Sleep Debt and Academic Success in Relation to School Type

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Abstract How do hours of sleep and school type impact the academic success of high school

students? According to the CDC, 1 out of 10 people are affected by sleep deprivation. Two independent variables were tested, public and STEM high school students. Questions about their stress levels, GPAs, and average hours of sleep were asked of the participants using an online survey at http://www.quia.com/sv/620383.html. The study shows that there is a weak negative correlation between Sleep and GPA. The study also shows that students who feel pressure from their parents to succeed tend to have higher GPAs compared to students who do not. An ANOVA test supported the null hypothesis; however observations can still be made of the trends seen. According to the data getting more sleep does not mean your GPA will necessarily improve and students who are encouraged to succeed are more likely to have higher GPAs. The issue is more about time usage during waking hours than actual number of hours slept. It is evident that students must decide if they are willing to trade the benefit of sleep for the benefit of more time to do work. So, while sleeping more will not inherently improve your grades, it cannot be concluded that sleep is unimportant. Further study should evaluate quality of time management as well as sleep.

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Introduction According to the Centers for Disease Control and Prevention more than 1 in 10 people

did not get enough sleep or rest on any night in the past 30 days. The purpose of this project is to

determine if there is a correlation between sleep and study habits among high school and college

students and if their educational environment affects these results. The basic rationale is that

sleep is an important biological process that heals the body and refreshes the mind. Today sleep

deprivation is a major issue. Teens and young adults are especially impacted by sleep deprivation

as perceived workload, stress, and peer pressure keep them awake. Sleep deprivation is

associated with a wide variety of disorders and health problems that may impair behavior,

growth and development, and basic functionality.

Sleep deprivation can contribute to health issues like: heart failure, stroke, obesity,

mental impairment, injury from accidents, attention deficit disorder (add), and psychiatric

problems, including depression and other mood disorders. A compounding lack of sleep has been

proven to lead to a significantly decreased quality of life. One of the primary goals of this project

is to determine the effect of environment; specifically the learning and/or work environment, on

the individuals sleep patterns.

The research hypothesis for this experiment was that there will be a greater negative

correlation between sleep debt (negative hours of sleep) and academic success in Ivy League

University students and the null hypothesis is that there will be no correlation between sleep

debt, academic success, or school type.

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Literature Review “Neurocognitive consequences of sleep deprivation” by Durmer, J. S., & Dinges, D. F

highlights some detrimental day time behaviors caused by sleep deprivation. Issues like micro-

sleeps, sleep attacks, and lapses in cognition increase with sleep loss as a function of state

instability are discussed. Serious Issues are described involving previous sleep studies are

discussed within this work. Deficits in daytime performance due to sleep loss are experienced

universally and associated with a significant social, financial, and human cost.. Sleep deprivation

studies repeatedly show a variable (negative) impact on mood, cognitive performance, and motor

function due to an increasing sleep propensity and destabilization of the wake state. Specific

neurocognitive domains including executive attention, working memory, and divergent higher

cognitive functions are particularly vulnerable to sleep loss. In humans, functional metabolic and

neurophysiological studies demonstrate that neural systems involved in executive function (i.e.,

prefrontal cortex) are more susceptible to sleep deprivation in some individuals than others.

Recent chronic partial sleep deprivation experiments, which more closely replicate sleep loss in

society, demonstrate that profound neurocognitive deficits accumulate over time in the face of

subjective adaptation to the sensation of sleepiness. Sleep deprivation associated with disease-

related sleep fragmentation (i.e., sleep apnea and restless legs syndrome) also results in

neurocognitive performance decrements similar to those seen in sleep restriction studies.

Performance deficits associated with sleep disorders are often viewed as a simple function of

disease severity; however, recent experiments suggest that individual vulnerability to sleep loss

may play a more critical role than previously thought.

