Content uploaded by Andrew P Smith
Author content
All content in this area was uploaded by Andrew P Smith on Oct 29, 2015
Content may be subject to copyright.
Journal of Psychopharmacology
1 –12
© The Author(s) 2015
Reprints and permissions:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/0269881115612404
jop.sagepub.com
Introduction
Dose-dependent effects of caffeine on
behaviour
Short-term effects of caffeine consumption include enhanced
mood and alertness (Ferré, 2008; Kaplan et al., 1997; Lorist and
Tops, 2003), improved exercise performance (Doherty and
Smith, 2004), increased blood pressure (Riksen et al., 2009),
improved ability to remain awake and mentally alert after fatigue
(Smit and Rogers, 2002), faster information processing speed
and reaction time, and heightened awareness and attention
(Cysneiros et al., 2007). When consumed in moderation it
appears that there are no serious adverse health effects associated
with its use by adults (Nawrot et al., 2003) or children (Higdon
and Frei, 2006; Mandel, 2002). However, it has been advised that
those who are highly sensitive should not consume >400 mg/d,
in order to avoid headaches, drowsiness, anxiety, and nausea
(Nawrot et al., 2003). A sensitive individual might experience
adverse effects at a lower dose than less sensitive individuals.
Children are often considered as sensitive individuals because of
their size and developing central nervous system. This is con-
cerning because many children and adolescents are frequent caf-
feine consumers (for instance, a recent US study found 73% of
children to consume caffeine on a given day; Branum et al.,
2014). It is important, therefore, to identify thresholds above
which negative effects might occur. In the context of the current
study, the thresholds in question relate to the group as a whole,
with potential sensitivity to caffeine being defined by the partici-
pants being children.
The relatively recent introduction of ‘energy drinks’ to the
consumer market has been highlighted as a cause for concern
(e.g. Reissig et al., 2009). Energy drinks are soft drinks that man-
ufacturers claim boost performance and endurance (Meadows-
Oliver and Ryan-Krause, 2007), with the main active ingredient
being caffeine (McLellan and Lieberman, 2012). These products
are often strategically marketed towards the young consumer
(Reissig et al., 2009), with 30–50% of adolescents and young
adults now known to consume them (Seifert et al., 2011). Energy
drinks have also been associated with behavioural problems
(Richards et al., 2015a), and a number of serious health compli-
cations (Reissig et al., 2009).
A potential avenue by which energy drink use may negatively
affect health is through their association with risk-taking behav-
iours (see Arria et al., 2014). Miller (2008a), for instance,
reported that the frequency of energy drink consumption in US
Caffeine consumption and self-assessed
stress, anxiety, and depression in
secondary school children
Gareth Richards and Andrew Smith
Abstract
Previous research suggests that effects of caffeine on behaviour are positive unless one is investigating sensitive groups or ingestion of large amounts.
Children are a potentially sensitive subgroup, and especially so considering the high levels of caffeine currently found in energy drinks. The present
study used data from the Cornish Academies Project to investigate associations between caffeine (both its total consumption, and that derived
separately from energy drinks, cola, tea, and coffee) and single-item measures of stress, anxiety, and depression, in a large cohort of secondary school
children from the South West of England. After adjusting for additional dietary, demographic, and lifestyle covariates, positive associations between
total weekly caffeine intake and anxiety and depression remained significant, and the effects differed between males and females. Initially, effects
were also observed in relation to caffeine consumed specifically from coffee. However, coffee was found to be the major contributor to high overall
caffeine intake, providing explanation as to why effects relating to this source were also apparent. Findings from the current study increase our
knowledge regarding associations between caffeine intake and stress, anxiety, and depression in secondary school children, though the cross-sectional
nature of the research made it impossible to infer causality.
Keywords
Adolescent behaviour, anxiety, caffeine, depression, energy drinks, sex differences, stress
Centre for Occupational and Health Psychology, School of Psychology,
Cardiff University, Cardiff, UK
Corresponding author:
Gareth Richards, Centre for Occupational and Health Psychology,
School of Psychology, Cardiff University, 63 Park Place, Cardiff, CF10
3AS, UK.
Email: RichardsG6@Cardiff.ac.uk
612404JOP0010.1177/0269881115612404Journal of PsychopharmacologyRichards and Smith
research-article2015
Original Paper
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
2 Journal of Psychopharmacology
undergraduates was positively associated with smoking, drink-
ing, alcohol problems, use of illicit prescription drugs and mari-
juana, sexual risk-taking, fighting, seatbelt omission, and taking
risks on a dare. However, it should be noted that such effects
might also be explainable by personality characteristics of high
users of energy drinks (for example, adherence to a ‘toxic jock’
identity; Miller, 2008b), rather than necessarily to the products
themselves.
Another potential route that energy drinks may negatively
affect health is through caffeine’s capacity to disrupt sleep. Energy
drink use has been associated with daytime sleepiness and weekly
‘jolt and crash’ episodes (Kristjánsson et al., 2011; Malinauskas
et al., 2007), though the products also appear to be used to counter
the effects of insufficient sleep (Malinauskas et al., 2007).
Although findings such as these may implicate energy drinks in
particular, Kristjánsson et al. (2013) have reported that caffeine
consumption itself is positively associated with self-reported vio-
lent behaviour and conduct disorder. Furthermore, James et al.
(2011) observed a strong inverse relationship between caffeine
intake and academic attainment, 32% of which was explained by
mediating effects of daytime sleepiness and other licit substance
use. Due to findings such as these it is considered to be of particu-
lar importance to investigate the effects of caffeine from differ-
ence sources, as well as its overall intake.
Associations between caffeine intake and
stress, anxiety, and depression
The consumption of caffeinated beverages is known to be a cop-
ing strategy used by college students in the management of
stressful academic situations (Lazarus, 1993; Thoits, 1995), with
49% of a representative stratified sample of Puerto Rican stu-
dents reporting caffeinated products to be useful for coping with
stress (Ríos et al., 2013). Pettit and DeBarr (2011) have also
reported a positive relationship between energy drink consump-
tion and perceived stress levels in undergraduate students.
Though the use of caffeine is moderately related to a range of
psychiatric and substance use disorders in the general population,
the relationships appear not to be causal (Kendler et al., 2006),
and results between studies are equivocal (for a review of the
area see Lara, 2010). Discerning the nature and direction of rela-
tionships between such variables becomes even more difficult
when considering the self-medication hypothesis (e.g. Khantzian,
1997). The idea here is that people may self-medicate with legal
and/or illicit substances, with evidence having already been pro-
vided to suggest that some individuals with mental health prob-
lems use caffeinated energy drinks for such purposes (Chelben
et al., 2008).
In some cases positive effects of caffeine have been observed.
For instance, low doses have been shown to reduce anxiety and
elevate mood (Haskell et al., 2005; Lieberman et al., 1987, 2002;
Smith, 2009a; Smith et al., 1999). Smith (2009b) also reported
that caffeine consumption was associated with reduced risk
of depression compared with non-consumption in a population
study.
