Psychology, 2010, 1, 88-95
doi:10.4236/psych.2010.12012 Published Online June 2010 (http://www.SciRP.org/journal/psych)
Salivary Cortisol in Relation to the Use of
Information and Communication Technology
(ICT) in School-Aged Children*
Marjut Wallenius1,2, Ari Hirvonen3, Harri Lindholm4, Arja Rimpelä5, Clas-Hakan Nygård5, Lea
Saarni5, Raija-Leena Punamäki1
1Department of Psychology, University of Tampere, Tampere, Finland; 2Research Unit of Pirkanmaa Hospital District, Tampere
University Hospital, Tampere, Finland; 3Biomonitoring Laboratory, Finnish Institute of Occupational Health, Helsinki, Finland;
4Department of Physiology, Finnish Institute of Occupational Health, Helsinki, Finland; 5Tampere School of Public Health, Univer-
sity of Tampere, Tampere, Finland.
Received April 27th, 2010; revised May 27th, 2010; accepted May 29th, 2010.
ABSTRACT
Long periods of use of Information and Communication Technology (ICT) may raise strong emotions and lead to cog-
nitive fatigue. The current study focused on the impact of ICT use the preceding day on the next-day salivary cortisol
pattern in 72 school-aged children (39 at the age ten and 33 at the age 13). Salivary cortisol levels were measured at
five time points from awakening to bedtime. Time spent in different ICT activities the day b efore salivary sampling was
measured by an activity diary. Results showed that the participants who had used ICT on an average three hours the
preceding day showed a significantly reduced cortisol increase one hour after awakening (awakening response) com-
pared to those who had used ICT not at all or less than one hour after controlling for pubertal status and the level of
depression. The results suggest a stress response as a consequence of a long period of ICT use.
Keywords: ICT, Salivary Cortisol, Stress, Children, Adolescents
1. Introduction
Information and communication technology (ICT; mobile
phone, digital games, computer, the Internet) is matching
the popularity of tradition al media in the liv es of Western
children and adolescents. Recent findings show that digi-
tal game playing exceeds time spent in television viewing
among children [1]. Younger children and boys spend
more time in digital game playing while adolescents
(14-18 years) spend increasingly time in Internet surfing
and chatting [2,3]. ICT use serves many motives and
functions among children and adolescents. For example,
digital game playing offers restoration, social compensa-
tion as well as possibilities to experience and learn new
or escape worries [4]. Thus, ICT may provide an extra
dimension to life in which children and adolescents are
able to accomplish thing s which are not possib le for them
in reality [5]. The psychophysiological effects of daily
ICT use among children and adolescents have not been
studied so far. In this study, we examine relations be-
tween the intensity of daily ICT use and psychophysi-
ological stress indicated by diurnal salivary cortisol se-
cretion in school-aged ch ildren.
Interactions with ICT can be understood as mental
tasks which may comprise different components [6].
These interactions may elicit strong emotional responses,
such as enthusiasm, fear, and surprise, affect the individ-
ual’s arousal level, and demand voluntary, directed atten-
tion and cognitive processing. Although adolescents ex-
perience the different ICT activities mainly positively,
long-lasting use of ICT may, however, result in more
negative consequences, such as directed attention fatigue
[7] and heighten arousal level which is often antecedents
of physiological stress response [7]. Directed attention
fatigue as a result of prolonged period of mental effort is
independent of the content of a task [8]. Physiological
measures of stress can be seen even after a long per iod of
a pleasant task [9]. Thus, the mental tasks during ICT use,
*This research was supported by the grants from the Academy o
f
Finland (201669), the Information Society Institute of the University o
f
Tampere and the Tampere University of Technology (16-01), and the
Competitive Research Funding of the Pirkanmaa Hospital District
(9G211).

Salivary Cortisol in Relation to the Use of Information and Communication Technology (ICT) in School-Aged Children 89
although experienced positively, may cause stress. In
addition, findings suggest that the auditory input, such as
built-in music during video game playing, may contribute
significantly to the stress response [10]. Recovery from
directed attention fatigue occurs during sleep and during
involuntary attention, or fascination, requiring no effort,
for instance in nature environments [11]. In everyday life
children and adolescents may use multiple forms of ICT
daily or even at the same time. Also , directed attention is
needed most of the time during schooldays and when
doing home works. We hypothesize that directed atten-
tion fatigue is increased by increasing the total time used
in ICT. We further hypothesize that psychophysiological
stress results as a consequence of directed attention fa-
tigue and insufficient attentional resources with joint ef-
fect of possible other stress-related features of ICT inter-
actions, such as high arousal level and noise.
When an individual is exposed to stress, a cascade of
physiological events occur along the hypothlamic- pitui-
tary-adrenal (HPA) axis. Subsequent release of the ster-
oid hormone cortisol is considered the body’s major
neuroendocrine response to stress [12]. Prior research
among children and adolescents shows that the daily
rhythm characteristic to cortisol can be disrupted by psy-
chological and environmental influences, such as cumu-
lative environmental risks [13], poverty [14], day-care
[15], post-traumatic stress disorder [16], social depriva-
tion [17], performance challenge [18], and violence ex-
posure [1 9 ].
