07-10-2016, 11:27 AM
Sleep Quality and Sleep Patterns in Relation to Consumption of
Energy Drinks, Caffeinated Beverages and Other Stimulants
among Thai College Students
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Abstract
Purpose—Poor sleep and heavy use of caffeinated beverages have been implicated as risk
factors for a number of adverse health outcomes. Caffeine consumption and use of other
stimulants are common among college students globally. However, to our knowledge, no studies
have examined the influence of caffeinated beverages on sleep quality of college students in
Southeast Asian populations. We conducted this study to evaluate the patterns of sleep quality;
and to examine the extent to which poor sleep quality is associated with consumption of energy
drinks, caffeinated beverages and other stimulants among 2,854 Thai college students.
Methods—A questionnaire was administered to ascertain demographic and behavioral
characteristics. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep habits and
quality. Chi-square tests and multivariate logistic regression models were used to identify
statistically significant associations.
Results—Overall, the prevalence of poor sleep quality was found to be 48.1%. A significant
percent of students used stimulant beverages (58.0%). Stimulant use (OR 1.50; 95%CI 1.28-1.77)
was found to be statistically significant and positively associated with poor sleep quality. Alcohol
consumption (OR 3.10; 95% CI 1.72-5.59) and cigarette smoking (OR 1.43; 95% CI 1.02-1.98)
also had statistically significant association with increased daytime dysfunction. In conclusion,
stimulant use is common among Thai college students and is associated with several indices of
poor sleep quality.
Conclusion—Our findings underscore the need to educate students on the importance of sleep
and the influences of dietary and lifestyle choices on their sleep quality and overall health.
Introduction
Sleep is physiologically essential for maintaining overall well-being [1]. Sleep research has
led to the discovery of a plethora of adverse health conditions associated with poor sleep.
The immediate effects of sleep problems—decreases in the ability to perform tasks, daytime
sleepiness, and fatigue—are commonly recognized and experienced [2]. Additionally, poor
sleep has been shown to be associated with serious cardiometabolic and psychiatric
conditions including type 2 diabetes, hypertension, obesity, as well as higher frequencies of
depression, anxiety, eating disorders, and dementia [2-5].
Measures of poor sleep in the literature include both indices of sleep duration and sleep
quality. Though both are found to be important to subjective well-being, health and wellness
indices are found to be better correlated to sleep quality which encompasses measures of
depth of sleep, how the individual feels upon waking, and general satisfaction with sleep [6].
Patterns and disparities in sleep quality have been found to vary by race, ethnicity, gender,
and age [7, 8].
Up to this point, however, not much epidemiological data on the sleep patterns and sleep
quality of older adolescents/young adults can be found in the literature and data are
particularly limited for college students [9]. Moreover, available studies on sleep patterns
and quality of college students are not representative of all nationalities and ethnicities; most
examine Western populations with the U.S. being most represented. It is important that
researchers contribute to knowledge in this area as this is the age interval when sleep
patterns change due to biological factors such as changes in circadian timing, behavioral
factors such as changes in social and educational demands, and dietary modifications such
as changes in the pattern of consumption of caffeinated beverages [10].
Energy drinks, especially, are targeted to young adult consumers [11]. According to a selfreport
survey, energy drinks are consumed by 30 - 50 % of young adults and adolescents in
the United States [12]. Another survey of US college students found that 51% of those
surveyed regularly consumed more than one energy drink per month, and the majority of
students reported regular consumption of energy drinks several times per week [11].
Surveyed college students reported using energy drinks for insufficient sleep, when energy
was needed in general, when studying for an exam or completing a major course project, and
to mix with alcohol for partying [11].
Moderate caffeine consumption is associated with common symptoms including jitteriness
and anxiety. On the other hand high-dose caffeine use is associated with insomnia,
palpitations and arrhythmias, seizures, and stroke [12]. In Germany, Ireland, and New
Zealand, where energy drink-related toxicity incidents are kept on record, cases of liver
damage, kidney failure, heart failure, and death have been found to be associated with
energy drink consumption [12]. The most common adverse effects implicated in caffeine use
in adults are chest discomfort, heart rhythm irregularities, increased blood pressure,
electrolyte disturbances, nausea and vomiting, and anxiety [13]. In children, caffeine
consumption was found to increase blood pressure and complaints of sleep disturbances [13]
whilst energy drink consumption may lead to increased body mass index (BMI), diabetes,
and the incidence of dental caries [12].
Currently, few studies have examined sleep patterns and quality in Asian populations
[14-17]. Moreover, to the best of our knowledge, very few studies have examined the
influence of caffeinated beverages on the sleep quality and sleep patterns of college students
[18, 19]. As Thailand leads the world in the consumption of energy drinks per person [20], it
is a fitting setting to examine such an association. The present study aims to augment the
understanding of sleep among Thai college students by evaluating reports of sleep hygiene
and sleep quality in relation to stimulant use and other behavioral risk factors. Given prior
studies [21, 22] we hypothesized that those students who use stimulants were more likely
than non-users to report poor sleep quality and that this relationship may vary by sex and
age.