Negative effects of caffeine on stress and mental health have
also been observed. Gilliland and Andress (1981), for instance,
reported higher anxiety levels in moderate and high caffeine con-
sumers compared with abstainers in a student sample. Case
reports also suggest that mania can be induced by a high intake of
caffeine (Ogawa and Ueki, 2003) or energy drinks (Sharma,
2010). These results are supported by the finding of Kaplan et al.
(1997), that 250 mg of caffeine can increase elation in healthy
volunteers, whereas 500 mg increases irritability. Other studies,
however, have reported null findings. James et al. (1989), for
instance, found no relationships between caffeine intake and
anxiety or depression in medical students.
In the general population, negative effects of caffeine are usu-
ally observed in relation to excessive intake. At extremely high
doses its consumption can induce a condition known as ‘caffein-
ism’. Symptoms include anxiety, nervousness, restlessness,
insomnia, excitement, psychomotor agitation, dysphoria, and a
rambling flow of thoughts and speech (Gilliland and Andress,
1981; Greden, 1974), which have been considered to mimic a
clinical picture known as ‘mixed mood state’ (Lara, 2010).
Larger effects of caffeine seem to occur in sensitive individu-
als, with psychiatric patients appearing to make up one such
group. Higher sensitivity to the anxiogenic effects of high doses
(typically >400 mg), for instance, has been observed in patients
with panic disorder (Boulenger et al., 1984; Charney et al., 1985),
generalised panic disorder (Bruce et al., 1992), and to a lesser
extent, depression (Lee et al., 1988). Similar findings have also
been made in patients with performance social anxiety disorder
(though not generalised social anxiety disorder; Nardi et al.,
2009), and excessive intake may interfere with the recovery of
patients with bipolar disorder and manic-type mood episodes
(Caykoylu et al., 2008; Dratcu et al., 2007; Tondo and Rudas,
1991).
Another potentially sensitive subgroup is that of young con-
sumers. Certain psychiatric symptoms appear to occur at an
alarming rate in this group. For example, the prevalence of major
depressive disorder is known to range from 0.4% to 8% in ado-
lescents (Birmaher et al., 1996; Fleming and Offord, 1990;
Roberts et al., 1995), with approximately 30% reporting at least
one current symptom of a major depressive episode (Roberts
et al., 1995). Depressive symptoms have also been found to cor-
relate positively with coffee consumption in middle- and high-
school students (Fulkerson et al., 2004), and positive associations
with the Children’s Depression Inventory have been reported in
both children and adolescents (Luebbe and Bell, 2009). However,
as with research in adults, some studies have also reported null
findings. Luebbe and Bell (2009), for instance, found no relation-
ship between anxiety and caffeine in children and adolescents.
Aims of the current research
The general lack of research relating to the effects of caffeine on
stress, anxiety, and depression in children is an area that the cur-
rent paper will try to address. In order to do this, the Diet and
Behaviour Scale (DABS; Richards et al., 2015b), a measure of
intake of food and drinks (including caffeinated products) that
may affect psychological outcomes, was administered to a large
cohort of secondary school children from the South West of
England. The current paper used the DABS for two purposes: (1)
to provide estimates of weekly caffeine intake from energy
drinks, cola, tea, and coffee, and (2) so that additional aspects of
diet could be controlled for in multivariate analyses.
Along with the DABS, single-item measures of self-assessed
stress, anxiety, and depression were administered. Single items
were chosen as they have been shown to be valid and reliable,
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
Richards and Smith 3
allowing for the identification of overall risk whilst reducing the
time costs associated with administering multi-item measures
(Williams and Smith, 2012). The items themselves came from
the Wellbeing Process Questionnaire (Williams, 2014), have
been validated against full measures, demonstrated to correlate
well, and appear to be as sensitive as the full-length measures
with which they were compared (Williams, 2015; Williams and
Smith, 2013).
It was hypothesised that high consumption of caffeine would
be associated with high stress, anxiety, and depression, and that
such relationships would not be dependent on the source from
which caffeine was obtained. However, as no interventions were
conducted, and data presented here are only cross-sectional in
nature, it should be acknowledged that it is not possible to infer
causality or the direction of relationships observed.
Method
The Cornish Academies Project was a large-scale longitudinal
programme of research designed to investigate dietary effects on
school performance, general health, and stress, anxiety, and
depression in secondary school children. Two cross-sections of
data were collected from three academies in the South West of
England. The first cross-section (T1) was collected 6 months
prior to the second (T2). The current paper presents analyses
using data from the latter cross-section only, as information relat-
ing to stress, anxiety, and depression were not collected at the
former.
Participants
In total, 3071 secondary school pupils were asked to take part in
the Cornish Academies Project at T1; 2610 (85%) agreed to par-
ticipate. At T2, the cohort consisted of 3323 pupils, and 2307
completed the questionnaires. A relatively balanced sex ratio
(48.5% male, 51.5% female), and an age range of 11–17 (M =
13.6, SD = 1.49) were observed (for a more detailed description
of the sample see Richards et al., 2015b).
Apparatus/materials
The DABS (Richards et al., 2015b) is a 29-item questionnaire
developed for the purpose of assessing intake of common dietary
variables with an onus on functional foods, and foods and drinks
of current concern. The DABS contains 18 questions that assess
frequency of consumption on a five-point scale (1 = never, 2 =
once a month, 3 = once or twice a week, 4 = most days [3–6], 5 =
every day), and 11 questions to assess amounts typically con-
sumed. It has been associated with a four-factor structure in sec-
ondary school children labelled Junk Food, Caffeinated Soft
Drinks/Gum, Healthy Foods, and Hot Caffeinated Beverages
(see Richards et al., 2015b).
Because caffeine content is known to vary considerably
between energy drink products (Reissig et al., 2009), participants
were asked to state the brand names of those that they consumed.
This measure was included in order to increase the accuracy of
estimating caffeine consumption. In addition to this, as diet may
reflect general lifestyle (e.g. Akbaraly, 2009), five further ques-
tions were administered. Three items were used to gauge exercise
frequency (mildly energetic, moderately energetic, and vigor-
ous), with answers being given on a four-point scale (1 = three
times a week or more, 2 = once or twice a week, 3 = about once
to three times a month, 4 = never/hardly ever). In addition to this,
participants were asked to state how many hours per night they
typically spent asleep, and to give an indication of how good they
perceived their general health to have been over the previous 6
months (1 = very good, 2 = good, 3 = fair, 4 = bad, 5 = very bad).
Participants were then asked to state how frequently they had
experienced stress, anxiety, and depression over the previous 6
months, on a five-point scale (1 = not at all, 2 = rarely, 3 = some-
times, 4 = frequently, 5 = very frequently), though no clinical
evaluations were made. No further descriptions of ‘stress’, ‘anxi-
ety’, or ‘depression’ were provided as it was assumed that partici-
pants would understand the concepts at hand.
Design and procedure
Schoolteachers administered the DABS as well as the lifestyle,
stress, anxiety, and depression questions to the pupils at their
respective academies. Demographic information was acquired
through the School Information Management System (SIMS)
and stored in a confidential database in Cardiff. This information
included age, sex, school attendance, number of detentions/
behavioural points received, English and maths attainment at
Key Stage 3/Key Stage 4, school year, ethnicity, presence/
absence of a special educational needs (SEN) status, eligibility/
ineligibility to receive free school meals (FSM; a proxy indica-
tion of socioeconomic status; Shuttleworth, 1995), whether or
not English was spoken as an additional language, and whether
or not children were cared for by a non-parental guardian.
