Content uploaded by Marco Fabbri
Author content
All content in this area was uploaded by Marco Fabbri on Apr 29, 2016
Content may be subject to copyright.
1 23
European Journal of Applied
Physiology
ISSN 1439-6319
Volume 115
Number 3
Eur J Appl Physiol (2015) 115:579-587
DOI 10.1007/s00421-014-3033-4
Effects of dawn simulation on attentional
performance in adolescents
Lorenzo Tonetti, Marco Fabbri, Alex
Erbacci, Marco Filardi, Monica Martoni
& Vincenzo Natale
1 23
Your article is protected by copyright and
all rights are held exclusively by Springer-
Verlag Berlin Heidelberg. This e-offprint is
for personal use only and shall not be self-
archived in electronic repositories. If you wish
to self-archive your article, please use the
accepted manuscript version for posting on
your own website. You may further deposit
the accepted manuscript version in any
repository, provided it is only made publicly
available 12 months after official publication
or later and provided acknowledgement is
given to the original source of publication
and a link is inserted to the published article
on Springer's website. The link must be
accompanied by the following text: "The final
publication is available at link.springer.com”.
1 3
Eur J Appl Physiol (2015) 115:579–587
DOI 10.1007/s00421-014-3033-4
ORIGINAL ARTICLE
Effects of dawn simulation on attentional performance
in adolescents
Lorenzo Tonetti · Marco Fabbri · Alex Erbacci ·
Marco Filardi · Monica Martoni · Vincenzo Natale
Received: 13 May 2014 / Accepted: 15 October 2014 / Published online: 29 October 2014
© Springer-Verlag Berlin Heidelberg 2014
(45.97 ± 32.76 ms) that significantly increased in compari-
son to the baseline (31.57 ± 26.97 ms) (p < 0.05). On the
contrary, the sleep quality, sleep quantity and sleep timing
did not significantly change.
Conclusion These results show for the first time that, con-
trolling for sleep quality, sleep duration and sleep timing,
the use of dawn simulator across 2 weeks is able to deter-
mine an alerting effect in adolescents.
Keywords Adolescents · Attention network test ·
Attentional performance · Dawn simulation · Social jetlag ·
Actigraphy
Abbreviations
ANT Attention network test
BT Bed time
GUT Get up time
MA Mean motor activity
MIDF Midpoint of sleep during free days
MIDS Midpoint of sleep during school days
MMW Micro Motionlogger® Watch actigraph
SE Sleep efficiency
SJ Social jetlag
SOL Sleep onset latency
TIB Time in bed
TST Total sleep time
WASO Wake after sleep onset
WB Wake bouts
Introduction
Adolescence is a period of life which is a challenge for teen-
agers, and is also full of changes from a chronobiological
point of view (Adan et al. 2012). Indeed, during this stage
Abstract
Purpose This study aimed at examining the effects of
2 weeks of dawn simulation on attentional performance in
adolescents.
Methods On the whole, 56 adolescents (24 females
and 32 males) took part to the study, with a mean age of
17.68 ± 0.97 years (age ranging between 15 and 20 years).
Each adolescent was requested to participate for 5 consecu-
tive weeks and the research design included the baseline
and two counterbalanced conditions, dawn simulator and
control (no dawn simulator). Attentional performance of
adolescents was measured through the attention network
test (ANT) that allowed assessing the efficiency of three
separable attentional networks, namely alerting, orienting
and executive. Overall, participants performed the ANT
three times (i.e., one time for each condition), while sleep
quality, sleep duration and sleep timing were concurrently
monitored by means of actigraphy and were treated as
potential confounders.
Results The only improvement of the attentional
performance attributable to the use of dawn simula-
tor was observed for the efficiency of alerting network
Communicated by Dick F. Stegeman.
L. Tonetti (*) · A. Erbacci · M. Filardi · V. Natale
Department of Psychology, University of Bologna, Viale Berti
Pichat 5, 40127 Bologna, Italy
e-mail: lorenzo.tonetti2@unibo.it
M. Fabbri
Department of Psychology, Second University of Naples,
Caserta, Italy
M. Martoni
Department of Experimental, Diagnostic and Specialty Medicine,
University of Bologna, Bologna, Italy
Author's personal copy
580 Eur J Appl Physiol (2015) 115:579–587
1 3
of development, several alterations occur in circadian pref-
erence, which is known as one of the most marked individ-
ual differences in circadian rhythms. From the age of 12–
13 years, a shift from a prevalent morning preference (i.e.,
people characterized by earlier bedtime and rising time and
better morning performance) to a prevalent evening prefer-
ence (i.e., persons that have later bedtime and rising time
and show more irregular sleep–wake habits) has been docu-
mented in adolescents from different countries all over the
world (e.g., Carskadon et al. 1993; Gau and Soong 2003;
Tonetti et al. 2008). The possible causes of this change are
both biological (Carskadon et al. 1993; Roenneberg et al.
2004) and psychosocial (Diaz-Morales et al. 2014; Randler
et al. 2009). It is worth noting that the exposure to light late
at night, for example through the use of electronic media
devices (Cain and Gradisar 2010), has been related to even-
ing preference in adolescents (Vollmer et al. 2012).
During adolescence a discrepancy occurs between the
biological clock and the social clock. The biological clock is
characterized by a phase delay, with the dark/light cycle as the
main environmental synchronizer, while the most important
synchronizer of the social clock in the high school student
population is school start time, which does not vary according
to the biological phase delay. This lack of synchrony has been
called social jetlag (Wittmann et al. 2006). One of the most
detrimental consequences of social jetlag in adolescence is
sleep deprivation (Loessl et al. 2008), which in turn has been
related to poor scholastic performance (Dewald et al. 2010),
sleepiness (Dewald et al. 2010), obesity (Garaulet et al. 2011)
and more negative affect (Talbot et al. 2010).
