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Annals of Psychophysiology                                                                                                        Volume 3, October 2016                  
©Advance Educational Institute & Research Centre                                                                    www.aeirc-edu.com 

ISSN 2412 - 3188 
 

Original Article 

Determinants of Sleep Quality among Undergraduate 

Students of Universities of Karachi 
Fahad Khan1,2, Husna Haroon2, Hafsa Murtaza2 & Erum Anwar2 

1. Civil Hospital Karachi, Pakistan. 

2. Dow Medical College, DUHS Karachi, Pakistan 

Corresponding Author: husnadragneel@gmail.com 

 

Abstract 

Background: The goal of this study is to evaluate the extent to which caffeine consumption, cigarette smoking, 

technology use, and academics are associated with the sleeping habits of university students.  

Methods: 643 undergraduate students aged 18 to 23 from five universities of Karachi completed a cross-sectional 

survey about sleep patterns and lifestyle habits between December 2014 and November 2015. Pittsburgh Sleep 

Quality Index (PSQI) was used to assess the sleep quality. Data was entered and analyzed using SPSS version 22. 

Pearson Chi- square test was applied to determine relationship between sleep patterns and factors affecting sleep. 

Threshold of significance was set at <0.05. Results: Majority of the participants (60.5%) reported poor sleep quality 

(PSQI score >5), with an average PSQI score of 6.5 (±3.033). Sleep duration was less than 7 hours for 71.8% 

(N=462) of the participants. Also, most participants went to bed between 9pm to 12am, with 52.5% of males 

(N=136) sleeping after 12am compared to 46.1% (N=177) of the females. Other than energy drinks, no stimulant 

beverage showed significant relation with sleep quality. However, coffee, tea and energy drinks had a negative 

impact on sleep onset latency. Smoking too showed an association with poor sleep quality (p-value <0.009) and 

delayed sleep onset (p-value <0.009). Mobile phone was by far the most frequently used technological device.  

Conclusions: Poor sleep quality is prevalent among college students of Karachi. Behavioral habits like consumption 

of caffeinated drinks, smoking, and technology use are associated with increased odds of poor sleep quality. 

 

Keywords 
Sleep quality, PSQI, technology, beverages, college students 

Introduction 

College, a terminal educating and nurturing ground 

for professional education, is indispensable for the 

overall development and progress of any nation. It 

provides young individuals with the requisite skills 

for their preferred fields, along with enabling them to 

support themselves and contribute to their society. 

But the price paid can be dear. The hectic schedules, 

towering burden of studies and assignments, and the 

never ending stress of deadlines dispose individuals 

to constant self-neglect, not the least of which is 

compromise on their night-time sleep.  

 

On an average, a young adult needs around 8 hours of 

sleep per day (Ohayon, Carskadon, Guilleminault, & 

Vitiello, 2004; Van Dongen, Maislin, Mullington, & 

Dinges, 2003). Yet majority of the students are sleep 

deprived, as shown by one study in which 70.6% of 

the college students reported sleeping less than 8 

hours with mean total sleep time being 7.02 hours 

(Lund, Reider, Whiting, & Prichard, 2010). College 

students are prone to sleep related problems (Peltzer 

& Pengpid, 2016; A. A. Schlarb, D. Kulessa, & M. 

D. Gulewitsch, 2012). A research conducted among 

Lebanese university students (Kabrita, Hajjar-Muca, 

& Duffy, 2014) found out that more than half of the 

students scored in the poor-sleeper category on the 

Pittsburgh Sleep Quality Index (PSQI).  

 

Sleep repairs physical and mental functions of one’s 

body and restores its normal activity along with 

consolidation of learning and memory (Ohlmann & 

O'Sullivan, 2009). The repercussions of 

compromising on sleep can be severe as sleep 

deprivation and unhealthy sleep hygiene can result in, 

loss of cognitive functions (Wiebe, Cassoff, & 

Gruber, 2012), poor academic performance (Dewald, 

Meijer, Oort, Kerkhof, & Bögels, 2010), as well as 

increased risk of road accidents (Horne & Rumbold, 

2015). According to the current literature, chronic 

sleep loss has been shown to have a link with several 

common health problems, such as cardiovascular 

diseases, weight gain, type II diabetes, poor memory, 

depression, digestive problems, and cancer (Ohlmann 

& O'Sullivan, 2009).  

 

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Annals of Psychophysiology                                                                                                        Volume 3, October 2016                  
©Advance Educational Institute & Research Centre                                                                    www.aeirc-edu.com 

ISSN 2412 - 3188 
 

Voluntarily staying up late is only one part of the 

problem, however. Different habits adopted by 

students contribute to unhealthy sleep hygiene; for 

instance, the untimely use of caffeine, cigarettes, and 

media devices. The present study is aimed at finding 

out the effect on sleep of these activities.  

 

Caffeine has a well-documented effect on sleep 

quality. Present in varying amounts in coffee, energy 

drinks, tea, soft drinks and even chocolate, it is 

commonly consumed by young adults. It was found 

that consumers of any stimulant beverage were 80% 

more likely to have poor sleep quality than non-

consumers (Velez et al., 2013). Drake et al (Drake, 

Roehrs, Shambroom, & Roth, 2013) showed that 

consuming caffeine 0,3 or even 6 hours before 

bedtime significantly disrupts sleep. 