All questionnaire and demographic data were anonymised
prior to being merged into a single database. Ethical clearance was
granted by Cardiff University’s School of Psychology Ethics
Committee, and informed consent was acquired from all partici-
pants (as well as their parents) before data were collected. All data
analysis was conducted using IBM SPSS Statistics Version 20.
Statistical analysis
The representativeness of the sample was investigated by compar-
ing SIMS data for those who completed the questionnaires with
that of those who did not, though frequency data relating to stress,
anxiety, and depression are not provided here because they have
already been reported elsewhere (see Richards and Smith, 2015).
Weekly caffeine consumption was calculated from the DABS
items used to measure the amount of consumption of energy
drinks, cola, tea, and coffee. Linear-by-linear trends were then
investigated between total weekly caffeine intake and stress, anxi-
ety, and depression, and were followed up with binary logistic
regression analyses (using the ‘enter’ method), so that additional
variance from diet, demography, and lifestyle could be controlled
for statistically. In order to investigate interactions between caf-
feine use and sex, multivariate analyses were conducted for males
and females separately. It was also deemed important to examine
the effects of each individual source of caffeine (i.e. that con-
sumed specifically from energy drinks, cola, tea, and coffee). As
with the analyses of total weekly caffeine intake, these effects
were initially investigated using linear-by-linear trends, and then
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
4 Journal of Psychopharmacology
with binary logistic regression to control for additional covariates
(though in this instance separate analyses were not conducted for
males and females).
Results
Demographic and lifestyle variables
Considerable variance in demographic background and lifestyle
was observed within the sample; for frequency data, see Table 1.
Participants’ average number of sleep hours and frequency of
exercise (a single-factor analysed variable derived from items
measuring mild, moderate, and vigorous exercise) that are used
as control variables in the current study have been described else-
where (see Richards et al., 2015b).
Representativeness of the sample
A relatively high response rate of 88.4% was observed for com-
pletion of the DABS. To investigate how representative the sam-
ple was in reference to the academies from which it came,
Chi-square tests were conducted to determine if SIMS data for
those who completed the DABS differed from SIMS data of
those that did not. It should be noted that a similar analysis pre-
sented in Richards et al. (2015b) relates to T1 from the Cornish
Academies Project, whereas that presented here relates to T2.
It was found that the academy a pupil came from was signifi-
cantly related to their likelihood of responding to the question-
naires, with Academy 1 and Academy 3 providing fewer
respondents, and Academy 2 providing more respondents, than
expected, χ2 (2, N = 3323) = 241.172, p < .001. The school year
that a participant came from was also related to their likelihood
of completing the questionnaires, χ2 (4, N = 3049) = 34.681, p <
.001. A significant linear-by-linear association was observed,
with the likelihood of responding being negatively associated
with school year, χ2 (1, N = 3049) = 30.245, p < .001. Children
with a SEN status were also less likely to answer the question-
naire, χ2 (1, N = 3083) = 23.142, p < .001, as were children who
were eligible to receive FSM, χ2 (1, N = 3049) = 25.116, p < .001.
Associations between total weekly caffeine
intake and stress, anxiety, and depression
Univariate associations between total weekly caffeine
intake and stress, anxiety, and depression. Single items
from the DABS were used to estimate weekly caffeine intake,
with the following values being assigned: cup of coffee (80 mg),
cup of tea (40 mg), can of cola (25 mg), can of energy drink (133
mg). The values used for coffee, tea, and cola, were based on
updated versions of those reported by Brice and Smith (2002),
which were themselves based on values provided by Barone and
Roberts (1996) and Scott et al. (1989); the value used for energy
drinks was the mean caffeine content of the three brands most
commonly reported by the current sample (which together
accounted for 53.2% of all cases). Caffeine totals consumed from
coffee, tea, energy drinks, and cola were then added together to
create a variable for total weekly consumption. It was found that
caffeine intake was higher in males than females, both in total
amount, as well as in that consumed from energy drinks, cola,
and coffee (though there was no difference regarding caffeine
consumed from tea; for descriptive statistics, see Table 2). Total
weekly caffeine was subsequently recoded into a categorical
variable consisting of the following six consumption groups: 0
mg/w, 0.1–250 mg/w, 250.1–500 mg/w, 500.1–750 mg/w, 750.1–
1000 mg/w, >1000 mg/w.
Self-assessed stress, anxiety, and depression were all found to
be significantly higher in females compared with males (for
descriptive statistics, see Table 2). The single-item measures
were then dichotomised, with those answering with 1 or 2 (‘not at
all’ or ‘rarely’ experienced stress, anxiety, or depression) making
up the above average mental health group, and those answering
with 3, 4, or 5 (‘sometimes’, ‘frequently’, or ‘very frequently’
experienced stress, anxiety, or depression) making up the below
average mental health group.
Linear-by-linear associations were investigated between the
dichotomous variables for stress, anxiety, and depression, and the
categorical variable created from total weekly caffeine intake.
The analysis found the >1000 mg/w condition to be associated
with high stress, anxiety, and depression. In addition to this, con-
suming 0.1–250 mg/w was associated with low stress, and non-
consumption was associated with low depression, though the
latter effect was not significant. For linear-by-linear associations
and cross-tabulations between total weekly caffeine intake and
stress, anxiety, and depression, see Table 3.
Multivariate associations between total weekly caffeine
intake and stress, anxiety, and depression. The analyses
described in the previous section indicate that being a very high
consumer of caffeine is a predictor of high levels of stress, anxi-
ety, and depression. It was therefore deemed important to further
investigate such effects at the multivariate level, so that addi-
tional variance could be controlled for statistically. In order to do
this, binary logistic regression analyses (using the ‘enter’ method)
Table 1. Frequency information for demographic variables.
N %
Academy 1 971 29.2%
2 1375 41.4%
3 977 29.4%
School year 7 573 18.8%
8 602 19.7%
9 618 20.3%
10 616 20.2%
11 640 21%
Sex Male 1018 48.5%
Female 1079 51.5%
SEN status Yes 899 29.2%
No 2184 70.8%
Eligible for FSM Yes 398 13.1%
No 2651 86.9%
Ethnicity White 2946 97.2%
Not white 84 2.8%
English as additional language Yes 51 1.7%
No 2868 98.3%
Non-parental guardian Yes 17 .6%
No 2909 99.4%
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
Richards and Smith 5
were conducted upon the dependent variables of stress, anxiety,
and depression. The same categorical variable for total weekly
caffeine intake described in the previous section was used, and
the non-consumption group was set as the comparison. The addi-
tional covariates entered were diet (the DABS subscale scores for
Junk Food and Healthy Foods; Caffeinated Soft Drinks/Gum and
Hot Caffeinated Beverages were not entered as they were com-
prised of caffeinated products; for a description of these variables
see Richards et al., 2015b), demography (sex, school, school
year, presence/absence of a SEN status, and the eligibility/ineli-
gibility to receive FSM), and lifestyle (sleep hours, exercise fre-
quency, and school attendance). It was, however, deemed
inappropriate to attempt to control for ethnicity, whether English
was spoken as an additional language, and whether or not the
child was cared for by a non-parental guardian, due to the num-
bers present in the minority groups being particularly small.