Aiming to counteract the negative consequences of
social jetlag in adolescence, some studies have introduced a
modification at the social clocks level, delaying the school
start time. Those studies have shown positive effects on the
self-assessment of sleepiness (Owens et al. 2010; Wahl-
strom 2002), depression (Owens et al. 2010; Wahlstrom
2002), scholastic absenteeism (Owens et al. 2010; Wahl-
strom 2002), sleep length (Dexter et al. 2003; Lufi et al.
2011; Owens et al. 2010; Vedaa et al. 2012), as well as
on the objectively evaluated attentional performance (Lufi
et al. 2011; Vedaa et al. 2012). In spite of the positive out-
comes found in these studies, the delay of the school start
time is scarcely adopted since it requires a deep reorgani-
zation of the scholastic (i.e., other than the changes of the
school timetable, also adjustments of routines of teachers,
students and parents) and extra-scholastic systems (e.g.,
changes of the public transport timetable).
Aiming to avoid the difficulties related to the changes
at the social clocks level, a possible alternative to reduce
the consequences of social jetlag in adolescence could con-
sist in acting on the biological clocks, for example through
the administration of light. Indeed the circadian system
is biologically determined, but it is also sensitive to the
environmental stimuli, in particular those related to the
dark/light cycle. As stated above, light is one of the most
relevant environmental synchronizers of the circadian sys-
tem that it needs to be synchronized to the environmental
and cyclic alternation of light and dark to efficiently oper-
ate. It is well known that light is able to determine several
neurobiological effects in humans, such as the resetting of
the endogenous circadian pacemaker (Boivin et al. 1996),
the increasing of body temperature (Dijk et al. 1991) and
cortisol levels (Scheer and Buijs 1999). The administration
of light also determinates activating/stimulating effects,
as shown through the decrease of subjective sleepiness,
reaction times and attentional failures (Badia et al. 1991;
Daurat et al. 1993; Lavoie et al. 2003; Rahman et al. 2014;
Smolders et al. 2012). On the basis of its properties, light
could be potentially administered to high school students
to obtain a better synchronization of their circadian system
to the dark/light cycle and/or reducing the consequences of
social jetlag.
A possible way to administer the light is represented by
the dawn simulator that emits light at low intensity (maxi-
mum intensity of 250–300 lux) that gradually increases in
intensity before the morning awakening. The use of the
dawn simulator has led to an improvement of the percep-
tion of sleep quality in healthy adults and young adults
(Leppämäki et al. 2003; Thompson et al. 2014) and an
increasing of the cortisol levels at the morning awakening
in healthy adults (Thorn et al. 2004). Furthermore, the dawn
simulator has been successfully used to reduce the conse-
quences of sleep inertia in healthy young adults (Giménez
et al. 2010; Thompson et al. 2014; van de Werken et al.
2010) and to improve, in a laboratory-setting, subjective
well being, mood, alertness and the objectively assessed
cognitive and physical performance in healthy young adults
(Gabel et al. 2013; Thompson et al. 2014). Gabel et al.
(2013) underlined that some caution was needed to extend
their conclusions to real-life setting and stated that further
studies under such condition were required. To the best of
our knowledge, to date only one study has employed the
dawn simulator in a population of adolescents (Fromm
et al. 2011); the main outcomes were an improving of the
quality of the self-assessed rising, awakening and well-
being, after the use of such device. However, the authors
highlighted some limitations of their study, in particular the
short duration of their research design (2 weeks) and the
lack of an objective assessment of vigilance (e.g., through
psychomotor vigilance tasks, reaction time tests) during
the school week, encouraging future studies in the real-life
setting.
The main goal of the present real-life study was to
explore if the use of dawn simulator across 2 weeks in
adolescents could enhance their attentional performance.
Aiming to address the previous aspects highlighted by
Author's personal copy
581Eur J Appl Physiol (2015) 115:579–587
1 3
Gabel et al. (2013) and Fromm et al. (2011), we objectively
assessed, in the real-life setting, the attentional perfor-
mance through the attention network test (ANT) (Fan et al.
2002), in a long-lasting research design (i.e., 5 weeks). The
ANT assesses three different attentional networks: alerting,
orienting and executive. On the basis of the results of previ-
ous studies on dawn simulator (Fromm et al. 2011; Gabel
et al. 2013; Giménez et al. 2010; Thompson et al. 2014;
Thorn et al. 2004; van de Werken et al. 2010) and taking
into account that the well-known alerting effects of light
(Cajochen 2007) seem to be modulated by some cortical
networks that partially overlap with the suggested cortical
substrate of the alerting network (i.e., parietal, insular and
thalamic areas) (Fan et al. 2005), we could expect a bet-
ter attentional performance after the use of dawn simula-
tor, especially with reference to the alerting network.
Since the likelihood that the attentional performance at the
ANT could be modulated by sleep quality and sleep dura-
tion (Jugovac and Cavallero 2012) as well as sleep timing
(Matchock and Mordkoff 2009), here such parameters were
objectively monitored through actigraphy and treated as
potentially confounding variables.
Methods
Participants
Sixty Italian high school students were invited to partici-
pate in the study; overall, 56 (24 females and 32 males)
voluntarily agreed. The mean age of participants was
17.68 years (SD 0.97 years), with age ranging between 15
and 20 years (mode and median = 18 years). The mean
age of females (17.56 ± 1.06 years) was not signifi-
cantly different from that of males (17.84 ± 0.88 years)
(t
54
= −1.48; p = 0.14). The distribution of males and
females among the different ages was not significantly dif-
ferent (
χ
2
5
= 9.73; p = 0.08).
The research project was approved by the ethics com-
mittee of the Department of Psychology of the University
of Bologna and complied with the tenets of the Declaration
of Helsinki. The present research project was presented
to and approved by principals, teachers and parents of the
high school students. If 18-year-old or older, students pro-
vided written informed consent prior to their participation
in the study; if they were underage, the written informed
consent was given by parents.
