Technology use and sleep- a study on how the two interact to affect academic performance in university students

2014-15 Eveliina Glogan, The University of Glasgow

Technology use and sleep: a study on how the two interact to affect academic

performance in university students

ABSTRACT

Technology use and sleep disturbances seem to be growing phenomena in today’s society,

and the two seem to have a unique relationship. In addition, both have been separately found

to have negative effects on academic performance. The objective of the study was to

investigate the relationship between technology use and sleep, and whether the two together

affect academic performance. 60 university students completed a questionnaire concerning

their sleeping habits, technology use habits, and latest grades. Correlations were examined to

investigate the relationships between technology use, sleep and grades. No significant

relationships were found between any of the variables. It is concluded that technology use has

no generalisable effect on either sleep or academic performance, but research into heavy

technology use could generate more generalisable results.

INTRODUCTION

Prolonged wakefulness is a widespread phenomenon in today’s society. Some studies have

reported a prevalence rate of 20% to 25% of sleeping problems in U.S. children and

adolescents (e.g. NSF, 2006), while other studies report prevalence rates of up to 40% in

adolescents (Owens & Witmans, 2004). In addition, Ohayon and Paatinen (2002) state that

insomnia symptoms of the population are estimated to be between 10% and 35%. It is widely

accepted among researchers and professionals, that insufficient sleep has adverse effects on

attention, and a plethora of empirical research exists to back up this notion (e.g. Choo et al.

2005; Karakorpi et al. 2006). Additionally, a vast and rapidly growing amount of scientific

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evidence supports a role for sleep in the memory and learning processes, consequently

demonstrating the detrimental effects that deficient of sleep has on the functioning of these

processes (e.g. Yoo et al. 2007; Martella et al. 2012)

Simultaneously, the use of technology has grown rapidly in the past years, with 7 out

of 10 Americans having access to the Internet in their homes in 2009 compared to 2 out of 10

Americans having personal access to the Internet in the mid-1990s (United States Census

Bureau, 2010). This is especially evident in the younger population with 95% of

undergraduate students reporting having access to the Internet at home, and 96% owning a

mobile phone (Smith et al. 2011). The portability of these devices has resulted in their

entering into bedrooms. In a report by the National Sleep Foundation (2006), 97% of

American teens had at least one technological device in their bedroom, with mp3’s and

televisions being the most popular. However, according to more recent evidence, it would

seem that the more interactive devices have become the more popular ones being used in

association with bed, with 72% of adolescents and 67% of young adults using their mobile

phones within the hour before bed (National Sleep Foundation, 2011).

In addition, there is growing evidence that technology use, and especially

communicative technology use, may also have negative effects on academic performance

(e.g. Wentworth & Middleton, 2014). With the adverse effects of insufficient sleep on

attention and learning, together with the growing evidence of technology use having adverse

effects on sleep (e.g. NSF, 2011; Munezawa et al. 2011), it is important to study whether the

root of the problem, in part of the population, might lie in the usage of technological devices

in association with bed, and whether managing these behaviours might improve attention and

consequently, academic performance.

Sleep loss and attention

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The decrease in attention due to sleep deprivation (SD) is well established. Vigilance (as

measured in reaction times) is especially impaired, but other attentional tasks, such as tasks of

working memory have also been found to show a decline (e.g. Choo et al. 2005). A number

of studies show that insufficient sleep results in poor academic performance (e.g. Kahn et al.

1989), and a decline in attention due to insufficient sleep could explain these findings.

Effects on alertness and attention

The main reason for the decrease in cognitive performance has been considered to be lapses

in attention: brief periods of inattentiveness accompanied by extreme drowsiness (Bjerner,

1949). These lapses are caused by extremely short periods of declined, sleep-like electro-

encephalography (EEG) activity that cause microsleeps (Priest et al. 2001). Williams et al.

(1959) proposed the ‘lapse hypothesis’ after noting that sleep deprived participants showed

relatively generalised features of lapses, e.g. the lapses increased in frequency and duration as

sleep loss progressed, they were strongly affected by stimulus monotony, and their specific

effect on performance varied with the properties of the task. Specifically, performance during

SD would be most likely to deteriorate during long, simple and monotonous tasks.