Table 2. Descriptive statistics and sex differences for self-reported stress, anxiety and depression, and weekly caffeine intake as calculated from the
DABS.
Overall Males Females Sex difference
N M SD N M SD N M SD t df p
Mental health Stress 2249 2.88 1.08 984 2.67 1.07 1060 3.08 1.05 −8.7 2024.191 < .001
Anxiety 2239 2.43 1.05 979 2.16 1 1058 2.66 1.05 −10.89 2033.779 < .001
Depression 2237 2.17 1.15 980 1.97 1.06 1054 2.34 1.19 −7.411 2028.44 < .001
Caffeine
intake (mg/w)
Total 2200 421.77 550 963 467.34 557.01 1033 364.99 512.84 4.262 1948.766 < .001
Energy drinks 2254 123.74 246.99 989 158.69 270.54 1055 89.51 223.12 6.284 1918.455 < .001
Cola 2253 36.7 55.52 991 41.45 60.31 1053 32 49.49 3.857 1917.632 < .001
Coffee 2265 113.77 322.51 996 130.6 348.42 1061 92.97 278.13 2.696 1902.424 .007
Tea 2267 152.32 261.65 996 142.49 241.87 1063 155.6 266.93 −1.165 2057 .244
Table 3. Cross-tabulations between total weekly caffeine intake and stress, anxiety, and depression.
0 mg/w 0.1–250 mg/w 250.1–500 mg/w 500.1–750 mg/w 750.1–1000 mg/w >1000 mg/w
Stress Low Count 81 342 165 89 42 66
Expected count 81.6 318.5 166.9 88.2 44.8 84.9
Column % 36.2% 39.1% 36% 36.8% 34.1% 28.3%
Adjusted residual −.1 2.1 −.2 .1 −.5 −2.7
High Count 143 532 293 153 81 167
Expected count 142.4 555.5 291.1 153.8 78.2 148.1
Column % 63.8% 60.9% 64% 63.2% 65.9% 71.7%
Adjusted residual .1 −2.1 .2 −.1 .5 2.7
Linear-by-linear 6.599, p = .01
Anxiety Low Count 134 519 258 143 75 110
Expected count 128.7 502.9 262.7 139.1 71 134.5
Column % 60.1% 59.6% 56.7% 59.3% 61% 47.2%
Adjusted residual .8 1.4 −.5 .5 .7 −3.4
High Count 89 352 197 98 48 123
Expected count 94.3 368.1 192.3 101.9 52 98.5
Column % 39.9% 40.4% 43.3% 40.7% 39% 52.8%
Adjusted residual −.8 −1.4 .5 −.5 −.7 3.4
Linear-by-linear 6.976, p = .008
Depression Low Count 158 574 308 157 77 131
Expected count 146.1 569.5 300.1 157.3 80.6 151.4
Column % 70.9% 66.1% 67.2% 65.4% 62.6% 56.7%
Adjusted residual 1.8 .4 .9 .0 −.7 −3
High Count 65 295 150 83 46 100
Expected count 76.9 299.5 157.9 82.7 42.4 79.6
Column % 29.1% 33.9% 32.8% 34.6% 37.4% 43.3%
Adjusted residual −1.8 −.4 −.9 .0 .7 3
Linear-by-linear 9.101, p = .003
Note. Mean weekly caffeine intake for each consumption group was as follows: 0 mg M = 0 mg (SD = 0), 0.1–250 mg/w M = 117.83 (SD = 69.32), 250.1–500 mg/w
M = 355.94 (SD = 70.61), 500.1–750 mg/w M = 616.37 (SD = 69.99), 750.1–1000 mg/w M = 865.09 (SD = 72.71), >1000 mg/w M = 1651.74 (SD = 750.33).
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
6 Journal of Psychopharmacology
After controlling for covariates, the overall effect of caf-
feine on stress was not significant, Wald = 6.252, p = .283, and
none of the consumption groups differed from the non-con-
sumption group. However, total weekly caffeine intake
remained a significant predictor of anxiety, Wald = 12.39, p =
.03. This effect reflected increased risk of high anxiety occur-
ring in the >1000 mg/w group, though none of the other condi-
tions differed significantly from the non-consumers. For odds
ratios and 95% confidence intervals for the multivariate asso-
ciation between total weekly caffeine intake and anxiety, see
Figure 1.
The effect of caffeine on depression also remained significant
after controlling for covariates, Wald = 14.682, p = .012. In this
case increased risk was associated with each of the consumption
groups compared with the non-consumers (though the effect
relating to the 250.1–500 mg/w group was only marginally sig-
nificant, and the effect relating to the 500.1–750 mg/w group was
not significant). For odds ratios and 95% confidence intervals for
the multivariate associations between caffeine and depression,
see Figure 2.
Sex differences in associations between total weekly caf-
feine intake and stress, anxiety, and depression. Due to the
large sample size available, and because sex differences in
responses to caffeine in adolescents have been reported (e.g. Tem-
ple and Ziegler, 2011), it was deemed meritorious to investigate
interactions between sex and caffeine intake. To do this, the same
methodology outlined in the previous section was used (i.e. binary
logistic regression analyses were conducted, and the same covari-
ates were entered), except that the caffeine*sex interaction term
was included instead of the main effects of caffeine and sex.
Significant interactions were observed for each of the outcome
variables: stress, Wald = 31.927, p < .001, anxiety, Wald = 50.341,
p < .001, depression, Wald = 45.038, p < .001.
In order to further investigate the interactions between sex and
caffeine intake on stress, anxiety, and depression, separate multi-
variate analyses were conducted in males and females. The overall
effect of caffeine on stress was not significant in males, Wald =
5.193, p = .393, or females, Wald = 4.243, p = .515, though males
who consumed >1000 mg/w were marginally more likely to report
high stress compared with controls, OR = 1.891, 95% CI [.943,
3.792], p = .073. The effect of caffeine on anxiety was not signifi-
cant in females, Wald = 8.307, p = .14, and none of the consump-
tion groups differed significantly from the control. In males,
however, the effect was significant, Wald = 13.186, p = .022. This
reflected increased risk of high anxiety in the 0.1–250 mg/w,
250.1–500 mg/w, and >1000 mg/w conditions, with the effect
being most apparent in the last condition. For odds ratios and 95%
confidence intervals for the multivariate association between total
weekly caffeine intake and anxiety in males see Figure 3.
The overall effect of caffeine on depression in males was not
significant, Wald = 7.882, p = .163. However, each caffeine con-
sumption group was associated with increased risk compared
with the control (though the effect relating to the 500.1–750
mg/w group was only marginally significant, and the effect in the
750.1–1000 mg/w condition was not significant). The overall
effect in females was significant, Wald = 13.137, p = .022, and
reflected increased risk in both the 750.1–1000 mg/w and >1000
mg/w groups. For odds ratios and 95% confidence intervals for
the multivariate associations between total weekly caffeine
intake and depression in males and females, see Figures 4 and 5,
respectively.