In the diurnal profile of cortisol secretion, the cortisol
awakening respon se (CAR) has been recognised as a dis-
tinct phenomenon. Under basal co nditions, cortisol secre-
tion follows a circadian rhythm, manifested in 50-160%
increase in salivary-free cortisol during the first 30-40
min after awakening, drop rapidly in the next few hours
and then more gradually throughout the day [20]. The
cortisol awakening response has shown to be an in-
tra-individually stable phenomenon [21]. A majority ( 7 0 %)
of 10-12-year-old children show a cortisol awakening rise,
although lower than in adults, with higher levels in girls
than boys [22,23]. Results concerning raltions between
cortisol measures and pubertal stage are somewhat
contradictory. While some studies have found no rela-
tionships [22,23], some findings show correlations be-
tween higher pubertal stage and elevated cortisol curves
and lower cortisol awakening response [24]. The cortisol
awakening response is unrelated to the mean underlying
level of cortisol secretory activity throughout the rest of
the day [25].
Many studies have found perceived stress to be linked
to some aspects of the cortisol awakening response for a
review see [20]. Especially the attenuated cortisol awa
kening response is related to stress, such as acute [26]
and long-lasting stress [27], chronic fatigue [28], early
loss experience [29] and violence exposure [19]. Con-
trary to adult studies, adolescents with higher levels of
depressive symptoms showed lower diurnal cortisol lev-
els [24].
Results on the association between ICT use and co rti sol
levels are limited, describing only short-term experiment-
tal conditions concerning digital playing. Some studies
have found raised cortisol levels [30] but others have
found no chance during a digital game [31]. Still some
results suggest that higher cortisol levels are due to built-in
music of digital games [10]. Instead, psychophysiological
effects of daily ICT use have not been studied.
The present study investigated whether the amount of
time used on ICT the preceding day was related to salivary
cortisol among school-aged children. For cortisol, we used
both levels and dynamics of cortisol concentration. We
hypothesized that higher ICT use the preceding day is
related to higher total cortisol levels and an attenuated
cortisol awaken ing respons e.
2. Methods
2.1 Participants
Participants for the laboratory measurements were selected
from those 222 (123 girls) fourth and 256 (137 girls)
seventh graders from seven schools (five elementary and
two middle schools) in a city in Finland who completed a
survey questionnaire. The age groups were chosen acc-
ording to the developmental saliency in transition from
middle childhood to adolescence [32]. Altogether 88 sub-
jects were stratified into fourth and seventh graders (10-
and 13-year-olds), and boys and girls.
Altogether 72 (33 girls, 39 boys; 39 10-year-olds, 33
13-year-olds) schoolchildren took part in the study. The
dropouts were due to refusals and unwillingness to provide
informed consent on the part of either the schoolchildren
or their parents.
2.2 Procedure
Before data collection the approval of the Ethical Co-
mmittee of Pirkanmaa Hospital District (Code Nr. R
04050) was acquired. Permission was also obtained from
the school principals. At each school an information
meeting was held, usually for each participating class
separately, and an information letter was delivered both
for the pupils selected for the study and their parents.
Written consents for the participation were o btained from
all children willing to participate, and their p a re n t / g u a r di an.
The researcher gave directions for an activity diary and
the subjects had possibility to ask questions.
At each school, in a peaceful room, the participants
were given the saliva collection kits with verbal and
written instructions. Children took their saliva samples
during one day, and next day they returned the samples
and the activity diary to the researcher. One day saliva
samples of all the participants were collected during three
weeks, from the end of April to the beginning of May.

Salivary Cortisol in Relation to the Use of Information and Communication Technology (ICT) in School-Aged Children 90
The sampling day was always an ordinary school day. As
a reward for taking part in the study, the subjects received
a cinema ticket each.
2.3 Measures
2.3.1 Content and Intensity of ICT Use
Exposure to ICT was measured by use of an activity di-
ary covering the day preceding saliva sample collection.
High subject compliance rate and data reliability have
been obtained with diary method using objective measures
as criterion, both among children and adolescents [33].
The participants were instructed to record in 15 minutes
intervals the time spent on different ICT activities with
alternatives 1) using mobile phone e.g. for phone calls,
text messages, 2) playing mobile phone games, 3) playing
TV or console games, 4) playing computer or Internet
games, 5) using computer for homework, writing etc., 6)
using computer for communication by e-mail, discussion
groups, and chatting, and 7) general surfing on the Int e r n e t .
Participants also recorded their sleeping hours, from go-
ing to bed until waking up the next morning. Total time
spent on different ICT activities was calculated summing
up the 15 min intervals for each activity. Child- reports on
time they use for different activities have been shown to
have moderately high validity [34].