Doran et al. (2001) found that SD resulted in cognitive performance variability that

involved both errors of omission (lapses resulting in failure to respond to a stimulus in a

timely fashion) and errors of commission (responses to the wrong stimulus or when no

stimulus is present). This led the authors to propose the state instability hypothesis (Doran et

al. 2001). The theory states that during SD two competing neurological systems work to

influence behaviour during extended periods of SD. Some of these systems exert a drive to

sustain alertness, while others increase the involuntary drive to fall asleep. The interaction of

these drives causes unreliable and unexpected behaviour, including heightened variability in

cognitive functioning that can change moment to moment (Goel et al. 2009). Wake state

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instability thus occurs when sleep-initiating mechanisms repeatedly interfere with

wakefulness. Depending on how severe the SD is, cognitive performance becomes

increasingly variable and dependent on compensatory mechanisms (Dorrian et al. 2005).

A survey on Italian high-school students by Giannotti and Cortesi (2002) found that

poor academic performance in adolescents was associated with attention problems in school.

These adolescents also reported having more irregular bedtimes and thus tend to sleep less

than their peers who did not report attention problems. This would suggest that sleep loss and

declined attention are associated with poor academic performance.

Sleep loss and its effects on memory and learning

Sleep seems to be a vital process for memory and learning, and it would seem that this is the

case for both before and after learning: the brain appears to be less able to acquire and encode

new information without sufficient sleep prior to learning, while the ability to consolidate

(the process by which a memory becomes stable and more resistant to interfering factors

(McGaugh, 2000)) new information after learning is also hindered by subsequent lack of

sleep (Diekelmann and Born, 2010; Walker and Stickgold, 2006). Therefore, if an individual

is not well rested before learning, the acquisition of information decreases, whereas if they do

not get sufficient sleep after learning, the consolidation and integration of this new

information into existing memory structures is prevented (Diekelmann and Born, 2010).

Van Der Werf et al. (2009) found that inducing a shallow sleep due to the suppression

of slow-wave sleep was sufficient to reduce hippocampal memory encoding, therefore

demonstrating that deep sleep before learning allows optimal hippocampal activity and

benefits memory encoding, while Drummond et al. (2000) found that participants performed

significantly worse on a verbal memorising task after 35 hours of sleep deprivation compared

to baseline performance of the same participants.

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In Hu et al.’s (2006) study, participants attended a study session where they had to

memorise emotionally salient or neutral pictures. 12 hours later, either after sleeping or

staying awake, participants returned to perform a recognition test. Participants who had been

allowed to sleep, performed significantly better than participants who had stayed awake.

Thus, it would seem that sleep after learning facilitates the consolidation of memories.

More recent research also suggests that sleep obtained immediately after memory

acquisition may be critical in hippocampal-dependent memory consolidation. Hagewoud et

al. (2010) studied the effects of sleep deprivation in rats, using fear conditioning. After

training (receiving the electrical shock), rats would immediately be deprived of sleep for 6

hours. These rats showed a reduced freezing response at re-exposure to the fear context

compared to rats that were allowed to sleep after training. This suggests that sleep deprivation

immediately after memory acquisition impairs the formation of the memory. However, when

the rats were conditioned during night-time (when rats are most active, i.e. naturally sleep

less), rats did not show the learned fear-response until 12 hours of immediate sleep

deprivation. This would suggest that the amount of required hours of sleep that are lost, rather

than continuous wakefulness per se, may have an effect on memory consolidation

(Hagewoud et al. 2010).

These studies, among others, demonstrate the importance of sleep for memory and

learning; functions that are immensely important for academic performance.

Technology and its effect on sleep

It is thus evident that insufficient sleep can cause attention and learning to decrease. In real

life, however, lack of sleep rarely takes form in total sleep deprivation. Rather, it manifests

itself as something resembling chronic partial sleep restriction (Alhola & Polo-Kantola,

2007), and a variety of research suggests that technology is, at least partially, to blame. This

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paper will focus mainly on the more interactive technological devices, as they have been

found to disrupt sleep more than passive devices (e.g. NSF, 2011; Gamble et al. 2014).

Mobile phones

The mobile phone is possibly the most used technological device in today’s society. In 2011

there were 6 billion mobile phone subscriptions worldwide, which was enough to provide for

87% of the world’s population (International Telecommunication Union, 2011). In addition

to being the most portable technological device, mobile phones are also becoming

increasingly multifunctional. For example, mobile phones now allow the user to instant

message, surf the Internet, listen to music, and email, all on top of making phone calls. This

has resulted in very high use by young people. According to the National Sleep Foundation’s

2011 Sleep in America Poll 67% of young adults and 72% of adolescents use their mobile

phone within the hour before bed. In addition, 56% of adolescents reported sending or

receiving text messages every night or almost every night. Results also showed that reports of

texting within the hour before attempting to fall sleep at least a few nights a week, was

associated with daytime sleepiness and unrefreshing sleep (NSF, 2011). Similarly, Munezawa

et al. (2011) found that using mobile phones for calling and text messaging after lights out

was associated with sleep disturbances such as short sleep duration, subjective poor sleep

quality, excessive daytime sleepiness, and symptoms of insomnia. Furthermore, mobile

phones seem to be the main technological device to cause waking up from sleep. According

to the NSF (2011) 28% of adolescents sleep with their phones in their bedrooms with the

ringer turned on, and 18% of adolescents reported being woken up by text messages or phone

calls at least a few nights a week (NSF, 2011).