METHODS
Study Setting and Sample
This cross-sectional study was conducted between December 2010 and February 2011 at
seven colleges (Chulalongkorn University, Thammasat University, Dhurakij Pundit
University, Rangsit University, Kasetsart University, University of the Thai Chamber of
Commerce, and Walailak University) in Thailand; 56.3% of our data were collected from
Chulalongkorn University and Walailak University. Chulalongkorn University in Thailand
has more than eighteen faculties and a number of schools and institutes. Currently, the
University has more than 36,000 full time students. Walailak University, which is located in
Nakhon Si Thammarat Province in Southern Thailand. It has more than 3,500 undergraduate
students.
Recruitment—Flyers were posted in each campus to invite participants to the study.
Students who expressed an interest in participating were asked to meet in a large classroom
or an auditorium where they were informed about the purpose of the study. Students
consenting to participate were asked to complete a self-administered individual survey.
Vision impaired students and those who could not read the consent and questionnaire forms
were not eligible to participate. Those enrolled in correspondence, extension, or night school
programs were not included as well since their experience might be different from regular
time students. A total of 3,000 undergraduate students participated in the study. For the
analysis described here, we excluded subjects with incomplete questionnaires and missing
sleep quality scores (n=146). The final analyzed sample included 2,854 students (930 male
and 1,924 female). All the completed questionnaires were anonymous, and no personal
identifiers were collected. All study procedures were approved by the institutional review
boards of the Faculty of Medicine Chulalongkorn University, Walailak University and the
University of Washington, USA. The Harvard School of Public Health Office of Human
Research Administration, USA, granted approval to use the de-identified data set for
analysis.
Data Collection and Variables
Demographics—A self-administered questionnaire was used to collect information for
this study. The questionnaire ascertained demographic information including age, sex, and
education level. Questions also included regarding behavioral risk factors such as smoking,
energy drinks, caffeinated beverages, physical activity, and alcohol consumption.
Measurements of the student’s height, weight, waist, and hip circumference were also
collected by research nurses after the questionnaire was administered.
Use of energy or stimulant beverages—Energy drinks are a group of beverages used
to provide an extra boost in energy, promote wakefulness, and provide cognitive and mood
enhancement[23]. They are often referred as stimulant beverages. Hence in this study, we
will be using the terms energy drinks and stimulant beverages interchangeably. Participants
were first asked if they consumed more than one stimulant or energy drink per week during
the current academic semester/quarter. Those who responded affirmatively were further
asked to identify the specific type of energy drinks. To provide a frame of reference
regarding what constituted an energy drink, we included examples of energy drinks that
were popular on the campus and in social establishments in the immediate geographic region
where the survey was administered. These included international and local brands such as:
Red Bull, M100, M150, Carabao Daeng, Lipovitan-D or Lipo, Wrangyer, and Sharks. For
the purpose of this analysis we grouped the less commonly used (i.e., Carabao Daeng,
Lipovitan-D or Lipo, Wrangyer, and Shark) energy drinks together and hereinafter refer to
this group as “other energy drinks.” Consumption of caffeine-containing beverages included
coffee, black tea, and stimulant beverages [colas such as Coke and Pepsi were each
categorized as dichotomous variables (no vs. yes)]. Participants were also asked to specify
whether they consumed sugar-containing and/or sugar-free caffeinated beverages. We then
summed each of the drinks to estimate the number different types of energy drinks or
stimulants used per week.
Pittsburgh Sleep Quality Index (PSQI)—Sleep quality was assessed using the
previously validated Pittsburgh Sleep Quality Index (PSQI) [24]. The PSQI instrument has
been widely used among college students globally including Southeast Asia [16]. The PSQI
is a 19-item self-reported questionnaire that evaluates sleep quality within the past month.
The PSQI consists of seven sleep components related to sleep habits including duration of
sleep, sleep disturbance, sleep latency, habitual sleep efficiency, use of sleep medicine,
daytime dysfunction and overall sleep quality. The sleep components yield a score ranging
from 0 to 3, with three indicating the greatest dysfunction [24]. The sleep component scores
are summed to yield a total score ranging from 0 to 21 with higher total scores (referred to
as global scores) indicating poor sleep quality. Based on prior literature [24], participants
with a global score of greater than 5 were classified as poor sleepers. Those with a score of 5
or less were classified as good sleepers.