 

Electronic media occupies an indispensable niche in 

young adults’ life. Increased portability and 

affordability has made these devices available to all 

and sundry. According to National Sleep Foundation 

2006, almost all (97%) adolescents had at least one 

technological device in their bedroom. However, in 

concern with their effect on sleep, not only the 

frequency, but the time and duration of media use are 

essential variables to be examined. Gradisar et al. 

(2013) reported in their research that 90% of 

American adolescents used some form of technology 

in the hours before bedtime (TV 60%, mobile 39%, 

computer/laptops 36%). They found a significant 

relation of evening technology use with sleep, where 

the quality of sleep and sleep latency were both 

affected by technology use in the hour before 

bedtime. High screen time was also found to have a 

positive significant relation with poor sleep quality 

among Chinese college students (Wu, Tao, Zhang, 

Zhang, & Tao, 2015). 

 

In Pakistan, a few studies have been carried out on 

sleep quality of students (U. Bhatti, Rani, Memon, & 

Wali, 2015; Surani et al., 2015). One study was 

conducted among school-going children which found 

that more than three-fourth of the secondary school 

children of Karachi slept late, with homework and 

TV shows being more frequent reasons for later 

bedtimes, whereas parental influence was reported 

more by the early-sleepers (Nusrat, Khan, Hamid, 

Hussain, & Kadir, 2012). The sleep habits of first and 

final year medical students have also been evaluated 

(A. A. Bhatti et al., 2012). Yet another research 

studied the relation between sleep and academic 

performance in medical students (Waqas, Khan, 

Sharif, Khalid, & Ali, 2015). But, to the best of our 

knowledge, the present study is the first in Pakistan 

to assess sleep quality with its determinants among 

college students in detail. 

 

Methodology 

a) Subjects: 

The study was approved by ethical review board of 

Dow University of Health and Sciences. This was a 

cross-sectional study conducted in 5 private and 

public universities of Karachi in late fall 2014 and 

early spring 2015. These include Dow Medical 

College, NED university of engineering and 

technology, Karachi University, Indus University and 

Iqra University. Subjects for this study were 

undergraduate students aged 18-23 years, who did 

not have any known disorder or acute or chronic 

psychiatric illness. Approval to conduct this study 

was obtained from deans of all selected universities. 

 

b) Apparatus/ equipment: 

700 questionnaires were distributed among students, 

out of which 213 were sent through Facebook via 

Google docs as online forms. The students were also 

encouraged to pass on the questionnaires to students 

of other universities who could fit into our criteria. 

The remaining 487 were distributed hand-to-hand. 

Students were first orally briefed about the purpose 

and importance of the study. Those who expressed an 

interest were given the questionnaires after obtaining 

consent. The questionnaires were anonymous and no 

personal identifiers were collected. Students enrolled 

in online or night time school programs were not 

included in the study. After excluding subjects of 

missing sleep quality components, the final analyzed 

sample consisted of 643 participants. 

 

A 32-item self-administered questionnaire, consisting 

of four parts was used. The first part comprised of 

demographics including age (years), sex, 

employment status, participation in physical exercise. 

Height and weight were also asked for BMI and 

thresholds were set according to WHO protocol 

(underweight: <18.5kg/m2; normal: 18.5–24.9kg/m2; 

overweight:25.0–29.9kg/m2; obese: ≥30kg/m2) 

(Samuelson, 1997). 

 

The second part consisted of Pittsburgh sleep quality 

index (PSQI), by which sleep quality was assessed 

(Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). 

It is a 19-item self-reported questionnaire that 

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Annals of Psychophysiology                                                                                                        Volume 3, October 2016                  
©Advance Educational Institute & Research Centre                                                                    www.aeirc-edu.com 

ISSN 2412 - 3188 
 

evaluates sleep quality over past month. It possesses 

7 sleep components including sleep duration, sleep 

disturbance, sleep latency, habitual sleep efficiency, 

sleep medicine, daytime dysfunction, and overall 

sleep quality. The questionnaire was further modified 

by including bedtimes and arousal times on weekdays 

and weekends. These components yield a score of 0-3 

with 3 indicating greatest dysfunction. All the 

components are summed up to give a total score 

(global score) from 0 to 21 with higher total score 

indicating poor sleep quality. Participants with a 

score below 5 are referred to as good sleepers while 

those ≥5 are poor sleepers (Buysse et al., 1989). 

 

The third part consisted of inquiry about technology 

use before bedtime over the past month. Participants 

were asked about the presence of technological 

devices in the bedroom (TV, computer/laptop, 

mobile, video games, DVD) in Yes or No format and 

frequency of technology use per week. Questions 

were also asked about duration of device(s) used, the 

activities performed online, the contents viewed on 

TV/DVD and the type of music listened in the hour 

before bedtime. Participants were also asked self-

analysis about the technology device affecting the 

sleep most and frequency of sleep disturbance due to 

technology use in a 4-point Likert scale ranging from 

Always to Never.  

 

The fourth part consisted of questions on 

consumption of stimulants and academics. 

Participants were asked if they consumed any 

caffeinated beverage during past month. those 

answering Yes were further asked about type of 

caffeinated beverages (coffee, tea, carbonated soft 

drinks, energy drinks, others), frequency of use per 

week and their time of consumption. We asked the 

participants whether they smoked during the past 

month. Those answering Yes were asked further 

about number of cigarettes (frequency) smoked per 

week. In the academics portion we asked the 

participants about frequency of attending university 

per week, their study onset timings and grade points 

average (GPA). They were also asked their self-

rating whether admission in university and academic 

stress affected their sleep. 

