the night caused a difference in sleep quality (Appendix C).

For each gender group, the mean sleep scores were 6.77 for women and 6.78 for men (Figure 5). Compared to males, females’ sleep quality was slightly worse. This difference was verified not to be statistically significant through the Mann-Whitney test (p-value = 0.9398, Appendix C). However, prior research found that women slept better than men (Okano et al., 2019).

19 Figure 5. Means of Sleep Health by gender

Whereas, sleep health by employment group and degree exhibits greater differences between female’s and male’s sleep (Figure 6). Although, statistical tests did not prove significant differences through the several comparison of gender sleep (Appendix C).

Figure 6. Means of Sleep Health by gender, degree and employment group

Another possible contrast was through age (Figure 7). Sleep quality in younger students tended to be relatively similar between the ages 18 and 27 mean of sleep health was 6.79 for female and 6.66 for male. The same relation happened to students aging between 28 and 37, the difference between gender was very slight.

20 Figure 7. Means of Sleep Health by gender and age group

4.3 M

H

As previously noted, a measure from 0 to 12 was used to assess mental health. The working students in this group displayed, on average, a score of 6.36 on sleep-related mental health issues (Table 4).

Mental Health

n 126

Mean 6.3571

Standard Error 0.2761 Standard Deviation 3.0996 Sample Variance 9.6074

Table 4. Descriptive statistics of Mental Health (scale: 0-12)

Due to lack of sleep during the previous month, 45% of the participants typically lose interest in their work or studies (Figure 8). Moreover, difficulty to think and headaches as an outcome of poor sleep occur often to 38% and 30% of the sample, respectively.

21 Figure 8. Frequency of the effects of poor sleep during the previous month

Concerning emotions felt as a direct consequence of a restless night, 65% of the sample (82 participants) had an Emotions count above 5. The average of the Emotion’s result was 6.45 on a scale ranging between 0 and 12, where higher scores indicate issues related to sleep. Table 5 summarises the descriptive statistics of this variable.

Emotions

n 126

Mean 6.4524

Standard Error 0.2198 Standard Deviation 2.4677 Sample Variance 6.0897

Table 5. Descriptive statistics of Emotions (scale: 0-12)

Most students found that a night with poor sleep causes them to feel exhausted (moderately 40% and extremely 49%). In addition, feelings related to irritability and anxiety the following day were also moderately observed (39% and 42%). Depression caused by a restless night was relatively less perceived. Figure 9 displays a graphic illustrating the frequency of each feeling.

22 Figure 9. Frequency of each emotion as effects of a poor night’s sleep

4.4 C

A

A score between 0 to 12 evaluates the cognitive abilities impaired as a consequence of tiredness. Under which higher results suggest sleep-related difficulties, the mean score was 6.67. The descriptive data for this variable are shown in Table 6

Cognitive Abilities

n 126

Mean 6.6667

Standard Error 0.2719 Standard Deviation 3.0516 Sample Variance 9.3120

Table 6. Descriptive statistics of Cognitive abilities (scale: 0-12)

The participants’ perception of which cognitive abilities are impacted by sleepiness is displayed in the following figure. It indicates that learning or studying while being tired is the most impaired (moderately 42% and extremely 25%).

23 Figure 10. Frequency of cognitive abilities impaired by tiredness

4.5 P

The tendency to procrastinate was determined using a score ranging from 7 to 35, higher values imply a greater tendency. Overall, the sample’s average procrastination value was 22.97.

Furthermore, 77 students (61%) had a total count above 21.

Procrastination

n 126

Mean 22.9683

Standard Error 0.4361 Standard Deviation 4.8956 Sample Variance 23.9670

Table 7. Descriptive statistics of Procrastination (scale: 7-35)

24

4.6 C

Most working students were unaffected or unaware of the Covid pandemic's outcomes on their sleep (52%). Figure 11 illustrates the frequency of the effects the pandemic had on sleep. Participants who reported changing sleep patterns range in quality from better to worse: 25% of the sample noticed that the pandemic affected positively their sleep, while 24% reported a decrease in quality or quantity of sleep.

Research on this particular subject suggests that the pandemic affected negatively sleep patterns (Marelli et al., 2021). The mandatory lockdown decreased sleep quality according to a study on Spanish university students (Maestro-Gonzalez et al., 2021).

The gathered data might not be accurate since the time of questioning was distant from the experience.

The working students might not remember.

4.7 E

The assessment of performance both academic and professional was done through self-report, similarly to previous studies (Chiang et al., 2020; Zeek et al., 2015). The students’ mean Grade Point Average (GPA) was 15 (± 1.91) and professional success was 7.35 (± 1.31). Table 8 details the descriptive statistics of these variables.