For sleep quality component subscales, namely subjective sleep efficiency, sleep latency,
sleep medication use, and daytime dysfunction due to sleepiness, we computed dichotomous
variables of optimal and suboptimal sleep quality. Specific categories were long sleep
latency (≥30 minutes vs. < 30 minutes), poor sleep efficiency (<85% vs. ≥85%), daytime
dysfunction due to sleep (< once a week vs. ≥ once per week), and use of sleep medication
during the past month (< once per week vs. ≥ once per week). Sleep duration was assessed
using the PSQI questionnaire that queried participants about how many hours of actual sleep
the participants got at night during the previous month. Given the lack of prior data on
cutoffs for defining “short sleep duration” among college students, we used quartiles. The
following quartiles were used to define sleep duration: ≤6.0 hours, 6.1-7.0 hours, 7.1-8.0
hours, and ≥ 8.1 hours. The group with the lowest quartile of sleep duration (≤6 hours) was
classified as short duration sleepers.
Other Covariates—We defined alcohol consumption as low (< 1 alcoholic beverage a
week), moderate (1 19 alcoholic beverages a week), and high to excessive consumption (>
19 alcoholic beverages a week) [25, 26]. Other covariates were categorized as follows: age
(years), sex, cigarette smoking history (never, former, current), and participation in
moderate or vigorous physical activity (no vs. yes), BMI was calculated as weight (kg)/
height squared (m2
). BMI thresholds were set according to the World Health Organization
(WHO) protocol (underweight: <18.5 kg/m2
; normal: 18.5–24.9 kg/m2
; overweight: 25.0–
29.9 kg/ m2
; and obese ≥30 kg/m2
)[27].
Data Analysis
We first examined frequency distributions of socio-demographic and behavioral
characteristics of study participants. Characteristics were summarized using means (±
standard deviation) for continuous variables and counts and percentages for categorical
variables. Chi-square test and Student’s t-test were used to determine bivariate differences
for categorical and continuous variables, respectively. Next, we calculated the distribution of
poor sleep quality across demographic and behavioral groups. The distributions of PSQI
scores among male and female students, as well as the sex-specific prevalence of poor sleep
quality across age groups were also estimated. To examine the association between sleep
quality and energy drinks, we compared the distribution of overall poor sleep quality
according to any energy drink use and specific type studied. Prevalence estimates were also
determined for suboptimal dichotomous sleep quality subscales in relation to consumption
of stimulant drinks and other lifestyle characteristics. Forward logistic regression modeling
procedures combined with the change-in-estimate approach were used to calculate odds
ratios (OR) and 95% confidence intervals (95% CI) for the associations between poor sleep
quality and socio-demographic and behavioral factors [28]. Variables of a priori interest
(e.g., age and sex) were forced into final models. All analyses were performed using IBM’s
SPSS Statistical Software for Windows (IBM SPSS Version 20, Chicago, Illinois, USA). All
reported p-values are two-sided and deemed statistically significant at α=0.05.
RESULTS
Of the 2,854 college students who completed the survey and met participant guidelines,
67.4% were females and the average reported age was 20.3 (±1.3) years. Overall, a total of
1,373 (48.1%) students had poor sleep quality (PSQI > 5). The mean PSQI total score across
all participants was 5.76 ± 2.50 (5.80±2.49 for females and 5.67±2.52 for males). The
distributions of PSQI scores for males and females were similar (Fig. 1).
The behavioral and demographic characteristics of the study samples in relation to sleep
quality are reported in Table 1. A total of 34.1% of the students reported consuming at least
1 alcoholic drink per month whilst current smoking was reported by approximately 7% of
participants. More than two-thirds (68.6%) of participants were found to be of normal BMI
(18.5–24.9), 71.2% were of normal health, and 77.9% were physically active. Overall there
were no statistically significant associations between sleep quality and demographic or
lifestyle characteristics, except for alcohol consumption. Those who reported consuming at
least 1 alcoholic drink per month were more likely to report poor sleep quality than nondrinkers
(p-value= 0.02).
Table 2 shows the distribution of PSQI sleep components subscales across male and female
students. Notably, 38.9% of our study samples reported sleeping ≤ 6 hours per day.
Approximately 26% of the study samples reported longer sleep latency (≥ 30 minutes), and
25.3% reported having daytime dysfunction due sleepiness at least once per week. A total of
25.1% were classified as having poor sleep efficiency (< 85%), and 2.0% reported using
sleep medicine at least once per week. There were no significant associations between any
of the sleep components reported in Table 2 and sex, though slightly more females reported
having poor sleep as compared to males (48.7% compared to 46.8% respectively), and
females were more likely to report daytime dysfunction due to sleepiness (17.5% of males
reported never having daytime dysfunction due to sleepiness compared to 14.5% of
females). The prevalence of poor sleep quality in relation to age and sex is illustrated in
Figure 2. The highest prevalence of poor sleep was reported by 21-year old males and
females ≥ 22 year of age.