 

Data were entered and analyzed using SPSS version 

22 and Microsoft Excel 2013 for Windows. 

Frequency and percentages were calculated for 

categorical data and mean and standard deviation for 

continuous data. Pearson Chi- square test was applied 

to determine relationship between sleep patterns and 

other variables. Threshold of significance was set at 

<0.05. 

 

Results 

 

Demographic Characteristics and Lifestyle Habits:  

A total of 643 students completed the questionnaires. 

Their average age was 20.03 ± 1.245 years. Majority 

of the respondents were females 59.7% (N=384); 

lived with their family (93.1%); attended university 

daily (59.7%) and were unemployed (79%). About 

62.4% said they exercised regularly or occasionally. 

Only 6.8% (N=44) reported having stress. Table 1 

summarizes the demographic characteristics of 

respondents.

 

Sleep Variables and Patterns: 

 

Majority of the participants, about 60.5% (N=389) 

reported poor sleep quality (PSQI score >5), with an 

average PSQI score of 6.5 (±3.033). Sleep duration 

was less than 7 hours for 71.8% (N=462) of the 

participants with most sleeping 6 to 7 hours each 

night. Mean sleep duration for weekdays was 6.82 

(±1.758) and for weekends was 8.73(±2.695). For 

67.5% of the participants sleep latency was ≤30 

minutes. Also, most participants went to bed between 

9pm to 12am with majority of males 52.5% (N=136) 

sleeping after 12am compared to 46.1% (N=177) of 

the females. Most of the females took daytime naps 

60.4% (N=232) compared to 45.6% (N=118) males. 

The results are represented in Table 2. 

 

Association of Sleep and Lifestyle Habits: 

 

There was no significant relationship between PSQI 

and age, sex, BMI, place of living, employment, or 

education level. Behaviorally, exercise and university 

attendance were not significantly related to PSQI 

either. Naps were found to decrease nocturnal sleep 

duration. 76.3% of the people who took naps had 

sleep duration of <7 hours compared to the 66.6% 

who did not (p- value 0.006). 

 

 

 

 

 

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Annals of Psychophysiology                                                                                                        Volume 3, October 2016                  
©Advance Educational Institute & Research Centre                                                                    www.aeirc-edu.com 

ISSN 2412 - 3188 
 

Table 1: Demographics and lifestyle characteristics of respondents 

Variables n (N=643) % Mean ± SD 

Age(years)   20.03±1.245 

Gender    

Male 259 40.3  

Female 384 59.7  

Educational level    

Junior 233 36.2  

Sophomore 201 31.3  

Senior 209 32.5  

Employment status    

Yes 65 10.1  

No 578 89.9  

Exercise    

Yes 401 62.4  

No 242 37.6  

Smoking status    

Yes 98 15.2  

No 545 84.8  

Body Mass Index (BMI)    

Underweight 212 33.0  

Normal 365 56.8  

Overweight 47 7.3  

Obese 19 2.9  

 

Table 2: Sleep Variables 

Characteristics 

 

All 

N = 643 n (%) 

Male 

N =259 n (%) 

Female 

N = 384 n (%) 

P-value 

1. TST (hours) 

<5    75(11.7) 35(13.5) 40(10.4) 0.536 

 5-6   163(25.3) 63(24.3) 100(26.0) 

 6-7 224(34.8) 85(32.8) 139(36.2) 

>7 181(28.2) 76(29.4) 105(27.4) 

2.SOL (minutes) 

 0-15 250(38.9) 108(41.7) 142(37.0) 0.669 

>15-30 184(28.6) 72(27.8) 112(29.2) 

>30-60 105(16.3) 39(15.1) 66(17.2) 

>60 104(16.2) 40(15.4) 64(16.6) 

3.Bedtime  

  6pm-9pm 10(1.6) 2(0.8) 8(2.1) 0.024 

>9pm-12am 321(49.9) 121(46.7) 200(52.0) 

>12am-3am 282(43.9) 117(45.2) 165(43.0) 

>3am-6am 30(4.6) 19(7.3) 11(2.9) 

4.Naps 

    Yes 350(54.4) 118(45.6) 232(60.4) <0.01 

   No 293(45.6) 141(54.4) 152(39.6)  

 

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Fahad Khan 8 

  

 

Annals of Psychophysiology                                                                                                        Volume 3, October 2016                  
©Advance Educational Institute & Research Centre                                                                    www.aeirc-edu.com 

ISSN 2412 - 3188 
 

Table 3: Consumption of caffeinated beverages and cigarette smoking 

Stimulants Frequency of 

consumption 

Sleep quality 

n (%) 

p-

value 

Sleep latency 

n (%) 

p-

value 

  Good sleep 

quality  

Poor sleep 

quality 

 <30 mins ≥30 mins  

Coffee 0-3 times/week 

N = 530 

205(38.7) 325(61.3) 0.355 367(69.2) 163(30.8) 0.04 

≥4 times/week 

N = 113 

49(43.4) 64(56.6)  67(59.3) 46(40.7)  

Tea 0-3 times/week 

N = 297 

124(41.8) 173(58.2) 0.280 212(71.4) 85(28.6) 0.051 

≥4 times/week 

N = 346 

130(37.6) 216(62.4)  222(64.2) 124(35.8)  