2.3.2 Salivary Cortisol
Participan ts to ok five sa liva samp le s (i mmedi ate ly a t wake-
up, at 1 hour after wake-up, at 3 hours after wa ke-up, late
in the afternoon and before going to bed) using the Sali-
vette sampling device (Salivette®, Sarstedt, Germany). The
Salivette tube consists of a plastic sampling vessel with a
suspended insert containing a sterile neutral cotton wool
swab that has to be chewed for about one minute and then
returned to the insert. Children were instructed not to
drink or eat or brush teeth half an hour before collecting
the saliva sample. Salivary cortisol sampling compliance
has shown to be reasonably high when verified electroni-
cally in adults [35]. The next day, the saliva samples
were returned to the researcher and immediately mailed to
the Finnish Institute of Occupational Health for cortisol
assay. Free cortisol levels in saliva were measured using a
commercially available chemiluminence assay (IBL,
Hambur g, Ge rmany).
2.3.3 Pubertal Status
In the survey questionna ire, pubertal status was measured
by asking ‘How old were you when you got your first
period/spermarche?’ The adolescents had to select one of
the options: not yet, at the age of 10, 11, 12, 13, 14, and
15. For this study pubertal status was dummy coded: yes/
no at puberty.
2.3.4 Depression
In the survey questionnaire, depression was measured by
depressive and anxiety symptoms. Six items indicating
depressiveness were derived from the Child Depression
Inventory CDI; [36], and included items such as “I am
sad”, “I cry easily and often” and “I feel that nobody ca-
res about me”. Five items indicating anxiety and fears
were from the Screen for Anxiety Related Emotional
Disorders 5-item SCARED; [37] and included items such
as “I fear that something bad will happen to me”, “Many
things bother me”, and “I fear that I will fail”. The par-
ticipants estimated how well the descriptions fitted them
on a 5-point scale: (0) not at all, (1) somewhat, (2) quite
well, (3) well, and (4) very well. The depressiveness and
anxiety scales have been validated in a Finnish interven-
tion study among children in depressive families [38].
Based on both scales a mean variable of depression was
formed, and it had sufficient internal consistency of
Cronbach’s alpha 0.79.
2.4 Statistical Analysis
For statistical analysis the participants were divided into
two groups on the basis of the to tal sum of hours spent in
different ICT activities during the day preceding saliva
sample collection. Participants who h ad used ICT for less
than one hour formed the low user group (N = 42). In this
group 32 participants (76%) had not used ICT at all the
day preceding salivary collection. The rest of the partici-
pants were called the user group (N = 30). In the user
group, the total time of ICT usage of 19 participants
(63%) was one to less than 3 hours, of six participants
(20%) 3 to less than 6 hours, and of five participants
(17%) 6-8 ho urs.
Differences between the ICT user groups in sex, age,
classroom, and pubertal status were tested with Chi-
square tests. Classroom was included in these analyses to
be sure that the groups did not differ systematically ac-
cording to the amount of ICT use or any other activity
during the school day preceding cortisol measurements.
Independent-samples t-tests were used to test group dif-
ferences in depression, time spent in different ICT activi-
ties, wake-up time, sleeping hours before sampling, sali-
vary sample times, and single salivary cortisol values.
Group differences in daytime cortisol profiles were tested
with general linear model (GLM) where five cortisol
measures were within-subject repeated measures, and sex,
level of depression and pubertal status were as covariates.
Greenhouse-Geisser correction was applied because the
assumption of sphericity was violated. Preliminary analysis
indicated that girls showed significantly higher awakening
cortisol levels than boys (t(70) = 2.22, p = 0.030) but
cortisol values did not differ by age and pubertal status
and were not correlated with the level of depression.
In addition to short daytime cortisol profiles, areas un-
der the curve with respect to ground (AUCG) and with
respect to increase (AUCI) based on five measurements
were computed according to the formula described by Pre-

Salivary Cortisol in Relation to the Use of Information and Communication Technology (ICT) in School-Aged Children 91
ussner, Kirschbaum, Meinlschmid, and Hellhammer [39].
These data were analysed with one-way A NCOVA using
sex, the level of depression and pubertal status as covari-
ates.
3. Results
3.1 Descriptive Statistics
The sample characteristics and the data for the two ICT
user groups are summarised in Table 1. The low user and
user groups did not differ by sex, age, classroom, level of
depression or pubertal status. Participants in the user
group differed significantly from the low user group both
in the total time used ICT and in using different form of
ICT except for mobile phone use. Hours slept before the
salivary sampling, waking time, and sampling times did
not differ between the ICT user groups.
3.2 Relations between ICT Use and Salivary
Cortisol
The focus of the study was to examine the relation of the
amount of time used on ICT to next-day salivary cortisol.
In Ta ble 1 are shown group values for the different corti
sol measures. The sample 2 cortisol value tended to be
higher in the low user group than in the user g r o u p. A n a l y -
sis of the short daytime cortisol profiles revealed no sig-
nificant difference between the groups (F(3.22, 215.90) =
1.58; p = 0.191) (Figure 1). The total cortisol level indi-
cated by AUCG tended to be higher in the low user group
than in the user group (p = 0.09). A sig nificant group
difference was found in AUCI. As expected,the user
group showed a significantly reduced awakening re-
sponse compared to the low user group (p = 0.03) al-
though the effect size was relatively small.