Dawn simulator
Participants used a dawn simulator (Wake-Up Light
HF3485/01; Philips, Netherlands) that emits light that grad-
ually increases in intensity before the morning awakening.
In the present study, the length of the dawn simulation
was set to 20 min. and the level of the intensity of light, on
a scale from 0 to 20, at twenty that corresponded to 250 lux.
Students were asked to place the dawn simulator on the night
table at eye level, with a distance of 40–50 cm from their
face, because the intensity of light ranged between 0 and
250 lux at that distance, as reported in the technical speci-
fications of this particular model of dawn simulator (http://
download.p4c.philips.com/files/h/hf3485_01/hf3485_
01_pss_aenbe.pdf). Moreover, participants had to verify that
light was not obstructed by duvet or pillow.
Attention network test (ANT)
Participants performed at the pc the attention network test
(ANT) (Fan et al. 2002). This cognitive task was devel-
oped aiming to evaluate the attentional model put forward
by Posner and Petersen (1990). Following this model, the
sources of attention can be localized in specific and sepa-
rate anatomical areas that can be further divided into three
distinct and relatively independent neural networks: alert-
ing, orienting and executive. These networks are defined on
the basis of anatomical and functional features, through a
correspondence between the areas of cortical activation and
the performance in attentional tasks that assess different
functions of attention. Evidences for the model come from
studies that used functional magnetic resonance (fMRI)
(Corbetta et al. 2000), event-related potential (ERP) (Neu-
haus et al. 2007) and genetics (Fan et al. 2001).
Alerting refers to the changes in the mental and physi-
ological state, caused by the appearance of a signal that
transmits relevant information to the task (Posner 1978), as
well as to the ability to reach and keep an alerting state.
Such network has been related to the activation of parietal
and frontal areas of the right and/or left hemisphere as well
as to thalamic and insular areas (Fan et al. 2005; Sturm and
Willmes 2001).
Orienting refers to the orientation of attention towards a
source of signals in the space and has been associated with
the left and right superior parietal lobes and thalami (Fan
et al. 2005; Posner and Raichle 1994).
Executive refers to the resolution of conflicts, control
over the decision making, identification and inhibition of
usual responses; such network has been related to the acti-
vation of the anterior cingulate cortex and the lateral pre-
frontal cortex (Bush et al. 2000; Fan et al. 2005).
The ANT is based on a combination of the cued reaction
time task (Posner 1980) and the flanker task (Eriksen and
Eriksen 1974).
1
In each trial, different types of cue (i.e., an
1
Please refer to Fan et al. (2002) to see the graphical representation
of the ANT experimental procedure.
Author's personal copy
582 Eur J Appl Physiol (2015) 115:579–587
1 3
asterisk) precede the appearance of a central arrow (i.e.,
target) on the screen, orientated towards left or right,
flanked by other arrows pointing in the same direction
(congruent condition) or the opposite direction (incongru-
ent condition) or by two lines (neutral condition). During
the test participants had to focus on a fixation point (i.e., a
cross) that appeared at the center of the screen and they had
to keep the left index finger on the key “Q” while the right
index finger on the key “P”. Students had to push as quickly
and accurately as possible the key “Q” on the keyboard,
with the left index finger, if they think that the central arrow
pointed towards left. On the contrary, if they thought that
the central arrow pointed towards right, they had to push as
quickly and accurately as possible the key “P” on the key-
board using the right index finger. The four cue conditions
that anticipated the appearance of the target were: double
cue (simultaneous presentation of an asterisk above and
below the fixation point), spatial cue (appearance of one
asterisk, above or below the fixation point), no cue (the tar-
get is not preceded by any cue) and center cue (the asterisk
appears on the fixation point). The flankers that accompa-
nied the target can be oriented towards left or right, deter-
mining three conditions: congruent (the target is flanked by
arrows that point towards the same direction), incongruent
(the target is flanked by arrows that point towards the oppo-
site direction) and neutral (the target is flanked by lines).
In the present study we have calculated the overall
mean reaction times, in spite of the specific tested condi-
tion. Moreover, we have also computed the efficiency of
the three attentional networks as follows. The efficiency of
the alerting network was defined by the difference between
the reaction times in the no cue condition and those in the
double-cue condition (higher values correspond to a better
efficiency), while the efficiency of the orienting network
was defined by the difference between the reaction times
in center cue condition and those in spatial cue condition
(higher values indicate an improved efficiency). Finally,
the efficiency of the executive network was computed sub-
tracting the reaction times in the congruent condition from
those observed in the incongruent condition, with lower
values corresponding to a better efficiency.
Actigraphy
The Micro Motionlogger® Watch (MMW) actigraph
(Ambulatory Monitoring, Inc., Adrsley, NY) was used in
the present study. MMW was initialized through the Watch-
ware® software (version 1.94.0.0) (Ambulatory Monitor-
ing, Inc., Adrsley, NY) to collect motor activity data in zero
crossing mode, with a sampling rate of 1 min. The MMW
data were analyzed through Action W-2® software, version
2.7.1 (Ambulatory Monitoring, Inc., Adrsley, NY). Such
software identified each epoch as sleep or wake using the
mathematical model validated by Cole and Kripke (1988)
and Cole et al. (1992).
Participants were requested to push the mark-event
button on the actigraph to signal the bedtime and the get-
up time. Furthermore, they filled in a sleep log each day
within 30 min after morning awakening. When participants
forgot to push the mark-event button, the bedtime and the
get-up time reported on the sleep log were used to set the
time in bed (TIB) for actigraphic analysis. When both types
of information were missing, the corresponding night was
discarded.