Energy 

drinks 

0-3 times/week 

N = 541 

223(41.2) 318(58.8) 0.040 383(70.8) 158(29.2) <0.05 

≥4 times/week 

N = 102 

31(30.4) 71(69.6)  51(50.0) 51(50.0)  

Carbonated 

soft drinks 

0-3 times/week 

N = 519 

213(41.0) 306(59.0) 0.103 352(67.8) 167(32.2) 0.718 

≥4 times/week 

N = 124 

41(33.1) 83(66.9)  82(66.1) 42(33.9)  

Others 0-3 times/week 

N = 632 

252(39.9) 380(60.1) 0.145 427(67.6) 205(32.4) 0.783 

≥4 times/week 

N = 11 

2(18.2) 9(81.8)  7(63.6) 4(36.4)  

Cigarette 

smoking 

Smokers 

N = 98 

27(27.6) 71(72.4) 0.009 55(56.1) 43(43.9) 0.009 

Non-smokers 

N = 545 

227(41.7) 318(58.3)  379(69.5) 166(30.5)  

 

Figure 1: Frequency of technological devices used in the hour before bedtime. 

 
 

 

 

 

292
337

604

491

140

364351
306

39

152

503

279

0

100

200

300

400

500

600

700

Computers/
laptops

TV DVD Games Mobiles Music

<4 times per week

4 times or more per week

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Annals of Psychophysiology                                                                                                        Volume 3, October 2016                  
©Advance Educational Institute & Research Centre                                                                    www.aeirc-edu.com 

ISSN 2412 - 3188 
 

Caffeine Consumption: 

Overall, 548 respondents (85.2%) consumed at least 

one type of caffeinated beverage once during the 

previous month. Tea was the most frequently 

consumed stimulant beverage (63.8%), followed by 

carbonated soft drinks (43.2%), coffee (35%), and 

energy drinks (29.8%).  

 

Frequency of consumption 

Of the 643 participants who consumed energy drinks 

greater or equal to 4 times per week 69.6% had poor 

sleep quality whereas 58.8% of those who consumed 

< 3 times per week had poor sleep. No significant 

association was found between other beverages and 

PSQI. On the other hand, frequency of consumption 

of tea, coffee and energy drinks was found to 

significantly increase sleep latency by ≥30 minutes 

when consumed ≥ 4 times per week compared to 3 

times or less per week consumption (Table 3). 

No relation was found with sleep duration. 

 

Time of consumption 

Time of consumption of stimulant beverages was 

found to have a significant relation with PSQI and 

bedtime of the respondents. Late- night consumption 

of stimulant beverage led to poor sleep quality as 

demonstrated by 77.9% of late- night consumers 

having poor sleep quality compared to 57.1% of 

those who were not (p-value<0.01). Also, majority of 

those participants (61.3%) consuming beverage in the 

morning had bedtimes before 12(p-value=0.003) 

whereas majority of the late- night consumers 

(67.3%, p-value <0.05) had their bedtimes delayed 

until after midnight. 

 

Smoking: 

Smokers (15.2%), who smoked during the previous 

month, showed poor sleep quality (72.4%) compared 

to 58.3% of non-smokers who had poor sleep quality 

(p- value 0.009). Also, more smokers showed 

delayed sleep latency as compared to non-smokers 

(p- value 0.009) (Table 3). Furthermore, majority 

(80%) of respondents who woke up during the night 

to smoke displayed poor sleep quality and delayed 

sleep. 

 

Technological Devices: 

98.25% of the participants had at least one 

technological device in their bedrooms, with 53% 

(N=341) having at least 3 devices in their bedrooms. 

Mobile phones were found to be the most frequently 

used device in the hour before bedtime, followed by 

computer/laptops and television. (Figure 1) Those 

respondents who watched television (p-value 0.007), 

used mobile phones (p-value 0.012) or listened to 

music (p-value 0.001) in the hour before bedtime ≥ 4 

times per week during the previous month had a 

greater tendency of having a sleep onset latency of 

greater than 1 hour compared to those who used those 

devices ≤3 times. Also, increased television, DVD 

and music player usage had increased prevalence of 

poor sleep quality. 61.6% of those who used mobile 

phones more frequently had poor sleep compared to 

56.4% of the people who used it less frequently, 

though the association was not significant. 

 

Presence of videogames in bedroom had a significant 

relation with PSQI. 72.9% of those who had video 

games in their bedrooms had poor sleep quality 

compared to 58.6% who did not (p-value 0.012). 

Content viewed on television or activities performed 

online were not found to have any significant relation 

with sleep quality or other sleep variables. Type of 

music listened to in the hour before bedtime was 

found to have a significant relation with sleep 

latency. 38.7% of heavy music listeners reported 

sleep onset latency of >30 minutes, whereas 29.9% of 

light music listeners and those who did not listen to 

any music had delayed sleep (p value 0.028). 

43.1% of the respondents reported mobile phone 

being the major device affecting their sleep. Yet, 

78.4% said their sleep was never or rarely disturbed 

by any incoming call or text message. 36.2% of the 

participants admitted that their sleep was frequently 

or always affected by the use of technological 

devices. 

 

Academics: 

GPA and study onset time of the participants were 

not found to have any significant relation with sleep 

quality or its variables. 35.9% of the students who 

attended university regularly reported a sleep onset 

latency of >30mins compared to the 29.6% of the 

ones who attended 2-4 times and 21.9% who attended 

once or less per week (p- value 0.038). 