Table 1. Demographic characteristics, intensity of using information and communication technology (ICT), time of saliva
collection, and salivary cortisol values in low ICT user and ICT user groups
Intensity of ICT-usage
Variable Low user1
(N = 42) User1 (N = 30) Statistic
Sex (girl/boy) 20/22 14/16  p = 0.990
Age-group (10/13) 21/19 15/15  p = 0.624
Classroom (17 classrooms) 0-6 students/ 0-4 students/  (16, 72) = 17.7 0, p = 0.342
classroom classroom
At puberty (yes/no) 14/28 11/19  p = 0.716
Depression 7.86 (7.43) 9.00 (7.29) t (71) = –0.649; p = 0 .518
Hours used on ICT by activity
Mobile phone calls, text messages 15 (0.29) 0.17 (0.38) t (71) = –0.27; p = 0.786
Mobile phone games 01 (0.05) 09 (0.30) t (71) = –1.73; p = 0.088
Playing TV and console games 04 (0.13) 56 (1.20) t (71) = –2.81; p = 0.006
Playing computer games 05 (0.17) 1.16 (1.89) t (71) = –3.85; p = 0.000
Using computer for writing, homework 01 (0.04) 37 (0.88) t (71) = –2.69; p = 0.009
Using computer for
communication 02 (0.09) 37 (0.89) t (71) = –2.53; p = 0.014
Surfing in the Internet 00 (0.00) 52 (1.26) t (71) = –2.71; p = 0.009
Total hours of ICT usage 14 (0.26) 3.15 (2.52) t (71) = –7.80; p = 0.000
Wake-up time (h) 0648 (0054) 0707 (0055) t (71) = –1.63; p = 0.108
Sleeping hours before
sampling 8.60 (1.04) 8.98 (1.27) t (71) = –1.39; p = 0.170
Time of saliva collection (h)
Sample 1 0655 (0036) 0649 (0037) t (71) = 0.53; p = 0.59 6
Sample 2 0758 (0036) 0754 (0050) t (71) = 0.26; p = 0.79 4
Sample 3 1010 (0042) 1001 (0049) t (71) = 0.90; p = 0.37 2
Sample 4 1528 (0100) 1553 (0114) t (71) = –1.58; p = 0.11 8
Sample 5 2103 (0316) 2057 (0400) t (71) = 0.11; p = 0.91 3
Salivary cortisol (nmol/l)
Sample 1 18.28 (8.55) 20.32 (7.86) t (71) = –1.03; p = 0.308
Sample 2 23.14 (17.87) 17.27 (7.88) t (71) = 1.68; p = 0.097
Sample 3 16.24 (19.06) 10.94 (9.07) t (71) = 1.41; p = 0.163
Sample 4 11.22 (13.49) 7.91 (8.2 7) t (71) = 1.19; p = 0.237
Sample 5 6.70 (8.22) 4. 7 7 (6.27) t (71) = 1.08; p = 0.282
AUCG 191.03 (142.06) 154.72 (85.05) F (1, 70) = 2.88, p = 0.094,
 = 0.041
AUCI –79.95 (142.83) –201.59 (175.68) F (1, 70) = 4.82, p = 0.032,
 = 0.067
1For sex, age group and puberty, frequency; for others, mean (standard deviation). AUCG, area under curve with respect to ground; AUCI, area under
curve with respect to increase. Samples: 1 = at wake-up, 2 = at 1 hour after wake-up, 3 = at 3 hours after wake up, 4 = late afternoon, 5 = before going
to bed

Salivary Cortisol in Relation to the Use of Information and Communication Technology (ICT) in School-Aged Children 92
4. Discussion
This study provides sup port for a link between time used
ICT and next-day salivary cortisol pattern among school-
children. As expected, the participants who had used ICT
on the average for three hours the preceding day showed
a significantly reduced cortisol awakening response co-
mpared to those most of whom had used ICT not at all.
This is in line with earlier results which show attenuated
cortisol awakening response in relation to stress [19,
26-29].
In measuring cortisol different confounding factors
have been identified, most of which, however, have been
controlled in our study. According to earlier results both
cortisol levels and the cortisol awakening response of
10-12-year-old children show seasonal variability, being
highest during summer months [23] wh ich may be re lated
to greater amount of light [40]. Further, greater response
has been reported on weekdays compared to weekends
both by working adults [41] and children in day-care [15].
This kind of anticipation of a po tentially stressful day has
been observed in school-aged children, too [42]. In our
sampling times there was no seasonal variation, and the
sample collection day was always a weekday. Moreover,
although earlier findings about the influence of awakening
time on cortisol secretion are somewhat mixed [20] , s ome
recent studies imply that awakening time may be a con-
founding variable [43,44]. In this study, participants in
the two ICT user groups did not differ according to wak-
ing-time or sleeping hours before salivary sampling, nor
by sex, age, pubertal status or depression.