Associations between individual caffeine
sources and stress, anxiety, and depression
Univariate associations between individual caffeine sources
and stress, anxiety, and depression. In order to determine
whether the source of caffeine was important regarding the rela-
tionships reported in the previous section, caffeine from energy
drinks, cola, tea, and coffee were recoded into three groups (non-
consumption, low consumption, and high consumption), and
linear-by-linear associations were investigated in relation to
stress, anxiety, and depression. Because the distributions were
skewed, the cut-off points to define what constituted ‘low con-
sumption’ and ‘high consumption’ were determined in a manner
that assigned relatively balanced numbers of participants to each
group. These distinctions are shown in Table 4; essentially ‘low
consumption’ related to one can of energy drink, one can of cola,
two cups of coffee, and three cups of tea per week, and ‘high
consumption’ related to any values in excess of these.
Caffeine consumed from energy drinks and tea was not asso-
ciated with stress, anxiety, or depression. Interestingly, although
Figure 1. Odds ratios and 95% confidence intervals for multivariate
associations between total weekly caffeine intake and anxiety.
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
Richards and Smith 7
consumption of caffeine from cola was not associated with anxi-
ety or depression, its non-consumption was associated with high
stress levels, and being a low consumer was associated with low
stress levels.
Positive linear relationships were observed between caffeine
consumption from coffee and stress, anxiety, and depression (for
linear-by-linear associations and cross-tabulations between stress,
anxiety, and depression, and caffeine consumed from individual
sources, see Table 4). However these associations are likely
explained by coffee being the major contributor to high overall
caffeine intake. This is reflected in the observation that those
above the median for caffeine intake from coffee consumed more
total caffeine than did those above the median for each of the other
sources: caffeine from coffee low M = 261.42 (SD = 331.82), high
M = 827.65 (SD = 748.51); caffeine from energy drinks low M =
247.63 (SD = 382.38), high M = 674.24 (SD = 649.38); caffeine
from tea low M = 225.97 (SD = 365.43), high M = 640.55 (SD =
633.11); caffeine from cola low M = 295.12 (SD = 448.63), high
M = 486.88 (SD = 585).
Multivariate associations between individual caffeine
sources and stress, anxiety, and depression. In order to fur-
ther investigate associations between caffeine from different
sources and stress, anxiety, and depression, the non-consump-
tion/low consumption/high consumption variables for caffeine
from energy drinks, cola, tea, and coffee were entered together
into binary logistic regression analyses using the ‘enter’ method.
The same dietary, demographic, and lifestyle variables that were
controlled for in the multivariate analyses of total weekly caf-
feine intake were again entered as covariates here.
Low consumption of caffeine from energy drinks was associ-
ated with high stress, though the overall effect was not signifi-
cant. Both low and high consumption of caffeine from cola, on
the other hand, were significantly associated with low stress.
Low caffeine from energy drinks and high caffeine from coffee
were both marginally associated with high anxiety, though nei-
ther effect was significant overall. Low consumption of caffeine
from tea was associated with high depression, and the overall
effect was significant. High caffeine consumption from coffee
was also associated with high depression, though in this case the
overall effect was not significant. For odds rations, 95% confi-
dence intervals, and p-values for all multivariate level associa-
tions between individual caffeine sources and stress, anxiety, and
depression, see Table 5.
Discussion
The current study aimed to present cross-sectional data from
the Cornish Academies Project to investigate associations
between caffeine consumption and stress, anxiety, and depres-
sion in secondary school children. Based on findings from the
literature it was predicted that excessive caffeine intake would
Figure 2. Odds ratios and 95% confidence intervals for multivariate
associations between total weekly caffeine intake and depression. Figure 3. Odds ratios and 95% confidence intervals for multivariate
associations between total weekly caffeine intake and anxiety in males.
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
8 Journal of Psychopharmacology
be associated with high stress, anxiety, and depression, and that
such effects would not be dependent on any particular source of
caffeine. In addition to this, separate analyses were conducted
in males and females in order to investigate interactions
between caffeine and sex.
Relationships between total weekly caffeine
intake and stress, anxiety, and depression
Initial positive relationships were observed between total
weekly caffeine intake and stress, anxiety, and depression.
After adjusting for dietary, demographic, and lifestyle covari-
ates, the effect on stress disappeared. However, consuming
>1000 mg/w remained a predictor of high anxiety, and caffeine
consumption in general appeared to be associated with higher
instances of depression compared with non-consumption
(although the effect was also most pronounced in those who
consumed >1000 mg/w).
Though the above findings mainly replicated those reported
in adults (e.g. Gilliland and Andress, 1981; Pettit and DeBarr,
2011), the effects appeared to occur at lower doses, which is most
likely a reflection of the lower bodyweight of children compared
with adults. One finding that differed considerably from those
made in adult populations was that of depression. Smith (2009b)
observed caffeine consumption to be beneficial compared with
its abstinence, whereas the opposite pattern of results was
observed here. This finding is therefore likely to highlight differ-
ences between the populations studied.
As significant interactions between total weekly caffeine
intake and sex were observed in relation to each of the outcome
variables, separate multivariate analyses were conducted for
males and females. No association between caffeine and anxi-
ety appeared in females; in males, higher instances of anxiety
occurred in the 0.1–250 mg/w, 250.1–500 mg/w, and >1000
mg/w conditions, with the largest effect occurring in the last
group. For depression, effects occurred in both males and
females. In males, increased risk was associated with each
group that consumed caffeine compared with non-consumers
(though consuming 500.1–750 mg/w was only a marginally
significant predictor, and consuming 750.1–1000 mg/w was
not significantly related). In females, consuming either 750.1–
1000 mg/w or >1000 mg/w was significantly associated with
higher reporting of depression. These observations are consist-
ent with other findings, such as caffeine having been shown to
produce greater arousal effects in young males compared with
females (Adan et al., 2008), and to have a higher propensity for
reinforcement in adolescent males compared with females
(Temple et al., 2009). Though it may be that male adolescents
are more vulnerable to harmful effects of caffeine than are
Figure 4. Odds ratios and 95% confidence intervals for multivariate
associations between total weekly caffeine intake and depression in
males.
Figure 5. Odds ratios and 95% confidence intervals for multivariate
associations between total weekly caffeine intake and depression in
females.
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
Richards and Smith 9
Table 4. Cross-tabulations between weekly caffeine intake from energy drinks, cola, coffee, and tea, and stress, anxiety, and depression.