 

Majority (75.6%) agreed that admission in university 

had altered their sleeping habits, while 54.7% of 

students reported that they always or frequently 

sacrificed their nocturnal sleep for studies and 

assignments.  39.2% of students admitted that they 

had difficulty sleeping due to anxiety for studies or 

academic stress. 

 

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Annals of Psychophysiology                                                                                                        Volume 3, October 2016                  
©Advance Educational Institute & Research Centre                                                                    www.aeirc-edu.com 

ISSN 2412 - 3188 
 

Discussion 
Poor sleep quality as determined by PSQI global score 

of greater than 5 was prevalent among majority of the 

university students, as was found in a prior study 

conducted among medical students of Karachi (Surani 

et al., 2015) and college students in North America 

(Lund et al., 2010), as well as among Lebanese 

(Kabrita et al., 2014) and Chinese university (Suen, 

Tam, & Hon, 2010)  students. Also, majority of the 

participants slept between 9- 12 am, a habit that was 

similar to the majority of German university students 

(72.4%) who went to bed between 10 pm and 12 am. 

On the other hand, unlike German students (Angelika 

A Schlarb, Dominika Kulessa, & Marco D 

Gulewitsch, 2012), most of whom (84.1%) fell asleep 

within 30 minutes, majority of University students of 

Karachi had delayed sleep latency of ≥ 30 minutes. 

 

Consumption of caffeine, cigarette smoking and 

sleep: 

Caffeine, a well-known adenosinergic receptor 

antagonist, is one of the most widely consumed 

psychoactive substance mostly taken to restore low 

levels of wakefulness, modulate the activities of brain 

and improve task performance. Our study reveals that 

caffeine consumption is prevalent among the 

undergraduate university students of Karachi. Majority 

of the poor sleepers are tea consumers, though it did 

not reach statistical significance, which corroborate 

with a similar study (Hindmarch et al., 2000). The 

study also revealed that 3 out of 10 respondents 

consumed energy drinks, which is found to be strongly 

associated with sleep quality. This is in general 

agreement with prior studies (Aslam et al., 2013; 

Lemma et al., 2012; Reissig, Strain, & Griffiths, 2009; 

Velez et al., 2013). Besides caffeine, which is the main 

stimulant of energy drink, it may also contain other 

stimulating ingredients. The amino acid, taurine, a 

frequent ingredient in energy drinks, is thought to 

increase the effects of caffeine (Rath, 2012).  

 

Out of all the sleep quality variables, only sleep latency 

is found to be associated with frequency of caffeine 

consumption (tea, coffee, energy drinks), no other 

sleep parameters approached significance. Majority of 

the respondents who said that they consumed 

beverages right after wake up had early bedtimes, that 

is, before midnight, while more than half of the late 

night consumers reported going to bed after midnight. 

More than three fourth of the late night consumers are 

poor sleepers. Thus, morning consumption is not found 

to be associated with sleep quality whereas late night 

consumption showed significant impact on sleep. The 

results are in accordance with previous findings (Drake 

et al., 2013). However, this result is contrary to some 

previous studies that found no association between 

caffeine use and sleep quality (Brick, Seely, & 

Palermo, 2010; Lund et al., 2010). Investigators have 

shown that caffeinated beverages have a dose-

dependent negative effect on sleep onset, sleep time, 

and sleep quality (Hindmarch et al., 2000). However, 

we did not have information regarding dose of caffeine 

consumption to confirm previous findings. 

 

Smokers demonstrated considerably more often than 

never smokers an overall reduced sleep quality 

represented by a PSQI global score of above 5. This 

was consistent with the results of previous researches 

(Araujo et al., 2014; Cohrs et al., 2014). Furthermore, 

the finding of a disturbed sleep latency with a higher 

number of smokers reporting an increased time to fall 

asleep is in accordance with studies reporting smokers 

to have more difficulty getting to sleep (Botello-

Harbaum, Haynie, Murray, & Iannotti, 2011; Cohrs et 

al., 2014; McNamara et al., 2014) On the other hand, 

variables such as sleep duration and bedtime did not 

show any significant association with smoking. No 

significant association was found between the number 

of cigarettes smoked per day and sleep quality or sleep 

duration, as was found in a prior research (Cohrs et al., 

2014). However, our study found that increased sleep 

latency was found to be associated with a decreased 

number of cigarettes smoked (Table 3). A possible 

reason could be the role of other confounding factors 

affecting sleep.  

 

Electronic media and sleep: 

We studied the effect of technology usage on sleep of 

young adults. Considering the extent to which media 

devices have infiltrated our day to day lives, it was not 

surprising to find that there was an association found 

between the frequency of use of television, cell phone 

and music player, and the sleep onset latency. Those 

who used these devices ≥ 4 times per week had more 

chance of having sleep latency of greater than one 

hour, compared with those people who used the 

devices ≤ 3 times per week. This finding is consistent 

with that of previous studies (U. Bhatti et al., 2015; 

Gradisar et al., 2013; Polos et al., 2015) which found 

that the stimulating activities performed on interactive 

devices (cell phones, computers, video games) in the 

hour before bedtime were associated with difficulty 

falling asleep. It is possible that the cognitive and 

physiological arousal resulting from their usage 

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Annals of Psychophysiology                                                                                                        Volume 3, October 2016                  
©Advance Educational Institute & Research Centre                                                                    www.aeirc-edu.com 

ISSN 2412 - 3188 
 

inhibits normal sleep initiation (Ivarsson, Anderson, 

Åkerstedt, & Lindblad, 2009; Weaver, Gradisar, 

Dohnt, Lovato, & Douglas, 2010). It has been found 

that evening exposure to LED-backlit computer 

screens, despite increasing the cognitive functioning, 

can suppress melatonin and interfere with our normal 

sleep cycle (Cajochen et al., 2011). A recent research 

further highlighted the positive correlation between 

poor sleep quality and greater texting and iPod 

dependence (Ferraro, Holfeld, Frankl, Frye, & 

Halvorson, 2015).  