Measurement of salivary cortisol, compared to serum
cortisol, has the advantage of a non-invasive and stress-
free sampling procedure in the subjects’ natural environ-
ment [23]. However, when saliva samples are collected in
a domestic setting, the accuracy of the meas urements
depends on participant adhe rence to the instructions [45].
Since no electronic monitoring was used, we have no
means to objectively assess participants’ com pliance to
saliva sampling in this study. However, we have no reason
to believe that the ICT users were less comp liant than the
Figure 1. Salivary cortisol daytime profiles (1 = at wake-up,
2 = at 1 hour after wake-up, 3 = at 3 hours after w ake-up, 4
= late afternoon, 5 = before going to bed) in the low ICT
user group (N = 42) and ICT user gr oup (N = 30)
other participants. Cortisol level is usually at its highest
at 30 minutes after awakening. Had this measurement
included, the cortisol level in the low user group might
have b een even higher during the s econd sample.
Generally, an altered cortisol awakening response is
seen as an indicator of stress and stress-related changes in
HPA-regulation. Adolescents rarely describe gaming and
surfing in the Internet as stressing activities but, instead,
as a way of passing time, getting positive experiences,
and social communication. Gaming, Internet surfing, and
other computer-based activities demand e.g. applying
rules and strategies, quick reactions, and processing a lot
of new information and experiences. Thus, in spite of the
pleasant content, long period of focusing attention to
game playing or Internet surfing may result in attention
overload and directed attention fatigue [26]. Results have
shown that subjective appraisals of stress and physiological
reactions do not always mach [46,47]. Besides directed
attention fatigue, strong emotions may be associated with
game and Internet contents, social communication, and
successes and failures around these activities. Emotional
processes involve both the subjective feelings of the indi-
vidual and the neural and biochemical basis [9,30]. Thus,
strong emotions are another possible source of physio-
logical stress. Earlier results show physiological stress
reactions immediately after a play session [30]. Our re-
sults suggest that a psych o physiological load due to ICT
use can persist over night and have an impact on the
regulation of HPA-activity even the next morning. Part of
the participants in the user group had spent 1-3 hours on
ICT which could be seen quite a moderate and usual
amount of ICT usage per day at present. An experimental
design would be needed to examine does even quite a
moderate period of ICT use alone or only when combined
with school work have an impact on diurnal cortisol se-
cretion. The possibility of the low user group to recover
and restore after school work may be as important as di-
rected attention fatigue in the ICT user group in explain-
ing group differenc es.
According to our results salivary cortisol may be a
variable of interest when studying stress, strain and res-
toration in connection with ICT use. Some methodologi-
cal questions need to be addressed, however. The result
should be replicated in other, possibly larger samples,
including salivary cortisol measurement at 30 minutes
after awakening. Electronic monitoring of wake-up time
and saliva sampling, and more obj ective estimates of time
spent in different activities would be advantageous. In-
clusion of more than one day would allow assessment of
cortisol baseline values and enable measurement of within-
subject differences based on ICT use. There are no stan-
dards for measuring pubertal development, but the selec-
tion of the method depends on which aspect of the pu-
berty may be the most relevant to the research question
[48]. In the current study, we were interested in the
physical processes related to puberty which could affect

Salivary Cortisol in Relation to the Use of Information and Communication Technology (ICT) in School-Aged Children 93
the diurnal cortisol secretion, not e.g. social or psycho-
logical development. Especially menarche appears quite
late in the pubertal development [48]. Thus, a more fine-
grained measure of stage of pubertal development [49]
could be used.
In conclusion, our results suggest that long hours on ICT
may imply stress responses during which the p h y si o lo g i ca l
regulation system is in imbalanced allostatic state [50].
Excessive use of ICT can be seen as a modern form of
Type 2 allostatic load which refers to an individual’s ca-
pacity to cope in th e surrounding social context. It seems
possible that long hours of ICT use day after day might
work like a naturally occurring stressor, which would
predispose some adolescents to the development of al-
lostatic load. Stress from different sources may also have
interactive effects [51]. In the face of multiple stressors,
for instance, problems in family or peer rel atio ns, troubles
at school, or living in a noisy or crowded env i ro n men t , the
negative effects of intensive ICT use may be strengthened.
Turner-Cobb [50] suggests that under naturalistic condi-
tions even changes within the normal range may be subtle
early indicators of, and contributors to, physical health
outcomes in adulthood.
Directed attention fatigue can be defined as an insuffi-
cient reserve of attention to perform demanding tasks
[52]. In addition to physiological signs of stress, negative
after-effects have also been observed in next-day per-
formance [53]. It would be urgent to study whether ex-
cessive ICT use in the evening negatively affects adoles-
cents’ next-day school performance, for instance, in the
form of poorer attention, persistence, or memory. There
is evidence that changes in cortisol levels are associated
with impaired cognitive performance, such as memory
[54].