In their study, “Functional consequences of sustained sleep deprivation in the rat”

Everson, C. A. aimed to discover the physiological changes that occur as a result of sleep

deprivation by testing lab rats. In the rat, the course of prolonged sleep deprivation has a

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syndromic nature resulting in a steady decline of health. The study shows the change in various

conditions of the rate including metabolic brain activity. Metabolic mapping of the brain

revealed dissociation between the energy metabolism of the brain and that of the body. Sleep

deprivation's effects on cerebral structures are heterogeneous and unidirectional toward

decreased functional activity. the four major abnormalities identified in the sleep deprived rats

were -- (1) a deep negative energy balance and associated malnutrition; (2) heterogeneous

decreases in cerebral function; (3) low thyroid hormone concentrations; and (4) decreased

resistance to infection - can be viewed as having an early origin during the sleep deprivation

process to signify the foremost pathogenic situation to which the other abnormalities might be

secondarily related. The findings therefore remain unclear for a single function for sleep, but can

support accepted roles for sleep in thermoregulation, energy conservation, immune system

integrity, and tissue restoration.

This study, Effects of prolonged sleep deprivation on local rates of cerebral energy

metabolism in freely moving rats, focused on how the brain, a rat’s brain in this experiment,

changed and the rate of metabolism. It was conducted 1-2 days and the results showed an

unidirectional change toward unidirectional toward decreased energy metabolism, primarily in

regions associated with mechanisms of thermoregulation, endocrine regulation, and sleep.

However, correspondence was found between the hypo-metabolic brain regions and some

aspects of peripheral symptoms.

In this review, Sleep and circadian rhythms: Key components in the regulation of energy

metabolism, the researchers Laosky, A. D., Bass, J., Kohsaka, A., & Turek, F. W. present

evidence from human and animal studies to evaluate their hypothesis that sleep and circadian

rhythms have direct impacts on energy metabolism, and represent important mechanisms

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underlying the major health epidemics of obesity and diabetes. The first part of this review

focused on studies that support the idea that sleep loss and obesity are ‘‘interacting epidemics.’’

The second part discussed recent evidence that the circadian clock system plays a fundamental

role in energy metabolism at both the behavioral and molecular levels. These lines of research

are still recent, but nevertheless, have provided an experimental framework that could prove

instrumental in understanding metabolic health and disease.

Why we sleep: The temporal organization of recovery goes into detail on the neural

processes during wake, NREM Sleep, and REM sleep with detailed diagrams of brain activity. It

explains the effects of sleep illnesses like insomnia. Naturally the paper’s goal is to discovery the

purpose for sleep and includes much information on the effects sleep has on the body. In the

paper it is stated that sleep is necessary, though it is unclear at this time, why it is required and

maintained by evolution. Recent work suggests multiple roles, a correlation with synaptic

plasticity changes in the brain, and widespread changes in gene expression, not unlike what has

been recently discovered in circadian biology. Functional data are however still largely lacking,

and studies such as functional genomic screens in model organisms, comparative sleep

neuroanatomy through phylogeny, and the study of molecular changes within specific wake,

REM sleep, and NREM sleep regulatory systems are needed. The resilience of behavioral sleep

in evolution and after experimental manipulations may be secondary to the fact that it is

grounded at the molecular, cellular, and network levels.

The entirety of “Never enough sleep: A brief history of sleep recommendations for

children” discusses patterns of recommended sleep for children. The aim of this study was to

describe historical trends in recommended and actual sleep durations for children and

adolescents, and to explore the rationale of sleep recommendations. The paper is basically an

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extensive literature review over the topic designed to identify recommendations for children’s

sleep requirements and data reporting children’s actual total sleep time. A thematic analysis was

conducted to determine the rationale and evidence-base for recommendations. Thirty-two sets of

recommendations were located dating from 1897 to 2009. On average, age-specific

recommended sleep decreased at the rate of –0.71 minute per year. This rate of decline was

almost identical to the decline in the actual sleep duration of children (–0.73 minute per year).

Recommended sleep was consistently ∼37 minutes greater than actual sleep, although both

declined over time. In conclusion the study suggested that inadequate sleep was seen as a

consequence of “modern life,” associated with technologies of the time and that no matter how

much sleep children are getting, it has always been assumed that they need more.