 

An interesting finding was that the type of music 

listened to in the hour before bedtime was significantly 

associated with sleep onset latency. Individuals who 

preferred listening to heavy music reported greater 

difficulty in falling asleep as compared to those who 

listened to light music or none at all. Studies show that 

while soothing music is conducive to relaxation and 

falling asleep (de Niet, Tiemens, Lendemeijer, & 

Hutschemaekers, 2009) and can improve sleep quality 

in insomniacs (Harmat, Takacs, & Bodizs, 2008), 

music can also elevate the heart rate and thus play a 

role in maintaining arousal and causing difficulty in 

falling asleep (Bonnet & Arand, 2000). 

 

Even though 43.1% of the entire sample reported cell 

phones to be the device which most affected their 

sleep, majority (78.4%) claimed that their sleep was 

never or rarely disturbed by an incoming call or text. In 

light of a research conducted among Americans 

(Gradisar et al., 2013) which found that 10% of all the 

participants went to bed with the cell phone ringer 

turned on and consequently had difficulty returning to 

sleep after an awakening, a possible reason for the 

present finding could be that those participants left 

their ringer turned off. 

 

Academics and sleep: 

Sleep problems in university students might lead to 

suppressed academic performance (Genzel et al., 2013; 

Haraszti, Ella, Gyongyosi, Roenneberg, & Kaldi, 2014; 

Lund et al., 2010). One cannot remain refreshed and 

alert in university after insufficient or poor sleep the 

night before. Our findings showed that 6 of 10 

participants regularly attended the university during 

past month. Of those, majority of the students went to 

bed before midnight. However, more than half of the 

students, who attended the university ≤ 1 time per 

week, went to bed after midnight which reached 

significance. An overwhelming 39.2% of students 

stated that academic stress negatively impacted their 

sleep. This is consistent with the results of a previous 

study (Lund et al., 2010). Prior studies also reported 

that later bedtimes are more likely to affect one’s 

academic performance than short sleep duration 

(Genzel et al., 2013; Haraszti et al., 2014). 

 

Limitations of the study: 

Our study has certain limitations. The non-longitudinal 

and cross-sectional nature of the study made it 

impossible to infer causality i.e. what came first. Data 

collection was done using self-reported questionnaires 

which might lead to recall bias.  Respondents were 

inquired about their past month only, without taking 

into consideration their academic schedules, which 

could have influenced on their sleep quality ratings. 

Sleep quality was assessed using PSQI having cut-off 

value 5, below which are good sleepers and above are 

poor sleepers. This grouping leads to substantial 

heterogeneity among large group of poor sleepers, 

hiding essential correlations. Finally, we asked the 

respondents about frequency of consumption of 

stimulants without asking them brands (companies) 

and dose of consumption (in grams or milligrams). It 

might be possible that caffeine content varied for 

beverages/stimulants of different brands. Future 

researches must use sleep/health diaries for subjective 

measures or actigraph for objective measures so that 

their self-report biasing might be eliminated. 

 

Conclusion 

In summary, poor sleep quality is prevalent among 

college students of Karachi, Pakistan. Despite the 

aforementioned limitations, we found evidence that 

behavioural habits like consumption of caffeinated 

drinks, smoking, and technology use can adversely 

affect sleep to various extents. Considering these 

factors are the zeitgeist of youth, and have many 

benefits if used wisely, interventions can be made to 

ensure that students are benefited more than harmed by 

them. College students in Karachi, indeed all over 

Pakistan, need to be made aware of the impact their 

activities have on their sleep. Emphasis needs to be 

placed on the fact that it isn’t simply how often they 

perform these activities, but also the time that is 

important and with proper education we can hope for 

students to adjust their schedule such that due 

importance is given to night time sleep. 

 

Acknowledgements 

The authors thank Tayyab Raza Fraz, for his 

contribution in the early stages of data input and 

analysis. 

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Annals of Psychophysiology                                                                                                        Volume 3, October 2016                  
©Advance Educational Institute & Research Centre                                                                    www.aeirc-edu.com 

ISSN 2412 - 3188 
 

 

Conflicts of interest 

All authors declare that they have no conflict of 

interest. 

 

 

Disclosure of financial support 

The authors have not used any financial support 

 

References 

 Araujo, M. F., Freitas, R. W., Lima, A. C., Pereira, 

D. C., Zanetti, M. L., & Damasceno, M. M. (2014). 

[Health indicators associated with poor sleep quality 

among university students]. Rev Esc Enferm USP, 

48(6), 1085-1092.  

 Aslam, H. M., Mughal, A., Edhi, M. M., Saleem, S., 

Rao, M. H., Aftab, A., Khan, A. M. (2013). 

Assessment of pattern for consumption and awareness 

regarding energy drinks among medical students. Arch 

Public Health, 71(1), 31.  