In a study by Van den Bulck (2007), adolescents aged 13-17 gave self-reports

concerning their mobile phone use after lights out, and filled in questionnaires concerning

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their subjective tiredness in a follow-up study a year later. Self-reports showed that only 38%

of participants reported never using phones after lights out. Results from the follow-up study

a year later revealed that those who had reported using phones after lights out about once a

week, were 3 times more likely to report being very tired than those who had not reported

using their mobile phones in this way. It was concluded that it was likely that adolescents

keep each other awake at night by texting, rather than being poor sleepers who communicate

during night-time (Van den Bulck, 2007). These results suggest that a large amount of young

people in today’s society are being kept awake at night by their technological devices, and

that this can have long-lasting effects on their quality of sleep, leading to chronic insufficient

sleep.

Computers

In 2013 83,3% of American households reported ownership of a computer, with 74,4% also

reporting having Internet access, and with 73,4% reporting a high-speed connection (U.S.

Census Bureau, 2013). Li et al. (2007) report that computers and laptops have become more

and more commonplace in children’s bedrooms, and although some studies suggest that

children spend more time watching television than they do on computers (e.g. Olds et al.

2006), the fact that many television shows have started showing on the Internet, could mean

that in just a few years the watching of television programs could shift completely from the

television to the computer (NSF, 2011).

Greater computer use has been associated with shorter sleep duration and greater

tiredness during the day (Punamäki et al. 2007), and adolescents who use a computer at night

report worse sleep quality, increased daytime sleepiness, and more sleep disorders (Mesquita

& Reimao, 2007). What comes to Internet use, Oka et al. (2008) found that school children

aged 6-12 who used the internet before bed were more likely to have later bedtimes on

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weekdays and weekends, less sleep time on weekdays, and later waking times on weekends.

Additionally, Yen et al. (2008) found that Internet addiction was associated with increased

subjective insomnia. According to the NSF Sleep in America Poll (2011), 60% of adolescents

reported using computers or laptops within the hour before attempting to fall asleep at least a

few nights a week, and 53% of adolescents reported using computers and laptops for

accessing the Internet and 20% for watching videos. These adolescents were significantly less

likely to report having a good night’s sleep (NSF, 2011).

Mesquita and Reimao (2007) studied nocturnal computer use of adolescents aged 15-

18 using a questionnaire about computer use, in order to gain knowledge about the time of

day/night and number of hours that adolescents use computers. The Pittsburgh Sleep Quality

Index was employed to assess sleep quality and report cards were used to gain knowledge

about grades. They found that 65% of the sample of adolescents used their computers at

night. Results also showed that adolescents were using computers indiscriminately of time, as

they reported using them at irregular hours and at late hours, even during the week. Authors

concluded that night time use of computers directly induces poor quality of sleep, high

indexes of daytime sleepiness, and sleep disorders, which were all significantly more

common in adolescents who used computers at night time. It was also concluded that the

findings of the study are most likely generalisable to other young people who are using the

Internet at night and who are submitted to modern conditions of life (Mesquita & Reimao,

2007).

Potential mechanisms

There are several mechanisms that have been proposed to explain the relationship between

technology use and sleep. These include sleep displacement, arousal, light exposure,

electromagnetic field exposure and sleep interruption. It is likely that more than one of these

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mechanisms is responsible for the affect technology use has on sleep, and the mechanisms are

likely to vary according to variables such as timing and quantity of technology use, and age

and socioeconomic status of the individual (Gradisar & Short, 2013). The relationship

between these mechanisms and their possible effects are unclear, yet a few hypotheses exist.

Sleep Displacement

According to the displacement hypothesis, technology use affects sleep by displacing the

time that would normally be spent sleeping, i.e. using technology before going to bed results

in delayed bedtimes (e.g. Eggermont & Van den Bulck, 2006). Van den Bulck (2004) adds

that technology use is more likely to displace sleep than structured activities, such as sport,

that have a beginning and an end point.