Caffeine from energy drinks Caffeine from cola Caffeine from coffee Caffeine from tea
0 mg 0.1–133 mg >133 mg 0 mg 0.1–25 mg >25 mg 0 mg 0.1–160 mg >160 mg 0 mg 0.1–120 mg >120 mg
Low Count 493 164 145 245 295 266 602 110 94 333 227 248
Stress Expected count 476.6 175.6 149.8 275.7 272.4 257.8 576.3 110.5 119.2 335 216.3 256.7
Column % 37.6% 34% 35.2% 32.5% 39.6% 37.7% 38% 36.2% 28.7% 36.2% 38.2% 35.2%
Adjusted residual 1.5 −1.2 −.5 −2.9 2.1 .8 2.5 −.1 −3.1 −.2 1.1 −.8
High Count 818 319 267 509 450 439 984 194 234 587 367 457
Expected count 834.4 307.4 262.2 478.3 472.6 447.2 1009.7 193.5 208.8 585 377.7 448.3
Column % 62.4% 66% 64.8% 67.5% 60.4% 62.3% 62% 63.8% 71.3% 63.8% 61.8% 64.8%
Adjusted residual −1.5 1.2 .5 2.9 −2.1 −.8 −2.5 .1 3.1 .2 −1.1 .8
Linear-by-linear 1.426, p = .232 4.477, p = .034 9.308, p = .002 .121, p = .728
Low Count 755 277 233 412 447 408 933 172 166 524 352 394
Anxiety Expected count 752.8 276.3 236 432.9 427.7 406.4 907.9 174.9 188.1 525.8 339.6 404.6
Column % 57.7% 57.7% 56.8% 54.9% 60.3% 58% 59.1% 56.6% 50.8% 57.3% 59.6% 56%
Adjusted residual .2 .1 −.3 −1.9 1.8 .2 2.4 −.4 −2.7 −.2 1.2 −1
High Count 553 203 177 338 294 296 645 132 161 391 239 310
Expected count 555.2 203.7 174 317.1 313.3 297.6 670.1 129.1 138.9 389.2 251.4 299.4
Column % 42.3% 42.3% 43.2% 45.1% 39.7% 42% 40.9% 43.4% 49.2% 42.7% 40.4% 44%
Adjusted residual −.2 −.1 .3 1.9 −1.8 −.2 −2.4 .4 2.7 .2 −1.2 1
Linear-by-linear .081, p = .776 1.434, p = .231 7.62, p = .006 .196, p = .658
Depression Low Count 864 316 255 497 491 447 1048 198 193 612 369 460
Expected count 853.4 313.7 267.9 489.5 485.5 460 1029.5 197.6 211.9 597.8 384.4 458.8
Column % 66.2% 65.8% 62.2% 66.4% 66.2% 63.6% 66.4% 65.3% 59.4% 66.8% 62.6% 65.4%
Adjusted residual 1 .3 −1.5 .7 .5 −1.3 1.8 .1 −2.4 1.3 −1.6 .1
High Count 442 164 155 251 251 256 531 105 132 304 220 243
Expected count 452.6 166.3 142.1 258.5 256.5 243 549.5 105.4 113.1 318.2 204.6 244.2
Column % 33.8% 34.2% 37.8% 33.6% 33.8% 36.4% 33.6% 34.7% 40.6% 33.2% 37.4% 34.6%
Adjusted residual −1 −.3 1.5 −.7 −.5 1.3 −1.8 −.1 2.4 −1.3 1.6 −.1
Linear-by-linear 1.805, p = .179 1.288, p = .257 5.164, p = .023 .465, p = .495
Note. Caffeine amounts listed relate to weekly consumption.
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
10 Journal of Psychopharmacology
females, these results may also reflect sexually dimorphic per-
sonality characteristics, or the observation that overall caffeine
consumption in the current study was higher in males than in
females.
When individual caffeine sources were investigated, negative
effects were observed in relation to coffee, tea, and energy drinks,
though they were not consistent across variables and often only
marginally statistically significant. One relationship of particular
interest was however observed: both low (0.1–25 mg/w) and high
(>25 mg/w) levels of caffeine consumed from cola were associ-
ated with low stress. This finding may reflect reports of students
using caffeinated products to cope with stress (e.g. Ríos et al.,
2013). However, the general lack of consistent findings from this
analysis as a whole suggests that, when investigating its effects
on stress, anxiety, and depression, caffeine is best examined in
terms of total intake rather than by differentiating between indi-
vidual sources.
Methodological limitations and directions for
future research
Though the current study has addressed a gap in the literature,
several methodological limitations should be considered when
interpreting the findings. One such limitation is that the partici-
pants who completed the questionnaires were not fully represent-
ative of the schools from which they came. However, taking a
multivariate approach to data analysis in which demographic and
lifestyle variables could be controlled for statistically is deemed
to have been an effective method for addressing this issue.
Nevertheless, as the population studied came from a very specific
demographic group (i.e. 11–17-year-old White children from the
South West of England), further research is needed that focuses
on more representative samples.
Another limitation of the current research was that the chro-
nicity of caffeine use was not taken into account. For instance, a
Table 5. Multivariate associations between individual sources of caffeine and stress, anxiety, and depression.
Caffeine source OR 95% CI p
Stress Energy drinks Low 1.377 1.051, 1.803 .02
High 1.099 .804, 1.502 .555
Wald 5.41, p = .067
Cola Low .721 .557, .935 .013
High .68 .517, .895 .006
Wald 8.986, p = .011
Coffee Low .957 .705, 1.3 .779
High 1.293 .93, 1.8 .127
Wald 2.625, p = .269
Tea Low 1.014 .784, 1.312 .915
High 1.052 .818, 1.353 .693
Wald .159, p = .923
Anxiety Energy drinks Low 1.259 .967, 1.638 .087
High 1.05 .77, 1.43 .759
Wald 3.008, p = .222
Cola Low .862 .669, 1.109 .248
High .83 .635, 1.085 .173
Wald 2.151, p = .341
Coffee Low 1.138 .842, 1.538 .401
High 1.348 .988, 1.838 .059
Wald 3.829, p = .147
Tea Low .944 .731, 1.217 .655
High .958 .75, 1.224 .731
Wald .231, p = .891
Depression Energy drinks Low .994 .756, 1.306 .964
High 1.11 .811, 1.52 .516
Wald .449, .779
Cola Low 1.184 .911, 1.539 .206
High 1.227 .93, 1.619 .148
Wald 2.443, p = .295
Coffee Low .931 .681, 1.273 .655
High 1.369 1.001, 1.872 .049
Wald 4.514, p = .105
Tea Low 1.408 1.086, 1.825 .01
High 1.104 .856, 1.422 .447
Wald 6.809, p = .033
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
Richards and Smith 11
weekly cycle of caffeine use in adolescents was reported by
Pollak and Bright (2003), in which consumption peaked during
the weekend (Saturday), and was lowest in the middle of the
week (Wednesday). Coupled with the observations that adoles-
cents sometimes use caffeinated products to delay sleep onset
(e.g. Calamaro et al., 2009) and to counteract the effects of sleep-
iness during the day (Malinauskas et al., 2007), it is possible that
the timing of administration of the questionnaire may have been
of importance.
A further limitation of the current study is that it utilised a
cross-sectional design. This means that all effects observed here
are correlational, and that causation cannot be inferred.
Therefore the possibility of reverse-causation, or indeed bi-
directionality, cannot be disregarded. For instance, high caf-
feine consumption may be a cause of high stress, anxiety, and
depression, but suffering from such conditions may also lead
towards the high consumption of caffeinated products as a cop-
ing strategy. Furthermore, it is possible that the effects observed
here are attributable to personality characteristics associated
with caffeine users, rather than to their use of caffeine. Future
research should therefore aim to conduct intervention studies in
order to investigate the nature of these relationships further.