 Bhatti, A. A., Khan, U. A., Sabih, F., Naeem, N., 

Khan, M., & Riaz, F. (2012). Sleep habits of first year 

and final year female MBBS students. J Behav Health, 

1(2), 110-113.  

 Bhatti, U., Rani, K., Memon, M. Q., & Wali, H. 

(2015). Effects of advanced cell phone technology on 

the duration of sleep. New York Sci J, 8, 86-88.  

 Bonnet, M. H., & Arand, D. L. (2000). The impact of 

music upon sleep tendency as measured by the multiple 

sleep latency test and maintenance of wakefulness test. 

Physiol Behav, 71(5), 485-492.  

 Botello-Harbaum, M., Haynie, D. L., Murray, K. W., 

& Iannotti, R. J. (2011). Cigarette smoking status and 

recurrent subjective health complaints among US 

school-aged adolescents. Child Care Health Dev, 

37(4), 551-558.  

 Brick, C. A., Seely, D. L., & Palermo, T. M. (2010). 

Association between sleep hygiene and sleep quality in 

medical students. Behav Sleep Med, 8(2), 113-121.  

 Buysse, D. J., Reynolds, C. F., 3rd, Monk, T. H., 

Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh 

Sleep Quality Index: a new instrument for psychiatric 

practice and research. Psychiatry Res, 28(2), 193-213.  

 Cajochen, C., Frey, S., Anders, D., Spati, J., Bues, 

M., Pross, A., Stefani, O. (2011). Evening exposure to 

a light-emitting diodes (LED)-backlit computer screen 

affects circadian physiology and cognitive 

performance. J Appl Physiol (1985), 110(5), 1432-

1438.  

 Cohrs, S., Rodenbeck, A., Riemann, D., Szagun, B., 

Jaehne, A., Brinkmeyer, J., Winterer, G. (2014). 

Impaired sleep quality and sleep duration in smokers-

results from the German Multicenter Study on Nicotine 

Dependence. Addict Biol, 19(3), 486-496.  

 de Niet, G., Tiemens, B., Lendemeijer, B., & 

Hutschemaekers, G. (2009). Music-assisted relaxation 

to improve sleep quality: meta-analysis. J Adv Nurs, 

65(7), 1356-1364.  

 Dewald, J. F., Meijer, A. M., Oort, F. J., Kerkhof, G. 

A., & Bögels, S. M. (2010). The influence of sleep 

quality, sleep duration and sleepiness on school 

performance in children and adolescents: a meta-

analytic review. Sleep medicine reviews, 14(3), 179.  

 Drake, C., Roehrs, T., Shambroom, J., & Roth, T. 

(2013). Caffeine effects on sleep taken 0, 3, or 6 hours 

before going to bed. J Clin Sleep Med, 9(11), 1195-

1200. doi:10.5664/jcsm.3170 

 Ferraro, F., Holfeld, B., Frankl, S., Frye, N., & 

Halvorson, N. (2015). Texting/iPod dependence, 

executive function and sleep quality in college 

students. Computers in Human Behavior, 49, 44-49.  

 Genzel, L., Ahrberg, K., Roselli, C., Niedermaier, S., 

Steiger, A., Dresler, M., & Roenneberg, T. (2013). 

Sleep timing is more important than sleep length or 

quality for medical school performance. Chronobiol 

Int, 30(6), 766-771.  

 Gradisar, M., Wolfson, A. R., Harvey, A. G., Hale, 

L., Rosenberg, R., & Czeisler, C. A. (2013). The sleep 

and technology use of Americans: findings from the 

National Sleep Foundation's 2011 Sleep in America 

poll. J Clin Sleep Med, 9(12), 1291-1299. 

doi:10.5664/jcsm.3272 

 Haraszti, R. A., Ella, K., Gyongyosi, N., Roenneberg, 

T., & Kaldi, K. (2014). Social jetlag negatively 

correlates with academic performance in 

undergraduates. Chronobiol Int, 31(5), 603-612.  

 Harmat, L., Takacs, J., & Bodizs, R. (2008). Music 

improves sleep quality in students. J Adv Nurs, 62(3), 

327-335.  

 Hindmarch, I., Rigney, U., Stanley, N., Quinlan, P., 

Rycroft, J., & Lane, J. (2000). A naturalistic 

investigation of the effects of day-long consumption of 

tea, coffee and water on alertness, sleep onset and sleep 

quality. Psychopharmacology (Berl), 149(3), 203-216.  

 Horne, J., & Rumbold, J. (2015). Sleep-related road 

collisions. Med Sci Law.  

 Ivarsson, M., Anderson, M., Åkerstedt, T., & 

Lindblad, F. (2009). Playing a violent television game 

affects heart rate variability. Acta paediatrica, 98(1), 

166-172.  

http://www.aeirc-edu.com/


 
 

Fahad Khan 13 

  

 

Annals of Psychophysiology                                                                                                        Volume 3, October 2016                  
©Advance Educational Institute & Research Centre                                                                    www.aeirc-edu.com 

ISSN 2412 - 3188 
 

 Kabrita, C. S., Hajjar-Muca, T. A., & Duffy, J. F. 

(2014). Predictors of poor sleep quality among 

Lebanese university students: association between 

evening typology, lifestyle behaviors, and sleep habits. 