Arousal

Physiological, cognitive, and emotional arousal are frequently brought forward as

explanations for the affect technology use has on sleep. Munezawa et al. (2011), among

others, argue that media with stimulating content is likely to cause heightened arousal, which

hinders sleep onset, and potentially shortens sleep (Munezawa et al. 2011). In a study by

Paavonen et al. (2006) the content of television programs predicted sleeping problems over

quantity of television viewing or exposure, and Van den Bulck (2000) showed that 1 in 10

adolescent boys and 1 in 4 adolescent girls reported difficulty falling asleep after watching a

suspenseful television program at night.

Light

Light is an important zeitgeber that helps the individual in adjusting to the 24-hour circadian

rhythm. This is especially important in the morning, as light informs the individual that it is

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time to wake up. Exposure to light at night would thus increase the risk of shifting circadian

rhythms later. Zeitzer et al. (2000) demonstrated that low-intensity light of 100 lux

suppressed melatonin; a hormone associated with sleep onset, and shifted circadian rhythms

of healthy adult participants (Zeitzer et al. 2000). Gradisar and Short (2013) conclude that

whether or not light from technological devices has any affect on sleep, is dependent on the

intensity, timing and duration of exposure to light (Gradisar & Short, 2013).

Electromagnetic fields

Exposure to electromagnetic fields emitted from mobile phones has also been suggested to be

a possible mechanism to interfere with sleep; either by the effect of electromagnetic

emissions on sleep architecture (the structure and pattern of sleep), melatonin secretion or

both (e.g. Loughran et al. 2005; Munezawa et al. 2011). Loughran et al. (2005) examined

whether aspects of sleep architecture show sensitivity to electromagnetic fields from phones.

Participants were exposed to electromagnetic fields for 30 minutes before sleep. Results

showed a decrease in REM sleep and an increase in EEG frequency during the initial stage of

sleep, suggesting that mobile phone exposure prior to sleep may reduce REM sleep and

modify neural activity. In addition, Wood et al. (2006) tested whether exposure to emissions

from mobile phones 30 minutes before sleep altered melatonin secretion in adult participants.

Results showed that melatonin output at bedtime was significantly reduced following mobile

phone exposure. Findings on the affect of electromagnetic fields on sleep have, however,

remained mostly inconsistent, and the relationship between the two is still unclear.

Sleep interruption

While technology is often used pre-bedtime, and is thus more likely to affect the timing and

onset of sleep, the impact of technology during sleep may also be a contributing mechanism,

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especially among adolescents and young adults. This mechanism mainly concerns the mobile

phone, which has become ubiquitous worldwide. In Van den Bulck’s (2007) study, 20.3% of

teens reported text messaging, and 17.3% reported making phone calls between midnight and

3am. In addition, 18.6% of those who reported text messaging and 20.2% of those who

reported making calls, reported using their mobile phones at any time of the night. What

comes to being woken up by the mobile phone: 1 in 10 adolescents reported being woken up

by text message at least once a week, 8.9% several times a week, and 2.9% reported being

woken up by their phone every day (Van den Bulck, 2003). This suggests that a number of

young people are being either kept awake or being roused in the middle of the night, by

mobile phone calls and text messages.

It is thus evident that, while making our environments highly stimulating and entertaining,

technology use is interfering with the amount of sleep we are getting, to the extent that the

population is chronically getting insufficient amounts of sleep.

Academic Performance

Sleep loss

It is quite evident in the literature that sleep goes hand in hand with academic performance.

Kahn et al. (1989) found that 21% of children who were poor sleepers, compared to 11% of

children who were normal sleepers, failed 1 or more years at school. Moreover, difficulties in

school achievement were more common in poor than normal sleepers (Kahn et al. 1989).

Blum et al. (1990) conclude that children’s fatigue (i.e. difficulties in rousing in the morning

and the need to take afternoon naps) is one of the best predictors of low school achievement.

Paavonen et al. (2000) studied a sample of 5813 school-aged children and found that

17.8% of children reported sleep problems, and that severe self-reported sleep problems were

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significantly correlated with reduced academic performance, reported by teachers, compared

to reports of normal sleepers.

Trockel et al. (2000) found that university students’ sleeping habits were highly

correlated with academic performance. Using interviews and surveys to obtain information

about sleeping habits, and official grades provided by the university register, the study

showed that students who had later bedtimes and wake up times both on weekdays and

weekends were those who also had lower grades.

These studies, among others, show that increased daytime sleepiness, resulting from

poor quality of sleep, can severely impair students’ cognitive functioning and academic

performance, and that academic performance is clearly linked to sleeping habits and daytime

sleepiness (e.g. Wolfson & Carskadon, 1998).