In the present study, the following sleep actigraphic
parameters were considered: bed time (BT), the clock time
at which the participants went to bed attempting to sleep,
pushed the event-marker button on the actigraph to signal
it and switched off the light; get-up time (GUT), the clock
time at which participants switched on the light, pushed the
event-marker button on the actigraph to signal it and got out
of bed; time in bed (TIB), interval, in minutes, between BT
and GUT; midpoint of sleep during the free days (MIDF),
in hours and minutes, the midpoint between BT and GUT;
midpoint of sleep during the school days (MIDS); social
jetlag (SJ), in hours, as the difference between MIDF and
MIDS; sleep onset latency (SOL), the interval, in minutes,
between BT and sleep start; total sleep time (TST), the
sum, in minutes, of all sleep epochs between sleep onset
and GUT; wake after sleep onset (WASO), the sum, in min-
utes, of all wake epochs between sleep onset and GUT;
sleep efficiency (SE), the ratio of the TST to TIB multi-
plied by 100; mean motor activity (MA), the mean value
of activity counts in 1 min epoch during the assumed sleep;
wake bouts (WB), the actual number of episodes of activity
from sleep onset until wake-up time.
Procedure
The study was carried out during autumn seasons of 2011
and 2012, and the winter season of 2012. This choice was
taken aiming to maximize the effects of light emitted by
dawn simulator (Fromm et al. 2011; Gabel et al. 2013).
Each high school student participated for 5 consecu-
tive weeks. The research design included the baseline and
two counterbalanced conditions, dawn simulator and con-
trol (no dawn simulator). Each of the last two conditions
consisted of 2 weeks: the first one was of adaptation and
the second one was the recording week. At the end of each
of the recording weeks (the first, the third and the fifth of
the research design, respectively) participants performed
the ANT together, according to the group they belonged
to, during regular school activities at the same time of day,
i.e., 12:30 hours. During the three recording weeks, par-
ticipants’ sleep quality, sleep duration and sleep timing
were continuously monitored through an actigraph placed
Author's personal copy
583Eur J Appl Physiol (2015) 115:579–587
1 3
around the non-dominant wrist. The length of the acti-
graphic recording, 1 week for each condition, represents
the standard to get reliable measures (Acebo et al. 1999;
Van Someren 2007).
Aiming to reduce the learning effect, the order of the
conditions was balanced between participants. One half of
students (group 1, n = 28, 12 females) used the dawn simu-
lator at the beginning of the research (from the second to
the third week), following the baseline, while the other half
(group 2, n = 28, 12 females), after the baseline, first par-
ticipated in the control condition and second in the dawn
simulator condition (from the fourth to the fifth week of the
research design).
During the baseline and control conditions, the dawn
simulator was not present in the students’ bedrooms; it
was used only during the dawn simulator condition, for
14 consecutive days. Since the study was carried out in
a real-life setting, each participant was asked to keep
her/his own habits and thus the timing of the switch-on
of light exposure, during the dawn simulator condi-
tion, was flexible. However, since the school start time
(08:05 hours) was the same for all participants, the timing
was similar and did not lead to a significantly different
get-up time between the three conditions (see below the
paragraph about sleep actigraphic parameters). Further-
more, students were also instructed to keep their own hab-
its as regards the way of wake-up, which did not result in
a significant difference between the get-up times recorded
in the three conditions (see below the paragraph concern-
ing sleep actigraphic parameters).
Data analysis
We left out from the analysis the ANT trials in which par-
ticipants made an error. Furthermore we excluded the reac-
tion times that were different ±3 standard deviation from
the mean.
Aiming to control the possible effect of the order of the
conditions, we performed a preliminary mixed ANOVA,
with the order as a between-subjects factor (2 levels)
(group 1: baseline, dawn simulator, control; group 2: base-
line, control, dawn simulator). The within-subjects factor
(three levels) was the mean value of the examined param-
eter observed in the baseline, dawn simulator and control
conditions. These mixed ANOVAs were performed with
reference to each of the ANT examined parameters, i.e.,
the overall mean reaction times and the efficiency of the
alerting, orienting and executive networks. Moreover, aim-
ing to control for the possible variation of sleep quality,
sleep quantity and sleep timing during the recording weeks,
these mixed ANOVAs were performed also with reference
to each actigraphic sleep parameter, i.e., BT, GUT, TIB,
MIDF, MIDS, SJ, SOL, TST, WASO, SE, MA and WB. If
the between-subjects factor was significant, the HSD Tukey
post hoc test was performed. If the within-subjects factor
reached the significant level, we performed the Scheffé post
hoc test.
Furthermore, for each attentional network, we carried
out a repeated measures ANOVA with the following two
within-subjects factors: conditions factor (3 levels) (base-
line, dawn simulator and control) and type of cue or flanker
that defined the specific network (2 levels) (i.e., no cue
and double cue for the alerting, central cue and spatial cue
for the orienting network, incongruent and congruent for
the executive). If significant effects were observed (main
effects and interaction), the Scheffé post hoc test was per-
formed. The significant level was set at p < 0.05.
Results
With reference to each of the dependent variables exam-
ined, the preliminary mixed ANOVA did not show any sig-
nificant effect of the order of the conditions between par-
ticipants. For this reason, this between-subjects factor was
no longer considered.
ANT-Overall mean reaction times
With reference to the overall mean reaction times, regard-
less of the attentional network examined, the within-sub-
jects factor was significant (F
2,110
= 14.37; p < 0.001). At
the Scheffé post hoc test, the mean reaction times in base-
line (526.84 ± 52.44 ms) were significantly higher than
those observed in dawn simulator (506.82 ± 54.26 ms)
(p < 0.001) and control (509.84 ± 53.37) (p < 0.001)
conditions.
ANT-Alerting network
A significant effect of the within-subjects factor with
reference to the efficiency of the alerting network
(F
2,110
= 3.97; p < 0.05) (Fig. 1) was observed. The
Scheffé post hoc test showed a significant improvement
of the efficiency of such network in the dawn simula-
tor condition (45.97 ± 32.76 ms) compared to the base-
line (31.57 ± 26.97 ms) (p < 0.05), while no compari-
sons referred to the control condition (41.27 ± 28.51 ms)
reached the significance.