Oka, Y., Suzuki, S., & Inoue, Y assessed the impact of bedtime activities and sleep

environment on sleep/wake patterns. 509 Japanese elementary school students (6–12 years of

age; 252 males and 257 females) participated in thier study, Bedtime activities, sleep

environment, and sleep/wake patterns of japanese elementary school children. Most activities

involving electronics had a negative impact on the sleep/wake patterns. The presence of a device

in the child’s room increased the activity before bed. Curfews later than 8 p.m. were shown to

have a negative effect as well. This information can help health care professionals inform parents

of the detrimental effects of these activities on sleep.

The goal of the study, Online assessment of sustained attention following sleep

restriction, was to assess the feasibility of conducting home-based sleep restriction studies with

actigraphic monitoring of sleep and a new online continuous performance test (OCPT). 34

female university undergraduate students repeated home assessments using self-administered

OCPT following a regular night of sleep (8 h or more) and following sleep restriction (4 h of

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sleep) in a within–between subjects counter-balanced design. Actigraphy was used to monitor

sleep. OCPT sessions were scheduled in the morning and the evening of days following normal

and restricted sleep. OCPT measures demonstrated acceptable test–retest reliability. Actigraphic

monitoring revealed good compliance with sleep requirements, and reported alertness reflected

significant effects of sleep manipulation (p < .0001). In comparison to performance following an

8-h sleep night, sleep restriction to 4 h was associated with a significant increase in omission

errors in the high-target section of the test (p < .0005) and with a significant increase in omission

errors in the low-target section of the test (p < .01). The researchers concluded that these

preliminary results support the feasibility of conducting home-based sleep restriction studies and

the validity of the online version of the OCPT, suggesting that it may serve as a sensitive tool for

assessment of sleep restriction/deprivation

Quality of sleep among university students effects of nighttime computer and television

use was a study based on subjective questionnaires that assessed nighttime habits of television

viewing and Internet use during weekdays and perceived sleep quality among university

students. Sleep perception was measured using the Pittsburgh Sleep Quality Index (PSQI). The

study group comprised 710 university students aged 17-25 years. Analysis of sleep perception in

relation to internet use revealed that 58.06% of subjects who accessed the internet between 19:00

and 21:00 slept poorly; 71.43% between 19:00 and 22:00; 73.33% between 19:00 and 24:00; and

52.38% between 19:00 and 03:00 (p=0.0251). Concerning the relationship between television

exposure and perceived sleep, the groups did not differ from each other (p=0.9303). This study

showed that internet use between 19:00 and 24:00 increases the risk of poor sleep among young

adults, in comparison with television viewing times.

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Research Methodology First data pertaining to the project was gathered and analyzed. The information gathered

was used to write the literature review. The information was then used to create a survey. The

survey was alpha tested to confirm the viability of the questions. When negative feedback was

received the material would be reviewed again and the question would be rewritten. An email

was then written detailing the primary goal of the study along with a link to the

survey, http://www.quia.com/sv/620383.html. The email was then distributed to high school

teachers. The educators then distributed the survey to their students. Each student participated in

the online survey and their results were analyzed using an ANOVA test. Conclusions were then

formed based on correlations between GPA, school type, and average hours of sleep.

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Data Analysis The Confidence level for this experiment was 95%, p < 0.05 indicating significance

One-way ANOVA:GPA versus School Type

• P =0.52

• Accept the null hypothesis: School type does not have an influence on GPA.

• One-way ANOVA: Average Hours of Sleep versus School Type

• P = 0.71

• Accept the null hypothesis: School type does not have an influence on Average Hours of Sleep

• One-way ANOVA: Average Hours of Sleep versus Gender

• P =0.959

• Accept the null hypothesis: Gender does not have an influence on Average Hours of Sleep

In the preceding graph you can see a weak negative correlation between Average Hours Sleep and GPA. This indicates that sleep has either a negative effect on Grade Point averages or has little to no impact.

Average Hours of Sleep

GPA

98765432

4.0

3.5

3.0

2.5

Scatterplot of GPA vs Average Hours of Sleep

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In the Graph above you can see there isn’t a great deal of difference between the average GPAs of Males versus the average age of Females. Females though can be said to have a wider range of sleep hours than males.