Technology use

Students are among the heaviest users of modern technologies, especially when it comes to

communication technologies. Smith et al. (2011) found that almost all university students

access the internet, connect wirelessly, and own computers and 99% of Hakoama and

Hakoyama’s (2011) sample of university students owned a mobile phone.

The negative effects of technology use on academic performance have been

demonstrated in a number of studies, yet some studies have failed to find any effect. Chen

and Peng (2008) studied a large sample of university students by asking them to fill in an

online questionnaire. Results showed that heavy users of the internet had lower grades, lower

learning satisfaction, and their relationships with administrative staff weren’t as good as those

of non-heavy internet users.

Similarly, Kubey et al. (2001) found that heavier Internet use was highly associated

with poorer academic performance. Self-reports of Internet caused impairment were also

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correlated with staying up late, tiredness, and missing classes. Greater use of all Internet

applications was associated with self-reported internet dependency and poor academic

performance, but this was especially the case with socially interactive applications, as

opposed to applications such as email.

However, Pasek et al. (2009) studied a cross-sectional sample of 14-22 year olds as

well as a longitudinal sample of youths aged 14 to 23 and found no significant negative

relationship between Facebook use and grades. In fact, the results suggested that Facebook

use is more common among students with higher grades. The authors went on to conclude

that, albeit the convincing evidence that Facebook use (and the use of other popular

technologies) negatively affects academic performance, the matter is not as simple as it

seems, and that further investigations are needed in order to attain more reliable knowledge

of the matter.

Firstly, the relationship between technology use (especially communication technologies and

social media) and sleep requires further research in order to find clarification. Secondly, as

evidenced in this review, most of the research has mainly focused on how technology use

affects the sleeping habits and academic performances of school-aged children and

adolescents. Young adults, and especially university students, are among the most frequent

users of modern technologies (Smith et al. 2011; Hakoama & Hakoyama, 2011), and it is

therefore important to study the effects of technology use on sleep and academic performance

in this population. Finally, while most previous research has studied the effects of sleep and

technology use on academic performance individually, it has rarely been considered in the

literature, that the two might work in conjunction to affect academic performance. The

current study attempts to provide clarification by studying correlations between university

students’ self-reports of sleeping habits, technology use habits, and their academic

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performance. The first aim of the study will be to investigate whether the use of technological

devices will interfere with sleeping habits, and might therefore have an indirect affect on

academic performance. The second aim of the current study is to investigate whether

insufficient sleep or poor quality of sleep will have an affect on academic performance within

the student population. The final aim of the study will be to investigate whether the use of

technological devices will be associated with academic performance.

Hypotheses

Three hypotheses are investigated in the current study:

1. Higher reported use of technological devices will correlate with less sleep/worse

quality of sleep.

2. Less sleep/worse quality of sleep will correlate with poorer academic performance.

3. Higher reported use of technological devices will correlate with poorer academic

performance.

METHODS

Design

Using a within-subjects correlational design, each participant took part in one condition in

which they completed a questionnaire that provided information about their sleeping habits,

technology use habits, and university grades.

Participants

Participants were 60 students from the University of Glasgow, who were recruited personally

by the researchers. 22 of the participants were male, and 38 were female. Participation was

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entirely voluntary and each participant gave written consent to take part in the study.

Participants did not receive any kind of reward for taking part.

Materials

Information about participants’ sleeping habits, technology use, and grades were collected

using a questionnaire, which was grouped into five sections of response items. The first

section of the questionnaire concerned participants’ general sleeping habits (e.g. average

waking and sleeping hours on weekdays and weekends). The second section concerned

participants’ general pattern of technology use (e.g. how many hours they spend on various

technological devices, including phone and computer). The third section included questions

concerning participants’ technology use and sleep patterns together, i.e. how these behaviours

affect each other (e.g. how much time spent on devices before falling asleep, whether these

devices ever interrupt sleep). The fourth section requested usage of technological devices

during lectures and studying (e.g. how often device use ever takes place during lectures), and

the last section requested most recent grades and participants’ subjective satisfaction with

these grades. For the questionnaire in its entirety refer to the appendix section.