5. Acknowledgements
We are grateful to the schools for cooperation and to the
children and adolescents who participated in the study.
We thank Heli Sistonen, Jukka Koskelo, and Marjatta
Radecki for assistance in data collection and Mrs. Marja
Vajaranta and Virginia Mattila for language checking.
REFERENCES
[1] D. A. Christakis, B. E. Ebel, F. P. Rivara and F. J. Zim-
merman, “Television, Video, and Computer Game Usage
in Children Under 11 Years of Age,” The Journal of Pe-
diatrics, Vol. 145, No. 5, 2004, pp. 652-656.
[2] D. F. Roberts, “Media and Youth: Access, Exposure, and
Privatizaton,” Journal of Adolescent Health, Vol. 27, No.
2, 2000, pp. 8-14.
[3] R.-L. Punamäki, M. Wallenius, C.-H. Nygård, L. Saarni
and A. Rimpelä, “Use of Information and Communication
Technology (ICT) and Perceived Health In Adolescence:
The Role of Sleeping Habits and Waking-Time Tired-
ness,” Journal of Adolescence, Vol. 30, No. 4, 2007, pp.
660-670.
[4] M. Wallenius, A. Rimpelä, R.-L. Punamäki and T. Linto-
nen, “Digital Game Playing Motives among Adolescents.
Relation to Parent-Child Communication, School Per-
formance, Sleeping Habits, and Perceived Health,” Jour-
nal of Applied Developmental Psychology, Vol. 30, No. 4,
2009, pp. 463-474.
[5] S. McNamee, “Foucaults’s Heterotopia and Children’s
Everyday Lives,” Childhood, Vol. 4, No. 4, 2000, pp.
479-492.
[6] S. Lenot-Ledunois, G. Vardon, P. Perr uchet and J. Gallego,
“The Effects of Attentional Load on the Breathing Pattern
in Children,” International Journal of Psychophysiology,
Vol. 29, No. 1, 1998, pp. 13-21.
[7] J. A. Veltman and A. W. Gaillard, “Physiological Work-
load Reactions in Increasing Levels of Task Difficulty,”
Ergonomics, Vol. 41, No. 5, 1998, pp. 656-669.
[8] S. Kaplan, “The Restorative Benefits of Nature: Toward
an Integrative Framework,” Journal of Environmental
Psychology, Vol. 15, No. 3, 1995, pp. 169-182.
[9] U. Lundberg, B. Melin, G. W. Evans and L. Holmberg,
“Physiological Deactivation after Two Contrasting Tasks
at a Video Display Terminal: Learning vs. Repetitive Data
Entry,” Ergonomics, Vol. 36, No. 6, 1993, pp. 601-611.
[10] S. Hérbert, R. Béland, O. Dionne-Fournelle, M. Crête and
S. J. Lupien, “Physiological Stress Response to Video-
Game Playing: The Contribution of Built-in Music,” Life
Sciences, Vol. 76, No. 20, 2005, pp. 2371-2380.
[11] G. Felsten, “Whe re to Take a Study Break on the College
Campus: An Attention Restoration Theory Perspective,”
Journal of Environmental Psychology, Vol. 29, No. 1,
2009, pp. 160-167.
[12] J. F. Lopez, H. Akil and S. J. Watson, “Neural Circuits
Mediating Stress,” Biological Psychiatry, Vol. 46, No. 1,
1999, pp. 1461-1471.
[13] G. W. Evans and L. A. Marcynyszyn, “Environmental
Justice, Cumulative Environmental Risk, and Health
among Low and Middle-Income Children in Upstate New
York,” American Journal of Public Health, Vol. 94, No.
11, 2004, pp. 1942-1944.
[14] S. J. Lupien, S. King, M. J. Meaney and B. S. McEwen,
“Can Poverty Get Under Your Skin? Basal Cortisol Lev-
els and Cognitive Function in Children from Low and
High Socioeconomic Status,” Development and Psycho-
patology, Vol. 13, No. 3, 2001, pp. 653-676.
[15] A. Dettling, M. R. Gunnar and B. Donzella, “Cortisol
Levels of Young Children in Full-Day Childcare Centers:
Relations with Age and Temperament,” Psychoneuroen-
docrinology, Vol. 24, No. 5, 1999, pp. 519-536.
[16] A. K. Goenjian, et al., “Hypothalamic-Pituitary-Adrenal
activity among American Adolescents with PTSD Sym-
ptoms,” Journal of Traumatic Stress, Vol. 16, No. 4, 2003,
pp. 319-323.
[17] M. Carlson, et al., “Effects of Social Deprivation on Cor-
tisol Regulation in Institutionalized Romanian Infants,”

Salivary Cortisol in Relation to the Use of Information and Communication Technology (ICT) in School-Aged Children 94
Society of Neuroscience Abstracts, Vol. 218, No. 12, 1995,
p. 524.