The repeated measures ANOVA with conditions (base-
line, dawn simulator and control) and the type of cue
(double cue and no cue) as within-subjects factors showed
a significant effect of the former factor (F
2,110
= 6.87;
p < 0.005). The Scheffé post hoc test highlighted that the
mean reaction times in baseline (541.91 ms) were signifi-
cantly higher than those observed in the dawn simulator
Author's personal copy
584 Eur J Appl Physiol (2015) 115:579–587
1 3
(525.63 ms) (p < 0.005) and control (529.83 ms) (p < 0.05)
conditions. Also the type of cue reached the significant level
(F
1,55
= 241.80; p < 0.001), with lower mean reaction times
in the double-cue condition (512.65 ms) compared to the
no-cue condition (552.26 ms) (p < 0.001). Also the interac-
tion between the two factors was significant (F
2,110
= 3.97;
p < 0.05) (Table 1); that result was due to a significant
improvement in the mean reaction times in the double-cue
condition between baseline (526.13 ± 56.57 ms) and dawn
simulator (502.64 ± 58.38 ms) (p < 0.001), as opposed to
the comparison between the mean reaction times to the no-
cue condition in baseline (557.70 ± 54.69 ms) and dawn
simulator (548.61 ± 62.12 ms), that did not reach the sig-
nificant level (p = 0.31).
ANT-Orienting network
With reference to the efficiency of the orienting network,
no significant difference was detected between baseline
(48.04 ± 39.78 ms), dawn simulator (46.79 ± 29.86 ms)
and control (47.51 ± 35.70 ms) (F
2,110
= 0.03; p = 0.97)
(Fig. 1).
Following the repeated measures ANOVA with 3 con-
ditions (baseline, dawn simulator and control) and 2 types
of cue (center cue and spatial cue) as within-subject fac-
tors, the condition factor was significant (F
2,110
= 19.15;
p < 0.001). The Scheffé post hoc test showed higher
mean reaction times in baseline (513.96 ms) compared to
both dawn simulator (489.08 ms) (p < 0.001) and control
(492.13 ms) (p < 0.001). Also the type of cue was signifi-
cant (F
1,55
= 173.89; p < 0.001), with significantly lower
mean reaction times to the spatial cue condition (474.67 ms)
compared to the center cue (522.11 ms) (p < 0.001). The
interaction between two factors did not reach the significant
level (F
2,110
= 0.03; p = 0.97) (Table 1).
ANT-Executive network
As regards the efficiency of the executive network, the
ANOVA showed a significant effect of the within-subjects
factor (F
2,110
= 5.94; p < 0.005) (Fig. 1). Through the post
hoc test, a significantly lower efficiency was observed in
baseline (45.69 ± 47.16 ms) than those observed both in
dawn simulator (28.49 ± 43 ms) (p < 0.05) and control
(32.29 ± 39.43 ms) (p < 0.05) conditions.
The repeated measures ANOVA with conditions
(baseline, dawn simulator and control) and the type
of flanker (congruent and incongruent) as within-sub-
jects factors showed that the former was significant
(F
2,110
= 22.56; p < 0.001), with mean reaction times in
baseline (554.22 ms) higher in comparison to dawn simu-
lator (526.54 ms) (p < 0.001) and control (530.09 ms)
(p < 0.005). The type of flanker factor also reached the sig-
nificant level (F
1,55
= 51.75; p < 0.001), with mean reac-
tion times lower in the congruent condition (519.20 ms)
as opposed to the incongruent (554.70 ms) (p < 0.001).
The interaction between the two factors was significant
too (F
2,110
= 5.94; p < 0.005) (Table 1), with the mean
reaction times in the congruent flanker condition that
improved comparing baseline (531.37 ± 54.30 ms) with
dawn simulator (512.30 ± 52.78 ms) (p < 0.005) as well
as baseline with control (513.94 ± 53.10 ms) (p < 0.01).
Moreover, the mean reaction times in the incongruent
flanker condition were significantly improved between
baseline (577.07 ± 76.03 ms) and dawn simulator
(540.79 ± 66.46 ms) (p < 0.001) besides baseline and con-
trol (546.23 ± 70.48 ms) (p < 0.001).
20
30
40
50
60
70
80
90
100
Alerting OrientingExecutive
Efficiency (ms)
Baseline Dawn simulator
Control
Fig. 1 Efficiency (expressed in milliseconds) of the alerting, orient-
ing and executive networks in baseline, dawn simulator and control
conditions. Means and standard deviations are shown. With reference
to the alerting and orienting networks, higher values correspond to a
better efficiency, while the opposite is true for the executive network
Table 1 Means and standard
deviations of the reaction times
(expressed in milliseconds) to
the different types of cue and
flanker, that define the three
attentional networks, in the
three conditions
Attentional networks Types of cue/flanker Baseline Dawn simulator Control
Alerting No cue 557.70 ± 54.69 548.61 ± 62.12 550.47 ± 58.12
Double cue 526.13 ± 56.57 502.64 ± 58.38 509.20 ± 57.20
Orienting Center cue 537.97 ± 61.23 512.48 ± 53.43 515.89 ± 54.58
Spatial cue 489.94 ± 57.70 465.69 ± 59.57 468.38 ± 60.67
Executive
Incongruent flanker 577.07 ± 76.03 540.79 ± 66.46 546.23 ± 70.48
Congruent flanker 531.37 ± 54.30 512.30 ± 52.78 513.94 ± 53.10
Author's personal copy
585Eur J Appl Physiol (2015) 115:579–587
1 3
Sleep actigraphic parameters
Table 2 shows the mean values of each actigraphic sleep
parameter in baseline, dawn simulator and control
conditions.