Measures

The questionnaire included a variety of response options that varied according to the

questions. Participants were either given the option to choose from a specific range of

numbers with different meanings (i.e. a range from 1 to 7 with 1 signifying “not at all” and 7

signifying “very”), or ranges of numbers in which the numbers were specific measures, i.e.

measures of time (e.g. a range from 0 to 60 minutes) or measures of how many times a

participant woke up during the night (a range from 1 to 5). Other questions had verbal

response options, such as “rarely”, “sometimes” or “often”, while other questions had the

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option of simply answering “yes” or “no”. Questions also had response options, in which the

participant could freely give a numerical answer (e.g. hours spent on devices or latest grades

received).

Procedure

Participants performed the experiment separately in university laboratories, at times

convenient to them. At arrival, participants signed a form of informed consent. After this they

filled in the questionnaire, which took approximately 10 minutes to complete. Each

participant either received a debrief form or was debriefed by the experimenter after the

experiment.

Data-analysis

The data from the questionnaire was collected and organised using Excel. Responses for

tablet-use were excluded from the data, as only a very small part of the sample reported using

this technological device. Excel was used to calculate means and standard deviations for

weekly technology use (hours), weekly sleep (hours), grades, frequency of waking up during

the night, device use during lectures (rarely/sometimes/often) (these data were given the

numerical values of 1-3 in the analysis), time spent on devices before bed (minutes), and time

it takes to fall asleep (minutes). Correlations were examined to evaluate relationships

between technology frequency-and-amount-of-use measures, sleep measures and grades.

Using Pearson’s r, six simple linear regression analyses were conducted to determine whether

there was a relationship between the three variables (technology use, sleep, grades).

Correlations were calculated for relationships between technology use/amount of sleep

obtained, time spent on devices before bed/time it takes to fall asleep, technology use/grades,

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technology use before bedtime/grades, frequency of waking up during the night/grades, and

device use during lectures/grades.

RESULTS

Means and Standard deviations

For weekly technology use, the mean was 48.73 (hours), and standard deviation (std) 24.84.

For weekly device use before bed, the mean was 3.66 (hours) and the std 2.7. The average

response for frequency of device use during lectures/studying was “sometimes” which had a

numerical value of 2. The mean for weekly sleep was 60.24 (hours), and the std 9.53, and the

mean for frequency of waking up during the night was 1.25 (times a night) with a std of 0.97.

For time taken to fall asleep, the mean was 28.95 (minutes), and the std was 22.55. The

average grades of the sample were 16.22, with an std of 1.97. All but one participant reported

using social media. The mean for hours spent on social media per day was 2.27 and the std

2.06.

Mean Standard Deviation

Weekly technology use (h) 48.73 24.84

Weekly device use before

bed (h)

3.66 2.7

Technology use in lectures 2

Weekly sleep (h) 60.24 9.53

Frequency of waking up

during the night

1.25 0.97

Time it takes to fall asleep

(min)

28.95 22.55

Grades 16.22 9.53

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Daily use of social media

(hours)

2.27 2.06

Correlations

The first correlation that was run investigated the relationship between weekly technology

use and the amount of sleep, and whether hours spent on devices per week affected the

amount of hours of sleep attained per week. The analysis proved the correlation to be non-

significant (Pearson’s r= .01, p= .93).

0 20 40 60 80 100 120 140 1600

20

40

60

80

100

120

f(x) = 0.00445990175366015 x + 60.0215677873258hours of sleep per weekLinear (hours of sleep per week)

Hours spent on devices (per week)

Hou

rs o

f atta

ined

slee

p (p

er w

eek)

The second analysis examined whether time (minutes) spent on devices before attempting to

fall asleep affected the ability to fall asleep; whether falling asleep would be more difficult

after spending time on devices only a little bit before falling asleep. The correlation was non-

significant

(Pearson’s r= .21, p= .10)

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0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

f(x) = 0.208652512536008 x + 22.4122212738718Minutes to fall asleep

Time spent on devices before aleep attempt (minutes)

Tim

e it

take

s to

fall

asle

ep

(min

utes

)

Thirdly, it was investigated whether hours spent on technological devices would have an

affect on grades. The correlation between the variables was non-significant

(Pearson’s r= .22, p= .08).

0 20 40 60 80 100 120 140 1600

5

10

15

20

25

f(x) = 0.0172715620448825 x + 15.330395650958

gradesLinear (grades)

Hours spent using devices (per week)

Gra

des

It was then examined whether the total weekly time spent on devices before bed would have

an affect on grades. The correlation was non-significant

(Pearson’s r=. 17, p= .19).

19


0 1 2 3 4 5 6 7 80

5

10

15

20

25

f(x) = 0.125558214323818 x + 15.7576816387496


Hours spent on devices before bed (per week)

Gra

des

The fifth analysis investigated whether frequency of night-time waking would have an affect

on grades. The correlation proved non-significant

(Pearson’s r= -0.01, p= .93).