[18] M. Lindahl, T. Theorell and F. Lindblad, “Test Perform-
ance and Self-Esteem in Relation to Experienced Stress in
Swedish Sixth and Ninth Graders-Saliva Cortisol Levels
And Psychological Reactions to Demands,” Acta Pae-
diatrica, Vol. 94, No. 4, 2005, pp. 489-495.
[19] W. Kliewer, “Violence Exposure and Cortisol Responses
in Urban Youth,” International Journal of Behavioral
Medicine, Vol. 13, No. 2, 2006, pp. 109-120.
[20] A. Clow, L. Thorn, P. Evans and F. Hucklebridge, “The
Awakening Cortisol Response: Methodological Issues and
Significance,” Stress, Vol. 7, No. 1, 2004, pp. 29-37.
[21] S. Wust, J. Wolf, D. H. Hellhammer, I. Federenko, N.
Schommer and C. Kirschbaum, “The Cortisol Awakening
Response-Normal Values and Confounds,” Noise and
Health, Vol. 2, No. 7, 2000, pp. 79-99.
[22] U. Knutsson, et al., “Circadian Cortisol Rhythms in
Healthy Boys and Girls: Relationship with Age, Growth,
Body Composition, and Pubertal Development,” Journal
of Clinical Endocrinology and Metabolism, Vol. 82, No. 2,
1997, pp. 536-540.
[23] J. G. M. Rosmalen, A. J. Oldehinkel, J. Ormel, A. F. de
Winter, J. K. Buitelaar and F. C. Verhulst, “Determinants
of Salivary Cortisol Levels in 10-12 Year Old Children: A
Population-Based Study of Individual Differences,” Psy-
choneuroendocrinology, Vol. 30, No. 5, 2005, pp. 483-
495.
[24] E. K. Adam, “Transactions among Adolescent Trait and
State Emotion and Diurnal and Momentary Cortisol Ac-
tivity in Naturalistic Setting,” Psychoneuroendocrinology,
Vol. 31, No. 5, 2006, pp. 664-679.
[25] S. Edwards, A. Clow, P. Evans and F. Hucklebridge,
“Exploration of the Awakening Cortisol Response in Re-
lation to Diurnal Cortisol Secretory Activity,” Psycho-
neuroendocrinology, Vol. 26, No. 6, 2001, pp. 613-622.
[26] J. Gaab, L. Sonderegger, S. Scherrer and U. Ehlert, “Psy-
choneuroendocrine Effects of Cognitive-Behavioral Stress
Management in a Naturalistic Setting—A Randomized
Controlled Trial,” Psychoneuroendocrinology, Vol. 31,
No. 4, 2006, pp. 428-438.
[27] M. Wessa, N. Rohled er, C. Kirschbaum and H. Flor, “Al-
tered Cortisol Awakening Response in Posttraumatic
Stress Disorder,” Psychoneuroendocrinology, Vol. 31, No.
2, 2006, pp. 209-215.
[28] A. D. Roberts, S. Wessely, T. Chalder, A. Papadopuolod
and A. J. Cleare, “Salivary Cortisol Response to Awa-
kening in Chronic Fatigue Syndrome,” British Journal of
Psychiatry, Vol. 184, No. 2, 2004, pp. 136-141.
[29] G. Meinlschmid and C. Heim, “Decreased Cortisol Awak-
ening Response after Early Loss Experience,” Psycho-
neuroendocrinology, Vol. 30, No. 6, 2005, pp. 568- 576.
[30] L. M. Fulgham, “Effects of Videogame Play in Boys: An
Examinaion of Adrenocortical Activity and Behaviour,”
Dissertation Abstracts International: Section B: The Sci-
ences & Engineering, Vol. 64 (2-B), No. 2, 2003, pp. 983.
[31] S. Denot-Ledunois, G. Vardon, P. Perruchet and J. Gallego,
“The Effects of Attentional Load on the Breathing Pettern
in Children,” International Journal of Psychophysiology,
Vol. 29, No. 1, 1998, pp. 13-21.
[32] L. Steinberg and A. S. Morris, “Adolescent Develop-
ment,” Annual Review of Psychology, Vol. 52, No. 1,
2001, pp. 83-110.
[33] E. Thieden, P. A. Philipsen and H. C. Wulf, “Compliance
and Data Reliability in Sun Exposure Studies with Diaries
and Personal, Electronic UV Dosimeters,” Photoderma-
tology, Photoimmunology & Photomedicine, Vol. 22, No.
2, 2006, pp. 93-99.
[34] T. H. A. Van der Voort and M. W. Vooijs, “Validity of
Children’s Direct Estimates of Time Spent Television
Viewing,” Journal of Broadcasting & Electronic Media,
Vol. 34, No. 1, 1990, pp. 93-99.
[35] J. Jacobs, N. A. Nicolson, C. Derom, P. Delespaul, J. Van
Os and I. Myin-Germeys, “Electronic Monitoring of
Salivary Cortisol Sampling Compliance in Daily Life,”
Life Sciences, Vol. 76, No. 21, 2005, pp. 2431-2443.