Nat Sci Sleep, 6, 11-18. doi:10.2147/nss.s55538 

 Lemma, S., Patel, S. V., Tarekegn, Y. A., Tadesse, 

M. G., Berhane, Y., Gelaye, B., & Williams, M. A. 

(2012). The Epidemiology of Sleep Quality, Sleep 

Patterns, Consumption of Caffeinated Beverages, and 

Khat Use among Ethiopian College Students. Sleep 

Disord, 2012, 583510.  

 Lund, H. G., Reider, B. D., Whiting, A. B., & 

Prichard, J. R. (2010). Sleep patterns and predictors of 

disturbed sleep in a large population of college 

students. J Adolesc Health, 46(2), 124-132.  

 McNamara, J. P., Wang, J., Holiday, D. B., Warren, 

J. Y., Paradoa, M., Balkhi, A. M., . . . McCrae, C. S. 

(2014). Sleep disturbances associated with cigarette 

smoking. Psychol Health Med, 19(4), 410-419. 

doi:10.1080/13548506.2013.832782 

 Nusrat, M., Khan, A., Hamid, S., Hussain, A. A., & 

Kadir, M. M. (2012). Bedtime and its correlates among 

secondary school children in Karachi, Pakistan. J Pak 

Med Assoc, 62(11), 1168-1173.  

 Ohayon, M. M., Carskadon, M. A., Guilleminault, C., 

& Vitiello, M. V. (2004). Meta-analysis of quantitative 

sleep parameters from childhood to old age in healthy 

individuals: developing normative sleep values across 

the human lifespan. Sleep-new york then westchester-, 

27, 1255-1274.  

 Ohlmann, K. K., & O'Sullivan, M. I. (2009). The 

costs of short sleep. AAOHN J, 57(9), 381-385; quiz 

386-387. doi:10.3928/08910162-20090817-02 

 Peltzer, K., & Pengpid, S. (2016). Sleep duration and 

health correlates among university students in 26 

countries. Psychology, health & medicine, 21(2), 208-

220.  

 Polos, P. G., Bhat, S., Gupta, D., O'Malley, R. J., 

DeBari, V. A., Upadhyay, H., . . . Chokroverty, S. 

(2015). The impact of Sleep Time-Related Information 

and Communication Technology (STRICT) on sleep 

patterns and daytime functioning in American 

adolescents. J Adolesc, 44, 232-244.  

 Rath, M. (2012). Energy drinks: what is all the hype? 

The dangers of energy drink consumption. J Am Acad 

Nurse Pract, 24(2), 70-76.  

 Reissig, C. J., Strain, E. C., & Griffiths, R. R. (2009). 

Caffeinated energy drinks--a growing problem. Drug 

Alcohol Depend, 99(1-3), 1-10.  

 Samuelson, G. (1997). Physical Status: The Use and 

Interpretation of Anthropometry. WHO Technical 

Report Series. Acta Pædiatrica, 86(3), 280-280.  

 Schlarb, A. A., Kulessa, D., & Gulewitsch, M. D. 

(2012). Sleep characteristics, sleep problems, and 

associations of self-efficacy among German university 

students. Nat Sci Sleep, 4, 1-7. doi:10.2147/nss.s27971 

 Schlarb, A. A., Kulessa, D., & Gulewitsch, M. D. 

(2012). Sleep characteristics, sleep problems, and 

associations of self-efficacy among German university 

students. Nature and science of sleep, 4, 1.  

 Suen, L. K., Tam, W. W., & Hon, K. L. (2010). 

Association of sleep hygiene-related factors and sleep 

quality among university students in Hong Kong. Hong 

Kong Med J, 16(3), 180-185.  

 Surani, A. A., Zahid, S., Surani, A., Ali, S., Mubeen, 

M., & Khan, R. H. (2015). Sleep quality among 

medical students of Karachi, Pakistan. J Pak Med 

Assoc, 65(4), 380-382.  

 Van Dongen, H. P., Maislin, G., Mullington, J. M., & 

Dinges, D. F. (2003). The cumulative cost of additional 

wakefulness: dose-response effects on neurobehavioral 

functions and sleep physiology from chronic sleep 

restriction and total sleep deprivation. Sleep, 26(2), 

117-126.  

 Velez, J. C., Souza, A., Traslavina, S., Barbosa, C., 

Wosu, A., Andrade, A., . . . Williams, M. A. (2013). 

The Epidemiology of Sleep Quality and Consumption 

of Stimulant Beverages among Patagonian Chilean 

College Students. Sleep Disord, 2013, 910104.  

 Waqas, A., Khan, S., Sharif, W., Khalid, U., & Ali, 

A. (2015). Association of academic stress with sleeping 

difficulties in medical students of a Pakistani medical 

school: a cross sectional survey. PeerJ, 3, e840.  

 Weaver, E., Gradisar, M., Dohnt, H., Lovato, N., & 

Douglas, P. (2010). The effect of presleep video-game 

playing on adolescent sleep. Journal of clinical sleep 

medicine: JCSM: official publication of the American 

Academy of Sleep Medicine, 6(2), 184.  

 Wiebe, S. T., Cassoff, J., & Gruber, R. (2012). Sleep 

patterns and the risk for unipolar depression: a review. 

Nature and science of sleep, 4, 63.  

 Wu, X., Tao, S., Zhang, Y., Zhang, S., & Tao, F. 

(2015). Low Physical Activity and High Screen Time 

Can Increase the Risks of Mental Health Problems and 

Poor Sleep Quality among Chinese College Students. 

PloS one, 10(3), e0119607.

 

http://www.aeirc-edu.com/