Conclusions
The current study has presented results that suggest caffeine con-
sumption may be associated with stress, anxiety, and depression
in secondary school children, though the effect on stress disap-
peared after additional dietary, demographic, and lifestyle vari-
ance was controlled for statistically. The effects observed also
appeared to differ between males and females. Though caffeine
consumption was associated with anxiety in males at the multi-
variate level, no such observation was made in females.
Furthermore, though the effects relating to depression occurred
in both sexes, the threshold at which they appeared was lower in
males than it was in females.
Initial analyses of individual caffeine sources implied that
coffee may have been responsible for the effects observed in rela-
tion to total caffeine intake, but further investigations suggested
this not to have been the case, and that they were likely attribut-
able to caffeine consumption in general rather than to any par-
ticular source. The study also identified very high caffeine intake
(>1000 mg/w) to be a risk factor associated with anxiety and
depression, although effects were sometimes detected at lower
doses. These findings may therefore be a concern for public
health and school policy, and should be considered an important
area for further investigation.
Acknowledgements
The authors would like to acknowledge the contribution of The Waterloo
Foundation for funding the research. In addition, the authors wish to
express their gratitude for the on-going support and collaboration with
Pool Academy, Penrice Community College, and Treviglas Community
College, as well as to thank each of the teachers and pupils who made the
research possible.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the
research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the
research, authorship, and/or publication of this article: The current
research was supported by a grant from The Waterloo Foundation (grant
number: 503692).
References
Adan A, Prat G, Fabbri M, et al. (2008) Early effects of caffeinated and
decaffeinated coffee on subjective state and gender differences.
Prog Neuropsychopharmacol Biol Psychiatry 32: 1698–1703.
Akbaraly TN (2009) Dietary pattern and depressive symptoms in middle
age. Br J Psychiatry 195: 408–413.
Arria AM, Bugbee BA, Caldeira KM, et al. (2014) Evidence and knowl-
edge gaps for the association between energy drink use and high-risk
behaviors among adolescents and young adults. Nutr Rev 72: 87–97.
Barone JJ and Roberts HR (1996) Caffeine consumption. Food Chem
Toxicol 34: 119–129.
Birmaher B, Ryan N, Williamson D, et al. (1996) Childhood and adoles-
cent depression: A review of the past 10 years. Part II. J Am Acad
Child Adolesc Psychiatry 35: 1575–1583.
Boulenger J-P, Uhde TW, Wolff EA III, et al. (1984) Increased sensitiv-
ity to caffeine in patients with panic disorders. Preliminary evidence.
Arch Gen Psychiatry 41: 1067–1071.
Branum AM, Rossen LM and Schoendorf KC (2014) Trends in caffeine
intake among US children and adolescents. Pediatrics 133: 386–393.
Brice CF and Smith AP (2002) Factors associated with caffeine con-
sumption. Int J Food Sci Nutr 53: 55–64.
Bruce M, Scott N, Shine P, et al. (1992) Anxiogenic effects of caffeine in
patients with anxiety disorders. Arch Gen Psychiatry 49: 867–869.
Calamaro CJ, Mason TBA and Ratcliffe SJ (2009) Adolescents living the
24/7 lifestyle: Effects of caffeine and technology on sleep duration
and daytime functioning. Pediatrics 123: e1005–e1010.
Caykoylu A, Ekinci O and Kuloglu M (2008) Improvement from treat-
ment-resistant schizoaffective disorder, manic type after stopping
heavy caffeine intake: A case report. Prog Neuropsychopharmacol
Biol Psychiatry 32: 1349–1350.
Charney DS, Heninger GR and Jatlow PI (1985) Increased anxiogenic
effects of caffeine in panic disorders. Arch Gen Psychiatry 42: 233–243.
Chelben J, Piccone-Sapir A, Lanco L, et al. (2008) Effects of amino acid
energy drinks leading to hospitalization in individuals with mental
illness. Gen Hosp Psychiatry 30: 187–189.
Cysneiros RM, Farkas D, Harmatz JS, et al. (2007) Pharmacokinetics and
pharmacodynamic interactions between zolpidem and caffeine. Clin
Pharmacol Ther 82: 54–62.
Doherty M and Smith PM (2004) Effects of caffeine ingestion on exercise
testing: A meta-analysis. Int J Sport Nutr Exerc Metab 14: 626–646.
Dratcu L, Grandison A, McKay G, et al. (2007) Clozapine-resistant psy-
chosis, smoking, and caffeine: Managing the neglected effects of sub-
stances that our patients consume every day. Am J Ther 14: 314–318.
Ferré S (2008) An update in the mechanisms of the psychostimulant
effects of caffeine. J Neurochem 105: 1067–1079.
Fleming JE and Offord DR (1990) Epidemiology of childhood depressive
disorders: A critical review. J Am Acad Child Adolesc Psychiatry
29: 571–580.
Fulkerson JA, Sherwood NE, Perry CL, et al. (2004) Depressive symp-
toms and adolescent eating and health behaviors: A multifaceted
view in a population-based sample. Prevent Med 38: 865–875.
Gilliland K and Andress D (1981) Ad lib caffeine consumption, symp-
toms of caffeinism, and academic performance. Am J Psychiatry
138: 512–514.
Greden JF (1974) Anxiety or caffeinism: A diagnostic dilemma. Am J
Psychiatry 131: 1089–1092.
Haskell CF, Kennedy DO, Wesnes KA, et al. (2005) Cognitive and mood
improvements of caffeine in habitual consumers and habitual non-
consumers of caffeine. Psychopharmacology 179: 813–825.
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
12 Journal of Psychopharmacology
Higdon JV and Frei B (2006) Coffee and health: A review of recent
human research. Crit Rev Food Sci Nutr 46: 101–123.
James JE, Bruce MS, Lader MH, et al. (1989). Self-report reliability and
symptomatology of habitual caffeine consumption. Br J Clin Phar-
macol 27: 507–514.
James JE, Kristjánsson AL and Sigfúsdóttir ID (2011) Adolescent sub-
stance use, sleep, and academic achievement: Evidence of harm due
to caffeine. J Adolesc 34: 665–673.
Kaplan GB, Greenblatt DJ, Ehrenberg BL, et al. (1997) Dose-dependent
pharmacokinetics and psychomotor effects of caffeine in humans.
J Clin Pharmacol 37: 693–703.
Kendler KS, Myers J and Gardner CO (2006) Caffeine intake, toxicity
and dependence and lifetime risk for psychiatric and substance use
disorders: An epidemiologic and co-twin control analysis. Psychol
Med 36: 1717–1725.
Khantzian EJ (1997) The self-medication hypothesis of substance use
disorders: A reconsideration and recent applications. Harv Rev Psy-
chiatry 4: 231–244.
Kristjánsson AL, Sigfúsdóttir ID, Allegrante JP, et al. (2011) Adolescent
caffeine consumption, daytime sleepiness, and anger. J Caffeine Res
1: 75–82.
Kristjánsson AL, Sigfúsdóttir ID, Frost SS, et al. (2013) Adolescent caf-
feine consumption and self-reported violence and conduct disorder.
J Youth Adolesc 42: 1053–1062.