3.61 3.24

3.63 3.427941176 3

3.5125 3.355 3.55

1 3.00 5.00 7.00 9.00

Aver

age

GPA

s

Average Hours of Sleep

GPA in Relation to Sleep and Gender

Females Males

3.75 3.7

3.4 3.433333333

3.316666667

3.573684211

1 3.00 5.00 7.00 9.00

Aver

age

GPA

s

Average Hours of Sleep

GPA in Relation to Sleep and School Type Amoung Males

STEM School Males Public School Males

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This graph shows that males in public schools are more likely to have lower GPAs and are also likely to get more sleep. It is interesting to note that public school students who ordinarily get about 7 hours of sleep have a higher average GPA than those of STEM school students who get the same amount of sleep.

This graph shows that there isn’t a great deal of variation between females GPAs. It is interesting to see that only STEM School Females had fewer than 2 hours of sleep where the only public school females got 8 or more hours.

3.61 3.566666667 3.516666667 3.24 3.6365 3.408928571

3

1 3.00 5.00 7.00 9.00

Aver

age

GPA

s

Average Hours of Sleep

GPA in Relation to Sleep and School Type Amoung Females

STEM School Females Public School Females

Average Hours of Sleep

GPA

98765432

4.0

3.5

3.0

2.5

NoYes

the Parefrom

PressureIs There

Scatterplot of GPA vs Average Hours of Sleep

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This graph could be used to support the hypothesis that Students who are externally motivated are more likely to have higher GPAs and are also more likely to get more sleep.

Sources of Error

It is possible for the participants to lie. Any incorrect answers would cause the trend to deviate from the actual trend. Disproportionate representation is another possible source of data distortion.. There were more participants from public schools and all of the public school students were upperclassmen. There were more female participants. This is a problem because it does not represent the entire population and only expresses the data given by specific subgroups.

Conclusions Though the results cannot be considered statistically significant the trends seen show a

slight negative correlation between Sleep and GPA and Students who receive encouragement to succeed are more likely to have higher GPAs. This shows that students can overcome the negative effects of sleep deprivation and still have good grade point averages. The study does not show the effects of Sleep deprivation on heath however so getting less sleep does not necessarily beneficial.

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Dinges, D. F., Durmer, J. F., Goel, N., & Rao, H. (n.d.). Neurocognitive consequences of sleep deprivation. (2009). SEMINARS IN NEUROLOGY, 29(4), 320-339. doi: 10.1055/s-0029-1237117. ISSN 0271-8235

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Eliasson, A., Eliasson, A., King, J., Gould, B., & Eliasson, A. (2002). Association of sleep and academic performance. Emerging Research, 6(1), 45-48. Retrieved from http://web.mit.edu/writing/2010/July/EliassonEtAl2002.pdf

Ellenbogen, J. M. (n.d.). Cognitive benefits of sleep and their loss due to sleep deprivation. (2013).Neurology, 64(25), 25-27. doi: 10.1212/01.wnl.0000164850.68115.81

Everson, C. A. (1995). Functional consequences of sustained sleep deprivation in the rat. Behavioural Brain Research, (69), 43-54. Retrieved from http://postcog.ucd.ie/files/Everson.pdf

Everson, C. A., Smith, C. B., & Sokoloff, L. (1994). Effects of prolonged sleep deprivation on local rates of cerebral energy metabolism in freely moving rats.The Journal of Neuroscience, 14(11), 67869-6778. Retrieved from www.jneurosci.org/content/14/11/6769.full.pdf

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Laosky, A. D., Bass, J., Kohsaka, A., & Turek, F. W. (2008). Sleep and circadian rhythms: Key components in the regulation of energy metabolism. FEBS Letters, (582), 142–151. doi: 10.1016/j.febslet.2007.06.079

Longordo, F., Kopp, C., & Luthi, A. (2009). Consequences of sleep deprivation on neurotransmitter receptor expression and function.European Journal of Neuroscience, 29, 1810-1819. doi: 10.1111/j.1460-9568.2009.06719.x