With reference to only one actigraphic parameter, the
TIB, a significant effect of the within-subjects factor was
observed (Table 2); performing the Scheffé post hoc test,
TIB in dawn simulator condition resulted significantly
shorter (14 min of difference) than that observed in base-
line (p < 0.05).
Discussion
The main finding of the present study was a significant
improvement of the ANT-alerting network efficiency, after
the use of dawn simulator for 2 weeks’ time in the real-
life setting, in comparison to the baseline condition, in
high school students. These data are meaningful bearing in
mind that the sleep quality, sleep quantity and sleep timing,
potential confounders of the results (Jugovac and Caval-
lero 2012; Matchock and Mordkoff 2009), were objectively
assessed through actigraphy (Table 2) and did not signifi-
cantly change during the study. Furthermore, on the basis
of the features of our research design (specifically with ref-
erence to the control condition), we were able to distinguish
a learning effect, observed for the overall mean reaction
times and the efficiency of the executive network (Ishigami
and Klein 2010, 2011), from the effect of the dawn simu-
lation. The latter was highlighted by the fact that the effi-
ciency of the alerting network significantly improved only
in the dawn simulator condition, compared to the baseline.
This result corroborates our main expectation, confirming
the alerting effect of light (Cajochen 2007); such specific
effect of dawn simulation on the efficiency of the alerting
network could be explained on the basis of the partial over-
lap between the cortical networks mediating the alerting
effects of light (Cajochen 2007) and the neural substrate
of the ANT alerting network (Fan et al. 2005). However,
to date, the specific physiologic mechanisms underlying
this improvement are still unclear and should be thoroughly
investigated by future studies.
The improvement of the ANT-alerting network effi-
ciency in the dawn simulator condition, compared to the
baseline, was explored performing a repeated measures
ANOVA with conditions (baseline, dawn simulator and
control) and type of cue (double cue and no cue) as within
subjects factors; this analysis highlighted a significant inter-
action between the two factors (Table 1), with an improv-
ing of the mean reaction times to the targets preceded by
the appearance of the double cue in the dawn simulator
condition compared to the baseline, while the mean reac-
tion times in the no cue condition were not significantly
Table 2 Means and standard
deviations of each actigraphic
sleep parameter, in the three
conditions
The significant difference is in
bold
BT bedtime, GUT get-up
time, TIB time in bed, MIDF
midpoint of sleep during free
days, MIDS midpoint of sleep
during school days, SJ social
jetlag, SOL sleep onset latency,
TST total sleep time, WASO
wake after sleep onset, SE sleep
efficiency, MA mean motor
activity, WB wake bouts
Baseline Dawn simulator Control Significance
BT 24:05 ± 0.85 24:11 ± 0.84 24:10 ± 0.82
F
2,106
= 0.72;
p = 0.49
GUT 7:32 ± 0.52 7:25 ± 0.46 7:29 ± 0.40
F
2,106
= 1.51;
p = 0.22
TIB 449.74 ± 47.25 435.17 ± 44.98 440.23 ± 49.93 F
2,106
= 4.52;
p < 0.05
MIDF 5:49 ± 1.46 5:55 ± 1.55 5:29 ± 1.35
F
2,92
= 1.38;
p = 0.26
MIDS 3:20 ± 0.52 3:25 ± 0.54 3:23 ± 0.46
F
2,104
= 1.36;
p = 0.26
SJ 2.29 ± 1.43 2.32 ± 1.53 2.08 ± 1.36
F
2,90
= 1.07;
p = 0.35
SOL 8.92 ± 6.47 8.73 ± 5.09 9.48 ± 6.48
F
2,106
= 0.47;
p = 0.62
TST 405.05 ± 46.03 395.86 ± 42.87 400.33 ± 47.36
F
2,106
= 2.57;
p = 0.08
WASO 30.02 ± 14.30 28.57 ± 14.25 28.25 ± 14.13
F
2,106
= 1.23;
p = 0.30
SE 90.60 ± 3.50 91.01 ± 3.54 90.94 ± 3.31
F
2,106
= 1.03;
p = 0.36
MA 15.57 ± 5.73 15.01 ± 5.87 14.65 ± 5.16
F
2,106
= 2.01;
p = 0.14
WB 17.50 ± 7.54 17.09 ± 7.57 16.81 ± 7.32
F
2,106
= 0.90;
p = 0.41
Author's personal copy
586 Eur J Appl Physiol (2015) 115:579–587
1 3
different between baseline and dawn simulator. Such data
indicate that the overall improving of the alerting network
efficiency was due to a greater reactivity of participants fol-
lowing the appearance of the double cue that forestalled the
presentation of the target.
Some limitations of the present study should be under-
lined. First, this is a study carried out in the real-life set-
ting; thus we were not able to control for some potential
confounders. For example, it was not possible to control
the bedrooms’ ambient temperature at the same degree, as
previously done by Thompson et al. (2014) in their labora-
tory study. Second, participants performed the ANT in the
late morning, several hours after the exposure to the light;
indeed due to scholastic requirements, it was not possible
to perform the test more times at regular intervals, from the
morning awakening. Despite this limit, it is necessary to
underline that even if the ANT was performed several hours
after the exposure to dawn simulation, a significant improve-
ment of the alerting network efficiency was observed.
In comparison to the previous work carried out by
Fromm et al. (2011), the only study that has investigated
the effect of artificial dawn in adolescents, the strength of
the present study lies in the longer duration of the research
design (5 vs. 2 weeks) and in the objective assessment of
cognitive performance as well as sleep quality, sleep quan-
tity and sleep timing that were lacking in Fromm et al.