[36] M. Kovacs, “Children’s Depression Inventory,” Multi-
health Systems, New York, 1992.
[37] B. Birmaher, “Psychometric Properties of the Screen for
Child Anxiety Related Emotional Disorders (SCARED):
A Replication Study,” Journal of the American Academy
of Child and Adolescent Psychiatry, Vol. 38, No. 4, 1999,
pp. 1230-1236.
[38] T. Solantaus and S. Toikka, “The Efficient Family: An
Intervention Study on the Prevention of Mental Disorders
in Children with Affectively Ill Parents,” Juvenes Print,
Tampere 2005.
[39] J. C. Preussner, C. Kirschbaum, G. Meinlschmid and D. H.
Hellhammer, “Two Formulas for Computation of the Area
Under the Curve Represent Measures of Total Hormone
Concentration Versus Time-Dependent Change,” Psy-
choneuroendocrinology, Vol. 28, No. 7, 2003, pp. 916-
931.
[40] F. A. J. L. Scheer and R. M. Buijs, “Light Affects
Morning Salivary Cortisol in Humans,” Journal of
Clinical Endocrinology and Metabolism, Vol. 84, No. 9,
1999, pp. 3395-3398.
[41] S. R. Kunz-Ebrecht, C. Kirschbaum, M. Marmot and A.
Steptoe, “Differences in Cortisol Awakening Response on
Work Days and Weekends in Women and Men from the
Whitehall II Cohort,” Psychoneuroendocrinology, Vol. 29,
2004, pp. 516-528.
[42] J. Bruce, E. P. Davis and M. R. Gunnar, “Individual Dif-
ferences in Children’s Cortisol Reponse to the Beginning
of a New School Year,” Psychoneuroendocrinology, Vol.
27, No. 6, 2002, pp. 635-650.
[43] B. M. Kudielka and C. Kirschbaum, “Awakening Cortisol
Responses are Influenced by Health Status and Awaken-
ing Time but not by Menstrual Cycle Phase,” Psychoneu-
roendocrinology, Vol. 28, No. 1, 2003, pp. 35-47.
[44] I. Federenko, S. Wust, D. H. Hellhammer, R. Dechoux, R.
Kumsta and C. Kirschbaum, “Free Cortisol Awakening

Salivary Cortisol in Relation to the Use of Information and Communication Technology (ICT) in School-Aged Children 95
Responses are Influenced by Awakening Time,” Psycho-
neuroendocrinology, Vol. 29, No. 2, 2004, pp. 179-184.
[45] J. E. Broderick, D. Arnold, B. M. Kudielka and C. Kir-
schbaum, “Salivary Cortisol Sampling Compliance: Com-
parison of Patients and Healthy Volunteers,” Psychoneu-
roendocrinology, Vol. 29, No. 5, 2004, pp. 636-650.
[46] R. J. Davidson and W. Irwin, “The Functional Neuro-
anatomy of Emotion and Affevtive Style,” Trends in
Cognitive Science, Vol. 3, No. 1, 1999, pp. 11-21.
[47] A. R. Damasio, “A Second Chance for Emotions,” In: R.
D. Lane and L. Nadel, Eds., Oxford University Press,
New York, 2000, pp. 12-13.
[48] L. D. Dorn, R. E. Dahl, H. R. Woodward and F. Biro,
“Defining the Boundaries of Early Adolescence: A User’s
Guide to Assessing Pubertal Status and Pubertal Timing
in Research With Adolescents,” Applied Developmental
Science, Vol. 10, No. 1, 2006, pp. 30-56.
[49] J. M. Tanner and R. H. Whitehouse, “Atlas of Children’s
Growth: Normal Variation and Growth Disorders,” Aca-
demic Press, London, 1982.
[50] J. M. Turner-Cobb, “Psychological and Stress Hormone
Correlates In Early Life: A Key to HPA-Axis Dysregula-
tion and Normalisation,” Stress, Vol. 8, No. 1, 2005, pp.
47-57.
[51] R. M. Sapolsky, “Why Stress is Bad for Your Brain,”
Science, Vol. 273, No. 5276, 1996, pp. 749-750.
[52] G. W. Evans, K. M. Allen, R. Tafalla and T. O’Meara,
“Multiple Stressors: Performance, Psychophysiological
and Affective Responses,” Journal of Environmental Psy-
chology, Vol. 16, 1996, pp. 147-154.
[53] S. J. Lupien, C. W. Wilkinson, S. Briere, C. Menard, N.
M. N. Y. Kin and N. P. Nair, “The Modulatory Effects of
Corticosteroids on Cognition: Studies in Young Human
Populations,” Psychoneuroendocrinology, Vol. 27, No. 3,
2002, pp. 401-416.
[54] B. S. McEwen and J. C. Wingfield, “The Concept of Al-
lostasis in Biology and Biomedicine,” Hormones and Be-
havior, Vol. 43, No. 1, 2003, pp. 2-15.