0 0.5 1 1.5 2 2.5 3 3.5 4 4.50

5

10

15

20

25

f(x) = − 0.0226244343891402 x + 16.2449472096531


Frequency of waking up during the night (per night)

Gra

des

Finally, it was investigated whether frequency of technology use during lectures and/or

studying was related to grades. The correlation proved non-significant

20


(Pearson’s r= -0.10, p= .44).

0.5 1 1.5 2 2.5 3 3.50

5

10

15

20

25

f(x) = − 0.314285714285715 x + 16.95


Frequency of technology use during lectures/studying

Gra

des

DISCUSSION

The current study intended to investigate whether there is a relationship between technology

use and sleep that might consequently affect the academic performances of university

students. It also intended to further establish the relationships between technology use and

sleep, sleep and academic performance and technology use and sleep. The results of the

current study suggest no relationship between technology use and sleep, technology use and

academic performance, sleep and academic performance, or technology use/sleep and

academic performance. For the first hypothesis: higher reported use of technological devices

will correlate with less sleep/worse quality of sleep, two analyses were run, but no support

was found. There was no relationship between hours spent on devices each week and hours

spent sleeping each week, nor was there a relationship between time spent on devices before

bed and time taken to fall asleep (i.e. trouble falling asleep). This was also the case for the

second hypothesis: less sleep/worse quality of sleep will correlate with poorer academic

21


performance. The relationship between the amount of times waking up during the night and

grades was non-significant. For the third hypothesis: higher reported use of technological

devices will correlate with poorer academic performance, the three analyses all proved non-

significant. Surprisingly, a positive, yet weak correlation was found for weekly hours spent

on devices and grades, suggesting a possible benefit from technology use to academic

performance. However, the correlation was not strong enough to be significant. Although

weak, the correlation between frequency of technology use during lectures and/or studying

and grades was negative. Because of the weak correlations between all analyses that were

made, the hypotheses are not supported by the results of the current study.

The results of the current study contradict the majority of the previous literature. For

example, the NSF 2011 Sleep in America Poll showed that evening technology use was

significantly associated with poor sleep. Similarly, Munezawa et al. (2011) reported that

mobile phone use after lights out was related to increased levels of tiredness, while Mesquita

and Reimao (2007) found that night-time computer use highly correlated with poor quality of

sleep. The differences in results may have been due to large differences in sample sizes, as

the sample in the current study might not have been large enough to show any effects. The

number of respondents in the NSF (2011) poll was 1,498 and Munezawa et al. (2011) had

1656 participants. Mesquita and Reimao (2007), however, did not have a sample that much

larger than the one in the current study, thus decreasing the validity of this argument.

However, in Munezawa et al. (2011) and Mesquita and Reimao’s (2007) studies, the

samples consisted of school-aged children and teenagers. Children and teenagers need more

sleep than adults (NSF, 2015), and as stated in the literature review of the current paper, most

of the research on the issue of technology and sleep or sleep and academic performance has

been performed on children. It could be possible that the current study found no effect

22


between the variables because university students can function better with less sleep than can

children and adolescents.

Additionally, it would seem that night-time text messaging and being woken up by

mobile phones is more prevalent in younger age groups, the lack of which also might explain

the lack of correlation found between technology use and sleep in the current study.

According to the NSF Sleep in America Poll (2011) 56% of adolescents sent or received text

messages every night or almost every night, and this proportion was significantly lower in

older age groups. In addition, 18% of adolescents reported being woken up by their mobile

phones at least a few nights a week (NSF, 2011). In the current study, only four participants

reported being woken up by their mobile phone often during the night, as opposed to rarely

(39 participants) or sometimes (17). This would suggest that the hours of technology use

reported in the current study did not happen as much during night-time (and did thus not

interfere with sleep), as it has in the results of previous research.

Assuming that most of the technology use reported by participants in the current study

happens during daytime, the results from the current study, in comparison with the reviewed

literature, are in line with the findings of Hagewoud et al. (2010). If being deprived of sleep

at the point of the circadian rhythm where one naturally needs more sleep results in decreased

learning, then it would make sense for children/adolescents who stay awake on their devices

during night-time to show a larger decrease in academic performance, than young adults who

do not stay awake on devices. Furthermore, if children/adolescents need more sleep at night

than do adults, it would make sense for children and adolescents to show a larger decrease in

academic performance, than do young adults who do use devices at night-time.