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Smyth, C. (2012). The pittsburgh sleep quality index (psqi).try this: Best Practices in Nursing Care to Older Adults, 6(1), Retrieved from http://consultgerirn.org/uploads/File/trythis/try_this_6_1.pdf

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Glossary

Thalamus The thalamus is a midline symmetrical structure within the brains of vertebrates including humans, situated between the cerebral cortex and midbrain. Its function includes relaying sensory and motor signals to the cerebral cortex, along with the regulation of consciousness, sleep, and alertness

Prefrontal Cortex The prefrontal cortex (PFC) is the anterior part of the frontal lobes of the brain, lying in front of the motor and premotor areas. The basic activity of this brain region is considered to be orchestration of thoughts and actions in accordance with internal goals.

Neuroimaging Neuroimaging includes the use of various techniques to either directly or indirectly image the structure, function/pharmacology of the brain. It is a relatively new discipline within medicine and neuroscience/psychology.

Psychomotor learning Psychomotor learning is the relationship between cognitive functions and physical movement. Psychomotor learning is demonstrated by physical skills such as movement, coordination, manipulation, dexterity, grace, strength, speed; actions which demonstrate the fine motor skills such as use of precision instruments or tools, or actions which evidence gross motor skills such as the use of the body in dance, musical or athletic performance.

Behavioral neuroscience Behavioral neuroscience, also known as biological psychology,[1] biopsychology, or psychobiology[2] is the application of the principles of biology (in particular neurobiology), to the study of physiological, genetic, and developmental mechanisms of behavior in human and non-human animals. It typically investigates at the level of nerves, neurotransmitters, brain circuitry and the basic biological processes that underlie normal and abnormal behavior.

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Appendix A

Experimental Design:

Research Hypothesis: There will be a greater negative correlation between sleep debt (negative hours of sleep) and academic success in Ivy League University students.

Null Hypothesis: There will be no correlation between sleep debt, academic success, or school type.

IV: School Type Public High School

STEM High School

Private High School

Home school

Home school (Online)

State University

Ivy League University

Liberal Arts College

Military Academy

100 100 100 100 100 100 100 100 100 DV: The relationship between sleep debt and academic success. Constants: Age ranges of participants, nationality of participants, survey questions. Control: No control is needed, this experiment is for comparison between the established groups.

Slope intensity and correlation will be measured. Slope intensity=absolute value of slope, higher values=steeper slopes.

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Appendix B Materials

• Internet connection • Quia survey • Microsoft Excel Program • Microsoft Word Program • Microsoft PowerPoint Program

Budget: Total Cost of Reusable Materials & New Equipment Ordered: $ ___0______ Total Cost of Disposable Materials Needed (cannot be reused): $ ___0_____

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Detailed Procedures

Analyze data using school type, GPA, and sleeping

habits

Crate survey concerning sleep

Testing Methodology

Alpha Test Questions

Receive feed back from

Alpha- participants

Positive results N

egative results

Is the feedback

positive or

Rewrite questions

Review m

aterial and feed back

Find flaws in the questions

Post survey on Quia

Send link to high school teachers and college

professors

Advertise the survey on Social N

etworking sites

Make a graph for both the

median m

ean and mode of

both sets of data

Was the

hypothesis supported?

Yes N

o

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1. Week 1 and 2 work on survey and alpha test in class.

2. Week 3 Distribute survey, http://www.quia.com/sv/620383.html to high school and

college students ages 13-24. The IV level for this study Is School and Sleep dept. The DV

is GPA.

3. Participants will then complete the survey.

4. The survey results will then be analyzed using an ANOVA test and Microsoft excel.

Correlations and other patterns will be observed and recorded.

5. Reject or accept hypothesis based on analysis.

6. Write conclusions

Safety

All participants will remain anonymous. No personal information will be asked for. There is no perceived danger in participating in the study. The entire study will take place online.

Materials • Internet connection • Quia survey • Microsoft Excel Program • Microsoft Word Program • Microsoft PowerPoint Program