(2011). Moreover, the methodological features of our
work, even if from one side can be considered as a limit,
on the other can be interpreted as strength. Indeed the stud-
ies that explore the functioning of the biological clock in
the real-world are rare and they have the main advantage
of investigating the behavior of participants in their natu-
ral environment, thus having a higher ecological validity
in comparison to the laboratory studies, carried out in con-
trolled but artificial conditions. The studies in the real-life
setting also allow verifying the applicability of the chrono-
biological knowledge derived from laboratory studies to
the real-life, with possible applied consequences.
As regards the potential applied outcomes of this study,
on the basis of the activating/alerting effect determined by
the dawn simulator, this lamp could be potentially used as
a countermeasure to some of the well-known consequences
of social jetlag in adolescence, e.g., the effects of the sleep
restriction on cognitive performance. Specifically, dawn
simulator could be used as a potential tool to improve the
attentional performance in high school students. A more
efficient attentional system could allow students to partici-
pate more actively during classes, with hypothetical posi-
tive consequences on their scholastic performance.
To sum up, the present study has shown for the first time
that, controlling for sleep quality, sleep quantity and sleep
timing, the use of dawn simulator for 2 weeks is able to
determine an alerting effect in adolescents; nevertheless at
the present moment it is not possible to predict the effects
due to the use of such device for a longer time, which
should be investigated in future studies.
Acknowledgments We wish to thank all students, parents, teachers
and principals of the high schools who supported our study. The dawn
simulators used in the present study were provided by Philips Con-
sumer Lifestyle, Monza, Italy, while the actigraphs were supplied by
Ambulatory Monitoring, Inc., Ardsley, NY, USA.
Conflict of interest The authors declare no conflict of interest.
References
Acebo C, Sadeh A, Seifer R, Tzischinsky O, Wolfson AR, Hafer A,
Carskadon MA (1999) Estimating sleep patterns with activity
monitoring in children and adolescents: how many nights are
necessary for reliable measures? Sleep 22:95–103
Adan A, Archer SN, Hidalgo MP, Di Milia L, Natale V, Randler C
(2012) Circadian typology: a comprehensive review. Chronobiol
Int 29:1153–1175
Badia P, Myers B, Boecker M, Culpepper J (1991) Bright light effects
on body temperature, alertness, EEG and behaviour. Physiol
Behav 50:583–588
Boivin DB, Duffy JF, Kronauer RE, Czeisler CA (1996) Dose-
response relationships for resetting of human circadian clock by
light. Nature 379:540–542
Bush G, Luu P, Posner MI (2000) Cognitive and emotional influences
in anterior cingulated cortex. Trends Cogn Sci 4:215–222
Cain N, Gradisar M (2010) Electronic media use and sleep in
school-aged children and adolescents: a review. Sleep Med 11:
735–742
Cajochen C (2007) Alerting effects of light. Sleep Med Rev
11:453–464
Carskadon MA, Vieira C, Acebo C (1993) Association between
puberty and delayed phase preference. Sleep 16:258–262
Cole RJ, Kripke DF (1988) Progress in automatic sleep/wake scoring
by wrist actigraph. Sleep Res 17:331
Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC (1992)
Automatic sleep/wake identification from wrist activity. Sleep
15:461–469
Corbetta M, Kincade J, Ollinger JM, McAvoy MP, Shulman GL
(2000) Voluntary orienting is dissociated from target detection in
human posterior parietal cortex. Nat Neurosci 3:292–297
Daurat A, Aguirre A, Foret J, Gonnet P, Keromes A, Benoit O (1993)
Bright light affects alertness and performance rhythms during a
24-h constant routine. Physiol Behav 53:929–936
Dewald JF, Meijer AM, Oort FJ, Kerkhof GA, Bögels SM (2010) The
influence of sleep quality, sleep duration and sleepiness on school
performance in children and adolescents: a meta-analytic review.
Sleep Med 14:179–189
Dexter D, Bijwadia J, Schilling D, Appelbaugh G (2003) Sleep,
sleepiness and school start times: a preliminary study. Wis Med
J 102:44–46
Diaz-Morales JF, Escribano C, Jankowski KS, Vollmer C, Randler C
(2014) Evening adolescents: the role of family relationships and
pubertal development. J Adolesc 37:425–432
Dijk DJ, Cajochen C, Borbély AA (1991) Effect of a single 3-hour
exposure to bright light on core body-temperature and sleep in
humans. Neurosci Lett 121:59–62
Eriksen BA, Eriksen CW (1974) Effects of noise letters upon the
identification of a target letter in a nonsearch task. Percept Psy-
chophys 16:143–149
Author's personal copy
587Eur J Appl Physiol (2015) 115:579–587
1 3
Fan J, Wu Y, Fossella JA, Posner MI (2001) Assessing the heritability
of attentional networks. BMC Neurosci 2:14
Fan J, McCandliss BD, Sommer T, Raz A, Posner MI (2002) Testing
the efficiency and independence of attentional networks. J Cogn
Neurosci 14:340–347
Fan J, McCandliss BD, Fossella J, Flombaum JI, Posner MI (2005)
The activation of attentional networks. Neuroimage 26:471–479
Fromm E, Horlebein C, Meergans A, Niesner M, Randler C (2011)
Evaluation of a dawn simulator in children and adolescents. Biol
Rhythm Res 42:417–425
Gabel V, Maire M, Reichert CF, Chellappa SL, Schmidt C, Hommes
V, Viola AU, Cajochen C (2013) Effects of artificial dawn and
morning blue light on daytime cognitive performance, well-
being, cortisol and melatonin levels. Chronobiol Int 30:988–997
Garaulet M, Ortega FB, Ruiz JR, Rey-López JP, Béghin L, Manios Y,
Cuenca-García M, Plada M, Diethelm K, Kafatos A, Molnár D,
Al-Tahan J, Moreno LA (2011) Short sleep duration is associated
with increased obesity markers in European adolescents: effect
of physical activity and dietary habits. the HELENA study. Int J
Obes 35:1308–1317
Gau SF, Soong WT (2003) The transition of sleep-wake patterns in
early adolescence. Sleep 26:449–454
Giménez MC, Hessels M, van de Werken M, de Vries B, Beersma
DGM, Gordijn MCM (2010) Effects of artificial dawn on sub-
jective ratings of sleep inertia and dim light melatonin onset.