GPA Professional success

n 158 152

Range 0 - 20 0 - 10

Mean 15 7.3544

Standard Error 0.1519 0.1060

Standard Deviation 1.9087 1.3075

Sample Variance 3.6433 1.7095

Table 8. Descriptive statistics of GPA and Professional success

Each responder was asked to choose up to five factors that are the most difficult to manage as a working student (Figure 12). The majority of students from the sample struggle to balance everything (71%) and to cope with stress (62%). The following difficulties include exhaustion (50%), and a lack of

Figure 11. Impact on sleep caused by Covid pandemic

25 free time for resting (48%) and socializing (47%). Graphic in Figure 12 depicts the data on this particular question.

Figure 12. The most difficult aspects of being a working student

Participants were requested to report an evaluation of being a working student. The rate ranges between zero and ten, with higher numbers denoting more challenging managing both job and academic lives, while lower values represent easier management. The sample’s mean of this variable was 7.20 (Table 9).

Rate Working while Studying

n 113

Mean 7.2012

Standard Error 0.1628 Standard Deviation 1.7309 Sample Variance 2.9959

Table 9. Descriptive statistics of Rate Working while Studying (scale: 0-10)

Figure 13 displays the thoughts of students regarding the quality of the assistance offered by the university and the employer. Regarding the support given by the company, 24% of the students felt that it was extremely adequate and 30% that it was somewhat suitable, whereas 43% thought the academic support was neither adequate nor inadequate. Moreover, 24% of respondents found it fairly inadequate.

26 Figure 13. Support given to working students

4.8 C

The Spearman’s rank correlation test revealed no significant association between sleep health and the two types of performance, GPA and professional success (Table 10). Moreover, sleep health had no significant relationship with the other variables.

Regarding academic success (GPA), results revealed only a significant association with mental health (a=-19, p<0.05), which implies that students with lower grades tend to suffer more from mental health problems.

Furthermore, professional success appears to have significant correlation with rate of working while studying (r=-0.31, p<0.01) and a significant association with procrastination (a=-0.32, p<0.01).

Students who perform better at their job tend to find life to be easier and procrastinate less. Chapters 4.10 and 4.11 present the analysis on these two relationships.

The older the participant the more likely to describe life as a working student harder to manage than younger participants. This was expressed by the significant and positive relationship between the rate of working while studying and age (a=0.32, p<0.01). This relationship was further investigated using statistical testing (section 4.9).

Working students with a higher mental health score were more likely to assess studying and working simultaneously as more demanding, indicated by the positive and significant association between mental health and the rate of working while studying (a=0.21, p<0.05). The rate of being a working student had similar relationships with emotions (a=0.21, p<0.05). Participants that evaluate life as harder to manage as a working student are more likely to experience more negative emotions because of poor sleep than those who describe life as easier.

27 There was a positive association between mental health and emotions (a=0.54, p<0.01), the participants were more likely to experience negative feelings when dealing with mental health.

Similarly, cognitive abilities associated with mental health (a=0.58, p<0.01). Students with mental health issues had more trouble executing tasks due to poor sleep. As for the relationship between mental health and procrastination, results suggest that procrastinators were more frequently to struggle with mental health than non-procrastinators (a=0.33, p<0.01).

Emotions and cognitive abilities have a significant association (a=0.50, p<0.01). Due to poor sleep, students with stronger negative emotions typically perform worse through lower cognitive skills.

Moreover, procrastination and cognitive abilities had a positive relationship (a=0.18, p<0.05), indicating that participants with greater cognitive issues due to tiredness are more likely to procrastinate.

Age had two negative and significant associations: mental health (a=-0.27, p<0.01) and procrastination (a=-0.20, p<0.05). Younger students tend to have more mental health issues and procrastinate more.

Furthermore, working students with worse sleep-related behaviours tended to have higher scores in mental health (a=-0.32, p<0.01), emotions (a=-0.27, p<0.01) and cognitive abilities (a=-0.20, p<0.05). Therefore, habits influence the way working students feel.

1 2 3 4 5 6 7 8 9 10

1. Age 1

2. GPA -0.05 1

3. Professional

Success 0.10 0.09 1

4. Rate Working

While Studying 0.32** 0.02 -0.31** 1

5. Sleep Health 0.10 0.00 0.12 -0.13 1 6. Sleep-related

Habits 0.14 0.05 0.10 -0.06 0.08 1

7. Mental Health -0.27** -0.19* -0.17 0.21* -0.16 -0.32** 1

8. Emotions -0.09 -0.05 -0.14 0.21* -0.11 -0.27** 0.54** 1 9. Cognitive

Abilities -0.14 -0.08 -0.15 0.18 -0.04 -0.20* 0.58** 0.50** 1

10. Procrastination -0.20* -0.09 -0.32** 0.08 -0.10 -0.17 0.33** 0.15 0.18* 1 Table 10. Spearman’s correlation coefficients of the major variables.

Note: *: p < .05; **: p < .01