The results of the current study also contradict research on the relationship between

technology and academic performance. Chen and Peng (2009) found the academic

performance of heavy Internet users to be significantly poorer than that of non-heavy Internet

23


users. Similarly, Kubey et al. (2001) found that heavy recreational use of the Internet,

especially socially interactive websites, correlated highly with impaired grades. Again, the

differing results between previous studies and the current study could be due to differences in

sample sizes (49,609 in Chen and Peng (2009) and 572 in Kubey et al. (2001)). In addition,

the current study did not differentiate between heavy Internet users and non-heavy users, as

has been done in the previous research. Consequently, in the previous studies mentioned, it

has mostly been heavy users of the Internet (defined as those who use the Internet 30-40

hours per week (Chen & Peng, 2009)), who have shown negative effects in their academic

performance. Although almost all participants in the current study reported using

technological devices and social media on a daily basis, only two of those participants would

be defined as heavy users. As most of the sample would be defined as non-heavy users, the

amount of technology/internet use reported might not have been high enough to have an

affect on academic performance.

The results of the current study are similar to those of Pasek et al. (2009). The authors

found no negative relationship between Facebook use and academic performance in a large

sample of young people ranging between the ages of 14-22. Two of the analyses in Pasek et

al.’s (2009) study showed that Facebook-users were no more or less likely to get good grades

than non-users, and in fact one of the analyses showed a positive relationship between high

grades and Facebook use. While analyses weren’t made in the current study for a correlation

between Facebook use and grades per se, almost all participants in the current sample

reported using social networking sites on a daily basis. According to statistics from a Pew

Research Centre report by Duggan et al. (2014), 71% of Internet users use Facebook, and

89% of those who use Facebook are between the ages of 18-29, which would make it

reasonable to assume that a substantial amount of the current sample also use this particular

social networking site. If this is the case, the results from the current study closely replicate

24


those of Pasek et al. (2009), as, the correlations between total technology use per week and

grades, and the correlation between technology use before bed and grades, were slightly

positive, rather than negative, although non-significant.

Pasek et al. (2009) point out that as new media and technologies are continuously

evolving, the changing nature of these media and technologies may lead to continuously

changing effects; maybe students are continuously learning to better juggle or balance

technology use with studying. This does not, however, suggest that Facebook and technology

use cannot have negative effects on academic performance. As previously pointed out in the

current study, and by Pasek et al. (2009), it is excessive users who seem to be most at risk.

A notable limitation to the current study is its small sample size. A larger sample

would have made the study more reliable. In addition, the response items in the questionnaire

could have benefitted from being designed more prospectively in terms of data-analysis and

in terms of comparability with previous research. Some correlations were impossible to

analyse, as part of the data was categorical. Also, a number of questionnaire responses were

not included in the data-analysis, as they proved not to be useful. Had the questionnaire been

designed more carefully, and more prospectively, there could have been more useful

questions and responses. The results could have been more comparable, had the questionnaire

questions been designed to measure similar effects as previous studies. For example, it would

have been helpful to know whether any technology use occurred at night-time, and if so, how

much of it occurred at night-time, as this would have made it easier to compare night-time

use of technologies between children/adolescents and university students. More meaningful

effects could have been found had the questionnaire been more specific, (e.g. how many

times a week were participants woken up by their mobile phone) instead of including vague

response options such as rarely/sometimes/often.

25


It would be useful to replicate the current study, as it still holds true that the effects of

technology use and sleep together on academic performance, have not received much

attention in the previous literature. However, further research would benefit from taking into

account the limitations of the current study, and use more similar measures to previous

research in order to make the results more comparable. Alternatively, future studies could use

a between subjects design to directly compare differences between age groups. This kind of

study would, however, need to take into account the differences in amounts of required sleep

and the differences in academic requirements between different age groups, as children and

adolescents generally require more sleep than young adults do in order to function fully, and

because school grades and university grades are not directly comparable. Because prior

research seems to suggest that it is the excessive users of technologies that show most

detrimental effects to academic performance, it would be useful to further investigate this

group by replicating the current study using an experimental group of heavy technology users

and a control group of non-heavy users.

In conclusion, the results of the current study suggest that technology use has no

generalisable impact on either sleep or academic performance, although, due to limitations of

the current study, further research is required for more reliable conclusions. However, based

on previous research, it would seem that there is a stronger effect when it comes to heavy

users of technologies, as they have been more generally found to show impaired academic

performance, than non-heavy users. This issue should be tackled in future research in order to

acquire a more reliable and generalisable understanding of the matter.

26


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Technology use and sleep- a study on how the two interact to affect academic performance in university students