Chronobiol Int 27:1219–1241
Ishigami Y, Klein RM (2010) Repeated measurement of the compo-
nents of attention using two versions of the attention network test
(ANT): stability, isolability, robustness, and reliability. J Neurosci
Methods 190:117–128
Ishigami Y, Klein RM (2011) Repeated measurement of the compo-
nents of attention of older adults using the two versions of the
attention network test: stability, isolability, robustness, and reli-
ability. Front Aging Neurosci 3:1–13
Jugovac D, Cavallero C (2012) Twenty-four hours of total sleep dep-
rivation selectively impairs attentional networks. Exp Psychol
59:115–123
Lavoie S, Paquet J, Selmaoui B, Rufiange M, Dumont M (2003) Vigi-
lance levels during and after bright light exposure in the first half
of the night. Chronobiol Int 20:1019–1038
Leppämäki S, Meesters Y, Haukka J, Lönnqvist J, Partonen T (2003)
Effect of simulated dawn on quality of sleep––a community-
based trial. BMC Psychiatry 3:14
Loessl B, Valerius G, Kopasz M, Hornyak M, Riemann D, Voder-
holzer U (2008) Are adolescents chronically sleep-deprived? an
investigation of sleep habits of adolescents in the Southwest of
Germany. Child Care Health Dev 34:549–556
Lufi D, Tzischinsky O, Hadar S (2011) Delaying school starting time
by one hour: some effects on attention levels in adolescents. J
Clin Sleep Med 7:137–143
Matchock RL, Mordkoff JT (2009) Chronotype and time-of-day influ-
ences on the alerting, orienting, and executive components of
attention. Exp Brain Res 192:189–198
Neuhaus AH, Koehler S, Opgen-Rhein C, Urbanek C, Hahn E, Det-
tling M (2007) Selective anterior cingulate cortex deficit dur-
ing conflict solution in schizophrenia: an event-related potential
study. J Psychiatr Res 41:635–644
Owens JA, Belon K, Moss P (2010) Impact of delaying school start
time on adolescent sleep, mood, and behavior. Arch Pediatr Ado-
lesc Med 164:608–614
Posner MI (1978) Chronometric explorations of mind. Erlbaum,
Hillsdale
Posner MI (1980) Orienting of attention. Q J Exp Psychol 32:3–25
Posner MI, Petersen SE (1990) The attention systems of the human
brain. Annu Rev Neurosci 13:25–42
Posner MI, Raichle ME (1994) Images of mind. Scientific American
Library, New York
Rahman A, Flynn-Evans EE, Aeschbach D, Brainard GC, Czeisler
CA, Lockley SW (2014) Diurnal spectral sensitivity of the acute
alerting effects of light. Sleep 37:271–281
Randler C, Bilger S, Diaz-Morales JF (2009) Associations among
sleep, chronotype, parental monitoring, and pubertal development
among German adolescents. J Psychol 143:509–520
Roenneberg T, Kuehnle T, Pramstaller PP, Ricken J, Havel M, Guth
A, Merrow M (2004) A marker for the end of adolescence. Curr
Biol 14:R1038–R1039
Scheer FA, Buijs RM (1999) Light affects morning salivary cortisol in
humans. J Clin Endocrinol Metab 84:3395–3398
Smolders KC, de Kort YA, Cluitmans PJ (2012) A higher illuminance
induces alertness even during office hours: findings on subjec-
tive measures, task performance and heart rate measures. Physiol
Behav 107:7–16
Sturm W, Willmes K (2001) On the functional neuroanatomy of
intrinsic and phasic alertness. Neuroimage 14:76–84
Talbot LS, McGlinchey EL, Kaplan KA, Dahl RE, Harvey AG (2010)
Sleep deprivation in adolescents and adults: changes in affect.
Emotion 10:831–841
Thompson A, Jones H, Gregson W, Atkinson G (2014) Effects of
dawn simulation on markers of sleep inertia and post-waking per-
formance in humans. Eur J Appl Physiol 114:1049–1056
Thorn L, Hucklebridge F, Esgate A, Evans P, Clow A (2004) The
effect of dawn simulation on the cortisol response to awakening
in healthy participants. Psychoneuroendocrinology 29:925–930
Tonetti L, Fabbri M, Natale V (2008) Sex difference in sleep-time
preference and sleep need: a cross-sectional survey among Ital-
ian pre-adolescents, adolescents, and adults. Chronobiol Int
25:745–759
van de Werken M, Giménez MC, de Vries B, Beersma DGM, van
Someren EJW, Gordijn MCM (2010) Effects of artificial dawn
on sleep inertia, skin temperature, and the awakening cortisol
response. J Sleep Res 19:425–435
Van Someren EJW (2007) Improving actigraphic sleep estimates
in insomnia and dementia: how many nights? J Sleep Res
16:269–275
Vedaa Ø, West Saxvig I, Wilhelmsen-Langeland A, Bjorvatn B,
Pallesen S (2012) School start time, sleepiness and functioning in
Norwegian adolescents. Scand J Educ Res 56:55–67
Vollmer C, Michel U, Randler C (2012) Outdoor light at night (LAN)
is correlated with eveningness in adolescents. Chronobiol Int
29:502–508
Wahlstrom K (2002) Changing times: findings from the first longi-
tudinal study of later high school start times. NASSP Bulletin
86:3–21
Wittmann M, Dinich J, Merrow M, Roenneberg T (2006) Social jet-
lag: misalignment of biological and social time. Chronobiol Int
23:497–509
Author's personal copy