Lara DR (2010) Caffeine, mental health, and psychiatric disorders.
J Alzheimers Dis 20(Suppl. 1): S239–S248.
Lazarus RS (1993) Coping theory and research: Past, present, and future.
Psychosom Med 55: 234–247.
Lee MA, Flegel P, Greden JF, et al. (1988) Anxiogenic effects of
caffeine on panic and depressed patients. Am J Psychiatry 145:
632–635.
Lieberman HR, Tharion WJ, Shukitt-Hale B, et al. (2002) Effects of caf-
feine, sleep loss, and stress on cognitive performance and mood during
U.S. Navy SEAL training. Psychopharmacology 164: 250–261.
Lieberman HR, Wurtman RJ, Emde GG, et al. (1987) The effects of low
doses of caffeine on human performance and mood. Psychopharma-
cology 92: 308–312.
Lorist M and Tops MM (2003) Caffeine, fatigue and cognition. Brain
Cogn 53: 82–94.
Luebbe AM and Bell DJ (2009) Mountain Dew or mountain don’t?: A
pilot investigation of caffeine use parameters and relations to depres-
sion and anxiety symptoms in 5th- and 10th-grade students. J School
Health 79: 380–387.
Malinauskas BM, Aeby VG, Overton RF, et al. (2007) A survey of
energy drink consumption patterns among college students. Nutr J
6(35). Available at: www.nutritionj.com/content/6/1/35 (accessed 27
May 2014).
Mandel H (2002) Update on caffeine consumption, disposition and
action. Food Chem Toxicol 40: 1231–1234.
McLellan TM and Lieberman HR (2012) Do energy drinks contain active
components other than caffeine? Nutr Rev 70: 730–744.
Meadows-Oliver M and Ryan-Krause P (2007) Powering up with sports
and energy drinks. J Pediatr Health Care 21: 413–416.
Miller KE (2008a) Energy drinks, race, and problem behaviors among
college students. J Adolesc Health 43: 490–497.
Miller KE (2008b) Wired: Energy drinks, jock identity, masculine norms,
and risk taking. J Am Coll Health 56: 481–490.
Nardi AE, Lopes FL, Freire RC, et al. (2009) Panic disorder and social
anxiety disorder subtypes in a caffeine challenge test. Psychiatry Res
169: 149–153.
Nawrot P, Jordan S, Eastwood J, et al. (2003) Effects of caffeine on
human health. Food Addit Contam 20: 1–30.
Ogawa N and Ueki H (2003) Secondary mania caused by caffeine. Gen
Hosp Psychiatry 25: 138–139.
Pettit ML and DeBarr KA (2011) Perceived stress, energy drink con-
sumption, and academic performance among college students. J Am
Coll Health 59: 335–341.
Pollak CP and Bright D (2003) Caffeine consumption and weekly sleep pat-
terns in US seventh-, eighth-, and ninth-graders. Pediatrics 111: 42–46.
Reissig CJ, Strain EC and Griffiths RR (2009) Caffeinated energy drinks
- a growing problem. Drug Alcohol Depend 99: 1–10.
Richards G and Smith AP (2015) Risk factors for, and effects of, stress, anx-
iety, and depression in adolescents. Br J Educ Soc Behav Sci 10: 1–10.
Richards G, Malthouse A and Smith AP (2015b) The diet and behaviour
scale (DABS): Testing a new measure of food and drink consump-
tion in a cohort of secondary school children from the south west of
England. J Food Res 4: 148–161.
Richards G, Millward N, Evans P, et al. (2015a) Acute effects of energy
drinks on behavioural sanctions in secondary school children: A pre-
liminary study. J Food Res 4: 1–9.
Riksen NP, Rongen GA and Smits P (2009) Acute and long-term cardio-
vascular effects of coffee: Implications for coronary heart disease.
Pharmacol Ther 121: 185–191.
Ríos JL, Betancourt J, Pagán I, et al. (2013) Caffeinated-beverage con-
sumption and its association with socio-demographic characteristics
and self-perceived academic stress in first and second year students
at the University of Puerto Rico Medical Sciences Campus (UPR-
MSC). Puerto Rico Health Sci J 32: 95–100.
Roberts RE, Lewinsohn PM and Seeley JR (1995) Symptoms of DSM-
III-R major depression in adolescence: Evidence from an epidemio-
logical survey. J Am Acad Child Adolesc Psychiatry 34: 1608–1617.
Scott NR, Chakrabotry J and Marks V (1989) Caffeine consumption in the
United Kingdom: A retrospective survey. Food Sci Nutr 42: 183–191.
Seifert SM, Schaechter JL, Hershorin ER, et al. (2011) Health effects of
energy drinks on children, adolescents, and young adults. Pediatrics
127: 511–528.
Sharma V (2010) Red bull and mania. German J Psychiatry 13: 178–180.
Shuttleworth I (1995) The relationship between social deprivation, as
measured by individual free school meal eligibility, and educational
attainment at GCSE in Northern Ireland: A preliminary investiga-
tion. Br Educ Res J 21: 487–504.
Smit HJ and Rogers PJ (2002) Effects of energy drinks on mood and mental
performance: Critical methodology. Food Qual Prefer 13: 317–326.
Smith AP (2009a) Effects of caffeine in chewing gum on mood and atten-
tion. Hum Psychopharmacol 24: 239–247.
Smith AP (2009b) Caffeine, cognitive failures and health in a non-work-
ing community sample. Hum Psychopharmacol 24: 29–34.
Smith AP, Sturgess W and Gallagher J (1999) Effects of a low dose of
caffeine given in different drinks on mood and performance. Hum
Psychopharmacol 14: 473–482.
Temple JL and Ziegler AM (2011) Gender differences in subjective and
physiological responses to caffeine and the role of steroid hormones.
J Caffeine Res 1: 41–48.
Temple JL, Bulkley AM, Briatico L, et al. (2009) Sex differences in rein-
forcing value of caffeinated beverages in adolescents. Behav Phar-
macol 20: 731–741.
Thoits PA (1995) Stress, coping, and social support processes: Where are
we? What next? J Health Soc Behav 35: 53–79.
Tondo L and Rudas N (1991) The course of a seasonal bipolar disorder
influenced by caffeine. J Affect Disord 22: 249–251.
Williams G (2014) Researching and developing mental health and well-
being assessment tools for supporting employers and employees in
Wales. PhD Thesis, Cardiff University, UK.
Williams G (2015) Assessing multiple factors of well-being using sin-
gle-item measures. In: Sharples S, Shorrock S and Waterson P (eds)
Contemporary Ergonomics and Human Factors 2015. London: Tay-
lor & Francis, pp. 291–298.
Williams G and Smith AP (2012) A holistic approach to stress and well-
being. Part 6: The Wellbeing Process Questionnaire (WPQ Short
Form). Occup Health Work 9: 29–31.
Williams G and Smith AP (2013) Measuring wellbeing in the workplace:
Single item scales of depression and anxiety. In: Anderson M (ed)
Contemporary Ergonomics and Human Factors 2013. London: Tay-
lor & Francis, pp. 87–94.
at Cardiff University on October 29, 2015jop.sagepub.comDownloaded from
